ARTICLE | doi:10.20944/preprints202208.0176.v1
Subject: Social Sciences, Psychology Keywords: psychological distress/anxiety/depression; dementia; Alzheimer’s disease; Parkinson’s disease; short form of the Depression Anxiety Stress Scale 21; Depression Anxiety Stress Scale 8-items; factor structure; psychometric properties; structural validity; validation; measurement invariance; old age/elders/elderly; informal; family caregivers; spouse; adult children
Online: 9 August 2022 (08:44:11 CEST)
Dementia patients express a set of problematic and deteriorating symptoms, along self-care dependency. Overtime, the mental health of family caregivers of persons with dementia may suffer, putting them at a high risk for psychopathology, which may be associated with endangered wellbeing of demented people. This cross-sectional study examined the psychometric properties of the Depression Anxiety Stress Scale 8-items (DASS-8), DASS-12, DASS-21 in a convenient sample of 571 caregivers from northern Italy and southern Switzerland (Mean age = 53, SD = 12, range = 24–89 years). A bifactor structure of the three measures had the best fit; some items of the DASS-12/DASS-21 failed to load on their domain-specific factors. The three-factor structure was invariant across various groups (e.g., gender, education, etc.), expressed adequate reliability and convergent validity, and had strong positive correlation with the 3-item UCLA Loneliness Scale (UCLALS3). Dementia type had no effect on distress scores, which were higher among females, adult children caregivers, those caring for dependent patients, and those who received help with care. For 54.9 and 38.8% of the latter, care was provided by relatives and health professionals, respectively. Since the DASS-8 expresses adequate psychometrics comparable with the DASS-21, it may be used as a brief measure of distress in this population.
REVIEW | doi:10.20944/preprints202305.1471.v1
Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: full arch; mandibular atrophy; short dental implants; short implants; prosthetic
Online: 22 May 2023 (07:26:03 CEST)
Background: This study aimed to evaluate survival rate, marginal bone levels, and full arch prosthetic success on short implants when placed in areas of severely resorbed and edentulous mandibles. Methods: This is a systematic review of all randomized controlled trials of at least 10 patients with a control group in which bone augmentations were performed that were published between January 2010 and February 2023. Only 3 relevant studies met the inclusion criteria. Results: It obtained demonstrate that short-term dental implant survival rates ranged from 94.2% to 97.4% with a 5-year follow-up and the prosthetic success rate varied by 62% during the same follow-up. The mean marginal bone level values of the affected short implants ranged from 0.2 mm to 0.6 mm. Conclusions: The data obtained demonstrated that short dental implants positioned with criterion and precision as a full-arch fixed support are a valid therapeutic choice for the medium-long term rehabilitation of severe edentulous mandibular atrophy.
ARTICLE | doi:10.20944/preprints201612.0002.v1
Subject: Computer Science And Mathematics, Applied Mathematics Keywords: change point; estimation; consistency; panel data; short panels; boundary issue; structural change; bootstrap; non-life insurance; change in claim amounts
Online: 1 December 2016 (10:02:03 CET)
Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.
ARTICLE | doi:10.20944/preprints202308.0189.v1
Subject: Social Sciences, Psychology Keywords: aggression questionnaire; machine learning; short-form questionnaire; adolescents
Online: 2 August 2023 (11:19:27 CEST)
For adolescents, high aggression is often associated with suicide, physical injury, worse academic performance, and crime. Therefore, there is a need for early identification and intervention for highly aggressive adolescents. The Buss-Warren Aggression Questionnaire (BWAQ) consists of 34 items, and the longer the scale, the more likely participants are to make an insufficient effort response (IER), which reduces the credibility of the results and increases the cost of implementation. The study aimed to develop a shorter BWAQ using machine learning (ML) techniques to reduce the frequency of IER and decrease implementation costs meantime. First, an initial version of the short-form questionnaire was determined using Stepwise Regression and ANOVA F-test. Then, a machine learning algorithm determined the optimal short-form questionnaire (BWAQ-ML). Finally, the reliability and validity of the optimal short-form questionnaire were tested using independent samples. The BWAQ-ML has 88% fewer items than the BWAQ. It has AUC, accuracy, recall, precision, and F1 scores of 0.85, 0.85, 0.89, 0.83, and 0.86, respectively, and good psychometric properties. The BWAQ-ML can effectively measure individual aggression and can be used as a simplified version of BWAQ.
ARTICLE | doi:10.20944/preprints201609.0119.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: short-term load forecasting; radial basis function neural network; support vector regression; particle swarm optimization; adaptive annealing learning algorithm
Online: 29 September 2016 (12:22:20 CEST)
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.
ARTICLE | doi:10.20944/preprints202311.0560.v1
Subject: Environmental And Earth Sciences, Oceanography Keywords: sea ice concentration; recurrent neural network; Arctic sea ice prediction; short-term prediction
Online: 8 November 2023 (14:45:25 CET)
Arctic sea ice prediction holds significant importance for facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the model's capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction. Additionally, meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin ice region at the edge of the sea ice.
ARTICLE | doi:10.20944/preprints201907.0062.v1
Subject: Medicine And Pharmacology, Other Keywords: ballistocardiography; seismocardiography; ultra-short heart rate variability; stress evaluation; smartphone; accelerometers
Online: 3 July 2019 (10:49:55 CEST)
Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional ECG. Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.
ARTICLE | doi:10.20944/preprints201608.0206.v3
Subject: Engineering, Electrical And Electronic Engineering Keywords: impulse radio ultra-wideband (IR-UWB) radar; noncontact; short-range; vital signs; ensemble empirical mode decomposition (EEMD); continuous-wavelet transform (CWT)
Online: 10 November 2016 (07:13:01 CET)
The radar sensor described realizes healthcare monitoring capable of detecting subject chest-wall movement caused by cardiopulmonary activities, and wirelessly estimating the respiration and heartbeat rates of the subject without attaching any devices to the body. Conventional single-tone Doppler radar can only capture Doppler signatures because of a lack of bandwidth information with noncontact sensors. In contrast, we take full advantage of impulse radio ultra-wideband (IR-UWB) radar to achieve low power consumption and convenient portability, with a flexible detection range and desirable accuracy. A noise reduction method based on improved ensemble empirical mode decomposition (EEMD) and a vital sign separation method based on the continuous-wavelet transform (CWT) are proposed jointly to improve the signal-to-noise ratio (SNR) in order to acquire accurate respiration and heartbeat rates. Experimental results illustrate that respiration and heartbeat signals can be extracted accurately under different conditions. This noncontact healthcare sensor system proves the commercial feasibility and considerable accessibility of using compact IR-UWB radar for emerging biomedical applications.
ARTICLE | doi:10.20944/preprints202309.0676.v1
Subject: Engineering, Mechanical Engineering Keywords: remaining useful life; maximum correlation kurtosis deconvolution; multi-scale permutation entropy; long short-term memory
Online: 11 September 2023 (11:30:22 CEST)
The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.
ARTICLE | doi:10.20944/preprints202202.0055.v2
Subject: Social Sciences, Psychology Keywords: Coronavirus disease 2019/COVID-19; Internet Gaming Disorder Scale9—Short form (IGDS9-SF); university students; factorial structure/psychometric properties/structural validity/validation; cultur*/collectivisti*/individualis*; invariance; gender; game type
Online: 21 March 2022 (11:04:57 CET)
The prevalence of internet gaming disorders (IGD) is considerably high among youth, especially with social isolation imposed by the ongoing COVID-19 pandemic. IGD adversely affects mental health, quality of life, and academic performance. The Internet Gaming Disorder Scale (IGDS9-SF) is designed to detect IGD according to DSM-IV diagnostic criteria. However, inconsistent results are reported on its capacity to diagnose IGD evenly across different cultures. To ensure the suitability of the IGDS9-SF as a global measure of IGD, this study examined the psychometric properties of the IGDS9-SF in a sample of Sri Lankan university students (N = 322, mean age =17.2 ± 0.6, range = 16-18 years, 56.5% males) and evaluated its measurement invariance across samples from Sri Lanka, Turkey, Australia, and the USA. Among Sri Lankan students, a unidimensional structure expressed good fit, invariance across different groups (e.g., gender, ethnicity, and income), adequate criterion validity (strong correlation with motives of internet gaming, daily gaming duration, and sleep quality), and good reliability (alpha = 0.81). Males and online multiplayers expressed higher IGD levels, greater time spent gaming, and more endorsement of gaming motives (e.g., Social and Coping) than females and offline players. Across countries, the IGDS9-SF was invariant at the configural, metric, and scalar levels, albeit strict invariance was not maintained. The lowest and highest IGD levels were reported among Turkish and American respondents, respectively. In conclusion, the IGDS9-SF can be reliably used to measure IGD among Sri Lankan youth. Because the scale holds scalar invariance across countries, its scores can be used to compare IGD levels in the studied countries.
ARTICLE | doi:10.20944/preprints202307.1699.v1
Subject: Biology And Life Sciences, Life Sciences Keywords: microbiome; spaceflight; lymphatic; short chain fatty acid; trascriptome
Online: 25 July 2023 (10:46:09 CEST)
The microbiome is critical to the function of higher organisms and the gastrointestinal microbiome in chief among them. We do not know what impact the spaceflight GI microbiome has on organismal health in the absence of other confounding factors. We used a fecal transplant model to address this question and we found that we can recapitulate some of the functional aspects of the spaceflight microbiome on the ground but the impact on organismal health was ambiguous showing neither a defined improvement nor a impairment to lymph transport, growth, or immune populations. Histidine treatment was able to alter some of the functional aspects of the microbiome. Overall it we can conclude that the spaceflight microbiome is not pathological but has distinct impacts on organismal health.
ARTICLE | doi:10.20944/preprints201810.0345.v1
Subject: Physical Sciences, Mathematical Physics Keywords: Born-Jordan, quantization, short time propagator
Online: 16 October 2018 (08:59:09 CEST)
We have shown in previous work that the equivalence of the Heisenberg and Schrödinger pictures of quantum mechanics requires the use of the Born and Jordan quantization rules. In the present work we give further evidence that the Born--Jordan rule is the correct quantization scheme for quantum mechanics. For this purpose we use correct short-time approximations to the action functional, initially due to Makri and Miller, and show that these lead to the desired quantization of the classical Hamiltonian.
ARTICLE | doi:10.20944/preprints202001.0068.v1
Subject: Biology And Life Sciences, Food Science And Technology Keywords: prebiotics; polyols; short chain fatty acids, Headspace Analysis
Online: 9 January 2020 (04:44:49 CET)
This pilot study of Streptococcus mutans ATCC 35668 grown in media with and without polyols (erythritol) measured the resultant metabolites, including the short chain fatty acids by using head space analysis. Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing no polyols or either erythritol or xylitol and Streptococcus mutans (ATCC 35668) was grown aerobically. After 48 hours of growth the supernatant were harvested and centrifuged to pellet bacteria. Supernatants were removed from bacterial pellets then submitted for Short Chain Fatty Acid (SCFA) analysis with an Agilent Technologies (Santa Clara, CA 95051) system configured from three components, a 5973 mass selective detector, a 6890N gas chromatographer, and a 7697A headspace sampler. Streptococcus mutans growing in Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose but containing no polyols produced the following short chain fatty acids: methyl isovalerate, acetic acid, propionic acid, butanoic acid, pentanoic acid, ethyl butaric acid, 4-methylvaleric acid, hexanoic acid. When the Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing erythritol was used as media for this Streptococcus mutans strain, the following were produced: ethanol, acetoin, and acetic acid. Our results would suggest that constituents of the media may affect the bacterial metabolite production.
ARTICLE | doi:10.20944/preprints201910.0183.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: ARDL; Inflation; Interest; Long-run; RGDPPC; Short-run; Unemployment
Online: 16 October 2019 (09:40:00 CEST)
Research background: Relationship between inflation rate, unemployment rate, interest rate and real gross domestic product per capita in Nigeria. However, there seems to be a short-run or long-run relationship among the macroeconomic variables.Purpose: This study investigated the impact of the inflation rate, unemployment rate and interest rate on real gross domestic product per capita (RGDPPC) (proxy for economic growth) and proffered recommendations towards enhancing economic growth and to reduce the distasteful effects of inflation rate, unemployment rate and interest rate in Nigeria in this present time economic challenges.Research methodology: This study applied a linear dynamic model Autoregressive Distributed Lag (ARDL) modeling technique to analyze the short-run dynamics and long-run relationship of the economic growth in Nigeria over the sample period between 1984 and 2017 using annual secondary data extracted from World Bank Development Indicators Report (last updated January 2019).Results: The empirical results showed that there was long-run relationship between inflation rate, unemployment rate and interest rate on real gross domestic product per capita (proxy for economic growth) in Nigeria. The result further revealed that only unemployment rate had a significant positive impact on real gross domestic product per capita in the long-run and inflation rate had a significant negative impact on real gross domestic product per capita in the short-run.Novelty: Therefore, the study concluded that unemployment rate and inflation rate proved to have significant impacts on economic growth in the long-run and short-run respectively. Formulation of policies to reduce unemployment through the adoption of labour concentrated technique of production, entrepreneurship development and policy to keep the inflation rate at single digit.
ARTICLE | doi:10.20944/preprints202208.0170.v1
Subject: Computer Science And Mathematics, Applied Mathematics Keywords: neuron; astrocyte; network; short-term memory; spatial frequency; computational biology
Online: 9 August 2022 (04:04:31 CEST)
Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article we will leverage a computational neuron-astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we will demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage.
ARTICLE | doi:10.20944/preprints202209.0088.v1
Subject: Engineering, Mechanical Engineering Keywords: Short fiber-reinforced composite; Random fields; Plasticity; Numerical simulation
Online: 6 September 2022 (10:11:54 CEST)
For the numerical simulation of components made of short fiber-reinforced composites the correct prediction of the deformation including the elastic and plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches the information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields. In this study numerical simulations of tensile test specimens are conducted utilizing a finite deformation elastic-ideal plastic material model. A selection of the material parameters covering the elastic and plastic domain are represented by cross-correlated second-order Gaussian random fields to incorporate the probabilistic nature of the material parameters. To validate the modeling approach tensile tests until failure are carried out experimentally, that confirm the assumption of spatially distributed material behavior in both the elastic and plastic domain. Since the correlation lengths of the random fields cannot be determined by pure analytic treatments, additionally numerical simulations are performed for different values of the correlation length. The numerical simulations endorse the influence of the correlation length on the overall behavior. For a correlation length of 5mm a good conformity with the experimental results is obtained. Therefore, it is concluded, that the presented modeling approach is suitable to predict the elastic and plastic deformation of a set of tensile test specimens made of short fiber-reinforced composite sufficiently.
ARTICLE | doi:10.20944/preprints202104.0765.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: ribosome biogenesis; rRNA processing; RNase MRP; long/short 5.8S rRNA
Online: 29 April 2021 (07:54:07 CEST)
Processing of the RNA polymerase I pre-rRNA transcript into the mature 18S, 5.8S, and 25S rRNAs requires removing the “spacer” sequences. The canonical pathway for the removal of the ITS1 spacer, located between 18S and 5.8S rRNAs in the primary transcript, involves cleavages at the 3’ end of 18S rRNA and at two sites inside ITS1. The process generates a long and a short 5.8S rRNA that differ in the number of ITS1 nucleotides retained at the 5.8S 5’ end. Here we document a novel pathway that generates the long 5.8S for ITS1 while bypassing cleavage within ITS1. It entails a single endonuclease cut at the 3’-end of 18S rRNA followed by exonuclease Xrn1 degradation of ITS1. Mutations in RNase MRP increase the accumulation of long relative to short 5.8S rRNA; traditionally this is attributed to a decreased rate of RNase MRP cleavage at its target in ITS1, called A3. In contrast, we report here that the MRP induced switch between long and short 5.8S rRNA formation occurs even when the A3 site is deleted. Based on this and our published data, we propose that the switch may depend on RNase MRP processing RNA molecules other than pre-rRNA.
REVIEW | doi:10.20944/preprints202103.0712.v1
Subject: Medicine And Pharmacology, Oncology And Oncogenics Keywords: dietary fibers; short chain fatty acid; gut microbiota; colorectal cancer prevention; epigenetics
Online: 29 March 2021 (22:22:00 CEST)
Dietary factors play an important role in shaping the gut microbiome which, in turn, regulates the molecular events in colonic mucosa. The composition and resulting metabolism of the gut microbiome have been implicated in the development of colorectal cancer (CRC). Diets low in dietary fibers and phytomolecules as well as other lifestyle-related factors may predispose to CRC. Emerging evidence demonstrates that the predominance of microbes, such as Fusobacterium nucleatum, can predispose the colonic mucosa to malignant transformation. Dietary and lifestyle modifications have been demonstrated to restrict the growth of potentially harmful opportunistic organisms. In this study, we aim to present evidence regarding the relationship of dietary factors to the gut microbiome and development of CRC.
ARTICLE | doi:10.20944/preprints202305.0411.v1
Subject: Engineering, Civil Engineering Keywords: ab initio calculation; short-range ordering; rhenium effect; nickel alloys; additive manufacturing
Online: 6 May 2023 (10:13:49 CEST)
Abstract: Interactions in multicomponent Ni-Cr-Mo-Al-Re model alloy were determined by ab initio calculations in order to investigate the Re doping effect on Haynes 282 alloy. Simulation results provided an understanding of short-range interactions in the alloy and successfully pre-dicted the formation of a Cr and Re-rich phase. The Haynes 282 + 3 % wt. Re alloy was manu-factured using the additive manufacturing Direct Metal Laser Sintering (DMLS) technique, in which the presence of the (Cr17Re6)C6 carbide was confirmed by an XRD study. The results ob-tained provide useful information about the interactions between Ni, Cr, Mo, Al, and Re as a function of temperature. The 5-element model designed can lead to a better understanding of phenomena that occur during the manufacturing or heat treatment of modern, complex, multi-component Ni-based superalloys.
ARTICLE | doi:10.20944/preprints202009.0360.v1
Subject: Engineering, Civil Engineering Keywords: BFRP square tube; Concrete short column; Axial compression test; Simulation analysis
Online: 16 September 2020 (11:19:39 CEST)
Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.
ARTICLE | doi:10.20944/preprints202009.0189.v1
Subject: Engineering, Civil Engineering Keywords: BFRP square tube; Concrete short column; Axial compression test; Simulation analysis
Online: 8 September 2020 (11:29:11 CEST)
Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.
ARTICLE | doi:10.20944/preprints202107.0229.v1
Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: HPLC; NSAIDs; Isocratic; Short column; Drug mixture
Online: 9 July 2021 (15:23:57 CEST)
Nonsteroidal anti-inflammatory drugs (NSAIDs), which block the activity of cyclooxygenase (COX) isoenzymes and inhibit the synthesis of prostaglandin, have been used for pain relief. We have developed a method to separate a mixture of three NSAIDs, such as aspirin, paracetamol, and naproxen, using reverse-phase high-performance liquid chromatography (RP-HPLC). An isocratic mobile phase consisting of acidic water and acetonitrile was selected to run at a low flow rate, such as 0.8 mL/min. The mixture of three NSAIDs was injected at a low volume into a C18 column that was 150 mm in length and characterized using a UV detector at 230 nm. We identified three peaks in the chromatogram indicating the three compounds. The elution time of the peaks was less than 10 minutes. To identify multiple peaks on the isocratic flow using a short column, further studies are required regarding the proposed method to generate microfluidic devices for nanoLC.
REVIEW | doi:10.20944/preprints202210.0451.v2
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: economic agroforestry zone; Salix spp.; Populus spp.; Alnus spp.; short rotation coppice (SRC); short rotation forestry (SRF); energy wood.
Online: 31 October 2022 (09:26:58 CET)
The main goal of the review is to provide a summary and an assessment of the potential of fast-growing tree species for suitable transformation of agroforestry areas for biomass production in the Baltic Sea region. The article summarizes the research on the management process of agroforestry zones by establishing short rotation plantations with tree species Salix spp., Populus spp., Alnus spp. and looks at the perspectives of planning of these zones as biomass producers. Short rotation forestry (SRF) with a combination of species and a rotation time of 15 to 30 years, depending on the species used, is the most suitable approach for management of these agroforestry zones. Willows (Salix spp.) and poplars (Populus spp.) are suitable for short rotation coppice (SRC), as these tree species can be harvested at much shorter intervals, respectively, 1–5 and 4–10 years, facilitating their use in agricultural systems. In Alnus spp. short rotation plantation the life cycle for energy wood production is assumed to be 15-30 years. The black alder plantations in agroforestry zones are used for sawnwood and firewood production, with a rotation span of 20–40 years. Calculated economic agroforestry zone repayment period is about 10-15 years, if costs and prices as in 2021 are used.
REVIEW | doi:10.20944/preprints202105.0268.v1
Subject: Medicine And Pharmacology, Immunology And Allergy Keywords: achondroplasia; hypochondroplasia; dwarfism; short-limb; short stature; FGFR3; skeletal dysplasia; genetic condition; extensive limb lengthening
Online: 12 May 2021 (11:22:56 CEST)
Extensive limb lengthening (ELL) was completed in 75 patients: 66 achondroplasia and 9 hypochondroplasia. The average lengthening was 27cm for achondroplasia (12-40cm) and 17cm for hypochondroplasia (range 10-25cm). There were 48 females and 27 males. Lengthening was done either by 2-segment (14 patients; both tibias and/or both femurs) or by serial 4-segment lengthenings (64 patients; both femurs and tibias same time). Most patients also had bilateral humeral lengthening. Lengthenings were either juvenile-onset (31), adolescent-onset (38) or adult-onset (6). The average age at final follow-up was 26 years old (range 17-43 years). There were few permanent sequelae of complications. The most serious was one paraparesis. All patients returned to activities of normal living and only one was made worse by the surgery (paraparesis). This is the first study to show that ELL can lead to increase of height into the normal height range. Previous studies showed mean increases of height of up to 20cm, while this study consistently showed an average increase of 30 cm (range 15-40cm) for juvenile-onset and increase of 26cm (range 15-30cm) for adolescent-onset. This results in lower normal height at skeletal maturity for males and females. The adult-onset had a mean increase of 16.8 (range 12-22cm). This long-term follow-up study shows ELL can be done safely even with large lengthenings and that 4-segment lengthening may offer advantages over 2-segment lengthening. While the majority of cases were performed using external fixation, implantable limb lengthening promises to be an excellent alternative and perhaps an improvement.
REVIEW | doi:10.20944/preprints202304.0917.v1
Subject: Engineering, Energy And Fuel Technology Keywords: Predictive models; Weather research and forecasting (WRF); Uncertainty; Wind forecasting; Ultra short term and Short term; Wind power generation
Online: 25 April 2023 (10:10:36 CEST)
The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.
ARTICLE | doi:10.20944/preprints202305.2229.v1
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: KPC; blood culture; short-term culture; MALDI-TOF MS
Online: 31 May 2023 (10:50:42 CEST)
Carbapenemase resistance in Enterobacterales is a global public health problem and rapid and effective methods to detect resistance mechanisms are needed urgently. Our aim was to evaluate the performance of a MALDI-TOF MS based KPC detection protocol from patients’ positive blood cultures, short-term cultures and colonies at health care settings. Bacterial identification and KPC detection were achieved after protein extraction with organic solvents and target spot loading with suitable organic matrices. Confirmation of KPC production was performed by susceptibility tests, blaKPC amplification by PCR and sequencing. KPC direct detection (KPC-peak at approximately 28.681 Da) from patients’ positive blood cultures, short-term cultures and colonies, once bacterial identification was achieved, showed an overall sensibility and specificity of 100% (CI95: [95%,100%] and CI95: [99%, 100%], respectively). Concordance between hospital routine bacterial identification protocol and identification with this new methodology from the same extract used for KPC detection was ≥92%. This study represents the pioneering effort to directly detect KPC using MALD-TOF MS technology, conducted on patient-derived samples obtained at the hospitals for validation purposes, in a multi-resistance global context that requires concrete actions to preserve available therapeutic options and reduce the spread of antibiotic resistance markers.
ARTICLE | doi:10.20944/preprints202306.1694.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Tag7; Hsp70; TNFR1, signal transduction, cytotoxity, short peptides.
Online: 23 June 2023 (15:06:46 CEST)
TNF cytokine receptor TNFR1 protein plays an important role in immune response and autoimmune diseases. Previously, we demonstrated that the peptide of the innate immunity protein Tag7 (PGLYRP1), designated as 17.1, is able to perform the functions of a full-size Tag7 protein, namely, to inhibit the transmission of a signal through the TNFR1 receptor and activate this receptor in complex with the major heat shock protein Hsp70. In this study, we show that two peptides derived from 17.1 – 17.1A and 17.1B have different affinities to the TNFR1 receptor and the Hsp70 protein, and each of them is able to perform only one of the above functions: peptide 17.1A is only able to inhibit signal conduction through the TNFR1 receptor, and peptide 17.1B can only activate this receptor is in complex with Hsp70. Thus, it is possible to modulate the activity of TNFR1 receptor and further perform its specific inhibition or activation in the treatment of various autoimmune or oncological diseases.
ARTICLE | doi:10.20944/preprints202012.0784.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Bacillus subtilis; flow cytometry; gastrointestinal health; peripheral blood mononuclear cells (PBMC); probiotic; short chain fatty acid
Online: 31 December 2020 (09:46:43 CET)
Probiotics make up a large and growing segment of the commercial market of dietary supplements and are touted as offering a variety of human health benefits. Some of the purported positive impacts of probiotics include, but are not limited to, stabilization of the gut microbiota, prevention of gastrointestinal disorders and modulation of the host immune system. Current research suggests that the immunomodulatory effects of probiotics are strain specific and vary in mode of action. Here, we examined the immunomodulatory properties of Bacillus subtilis strain DE111 in a healthy human population. In a randomized, double blind, placebo-controlled four-week intervention, we examined peripheral blood mononuclear cells (PBMCs) at basal levels pre- and post-treatment as well as in response to stimulation with bacterial lipopolysaccharide (LPS). We observed an anti-inflammatory effect of B. subtilis, manifested as a decrease in immune cell populations within the basal state along with an increase in anti-inflammatory immune cells in response to LPS stimulation. Overall gastrointestinal health, microbiota, and circulating and fecal markers of inflammation and gut barrier function were largely unaffected by DE111 treatment. These data suggest that the novel probiotic B. subtilis DE111 may have clinical applications in modulating immune homeostasis via anti-inflammatory mechanisms.
ARTICLE | doi:10.20944/preprints202211.0437.v3
Subject: Engineering, Civil Engineering Keywords: deep neural network; long short-term memory; suspended sediment; discharge
Online: 16 December 2022 (08:08:08 CET)
The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.
REVIEW | doi:10.20944/preprints202101.0098.v2
Subject: Biology And Life Sciences, Cell And Developmental Biology Keywords: Cell proliferation; congenital heart disease; embryonic lethality; folliculogenesis; neuropsychological profile; prolonged cell cycle; short stature; Turner syndrome
Online: 3 March 2022 (04:27:26 CET)
Turner syndrome (TS) is a chromosomal disorder that is caused by a missing or structurally ab-normal second sex chromosome. Subjects with TS are at an increased risk of developing intrauterine growth retardation, low birth weight, short stature, congenital heart diseases, infertility, obesity, dyslipidemia, hypertension, insulin resistance, type 2 diabetes mellitus, metabolic syndrome, and cardiovascular diseases (stroke and myocardial infarction). The underlying pathogenetic mechanism of TS is unknown. The assumption that X chromosome-linked gene haploinsufficiency is associated with the TS phenotype is questioned since such genes have not been identified. Thus, other pathogenic mechanisms have been suggested to explain this phenotype. Morphogenesis encompasses a series of events that includes cell division, the production of migratory precursors and their progeny, differentiation, programmed cell death, and integration into organs and systems. The precise control of the growth and differentiation of cells is essential for normal development. The cell cycle frequency and the number of proliferating cells are essential in cell growth. 45,X cells have a failure to proliferate at a normal rate, leading to a decreased cell number in a given tissue during organogenesis. A convergence of data indicates an association between a prolonged cell cycle and the phenotypical features in Turner syndrome. This review aims to examine old and new findings concerning the relationship between a prolonged cell cycle and TS phenotype. These studies reveal a diversity of phenotypic features in TS that could be explained by reduced cell proliferation. The implications of this hypothesis for our understanding of the TS phenotype and its pathogenesis are discussed. It is not surprising that 45,X monosomy leads to cellular growth pathway dysregulation with profound deleterious effects on both embryonic and later stages of development. The prolonged cell cycle could represent the beginning of the pathogenesis of TS, leading to a series of phenotypic consequences in embryonic/fetal, neonatal, pediatric, adolescence, and adulthood life.
ARTICLE | doi:10.20944/preprints201908.0321.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Intra–Seasonal rain fall characteristics; Short rains; WRF Model
Online: 30 August 2019 (09:57:58 CEST)
Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.
Subject: Engineering, Electrical And Electronic Engineering Keywords: wind power forecasting; short-term prediction; hybrid deep learning; wind farm; long short term memory; gated recurrent network and convolutional layers
Online: 22 September 2020 (03:45:59 CEST)
Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.
ARTICLE | doi:10.20944/preprints201905.0306.v1
Subject: Business, Economics And Management, Finance Keywords: return reversals; exchange-traded funds (ETFs); attention hypothesis; disagreement hypothesis; short selling
Online: 27 May 2019 (09:53:03 CEST)
We find that the overnight returns of Korean exchange-traded index funds (ETFs) are significantly positive, whereas the subsequent intraday returns are negative. These intraday return reversals are caused by relatively higher opening prices than the closing prices. In the Korean ETF market, where institutional investors are dominant participants, the return reversals are not explained by the attention hypothesis as in Berkman et al. . Hence, we investigate whether the disagreement hypothesis can explain return reversals. Under the disagreement situations between positive and negative traders at the open, positive traders can have a positive influence on the ETF prices by increasing their investments. However, negative traders, who give up investments due to limited short selling opportunities in the ETF market, have no effects on the prices. Comparing ETF markets with KOSPI 200 Futures where there are no restrictions on short selling, we find that short selling constraints are significant factors for the return reversals. This implies that disagreement among the investors can cause return reversals even in the markets without noise traders. Using unique Korean market data, we conclude that return reversals cannot be completely explained by the attention hypothesis, and that disagreement among investors is also a significant factor for the return reversals. This study contributes to the existing literature by showing that the attention hypothesis does not explain return reversals in the ETF market completely, and suggesting the disagreement hypothesis as an alternative.
Subject: Engineering, Automotive Engineering Keywords: Computational fluid dynamic; Long short term memory; Vortex bladeless wind turbine; Prediction; Correlation matrix.
Online: 9 June 2021 (07:38:21 CEST)
Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.
ARTICLE | doi:10.20944/preprints202311.0410.v1
Subject: Medicine And Pharmacology, Pediatrics, Perinatology And Child Health Keywords: short bowel syndrome; lymphocyte subsets; immunoglobulins; children
Online: 7 November 2023 (10:16:37 CET)
Abstract Background: Massive resection of the small intestine leading to short bowel syndrome (SBS) deprives organism of many immunocompetent cells concentrated in gut associated lymphoid tissue- the largest immune organ in humans. We have aimed to access the influence of bowel resection on adaptive immunity in children, basing on peripheral lymphocyte subsets and serum immunoglobulins. Methods: 15 children, who underwent bowel resection in the first month of life and required further home parenteral nutrition were enrolled into the study.. Based on flow cytometry the following subsets of lymphocytes were evaluated: T, B, NK, CD4+, C8+and activated T cells. Results: Significantly statistical differences were found for the rates of lymphocytes B, T, CD8 + and NK cells. Absolute count of NK cells was lower in SBS group than in the control group. Absolute counts of lymphocytes, lymphocytes B, T, CD4+ and percentages of lymphocytes CD4+, and activated T cells inversely correlated with the time after resection. Conclusions: Children with SBS do not present with clinical signs of immunodeficiency as well as deficits in peripheral lymphocyte subsets and serum immunoglobulins. The tendency of the lymphocyte subpopulations to decrease over time after resection points out the necessity for longer follow- up.
ARTICLE | doi:10.20944/preprints201904.0058.v1
Subject: Business, Economics And Management, Econometrics And Statistics Keywords: load forecast; short term; probabilistic; Gaussian processes
Online: 4 April 2019 (16:01:54 CEST)
We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.
REVIEW | doi:10.20944/preprints202309.1023.v1
Subject: Engineering, Chemical Engineering Keywords: biomass gasification; lignocellulosic material; biofuels; ethanol; short chain alcohols
Online: 15 September 2023 (02:43:16 CEST)
The fermentation of syngas is an attractive technology that can be integrated with gasification of lignocellulosic biomass. The coupling of these two technologies allows for treating a great variety of raw materials. Lignin usually hinders microbial fermentations; thus, the thermal decomposition of the whole material into small molecules allows for the production of fuels and other types of molecules using syngas as substrate, a process performed at mild conditions. Syngas contains mainly hydrogen, carbon monoxide and carbon dioxide in varying proportions. These gases have a low volumetric energy density, resulting more interesting its conversion into higher energy density molecules. Syngas can be transformed by microorganisms, thus avoiding the use of expensive catalysts, which may be subject to poisoning. However, the fermentation is not free of suffering from inhibitory problems. The presence of trace components in syngas may cause a decrease in fermentation yields or cause a complete cessation of bacteria growth. The presence of tar and hydrogen cyanide are just examples of this fermentation's challenges. Syngas cleaning impairs significant restrictions in technology deployment. The technology may seem promising, but it is still far from large-scale application due to several aspects that still need to find a practical solution.
ARTICLE | doi:10.20944/preprints202304.0598.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: short wave infrared; LiDAR; photodetector array; dark current detection
Online: 20 April 2023 (03:09:20 CEST)
The shortwave infrared Ge-Si photodetector will become the core device of the LiDAR optical receiver. In order to meet the urgent demand for photodetectors in the LiDAR field, we have designed and produced a 32×32 pixel Ge-Si photodetector array proposed and developed to meet the performance requirements of the detector array. A dark current detection system for fast scanning and detecting large-scale Ge-Si detector arrays is proposed and developed to achieve rapid detection of dark current in each pixel of the detector. The system was used to validate the main performance indicators of the detector array we designed, achieving rapid discrimination of array performance and rapid localization of damaged pixels. The scanning test results show that the average dark current of the detector array chip we designed is at the nano ampere level, and the proportion of bad points is less than 1%. The consistency of the array chip is high, which can meet the requirements of light detection at the receiving end of the LiDAR. This work laid the foundation for our subsequent development of a LiDAR prototype system.
ARTICLE | doi:10.20944/preprints201801.0210.v1
Subject: Biology And Life Sciences, Endocrinology And Metabolism Keywords: fructose; intestinal microbiota; short-chain fatty acids; metabolic profiling
Online: 23 January 2018 (05:31:27 CET)
Increased sugar intake is implicated in Type-2 diabetes and fatty liver disease. Mechanisms by which glucose and fructose components promote these conditions are unclear. We hypothesize that alterations in intestinal metabolite and microbiota profiles specific to each monosaccharide are involved. Two groups of six adult C57BL/6 mice were fed for 10-weeks with a diet where either glucose or fructose was the sole carbohydrate component (G and F, respectively). A third group was fed with normal chow (N). Fecal metabolites were profiled every 2-weeks by 1H NMR and microbial composition was analysed by real-time PCR (qPCR). Glucose tolerance was also periodically assessed. N, G and F mice had similar weight gains and glucose tolerance. Multivariate analysis of NMR profiles indicated that F mice were separated from both N and G, with decreased butyrate and glutamate and increased fructose, succinate, taurine, tyrosine and xylose. Compared to N and G, F mice showed a shift in microbe populations from gram-positive Lactobacillus spp. to gram-negative Enterobacteria species. Substitution of normal chow carbohydrate mixture by either pure glucose or fructose for 10 weeks did not alter adiposity or glucose tolerance. However, F G and N mice generated distinctive fecal metabolite signatures with incomplete fructose absorption as a dominant feature of F mice.
ARTICLE | doi:10.20944/preprints202101.0518.v1
Subject: Social Sciences, Psychology Keywords: mobile phones; health promotions; short message service; health students
Online: 25 January 2021 (15:53:53 CET)
Students are regarded as frequent users of mobile phones which has proven to be a convenient and acceptable method to promote healthy lifestyle. Students usually engage in relatively high levels of risky behavior and make unhealthy lifestyle choices, a study that investigates how health students access health information is necessary. The study adopted a descriptive cross-sectional study which was undertaken among third-year nursing students from three nurses training institutions in Ghana. A total of 270 students participated in the study. Most of the respondents who were currently subscribers of the health messages reported that they usually received health information on reproductive health issues, nutrition, and practicing safe sex. Most of the health students revealed that they needed more information on safe sex, diet, managing weight, and stress management. The results also show that health students are likely to remember and share short messages with friends. The findings serve as an ‘eye-opener’ for health educators and mobile service providers concerning factors that should be taken into consideration when framing health text messages that will attract health students.
ARTICLE | doi:10.20944/preprints202109.0026.v1
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: Cefotaxime; S. haemolyticus; neonates; sub-MIC; biofilms; short-term evolution
Online: 1 September 2021 (14:39:54 CEST)
Critical care of neonates involves substantial usage of antibiotics and exposure to multidrug resistant (MDR) nosocomial pathogens. These pathogens are often exposed to sub-MIC doses of antibiotics which might result in a range of physiological effects. Therefore, to understand the outcome of sub-inhibitory dosage of antibiotics on Staphylococcus populations, nasal swab specimens were collected from 34 neonates admitted to the Sick Newborn Care Unit between 2017-2018, a total of 41 non-repetitive isolates were included in this study. Staphylococcus haemolyticus was the prevalent species (58.54%) with high non-susceptibility to cefotaxime (CTX) (79.16%), gentamicin (87.50%), and meropenem (54.17%). Biofilm forming abilities of S. haemolyticus isolates in the presence of sub-optimal CTX (30μg/mL), the predominantly prescribed β-lactam antibiotic, were then determined by crystal violet assays and extracellular DNA (eDNA) quantitation. CTX was found to significantly enhance biofilm production among the non-susceptible isolates (p-valueWilcoxin test- 0.000008) with increase in eDNA levels (p-valueWilcoxin test- 0.000004). Additionally, no changes in non-susceptibility were observed among populations of two MDR isolates, JNM56C1 and JNM60C2 after >500 generations of growth in the absence of antibiotic selection in vitro. These findings demonstrate that sub-MIC concentration of CTX induces biofilm formation and short-term non-exposure to antibiotics does not alter non-susceptibility among S. haemolyticus isolates.
ARTICLE | doi:10.20944/preprints202012.0310.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Variola major; phylogeographical analysis; long-term calibrations; short- term calibrations
Online: 14 December 2020 (09:21:34 CET)
In order to reconstruct the origin and pathways of variola virus (VARV) dispersion, we analyzed 47 VARV isolates available in public databases and their SNPs. The mean substitution rate of the whole genomes was 9.41x10-6 (95%HPD:8.5-11.3x10-6) substitutions/site/year. The time of the tree root was estimated to be a mean 68 years (95%HPD:60.5–75.9). The phylogeographical analysis showed that the Far East and India were the most probable locations of the tree root and of the inner nodes, respectively, whereas for the outer nodes it corresponded to the sampling locations. The Bayesian Skyline plot showed that the effective number of infections started to grow exponentially in 1915-1920, peaked in the 1940s, and then decreased to zero. Our results suggests that the VARV major strains circulating between 1940s-1970s probably shared a common ancestor originated in the Far East; subsequently moved to India, which became the center of its dispersion to eastern and southern Africa, and then to central Africa and the Middle East, probably following the movements of people between south-eastern Asia and the other places with a common colonial history. These findings may help to explain the controversial reconstructions of the history of VARV obtained using long- and short- term calibrations.
ARTICLE | doi:10.20944/preprints202103.0302.v2
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Searaser; Flow-3D; Prediction; Long short term memory; deep neural network; Root mean error.
Online: 13 April 2021 (09:51:25 CEST)
Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.
ARTICLE | doi:10.20944/preprints202312.0038.v1
Subject: Physical Sciences, Nuclear And High Energy Physics Keywords: quasi-elastic scattering; short-range correlations; cold dense nuclear matter
Online: 1 December 2023 (08:00:04 CET)
The fundamental theory of nuclear interactions, Quantum Chromodynamics (QCD), operates in terms of quarks and gluons at higher resolution. At low resolution the relevant degrees of freedom are nucleons. Two-nucleon Short-Range Correlations (SRC) help to interconnect these two descriptions. SRCs are temporary fluctuations of strongly interacting close pairs of nucleons. The distance between the two nucleons is comparable to their radii and their relative momenta are larger than the fermi sea level. According to the electron scattering experiments held in the last decade, SRCs have far-reaching impacts on many-body systems, the nucleon-nucleon interactions, and nuclear substructure. The modern experiments with ion beams and cryogenic liquid hydrogen target make it possible to study properties of the nuclear fragments after quasi-elastic knockout of a single nucleon or an SRC pair. The first SRC experiment at BM@N (2018) with a carbon beam has shown that detection of an intact 11B nucleus after interaction selects out the quasi-elastic knockout reaction with minimal contribution of initial- and final-state interactions. Also, 25 events of SRC-breakups showed agreement in SRC properties as knows from electron beam experiments. The analysis of the second measurement of SRC at BM@N held in 2022 with an improved setup is currently ongoing. The SRC project at JINR moved to a new experimental area in 2023, where the next measurement is being planned in terms of experimental setup and physics goals.
ARTICLE | doi:10.20944/preprints201807.0124.v1
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: Trypanosomatidae; Kraken taxonomic assignment tool; Bowtie2 fast short reads aligner; Ancient DNA; Parasitome; Co-infection
Online: 6 July 2018 (16:58:45 CEST)
Proper species identification from ancient DNA samples is a difficult task that sheds light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies (Santiago-Rodriguez et al. 2016) has called into question the identification of Leishmania in South America. Here, the metagenomic data filed in MG-RAST (Metagenomics RAST server) were used for a further attempt to identify members of the Trypanosomatidae family infecting these ancient remains. For this purpose, we used two metagenomic analysis tools. In the first step, data were analysed using the ultrafast metagenomic sequence classifier, based on exact alignment of k-mers (Kraken). In the second step, we used Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. We then compared the output results. These approaches highlight some interesting findings on potential infections by human pathogenic trypanosomatids in these three pre-Columbian mummies.
ARTICLE | doi:10.20944/preprints201912.0213.v1
Subject: Medicine And Pharmacology, Endocrinology And Metabolism Keywords: short stature; type 2 diabetes; end-stage renal disease; mortality
Online: 16 December 2019 (11:12:15 CET)
Short stature has been associated with increased various disease and all-cause death, but no reliable data exist the association between height and end-stage renal disease (ESRD) in diabetic patients. We investigated the relationship between short stature, development of ESRD, and mortality in type 2 diabetes. This study analyzed clinical data using the National Health Insurance Database in Korea. Height was stratified by five groups according to age and sex. Risk of ESRD and all-cause mortality was analyzed with Cox proportional hazards models. During a 6.9-year follow-up period, 220,457 subjects (8.4%) died and 28,704 subjects (1.1%) started dialysis. Short stature significantly increased the incidence of ESRD and all-cause mortality in the overall cohort analysis. In multivariable analysis, hazard ratios (HR) for development of ESRD comparing the highest versus lowest quartiles of adult height were 0.86 (95% confidence interval (CI), 0.83–0.89). All-cause mortality also decreased with highest height compared to patients with lowest height after fully adjusting for confounding variables (HR 0.79, 95% CI, 0.78–0.81). Adult height had an inverse relationship with newly diagnosed ESRD and all-cause in both males and females. Short stature is strongly associated with an increased risk of ESRD and all-cause mortality in type 2 diabetes.
ARTICLE | doi:10.20944/preprints202308.0682.v1
Subject: Engineering, Mechanical Engineering Keywords: Duffing equation; deep learning; neural networks; recurrent neural networks; long short term memory.
Online: 8 August 2023 (12:58:08 CEST)
This study uses machine learning to predict the convergence results of the Duffing equation with and without damping. The Duffing equation represents a nonlinear second-order differential equation with interesting behavior in undamped free vibration and forced vibration with damping. Convergence alternates randomly between 1 and -1 in undamped free vibration, depending on initial conditions. For forced vibration with damping, multiple factors influence vibration patterns. We utilize the fourth-order Runge-Kutta method to collect convergence results for both conditions. Machine learning techniques, specifically the long short term memory (LSTM) and LSTM-Neural Network (LSTM-NN) method, are employed to predict these convergence values. The LSTM-NN model is a hybrid approach that combines the LSTM method with the addition of hidden layers of neurons. Both the LSTM and LSTM-NN models are thoroughly explored and analyzed in this research. The research process involves three stages: data preprocessing, training, and verification. The results show that the LSTM-NN model becomes more adept at predicting binary datasets, boasting an impressive accuracy of up to 98%. However, when it comes to predicting multiple solutions, the traditional LSTM method outperforms the LSTM-NN approach.
Subject: Engineering, Electrical And Electronic Engineering Keywords: arm motion recognition; micro-doppler signature; time series analysis; dynamic time warping; long short-term memory
Online: 16 December 2019 (11:42:44 CET)
Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.
Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: zebrafish; Danio rerio; sperm motility; fertilization; short-term storage; extender
Online: 27 November 2020 (10:11:39 CET)
The zebrafish Danio rerio is suitable to study gametes as a model organism. There were > 70% of zebrafish spermatozoa activated, because they were contaminated with urine or excrement. The movement of spermatozoon in water was propagated along the flagellum at 16 s after sperm activation, then damped from the end of the flagellum for 35 s and fully disappear at 61 s after activation. For artificial fertilization, milt must be added to an extender, which stops the movement of sperm and keeps the sperm motionless until fertilization. E400 was shown to be the most suitable extender as it allows to store sperm for fertilization for 6 to 12 h at 0-2oC. Sperm motility decreased only to 36% at 12 h post stripping (HPS) for E400 extender and to 19% for Kurokura extender. To achieve an optimal level of fertilization and hatching, a test tube with a well-defined amount of 6,000,000 spermatozoa in E400 extender per 100 eggs and 100 µl of activation solution has proved to be more successful than using a Petri dish. The highest fertilization and hatching rates reached 80% and 40-60%, respectively, with milt stored for 1.5 h in E400 extender at 0-2oC.
ARTICLE | doi:10.20944/preprints202306.1404.v1
Subject: Biology And Life Sciences, Food Science And Technology Keywords: 16S rRNA sequencing; diet; popped amaranth; gut microbiota, short-chain fatty acids
Online: 20 June 2023 (08:19:25 CEST)
Popped amaranth is a nutritious food consumed since pre-Hispanic times, currently its consumption has been associated with recovery of malnourished children. However, there is no information on the impact that popped amaranth consumption has on gut microbiota composition. A non-randomized pilot trial was conducted to evaluate the changes on composition, structure, and function of gut microbiota of stunted children who received four grams of popped amaranth daily for three months. Stool and serum were collected at the beginning and at the end of the trial. Short-chain-fatty acids were quantified and gut bacterial composition was analyzed by 16S rRNA gene sequencing. Biome-try and hematology results showed that children have no other pathology more than to be low-height-for age. Increase of total SCFAs levels in feces was ob-served as well as a decrease in relative abundance of Alistipes putredinis, Bac-teroides coprocola, and Bacteroides stercoris, bacteria related with inflammation and colitis. On the contrary, an increase in relative abundance of Akkermansia muciniphila and Streptococcus thermophiles, bacteria associated with health and longevity, was observed. Results have proven that popped amaranth, a nutri-tious food, help to combat children malnutrition through gut microbiota modu-lation.
CASE REPORT | doi:10.20944/preprints202308.1913.v1
Subject: Medicine And Pharmacology, Gastroenterology And Hepatology Keywords: short bowel syndrome; parenteral nutrition; reconstructive gastrointestinal surgery
Online: 29 August 2023 (08:58:41 CEST)
Short bowel syndrome (SBS) in adults is defined as having less than 180 to 200 cm of remaining small bowel [1–4]. Many literature sources do not provide precise epidemiological data, challenges in estimating the prevalence of SBS include its multifactorial etiology and varying definitions [1–7]. The most common pathologies leading to SBS include Crohn disease, mesenteric ischemia, radiation enteritis, post-surgical adhesions, and post-operative complications [1,2,4–6]. In advanced SBS, parenteral nutrition may be required to ensure that all vital nutrients are delivered directly through the venous system, bypassing the gastrointestinal tract [1,8–10]. In this article, we present a clinical case of a patient who underwent parenteral nutrition for four months due to SBS. The surgical team performed reconstructive re-anastomosis of both the small and large bowel, leading to the discontinuation of parenteral nutrition and the resumption of a regular diet.
ARTICLE | doi:10.20944/preprints202107.0252.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: Recurrent neural network; Long-term short memory; Gated recurrent unit
Online: 12 July 2021 (12:03:06 CEST)
Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.
ARTICLE | doi:10.20944/preprints201707.0026.v2
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: agavins; prebiotics; microbiota; overweight; body weight loss; short chain fatty acids
Online: 25 July 2017 (04:52:34 CEST)
Agavins consumption has lead to accelerate body weight loss in mice. We investigated the changes on cecal microbiota and short chain fatty acids (SCFA) associated to body weight loss in overweight mice. Firstly, mice were fed with standard (ST5) or high fat (HF5) diet for 5 weeks. Secondly, overweight mice were shifted to standard diet alone (HF-ST10) or supplemented with agavins (HF-ST+A10) or oligofructose (HF-ST+O10), five more weeks. Cecal contents were collected before and after supplementation to determine microbiota and SCFA concentrations. At the end of first phase, HF5 mice showed a significant increase of body weight, which was associated with reduction of cecal microbiota diversity (PD whole tree; non-parametric t-test, P < 0.05), increased Firmicutes/Bacteroidetes ratio and reduced SCFA concentrations (t-test, P < 0.05). After diet shifted, HF-ST10 normalized its microbiota, increase its diversity and SCFA levels, whereas agavins (HF-ST+A10) or oligofructose (HF-ST+O10) led to partial microbiota restoration, with normalization of the Firmicutes/Bacteroides ratio as well as higher SCFA levels (P < 0.1). Moreover, agavins noticeably enriched Klebsiella and Citrobacter (LDA > 3.0); this enrichment has not been reported previously under a prebiotic treatment. In conclusion, agavins or oligofructose modulated cecal microbiota composition, reduced extent of diversity and increased SCFA. Furthermore, identification of bacteria enriched by agavins, opens opportunities to explore new probiotics.
Subject: Business, Economics And Management, Accounting And Taxation Keywords: Short-term trading; mean reversion; VIX; SPY; linear stochastic process; MACD; Bollinger Bands
Online: 29 July 2021 (16:24:34 CEST)
One of the key challenges of stock trading is the stock prices follow a random walk process, which is a special case of a stochastic process, and are highly sensitive to new information. A random walk process is difficult to predict in the short-term. Many linear process models that are being used to predict financial time series are structural models that provide an important decision boundary, albeit not adequately considering the correlation or causal effect of market sentiment on stock prices. This research seeks to increase the predictive capability of linear process models using the SPDR S\&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto-Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence (MACD), focusing on forecast performance. The profitability of various algorithmic trading strategies is compared based on a combination of these two indicators. This research finds that linear process models that incorporate the VIX Index do not improve the performance of algorithmic trading strategies.
REVIEW | doi:10.20944/preprints202307.1823.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: drought, food security, short-growing season, rainfed agriculture, subsistence farming
Online: 27 July 2023 (05:47:30 CEST)
In semi-arid regions, climate change has affected crop growing season length and sowing time, potentially causing low yield of the rainfed staple crop pearl millet (Pennisetum glaucum L.) and food insecurity among smallholder farmers. In this study, we used 1994–2023 rainfall data from Namibia's semi-arid North-Central Region (NCR), receiving November–April summer rainfall, to analyze rainfall patterns and trends and their implications on the growing season to propose climate adaptation options for the region. The results revealed high annual and monthly rainfall variabilities, with nonsignificant negative trends for November–February rainfalls, implying a shortening growing season. Furthermore, we determined the effects of sowing date on grain yields of the early-maturing Okashana-2 and local landrace Kantana pearl millet varieties and the optimal sowing window for the region, using data from a two-year split-plot field experiment conducted at the University of Namibia-Ogongo Campus, NCR, during the rainy season. Cubic polynomial regression models were applied to grain yield data sets to predict grain production for any sowing date between January and March. Both varieties produced the highest grain yields under January sowings, with Kantana exhibiting a higher yield potential than Okashana-2. Kantana, sown by 14 January, had a yield advantage of up to 36.0% over Okashana-2, but its yield gradually reduced with delays in sowing. Okashana-2 exhibited higher yield stability across January sowings, surpassing Kantana’s yields by up to 9.4% following the 14 January sowing. We determined the pearl millet optimal sowing window for the NCR from 1–7 and 1–21 January for Kantana and Okashana-2, respectively. These results suggest that co-cultivation of early and late pearl millet varieties and growing early-maturing varieties under delayed seasons could stabilize grain production in northern Namibia and enhance farmers' climate adaptation. Semi-arid agro-region policymakers could utilize this information to adjust local seed systems and extension strategies.
ARTICLE | doi:10.20944/preprints202305.1997.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: climate change; spring phenology; short–distance migrants; European Wren; Troglodytes troglodytes; MOI; NAO; SCAND
Online: 29 May 2023 (08:36:57 CEST)
Many studies link changes in avian phenology in Europe to the North Atlantic Oscillation (NAO), as a proxy of conditions at western Europe. But effects of climate variation in other regions of Eu-rope on phenology of short-distance migrants with wide non-breeding grounds remain unclear. We determined the combined influence of large-scale climate indices, NAO, the Mediterranean Oscillation Index (MOI), and the Scandinavian Pattern (SCAND), during the preceding year on spring migration timing of European Wren at the southern Baltic coast during 1982–2021. We modelled the effects of these climate variables on the entire passage and subsequent percentiles of Wren’s passage at Bukowo-Kopań and Hel ringing stations. The start and median of migration shifted earlier at Hel, but the end of passage shifted later at both stations over 1982–2021. In the effect, the duration of passage at Hel extended by 7.6 days. Early passage at Hel was related with high MOI1 in spring and in preceding autumn. Spring passage at Bukowo-Kopań was late after high NAO in the previous breeding season, and high winter and spring NAO. At both stations late spring passage occurred after high SCAND in previous summer. Early beginning or median of passage at our stations followed high local temperatures. We conclude that phenology of Wren’s spring migration at the Baltic coast was shaped by conditions that different populations encounter at wintering quarters in western Europe, where NAO operates, and in the south-eastern Europe, where the MOI1 operates, in combination with conditions in Scandinavia during previous breed-ing season. We showed that climate variability in different parts of the migrants’ range has com-bined carry-over effects on in migrant’s phenology in Europe.
ARTICLE | doi:10.20944/preprints202201.0398.v1
Subject: Physical Sciences, Atomic And Molecular Physics Keywords: B-spline R-matrix; R-matrix with time dependence; intense short-pulse extreme ultra14 violet radiation; time-dependent Schrdinger equation; Arnoldi-Lanczos propagation
Online: 26 January 2022 (13:01:07 CET)
Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation
ARTICLE | doi:10.20944/preprints201908.0155.v2
Subject: Engineering, Control And Systems Engineering Keywords: Long short-term memory; Brain dynamics; Data-driven modeling; Complex systems
Online: 18 September 2019 (13:05:22 CEST)
Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.
REVIEW | doi:10.20944/preprints202008.0346.v1
Subject: Medicine And Pharmacology, Dietetics And Nutrition Keywords: Integrative review; Short-term Calorie Reduction; Fasting; Cancer; Chemotherapy; Calorie Restriction
Online: 15 August 2020 (09:41:11 CEST)
Recent preclinical studies have shown the potential benefits of short-term calorie reduction (SCR) on cancer treatment. In this integrative review, we aimed to identify and synthesize current evidence regarding the feasibility, process, and effects of SCR in cancer patients receiving chemotherapy. PubMed, Cumulative Index to Nursing and Allied Health Literature, Ovid Medline, PsychINFO, and Embase were searched for original research articles using various combinations of Medical Subject Heading terms. Among the 311 articles identified, seven studies met the inclusion criteria. The majority of the reviewed studies was small randomized controlled trials or cohort study with fair quality. The results suggest that SCR is safe and feasible. SCR is typically arranged around the chemotherapy with the duration ranging from 24 to 96 hours. Most studies examined the protective effects of SCR on normal cells during chemotherapy. The evidence supports that SCR had the potential to enhance both physical and psychological wellbeing of patients during chemotherapy. SCR is a cost-effective intervention with great potential. Future well-controlled studies with sufficient sample sizes are needed to examine the full and long-term effects of SCR and its mechanism of action.
ARTICLE | doi:10.20944/preprints202309.0577.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: mid-pleistocene transition; obliquity–oblateness feedback; obliquity damping hypothesis; water- mass redistribution; glacio-eustatic sea level; short eccentricity
Online: 8 September 2023 (13:07:04 CEST)
A recent research has identified an inverse amplitude link between obliquity damping and short eccentricity amplification during Mid-Late Pleistocene based on LR04 δ18O and equatorial Pacific Site 846 sea surface temperature records, which is associated with the Earth's long-term cooling. In the present study, new evidence of this anticorrelation is presented from Antarctic D-CO2-CH4 records, global benthic-planktic δ18O, and regional (Atlantic, Pacific, Mediterranean, and Indian) climate-related proxies. Based on a critical review of theoretical constrains (Earth's oblateness changes and ice-volume phase lag in the obliquity band <5.0 kyr), this widespread and symmetric (bipolar) obliquity response damping has been interpreted as an effect of the obliquity-oblateness feedback, which could be the latent physical mechanism at the origin of the Mid-Pleistocene Transition (MPT). Indeed, results and considerations of the present work suggest that fast and positive/negative net variation of the Earth's oblateness in the obliquity band was controlled by dominant glacio-eustatic water mass component and, assuming a rapid response of the ice-volume to surface temperature changes, the mean obliquity lag response is estimated to be <5.0 kyr over the past 800 kyr. These elements may explain the interglacial/glacial damping observed in the obliquity response. The consolidation of the Earth's long-term icy-state in the subtrend IV, culminated with the post-MPT obliquity damping, might have contributed to the strengthening of the short eccentricity response by mitigating the obliquity ‘ice-killing’ during obliquity maxima (interglacials), favouring the obliquity-cycle skipping and a feedback amplified ice-growth in the short eccentricity band (obliquity damping hypothesis). This could suggest a different impact of the climate friction than what is generally believed, which is presumably the latent physical mechanism that triggers transient ‘competitive’ interaction between obliquity and short eccentricity started early during the Piacenzian.
ARTICLE | doi:10.20944/preprints202307.0789.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: accuracy; data-driven approach, feed forward neural network; gated recurrent unit; hyper-parameters tuning; long short-term memory; short-term demand forecasting
Online: 12 July 2023 (11:29:06 CEST)
Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.
ARTICLE | doi:10.20944/preprints202311.1002.v1
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: medium manganese steel; short-time partial austenitization; strength and toughness; multiphase hierarchical microstructure; (V, Mo)C precipitation
Online: 15 November 2023 (14:25:44 CET)
In order to obtain the good match between yield strength and low-temperature toughness, the short-time partial austenitization (SPA) process was employed for V-microalloying 0.22C-5.24Mn steel. The initial microstructure after intercritical tempering was dual-phase ferrite and reversed austenite (RA), while the final microstructure was consisted of ferrite, RA and secondary martensite (SM) after subjected to SPA process. (V, Mo)C with disc-like morphology mainly precipitated during intercritial tempering, and the aspect ratio of particle decreased, leading to the appearance of near-spherical morphology. After subjected to SPA process, the resultant multiphase hierarchical microstructure (three layers: out layer of ferrite, interlayer of SM and inner layer RA) enabled high yield strength of 1097 MPa, total elongation of 14%, and impressive impact energy of 33.3 J at -20 °C. The strengthening contribution of (V, Mo)C precipitation was estimated about 108 MPa.
ARTICLE | doi:10.20944/preprints202305.1372.v1
Subject: Biology And Life Sciences, Animal Science, Veterinary Science And Zoology Keywords: weaned pig; gut microbiota; red beetroot; short chain fatty acids; bile acids
Online: 19 May 2023 (04:11:50 CEST)
Red beetroot, is a well-recognized and established source of bioactives (e.g., betalains and polyphenols) with anti-inflammatory and antimicrobial properties. It is proposed as a potential alternative to zinc oxide, with a focus on gut microbiota modulation and metabolite production. In this study, weaned pigs aged 28-days were fed either a control diet, diet supplemented with zinc oxide (3,000 mg/kg), or 2% and 4% pulverized whole red beetroot (CON, ZNO, RB2 and RB4; respectively) for 14 days. After the pigs were euthanized, blood and digesta samples were collected for microbial composition and metabolite analyses. Results showed, red beetroot supplemented diet at 2% improved the gut microbial richness relative to other diets, but marginally influenced the caecal microbial diversity compared to zinc oxide supplemented diet. Further increase in red beetroot levels (4% -RB4) lead to loss of caecal diversity, decreased short chain fatty acids and secondary bile acid concentrations. An increased Proteobacteria abundance, presumably due to increased lactate/lactic acid producing bacteria was also observed. Summarily, red beetroot contains several components conceived to improve the gut microbiota and metabolite output of weaned pigs. Future studies investigating individual components in red beetroot will better elucidate their contributions to gut microbiota modulation and pig health.
ARTICLE | doi:10.20944/preprints201805.0066.v1
Subject: Biology And Life Sciences, Forestry Keywords: biomass yield; carbon storage; growth pattern; poplar; short-rotation coppices; seasonal trends
Online: 3 May 2018 (11:10:21 CEST)
It is required to manage sustainable Short-Rotation Coppices (SRCs) as an important role on carbon sink and bioenergy output, because most of SRCs were established in reclaimed land in South Korea. However, during the last three years, growth pattern of the SRCs was remarkably changed with soil condition. This study aimed to identify the sustainability of SRCs on carbon storage, biomass and fuel pellet production, monitoring the neighboring vegetation of SRCs by land-use exchange, physiological change of poplar on seasonal trend, and to evaluate whether poplar is suitable for making wood pellets. The calculated biomass yield per area of poplar grown was 103.07 Mg per total area (55.6 ha), and volumes of carbon dioxide absorption was estimated to be 330 Mg CO2. Wood pellet quality based on the criteria scored third grade, indicating that poplar is suitable for manufacturing fuel pellets. Moreover, monitoring of the flora distribution in SRCs revealed changes in species composition. As halophyte was increased during drought, soil organic matter, net growth and total chlorophyll of poplar were significantly decreased. These findings indicated that photosynthesis and growth pattern of SRCs may be negatively affected by microclimate and will provide valuable information for effective management of SRCs.
ARTICLE | doi:10.20944/preprints202310.0094.v1
Subject: Biology And Life Sciences, Virology Keywords: Podo virus; temperate phages; Short tail phages, novel phage terminase, Lederbergvirus
Online: 3 October 2023 (08:19:22 CEST)
Salmonella enterica includes enteric pathogens of zoonotic potential, possessing one of the largest pools of temperate phages in their genomes. One such class of phages previously called Podoviridae (Now genera placed directly under class Caudoviricetes temporarily) is the largest group of temperate phages that lysogenize clinically and economically important Salmonella enterica serovars. These phages are capable of generalized transduction and are well known for carrying foreign DNA (mobile genetic elements and resistance genes) to new bacterial species that play a major role in the evolution, pathogenicity, and host adaptability of Salmonella serovars. Here we report a novel species of podo viruses; Salmonella phage BIS08P22 (BIS08) that infects Salmonella enterica serovar Typhimurium strain SE-BS17 (Acc. NO: MZ503545). Phage BIS08 was viable only at biological pH and temperature (pH7 and 37 °C) and features 41,574 -base pair (bp) linear ds DNA genome with 47% GC content. It encodes 73 putative Open Reading Frames (ORFs), has a mosaic arrangement, and shares only 16 core genes with its closest homologs. BIS08 genome possesses only 52 % homology (genome-wide Nucleotide homology by VIRIDIC analysis) with members of the genus Lederbergvirus which is not sufficient to place it in the same genus. This phage has a unique terminase enzyme (DNA packaging motor) with no nucleotide and protein homology with any known member of the Lederbergvirus genus. We carried out a detailed phylogenetic and genome analysis of this novel phage and proposed its placement in a new genus. Our study also suggests a revision in the classification of the genus Lederbergvirus. We also performed in silico structural analysis of the BIS08 unique terminase enzyme comparing it to its close homologs.
ARTICLE | doi:10.20944/preprints202310.0020.v1
Subject: Engineering, Chemical Engineering Keywords: dynamic neural networks; industrial process; recurrent neural networks; long short-term memory
Online: 1 October 2023 (08:13:52 CEST)
Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely used in natural language processing, speech recognition and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM) and dynamic polynomial models. The obtained results are satisfactory, which suggests a good possibility of application.
ARTICLE | doi:10.20944/preprints201811.0126.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Speech/Music Classification; Enhanced Voice Service, Long Short-Term Memory, Big Data
Online: 5 November 2018 (17:02:36 CET)
Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, a new technique to improve the robustness of speech/music classifier for 3GPP enhanced voice service (EVS) using long short-term memory (LSTM) is proposed. For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. Experiments show that LSTM-based speech/music classification produces better results than conventional EVS under a variety of conditions and types of speech/music data.
ARTICLE | doi:10.20944/preprints202309.0947.v1
Subject: Biology And Life Sciences, Ecology, Evolution, Behavior And Systematics Keywords: food grinding； Brandt’s voles； food restriction； short-chain fatty acids； fecal microbiota
Online: 14 September 2023 (09:05:05 CEST)
Food-grinding occurs in rodents and is influenced by multiple-factors. However, the factors affecting this behavior remain unclear. In this study, we aimed to investigate the effect of food restriction on food grinding by Brandt’s voles (Lasiopodomys brandtii), as well as the potential role of the gut microbiota in this process, through a comparison of the variations between voles with different food supplies. Food restriction reduced the relative amount of ground food to a greater extent than it lowered the relative food consumption, and altered the abundance of Staphylococcus, Aerococcus, Jeotgalicoccus, and Un--s-Clostridiaceae bacterium GM1. Strong correlations between the ground-to-consumed food ratio and the abundance of fecal microbiota were found for Un--s-Clostridiaceae bacterium GM1 and Aerococcus. The content of fecal acetate and propionate for the 7.5 g-food supply group was lower than that for the 15 g-food supply group. Further, the relative amount of ground food and ground-to-consumed food ratio were both positively correlated with the acetate content. Our study indicated that food restriction can effectively inhibit food grinding. Further, Un--s-Clostridiaceae bacterium GM1 abundance, Aerococcus abundance, and acetate content were strongly related to food grinding. Variations in gut microbial abundance and metabolite short-chain fatty acid content induced by food restriction likely promote the inhibition of food grinding. These results could potentially provide guidance for reducing food waste during laboratory rodent maintenance.
ARTICLE | doi:10.20944/preprints202301.0502.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: working memory; STDP; short-term plasticity; spiking neural network; flexible cluster formation
Online: 27 January 2023 (10:32:08 CET)
Working memory (WM) is a brain system for short-term storage and manipulation of information and plays an important role in complex cognitive tasks. In the synaptic theory of WM memorized elements are stored in the form of short-term potentiated connections in a sample population of neurons. In this paper, we show that such populations can be formed due to the mechanisms of spike-timing-dependent plasticity (STDP) – the phase dependence associated with the ratio of the pulse times of the interacting neurons. We propose a WM model considering two types of plasticity: short-term plasticity and STDP. We have shown formation of neuronal clusters encoding items in the WM model, that can be formed by external stimulation of a group of neurons due to the mechanisms of STDP and hold and reactivated by short-term plasticity mechanisms. The dynamic formation of neuronal clusters instead of pre-formed clusters gives additional flexibility to the model.
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: amyloid-β; endotoxin; short chain fatty acids; clasmatodendrosis; cytokines; neurovascular unit; vagus nerve; Toll-like Receptor 4
Online: 26 April 2021 (13:22:47 CEST)
Much evidence has accumulated over the past decade in favor of a significant association between dysbiosis, neuroinflammation and neurodegeneration. Presently, the pathogenetic mechanisms triggered by molecules produced by the altered microbiota, also responsible for the onset and evolution of Alzheimer Disease will be described. Our attention will be focused on the role of astrocytes and microglia. Numerous studies have progressively demonstrated how these glial cells are important to ensure an adequate environment for neuronal activity in healthy conditions. Furthermore, it is becoming evident how both cell types can mediate the onset of neuroinflammation and lead to neurodegeneration when subjected to pathological stimuli. Based on this information, the role of major microbiota products in shifting the activation profiles of astrocytes and microglia from a healthy to a diseased state will be discussed focussing on Alzheimer Disease pathogenesis.
ARTICLE | doi:10.20944/preprints202308.1206.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: rail transit short-term passenger flow prediction; LSTM; wavelet denoising analysis; SVR
Online: 16 August 2023 (11:08:42 CEST)
Urban rail transit offers advantages like high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou's Shantang Street station. Shantang Street's high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case. Wavelet denoising and LSTM modeling were combined to predict short-term flows, comparing the results to standalone LSTM and SVR approaches. This study illustrates that the adopted algorithms exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance.
ARTICLE | doi:10.20944/preprints202302.0086.v2
Subject: Engineering, Civil Engineering Keywords: Deep neural network; long short-term memory; water quality; discharge; stream-water
Online: 17 April 2023 (07:21:31 CEST)
Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.
ARTICLE | doi:10.20944/preprints202307.0733.v1
Subject: Biology And Life Sciences, Plant Sciences Keywords: tomato; Sly-miR171d/e; short tandem target mimic (STTM); salt stress; fruit quality
Online: 11 July 2023 (12:38:29 CEST)
High salinity in soil affects the normal growth and development of crop. MicroRNA171 (miR171) plays a momentous role in plant resistance to adversity stress. The functions of Sly-miR171d and Sly-miR171e on growth of tomato and fruit nutritional quality under salt stress were studied. The results showed that the seed germination and seedling growth of tomato that silencing of Sly-miR171d/e by short tandem target mimic (STTM) were better than those of wild-type (WT). Silencing of Sly-miR171d/e seeds maintained a high germination rate, while WT tomato seeds barely germinated under 50 mM NaCl stress. Under 100 mM NaCl stress, silencing of Sly-miR171d/e increased the expression of their target gene SlGRAS24 with increasing duration of salt stress, while they enhanced the antioxidant activity, reduced reactive oxygen species (ROS) accumulation, and enhanced the salt tolerance of tomato plant by regulating gibberellin (GA) level. Moreover, the preharvest and postharvest fruit nutritional quality under salt stress was also studied, and the results showed that silencing of Sly-miR171d/e increased soluble solids, lycopene, total carotenoids, Vitamin C, organic acids and phenolic substances in tomato fruit. The present study demonstrated that silencing of Sly-miR171d/e enhanced plant salt tolerance through the GA pathway, and improved fruit nutritional quality under salt stress.
ARTICLE | doi:10.20944/preprints202107.0431.v1
Subject: Engineering, Automotive Engineering Keywords: masonry; composite; short fibers; natural hydraulic lime; sisal; three-point bending test; fracture energy; strengthening; preservation; sustainability; carbon foot print
Online: 20 July 2021 (09:31:59 CEST)
The present work aims to characterize the mechanical behavior of a new composite material for the conservation and development of the vast historical and architectural heritage that is particularly vulnerable to environmental and seismic actions. The new composite consists of natural hydraulic lime (NHL) -based mortar, reinforced by sisal short fibers randomly oriented in the mortar matrix. The NHL-based mortar ensures the chemical-physical compatibility with the original feature of the historical masonry structures (mostly in stone and clay) aiming to pursue both the effectiveness and durability of the intervention. The use of vegetable fibers (i.e. the sisal one) is an exciting challenge for the construction industry since they require a lower degree of industrialization for their processing, and therefore, their costs are also low, as compared to the most common synthetic/metal fibers. Beams of sisal-composite sizing 160x40x40 mm3 with a central notch are tested in three-point bending, aiming to evaluate both their bending strength and fracture energy. Also, tensile tests and compressive tests were performed on the composite samples, while water retention test and slump test were performed on the fresh mix. Finally, the tensile tests on the Sisal strand were carried out to evaluate the tensile strength of both strand and wire. A final comparison with unreinforced mortar specimens shows that the proposed composite ensures great workability and good performances in term of ductility and strength and it can be considered a promising alternative to the classic fiber-reinforcing systems.
ARTICLE | doi:10.20944/preprints202309.1400.v1
Subject: Business, Economics And Management, Econometrics And Statistics Keywords: hierarchical forecasting; short time series; local approach; global approach; household gas consumption
Online: 21 September 2023 (05:39:44 CEST)
This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country's administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.
ARTICLE | doi:10.20944/preprints202211.0278.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: Long Short-Term Memory; time series forecasting; commodities; technical analysis
Online: 15 November 2022 (07:00:55 CET)
This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.
ARTICLE | doi:10.20944/preprints202101.0401.v1
Subject: Engineering, Automotive Engineering Keywords: Electric arc furnace; Arc short circuit; transient power quality; transient voltage; voltage sag
Online: 20 January 2021 (14:24:47 CET)
Three-phase AC electric arc furnace (EAF) is a typical non-linear load, causing many power quality problems. Most of the researches on the voltage problems of EAF mainly focus on the voltage fluctuation, and less on the transient voltage problems caused by EAF short circuit and open circuit. In this paper, the relationship between voltage and current of EAF is obtained by combining hyperbolic function and exponential function, then the white noise and chaotic circuit are added to establish the EAF model which is suitable for the study of voltage fluctuation and transient voltage. This paper analyzes the causes of the transient voltage problem of the EAF, calculates the short-circuit current, reactive impact and the influence on voltage at the point of common coupling (PCC) in the three-phase short-circuit of the EAF, and compares the calculation results with the simulation results to prove the accuracy of this model. The results show that the reactive impact of three-phase short circuit is about twice as much as that of normal operation of EAF, resulting in about 30% voltage sag at the PCC, which is very unfavorable to the power grid. This paper provides reference for transient power quality evaluation and dynamic reactive power compensation of EAF.
ARTICLE | doi:10.20944/preprints202001.0169.v1
Subject: Engineering, Civil Engineering Keywords: residual compressive strength; steel; finite element analysis; short tubular steel column; local corrosion
Online: 16 January 2020 (11:17:19 CET)
Corrosion is considered as one of the main factors in the structural performance deterioration of steel members. In this study, experimental and numerical methods were used to assess the reduction in compressive strength of short tubular steel columns with local corrosion damage. The corrosion damage was varied with different depths (0, 1.5, 2, 3, 4, 4.5, and 6 mm), height (0, 20, 40, 60, 80, 100, 120, 140, 160, and 180 mm), circumference (0, 90, 180, 270, and 360°), and location along the column. A parametric numerical study was performed to establish a correlation between the residual compressive strength and the severity of corrosion damage. The results showed that as the corrosion depth, height and circumference increased, the compressive strength decreased linearly. As for the corrosion height, the residual compressive strength became constant after decreasing linearly when the corrosion height was greater than the half-wavelength of buckling of the short columns. An equation is presented to evaluate the residual compressive strength of short columns with local corrosion wherein the volume of the corrosion damage was used as a reduction factor in calculating the compressive strength. The percentage error using the presented equation was found to be within 11.4%.
ARTICLE | doi:10.20944/preprints202203.0140.v1
Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Left ventricular ejection fraction; Left ventricle segmentation; Convolutional long short-term memory; Echocardiography
Online: 10 March 2022 (04:19:30 CET)
Cardiovascular disease is the leading cause of death worldwide. A key factor in assessing the risk of cardiovascular disease is left ventricular functional evaluation. Left ventricular (LV) systolic function is evaluated by measuring the left ventricular ejection fraction (LVEF) using echocardiography data. Therefore, quick and accurate left ventricle segmentation is important for estimating the LVEF. However, it is difficult to accurately segment the left ventricle due to changes in the shape and area of the left ventricle during cardiac cycles. In this study, we proposed a framework that considers changes in the shape and area of the left ventricle during the cardiac cycle by applying the convolutional long short-term memory (CLSTM) approach. In addition, we evaluated the left ventricular segmentation and multidimensional quantification of the proposed system in comparison to manual and automated segmentation methods. In addition, to assess the validity of CLSTM, the values of multi-dimensional quantification metrics were compared and analyzed using graphs and Bland–Altman plots on a frame-by-frame basis. We demonstrated that the CLSTM method effectively segments the left ventricle by considering the LV activity. In conclusion, we demonstrated that LV segmentation based on our framework may be utilized to accurately estimate LVEF values.
ARTICLE | doi:10.20944/preprints202104.0269.v1
Subject: Engineering, Transportation Science And Technology Keywords: Travel Time Prediction; Deep Learning; Long Short Term Memory Networks; transit; temporal correlation
Online: 9 April 2021 (15:04:06 CEST)
This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.
ARTICLE | doi:10.20944/preprints201807.0312.v1
Subject: Physical Sciences, Nuclear And High Energy Physics Keywords: light-matter interaction, ultra-short laser pulses, high-pressure/density conditions, phase transitions
Online: 17 July 2018 (14:57:31 CEST)
It was demonstrated during the past decade that ultra-short intense laser pulse tightly focused deep inside a transparent dielectric generates the energy density in excess of several MJ/cm$^3$. Such energy concentration with extremely high heating and quenching rates leads to unusual solid-plasma-solid transformation paths overcoming kinetic barriers to formation of previously unknown high-pressure material phases, which are preserved in the surrounding pristine crystal. These results were obtained with the pulse of Gaussian shape in space and in time. Recently it was shown that the Bessel-shaped pulse could transform much larger amount of a material and allegedly create even higher energy density than that was achieved with the Gaussian (GB) pulses. Here we present a succinct review of previous results and discuss the possible routes for achieving higher energy density employing the Bessel beams (BB) and take advantage of its unique properties.
ARTICLE | doi:10.20944/preprints201804.0301.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: convex programming; wind power, hydropower; risk mitigation; CVaR; short-run marginal cost curve
Online: 23 April 2018 (17:35:32 CEST)
This study analyses the short-run hydro generation scheduling for the wind power differences from the contracted schedule. The approach for construction of the joint short-run marginal cost curve for the hydro-wind coordinated generation is proposed and applied on the real example. This joint short-run marginal cost (SRMC) curve is important for its participation in the energy markets and for economic feasibility assessment of such coordination. The approach credibly describes the short-run marginal costs which this coordination bears in “real life”. The approach is based on the duality framework of a convex programming and as a novelty combines the shadow price of risk mitigation capability and the water shadow price. The proposed approach is formulated as a stochastic linear program and tested on the case of the Vinodol hydropower system and the wind farm Vrataruša in Croatia. The result of the case study is a family of 24 joint short-run marginal cost curves.
REVIEW | doi:10.20944/preprints201912.0211.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: retinoic acid; retinol; retinaldehyde; short-chain dehydrogenase; vitamin a; retinol dehydrogenase; retinaldehyde reductase; embryogenesis
Online: 16 December 2019 (07:18:46 CET)
The concentration of all-trans-retinoic acid, the bioactive derivative of vitamin A, is critically important for the optimal performance of numerous physiological processes. Either too little or too much of retinoic acid in developing or adult tissues is equally harmful. All-trans-retinoic acid is produced by the irreversible oxidation of all-trans-retinaldehyde. Thus, the concentration of retinaldehyde as the immediate precursor of retinoic acid has to be tightly controlled. However, the enzymes that produce all-trans-retinaldehyde for retinoic acid biosynthesis and the mechanisms responsible for the control of retinaldehyde levels have not yet been fully defined. The goal of this review is to summarize the current state of knowledge regarding the identities of physiologically relevant retinol dehydrogenases, their enzymatic properties and tissue distribution, and to discuss potential mechanisms for the regulation of the flux from retinol to retinaldehyde.
ARTICLE | doi:10.20944/preprints202102.0461.v1
Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: Dead pericarps; salinity; short episodes of high temperature; stress response; reproductive phase; seed abortion; Phyohormones; Climate change; Brassica juncea
Online: 22 February 2021 (11:48:15 CET)
Climate change is expected to increase the frequency and severity of abiotic stresses that lead to loss of crop yield. We investigated the effect of salinity (S), short episodes of high temperature (HS) and combination of S+HS at the reproductive phase on dead pericarps properties and yield of the crop plant Brassica juncea. Three intervals of HS resulted in massive seed abortion; seeds from salt-treated plants germinated poorly. Pericarp extracts of salt-treated plants reduced seed germination of B. juncea; all pericarp extracts completely inhibited seed germination of tomato and Arabidopsis; removal of pericarp extracts restored seed germination. HS reduced all metabolites accumulated in dead pericarps, except for upregulation of isomaltose and cellobiose. Salt induced alteration in metabolite levels including increase in proline, reduction in TCA intermediates and changes in phytohormone levels. Proteome analysis revealed hundreds of proteins stored in dead pericarps whose levels and composition were altered under salt stress. The integration of metabolic and proteomic data showed that changes in metabolites were highly correlated with changes in proteins involved in their biosynthetic pathways. Thus, besides providing a physical shield for seed/embryo protection dead pericarps store beneficial substances whose levels, composition and biological function are altered under stress, further highlighting the elaborated function of dead organs enclosing embryos in seed biology and ecology. The detrimental effect of HS on crop production might have implications for global food security in the face of climate change.
ARTICLE | doi:10.20944/preprints202307.1115.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: InSAR-based method; coal mine goaf; prediction of long-term land subsidence; concatenation of multiple short-term monitoring data
Online: 18 July 2023 (02:35:57 CEST)
The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.
ARTICLE | doi:10.20944/preprints202201.0107.v1
Subject: Engineering, Energy And Fuel Technology Keywords: Very short term load forecasting; VSTLF; Short term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; extreme gradient boosting, energy consumption; ARIMA; time series prediction.
Online: 10 January 2022 (12:17:35 CET)
Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.
ARTICLE | doi:10.20944/preprints202305.0934.v1
Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Time Series; Forecasting, Deep Learning; Genetic Algorithm; Long Short-Term Memory
Online: 12 May 2023 (11:07:28 CEST)
Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.
ARTICLE | doi:10.20944/preprints202305.0406.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: Biosensors; ultra-short circulating tumor DNA; Lung Cancer; Liquid biopsy; EGFR
Online: 6 May 2023 (09:44:49 CEST)
Liquid biopsy is a rapidly emerging field which involves the minimal/non-invasive assessment of signature somatic mutations through analysis of circulating tumor DNA (ctDNA) shed by tumor cells in bodily fluids. The broad, unmet need for liquid biopsy lung cancer detection is the lack of multiplex platform that can detect a mutation panel of lung cancer genes using minimum amount of sample, especially for ultra-short ctDNA (usctDNA). Here we developed a non-PCR and non-NGS-based single droplet based multiplexing microsensor technology “Electric Field-Induced Released and Measurement (EFIRM) Liquid Biopsy” (m-eLB) for lung can-cer-associated usctDNA. The m-eLB provides multiplexable assessment of usctDNA within a single droplet of biofluid in only one well of micro-electrodes, as each electrode coated with dif-ferent probes for the ctDNA. In this m-eLB prototype, it demonstrates accuracy for 3 tyrosine ki-nase inhibitor related EGFR target sequences in synthetic nucleotides. The accuracy for the multi-plexing assay has an AUC of 0.98 for L858R, Ex19del, AUC of 0.94 for Ex19 deletion and AUC of 0.93 for T790M. By combining the 3 EGFR assay together, the AUC was 0.97 for the multiplexing assay.
ARTICLE | doi:10.20944/preprints202304.1010.v1
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: photovoltaic cells; antireflective coatings; solar glass transmittance; short-circuit current gain
Online: 27 April 2023 (03:08:05 CEST)
The aim of the study was to find the effect of polyethylene (PE) coatings on the short-circuit current of silicon photovoltaic cells covered with glass, in order to improve the short-circuit current of the cells. Various combinations of PE films (thicknesses ranging from 9 to 23 µm, number of layers ranging from 2 to 6) with glasses (greenhouse, float, optiwhite and acrylic glass) were investigated. The best current gain of 4.05% was achieved for the coating combining a 1.5 mm-thick acrylic glass with 2 × 12 µm-thick PE films. This effect can be related to the formation of an array of micro-wrinkles and micrometer-sized air bubbles with a diameter of 50 to 600 µm in the films, which serve as micro-lenses and enhance light trapping.
ARTICLE | doi:10.20944/preprints202212.0132.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: Short video; Sentiment Analysis; Feature; 3D Dense Net; 3D Residual Network
Online: 7 December 2022 (11:57:32 CET)
In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.
ARTICLE | doi:10.20944/preprints202102.0401.v1
Subject: Engineering, Automotive Engineering Keywords: Machine learning; Ultrasonic measurements; Long Short-Term Memory; Industrial Digital technologies
Online: 18 February 2021 (09:31:43 CET)
Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.
ARTICLE | doi:10.3390/sci1010007.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: quantum mechanics; EEG; short term memory; astrocytes; neocortical dynamics; vector potential
Online: 11 December 2018 (00:00:00 CET)
Background: Previous papers have developed a statistical mechanics of neocortical interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated Annealing (ASA) has been developed to perform fits to such nonlinear stochastic systems. An N-dimensional path-integral algorithm for quantum systems, qPATHINT, has been developed from classical PATHINT. Both fold short-time propagators (distributions or wave functions) over long times. Previous papers applied qPATHINT to two systems, in neocortical interactions and financial options. Objective: In this paper the quantum path-integral for Calcium ions is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Using fits of this SMNI model to EEG data, including these effects, will help determine if this is a reasonable approach. Method: Methods of mathematical-physics for optimization and for path integrals in classical and quantum spaces are used for this project. Studies using supercomputer resources tested various dimensions for their scaling limits. In this paper the quantum path-integral is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Results: The mathematical-physics and computer parts of the study are successful, in that there is modest improvement of cost/objective functions used to fit EEG data using these models. Conclusions: This project points to directions for more detailed calculations using more EEG data and qPATHINT at each time slice to propagate quantum calcium waves, synchronized with PATHINT propagation of classical SMNI.
ARTICLE | doi:10.20944/preprints202201.0391.v1
Subject: Chemistry And Materials Science, Metals, Alloys And Metallurgy Keywords: Cu-Pb; Fe-Si alloys; modelling; surface tension; viscosity; molar volume; short-range ordering
Online: 26 January 2022 (03:44:15 CET)
Among the thermophysical properties, the surface / interfacial tension, viscosity and density / molar volume of liquid alloys are the key properties for the modelling of microstructural evolution during solidification. Therefore, only reliable input data can yield accurate predictions preventing the error propagation in numerical simulations of solidification related processes. Due to experimental difficulties related to reactivity of metallic melts at high temperatures, the measured data are often unreliable or even lacking. The application of containerless processing techniques either leads to a significant improvement of the accuracy or makes the measurement possible at all. On the other side, accurate model predicted property values could be used to compensate the missing data; otherwise, the experimental data are useful for the validation of theoretical models. The choice of models is particularly important for the surface, transport and structural properties of liquid alloys representing the two limiting cases of mixing, i.e. ordered and phase separating alloy systems. To this aim, the thermophysical properties of the Fe-Si and Cu-Pb systems were analysed and the connections with the peculiarities of their mixing behaviours are highlighted.
ARTICLE | doi:10.20944/preprints201807.0019.v1
Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Clustering; Forecasting; Hierarchical Time-Series; Individual Electrical Consumers; Scalable; Short Term; Smart Meters; Wavelets
Online: 2 July 2018 (17:43:29 CEST)
Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16\% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.
ARTICLE | doi:10.20944/preprints202011.0205.v1
Subject: Engineering, Control And Systems Engineering Keywords: Neural Networks; Long-Short Term Model; Water demand; Forecasting; Sustainable development goals; Water Goal.
Online: 5 November 2020 (10:17:30 CET)
Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.
ARTICLE | doi:10.20944/preprints202310.0832.v1
Subject: Chemistry And Materials Science, Metals, Alloys And Metallurgy Keywords: CoCrFeNi alloy; dilatometry; differential scanning calorimetry; thermal conductivity; specific heat capacity; short-range ordering
Online: 12 October 2023 (12:55:59 CEST)
Key thermophysical properties necessary for the successful design and use of CoCrFeNi alloy in thermophysical applications have been measured experimentally, and the results have been compared with literature values and results previously obtained for commercial Ni-Cr alloys and equiatomic CoCrFeNi alloy. In particular, the thermal diffusivity, coefficient of thermal expansion (CTE), and specific heat capacity were measured for the as-cast and homogenized equiatomic CoCrFeNi alloy over a temperature range allowing the thermal conductivity to be calculated up to 1173 K. The thermal conductivity and thermal diffusivity of the equiatomic CoCrFeNi alloy were found to deviate from monotonic behavior in the temperature range from 773 to 1100 K. Such a deviation was previously observed in the behavior of the temperature dependence of CTE and specific heat capacity of the equiatomic CoCrFeNi alloy. The non-linear behavior is primarily the result of order/disorder phenomena for the as-cast and homogenized sample, as well as additionally for the as-cast sample, non-equilibrium solidification under arc melting conditions. The measured data of thermophysical properties are provided for thermally differently treated samples, and it is shown that there is a difference in the behavior of the temperature dependences of CTE, thermal diffusivity, and heat capacity.
ARTICLE | doi:10.20944/preprints201807.0613.v1
Subject: Biology And Life Sciences, Food Science And Technology Keywords: tempeh; lactic acid bacteria; short chain fatty acids; metabolic syndrome; high fat diet; feces
Online: 31 July 2018 (09:37:51 CEST)
The increased consumption of high fat-containing foods has been linked to the prevalence of obesity and abnormal metabolic syndromes. Rhizopus oligosporus, a fungus in the family Mucoraceae, is widely used as a starter for homemade tempeh. Although R. oligosporus can prevent the growth of other microorganisms, it grows well with lactic acid bacteria (LAB). Lactobacillus plantarum can produce β-glucosidase, which catalyzes the hydrolysis of glucoside isoflavones into aglycones (with greater bioavailability). Therefore, the development of a soybean-based functional food by the co-inoculation of R. oligosporus and L. plantarum is a promising approach to increase the bioactivity of tempeh. In this study, the ameliorative effect of L. plantarum in soy tempeh on abnormal carbohydrate metabolism in high-fat diet (HFD)-induced hyperglycemic rats was evaluated. The co-incubation of L. plantarum with R. oligosporus during soy tempeh fermentation reduced the homeostatic model assessment of insulin resistance, HbA1c, serum glucose, total cholesterol, triglyceride, free fatty acid, insulin, and low-density lipoprotein contents and significantly increased the high-density lipoprotein content in HFD rats. It also increased the LAB counts as well as the bile acid, cholesterol, triglyceride, and short-chain fatty acid contents in the feces of HFD rats. Our results suggested that the modulation of serum glucose and lipid levels by LAB occurs via alterations in the internal microbiota, leading to the inhibition of cholesterol synthesis and promotion of lipolysis. Tempeh, produced with both L. plantarum and R. oligosporus, may be a beneficial dietary supplement for individuals with abnormal carbohydrate metabolism.
ARTICLE | doi:10.20944/preprints202309.1232.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: lightning prediction; deep learning; spatio-temporal features; convolutional neural networks; long short-term memory networks
Online: 19 September 2023 (13:32:06 CEST)
The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.
ARTICLE | doi:10.20944/preprints201912.0058.v1
Subject: Medicine And Pharmacology, Pharmacology And Toxicology Keywords: mitochondrial dysfunction; idebenone; short-chain quinone; metabolic stability; HepG2 cell culture; reverse-phase liquid chromatography
Online: 5 December 2019 (03:56:02 CET)
Short chain quinones (SCQs) have been identified as potential drug candidates against mitochondrial dysfunction, which is largely dependent on their reversible redox characteristics of the active quinone core. We recently synthesized a SCQ library of > 148 naphthoquinone derivatives and identified 16 compounds with enhanced cytoprotection compared to the clinically used benzoquinone idebenone. One of the major drawbacks of idebenone is its high metabolic conversion in the liver, which significantly restricts is therapeutic activity. Therefore, this study assessed the metabolic stability of the 16 identified naphthoquinone derivatives 1-16 using hepatocarcinoma cells in combination with an optimized reverse-phase liquid chromatography (RP-LC) method. Most of the derivatives showed significantly better stability than idebenone over 6 hours (p < 0.001). By extending the side-chain of SCQs, increased stability for some compounds was observed. Metabolic conversion from the derivative 3 to 5 and reduced idebenone metabolism in the presence of 5 were also observed. These results highlight the therapeutic potential of naphthoquinone-based SCQs and provide essential insights for future drug design, prodrug therapy and polytherapy, respectively.
ARTICLE | doi:10.20944/preprints202307.1327.v1
Subject: Public Health And Healthcare, Public Health And Health Services Keywords: Short Physical Performance Battery; comprehensive geriatric assessment; older adults; institutionalization; psycometric properties
Online: 19 July 2023 (09:54:04 CEST)
Background: The validation of measuring instruments in health is a requirement to be able to use them safely and reliably. The Short Physical Performance Battery (SPPB) tool is an instrument widely used in the clinic and validated in numerous countries and languages and for different populations. The objective of this research was to determine the psychometric properties of SPPB for a sample composed of institutionalized Spanish older adults. Methods: Multicenter study of the psychometric properties of the Short Physical Performance Battery tool with a convenience sample of 194 institutionalized older adults. Reliability (internal consistency), validity (construct validity and convergent validity) tests were performed. Results: The results show a very good internal consistency, construct validity and convergent validity. In addition, the factorial structure of the SPPB is provided, which reflects that it is a unidimensional scale. Conclusions: In conclusion, the Short Physical Performance Battery is a valid and reliable tool for use with institutionalized older adults. Its use is recommended as part of the Comprehensive Geriatric Assessment for the evaluation of the physical or functional sphere. Authors should include one of the following statements about the registration status of the manuscript here: This study was prospectively registered and approved by the Ethics Committee of the University of Burgos (IR 11/2018).