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.

Statistics of languages are calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G – the semantic index G_C, proportional to the number of characters per word, and the syntactic index G_F, proportional to the reciprocal of the number of words per sentence −, G_F is dominant because G_C is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y=mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is, in author’s opinion, that between the short−term memory capacity, described by Miller’s “7∓2 law”, and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval is spread in the same of Miller’s law, and the time interval is spread in the same range of short−term memory response times. The connection between the word interval (and time interval) and short−term memory appears, at least empirically, justified and natural, and should further investigated. Technical and scientific writings (papers, essays etc.) ask more to their readers. A preliminary investigation of these texts shows clear differences: words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as Greek or Latin, could bring us a flavor of the short term−memory features of these ancient readers.

The purpose of this study is to analyse the feasibility of using waste from granite gang saws (GCW-GS) to manufacture ultra-high performance, steel-fibre reinforced concrete (UHPFRC). These machines cut granite blocks by abrasion using a steel blade and slurry containing fine steel grit. The waste generated by gang saws (GCW-GS) contains up to 15% Fe2O3 and up to 5% CaO. This is the main difference from the waste produced by diamond saws (GCW-D). Consequently, the second objective of this study is to compare the results of the waste obtained with gang saws with that from diamond saws, in order to determine the influence of iron and calcium oxides. The waste from cutting granite with gang saws was used in different percentages to replace micronized quartz powder of natural origin in the manufacture of UHPRFC. All the test specimens were analysed to determine their compressive strength, elasticity modulus, flexural strength and indirect tensile strength. The final conclusion is that wastes from both gang saws and diamond saws can be used to manufacture UHPFRC with an improvement in the mechanical properties up to a 35% replacement. The results for GCW-GS are better, mainly due to the pozzolanic effect of the iron dioxide. For higher percentage replacements the mechanical properties are close to the control concrete with small decreases.

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.

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving RDKs by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.

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.

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.

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.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

This paper describes the packaging models that are fundamental for the design of ultra-high-performance concrete (UHPC), and their evolution. They are divided into two large groups: continuous and discrete models. The latter are those that provide the best answer in obtaining an adequate simulation of the packing of the particles up to nanometric size. This includes the interaction among the particles by means of loosening and wall coefficients, allowing a simulation of the virtual and real compactness of such particles. In addition, a relationship between virtual and real compactness is obtained, through the compaction index, which may simulate the energy of compaction that the particles undergo in their placement in the mold. The use of last-generation additives allows such models to be implemented with water-cement (w/c) ratios close to 0.18. However, the premise of maximum packing as a basic pillar for the production of UHPC should not be the only one. The cement hydration process affected by nanoadditions and the ensuing effectiveness in the properties in both fresh and hardened state according to the respective percentages in the mixture should also be studied. An adequate ratio and proportion of these additions may lead to an obtaining of better results even with lower levels of compactness.

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

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.

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.

In this paper any superalgebra is generalized to a new form, namely ultra algebra. We study some algebraic structure results for ultra algebras and we have defined some maps on ultra algebras to know ultra derivation and Jordan ultra derivation. Moreover on certain assumptions we have proved that Jordan ultra derivation is an ultra derivation.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

The purpose of this study was to conduct a meta-analysis that shows the influence of fiber on ultimate compressive strength and tensile strength of ultra-high performance concrete. The internet scholarly search engines and ScienceDirect article references were used to illustrate the papers concerning the experimental investigations of mechanical properties of ultra-high strength concrete with and without fiber with clearly, completely and comparative raw data. The normal concrete test results were dismissed from this search. Seven trials were identified based on the adopted inclusion and exclusion criteria above. The meta-analysis based on standardized mean difference was carried out on the basis of a fixed-effects model for the major outcomes of the ultimate compressive and tensile properties of ultra-high performance concrete. A total of 888 test specimens were enrolled in these seven trials. The combined analysis yielded a sign of a significant improvement in ultimate compressive strength and tensile strength of ultra-high strength concrete with fiber addition of 2% by concrete volume. The summary effect size of ultimate compressive strength was 2.34 while a more improvement in term of tensile strength with effect size of 2.64. By addition fiber of 2% provides a significant benefit in mechanical properties of ultra-high performance concrete.

The VLSI industry is facing parasitic effects that trouble development in the nanoscale domain. However, instead of replacing the traditional MOSFET design, it would be more advantageous to apply different doping profiles and discerning which deal with specific parasitic effects the best. With a review of Gaussian doping, Uniform doping, and Delta doping profiles and analysis of the FET technology characteristics that use these doping profiles, a comparison can be made among them for integrated circuit design engineers. These doping profiles are compared based on how well they perform against non-ideal and ideal environments. Also, both digital and analog performance are measured to ensure the uniqueness of each doping profile that is present. After getting a list of benefits from each doping profile, it is derived to determine which doping profile works best against a host of parasitic effects and what type of application do these doping profiles have

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

Ultra-wideband wavelength division multiplexed networks enable operators to use more effectively the bandwidth offered by a single fiber pair and thus make significant savings, both in operational and capital expenditures. The main objective of this study is to minimize optical node resources, such as transponders, multiplexers and wavelength selective switches, needed to provide and maintain high quality of network services, in ultra-wideband wavelength division multiplexed networks, at low cost. A model based on integer programming is proposed, which includes a detailed description of optical network nodal resources. The developed optimization tools are used to study the ultra-wideband wavelength division multiplexed network performance when compared with the traditional C-band wavelength division multiplexed networks. The analysis is carried out for realistic networks of different dimensions and traffic demand sets.

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.

The Pierre Auger Observatory is the world's largest operating detection system for the observation of ultra high energy cosmic rays (UHECRs). The detector allows detailed measurements of their energy spectrum, mass composition and arrival directions of primary cosmic rays in the energy range above $10^{17}$ eV. The data collected at the Observatory over the last decade show the suppression of the cosmic ray flux at energies above $4\times10^{19}$ eV. However, it is still unclear if this suppression is caused by the propagation of cosmic rays or rather by energy limitation of their sources. The other puzzle is the origin of UHECRs. Some clues can be drawn from studying the distribution of their arrival directions. The recently observed dipole anisotropy has an orientation which indicates an extragalactic origin of UHECRs. The Auger surface detector array is also sensitive to showers due to ultra high energy neutrinos of all flavours and photons, and recent neutrino and photon limits provided by the Observatory can constrain models of the cosmogenic neutrino production and exotic scenarios of the UHECRs origin, such as the decays of super heavy particles. In this paper the recent results on measurements of the energy spectrum, mass composition and arrival directions of cosmic rays, and future prospects are presented.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

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.

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically-based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

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.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

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%.

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.

This paper seeks to optimize the mechanical and durability properties of ultra-high performance concrete (UHPC). To meet this objective, concrete specimens were manufactured by using 1,100 kg/m3 of binder, water/binder ratio 0.20, silica sand and last generation of superplasticizer. Silica fume, metakaolin and two types of nano silica were used for improving the performances of the concrete. Additional mixtures included 13mm long OL steel fibers. Compressive strength, electrical resistivity, mercury intrusion porosimetry tests and differential and thermogravimetric thermal analysis were carried out. The binary combination of nano silica and metakaolin, and the ternary combination of nano silica with metakaolin and silica fume, led to the best performances of the UHPC, both mechanical and durable performances.

The increasing use of energy resources recovered from the subsurface environments and the resulting carbon imbalance in the environment has motivated the need to develop thermodynamically downhill pathways to convert and store CO₂ as water insoluble calcium or magnesium carbonates. While previous studies extensively explored aqueous routes to produce calcium and magnesium carbonates from CO₂, there has been a limited scientific understanding of the phase evolution and textural changes during the direct gas-solid conversion routes to produce calcium carbonate from calcium hydroxide, which is one of the abundant constituents of alkaline industrial residues. With increasing interest in developing integrated pathways for capturing, converting and storing CO₂ from dilute flue gases, understanding the compositions of the product phases as they evolve is essential for evaluating the efficacy of a given processing route. Therefore, in this study, we investigate the phase evolution and the corresponding textural changes as calcium hydroxide is converted to calcium carbonate under the continuous flow of CO₂ at an ambient pressure of 1 atm and on heating continuously from 30 °C to 500 °C using in-operando wide angle X-ray scattering (WAXS), small angle X-ray scattering (SAXS), and ultra-small angle X-ray scattering (USAXS) measurements.

When an earthquake or a large fire has occurred, it is difficult to secure communication networks for rescue in the building due to the destruction of commercial communication networks. Although analog radio systems such as VHF (very high frequency) and UHF (ultra-high frequency) are used for rescue operation in general, communication failure occurs in closed spaces, causing difficulties in smooth rescue operations. When the communication infrastructures have been destroyed in a building in the disaster, an emergency wireless telecommunication environment should be constructed to secure a safer disaster response environment. In this study, along with comparison of the performances of diverse communication frequencies, UWB (Ultra-Wide Band) wireless telecommunication networks were evaluated under five building indoor environment conditions including open spaces. UWB communication modules were fabricated to satisfy the IEEE (The Institute of Electrical and Electronics Engineers) 802.15.4a standard performance to measure distances in which communications are possible according to the indoor environment for each of six channels with different UWB communication frequencies. The results indicated that the distances in which communications are possible for each the six channels were average 15.5 m, maximum 20 m in open spaces; average 17.33 m, maximum 20 m in corridors; average 15.3 m, maximum 20 m in indoor office environments with office fixtures; average 4.33 m, maximum 6 m in vertical spaces of stairs; and average 6.5 m, maximum 17 m in closed horizontal spaces with a fire door. In this case, the communication performance and distance performance were shown to be the most excellent at a frequency (Centre Frequency) of 6489.6 and a band of 5980.3 – 6998.9 MHz, which is UWB 7ch. In conclusion, it is judged that if UWB communication modules are installed in the disaster area at intervals of 20 m and multi-channels are used, communication environments can be constructed even in closed spaces

Excessive intake of fat and fatty acids is associated with major health hazards such as obesity or chronic diseases. The aim of this study was to provide the first data on total fat, saturated fatty acids (SFA) and trans fatty acids (TFA) intakes and their major food sources in Tunisian children. A total of 1200 children, aged 3 to 9 years old (yo), were randomly selected from primary schools and kindergarten under a cross-sectional design. The 24-hour recall method and food frequency questionnaire were used to assess dietary intake. The energy percentages of total fat, SFA and TFA in Tunisian children were respectively 29.6, 11.4 and 0.15. No sex differences were found. The WHO recommendations for total fat, SFA and TFA were adopted by 58 %, 39 % and 89 % of the study population, respectively. The leading food groups of fat and fatty acids were ultra-processed foods, breakfast cereals and dairy products. The meat, fish, eggs and fish alternatives were the fifth main contributors to the total fat and SFA intakes in Tunisian children. The implementation of a relevant strategy for fat reduction, especially from ultra-processed foods, considered as low nutrient energy-dense products, is needed to promote health among children and prevent diet-related chronic diseases.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

Since certain Mycobacterium tuberculosis complex (MTBC) members, like M. bovis, are endemic in specific South African wildlife reserves and zoos, cases of clinically important nontuberculous mycobacteria (NTM) in wildlife may be neglected. Additionally, due to the inability of tests to differentiate between host responses to MTBC and NTM, the diagnosis of MTBC may be confounded by the presence of NTMs. This may hinder control efforts. These constraints highlight the need for enhanced rapid detection and differentiation methods for MTBC and NTM, especially in high MTBC burden areas. We evaluated the use of the GeneXpert MTB/RIF Ultra, the Hain CMdirect V1.0, and novel amplicon sequencing PCRs targeting the mycobacterial rpoB and ku gene targets, directly on antemortem respiratory samples from known MTBC-infected and NTM culture-positive African elephants (n=26 animals) and rhinoceros (n=23 animals). Our findings suggest that the Ultra is the most sensitive diagnostic test for MTBC DNA detection directly in raw antemortem respiratory specimens and that the rpoB PCR is ideal for Mycobacterium genus DNA detection and species identification through amplicon sequencing.

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.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

Under the big unified framework of quantum gravitational field and quantum repulsive field, the earlier in this series of work has comprehensively expanded the general relativity and loop quantum ring gravitation theory, set up the quantum repulsive field equation corresponding to the quantum gravitational field equation, and then established the quantum hedge-unified field equation describing the interaction of quantum gravitational field and quantum repulsive field; On this basis, the quantum repulsive universe equation corresponding to the quantum gravitational universe equation has be established, thus a set of new equations describing the expansive universe model under the condition of fully considering the hedge effect of quantum gravitational field and quantum repulsion field, which can be called the expansive universe equation of quantum hedge-unified field theory, has be established. Now, let's go deep into the arche-unified physics behind the creation of the universe. Although all kinds of inflationary universe models can solve cosmological problems to a certain extent, there is no natural and reasonable analysis and explanation for all cosmological and physical consequences. For example, it is impossible to predict a large tensor-scale ratio and a relatively large spectral index run at the same time. At present, string theory, membrane theory, loop quantum gravity theory, super-gravitational holography principle and M theory, which are the theoretical basis of cosmology, are basically established by simplification in the case of breaking away from the extreme background conditions of the universe. While cosmology, which is deficient in nature, cannot provide strong support for particle physics to further advance to a wider field and a deeper level. Due to the lack of new ideas, the construction of existing theoretical models is difficult. Therefore, the existing research can only be further promoted by new astronomical observation. Unlike existing studies, we concentrate on the emergence of the big rip-rebound clusters of quantum chaotic-reticulate distribution between the outwards-push of the universe as a whole (related to pseudo-vacuum energy, Higgs field, dark energy, etc.) and the inwards-pull of the universe in local scale (related to dark matter, quantum gravity and initial gravity separated from high-dimensional supergravity, and the initial strong force separated from the grand unified force subsequently). On the one hand, we find a great impetus to make the inflation of the universe into firstly-imposed suppression, then-imposed acceleration and finally-imposed deceleration, which really explains why the universe expands at a critical rate and has the same temperature in different regions, on the other hand, we find a fundamental unit of matter and its emerging clusters evolving in the big bang, inflation and expansion of the universe accompanied by the great rip-rebound clusters of quantum chaos-reticulate distribution, which can be reduced to an arche-pulsator of neither punctiform particle nor linear string. From these two aspects, it can been seen that the evolution of the early universe is much more complicated than people originally thought. The complexity of the super inflation of the universe is mainly reflected in the big rip-rebound cluster emerging behind the gravitational disturbance (the pressure and tensor disturbance related to dark matter), the repulsive disturbance (the pressure and scalar disturbance related to dark energy), the cosmological disturbance (related to the interaction between high-dimensional supergravity and high-dimensional superrepulsion). This is a system of nonlinear stochastic differential dynamics, or a quantum statistical physical process of nonlinear nonequilibrium state. In the new research paradigms established in this series, the arche-conjugation between the high-dimensional supergravity and the high-dimensional superrepulsion, as well as between the quantum gravity and the quantum repulsion, is higher than the supersymmetry, and then becomes the core concept of the new theory. In this series, cluster-inflating configuration, spin network space and cosmic paradigm vectors are proposed, so the model of the super-inflated universe with quantum chaos-reticulate distribution emerged under the great impetus. The new model reveals that the unified inflation of the universe as a whole and the local inflation of the universe emerge together in the great impetus of the big rip-rebound cluster of quantum chaos-reticulate distribution. As a spin network model variable, the spatiotemporal mode variable is a vector composed of a series of variables, which variables include: dimension, curvature, range, expansion speed, dynamics, quantization, chaotic-reticulate distribution (inhomogeneity, concentration, fluctuation), synergy, and so on. Between particle physics and cosmology, this series of papers proposes the cosmic quantum evolution mode, the physical-sphere distributing configuration and the interaction situation variables, organizes the concepts, variables and models of these three levels, and establishes a systematic super-synergy paradigm. In this paradigm, the general Langevan equation and the general Fokker-Planck equation are established. For the quantum cosmic-sphere, a series of trend parameters are introduced to establish the dynamics of quantum gravitational mode and its master equation, and then to establish the operator distribution function and its motion equation of the action mode. For the Tachyon of upheaval in the rip-rebound-inflating of the universe, the hypothesis on the bifurcate-chaos wave of quantum is given, the quantum theory of the early photons in upheaval is set up, and the analysis on the particles of the ultrahigh energy cosmic ray from the γ ray burst is made. Finally, using Wigner-Ville distribution as a nonlinear time-frequency distribution, we establish the ultra-synergistic field equation of quantum-classic unifying action-sphere.

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.

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.

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.

Fabrics treated to repel water, superhydrophobic, and water and oil, superamphiphobic, have numerous industrial and consumer-level benefits. These coatings are typically non-permanent. This is largely due to chemical or physical changes of the coating to prolonged exposure to relatively harsh environments. To develop more durable fabric treatments for specific applications, it is necessary to measure the extent to which the treated fabrics retain their low-wettability after being subjected to controlled aggressive environmental conditions. In this study, plain weave fabrics made from polyester filaments and coated with silicone nanofilaments in-solution were exposed to aerodynamic icing conditions. The coated fabrics showed superhydrophobic behavior, or superamphiphobic for those that were fluorinated. The wettability of the fabrics was progressively evaluated by contact angle and roll-off-angle measurements. The fabrics were able to maintain their low-wettability characteristics after exposure to water droplet clouds at airspeeds up to 120 m/s, despite damage to the silicone nanofilaments, visible through scanning electron microscopy.

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.

Glycosyl inositol phospho ceramides (GIPCs) are the major sphingolipids on earth as they account for a considerable fraction of the total lipids in plants and fungi which in turn represent a large portion of the biomass on earth. Despite their obvious importance, GIPC analysis remains challenging due to the lack of commercial standards and automated annotation software. In this work, we introduce a novel GIPC glycolipidomics workflow based on reversed-phase ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry. For the first time, automated GIPC assignment was performed using the open-source software Lipid Data Analyzer based on platform-independent decision rules. Four different plant samples (salad, spinach, raspberry, strawberry) were analyzed and revealed 64 GIPCs based on accurate mass, characteristic MS2 fragments and matching retention times. Relative quantification using lactosyl ceramide for internal standardization revealed GIPC t18:1/h24:0 as the most abundant species in all plants. Depending on the plant sample, GIPCs contained mainly amine, N-acetylamine or hydroxyl residues. Most GIPCs revealed a Hex-HexA-IPC core and contained a ceramide part with a trihydroxylated t18:0 or t18:1 long chain base and hydroxylated fatty acid chains ranging from 16 to 26 carbon atoms in length (h16:0 – h26:0). Interestingly, six GIPCs containing t18:2 were observed in raspberry, which was not reported so far. The presented workflow supports the characterization of different plant samples by automatic GIPC assignment potentially leading to the identification of new GIPCs. For the first time, automated high‑throughput profiling of these complex glycolipids is possible by liquid chromatography-high-resolution mass spectrometry and subsequent automated glycolipid annotation based on decision rules.

The deep Cretaceous tight sandstone in Kuqa Depression of Tarim foreland basin is an ultra-low porosity and ultra-deep gas-bearing reservoir, which is characterized by small pores, fine throats, and poor connectivity. The wireline logging responses are so complex, and especially, it is difficult to identify fluid types from resistivity logs. Based on acoustic, density, and neutron logs response differences in gas and water layers, effective fluid sensitivity factors are constructed for gas layer identification. From conventional logs, acoustic-neutron porosity difference, density-neutron porosity difference, and triple-porosity ratio are all sensitive parameters to the gas layer. From the NMR logging response mechanism, the density and NMR porosity difference, and T2 geometric mean of the movable fluid are also two sensitive parameters to the gas layer. Based on these parameters, a series of fluid typing charts are constructed and their adaptabilities are analyzed and compared. By contrast, NMR log interpretation is better, and triple-porosity comprehensive method from conventional logs is also effective when NMR logging is not available. Finally, the comprehensive fluid typing strategy by combining some methods for ultra-low porosity tight sandstone is summarized and optimized. This study is another alternative for fluid identification using non-electrical logs.

Nowadays, technological advancement has transformed traditional vehicles into Au-tonomous Vehicles (A.V.s). In addition, in our daily lives, A.V.s play an important role since they are considered an essential component of smart cities. A.V. is an intelligent vehicle capable of main-taining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, A.V.s collect information about the outside environment using sensors to ensure safe navigation. Furthermore, A.V.s reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, A.V.s could be threatened by cyberattacks, posing risks to human life. For example, re-searchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW AVs. Therefore, more research is needed to detect cyberattacks targeting the components of A.V.s to mitigate their negative consequences. This research will contribute to the security of A.V.s by detecting cyberattacks at the early stages. First, we inject False Data Injection (FDI) attacks into an A.V. simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify FDI attacks targeting the control system of the A.V. through a compromised sensor. We use long short-term memory (LSTM) deep networks to detect FDI attacks in the early stage to ensure the stability of the operation of A.V.s. Our method classifies the collected dataset into two classifications: normal and anomaly data. The ex-perimental result shows that our proposed model's accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

Unhealthy diet is an important health problem in the region of the Americas, and Uruguay does not escape this reality. Nutritional problems in Uruguayan school-age children are overweight and obesity. Caloric intake is excessive for 60% of children and 28% of calories come from ultra-processed products (UPP) [CODICEN 2021] [MIDES 2020) (Köncke, Toledo, 2021). In 2018, an evaluation of food intake was conducted in a representative sample of public schools in the city of Montevideo. Food and preparations were categorized according to the NOVA system, later they were analyzed according to the Pan American Health Oganization nutrient profile model (PAHO NPM). Only 0.52% of children consumed exclusively natural foods, unprocessed, minimally processed or culinary ingredients. Twenty-five percent of children consumed ≥ 4 products categorized with excessive content of free sugars, total fat or saturated fat according to the PAHO NPM; in the case of excessive sodium this was 40%. In general, children who included products with excessive free sugars, sodium or saturated fat in their diet exceeded the limits established by PAHO and as a result their diet is of poorer nutritional quality compared to children who did not consume such products.

To address the severe distresses of asphalt pavement, a new type of pavement maintenance treatment, porous ultra-thin overlay (PUTO) with small particle size was proposed. The PUTO has a thickness of 1.5~2.5 cm and a large void ratio of 18~25%. As a newly asphalt mixture, the structure characteristics differ from traditional pavement. Therefore, it is necessary to investigated the fabrication schemes in laboratory and on-site, respectively. In this study, the optimal fabrication schemes, including compaction temperature and number of blows of PUTO were determined based on Cantabro test and volumetric parameters. Then, the corresponding relationship between laboratory and on-site compaction work was then established based on the energy equivalent principle. On this basis, the numbers of on-site rolling passes and the combination method were calculated. The results show that increased compaction temperature and number of blows reduce the height and enhance the compactness of the Marshall sample. With the same temperature and number of blows, the scattering resistance of coarse gradation (PAC-1) is better than that of fine gradation (PAC-2), and the increased asphalt viscosity significantly improves the scattering resistance of the asphalt mixture. To ensure the scattering resistance and volumetric characteristic, the initial compaction temperature of the PAC-1 and PAC-2 should not be lower than 150 °C and 165 °C, respectively. Then, the laboratory compaction work and on-site compaction work were calculated and converted based on the principle of energy equivalence. Consequently, the on-site compaction combination of rolling machines for four asphalt mixtures was determined. According to the volumetric parameters, the paving test section proved that the construction temperature and the on-site rolling combination determined by laboratory tests are reasonable, and ultra-thin overlay has good structural stability, drainage and skid resistance.

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.

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%.

As solar energy is a low-cost and clean energy source, there has been a great deal of interest in how to harvest it. To absorb solar energy efficiently, we have designed a broadband metamaterial absorber based on the principle of Fabry–Pérot (FP) cavity and surface plasmon resonances (SPRs). We propose a broadband perfect absorber consisting of a four-layer structure of silica-tungsten-silica-titanium (SiO2-W-SiO2-Ti) for the incident light wavelength range of 300–2500 nm. The structure achieves perfect absorption of incident light in the wavelength range of 351.8–2465.0 nm (absorption >90%), with an average absorption of 96.3%. The advantage of our proposed structure is that it combines the characteristics of both high and broadband absorption and has a high overall absorption efficiency for solar radiation. It is also independent of polarization and insensitive to incident angle. We investigated how absorption was affected by different structures, materials, geometrical parameters, and refractive indices for different dielectric materials, and we explored the reasons for high absorption. This structure is refractory and ultra-thin, and it offers a good trade-off between bandwidth and absorption. It therefore has premium application prospects and value.

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%.

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.

This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

Automated lying-posture tracking is important in preventing bed-related disorders such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research about how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are most effective in accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F-Score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

Among the components of a healthy life, sexuality is an essential part, contributing not only to psychophysical well-being, but also to the social well-being of women and, consequently to their quality of life. A poorly investigated standpoint is the acceptability of a contraceptive method, not only in terms of tolerability and metabolic neutrality, but also concerning the impact that it can have on sexual life. In this context, we will provide an overview of the different methods of contraception and their effects on female sexuality from the biological changes, to organic, social, and psychological factors, which can all shape sexuality.A MEDLINE/PUBMED review of the literature between 2010 and 2021 was conducted using the following key words/phrases: hormonal contraception, contraceptives, female sexual function, libido, sexual arousal and desire, and sexual pain. Recent studies have supported the effects of contraceptives on women’s sexuality, describing a variety of positive and negative events on several domains of the sexual function (desire, arous-al, orgasm, pain, enjoyment). However, satisfaction with sexual activity depends on factors that extend beyond sexual functioning alone. A more holistic approach is needed to better under-stand the multitude of factors linked to women’s sexuality and contraception. Contraceptive counselling must necessarily consider these important elements since they are closely related to good compliance.

The design of binders plays a pivotal role in achieving enduring high power in lithium-ion batteries (LIBs) and extending their overall lifespan. This review underscores the indispensable characteristics that a binder must possess when utilized in LIBs, considering factors such as electrochemical, thermal, and dispersion stability, compatibility with electrolytes, solubility in solvents, mechanical properties, and conductivity. In the case of anode materials, binders with robust mechanical properties and elasticity are imperative to uphold electrode integrity, par-ticularly in materials experiencing substantial volume changes. For cathode materials, the se-lection of a binder hinges on the crystal structure of the cathode material. Other vital consid-erations in binder design encompass cost-effectiveness, adhesion, processability, and envi-ronmental friendliness. Incorporating low-cost, eco-friendly, and biodegradable polymers can contribute significantly to sustainable battery development. This review serves as an invaluable resource for comprehending the prerequisites of binder design in high-performance LIBs and offers insights into binder selection for diverse electrode materials. The findings and principles articulated in this review can be extrapolated to other advanced battery systems, charting a course for the development of next-generation batteries characterized by enhanced perfor-mance and sustainability.

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.

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.

In the modern construction industry, lightweight aggregate concrete (LWAC) is often used in the production of load-bearing structural members. LWAC can be up to 40% lighter by volume in comparison to normal strength concrete. On the other hand, the lack of adequate numerical models often limits the practical application of innovative building materials, such as lightweight concrete, in real projects. This trend is due to the uncertainties in design standard methods and calculation errors, the level of which is generally unacceptable to civil engineers in terms of safety and reliability. In the present paper, a comparative numerical deformation analysis of a full-scale bridge deck slab and girder has been carried out. Using the physical model proposed by the authors and the finite element software ATENA, the deformations of full–scale lightweight and traditional reinforced concrete elements under short-term effects of permanent and variable loads was compared. Depending on the safety and serviceability limit requirements, it was found that the amount of longitudinal reinforcement in lightweight reinforced concrete elements can be reduced compared to normal reinforced concrete elements with the same parameters. The results of the numerical analysis show that the deformation analysis model proposed by the authors can be a reliable tool for the design of lightweight concrete flexural members by selecting the optimum geometrical and reinforcement parameters limited by the stiffness condition.

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.

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.

“All diseases originate in the gut.” Hippocrates (400 BC) A healthy gut microbiome via the gut-brain-axis (GBA) elevates heart rate variability (HRV), a general measure of health and well-being. A dysbiotic gut microbiome, low in biodiversity and butyrate producers, alters tryptophan metabolism with release of proinflammatory cytokines, predominantly TNF-α, IL-6, and IL-1β. These also characterize chronic inflammation, oxidative stress, and a multitude of diseases, all exhibiting low HRV. Gut dysbiosis upregulates IFN-γ and with it IDO (indoleamine 2,3 dioxygenase). Tryptophan pivots from serotonin synthesis to that of IDO induced kynurenine, increasing the kynurenine to tryptophan ratio (KTR). An elevated KTR is positively linked to neurodegenerative and autoimmune diseases and negatively linked to HRV. Elevated IDO activity is not only enzymatic but also an intracellular signal transducer potentiated by TGF-β. This cytokine is the primary determinant of the TME. Also proposed is the gut-lung dysbiosis concept and consequent degradation of ACE2 (richest in lungs and gut). Leaky gut induced autoantibodies related to G-protein coupled receptors (GPCRs) in combination with increased Ang II further potentiate oxidative stress. Aldosterone and paroxysmal orthostatic tachycardia syndrome (POTS) paradoxes are highlighted in the context of GPCR and gut dysbiosis, and the role of Candida is explored. The triple play of a prebiotic (d-mannose), probiotic (bifidobacteria and lactobacilli), and postbiotic (butyrate) might improve intestinal barrier integrity, oppose entry of GPCR antigens, suppress the inflammatory cytokine triad, balance IFN-γ and TGF-β, suppress oxidative stress, depress KTR, elevate HRV, and extend lifespan and its quality.

With the proportional reduction of MOSFET size, the leakage-to-barrier reduction (DIBL) effect leads to a more significant increase in the tunneling current on the gate, and the appearance of the gate tunneling current also seriously affects the static characteristics of the device. In this paper, a new theoretical model of the relationship between the direct tunneling current and the thickness of the oxide layer under the DIBL effect is proposed for the MOSFET device with ultra-thin oxide layer. On this basis, the characteristics of the MOSFET device are studied in detail by using HSPICE, and their working conditions are quantitatively analyzed. The characteristic variation trend of small-size devices under the influence of gate tunneling current is predicted. The simulation results using BSIM4 model are consistent with the theoretical model. The theory and data in this paper will provide useful reference for large scale integrated circuit design.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

FLASH-RT represents a novel therapeutic radiation modality that holds remarkable potential for mitigating radiation therapy’s adverse side effects. This cutting-edge technology allows for the sparing of healthy tissue while precisely targeting cancerous cells by administering an ultra-high dose-rates of typically between 10 and 30 Gy in less than a few hundred milliseconds. FLASH-RT has demonstrated impressive results in small-animal models, prompting scientists to adapt and advance existing technologies to make it a viable treatment option for humans. However, producing the ultra-high-dose-rate radiation required for the therapy remains a significant challenge. Several radiation sources, such as very high energy electrons (VHEEs), low energy electrons, x-rays, and protons, have been studied for their ability to deliver the necessary dose. Among them, FLASH-x-ray has gained the most attention owing to its capacity to penetrate deeply seated tumours. Despite the complexity of the process, the potential advantages of FLASH-RT make it an exciting area of research. To achieve the FLASH effect, high-frequency, pulsed irradiated accelerator technology can be employed. Sparing healthy tissue, it may allow for more aggressive and effective cancer treatments, leading to a better quality of life for patients. Ongoing research and development will be necessary to refine and optimize this approach to radiation therapy.

The purpose of this project was to demonstrate the potential of converting an existing internal combustion engine for hydrogen operation at relatively low cost and to optimize the combustion process for ultra-low NOx emissions. The conversion has been carried out on a 450cc naturally aspirated single cylinder engine. The first baseline measurements and setup have shown lower NOx emissions than the baseline gasoline engine (without catalytic converter) for similar power output and engine efficiency. Further optimization of the combustion process allowed to achieve Euro VI and (latest European Commission proposal) Euro VII heavy-duty NOx limits without exhaust gas aftertreatment system (EATS) in steady state conditions. The concept is very promising and the knowledge gained should allow for further optimization to achieve near zero NOx emissions at high engine efficiency and power output with supercharging. It should also allow an efficient conversion of other engine types for different applications. In particular, a project with the aim to convert a heavy duty 6 cylinder engine intended for public transport buses has started. It follows a feasibility study which has shown the competitiveness of the hydrogen engine for this application.

: This study was aimed at assessing adaptation and bonding of discontinuous (short) glass fiber-reinforced composite to intraradicular dentin EverX Flow (GC Corporation, Tokyo, Japan), when used as intracanal composite filling and anchorage instead of traditional fiber posts. (2) Methods: Seventy intact extracted human teeth were endodontically treated and randomly divided into 6 groups (n=10), depending on the materials used in the post space. In Group 1, a 2-bottle universal adhesive G2 Bond Universal + EverX Flow were tested. In group 2, a single-component universal adhesive G-Premio Bond + EverX Flow were used. In groups 3 and 4 the same materials are tested, but after cleaning of the canal walls with 17% EDTA and final irrigation with 5.25% NaOCl Ultrasound Activated. In the last three Groups (5-7) traditional prefabricated GC Fiber Posts 1.6 mm silanized with G-Multi Primer for 1 minute are cemented with a dual-cured composite resin cement (GradiaCore), after ultrasonic irrigation in the groups 6 and 7. In each group, 1 mm-thick slices from each sample (n=10) were cut for light microscope and SEM inspection for study materials adaption to the dentin and for measuring push-out strength of post / cemement material to the dentin / prefabricated post. These results were statistically analyzed: as the data distribution was not normal, the Kruskal-Wallis Analysis of Variance by Ranks had to be applied. The level of significance was set at p<0.05. Results: Push-out forces varied between 6.66-8.37 MPa. No statistically significant differences were recorded among the groups. Microscopic examination showed that ultrasonic irri-gation increased adaptation of the materials to the dentin surface. There was a trend of higher bond strength among the tested groups when EverX Flow was used. Also, the type of failure was more often cohesive when ultrasonic irrigation and two-step adhesive system were used. Conclusions: Within the limitations of this in vitro study, it may be concluded that when EverX Flow was used for intracanal anchorage in the post-endodontic recon-struction, similar push-out retentive forces and strength to those of traditional fiber posts cemented with particulate filler resin composite cements were achieved. Although further studies are necessary, EverX Flow represents an effective alternative to traditional fiber post adhesion in particular when used in combination with the two-step adhesive system and ultrasonic activation.

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.

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.

Autism spectrum disorder (ASD) is a neuropsychiatric condition characterized by impaired social interactions and repetitive stereotyped behaviors. Growing evidence highlights an important role of the gut-brain-microbiome axis in the pathogenesis of ASD. Research indicates an abnormal composition of the gut microbiome and the potential involvement of bacterial molecules in neuroinflammation and brain development disruptions. Concurrently, attention is directed towards the role of short-chain fatty acids (SCFAs) and impaired intestinal tightness. This comprehensive review emphasizes the potential impact of maternal gut microbiota changes on the development of autism in children, especially considering maternal immune activation (MIA). The following paper evaluates the impact of the birth route on the colonization of the child with bacteria in the first weeks of life. Furthermore, it explores the role of pro-inflammatory cytokines, such as IL-6 and IL-17a and mother’s obesity as potentially environmental factors of ASD. The purpose of the present analysis is to advance our understanding of ASD pathogenesis, while also searching for the positive implications of the latest therapies, such as probiotics, prebiotics or fecal microbiota transplantation, targeting the gut microbiota and reducing inflammation. This review aims to provide valuable insights that could instruct future studies and treatments for individuals affected by ASD.

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.

The passenger prediction flow is very significant to transportation sustainability. This is due to some chaos of traffic jams encountered by the road users during their movement to the offices, schools, or markets at earlier of the days and during closing periods. This problem is peculiar to the transportation system of the Federal University of Technology Minna, Nigeria. However, the prevailing technique of passenger flow estimation is non-parametric which depends on the fixed planning and is easily affected by noise. In this research, we proposed the development of a hybrid intelligent passenger frequency prediction model using the Auto-Regressive Integrated Moving Average (ARIMA) linear model, Convolutional Neural Network (CNN), and Kalman Filter Algorithm (KFA). The passengers’ frequency of arrival at the bus terminals is obtained and enumerated through the closed-circuit television (CCTV) and demonstrated using the Markovian Queueing Systems Model (MQSM). The ARIMA model was used for learning and prediction and compared the result with the combined techniques of using CNN-KFA. The autocorrelation coefficient functions (ACF) and partial autocorrelation coefficient functions (PACF) are used to examine the stationary data with different features. The performance of the models was analyzed and evaluated in describing the short-term passenger flow frequency at each terminal using the Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. The CNN-Kalman-filter model was fitted into the short-term series and the MAPE values are below 10%. The Mean Square Error (MSE) shows that the CNN-Kalman Filter model has the overall best performance with 83.33% of the time better than the ARIMA model and provides high accuracy in forecasting.

The accumulation of steel slag and other industrial solid wastes has caused serious environmental pollution and resource waste. The resource utilization of steel slag is imminent. In this paper, alkali activated materials ultra-high performance concrete (AAM-UHPC) was prepared by replacing GGBFS powder with different proportions of steel slag powder, and its workability, mechanical properties, curing condition, microstructure, and pore structure were investigated. The results illustrate that the incorporation of steel slag powder can significantly delay the setting time and improve the flowability of AAM-UHPC, making it possible for engineering applications. The mechanical properties of AAM-UHPC showed a tendency to increase and then decrease with the increase of steel slag dosing, and reached the best performance at 30% dosage of steel slag. The maximum compressive strength and flexural strength are 157.1 MPa and 16.32 MPa, respectively. High temperature steam or hot water curing at early age was beneficial to the strength development of AAM-UHPC, but continuous high temperature hot and humid curing would lead to strength inversion. When the dosage of steel slag is 30%, the average pore diameter of the matrix is only 8.43nm, and the appropriate steel slag dosage can reduce the heat of hydration and refine the pore size distribution, making the matrix more dense.

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.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

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.

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.

HER2-low and ultra-low breast cancer (BC) have been recently proposed as new subcategories of HER2 BC, re-considering the immunohistochemical negative score of 0, 1+ or score of 2+/in situ hybridization (ISH) negative phenotype. In the present review we have outlined the needed criteria to exactly distinguish HER2 low and ultra-low BC. Recent clinical trials have demonstrated significant clinical benefits of novel HER2 directing antibody-drug conjugates (ADCs) in treating these group sof tumors. In particular, Trastuzumab-deruxtecan (T-Dxd), a HER2-directing ADC has been recently approved by the U.S. Food and Drug Administration as the first targeted therapy to treat HER2-low BC. Furthermore, ongoing trials, such as the DESTINY-Breast06, are currently evaluating ADCs in patients with HER2-ultra low BC. Finally we hope that new guidelines may help to codify HER2 low and ultra-low BC, increasing our knowledge of tumor biology and improving a targetable new therapeutical treatment.

The correct classification of defects originating from partial discharges (PD) in medium-voltage (MV) switchgears with air insulation (AIS) remains a challenging research topic for scientists worldwide. In this article, the authors simulated four possible defects occurring in the power industry, including one that is a simultaneous combination of two commonly ones. In addition, the correctness of the algorithm was checked by adding a classification class without any fault. The measurement signals were recorded with TEV sensors. The effectiveness of various hy-brid-connected neural networks was tested and discussed: GoogleNet and SqueezeNet based on spectrograms, SAE with FNN, 2D-CNN with LSTM, and hybrid AE combined with CNN and LSTM. The highest effectiveness – approximately 97% – was demonstrated by the GoogleNet and SqueezeNet networks. The research results are expected to form the basis for the development of a universal and wireless capacitive sensor for monitoring the level of PD in switchgears.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in mobile wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and the key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning, quantum communication, and re-configurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and machine learning, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. This paper also addresses critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study not only maps out the current landscape but also lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI(Artificial Intelligence) technologies, we aim to illuminate the path forward in this rapidly evolving field.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

This paper introduces an innovative energy circuit that challenges conventional understandings of energy conservation, marking a significant departure from traditional frameworks. Seeking to enhance energy generation within existing paradigms, this innovation holds promise for addressing pressing global issues such as the energy crisis and environmental sustainability, while also stimulating scientific inquiry. In contrast to prevalent misconceptions rooted in philosophical and scientific constraints, this study refutes the notion of generating energy from nothing and questions the viability of perpetual motion machines as conceptual models. Instead, it proposes a novel circuit design derived from an unconventional electrical short circuit, which is shown to disrupt the established principles of energy conservation. This circuit offers a transformative approach with diverse applications, including self-recharging capabilities for electric vehicles, bolstering microgrid infrastructure, and facilitating the integration of renewable energy sources. Transcending conventional scientific boundaries, this paper prompts philosophical reflections on the evolving nature of scientific exploration. Ultimately, the energy circuit represents a pivotal step towards mitigating the global energy crisis by reducing reliance on finite resources and fostering sustainability. The paper concludes by illustrating the circuit's potential to revolutionize energy systems and contribute to a more resilient and sustainable future.

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.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.

Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

This paper investigates portfolio optimization methodologies and short-term investment strate-gies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from January 2020 to November 2023. We employ high frequency data and utilize the Kurtosis Mini-mization methodology, alongside other optimization strategies, to construct and evaluate port-folios under different rebalancing frequencies. The empirical analysis reveals that cryptocurren-cies exhibit significant volatility, skewness, and kurtosis, necessitating sophisticated portfolio management techniques. We find that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, delivering optimal returns to investors, particularly in shorter-term investment horizons. We demonstrate the diversification benefits of integrating cryptocurrencies into multi-asset portfolios and emphasize the importance of regular portfolio rebalancing in the volatile cryptocurrency market. Our findings offer insights for portfolio man-agers and investors seeking to optimize their investment outcomes in the cryptocurrency market, highlighting the importance of risk management, diversification, and dynamic portfolio man-agement strategies.

The introduction of stool as a readily obtainable sample and the recently developed simple-one-step (SOS) stool processing method offer an opportunity for TB diagnosis in children. We conducted this study at secondary health facilities in Ethiopia which are the first level referral facilities for childhood TB diagnosis and treatment with the aim to determine if stool-based TB diagnosis can be performed with reasonable level of concordance with sputum tests in using Xpert MTB/RIF Ultra. Eligible children 0-14 years with presumptive pulmonary TB were asked to provide stools in addition to routinely requested sputum samples. We computed Cohen’s kappa statistic in SPSS to determine the level of agreement between stool and sputum test results. Of the 373 children included in the study, 61% were &lt;5 years of age and 56% were male. Thirty-six children (9.7%) were diagnosed with TB and all started treatment. The rate of concordance between stool and sputum was high with kappa value of 0.83 (P&lt;0.001). There were more Xpert ultra positive test results on stool (n=27 (7.2%)) than on sputum/NGA (n=23 (6.2%)). Laboratories in secondary hospitals can perform stool-based TB diagnosis in children with high concordance between stool and sputum test results reaffirming the applicability of the SOS method.

Ultra high molecular weight polyethylene (UHMWPE) is a thermoplastic high-performance polymer in high demand for biomedicine, ship and machine building, anthropomorphic robots and smart prostheses. Highly oriented UHMWPE fibers possess record specific strength and may also be used for the fabrication of self-reinforced PE-PE composites (SRPEC). The temperature evolution study of the small-angle X-ray scattering (SAXS) signature of the supramolecular structure of UHMWPE can help reveal their important role in the mechanism of the shape memory effect in SRPEC. The laboratory SAXS–WAXS beamline XEUSS 3.0 was used for in situ studies of the nanostructure parameters in unidirectional SRPEC. Namely, the radius of gyration and the dimensionality factor were extracted from 2D SAXS patterns using several fitting algorithms. Significant changes of these parameters occurred in temperature ranges that correspond to the initiation of the shape memory effect and melting. The relationship between the material anisotropy and 2D SAXS patterns are discussed in the context of the supramolecular structure evolution.

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.

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.

Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.

Estimating the infection risks in indoor environments comprises the assessment of the behavior of virus-laden aerosols, i.e. spreading, mixing, removal by air purifiers etc. A promising experimental approach is based on using non-hazardous surrogate aerosols of similar size, e.g. salt particles, to mimic the virus aerosol behavior. This manuscript addresses the issue how a successful transfer of such experiments can be accomplished. Corresponding experiments in two very different environments, a large community hall and a seminar room, with optional use of air purifiers in various constellations were conducted. While high particle concentrations are advantageous in terms of avoiding influence of background aerosol concentrations, it is shown that appropriate consideration of aggregation and settling are vital to theoretically describe the experimentally determined course of particle concentrations. A corresponding model equation for a well-mixed situation is derived and the required parameters are thoroughly determined in separate experiments independently. It is demonstrated that clean air delivery rates (CADR) of air purifiers determined with this approach may differ substantially from common approaches not explicitly taking into account aggregation.

In recent years, there has been an increasing interest in adopting novel sensing technologies for the continuous monitoring of structural systems. In this respect, Micro-Electro-Mechanical System (MEMS) sensors are widely used in several applications, including Structural Health Monitoring (SHM). Thanks to their significantly lower cost, ease of installation on the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analysis show that MEMS can correctly detect modal frequencies, useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Human violence recognition is an area of great interest in the scientific community, given its broad spectrum of applications, especially in video surveillance systems, since detecting violence in real-time could prevent criminal acts and save lives. Despite the number of existing proposals and research, most focus on the precision of results, leaving aside efficiency and its practical implementation. Thus, this work proposes a model that is effective and efficient in recognizing human violence in real-time. The proposed model consists of three modules: a first module called Spatial Motion Extractor (SME), in charge of extracting regions of interest from a frame; a second module called Short Temporal Extractor (STC), whose function is to extract temporal characteristics of rapid movements, finally the Global Temporal Extractor (GET) module, responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated regarding efficiency, effectiveness, and ability to operate in real-time. The results obtained on Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to other alternatives. A VioPeru dataset was created to validate real-time applicability with violent and non-violent videos captured by real video surveillance cameras in Peru. The effectiveness results in this dataset outperformed the best existing proposal. Therefore, our proposal has contributions in efficiency, effectiveness, and real-time.

DEXTER (Detection of EXplosives and firearms to counter TERrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. The active microwave component of the DEXTER project is referred to as MIC (Microwave Imaging Curtain), supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real time to address non-checkpoint detection of explosives and fire-arms. The project, led by the ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra Wide-Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC's innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar im-ages using a deep-learning network. This paper will outline the project's objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results ob-tained during a live demonstration in a relevant environment.

With a view to sustainable development and circular economy, this work focused on the possibility to valorize a secondary waste stream of recycled carbon fiber (rCF) to produce a 3D printing usable material with PA6,6 polymer matrix. The reinforcing fibers implemented in the present research are the result of a double recovery action: starting with pyrolysis from which long fibers are obtained, used to produce non-woven fabrics and, subsequently, fiber agglomerate wastes obtained from this last process are ground in a ball mill. The effect of a different amount of reinforcement at 5% and 10% by weight on the mechanical properties of 3D printed thermoplastic composites was investigated. Although the recycled fraction was successfully integrated in the production of filaments for 3D printing and therefore in the production of specimens via Fused Deposition Modeling technique, the results showed that fibers did not improve the mechanical properties as expected, due to an unsuitable average size distribution and the presence of a predominant dusty fraction ascribed to the non-optimized ball milling process. PA6,6 + 10 wt.% rCF composites exhibited a tensile strength of 59.53 MPa and a tensile modulus of 2.24 GPa, which correspond to an improvement in mechanical behavior of 21 5 % and 5 21 % compared to the neat PA6,6 specimens, respectively. The printed composite specimens loaded with the lowest content of rCF provided the greatest improvement in strength (+ 9% over the neat sample). Then, a prediction of the “optimum” critical length of carbon fibers was proposed that could be used for future optimizations of the recycled fiber processing.