This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

The railway industry focus in the past years was to research, find and develop methods to mitigate noise and vibration resulted from wheel/rail contact along track infrastructure. This resulted in a wide range of abatement measures that are available for the professionals of the industry today. However, although there are many options in the market, their practical implementations depend upon general constraints that affect most technological application in the engineering world. The progression of these technologies have facilitated the selection of more adequate methods for each best case scenario, but further studies are ought to be made to proper assess if each one is fit for their purpose. Every method implementation must be analyzed through budget and timeframe limitations, which includes building, maintenance and inspection costs and time allocation, while also aiming to meet different benefits, such as environmental impact control and wear of the whole infrastructure. There are several situations and facilities in a railway project design that need noise and vibration mitigation methods and each design allocates different priorities for each one of them. Traditionally the disturbance caused by railways to the community are generated by wheel/rail contact sound radiation that expresses in different ways, depending on the movement of the rolling stock and track alignment, such as rolling noise, impact noise and curve noise. More specifically, in special trackworks such as turnouts, the main area of this study, there are two noises types that must be evaluated: impact noise and screeching noise. With respect to the second, it is similar to curve squeals and, being such, its mitigation methods are to be assigned as if it was to abate curve squeal in turnouts and crossings. The impact noise on the other hand, emerges from the sound made by the rolling stock moving through joints and discontinuities (i.e. gaps) that composes these special components of a railway track. A life cycle analysis is therefore substantial for this reality and in this case will be applied to Squeal and Impact Noise on Special Trackwork. The evaluation is based on a valid literature review and the total costs were assumed by industry reports to maintain coherency. The period for a life cycle analysis is usually of 50 years, hence it was the value assumed. As for the general parameters, an area with high density of people was considered to estimate the values for a community with very strict limits for noise and vibration.

The GLUT members belong to a family of glucose transporter proteins that facilitate glucose transport across the cell membrane. The mammalian GLUT family consists of thirteen members (GLUTs 1-12 and HMIT). Humans have a recently duplicated GLUT member, GLUT14. Avians express the majority of GLUT members. The arrangement of multiple GLUTs across all somatic tissues signifies the important role of glucose across all organisms. Defects in glucose transport have been linked to metabolic disorders, insulin resistance and diabetes. Despite the essential importance of these transporters, our knowledge regarding GLUT members in avians is fragmented. It has been clear that there are no chicken orthologs of mammalian GLUT4 and GLUT7. Our examination of GLUT members in the chicken revealed that some chicken GLUT members do not have corresponding orthologs in mammals. We review the information regarding GLUT orthologs and their function and expression in mammals and birds, with emphasis on chickens and humans.

This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.

Moso bamboo is famous for fast growing and biomass accumulation, as well as high annual output for timber and bamboo shoots. These high outputs require high nutrient inputs to maintain and improve stand productivity. Soil nitrogen (N), phosphorus (P) and potassium (K) are important micronutrients for plant growth and productivity. Due to high variability of soils, analysing spatial patterns of soil N, P and K stocks is necessary for scientific nutrient management in Moso bamboo forests. In this study, soils were sampled from 138 locations across Yong’an City and ordinary kriging was applied for spatial interpolation of soil N, P and K stocks. Soil N stock showed a strong spatial dependence while soil N and P stocks presented a moderate spatial dependence, indicating soil N was mainly controlled by intrinsic factors while soil N and P stocks were controlled by both intrinsic and extrinsic factors. Different spatial patterns were observed for soil N, P and K stocks across the whole study area, indicating that fertilizations with different ratios of N:P:K should be applied for different sites to maintain and improve stand productivity. The total soil N, P and K stocks within 0-60 cm were 0.624, 0.020 and 0.583 Tg, respectively.

This article aimed to evaluate, using statistical tools, the generation and composition of urban solid waste (MSW) in the city of Belém, Pará, from nine urban collection sectors in the municipality, and the average nominal income of the population. Approximately 900kg of urban solid waste were col-lected from the municipality in the period from 2021 to 2022. Statistical tests were carried out on hypotheses with 5% significance in comparative evaluations, and their respective average nominal incomes. The results indicated that the organic fraction corresponds to 55.57% of the waste generated in the municipality, 14.26% is inert waste (potentially contaminating) and 0.67% is miscellaneous waste. It was observed that the waste generated by families with high purchasing power tends to contain higher percentages of recyclables, while in families with lower purchasing power the highest percentages tend to be organic. Knowledge of the generation of MSW is fundamental for the choice and dimensioning of operations and processes involved in the management chain at the municipal level. The decentralization of services offered based on solid waste management would enable greater success in serving the population.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

Pain is a multifaceted multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central-stimulation (tDCS) and peripheral-stimulation (TENS) in pain-inhibition during a cognitive-task in healthy volunteers and to observe potential neuro-cognitive improvements. Healthy eighty volunteers went through a complete experimental protocol comprising pain-induction (CPT), administration of tDCS/TENS, EEG-data acquisition, and cognitive assessments. The protocol for this study is categorized into 04-groups: G1(Control), G2(TENS), G3(Anodal-tDCS), and G4(Cathodal-tDCS). Paired t-tests (p

Pollen tubes grow by spatially and temporally regulated expansion of new material secreted into the cell wall at the tip of the tube. A complex web of interactions among cellular components, ions and small molecule provides dynamic control of localized expansion and secretion. Cross-correlation studies on oscillating lily (Lilium formosanum Wallace) pollen tubes showed that an increase in intracellular calcium follows an increase in growth, whereas the increase in the alkaline band and in secretion both anticipate the increase in growth rate. Calcium, as a follower, is unlikely to be a stimulator of growth, whereas the alkaline band, as a leader, may be an activator. To gain further insight herein we reversibly inhibited growth with potassium cyanide (KCN), and followed the re-establishment of calcium, pH and secretion patterns as growth resumed. While KCN markedly slows growth and causes the associated gradients of calcium and pH to sharply decline, its removal allows growth and vital processes to fully recover. The calcium gradient reappears before growth restarts, however it is preceded by both the alkaline band and secretion, in which the alkaline band is slightly advanced over secretion. Thus the pH gradient, rather than the tip-focused calcium gradient, may regulate pollen tube growth.

Prostate cancer (Pca) is a highly heterogeneous disease and the second more common tumor in males. Molecular and genetic profiles have been used to identify subtypes and guide therapeutic intervention. However, roughly 26% of primary Pca are driven by unknown molecular lesions. We use Principal Component Analysis (PCA) and custom RNAseq-data normalization to identify a gene expression signature which segregates primary PRAD from normal tissues. This Core-Expression Signature (PRAD-CES) includes 33 genes and accounts for 39% of data complexity along the PC1-cancer axis. The PRAD-CES is populated by protein-coding (AMACR, TP63, HPN) and RNA-genes (PCA3, ARLN1) sparsely found in previous studies, validated/predicted biomarkers (HOXC6, TDRD1, DLX1), and/or cancer drivers (PCA3, ARLN1, PCAT-14). Of note, the PRAD-CES also comprises six over-expressed LncRNAs without previous Pca association, four of them potentially modulating driver’s genes TMPRSS2, PRUNE2 and AMACR. Overall, our PCA capture 57% of data complexity within PC1-3. GO enrichment and correlation analysis involving major clinical features (i.e., Gleason Score, AR Score, TMPRSS2-ERG fusion and Tumor Cellularity) suggest that PC2 and PC3 gene signatures might describe more aggressive and inflammation-prone transitional forms of PRAD. Of note, surfaced genes may entail novel prognostic biomarkers and molecular alterations to intervene. Particularly, our work uncovered RNA genes with appealing implications on Pca biology and progression.

Heteroresistance (HR) to colistin is especially concerning in settings where multi-drug resistant (MDR) K. pneumoniae are prevalent and empiric use of colistin might lead to treatment failures. This study aimed to assess the frequency of occurrence of colistin HR (CHR) among (MDR) K. pneumoniae (n=676) isolated from patients hospitalized in 13 intensive care units (ICUs) in 6 European countries in a clinical trial assessing the impact of decolonization strategies. All isolates were whole-genome-sequenced and studied for in vitro colistin susceptibility. Majority were colistin-susceptible (CS) (n=597, MIC 2 µg/ml). 288 CS isolates were randomly selected for population analysis profiling (PAP) to assess CHR prevalence. CHR was detected in 108/288 CS K. pneumoniae. No significant association was found between the occurrence of CHR and country, MIC-value, K-antigen type and O-antigen type. Overall, 92% (618/671) of the K. pneumoniae were MDR with high prevalence among CS (540/592) and CR (98.7%, 78/79) isolates. In contrast, proportion of carbapenemase-producing K. pneumoniae (CP-Kpn) was higher among CR (72.2%, 57/79) than CS isolates (29.1%, 173/594). Proportions of MDR and CP-Kpn were similar among CHR (MDR: 85%, 91/107; CP-Kpn: 29.9%, 32/107) and CS isolates (MDR: 91%, 539/592; CP-Kpn: 29.1%, 153/59). WGS analysis of PAP isolates showed diverse insertion elements in mgrB or even among technical replicates underscoring the stochasticity of the CHR phenotype. CHR isolates showed high ST diversity (Simpson’s diversity index, SDI: 0.97, in 52 of the 85 STs tested). CR (SDI: 0.85) isolates were highly associated with specific STs (ST101, ST147, ST258/ST512, P ≤ 0.003). The widespread nature of CHR among MDR K. pneumoniae in our study urge the development of rapid HR detection methods to inform on the need for combination regimens.

Investigation of oomycete diversity in rice paddies of Fars Province in Iran led to the identification of two new Pythium sensu lato (s.l.) species as Globisporangium izadpanahii sp. nov. and Pythium banihashemianum sp. nov. The identification was based on morphological and physiological features as well as the phylogenetic analysis of nuclear (ITS and βtub) and mitochondrial (cox1 and cox2) loci using Bayesian inference and Maximum Likelihood. A major characteristic of G. izadpanahii was the production of globose hyphal swellings; this species did not produce vesicles and zoospores. The present paper describes formally these two new species and defines their phylogenetic relationships with other congeneric species. According to multiple gene genealogy analysis, G. izadpanahii grouped with other species of Globisporangium in the former clade G of Pythium s.l. and was closely related to both G. nagaii and the recently described G. coniferarum. The second species, designated P. banihashemianum, grouped with other species of Pythium sensu stricto in the former clade B of Pythium s.l. and according to the phylogenetic analysis shared an ancestor with P. plurisporium. In pathogenicity tests on rice seedlings, P. banihashemianum isolates were highly pathogenic causing severe root and crown rot, while G. izadpanahii isolates were not pathogenic.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

This paper studies the flexural behavior of the bridge deck link slabs made with the Polyvinyl Alcohol-Strain Hardening Cementitious Composites (PVA-SHCC). Tensile and flexural properties of the PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1: 5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar and self-compacting PVA-SHCC; and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, cracking characteristics of the models with different types of link slabs were compared, and the relationship between Df and the flexural behavior of the models were studied. Numerical models were built to correlate with the results from the bending tests. It is illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed.

The variability of ozone over Egypt has been studied in this work. Higher values of coefficient of variation (COV) at eight stations occur at winter while lowest values occur at summer. The COV is function of latitude in annual, winter and spring where it decreases gradually from the north to south of Egypt. The trend analysis of ozone over the eight stations indicates negative trends over northern stations for both annual and seasonal time series with the greatest one at winter. The horizontal distribution of ozone trend values is negative over all Egypt in winter while it positive over middle and south Egypt in the other seasons. The long-term variability of the behavior of the annual ozone shows positive trend values in ozone are the dominant features during the period 1979- 1989 at the most stations. Negative trend values in ozone are the dominant features during the period 1990- 2014 at all stations. The Mann–Kendall test confirms that there is an abrupt change towards decreasing of ozone occurs in 1981, 1984, 2012, 1999, 2000, 1998 and 2010, while a change towards increasing of ozone appears in 2006, 2014, 1993 and 1989.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

Several studies have identified a relationship between air pollution and depression, particularly in relation to fine particulate matter (PM2.5) exposure. However, the strength of this association appears to be moderated by variables such as age, gender, genetic vulnerability, physical activity and climatic conditions, and has not been assessed at a cross-national level to date. The current study examines the association between the prevalence of depression in each country, based on the most recent Global Burden of Disease Study data, and the average national level of PM2.5 based on the World Health Organization’s database. The observed associations were adjusted for age, gender, level of physical activity, income, education, population density, climate, and type of depression. It was observed that there was a modest but significant positive correlation between PM2.5 level and the prevalence of depression even after adjusting for the above confounders. This association was more marked above a certain threshold and applied chiefly to major depressive episodes. These findings are of significant public health importance in terms of preventive strategies aimed at reducing the population-level burden of depression.

Capital structure decision plays an imperative role in firm’s performance. Recognizing the importance, there has been many studies inspected the rapport of capital structure with performance of firms and findings of those studies are inconclusive. In addition, there is relative deficiency of empirical studies examining the link of capital structure with performance of banks in Bangladesh. This paper attempted to fill this gap. Using panel data of 22 banks for the period of 2005-2014, this study empirically examined the impacts of capital structure on the performance of Bangladeshi banks assessed by return on equity, return on assets and earnings per share. Results from pooled ordinary least square analysis show that there are inverse impacts of capital structure on bank’s performance. Empirical findings of this study is of greater significance for the developing countries like Bangladesh because it will call upon concentration of the bank management and policy makers to pursue such policies to reduce reliance on debt and to accomplish optimal level capital structure. This research also contributes to empirical literatures by reconfirming (or otherwise) findings of previous studies.

Cerebellar ataxia, neuronopathy, and vestibular areflexia syndrome (CANVAS) is caused by biallelic pathogenic expansions, or compound heterozygosity with other pathogenic variants in the RFC1 gene. CANVAS is estimated to be underdiagnosed, both because of the lack of formal diagnostic criteria and molecular challenges that translate to lesser access and high cost of routine testing. Our aim was to address the need for making CANVAS genetic testing routine, by designing a streamlined two-step PCR consisting of a short-allele screening PCR and a confirmatory PCR with fragment capillary electrophoresis detection. Exome sequencing of RFC1 was additionally foreseen to resolve potential compound heterozygosity cases. Specificity of our approach was evaluated using ataxia patients with known non-CANVAS diagnoses, and optimized using Southern blot confirmed CANVAS patients. We evaluated our approach by testing patients consecutively referred for clinically suspected CANVAS using first the two-step PCR, followed by exome sequencing. Our approach was able to accurately identify negative and confirm positive cases in prospectively collected suspected CANVAS patients presenting with at least three typical clinical signs. The proposed testing approach provides an alternative method able to clearly distinguish between CANVAS negative and positive cases and can be easily incorporated into the genetic diagnostic laboratory workflow.

The world is facing new challenges every day; however, with the spread of the pandemic around the world, this new challenge is different. The pandemic is increasing and concentrating various challenges simultaneously. Although different sectors are facing consequences, the most important sectors, that is, health and economy are the most affected. When the pandemic began, it was not known how long it would last, which complicated health and economic planning. Therefore, it is important for decision makers and the public to know the predictions and expectations of the future of these challenges. In this work, the current situation is analyzed. Then, an expectation model is developed based on the statistics of the pandemic using a growth rate model based on an exponential and logarithmic rate of increase. Based on the available open data about the pandemic spread, the model can successfully predict future expectations, including the duration and maximum number of cases of the pandemic. The model uses the equilibrium point as the day the cases decrease. The model can be used for planning and the development of strategies to overcome these challenges.

Aim: To analysis the relationship between healthy adults right ventricular diameter (RVD) and geographical factors. Location: China’s 22 provinces, municipalities and autonomous regions. Methods: Using correlation analysis the relationship between RVD and nine geographic factors, build predictive models by factor analysis. Results: There is significant correlation between RVD and geographical factors, and the annual average temperature is most relevant geographical factors, a predictive model was built by factor analysis: Ŷ = 19.46 + 0.0007072X1 + 0.001034X2 -0.05412X3 -0.001405X4 -0.0006351X5 + 0.02903X6 + 0.01243X7 -0.01033X8 + 0.03816X9 ± 0.63. Then using Kriging interpolation interpolate the geographical distribution of Chinese healthy adult right ventricular diameter reference value in ArcGIS software. Main Conclusion: If geographic factors can be derived in anywhere of China, RVD can be calculated by the model, you can also get RVD from geographical distribution.

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

The effects of environmental media on the corrosion fatigue fracture behavior of 25CrMo steel were investigated. The media include air, and a 3.5 wt.% and a 5.0 wt.% NaCl solutions. Experimental results indicate that the media induces the initiation of corrosion fatigue cracks at multiple sites. The multi-cracking sites cause the changes in the crack growth directions, the crack growth rate during the coupling action of the media and the stress amplitude. The coupling effects are important for engineering applications and research. The probability and predictions of the corrosion fatigue characteristic life can be estimated using the 3-parameter Weibull distribution function.

As major natural disasters in grassland areas, fires and dust events seriously threaten human safety, property, and animal husbandry. Furthermore, these phenomena may be mutually reinforcing, which can lead to more severe cascading disasters. However, few studies have been on the mechanisms of grassland fire and dust events disaster chain. Therefore, we selected Dornod aimag (province), a typical temperate grassland, as the study area and analyzed the spatiotemporal variation patterns of grassland fires and dust weather, as well as the effect of grassland fires on dust weather based on MCD64A1 Burnt Area data and SYNOP dust data. The mechanism of grassland fires on dust weather was further investigated using the MOD13A3 vegetation index product and ERA5 wind speed, wind direction, and precipitation data. The results revealed that grassland fires and dust weather varied spatially across the study area. Furthermore, grassland fires occurred mainly in spring (April to May), summer (June), and autumn (October), while dust weather mainly occurred in spring (March to May). Moreover, autumn, winter, and spring cumulative grassland fires (both days and area) substantially affected the spring total dust weather days and dust storm days, particularly the spring cumulative dust storm days. Additionally, higher precipitation in the summer of 2014 resulted in higher vegetation coverage and more fuel in the autumn and winter of 2014, and even in the spring of 2015. As a result, the cumulative grassland fire days was higher, and the area was larger from September 2014 to April 2015, leading to a considerable increase in the cumulative dust storm days in May 2015. This study has important implications for disaster prevention and mitigation, ecological and environmental protection, and sustainable development in grasslands.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

In the era of large data, the cloud is increasingly used as a computing environment, necessitating the development of cloud-compatible pipelines that can provide uniform analysis across disparate biological datasets. The WDL Analysis Research Pipelines (WARP) repository is a GitHub repository of open-source, cloud-optimized WDL workflows for biological data processing that are semantically versioned, tested, and documented. A companion repository, WARP-Tools, hosts Docker containers and custom tools used in WARP workflows.

In recent years, online review system attracts attention to assess seller-customer relations in the world of e-commerce. To address the quality concern of online review, especially incentivized ones, this study evaluates credibility and consistency based on reviews’ volume, length, and content to distinguish the impact of incentives on customer review behavior, improving review quality, and purchase decision-making. Software product reviews collected from software review websites, including Capterra, Software Advice, and GetApp undergo Exploratory Data Analysis (EDA) to reveal critical features such as cost, support, usability, and product features. The indirect impact of companies’ size, direct impact of users’ experience, and different impacts of changing situations during years on the incentive reviews’ volume are major findings of sentiment analysis. A/B testing results show a minimal to no impact of such reviews on purchasing decisions, highlighting discrepancies in credibility and consistency in volume, length, and content. Employing advanced techniques like Sentence-BERT (SBERT) and TF-IDF, the study explores semantic differences in reviews to improve recommendation systems for a more customized shopping experience. This approach seeks to establish a framework that discerns between review types and their effect on customer behavior. The findings contribute to developing more sophisticated and consistent e-commerce solutions, emphasizing the importance of authentic and reliable online reviews in influencing consumer choices.

This study investigates how Electronic Livestock Health Recording Systems (ELHRs) facilitates the detection of disease burden and make cluster analysis by applying data analytics tools and techniques. A sample size of 18333 livestock disease cases reported from 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for data collection. The results showed that ELHRs are important as livestock disease data preservers, saving costs, and facilitating the extraction of up-to-date and complete information. Euclidean and Manhattan distance performed well at 98%, while cosine distance measurement metrics performed poorly. Finally, with the application of the selected clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect an optimal number of disease clusters and meet the objectives of the study.

As of 2020, public data for game ratings provided by Game Ratings And Administration Committee(GRAC) are more limited than public data for movie and video ratings provided by Korea Media Ratings Board and do not provide data which allow us to see information on ratings clearly and in detail. To get information on game ratings, we need to find information by searching for specific target on homepage which is inconvenient for us. In order to improve such inconvenience and extend scope of provision in public data, the author of this paper intends to study public data API which has been extended based on information on video ratings. To draw items to be extended, this study analyzes data for ratings on homepage of GRAC and designs collection system to build database. This study intends to implement system that provides data collected based on extended public data items in a form which users want. This study is expected to provide information on ratings to GRAC which will strengthen fairness and satisfy game users and people’s rights to know and contribute to promotion and development of game industry.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~ 40% of protein-coding genes in the model plant A. thaliana are still not categorized in the Gene Ontology Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus), the number of BP term annotation is far fewer, ~22%. In the current study we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and performed expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust high quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which, two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTL or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. In all, we have implemented a valuable strategy to better describe genes within specific biological processes.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

Reducing GHG emissions is a crucial challenge in urban areas, characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars analyzed both the ability of species-specific and layout-specific green infrastructures to increase carbon sequestration and the best location sites of new green infrastructures, to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement, based on vegetation species-specific performance and local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of the urban parks' performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between buildings’ demand and trees’ supply, to support local decisions about the best locations for new green infrastructures and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructures based on the demanded ecosystem service.

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth’s gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of O(10−6)m·s−2. Considering 00:00:00 on October 27, 2032 as the initial orbiting moment, under the influence of Earth’s gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is 1.6×105km, 32m·s−1 respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by 16.01% and 17.45%, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerises. The differences between ephemeris DE440 and DE430 are smaller than those between DE440 and DE421.

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been developed. The data set consists of 20 000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enables the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.

Energy efficiency of Data Center (DC) operations heavily relies on IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of IoT- enabled smart systems. Consequently, increased demand for computing power will bring about increased waste heat dissipation in data centers. In order to bring about a DC energy efficiency, it is imperative to explore the thermal characteristics analysis of an IT room (due to waste heat). This work encompasses the employment of an unsupervised machine learning modelling technique for uncovering weaknesses of the DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for energy efficiency improvement that will feed into DC recommendations. The methodology employed for this research includes statistical analysis of IT room thermal characteristics, and the identification of individual servers that frequently occur in the hotspot zones. A critical analysis has been conducted on available big dataset of ambient air temperature in the hot aisle of ENEA Portici CRESCO6 computing cluster. Clustering techniques have been used for hotspots localization as well as categorization of nodes based on surrounding air temperature ranges. The principles and approaches covered in this work are replicable for energy efficiency evaluation of any DC and thus, foster transferability. This work showcases applicability of best practices and guidelines in the context of a real commercial DC that transcends the set of existing metrics for DC energy efficiency assessment.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the missense mutations found in the 2,535 proteins involved in Mendelian rare diseases (MRD), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRD show different mutation site distributions at different three-dimensional protein depths. We then predicted the depth-dependent effects of mutations for the 20,248 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

The aim of this paper is to investigate whether multifractal analysis can be applied to study choroidal blood vessels and help ophthalmologists for the early diagnosis of retinitis pigmentosa (RP). In a case study, we use Spectral Domain Optical Coherence Tomography (SDOCT), which is a non-invasive and highly sensitive imaging technique of the retina and choroid. The image of a choroidal branching pattern can be regarded as a multifractal. Therefore, we calculate generalized Renyi point-centered dimensions, which is considered a measure of inhomogeneity of data, to prove that it increases in the patients with RP with respect to the control group.

This article presents a description of a new method for solving the factorization problem, based on the gradient descent algorithm. This approach demonstrates significant improvements in efficiency compared to traditional methods proposed in previous research. This article describes a new method for solving the factorization problem, based on the gradient descent algorithm, and demonstrates the transition from algebraic methods to approaches based on functional analysis. The proposed approach not only improves the efficiency of solving the problem but also allows the application of computational algorithms of functional analysis, opening new possibilities for research and optimization.

The permanent protection of groundwater and surface sources of drinking water in the required raw water quality from contamination is the primary foundation of preserving the life of society and the entire engineering infrastructure operability not only in the Czech Republic but also in other countries of the world. One of the basic ways of contamination prevention enhancement and risk mitigation of putting a water source out of operation is the identification of the given risk as well as soil contamination monitoring. The purpose of this study is to understand specific aspects of contamination threats to drinking water sources and determine possible solutions to eliminate the risk, shown in the example case study in the Czech Republic conditions. Based on the findings, a proposal to enhance the prevention of water source contamination has been elaborated in the form of recommendations regarding the ways and means used to monitor the risk areas and to implement technical measures so as to eliminate or terminate the specified risk. A complex approach based on the integration of interdisciplinary factors and methodology is required to minimize the threats of aquatic ecosystems contamination in the Czech Republic.

Abstract: Understanding social media networks and group interactions are crucial to the ad-vancement of linguistic and cultural behaviour. This includes the manner in which people ac-cessed advice on health, especially during the global lockdown periods. Some people turned to social media to access information on health where most activities were curtailed with isolation rules, especially for older generations. Facebook public pages, groups and verified profiles, using "senior citizen health", "older generations", and "healthy living" keywords were analysed over a 12-month period to analyse the engagement promoting good mental health. CrowdTangle was used to source English language status updates, photo and video sharing information which resulted in an initial 116,321 posts and 6,462,065 interactions Data analysis and visualisation were used to explore large datasets, including natural language processing for “Message” content discovery, word frequency and correlational analysis and co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media which showed correla-tions to global daily confirmed case and daily death totals. The results can be used to identify public concerns early on and address mental health issues in the senior generation on Facebook.

Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentation architecture is employed. The methodology also incorporates Markov field-based contextual analysis to assess post-segmentation features. In addition, building instantiation is performed using cluster analysis algorithms. Training this model to fit various datasets requires a large amount of annotated data, both from Quebec City, Canada, and from simulated data. Experimental results show that the use of simulated data improves segmentation accuracy, and the DBScan algorithm proves effective in extracting isolated buildings. This project paves the way for improved applications in 3D urban modeling, offering opportunities in fields ranging from urban planning to resource management. The positive results with simulated data reinforce the impact of this research on improving digital models of our ever-changing urban environments.

Background: Mental capacity is a fundamental aspect that enables patients to fully par-ticipate in various healthcare procedures. To assist healthcare professionals (HCPs) in assessing patients' capacity, especially in the mental health field, several standardized tools have been developed. These tools include the MacArthur Competence Assessment Tool for Treatment (MacCAT-T), the MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR), and the Competence Assessment Tool for Psychiatric Advance Directives (CAT-PAD). The core dimensions explored by these tools include Understanding, Appreciation, Reasoning, and Expression of a choice. Objective: This meta-analysis aimed to investigate potential differences in deci-sion-making capacity within the healthcare context among groups of patients with bi-polar disorder (BD) and schizophrenia spectrum disorder (SSD). Methods: A systematic search was conducted on Medline/Pubmed, and Scopus. Addi-tionally, Google Scholar was manually inspected, and a manual search of emerging re-views and reference lists of the retrieved papers was performed. Eligible studies were specifically cross-sectional, utilizing standardized assessment tools, and involving pa-tients diagnosed with BD and SSD. Data from the studies were independently extracted and pooled using random-effect models. Hedges g was used as a measure for outcomes. Results: Six studies were identified, with three studies using the MacCAT-CR, two studies the MacCAT-T, and one the CAT-PAD. The participants included 189 individuals with BD and 324 individuals with SSD. The meta-analysis revealed that patients with BD performed slightly better, and the difference was statistically significant, compared to patients with SSD, particularly in the domain of Appreciation (ES=0.23, 95% CI: 0.01 to 0.04, p=0.037). There was no statistically significant difference between the two groups for Understanding (ES=0.09, 95% CI: - 0.10 to 0.27, p=0.352), Reasoning (ES=0.18, 95% CI: - 0.12 to 0.47, p=0.074), and Expression of a choice (ES = 0.23, 95% CI: - 0.01 to 0.48, p=0.60). In the sensitivity analysis, furthermore, when considering only studies involving patients in symptomatic remission, the difference for Appreciation also resulted in non-significant (ES=0.21, 95% CI: -0.04 to 0.46, p=0.102). (ES=0.21, 95% CI: -0.04 to 0.46, p=0.102). Conclusions: These findings indicate that there are no significant differences between patients with BD and SSD during remission phases, while differences are minimal during acute phases. The usefulness of standardized assessment of capacity at any stage of the illness should be considered, both for diagnostic-therapeutic phases and for research and advance directives. Further studies are necessary to understand the reasons for the overlap in capacity between the two diagnostic categories compared in this study.

The analysis of the human movement patterns based on the mobile network data makes it possible to examine a very large population cost-effectively, and led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data and propose an automatized alternative to the current, questionnaire-based method. Commuting is predominantly analyzed by the census, but that is performed only once in a decade in Hungary. To analyze commuting, the home and the work locations of the subscribers are determined based on their appearances during and outside the working hours. The home locations were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It has been found that commuting analysis based on mobile network data strongly correlates with the census-based findings, even though home and work locations have been estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and demographic distribution of the commuters, show that mobile network data can be an automatized, fast, cost-effective, and relatively accurate way of commuting analysis, that could provide a powerful tool to the sociologists interested in commuting.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

Censoring occurs when complete follow-up time information is unavailable for patients enrolled in a clinical study. The process is considered to be informative (nonignorable) if the likelihood function for the censoring model cannot be partitioned into a set of response parameters that are independent of the censoring parameters. In such cases, estimated survival time probabilities may be biased, prompting the need for special statistical methods to remedy the situation. The problem is especially salient when censoring is skewed toward the early phase of a study. In this paper, we describe a method to impute censored follow-up times using a counting process method.

China’s transportation industry has made rapid progress, which has led to a mass of carbon emissions. However, it is still unclear how the carbon emission from transport sector is punctuated by shifts in underlying drivers. This paper aims to examine the process of China’s carbon emissions from transport sector as well as its major driving forces during the period of 2000 to 2015 at the provincial level. We firstly estimate the carbon emissions from transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the nation, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces of China, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socio-economic factors and their regional variations on the carbon emissions using panel data model. The results show that the development of secondary industry is the most significant variable in both the entire nation level and the regional level, while the effects of the other variables vary across regions. Among these factors, population density is the main motivator of the increasing carbon emissions per capita from transport sector for both the whole nation and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central region.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23' E, latitude 60 52' N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels' energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

Drinking water, in addition to the best-known chemical and biological agents, contains radionuclides of both natural and artificial origin, which can contribute significantly to the overall effective dose received by the population. The Italian Decree Law 28/2016, implementing 2013/51/EURATOM Directive, establishes the activities for risk management and the parameter values for different radionuclides activity concentrations. In addition to the institutions involved, annually the National Inspectorate for Nuclear Safety and Radiation Protection (ISIN), publishes the monitoring reports of environmental radioactivity in Italy, including radioactivity in drinking water. The purpose of the study was to integrate ISIN reports with 2018 to 2020 data by collecting measurements performed by institutional laboratories to obtain a more complete information and adding, for the Campania region, some data not yet published. This new updated report was not significantly different from ISIN’s one, meaning that those publications are nevertheless extremely representative of the radioactivity in Italian drinking water. However, the study allowed to obtain a more detailed data also including measurements not considered in ISIN reports, for instance, radon-222 activity concentrations. This may be of great usefulness for all radiation protection stakeholders in order to ensure environmental protection, pollution prevention and the population safety.

The objective of this article: analysis of research trends on the topic "Materials Science for Energy Engineering" for the period 2012-2021. Materials and methods: bibliometric analysis of data from the Scopus abstract database.Results. It is shown that this topic is developing faster than the general energy topics.The key feature of the research trends is a significant increase in work on the development of materials for renewable energy and a decrease in the number of publications related to nuclear power.The primary goal of many studies is to increase the efficiency of renewable energy production through the use of new materials.The importance of the topic is evidenced by the emergence of new journals that quickly entered the first quartile of the abstract databases Scopus and Web of Science, for example: ACS Applied Energy Materials.There has been a significant increase of interest in perovskite solar cells, layered semiconductors, triboelectric nanogenerators, cobalt compounds, sulfur compounds, and oxygen and hydrogen evolution reaction during Electrolysis.Publications on electric batteries are well represented in all time intervals. The focus is on electrode materials and battery efficiency.

This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods: pre-COVID-19 (January 1 to December 31, 2019), during the COVID-19 pandemic (November 1, 2020 to October 31, 2022), and post-COVID-19 (November 1, 2022 to October 31, 2023 ). After the COVID-19 outbreak, healing forest use behavior did not regress to pre-COVID-19 levels. Activity-based keywords such as "hiking," "trekking," and "walking" stood out as the main drivers of this change in behavior. Therefore, related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

Droughts are among the major natural hazards that are spreading to many parts of the world with huge multi-dimensional impacts. For Continental Croatia an extensive analysis of drought phenomenon is presented based on meteorological E-OBS gridded dataset (0.25° x 0.25°), within the period of 1950 to 2022. The drought events were characterized by the Standardized Precipita-tion Evapotranspiration Index (SPEI) applied to different time-scales (6 and 12 months) in order to describe the subannual and annual variability of drought. The spatiotemporal patterns of drought are obtained through principal component analysis (PCA) and K-means clustering (KMC) applied to the SPEI field. An areal drought evolution analysis and the changes in the frequency of occur-rence of the periods under drought conditions were achieved by using a kernel occurrence rate es-timator (KORE). The Modified Mann-Kendall (MMK) test coupled with the Sen’s slope estimator test are applied to the SPEI series in order to quantify the drought trends throughout the country. According to drought events history and considering the different morphoclimatic characteristics of the study area the results showed that Croatia could be divided into three different and spatial-ly well-defined regions with specific temporal and spatial characteristics of droughts (Central North, Eastern and Southern regions). It is shown that there is a manifest increase in the percent-age of area affected by drought as well as in the yearly drought occurrences rates in both Central North and Eastern regions and an evident decrease in the Southern region for both 6 and 12 month SPEI time-scales. In the observation of the drought temporal characteristics, it was found that downward trends expressing increasing drought severities were strongly significant in North and Eastern regions while a few significant upward trends were seen in Southern region. From this study, it is possible to obtain a broader view of the historical behaviour of droughts in Croatia with results providing useful support for drought risk assessment and decision-making processes.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

Business Intelligence, defined by [1] as "the ability to understand the interrelations of the facts that are presented in such a way that it can guide the action towards achieving a desired goal", has been used since 1958 for the transformation of data into information, and of information into knowledge, to be used when making decisions in a business environment. But, what would happen if we took the same principles of business intelligence and applied them to the academic environment? The answer would be the creation of Academic Analytics, a term defined by [2] as the process of evaluating and analyzing organizational information from university systems for reporting and making decisions, whose characteristics allow it to be used more and more in institutions, since the information they accumulate about their students and teachers gathers data such as academic performance, student success, persistence, and retention [5]. Academic Analytics enables an analysis of data that is very important for making decisions in the educational institutional environment, aggregating valuable information in the academic research activity and providing easy to use business intelligence tools. This article shows a proposal for creating an information system based on Academic Analytics, using ASP.Net technology and trusting storage in the database engine Microsoft SQL Server, designing a model that is supported by Academic Analytics for the collection and analysis of data from the information systems of educational institutions. The idea that was conceived proposes a system that is capable of displaying statistics on the historical data of students and teachers taken over academic periods, without having direct access to institutional databases, with the purpose of gathering the information that the director, the teacher, and finally the student need for making decisions. The model was validated with information taken from students and teachers during the last five years, and the export format of the data was pdf, csv, and xls files. The findings allow us to state that it is extremely important to analyze the data that is in the information systems of the educational institutions for making decisions. After the validation of the model, it was established that it is a must for students to know the reports of their academic performance in order to carry out a process of self-evaluation, as well as for teachers to be able to see the results of the data obtained in order to carry out processes of self-evaluation, and adaptation of content and dynamics in the classrooms, and finally for the head of the program to make decisions.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

The aim of this work is to explain the behaviour of the multiresistance percentage of Pseudomona aeruginosa in some countries of Europe through a multivariate statistical analysis and machine learning validation, using data from the European Antimicrobial Resistance Surveillance System, the World Health Organization and the World Bank. First, we will use a descriptive analysis and a principal components analysis. Then, we use a k-means clustering to determine the countries and regions that are most affected by the antibiotic resistance. Second, we expand the database by adding some socioeconomic, governance and antibiotic-consumption variables. We then run a data panel regression analysis to determine some functions that relates the multiresistance percentage with those new variables. Finally, we use machine learning techniques to validate a pooling panel data case, using XGBoost and random forest algorithms. The results of the data panel analysis indicate that the most important variables for the multiresistance percentage are corruption control and the rule of law. Similar results are found with the machine learning validation analysis, where the human development index is an additional important variable for the multiresistance percentage.

Mahalanobis distance is a useful multivariate statistic for determining how far apart two points are from one another. It is a very helpful statistic with excellent uses in multivariate anomaly detection, one-class classification, and classification on severely unbalanced datasets.

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry and science related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, rational design of combination therapies and the global response to the Covid19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human focused, systems-based approach to drug discovery and research.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

Tandem mass spectrometry (MS/MS) for diagnosing inborn errors of metabolism (IEM) is recognized as an ethical, safe, simple, and reliable screening test. Presented bibliometric analysis aims to describe the network structure of the scientific community in the study area at the level of countries, institutions, authors, papers, keywords, and sources; scientific productivity, directions, and collaboration efforts in a considered period (1991–2023.) Using the PRISMA method, we conducted a systematic search for articles reporting using MS/MS to screen for inherited metabolic disorders and inborn errors of metabolism collected from the Web of Science Core Collection (WOSCC.) A total of 451 articles out of 546, by 2,682 authors, published in 190 journals, with 15,440 citations in 8,181 citing articles, with an average citation of 34.24 per article, and an H-index of 64 were retrieved from the WOSCC. The research status of MS/MS in IEM screening was identified. The most relevant current research directions and future areas of interest were revealed: "selective screening for IEM," "new treatments for IEM," "new disorders considered for MS/MS testing," "ethical issues associated with newborn screening," "new technologies that may be used for newborn screening," "use of a combination of MS/MS and gene sequencing," etc.

This study employs Principal Component Analysis to examine cranial measurements obtained from both extant and fossil specimens spanning 38 species and comprising over 300 individuals within the subfamily Cervinae. Our findings elucidate craniometric distinctions among cervids characterized by varying body sizes and certain evolutionary adaptations. While our results generally corroborate previous assertions regarding the limited craniometric variability among plesiometacarpal deer, they also unveil specific cranial adaptations within certain genera and species. The Principal Component Analysis of craniometric data revealed that giant and large-sized deer display significantly broader ecomorphological diversity in cranial shape compared to small-sized deer. Secondly, small-sized deer exhibit greater uniformity in their cranial shape, appearing densely clustered on the factorial map. Thus, we conclude that body size imposes ecological constraints, limiting the available niches due to eco-physiological factors. The study demonstrates that endemic insular deer do not evolve consistent craniometric features attributable to insular isolation, while the cranial proportions of medium-to-small sized deer delineate a ubiquitous morphological archetype characteristic of numerous cervid taxa spanning diverse phylogenetic lineages and sharing comparable body sizes. This group of "Dama-like" deer, characterized by similar body size, metabolic rates, ecological needs, and cranial morphometry, is referred to here as the fundamental eco-physiological type, typical of warm regions within the Palearctic and Oriental biogeographic realms..

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

The present paper proposes a fractal analysis of the Covid-19 dynamics in 45 European countries. We introduce a new idea of using the box-counting dimension of the epidemiologic curves as a means of classifying the Covid-19 pandemic in the countries taken into consideration. The classification can be a useful tool in deciding upon the quality and accuracy of the data available. We also investigated the reproduction rate, which proves to have significant fractal features, thus enabling another perspective on this epidemic characteristic. Moreover, we studied the correlation between two meteorological parameters: global radiation and daily mean temperature and two Covid-19 indicators: daily new cases and reproduction rate. The fractal dimension differences between the analysed time series graphs could represent a preliminary analysis criterion, increasing research efficiency. Daily global radiation was found to be stronger linked with Covid-19 new cases than air temperature (with a greater correlation coefficient -0.386, as compared with -0.318), and consequently it is recommended as the first-choice meteorological variable for prediction models.

Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop's Boolean subtraction method, this study combined walled TPMS gyroid structures with normal TPMS gyroid lattice. This made a composite TPMS-gyroid lattice with relative densities ranging from 14% to 54%. Using the ideaMaker software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer aided design (CAD) models and fabricate 36 lattices samples precisely layer by layer technique. Shimadzu 100kN testing equipment was utilized for the mechanical compression experiments. And the validation using finite element analysis (FEA). Further, CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson-Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice's relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS-gyroid lattices in high-speed crash box scenarios, potentially enhancing vehicle safety and performance.

E-scooters are a micromobility transportation option for completing short trips. In recent years, many ‎cities welcomed shared e-scooters in an effort to offer more mode choices, increase travelers' ‎convenience, and reduce automobile use in their service areas. However, a knowledge gap still remains ‎regarding the acceptability of shared e-scooters as a transportation option, which motivates additional ‎research on user and non-user preferences and attitudes toward e-scooter use. This study investigates ‎latent variables impacting the adoption of shared e-scooters in urban areas, focusing on mode choice ‎factors and attitudes towards e-scooter use and car use. The study utilizes machine learning (ML) ‎techniques and SHAP analysis to analyze survey data (N=1196) collected from travelers in Washington, ‎D.C., Miami, FL, and Los Angeles, CA, in 2021 and 2022. A comparative analysis is performed to develop ‎comprehensive demographic profiles of e-scooter users. The analysis reveals gender (male), age (25-39 ‎years age group), higher income, and educational background as the most relevant factors toward e-‎scooter use. Attitudinal variations among e-scooter users and non-users underscore the complexity of ‎perceptions toward e-scooter use, with significant differences in mode choice factors and attitudes ‎toward the use of e-scooters and private vehicles. Notably, educational background ranked as a significant ‎factor in Washington, D.C., and Miami, while Factor 3, derived from factor analysis and encompassing car ‎use attitudes and the utilization of technology, emerged as influential in Los Angeles. This research ‎contributes fresh insights into factors shaping e-scooter adoption, offering a foundation for informed ‎urban transportation planning and policymaking. The holistic approach showcased in this study enhances ‎understanding of shared micromobility and its implications for urban mobility.

In review, the operating principles of the most common bio sensing devices, detection methods and the identification sensitivity of analyzed molecules were shown. The central focus was done on hybridization analysis of nucleic acids (NA), which are considered to be one of the most important analytes in terms of diagnostic point of view. Constructions enabling to transfer the fact of formation of nucleotide probe/target complex in to detectable signal by optical, electrochemical or micromechanical (piezoelectric) sensors were presented in this review.

Measurements of temperature and salinity in the Amazon River plume in the open ocean of the western tropical North Atlantic are considered. The measurements were carried out using the AML CTD probe in the upper layer and a flow-through system that measures salinity and turbidity of seawater while the vessel is on the way. Additionally, archive oceanographic data from the WOD18 database, data of satellite altimetry measurements (AVISO), and satellite salinity data from Aquarius and SMOS were used. It is shown that the width of the Amazon plume is about 300–400 km, the depth of desalination is from 50 to 100 m. Surface salinity decreases compared to the background (36 PSU) by 0.5 PSU in February and more than by 3 PSU in September during the period of maximum development of the plume, which is determined from satellite measurements of surface salinity. Lagrangian modeling of the back-in-time advection of passive markers simulating freshwater particles was carried out. It was shown that the source of fresh water in the measurement area is the discharge of the Amazon.

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

Real-time data processing and decision-making are increasingly crucial in various applications, driven by the continuous influx of data streams. Event-driven AI workflows within serverless computing environments offer a promising approach to handle these real-time demands efficiently. This paper presents a framework for simulating and analyzing the performance characteristics of such workflows. Our proposed approach utilizes simulated data with varying event rates and durations to investigate the impact on key performance metrics like latency, throughput, and resource utilization. This enables a comprehensive evaluation of the inherent trade-offs within event-driven AI systems. The key findings reveal a trade-off between latency and throughput. As the event rate increases, average processing latency generally increases while average throughput increases. Resource utilization remains relatively stable across different event rates in the simulated scenarios (e.g., 75.55\% at 2 events/second, 74.51\% at 10 events/second). This framework provides a valuable tool for understanding the performance characteristics of event-driven AI workflows and optimizing resource allocation strategies.

Huperzine A is an herbal reversible inhibitor of Acetylcholinesterase (AChE). A molecular docking analysis on Huperzine A molecule has been carried out to understand its structure, conformational flexibility, intermolecular interaction and the binding affinity in the active site of AChE enzyme. Further, the charge density distribution of huperzine A molecule (lifted from the active site of AChE) was determined from the high level quantum chemical calculations coupled with charge density analysis. The binding affinity of Huperzine A towards AChE was calculated from the molecular docking; the lowest docked energy is -8.46 kcal/mol. In the active site, huperzine A molecule interacts with acyl binding pocket-Phe330 of AChE, that is, the bicyclo ring group of huperzine A forms an intermolecular interaction with the oxygen atom of main chain of the amino acid residue Phe330 at the distances 3.02 and 3.25 Å respectively. On the other hand, a gas phase study on huperzine A molecule also performed using HF and DFT (B3LYP) methods with the basis set 6-311G**. The molecular structure, conformation, and the charge density distribution of huperzine A molecule in the gas phase have determined using quantum chemical calculations and the charge density analysis. The comparative studies between the gas phase and the active site forms of huperzine A molecule, explicitly reveals the degree of conformational modification and the charge density redistribution of huperzine A when present in the active site. The dipole moment of the molecule in the active site is 6.85 D, which is slightly higher than its gas phase value (5.91 D). The electrostatic potential (ESP) surface of active site molecule clearly shows the strong electronegative and positive ESP regions of the molecule, which are the expected strong reactive locations of the molecule.

Functional Data Analysis (FDA) is a relatively new field of statistics dealing with data expressed in the form of functions. FDA methodologies can be easily extended to the study of imaging data, an application proposed in Wang et al. (2020), where the authors settle the mathematical groundwork and properties of the proposed estimators. This methodology allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. This is especially relevant for the field of medical imaging, as one of the most extended research setups consists on the comparison between two groups of images, a pathological set against a control set. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups (Arias-López et al., 2021). In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases and then measure computing times for the construction of Delaunay triangulations, and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that previous researcher has been too conservative in its parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging.

The mantle mass flow interconnected with the process of Glacial Isostatic Adjustment (GIA) and the reformation of the Earth’s crust constantly perturbs the observed gravity field towards a hypothetic isostatic state. We analyse the temporal changes of the gravity field from the GRACE data, using different mathematical and/or statistical methods to detect the GIA amidst other gravity signals. A number of gravimetric post-glacial land uplift rate (LUR) modelling methods are investigated and compared with the data from a total number of 515 GPS stations and preferred GIA forward models in Fennoscandia and North America. We investigate three mathematical methods, namely regression, principal component, and independent component analysis (ICA) to extracting the GIA signal from the GRACE monthly geoid heights. We use some regularization techniques to exploit the GRACE monthly data to their maximum spatial resolution and to increase the Signal to Noise Ratio of their short wavelengths. Near the centres of the study areas the gravimetric LUR model using the fast-ICA algorithm of Hyvärinen and Oja (2000) is shown to be in a complete agreement with the GPS data and the predictions of the GIA forward models, and for the whole areas, subject to epeirogeny movement of the two regions, their discrepancies reach to the extrema at -1.8 and +3.3, and -4.5 and +7.5 mm/a, respectively. We show that the largest discrepancies between the gravimetric model using the ICA method and the GIA forward model, occur for the sub-regions likely collocated with strong ice mass change signals.

Primary ciliary dyskinesia (PCD) is an inherited disorder that impairs motile cilia, essential for respiratory health, with a reported prevalence of 1 in 16,309 within Hispanic populations. Despite having the RSPH4A [c.921+3_921+6del (intronic)] founder mutation in 70% of Puerto Rican patients, the characterization of the ciliary dysfunction remains unidentified due to the unavailability of advanced diagnostic modalities like High-Speed Video Microscopy Analysis (HSVA). Our study implemented HSVA for the first time on the island as a tool to better diagnose and characterize the RSPH4A [c.921+3_921+6del (intronic)] founder mutation in Puerto Rican patients. By applying HSVA, we analyzed the ciliary beat frequency (CBF) and pattern (CBP) in native Puerto Rican patients with PCD. Our results showed decreased CBF and a rotational CBP linked to the RSPH4A founder mutation in Puerto Ricans, presenting a novel diagnostic marker that could be implemented as an axillary test into the PCD diagnosis algorithm in Puerto Rico. The integration of HSVA technology in Puerto Rico substantially enhances the framework for PCD evaluation and diagnosis, facilitating prompt detection and early intervention for improved disease management. This initiative, demonstrating the potential of HSVA as an adjunctive test within the PCD diagnostic algorithm, could serve as a blueprint for analogous developments throughout Latin America.

The aim of this study is to solve the problems of complicated pretreatment and high analytical cost in the detection technology of trace drug toxicants in municipal wastewater. A high-performance magnetic sorbent was synthesized for enrichment of trace drug substances in wastewater to develop magnetic solid-phase extraction pretreatment combined with the acoustic ejection mass spectrometry (AEMS) analytical methods for the molecules in wastewater drug toxicants. The magnetic nanospheres was successfully prepared by magnetic nanoparticles modified with divinylbenzene and vinyl pyrrolidone.The results showed that the linear dynamic range of 17 drugs was 1-500 ng/L, the recovery was 43%-105%, the matrix effect was more than 67%, the limit of quantitative detection was 1-2 ng/L, and the detection time of each sample was 2.98s. It can be seen that the developed magnetic solid-phase extraction (MSPE) method is a good solution to the problem of complicated pre-treatment and analytical cost in the detection of drugs in wastewater. The developed magnetic material and acoustic excitation coupled mass spectrometry analysis method can realize the low-cost, efficient enrichment and fast analysis of different kinds of drug molecules in urban sewage.

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Soil quality is related to food security and human survival and development. In recent years, due to the acceleration of urbanization and the increase of abandoned land, land degradation occurs, poor topsoil quality. In this study, the minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics and influencing reasons of topsoil quality in Tieling County. The re-sults showed that MDS included total potassium (TK), Clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu). The MDS indicators can well replace all indicators to evaluate the topsoil quality in the study area. The overall soil quality of Tieling County showed a trend of low in the east and high in the west, and gradually increased from the hilly area to the plain area. The evaluation results are consistent with field research, which can provide reference for other topsoil quality evaluation, and it also provide a basis for the formulation of soil quality improvement measures.

The Benford method can be used to detect manipulation of epidemiological or trial data during the validation of new drugs. We extend here the Benford method after having detected particular properties for the Fibonacci values 1, 2, 3, 5 and 8 of the first decimal of 10 runs of official epidemiological data published in France and Italy (positive cases, intensive care, and deaths) for the periods of March 1 to May 30, 2020 and 2021, each with 91 raw data. This new method – called “BFP” for Benford-Fibonacci-Perez - is positive in all 10 cases (i.e. 910 values) with an average of favorable cases close to 80%, which, in our opinion, would validate the reliability of these basic data.

This study aimed to investigate the venom sac extracts (VSE) of the European hornet (EH) Vespa crabro (Linnaeus, 1758) (Hymenoptera: Vespidae), focusing on the differences between stinging females, gynes (G) and workers (W), at the protein level. Using a quantitative “Sequential Window Acquisition of all Theoretical Fragment Ion Mass Spectra” (SWATH-MS) analysis, we identified and quantified a total of 240 proteins. Notably, within the group, 45.8 % (n = 110) showed significant differential expression between VSE-G and VSE-W. In this set, 57.3 % (n = 63) were upregulated and 42.7 % (n = 47) downregulated in the G. Additionally, the 200 quantified proteins from the class Insecta belong to 16 different species, six of them to the Hymenoptera/Apidae lineage, comprising seven proteins with known potential allergenicity. Thus, phospholipase A1 (Vesp v 1), phospholipase A1 verutoxin 2b (VT-2b), hyaluronidase A (Vesp v 2A), hyaluronidase B (Vesp v 2B), and venom allergen 5 (Vesp v 5) were significantly downregulated in the G, and vitellogenin (Vesp v 6) was upregulated. Overall, 46 % of the VSE proteins showed differential expression, with a majority being upregulated in G. Data are available via ProteomeXchange with identifier PXD047955. These findings shed light on the proteomic differences in VSE between EH castes, potentially contributing to our understanding of their behavior and offering insights for allergy research.

This paper investigates women’s empowerment and their active involvement in governance aimed at promoting sustainable development. The study focuses on examining the roles and processes of co-creation of nature-based solutions (NBS) for water management in Milan. Three research directions were explored: 1) to determine gender-based roles within local governance; 2) to detect semantic groups of NBS based on stakeholders' gender; c) to analyze gender-based linguistic and conceptual patterns of NBS. To achieve our objectives, we converted a sample of audio-video interviews of local stakeholders into textual data along with metadata indicating speakers' roles and gender. Descriptive statistics were applied to explore the governance's roles. We employed a combination of textual analysis and network analysis to identify thematic groups of NBS, and applied a word embedding approach to uncover linguistic patterns indicative of semantic sub-structures. The findings revealed significant demographic diversity. Male stakeholders perceive sustainable development as a process driven by the circular economy and the coordination of territorial authorities, while female stakeholders envision a transformative approach rooted in science, culture, and collaboration. The semantic sub-structure analysis highlighted a tendency among male governance to emphasize specialized skills, whereas female governance placed greater emphasis on problem definition rather than resolution.

In volleyball, the jump serve is a fundamental and crucial skill that is one of the most commonly used serving techniques. This study aimed to examine the kinematic differences between straight line (SL) and diagonal line (DL) in volleyball jump serves by focusing on torso and attack arm joint motions. Three-dimensional coordinate data were captured using a motion capture system (200 Hz) in 14 right-handed male professional volleyball players who performed SL and DL jump serves. This result suggests that the pelvis and torso backward rotation angles during the arm-clocking phase were smaller when performing DL serves. During ball contact, the servers exhibited greater forward rotation angles of the pelvis and torso to control the upper body facing the diagonal direction of the target zone. In addition, lowering the forward tilt angle of the trunk helped the servers maintain body balance in the air and increased their wrist flexion angle, preventing the ball from flying out of bounds when performing DL. These findings highlight the importance of controlling pelvic and torso rotations in the transverse plane when adjusting for different serving lines. These insights can guide volleyball athletes and coaches in refining their serving techniques.

Motivated by diffraction experiments on the $\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$ reconstructed Si(111) due to deposition of rare earth elements (Dy, Tb) and silicide formation we analyse the splitting and non-splitting of superstructure spots. For this purpose, we model diffraction patterns for one dimensional structures generated by the binary surface technique and use supercell models to keep the analysis simple. Diffraction pattern are calculated in the framework of the kinematical diffraction theory and they are analyzed as a function of the domains and domain boundaries. Basic properties of the diffraction pattern are analyzed for model systems of a two-fold and a three-fold periodicity. The rules derived from these calculations are applied to the "real-world" system of Si(111)-($\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$)-RESi$2_{}$ (RE = Dy or Tb). Depending on the combination of domains and domain boundaries of different types a plethora of different features are observed in the diffraction patterns. These are analyzed to determine the sizes of both domain boundaries and domains from experimentally observed splitting of specific superstructure spots.

The aim of this paper is to study a semi-functional partial linear regression model (SFPLR) for spatial data with responses missing at random. The estimators are constructed by the kernel method, and some asymptotic properties such as probability convergence rates of the nonparametric component and asymptotic distribution of the parametric and nonparametric components are established under certain conditions. Next, the performances and the superiority of these estimators are presented and examined using a study on simulated data and on real data by carrying out a comparison between our semi-functional partially linear model with MAR estimator (SFPLRM), the semi-functional partially linear model with the full-case estimator (SFPLRC) and the nonparametric functional model estimator with MAR (FNPM). The results show that the proposed estimators outperform existing estimators as the number of random missing data increases.

Coccomyxa subellipsoidea KJ (C-KJ) is a green alga with unique immunoregulatory characteristics. Here, we investigated the mechanism underlying the modification of T cell function by C-KJ components. The water-soluble extract of C-KJ was fractionated into protein (P) and sugar (S) fractions [acidic (AS), basic (BS), and neutral (NS)]. These fractions were used for the treatment of peripheral blood mononuclear cells stimulated with toxic shock syndrome toxin-1. Transcriptome analysis revealed that both P and AS enhanced the expression of the genes encoding metallothionein (MT) family proteins, inflammatory factors, and T helper (Th) 17 cytokine and suppressed that of those encoding Th2 cytokines in stimulated T cells. The kinetics of MT1 and MT2A gene expression showed a transient increase in MT1 and maintenance of MT2A mRNA after T cell stimulation in the presence of AS. The kinetics of Th17-related cytokine secretion in the early period were comparable to those of MT2A mRNA. Furthermore, our findings revealed that static, a STAT-3 inhibitor, significantly suppressed MT2A gene expression. These findings suggest that the expression of MTs is involved in the immune regulatory function of C-KJ components, which is partially regulated by Th17 responses, and may help develop innovative immunoregulatory drugs or functional foods.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback and teaching. Phase duration itself and its correlation to clinical parameters has not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet. (2) Methods: We assess 100 robot-assisted partial nephrectomy videos for phase duration based on previously defined proficiency metrics. We develop an annotation framework and subsequently compare our annotations to an existing commercial solution (Touch Surgery, Medtronic™). We subsequently explore clinical correlations between phase durations and peri-operative parameters. (3) Results: Objective and uniform phase assessment requires precise definitions derived from an iterative revision process. Comparison to a commercial solution shows large differences in definitions across phases. BMI and duration of renal tumor identification correlate positively, as well as tumor complexity and both tumor excision and renorraphy duration. (4) Conclusions: Surgical phase duration can be correlated with certain clinical outcomes. Further research should investigate if retrieved correlations are also clinically meaningful. This requires increasing dataset sizes and facilitation through intelligent computer vision algorithms. Commercial platforms can facilitate this dataset expansion and help unlock the full potential, provided the disclosure of phase annotation details.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in the last section we present a concerning numerical example and the respective software.

The paper delineates the developed vision system of the UR5 cobot and the operating algorithm of the robotic quality control station. The hardware-software architecture of the developed robotic station was shown, consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system. Image processing and analysis algorithms were described, the method of control and communication between the station components was discussed, and two operating scenarios were presented as a single robotic station and a robotic line. Based on the results obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), one of the components constituting jumps, through a biomechanical model-based approach enables analysis even in environments where force plates or machine learning training data cannot be utilized. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs) and estimated GRFs and body loads using a 3D forward dynamics model. Our estimation method, based on forward dynamics and optimization calculations, generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of the dynamic acceleration in the optimization calculation, the 3D orientation using sensor fusion composed of acceleration and angular velocity data obtained from IMUs and an extended Kalman filter was estimated. As a result, by generating movements based on the cost function, it was possible to calculate biomechanically valid GRFs while following the measured movements even if not all joints are covered by IMUs. This estimation method allows for 3D motion analysis independent of measurement conditions or training data.

We propose a Gauss model (GM), a map from time to the bell-shaped Gauss function to model the deaths per day and country, as a quick and simple model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e. initial exponential spread to eventual saturation, we apply the GM to existing data, as of April 2, 2020, from 25 countries during first corona pandemic wave and study the model's predictions. We find that logarithmic daily fatalities caused by Covid-19 are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical chi2-fit with 95\% confidence, we are able to obtain the characteristic parameters of the GM, i.e. a width, peak height and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced.