Miracle-dz/UniFAR-FTUs huggingface dataset:
Mental Health Problems among Elementary School Students Mandated to e-Learning: A COVID-19 Rapid Review Caveat

Abstract Extended lockdowns during the COVID-19 pandemic mandated millions of students worldwide to e-learning and by default made many of their parents proxy homeschool teachers. Preliminary anecdotal, journalistic and qualitative evidence suggested that elementary school children and their parents were probably most vulnerable to this stressor and most likely to experience mental health problems because of it. We responded with a rapid review of 15 online surveys to estimate the magnitude of such risks and their predictors between 2020 and 2021. The pooled relative risk of mental health problems among school children and their parents was substantial (RR = 1.97). Moreover, this synthetic finding did not differ significantly between 10 child mental health outcomes (primarily measures of anxiety or depression) and five parental stress outcomes. Such risks to children and parents were incrementally greater among Latinx (RR = 1.81) and Black families (RR = 2.50) than among non-Hispanic White families (RR = 1.58) in the USA. Finally, such risks in the West (RR = 2.12) were observed to be greater than those in the East (RR = 1.36). Grave risks were experienced worldwide, but the pandemic once again clarified for the world that such structural violence, in this instance, in elementary school systems, was much more prevalent and virulent among Black and Brown families in places like the USA. Educational practice implications, future research and pandemic preparedness needs are discussed.
Stability analysis of geometrically non-homogeneous stratified slopes

Due to ongoing construction of cities and development of new buildings and infrastructures, many natural slopes in and around urban areas are often subjected to cuts. These excavations result in dramatic changes in the geometry of slope faces mostly by the slope angle to the ground level increasing which could potentially develop levels of risks of slope failures with catastrophic consequences. As most natural slopes are of non-homogeneous layered nature it will be of utmost importance to understand the stability behaviour of such slopes. The current practice in analysing slopes of complicated nature, geometrically and materially, is mostly to apply simplifications which could sacrifice accuracy resulting in application of large factors of safety which could question analytical and also economic feasibility of projects. In this research limit equilibrium and finite element methods are implemented by employing commercial software in order to empirically and numerically model and analyse stratified slopes with the aim of understanding effects of non-homogeneity of geometry and materials on stability. The analysis included determination of factors of safety as well as a sensitivity analysis looking into the combined effects of contributing parameters such as slope angle and non-homogeneity in stability analysis.
Time-Restricted Feeding and Intermittent Fasting as Preventive Therapeutics: A Systematic Review of the Literature

Multiple studies have shown that intermittent fasting (IF) is associated with better health conditions and longer lifespans, as is time-restricted feeding (TRF). One crucial explanation is that IF and TRF permit a set length of time for caloric ingestion, during which our systems activate a variety of mechanisms that lead to the enhancement and renewal of different body systems. Accordingly, the benefits of IF and TRF are a lot greater than those of complete calorie restriction (CR). Accordingly, TRF and IF offered the underpinnings for human studies that revealed that when we eat and when we are fasting, we experience fluctuations in all body systems. For relevant medical literature, we investigated medical databases such as PubMed/Medline, PubMed Central, Cochrane Library, and Google Scholar. The chosen articles were evaluated based on eligibility criteria and vetted by quality evaluation methods; 15 finished research papers were included in the study. Of the 15 recognized studies, four were systematic reviews of literature, and 11 were review articles. The chosen publications all examined the efficacy and comparability with other restrictive diets. The study articles indicated that the advantages of IF and TRF represent complex interplay involving periodic digestion of food, gut flora, and the circadian clock. Accordingly, further research is necessary to get a comprehensive grasp of this very complex molecular blueprint. This could aid in producing an effectively planned food treatment that can regulate numerous chronic health ailments and disorders. Furthermore, it might lead to the development and investigation of new pharmacological medicines that mimic the nutritional and therapeutic benefits of IF for those who are unwilling or unable to follow this kind of feeding regimen.
Single-particle dynamics in theoretical minimum emittance cell

Abstract We have developed a parameterization of the linear optics in a theoretical minimum emittance cell with three quadrupoles. It consists of five independent parameters: two phase advances, a dimensionless horizontal beta function at the center of the dipole, and a bending angle φ and a length L of the dipole. For the zero chromaticity cell, we again find that the dynamic aperture in the normalized phase space is scaled according to, Ā ∝ φ √ L. Moreover, we study nonlinear dynamics near the third-order coupled resonances in the framework of the resonance normal form. In particular, we derive the effective Hamiltonians using the Lie algebra method and show that the periodic orbits in tracking can be interpreted as solutions of the Hamilton’s equations. Surprisingly, we discover that the scheme is not only applicable to the single resonance but also to the double resonances.
DNA barcoding for species identification and phylogenetic investigation employing five genetic markers of Withania coagulans

Withania coagulans is an important medicinal plant in India, dispersed from the East Mediterranean to South Asia, but W. coagulans is usually misidentified with other Withania species. Accurate identification of medicinally important plant species is helpful for their effective use in medicine and would help to protect the globally declining threatened or endangered plants. The present study aimed to create barcodes for W. coagulans using five genetic markers ( rbcl , matK , ITS , psbA-trnH , and rpoB-trnCGAR ) from two samples of W. coagulans collected at ICAR-Anand and the Institute of Science, Mumbai, to test the discrimination capacity of the plant DNA barcode. The study findings confirmed that psbA and rbcl are better barcodes for investigating W. coagulans , which displayed 100% conservation even when geographical sites were changed, whereas the genetic loci rpoB, ITS, and matK helped to distinguish between the distinct evolutions of the Solanaceae family. ITS showed the highest GC content, 66.9% for WCNB1 and 62.6% for WCNB2. The maximum likelihood rpoB marker gave the highest probability value (–889.38), followed by rbcl (–967.83) compared to other genetic markers. The study conclusions would be used in the pharmaceutical sector to develop DNA-based species identification of the W. coagulans plant to point out adulteration while plant collection. The work provides insights into molecular-based identification and authentication of W. coagulans .
Z\gamma\gamma production with leptonic decays and triple photon production at NLO QCD

We present a calculation of the O(alpha_s) QCD corrections to the production of a Z boson in association with two photons and to triple photon production at hadron colliders. All final-state photons are taken as real. For the Z boson, we consider the decays both into charged leptons and into neutrinos including all off-shell effects. Numerical results are obtained via a Monte Carlo program based on the structure of the VBFNLO program package. This allows us to implement general cuts and distributions of the final-state particles. We find that the NLO QCD corrections are sizable and significantly exceed the expectations from a scale variation of the leading-order result. In addition, differential distributions of important observables change considerably. The prediction of two-photon-associated Z production with Z decays into neutrinos from the charged-lepton rate works well, once we use an additional cut on the invariant mass of the charged-lepton pair.
Venous Valves Ultrasonographic Evaluation of Vertebral

Summary: To date, vertebral venous valves have not been studied clinically or by sonography. This report reveals that these valves could be studied noninvasively by using sonographic techniques. Real-time sonography clearly showed the opening and closing of the two cusps of vertebral venous valves at the junction of the vertebral vein and the brachiocephalic vein, which move synchronously with the internal jugular venous valves. This is the first time the vertebral venous valves have been studied by using clinical sonography. This might provide a new point of view for evaluating the clinical significance of vertebral venous valves.
Tutorial: Reactive high power impulse magnetron sputtering (R-HiPIMS)

High Power Impulse Magnetron Sputtering (HiPIMS) is a coating technology that combines magnetron sputtering with pulsed power concepts. By applying power in pulses of high amplitude and a relatively low duty cycle, large fractions of sputtered atoms and near-target gases are ionized. In contrast to conventional magnetron sputtering, HiPIMS is characterized by self-sputtering or repeated gas recycling for high and low sputter yield materials, respectively, and both for most intermediate materials. The dense plasma in front of the target has the dual function of sustaining the discharge and providing plasma-assistance to film growth, affecting the microstructure of growing films. Many technologically interesting thin films are compound films, which are composed of one or more metals and a reactive gas, most often oxygen or nitrogen. When reactive gas is added, non-trivial consequences arise for the system because the target may become “poisoned,” i.e., a compound layer forms on the target surface affecting .
Exploiting the signal to noise ratio in multi-system predictions of summertime precipitation and maximum temperature in Europe

Droughts and heatwaves are among the most impactful climate extremes. Their co-occurrence can have devastating consequences on natural and human systems. Early information on seasonal timescales on their possible occurrence is beneficial for many stakeholders. Seasonal climate forecast has gradually become more used; but limited skill in certain regions and seasons still hinders a broader use. Here we show that a simple forecast metric from a multi-system ensemble, the signal 10 to noise ratio, can help overcome some limitations in the boreal summer. Forecasts of maximum daily near surface air temperature and precipitation in boreal summers with high signal to noise ratio tend to coincide with observed larger deviations from the mean than years with small signal to noise ratio. The same metric also helps identify processes relevant to seasonal climate predictability. Here we show that a positive phase of a North Atlantic Sea surface dipole during boreal spring may favor the occurrence of dry and hot summers in Europe.
Partial coherence and the influence of overlap and curvature in ptychography

In this paper, we use optical coherence theory to define the limit for the spatial coherence length with respect to the degree of overlap between adjacent probe positions in ptychography. The influence of the degree of curvature of the probe in relation to partial coherence in the Fresnel geometry for a fixed overlap is also considered. This work has implications for the application of ptychographic coherent imaging using partially coherent sources. We validate these results through a simulation study of coherence versus overlap parameter and curvature.
Dynamic Analysis of Piles under Lateral Harmonic Vibration

This paper presents a new mathematical approach for the analysis of harmonically vibrating horizontal, linear, elastic uniform pile. The soil properties may vary from layer to layer. No separation is allowed at the soil-pile interface. The pile is modeled as a number of cylindrical segments connected by rigid nodes. The length of each segment is chosen such that the effects of the soil inhomogenity are accounted for. The governing differential equation for an arbitrary pile segment is obtained and solved. According to the pile support types such as pinned, fixed and free conditions, first an arbitrary appropriate value for either toe force, bending moment, rotation, or displacement is assumed. The governing differential equation is then solved from the lower pile segment to the top one. The stiffness of the whole pile-soil system will then be computed. It is shown that the slenderness ratio, the stiffness ratio and toe fixity are the governing parameters affecting the stiffness of the soil-pile system. The new analytical model, which is verified using existing numerical and analytical solutions, is more efficient than the equivalent numerical solutions for example finite eminent methods.
Distributed Processing in c in Obit

—There are limits to the capacity of a single multicore shared memory system and for larger problems processing must be spread over nodes of a group or cluster of disjoint memory machines. This requires a different approach to dividing up the work than is optimal for vectorizatiion or over a number of cores in a shared memory system. This memo describes a conceptial design and test implementation for distributing processing over a number of processes without shared memory inside a c language program. This design uses xmlrpc for communicating control information and a globally visible distributed ﬁle system to communicate data.
Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

Background: There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. The recent utilization of nonnegative matrix factorization (NMF) in computational biology has demonstrated the capability to derive essential details from a high amount of data in particular gene expression microarrays. A common problem in NMF is finding the proper number rank (r) of factors of the degraded demonstration, but no agreement exists on which technique is most appropriate to utilize for this purpose. Thus, various techniques have been suggested to select the optimal value of rank factorization (r). Objective: In this work, a new metric for rank selection is proposed based on the elbow method, which was methodically compared against the cophenetic metric. Methods: To decide the optimum number rank (r), this study focused on the unit invariant knee (UIK) method of the NMF on gene expression data sets. Since the UIK method requires an extremum distance estimator that is eventually employed for inflection and identification of a knee point, the proposed method finds the first inflection point of the curvature of the residual sum of squares of the proposed algorithms using the UIK method on gene expression data sets as a target matrix. Results: Computation was conducted for the UIK task using gene expression data of acute lymphoblastic leukemia and acute myeloid leukemia samples. Consequently, the distinct results of NMF were subjected to comparison on different algorithms. The proposed UIK method is easy to perform, fast, free of a priori rank value input, and does not require initial parameters that significantly influence the model’s functionality. Conclusions: This study demonstrates that the elbow method provides a credible prediction for both gene expression data and for precisely estimating simulated mutational processes data with known dimensions. The proposed UIK method is faster than conventional methods, including metrics utilizing the consensus matrix as a criterion for rank selection, while achieving significantly better computational efficiency without visual inspection on the curvatives. Finally, the suggested rank tuning method based on the elbow method for gene expression data is arguably theoretically superior to the cophenetic measure.
IMPLEMENTATION AND EVALUATION OF REGISTER TILING FOR PERFECTLY NESTED LOOPS

Tiling is a crucial loop transformation for generating high-performance code on modern architectures to expose coarse grain parallelism in multi-core architectures and to maximize data reuse in deep memory hierarchies. Register tiling improves Instruction Level Parallelism and is critical for these architectures to maximize performance improvements. Tiled loops with parameterized tile sizes facilitate runtime feedback used in iterative compilation and empirical tuning. Chunky Loop Generator (CLooG) is a powerful polyhedral code generator used to generate syntactic code from polyhedral representation of statement domains and data dependences. However, optimizations like loop unrolling and register tiling can only be applied syntactically. We implement register tiling algorithm for perfectly nested loops for rectangular and non rectangular iteration spaces by post processing CLooG ASTs. There are numerous tools like Pluto, TLoG and HiTLoG that use CLooG to generate code after finding various optimizations through polyhedral approaches. An implementation of register tiling in CLooG can give higher performance improvements for generated code. Experimental results using a number of computational benchmarks comparing tiling techniques implemented in CLooG and TLoG, demonstrate the effectiveness of the implemented tiling algorithm.
Evaluating Functional Memory-Managed Parallel Languages for HPC using the NAS Parallel Benchmarks

Functiona1, memory-managed parallel languages (FMPLs) are a recent innovative approach to shared-memory parallel programming. Despite their rising prevalence in other areas, FMPLs have yet to gain traction in HPC. In this work, we explore the utility of FMPLs for HPC by re-implementing the NAS Parallel Benchmarks in an FMPL.For this study, we ported the benchmarks into the Parallel ML language. We discuss the advantages and disadvantages of using Parallel ML for HPC applications based on our development experience. We compare the performance of our Parallel ML implementation to the existing C/OpenMP version. The FMPL implementations are $1.02 \times -5.76 \times$ slower compared to OpenMP. Our positive development experience combined with some competitive performance results suggest that FMPLs have the potential to become a viable choice for HPC applications. We conclude by describing our future work to automatically manage distributed memory within an FMPL, creating a compelling new programming model for HPC.
Differentiating the sources of post‐election partisan affect warming

While scholars have closely examined the intensification of negative affect across party lines during elections, less is known about the decline of partisan hostility in the aftermath of election campaigns. Synthesizing insights from research on electoral rules and political psychology, we theorize and empirically test two such mechanisms of post‐election negative affect decline. The first is that of winners' generosity: the expectation that self‐perceived election winners will express warmer feelings towards political opponents. The second is that of co‐governance, which predicts that shared coalition status leads to warmer affective evaluations among governing parties. We provide evidence that these mechanisms operate as pressure valves of negative partisan affect. We also show that while co‐governance reduces negative affect between parties who govern together, it fuels negative affect among supporters of opposition parties. The empirical analyses leverage a uniquely uncertain political period following the 2021 Israeli elections, around which we conducted an original panel study. Our findings advance the comparative polarization literature and connect psychological and institutional accounts of temporal fluctuations in partisan affect.
THE ANTECEDENT OF ACTIVE MEMBERSHIP AND PERFORMANCE OUTCOMES OF EMPOWERING LEADERSHIP IN SPORTS FOR DEVELOPMENT ORGANIZATIONS

Purpose – The primary purpose of this paper was to identify the antecedent of active membership and performance outcomes of empowering leadership in sports for development organizations. The study also examined the mediating role of empowering leadership between active membership and performance outcomes. Design/Methodology/Approach – The research assumed a cross-sectional research design. Data was collected from a total population of 2993 youth/members drawn from three sports development organizations in Kenya. The authors conducted a series of confirmatory factor analyses (CFAs) to verify the validity of the constructs and adopted IBM SPSS AMOS Version 23 to test the hypotheses. Findings - The authors found that active membership is an important antecedent of empowering leadership and can enhance performance outcomes. Besides, empowering leadership plays a significant mediating role between active membership and performance outcomes. Conclusion / Recommendation - This study concluded that active membership has a positive and significant influence on performance outcomes among youth in sports for development organizations in Kenya. Empowering leadership mediates between active membership and performance outcomes in sports for development organizations in Kenya. The study recommended that leaders should organize activities and ensure regular participation for the youth in the sports for development organization.
Preview tracking control for discrete-time nonlinear Lur’e systems with sector-bounded nonlinearities

In this paper, the problem of preview tracking control for a class of discrete-time nonlinear Lur’e systems is investigated. To overcome the difficulty arising from the sector nonlinearity, the original system is transformed into a linear parameter varying (LPV) system, and a new auxiliary method is introduced. The difference between the state vector and the auxiliary variable is used instead of the usual difference between system states. At the same time, the tracking error and the previewable future reference information are also considered in order to construct an augmented error system (AES). Then, the preview tracking problem is reduced to a robust control problem. Note that the AES is still an LPV system. Next, a state feedback controller is developed to ensure that the closed-loop system is asymptotically stable with H∞ guaranteed cost performance. Based on this, a preview controller for the original system is proposed. Finally, two numerical examples are provided to illustrate the effectiveness of the controller.
Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation

Background: Artificial intelligence (AI) applications based on advanced deep learning methods in image recognition tasks can increase efficiency in the monitoring of medication adherence through automation. AI has sparsely been evaluated for the monitoring of medication adherence in clinical settings. However, AI has the potential to transform the way health care is delivered even in limited-resource settings such as Africa. Objective: We aimed to pilot the development of a deep learning model for simple binary classification and confirmation of proper medication adherence to enhance efficiency in the use of video monitoring of patients in tuberculosis treatment. Methods: We used a secondary data set of 861 video images of medication intake that were collected from consenting adult patients with tuberculosis in an institutional review board–approved study evaluating video-observed therapy in Uganda. The video images were processed through a series of steps to prepare them for use in a training model. First, we annotated videos using a specific protocol to eliminate those with