Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Care Supply Chain Design for Reducing Disparities in the Delivery of Care: Evaluating the Effects of Resource Dependence and IT Leveraging Competence

Fundamental to health care supply chain management, is the need to acknowledge that not everyone enjoys the same opportunities to access quality health care. As a quality improvement initiative, health information technology has been regarded as a critical component in reducing health care disparities. Yet, evidence supporting such benefits is scarce, more so in under-resourced clinical settings. Using clinic level data from 450 clinics in the Midwest, this study evaluates the effects of resource dependence and IT leveraging competence on the delivery of quality care in primary care settings. We find that clinics that operate in lower socioeconomic areas and provide care to a greater proportion of patients who are uninsured or with government assistance insurance are associated with lower quality care. We also find that leveraging an IT infrastructure, IT functionalities, and IT inter-organizational information exchange is associated with high quality care. Contrary to expectations, we find that the positive effects of higher levels of IT functionality utilization and IT inter-organizational information exchange on the delivery of high quality care do not increase, but potentially decrease as enabling resources decrease. These findings provide support to a growing concern of whether policy mandated health information technology interventions are further increasing the disparities in accessing quality health care and whether a more targeted approach that emphasizes alignment between need and resources is more appropriate.
The effects of childhood abuse on parenting The effects of childhood abuse on parenting

Abstract Childhood abuse has been associated with a broad range of problems in adulthood, including disruptions in parent-child relationships. The majority of research has focused on the effects of childhood sexual abuse on mothers, but it is also important to better understand the effects of childhood abuse on fathers. The current study examined the effect of various forms of childhood abuse on parent-child conflict and relationship quality using the recent replication of the National Comorbidity Survey (NCS-R). The NCS-R is a nationwide household survey of 9,282 participants that included an assessment of the presence/ absence of four forms of childhood abuse: physical abuse, rape, sexual molestation, and witnessing physical violence at home. In addition, victims of childhood abuse were asked to rate the level of conflict and the quality of relationships they have with their children. A series of 2(sex) X 2(exposure) MANOVAs were conducted with relationship quality and relationship conflict as dependent variables. A similar pattern of findings emerged across abuse type. Mothers with a history of childhood abuse reported more conflict with their own children than fathers but a better overall relationship. This study did not support the hypotheses that the effects of childhood abuse are similar for both males and females. Fathers with childhood abuse history may be more likely not to participate in their children’s lives.
ANALYSIS OF CUSTOMER KNOWLEDGE, HALAL BRAND, TRUST, AND CUSTOMER ENGAGEMENT ON CUSTOMER LOYALTY AT PT BANK SYARIAH INDONESIA IN SEMARANG

This study aims to analyze the significance effect of Customer Knowledge, Halal Brand, Trust, and Customer Engagement on Customer Loyalty on PT Bank Syariah Indonesia in Semarang both simultaneously and partially. The used data is primary data which collected by using questionnaire. Population in this study were all customer PT Bank Syariah Indonesia in Semarang, while the sample in this study was 115 participants customer PT Bank Syariah Indonesia in Semarang. Sampling is using a purposive sampling technique. The analysis model used in this research is multiple regression analysis, and the analysis technique uses the simultaneously significant test (F-Test), determination correlation test (R 2 ), and partial significance test (t-test) with the SPSS 25.00 program. The results of proving the hypothesis and discussion show that Customer Knowledge, Halal Brand, Trust, and Customer Engagement simultaneously have a significant effect on Customer Loyalty PT Bank Syariah Indonesia in Semarang. Customer Knowledge and Trust partially have positive and insignificant effect on Customer Loyalty, whereas Halal Brand and Customer Engagement partially have positive and significant effect on Customer Loyalty at PT Bank Syariah Indonesia in Semarang.
Isotopic Enrichment Resulting from Differential Condensation of Methane Isotopologues Involving Non-equilibrium Gas–Surface Collisions Modeled with Molecular Dynamics Simulations

: We employ molecular dynamics simulations to understand the energy transfer processes involved during the collisions of CH 4 and CD 4 with CH 4 layered surfaces at 20 K in order to explain our experimental finding of preferential adsorption of CD 4 compared to CH 4 . There is good agreement between our MD simulations and our experimental results. We find that gas − surface collisional energy accommodation is dominated by exchange involving the translational degrees of freedom of the incident molecule and intermolecular vibrations of the interface. This observation allows us to understand that the cause of CD 4 preferential sticking arises from its propensity to lose more energy during its first impact with the surface, inducing longer residence times and leading to increased probability of becoming trapped and condensed onto the surface. Systematic trends are seen for sticking probabilities and energy transfer when we explore the behavior of the other H/D-substituted isotopologues of methane. These molecular insights provide context into the adsorption behavior occurring on icy dust grains in our solar system. Because adsorption is often the first step, trapping efficiency differences between isotopologues have notable implications for condensed phase reaction probabilities involving isotopically substituted species and subsequent events leading to increased molecular complexity. Aside from astrophysical significance, our findings have direct implications for novel isotope enrichment mechanisms under non-equilibrium conditions involving the preferential condensation of heavier isotopes and isotopologues during gas − surface collisions under specifically selected substrate, gas mixture, and incident kinematic conditions.
A Neural Tangent Kernel Perspective on Function-Space Regularization in Neural Networks

Regularization can help reduce the gap between training and test error by systematically limiting model complexity. Popular regularization techniques such as ℓ 2 weight regularization act directly on the network parameters but do not explicitly take into account how the interplay between the parameters and the network architecture may affect the induced predictive functions. To address this shortcoming, we propose a simple technique for effective function-space regularization. Drawing on the result that fully-trained wide multi-layer perceptrons are equivalent to kernel regression under the Neural Tangent Kernel (NTK), we propose to approximate the norm of neural network functions by the reproducing kernel Hilbert space norm under the NTK and use it as a function-space regularizer. We prove that neural networks trained using this regularizer are arbitrarily close to kernel ridge regression solutions under the NTK. Furthermore, we provide a generalization error bound under the proposed regularizer and empirically demonstrate improved generalization and state-of-the-art performance on downstream tasks where effective regularization on the induced space of functions is essential.
On Sparse Graph Fourier Transform

In this paper, we propose a new regression-based algorithm to compute Graph Fourier Transform (GFT). Our algorithm allows different regularizations to be included when computing the GFT analysis components, so that the resulting components can be tuned for a specific task. We propose using the lasso penalty in our proposed framework to obtain analysis components with sparse loadings. We show that the components from this proposed {\em sparse GFT} can identify and select correlated signal sources into sub-graphs, and perform frequency analysis {\em locally} within these sub-graphs of correlated sources. Using real network traffic datasets, we demonstrate that sparse GFT can achieve outstanding performance in an anomaly detection task.
Relational Fabric: Transparent Data Transformation

A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a significant impact on the entire data system architecture. The multiple-decade-long journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question:"What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns? ". To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore's law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage components to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which profoundly impacts the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy allowing for better hardware utilization and, ultimately, better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware and the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control.
Bulletin of Electrical Engineering and Informatics

Received Nov 2, 2022 Revised Jan 29, 2023 Accepted Mar 24, 2023 Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.
Dual-Band Switched-Beam Antenna Using Slot Active Frequency Selective Surface

 Abstract —A dual-band electronically beam-switching antenna using a cylindrical active frequency selective surface (AFSS) is presented. The antenna structure consists of a slot AFSS and an omnidirectional dual-band feed antenna. The PIN diodes of half the AFSS are switched ON and OFF respectively to reconfigure the surface as transparent and opaque to the incident EM waves. Thus by controlling the bias voltages to different columns, the directive beams can be swept in 4 steps covering the whole azimuth plane. A design validation based on a metallic reflector antenna is performed to predict the performances of the proposed AFSS antenna. To validate the design concept, the transmission coefficients of the active FSS sheet are experimentally examined. Then an antenna prototype operating at 2.5 GHz and 5.3 GHz frequency bands is fabricated and measured. Simulation and measurement results of the fabricated antenna are presented to verify the design principle. A comparison between this work and previous (single-band) designs is given to better interpret the characteristics of the dual-band AFSS antenna.
ABBREVIATIONS BMI : Body Mass Index BSA : Body Surface Area CLA : Chronological Lung Age COPD : Chronic Obstructive Pulmonary Disease

Background. It have been demonstrated that published reference equations (Morris and Temple1985, Newbury2010, Hansen2010, Yamaguchi2012) predicting estimated lung age (ELA) didn’t reliably predict chronological lung age (CLA) data in healthy adults representing a sample of a North African population. Aim. To develop and to validate novel equations for ELA from varied anthropometric data and FEV1. Methods. Applying multiple regression analysis, equations predicting ELA were invented using data from 540 never-smokers with normal spirometry (group I ). Validation was made based on data from 41 never-smokers with normal spirometry (group II ). Equations were further applied for 91 subjects with confirmed chronic obstructive pulmonary disease (COPD). Results. Novel regression equations allowing prediction of reference value of ELA and normal limits of difference between ELA and CLA were elaborated in both sexes. In men, ELA (years) = 42.85 20.74 x FEV1 (l) + 47.41 x body surface area (m) 0.62 x body mass index (BMI, kg/m). In women, ELA (years) = 64.64 8.00 x FEV1 (l) 0.17 x BMI (kg/m ) + 8.82 x Height (m). Normal limits of difference between ELA and CLA were ±16.9 years in the men and ±14.8 years in the women. Established equations predicted ELA of group II with no significant difference (p > 0.05) between CLA and ELA in either sex (respectively, 42.9±16.6 years vs. 40.3±13.7 years in men, 42.0±13.5 vs. 45.6±7.7 in women). ELA was significantly (p < 0.05) older than CLA age only in COPD patients with grades III and IV ((ELA-CLA) (years) averaged -1.8 (grade I), +13.3 (grade II), +21.7 (grade III), +26.4 (grade IV)). Conclusion. The use of contemporary lung age equations may translate into greater success for smoking cessation programs. Key-Words Physiology ; Lung age ; Spirometry ; Reference value ; Normal limits ; Tunisia ; North Africa
BANANA MARKET INTEGRATION AND COINTEGRATION: IMPLICATIONS FOR SUSTAINABLE AGRICULTURAL DEVELOPMENT

The prices are controlled, guide and determine level of consumption, production, decisions of the market over time, form and places in the market-driven economy. Therefore, the market integration of agricultural commodities is deeply investigated which added valuable evidence regarding the mechanism of the market modification and justified government intervention. Morover, this paper was estimated the level of market integration in the provincial banana markets in Pakistan by using the Augmented dickey-fuller (ADF) and Johanson's Cointegration analysis and monthly wholesale price series from January 1981 to December 2020 are used to conduct the research. Furthermore, results of this study indicated that, banana provincial markets are well integrated and Hyderabad market is the dominant market in Pakistan. In addition to that, the other three provincial markets are well integrated with Hyderabad market. Furthermore, it was concluded that, higher levels of market integration between the banana market are fairly sustainable and viable. However, the banana markets will be further improved the market efficiency and enhance competitiveness by public and private sectors intervention.
Protection of a Multicast Connection Request in an Elastic Optical Network Using Shared Protection

Elastic Optical Networks (EONs) allow to solve the high demand for bandwidth due to the increase in the number of internet users and the explosion of multicast applications. To support multicast applications, network operator computes a tree-shaped path, which is a set of optical channels. Generally, the demand for bandwidth on an optical channel is enormous so that, if there is a single fiber failure, it could cause a serious interruption in data transmission and a huge loss of data. To avoid serious interruption in data transmission, the tree-shaped path of a multicast connection may be protected. Several works have been proposed methods to do this. But these works may cause the duplication of some resources after recovery due to a link failure. Therefore, this duplication can lead to inefficient use of network resources. Our work consists to propose a method of protection that eliminates the link that causes duplication so that, the final backup path structure after link failure is a tree. Evaluations and analyses have shown that our method uses less backup resources than methods for protection of a multicast connection.
Gradient dynamics of single-neuron autoencoders on orthogonal data

In this work we investigate the dynamics of (stochastic) gradient descent when training a single-neuron ReLU autoencoder on orthogonal inputs. We show that for this non-convex problem there exists a manifold of global minima all with the same maximum Hessian eigenvalue and that gradient descent reaches a particular global minimum when initialized randomly. Interestingly, which minimum is reached depends heavily on the batch-size. For full batch gradient descent, the directions of the neuron that are initially positively correlated with the data are merely rescaled uniformly, hence in high-dimensions the learned neuron is a near uniform mixture of these directions. On the other hand, with batch-size one the neuron exactly aligns with a single such direction, showing that when using a small batch-size a qualitatively different type of “feature selection” occurs.
ACELONG: Stata module to fit multilevel mixed-effects ACE, AE and ADE variance decomposition models

acelong estimates multilevel mixed-effects ACE variance decomposition models based on twin data formatted one twin per data row ("long" twin data format). An ACE variance decomposition is a behavioral genetic analysis method which partitions the variance of an observed outcome (called phenotype) that varies within twin pairs into three latent components: 1) A component caused by additive genetic effects (A), a component caused by environmental effects shared by both twins of a twin pair (C) and 3) a component caused by environmental effects which are unique to each twin (E). aelong estimates multilevel mixed-effects AE variance decomposition models, i.e., a behavioral genetic variance decompositions without component 2) (C). adelong estimates multilevel mixed-effects ADE variance decomposition models, i.e., a behavioral genetic variance decompositions with a component caused by dominant genetic effects (D).
Drainage via the Vertebral Venous System Diagnostic Pitfall: Atypical Cerebral Venous

Summary: We report a case of atypical cerebral venous drainage in a 38-year-old woman with symptoms of benign paroxysmal positional vertigo. Thrombosis of the left internal jugular vein and sigmoid sinus was suspected on the basis of spin-echo and time-of-flight MR findings, but mul-tisection CT angiograms showed a patent sigmoid sinus and predominant drainage via the emissary veins toward the vertebral plexus, with only a minor contribution of the jugular veins. This case illustrates the variability of the venous anatomy in the craniocervical region.
DOES OFF-COURT GENERIC PERIPHERAL PERCEPTION TRAINING IMPROVE BASKETBALL SPORTS PERFORMANCE?

Löppönen, S. 2022. Does off-court generic peripheral perception training improve basketball sports performance? University of Jyväskylä, Master’s thesis, 73 pp., 2 appendices. Peripheral perception is crucial in team sports where multiple players are simultaneously observed. Former research of sports vision has focussed on investigating the role of the central vision and the gaze behaviour whereas peripheral vision has been left in the shadow. Ecological dynamics and the information processing theories have fundamental differences in how perception and action are coupled. Elite athletes can extract sport-specific useful peripheral information more accurately and efficiently than lesser-skilled athletes or novices. Perceptual-cognitive interventions have successfully improved athletes’ peripheral perception, but it has not been connected to improved sports performance. There is no common understanding of how peripheral perception should be trained to gain transfer effects in sports performance. Thus, the purpose of the study was to investigate the useability of the information processing approach to train peripheral perception off-court using non-sportspecific stimuli. Nine intermediate basketball players were recruited to participate in the study. The study included pre-tests, placebo control period, mid-tests, peripheral training intervention and post-tests. Both study periods lasted for four weeks. Four peripheral reaction tests, visual field size tests, basketball skill tests and from the basketball transfer test (Pick and Roll), gaze location and decision accuracy were collected before and after intervention periods. Participants went first through the placebo control period followed by the peripheral training intervention. Peripheral training intervention, twice 30 minutes per week, included peripheral stimuli identification, attention allocation and multiple object tracking combined with correct motor response and/or sport-specific task. After the placebo control period significant increase (p<0,001) occurred in the horizontal visual field size and performance significantly improved (p<0,05) in two basketball skill tests. After peripheral training intervention, total simple reaction times decreased significantly (p<0,05) and a decrease in reaction time also occurred in the other two reaction tests, but insignificantly. The number of peripheral decisions made compared to foveal decisions increased in post-test, but insignificantly. The off-court generic peripheral training used in the present study does not seem to improve basketball offence performance. Reaction time results suggest peripheral training might improve reaction speed to non-sport-specific peripheral stimuli. Peripheral training potentially shifted participants to use peripheral perception in the decision-making process with no decrements in overall basketball on-court performance. More intervention studies investigating peripheral perception training’s effects on sports performance are needed.
Pressure-robust Flow Discretizations on General Polyhedral Meshes

Computational fluid dynamics (CFD) is a key technology of our modern society: It is of fundamental economic importance in weather forecasts for agriculture, air transportation, and emergency management, and can give substantial theoretical insight in various scientific disciplines like medicine, climate research, or astrophysics. Even hot topics like the spread of the Corona virus in aerosols or stability investigations of the gulf stream in relation to climate change are covered. What CFD