Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Computation of SHAP Values for Piecewise-Linear Decision Trees

The interpretability of machine learning models is important in many applied data analysis problems. In recent years, a universal SHAP interpretation paradigm based on Shapley values has become popular. However, to interpret the values using SHAP directly, it is necessary to calculate the model’s prediction the number of times that is exponential to the size of the feature space, which leads to the prohibitive complexity of this calculation method for complex models over complex feature spaces, for example, for neural networks or gradient boosting ensembles. It was shown that there are efficient algorithms for calculating SHAP values for decision trees and additive ensembles based on them, using structure of trees for an optimized calculation. One of interesting new classes of machine learning models is piecewise linear decision trees and gradient boosting ensembles based on them. In this paper, an efficient algorithm for computing SHAP values for piecewise linear decision trees and additive ensembles based on them was proposed.
Calculated Behavior and Effective Factors for Bolt Self-Loosening Under a Transverse Cyclic Load Generated by a Linearly Vibrating Washer

It is common knowledge that a bolt is apt to loosen due to slippage between the contact surfaces of joined parts. Loosening tests using real parts enable precise scrutiny of real phenomena under the influence of multiple factors such as slip distance, surface roughness, and coefficient friction. However, estimating the influence of the individual factors is very difficult because the friction forces of real contact surfaces are compiled based on variations in friction coefficients, meaning friction is not stable. Therefore, the effects of factors were investigated using Finite Element Analysis (FEA) to control friction coefficients. The procedures were as follows. Assuming a joined structure consisting of a bolt, nut, and washer, bolt axial tension was generated through constant movement of a washer in the bolt’s axial direction, following which the washer was constantly vibrated in one direction transverse to the bolt axis. This vibration generated displacements equivalent to the degree of slippage between the two clamped parts. During vibration, the rotating angles of the bolt and the contact pressure of the threads and bearing surfaces were calculated. The results were as follows. The vibrating displacements of a washer have considerable influence on the rotational loosening of a bolt. In cases where there was only minor displacement of the washer vibrations, the rotational loosening angle rapidly decreased, although the loosening did not cease completely. Therefore, the magnitude of what is called “critical slip” was not confirmed under the conditions of this study. In addition, the friction coefficient has a significant influence on the rotational loosening of a bolt. When the respective friction coefficient values of the threads and bearing surfaces are not balanced, rotational loosening cannot continue. Surface roughness readily affects contact pressure, so it tends to make the contact pressure localized. In particular, high-pressure areas were affected by several projections set on the threads. However, under those conditions the rotational loosening did not differ greatly from the results of the fine surface models subject to the same vibrating amplitude and friction coefficient. Consequently, the localized contact pressure had little evident effect on loosening. Above all, FEA reproduced the loosening of the bolt, and the reference made in this analysis is useful.Copyright © 2005 by ASME
AR2Concept Automatic Extraction Concepts From Arabic Text Language

: Our objective is the design and realization of an automatic system of extraction of concepts from a text in the Arabic language as a first step towards the creation of ontology. The architecture we adopt is an original approach for Arabic language texts that combines the semantic concept extraction method based on the Latent Semantic Analysis documentary search technique with the K-means algorithm. Faced with the problem posed by the K-means algorithm for the number of clusters to be fixed, we propose a solution that we have evaluated on a set of texts. The first results are satisfactory. Our AR2Concept system allowed the identification of concepts with an f-measure rate of around 80%.
To Change or Not to Change: Does it Affect my School?

An effective education system needs to be developed and changed to meet current and future claims. As such, in the face of today’s challenging global economic challenge, citizens with knowledge, skills and competencies are crucial to the country’s success, based on high performance education. This study aims to determine the predictors of school effectiveness in secondary schools based on the perception of the principals. Data was collected from 246 principals in low-performance schools using the survey questionnaire. The selection of the respondents based on proportionate stratified random sampling. The Structural Equation Modelling analysis showed that the model tested and evaluated was fit. Based on four constructs, namely the appropriateness of change, management support, change of efficacy and personal valence, the variance explained for school effectiveness is approximately 26.3%. The findings indicated that the appropriateness of change and support for management were the main predictors of school effectiveness. The study concluded that the appropriateness of change and management support plays a vital role in the efficiency of schools among principals in low-performance schools. Changes in educational settings need to be adapted to school development to ensure that the school communities successfully adapt changes.
Trends, Variation, and Factors Influencing Antibiotic Prescribing: A Longitudinal Study in Primary Care Using a Multilevel Modelling Approach

Antimicrobial resistance has become one of the greatest threats to global health. Over 80% of antibiotics are prescribed in primary care, with many prescriptions considered to be issued inappropriately. The aim of this study was to examine the association between prescribing rates and demographic, practice, geographic, and socioeconomic characteristics using a multilevel modelling approach. Antibiotic prescribing data by 320 GP surgeries in Northern Ireland were obtained from Business Services Organisation for the years 2014–2020. A linear mixed-effects model was used to identify factors influencing antibiotic prescribing rates. Overall, the number of antibacterial prescriptions decreased by 26.2%, from 1,564,707 items in 2014 to 1,155,323 items in 2020. Lower levels of antibiotic prescribing were associated with urban practices (p < 0.001) and practices in less deprived areas (p = 0.005). The overall decrease in antibacterial drug prescriptions over time was larger in less deprived areas (p = 0.03). Higher prescribing rates were linked to GP practices located in areas with a higher percentage of the population aged ≥65 (p < 0.001) and <15 years (p < 0.001). There were also significant regional differences in antibiotic prescribing. We advocate that any future antibiotic prescribing targets should account for local factors.
The role of values in coping with health and economic threats of COVID-19

ABSTRACT The current research examined the role of values in guiding people’s responses to COVID-19. Results from an international study involving 115 countries (N = 61,490) suggest that health and economic threats of COVID-19 evoke different values, with implications for controlling and coping with the pandemic. Specifically, health threats predicted prioritization of communal values related to caring for others and belonging, whereas economic threats predicted prioritization of agentic values focused on competition and achievement. Concurrently and over time, prioritizing communal values over agentic values was associated with enactment of prevention behaviors that reduce virus transmission, motivations to help others suffering from the pandemic, and positive attitudes toward outgroup members. These results, which were generally consistent across individual and national levels of analysis, suggest that COVID-19 threats may indirectly shape important responses to the pandemic through their influence on people’s prioritization of communion and agency. Theoretical and practical implications are discussed.
The complete chloroplast genome of Scoparia dulcis (Plantaginaceae)

Abstract Here, we report the complete chloroplast genome of Scoparia dulcis L. The genome is 153,701 bp in size. Two inverted repeats (IRs) with a total of 50,546 bp were identified. The rest of the sequence was separated into two single-copy regions, including a large single-copy (LSC) region (85,029 bp) and a small single-copy (SSC) region (18,126 bp), respectively. The genome of S. dulcis comprised of 129 genes, including 85 protein-coding genes, 8 rRNA genes, and 36 tRNA genes. Phylogenetic analysis showed that S. dulcis was strongly allied with Bacopa monnieri.
A novel three-phase high-power soft-switched DC/DC converter for low-voltage fuel cell applications

An efficient dc/dc converter is needed as the interface between a low-voltage fuel cell source and a high-voltage bus for inverter operation. In this paper, a three-phase transformer-isolated dc/dc converter utilizing phase-shift (PS) modulation is proposed. The converter must be able to boost the voltage significantly and operate at current levels above 240 A on the source side. Key features of the proposed converter include reduced transformer turns ratio by a factor of two while maintaining the same output voltage, reduced size of passive components including output filter and input dc bus capacitor using three-phase interleaving, eliminated inductor current ripple at PS angles above 120/spl deg/, and achieved soft switching over a wide load range without auxiliary circuitry. The proposed converter has been analyzed, simulated, and implemented in hardware. An efficiency of above 96% was achieved using the prototype unit. Experimental results were used to verify all designs and analyses.
Product Brokering Efficiency as a mediator of Online Product Recommendation and Customer Loyalty

Marketing strategies in e-commerce have a main goal, that is to pursue customer loyalty. Sociolla is an e-commerce company that sells cosmetic products and has a recommendation feature to make it easier for customers during the shopping process. These recommendations can trigger customer satisfaction and generate loyalty. The purpose of this study was to examine the correlation between online product recommendation and customer loyalty with product brokering efficiency as a mediator. 179 Sociolla customers were recruited in this study using convenience sampling. The data were analyzed using the SPSS-Process Hayes model 4. Results showed that perceived decision quality acts as a mediator in the relationship between enablers and customer loyalty (β = .20, [ .13; .27]). It can be concluded that recommendations that are comprehensive, clear, and meet the customer needs will make it easier for customers to make purchasing decisions, which ultimately leads the customers to form loyalty toward the products.
Estimation of multivariate treatment effects in contaminated clinical trials

The paper addresses estimating and testing treatment effects with multivariate outcomes in clinical trials where imperfect diagnostic devices are used to assign subjects to treatment groups. The paper focuses on the pre‐post design and proposes two novel methods for estimating and testing treatment effects. In addition, methods for sample size and power calculations are developed. The methods are compared with each other and with a traditional method in a simulation study. The new methods show significant advantages in terms of power, coverage probability, and required sample size. The application of the methods is illustrated with data from electroencephalogram (EEG) recordings of alcoholic and control subjects.
Immune Clonal Spectral Clustering for PolSAR Land Cover Classification

In this paper, a novel PolSAR land cover classification method is proposed based on spectral clustering (SC) and immune clonal selection principle. SC is employed to reduce dimension in the polarimetric feature space. However, it is susceptible to local optimum and sensitive to the initial partition. To address this issue, immune clonal clustering (ICC) is used due to its capability of global searching. The proposed method combines the complementary advantage of spectral clustering and immune clonal clustering. Experimental results demonstrate that the proposed method is more stable and efficient compared with the other methods.
A New Model for Hysteretic Behavior and Damage for Confined Masonry Walls

A new hysteretic and damage model is presented, aimed at predicting the behavior of confined masonry walls subjected to cyclic lateral forces. It takes into account the strength and stiffness degradation as functions of damage accumulation. It provides more accurate representations of the shapes of the hysteretic cycles and the rates of strength and stiffness degradation than those obtained with the aid of some currently used models. In particular, it is capable of representing the shapes of force-deformation functions, including pinching effects. Some illustrative examples are presented, based on the information provided by experimental data from full-scale masonry panels.
A Band-Gap Voltage Reference for LDO Circuit

A band-gap voltage reference applicable for LDO circuit was designed. The band-gap voltage reference with lower change rate and low temperature-drift and high PSRR was acquired, which uses the two stage operational amplifier as the input terminal, and the miller compensation circuit was adopted in order to improve the stability of the voltage reference circuit. The circuit was simulated with Cadence tool and 0. 5μm CMOS model. The results show that the reference voltage is 1.2182V, the PSRR of band-gap voltage reference is-76.8dB. The measured results indicated that the designed band-gap voltage reference is prospective for application in LDO circuit.
Research on the Effects of Task Repetition in TBLT and Its Recycling Use in EFL Learners’ Oral Production

This paper reports on a study that was primarily aimed at exploring the benefits of using TR for two times in English teaching for non-English major freshmen in a specific higher vocational college. It investigates the effect of applying an intermediate TR in TBLT procedure, and a second TR in one week's interval. Recordings were made of 3 students of markedly different English level performing the task in both 2 times of TR. Follow-up interviews were conducted on the teaching teacher and these 3 students. Results revealed that obvious improvement were achieved in oral production regardless of the students' English level in the first TR. However, when applied in the second time, no further gain was spotted in general, indicating that effect of TR may have been related to the teacher's scaffolds and the students' being notified of TR purpose in advance. The obtained results also show implications for teachers and practitioners in EFL
Performance, Physiological Parameters and Egg Quality Traits of Laying Hens as Affected by Dietary Supplementation of Date Palm Pollen

| The current study aimed to investigate the effect of supplementation of date palm pollen (DPP) on the performance, physiological parameters and egg quality traits of laying hens. A total number of 240 Fayoumi chicken hens were classified into two equal experiments, 120 hens each (4 groups × 3 replicates × 10 birds). In each experiment, birds were divided into 4 treatment groups as follow: Normal control, positive control (0.0125% Butylated hydroxytoluene; BHT), 0.1% and 0.3% DPP, respectively. The DPP was used in two forms i.e., powder (DPP) for Exp.1 and aqueous extract (DPPE) for Exp.2. The results revealed that egg production was significantly improved (P<0.01) in the 0.1% DPP group (Exp.1) whereas, all groups exhibited higher (P<0.05) egg production (Exp.2) comparing to normal control. All treated groups exhibited an enhancement (P<0.01) in yolk total cholesterol comparing to either normal or positive control groups (Exp.1 and Exp.2). Spleen percentage (Exp.1) and carcass percentage (Exp.2) was significantly improved (P<0.01) in the 0.3% DPP group compared with all other groups. Hens fed control positive and 0.3% DPP (Exp.1), and control positive and 0.1% DPPE (Exp.2) exhibited significantly higher (P<0.01) yolk percentage compared with the normal control. From the obtained results it could be concluded that the supplementation of DPP up to 0.3% in either powder or extract forms enhances laying hens performance, and egg quality traits.
Reinforcement Learning of Molecule Optimization with Bayesian Neural Networks

Creating new molecules with desired properties is a fundamental and chal-lenging problem in chemistry. Reinforcement learning (RL) has shown its utility in this area where the target chemical property values can serve as a reward signal. At each step of making a new molecule, the RL agent learns se-lecting an action from a list of many chemically valid actions for a given molecule, implying a great uncertainty associated with its learning. In a traditional implementation of deep RL algorithms, deterministic neural networks are typically employed, thus allowing the agent to choose one action from one sampled action at each step. In this paper, we proposed a new strategy of applying Bayesian neural networks to RL to reduce uncertainty so that the agent can choose one action from a pool of sampled actions at each step, and inves-tigated its benefits in molecule design. Our experiments suggested the Bayesian approach could create molecules of desirable chemical quality while maintained their diversity, a very difficult goal to achieve in machine learning of molecules. We further exploited their diversity by using them to train a generative model to yield more novel drug-like molecules, which were absent in the training molecules as we know novelty is essential for drug candidate molecules. In conclusion, Bayesian approach could offer a balance between exploitation and exploration in RL, and a balance between optimization and diversity in molecule design.
The Role of Supervision in Preventing Burnout among Professionals Working with People in Difficulty

The value of people in their various dimensions is a priority in the postmodern era. In this respect, programs are being implemented for disadvantaged social categories to compensate for differences, reduce discrepancies, and integrate marginalized people into society. This, however, is not easy, and the work of professionals with people in difficulty is frequently difficult, consuming multiple resources and, sometimes, leading to burnout. The professions involved in the recovery work of people in difficulty provide social, medical, psychological, and spiritual assistance services in order to restore or increase the well-being of disadvantaged people or social groups. This study presents an analysis of burnout among social workers and clerics and the effect of supervision on burnout. In support of this, a sociological survey (n = 502) was conducted on a convenience sample of Romanian social workers and clerics in June 2018. The main conclusion of the study is that supervising professionals working with people in difficulty significantly reduces the risk of burnout.
Auto-disturbance-rejection control for phase-shifted resonant converter

A robust auto-disturbance-rejection controller (ADRC) for phase-shifted resonant converters is proposed. The variation of load resistance is observed and estimated by an extended state observer, which is the core of the ADRC. A dedicated nonlinear state error feedback is proposed to synthesise the control actions to obtain superior dynamic performance. Laboratory experiments based on a Matlab/DSP system have been carried out and the results confirm the robustness of the controller.
3‐D Crustal Azimuthal Anisotropy Reveals Multi‐Stage Deformation Processes of the Sichuan Basin and Its Adjacent Area, SW China

The collision of the South China Block and the North China Block and the eastward extrusion of the Tibetan Plateau materials have caused complicated tectonic deformation and strong seismicity around the Sichuan Basin (SCB). In order to reveal regional stress distribution and deformation mechanisms, we invert for a 3‐D high‐resolution shear wave velocity and azimuthal anisotropy model from ambient noise tomography with data from 483 broadband seismographs deployed around the SCB. Our results reveal strong lateral contrasts of azimuthal anisotropy in the crust. The western Sichuan depression shows weak deformation. The central Sichuan uplift, used to be a stable microcontinent, shows relatively strong and coherent fossil anisotropy. In contrast, the eastern Sichuan folds shows strong anisotropy, indicating intense regional deformation. The fast axis direction is bifurcated when reaching the Longmenshan fault, which indicates the obstruction of the SCB to the crustal low velocity materials beneath the Songpan‐Ganzi Block. In the east, the rigid SCB and strike‐slip faults may transform the compressive stress to shear deformation in adjacent areas and cause a ring‐shaped anisotropy pattern below 15 km depth. Furthermore, anisotropy patterns display strong contrast at shallow depths in the Weiyuan and Changning regions, which may facilitate the accumulation of strain and more likely induce earthquakes during the shale gas exploration stage. Our new model provides important constraints for understanding the multi‐stage deformation processes in and around the SCB.
Frequency response function adaptation for reconstruction of combustion signature in a 9-L diesel engine

An accelerometer as a low-cost non-intrusive transducer for sensing the combustion events in a diesel engine was investigated via the reconstruction of in-cylinder pressure using an adapting frequency response function (FRF). As the noise introduced into the accelerometer signal and the response to combustion vary with the operating condition, the FRF computed from a single operating condition only works for the same or similar conditions. To overcome this limitation, an adaptation process for the FRF was explored. Robustness of FRF over additional operational conditions with start of injection, start of combustion, and load variations was greatly improved. Frequency domain analysis shows that only the low-frequency content is determinant for the in-cylinder pressure reconstruction, and the adaptation of the first and second (0 Hz and 121 Hz) harmonics of the FRF results in the greatest improvement for the in-cylinder pressure estimation accuracy. The 0 Hz harmonic is adjusted based on the pre-measured in-cylinder pressure offset and the online measured accelerometer signal offset. Particle swam optimization as a computational algorithm is applied to adapt the 121 Hz harmonic of FRF. The results show that the adapted FRF, in comparison to the unadapted FRF, can reduce the phase error up to 1.3 crank angle degrees and reduce the amplitude error by up to 90%.