Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDY ON SOFTWARE TESTING SUFFICIENCY BASED ON PROGRAM MUTATION TECHNOLOGY

—Software testing sufficiency means that the software’s performance on limited testing data can represent its performance on all input data. Ideally, in software testing, the testing should be carried on till all errors in the program are detected and removed. As a testing strategy to measure the completeness of the test cases set, mutation testing is a defects-oriented unit testing technology, and a feasible software testing method to generate a complete set of test cases. The article systematically simulates the different defects in software by using mutation operators to create mutants, and then construct testing data set to be able to kill these mutants. It analyzes the procedures of mutation testing and the generation of mutation operators with specific examples. Experiment indicates that the program mutation technology has enhanced the test cases, which greatly improved the software testing sufficiency.
A distributed deep reinforcement learning–based integrated dynamic bus control system in a connected environment

The bus bunching problem caused by the uncertain interstation travel time and passenger demand rate is a critical issue that impairs transit efficiency. Most current bus control studies focus on single or combined strategies while ignoring the bus system's real‐time environmental information. This paper proposed a distributed deep reinforcement learning (DRL)‐based generic bus dynamic control method to solve the bus bunching problem by maintaining the schedule adherence, headway regularity, and achieving the consensus in the multiagent system. This study built a bus system that utilizes the bus historical and traffic information by incorporating these characteristics into the environment. After that, a distributed DRL‐based bus dynamic control strategy is developed based on the bus system, enabling each bus to adjust its motion by any generic method utilizing the weighted downstream buses' information. Regarding the training process, a distributed proximal policy optimization algorithm is adopted for improving the converging performance. Simulated experiments are conducted to verify the control performance, robustness, feasibility, resilience, and generalization capability, which shows that our strategy can significantly reduce the schedule and headway deviations, prevent the accumulation of deviation downstream, and avoid bus bunching.
Distinguishing between Similar Miniproteins with Single-Molecule Nanopore Sensing: A Computational Study

A nanopore is a tool in single-molecule sensing biotechnology that offers label-free identification with high throughput. Nanopores have been successfully applied to sequence DNA and show potential in the study of proteins. Nevertheless, the task remains challenging due to the large variability in size, charges, and folds of proteins. Miniproteins have a small number of residues, limited secondary structure, and stable tertiary structure, which can offer a systematic way to reduce complexity. In this computational work, we theoretically evaluated sensing two miniproteins found in the human body using a silicon nitride nanopore. We employed molecular dynamics methods to compute occupied-pore ionic current magnitudes and electronic structure calculations to obtain interaction strengths between pore wall and miniprotein. From the interaction strength, we derived dwell times using a mix of combinatorics and numerical solutions. This latter approach circumvents typical computational demands needed to simulate translocation events using molecular dynamics. We focused on two miniproteins potentially difficult to distinguish owing to their isotropic geometry, similar number of residues, and overall comparable structure. We found that the occupied-pore current magnitudes not to vary significantly, but their dwell times differ by 1 order of magnitude. Together, these results suggest a successful identification protocol for similar miniproteins.
20-A to 100-A AC-DC Coaxial Current Shunts for 100 kHz Frequency Range

The paper describes a set of four cylindrical, coaxial, ac current shunts used as the new National Research Council (NRC) working standards in the frequency range up to 100 kHz. They were designed for currents from 20 A to 100 A. At the lower current range the shunts were compared to NRC coaxial shunts built from discrete resistors. At the higher current range they were compared to the old working standards, a set of commercial shunts. The paper presents the description of the shunt design as well as results of their experimental performance and uncertainty evaluation.
Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first step. Statistical features in time, frequency, and wavelet domains were extracted from the fault-specific frequency band. In the second step, all of the extracted features were combined into a single feature vector called a multi-domain feature pool (MDFP). The multi-domain feature pool results in a larger dimension; furthermore, not all of the features are best for representing the centrifugal pump condition and can affect the condition classification accuracy of the classifier. To obtain discriminant features with low dimensions, this paper introduces a novel informative ratio principal component analysis in the third step. The technique first assesses the feature informativeness towards the fault by calculating the informative ratio between the feature within the class scatteredness and between-class distance. To obtain a discriminant set of features with reduced dimensions, principal component analysis was applied to the features with a high informative ratio. The combination of informative ratio-based feature assessment and principal component analysis forms the novel informative ratio principal component analysis. The new set of discriminant features obtained from the novel technique are then provided to the K-nearest neighbor (K-NN) condition classifier for multistage centrifugal pump condition classification. The proposed method outperformed existing state-of-the-art methods in terms of fault classification accuracy.
A Novel Solution for $Z$-Numbers Based on Complex Fuzzy Sets and Its Application in Decision-Making System

<inline-formula><tex-math notation="LaTeX">$Z$</tex-math></inline-formula>-numbers have been proven to be advantageous in revealing uncertain information. This article focuses on applying uncertain <inline-formula><tex-math notation="LaTeX">$Z$</tex-math></inline-formula>-numbers to engineering practice. Considering that various uncertain data are inevitable in the process of addressing actual problems, uncertain <inline-formula><tex-math notation="LaTeX">$Z$</tex-math></inline-formula>-numbers are introduced to express information and measure information reliability. Powerful tools are essential when dealing with uncertain information; then, complex fuzzy sets (CFSs) are employed. First, the parameter <inline-formula><tex-math notation="LaTeX">$B$</tex-math></inline-formula> in <inline-formula><tex-math notation="LaTeX">$Z$</tex-math></inline-formula>-numbers is interpreted from the perspective of the phase term, and uncertain <inline-formula><tex-math notation="LaTeX">$Z$</tex-math></inline-formula>-numbers are transformed to interval-valued CFSs. Next, arithmetic operators, aggregation operator, generalized entropy, distance measure, and similarity measure are defined. Besides, a multicriteria group decision-making method is suggested by combining the proposed entropy, distance measure, and aggregation operator. The algorithm is applied to select the brand of the inertial navigation system. The comparison analysis with other widely used methods is conducted to verify the feasibility and validity of <inline-formula><tex-math notation="LaTeX">$Z$</tex-math></inline-formula>-information in the application.
Fluid-Oscillation Coupled Analysis for HAWT Rotor Blade (One Degree of Freedom Weak Coupling Analysis with Hinge-Spring Model)

Since large-scale commercial wind turbine generator systems such as MW-class wind turbines are becoming widely operated, the vibration and distortion of the blade are becoming larger and larger. Therefore the soft structure design instead of the solid-design is one of the important concepts to reduce the structural load and the cost of the wind turbine rotors. The objectives of the study are development of the fluid-structure coupled analysis code and evaluation of soft rotor-blade design to reduce the unsteady structural blade load. In this paper, fluid-structure coupled analysis for the HAWT rotor blade is performed by free wake panel method coupled with hinge-spring blade model for the flapwise blade motion. In the model, the continuous deflection of the rotor blade is represented by flapping angle of the hinge with one degree of freedom. The calculation results are evaluated by comparison with the database of the NREL unsteady aerodynamic experiment. In the analysis the unsteady flapwise moments in yawed inflow conditions are compared for the blades with different flapwise eigen frequencies.
Secondary Exfoliation of Electrolytic Graphene Oxide by Ultrasound Assisted Microwave Technique

Scalable production of large size and high quality graphene is an important prerequisite to fully realize its commercial applications. Herein, we propose a high-efficient route for preparing few-layer graphene. The secondary exfoliation of unexfoliated graphite flakes from electrochemical exfoliation was achieved by using ultrasonication assisted microwave exfoliation technique. The results show that the as-prepared sample has a C/O of 15.2, a thickness of about 1 nm and a transverse dimension of over 100 nm, and the Raman spectrogram shows low defects upon reduction of the sample. These results suggest that electrolytic graphene can be exfoliated to form graphene nanosheets under ultrasonic-assisted microwave technology, thus indicating that the current method has great potential for synthesizing high-quality graphene at an industrial-scale.
Measuring ego-centered social networks

In measuring ego-centered social networks, two general approaches can be distinguished. A very simple way to evaluate membership in a social network is to ask an ordinary survey question where response categories are types of relationships (e.g., partner, parents, children, friends, etc.). This approach is very appealing as it saves time and money. However, information obtained by this approach is very limited. Most often, when evaluating ego-centered networks, the name generator approach is used. The list of egos (respondents) is obtained in the first step. In the second step, existing ties are identified - all alters with whom the focal ego has some sort of relationship. When all ties have been identified, the contents and the characteristics of ties are assessed. In most cases the characteristics of the alters are also measured. The name generator approach yields more data and is also of higher quality. However, it is very time and money consuming, and it requires either considerable effort from respondents, when it is applied in self-administered mode, or complex coordination between interviewer and respondent, when it is applied in personal interviews (e.g., Kogovšek et al., 2002). In a series of studies, network composition was estimated using both approaches. Test-retest and split-ballot experiments on convenience samples of respondents were used to assess the stability of network composition. Findings are discussed with regard to survey complexity, respondent burden, costs and quality of network composition estimates.
On the stability of hydrostatic equilibrium in a uniform magnetic field with partial dissipation

In this paper, we establish the nonlinear stability and large time behavior of hydrostatic equilibrium in a uniform magnetic field for the 2D Boussinesq system with magnetohydrodynamics convection on spatial domain Ω=𝕋×ℝ with partial dissipation. This is the important part of further work about the stability/instability of shear flow which is the combination of hydrostatic equilibrium and Couette/shear flow in a uniform magnetic field with partial dissipation.
Regional Brain Analysis of Modified Amino Acids and Dipeptides during the Sleep/Wake Cycle

Sleep is a state in which important restorative and anabolic processes occur. Understanding changes of these metabolic processes during the circadian rhythm in the brain is crucial to elucidate neurophysiological mechanisms important for sleep function. Investigation of amino acid modifications and dipeptides has recently emerged as a valuable approach in the metabolic profiling of the central nervous system. Nonetheless, very little is known about the effects of sleep on the brain levels of amino acid analogues. In the present study, we examined brain regional sleep-induced alterations selective for modified amino acids and dipeptides using Ultra-high performance liquid chromatography-MS/MS (UHPLC-MS/MS) based metabolomics. Our approach enabled the detection and identification of numerous amino acid-containing metabolites in the cortex, the hippocampus, the midbrain, and the cerebellum. In particular, analogues of the aromatic amino acids phenylalanine, tyrosine and tryptophan were significantly altered during sleep in the investigated brain regions. Cortical levels of medium and long chain N-acyl glycines were higher during sleep. Regional specific changes were also detected, especially related to tyrosine analogues in the hippocampus and the cerebellum. Our findings demonstrate a strong correlation between circadian rhythms and amino acid metabolism specific for different brain regions that provide previously unknown insights in brain metabolism.
Automating Security Monitoring & Analysis for Space Station Freedom's Electric Power System

Operating a large, space power system requires classifying the system's status and analyzing its security. Conventional algorithms are used by terrestrial electric utilities to provide such information to their dis-patchers, but their application aboard Space Station Freedom will consume too much processing time. We present a new approach for monitoring and analysis using adaptive pattern recognition techniques. This approach yields an on-line security monitoring and analysis algorithm that is accurate and fast; and thus, it can free the Space Station Freedom's power control computers for other tasks.
Capability of two-dimensional Reynolds-averaged Navier—Stokes simulations for two-dimensional thrust vectoring nozzles

Abstract Numerical simulations of two-dimensional (2D) flow obtained by solving the Reynolds-averaged Navier—Stokes (RANS) equations served for the design of 2D thrust vectoring nozzles. Thrust is directed by means of deflectors with different heights placed at the end of the bottom wall. The nozzle was manufactured and tested. A series of experiments were performed in a trisonic wind tunnel. The experimental results obtained by optical methods and pressure measurements are compared with the results of a numerical flow simulation. Differences were observed in pressure distribution along the bottom wall. More detailed research revealed that the real flow considerably deviated from the 2D flow. The primary goal of this work is to provide applicability of the used numerical methods in nozzle design. The results show that RANS 2D simulations with turbulent models based on the Boussinesq approximation are not able to predict reliable enough results for 2D thrust vectoring nozzles.
Application of Dairy Manure Amended with Mineral Fertilizer on Stubble-Covered Soil: Effects on Ammonia Emissions

The reduction in the manure application rates through enrichment with mineral fertilizer has the potential to reduce costs, decrease environmental pollution, and extend the manure benefits to greater acreage. A pot experiment was carried out to assess ammonia emissions from dairy manure amended with mineral fertilizers applied on wheat stubble. The treatments were: control (no fertilization), urea (U), calcium ammonium nitrate (AN), dairy manure (MAN), urea + dairy manure (UMAN), and calcium ammonium nitrate + dairy manure (ANMAN). A dynamic chamber system was used to measure NH3 emissions during seven days after soil application. UMAN and ANMAN treatments led to higher NH3 emissions than each isolated component. This might be motivated by the manure pH. Thus, the enrichment of dairy manure with U or AN for application on stubble-covered soil should not be recommended. Nevertheless, some manure pre-treatments, such as acidification, or the use of other mineral fertilizers might improve such solution.
When the Bough Breaks: A systematic review and meta‐analysis of mental health symptoms in mothers of young children during the COVID‐19 pandemic

Abstract Parents have experienced considerable challenges and stress during the COVID‐19 pandemic, which may impact their well‐being. This meta‐analysis sought to identify: (1) the prevalence of depression and anxiety in parents of young children (<age 5) during the COVID‐19 pandemic, and (2) sociodemographic (e.g., parent age, being racially minoritized) and methodological moderators (e.g., study quality) that explain heterogeneity among studies. A systematic search was conducted across four databases from January 1, 2020 to March 3, 2021. A total of 18 non‐overlapping studies (8981 participants), all focused on maternal mental health, met inclusion criteria. Random‐effect meta‐analyses were conducted. Pooled prevalence estimates for clinically significant depression and anxiety symptoms for mothers of young children during the COVID‐19 pandemic were 26.9% (95% CI: 21.3–33.4) and 41.9% (95% CI: 26.7–58.8), respectively. Prevalence of clinically elevated depression and anxiety symptoms were higher in Europe and North America and among older mothers. Clinically elevated depressive symptoms were lower in studies with a higher percentage of individuals who were racially minoritized. In comparison, clinically elevated anxiety symptoms were higher among studies of low study quality and in samples with highly educated mothers. Policies and resources targeting improvements in maternal mental health are essential.
Performance Metric Analysis for a Jamming Detection Mechanism under Collaborative and Cooperative Schemes in Industrial Wireless Sensor Networks

The emergence of Industry 4.0 technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), has prompted a reconsideration of methodologies for network security as well as reducing operation and maintenance costs, especially at the physical layer, where the energy consumption plays an important role. This article demonstrates through simulations and experiments that, while the cooperative scheme is more efficient when a WSN is at normal operating conditions, the collaborative scheme offers more enhanced protection against the aggressiveness of jamming in the performance metrics, thus making it safer, reducing operation and maintenance costs and laying the foundations for jamming mitigation. This document additionally offers an algorithm to detect jamming in real time. Firstly, it examines the characteristics and damages caused by the type of aggressor. Secondly, it reflects on the natural immunity of the WSN (which depends on its node density and a cooperative or collaborative configuration). Finally, it considers the performance metrics, especially those that impact energy consumption during transmission.
Impact of class-imbalance on multi-class high-dimensional class prediction

The goal of multi-class supervised classification is to develop a rule that accurately predicts the class membership of new samples when the number of classes is larger than two. In this paper we consider high-dimensional class-imbalanced data: the number of variables greatly exceeds the number of samples and the number of samples in each class is not equal. We focus on Friedman's one-versus-one approach for three-class problems and show how its class probabilities depend on the class probabilities from the binary classification sub-problems. We further explore its performance using diagonal linear discriminant analysis (DLDA) as a base classifier and compare its performance with multi-class DLDA, using simulated and real data. Our results show that the class-imbalance has a significant effect on the classification results: the classification is biased towards the majority class as in the two-class problems and the problem is magnified when the number of variables is large. The amount of the bias depends also, jointly, on the magnitude of the differences between the classes and on the sample size: the bias diminishes when the difference between the classes is larger or the sample size is increased. Also variable selection plays an important role in the class-imbalance problem and the most effective strategy depends on the type of differences that exist between classes. DLDA seems to be among the least sensible classifiers to class-imbalance and its use is recommended also for multi-class problems. Whenever possible the experiments should be planned using balanced data in order to avoid the class-imbalance problem.
Willingness to Pay to Improve Quality of Public Healthcare Services in Mauritius

Mauritius has a universal free healthcare system, based on the Beveridge model which is financed by taxpayers. There are growing considerations over improving quality of healthcare services. The purpose of the study is to employ a contingency valuation (CV) to investigate the willingness of Mauritians people to pay to improve the quality of public healthcare services and the associated determinants using the double-bounded dichotomous choice model. A drop off survey with a sample size of 974 respondents from the working population is used. The empirical analysis shows that the majority of the sample was willing to pay for improving quality of public healthcare services. Other than the conventional determinants of respondents’ demographic and socioeconomic characteristics, the findings support the assertion that psycho-social constructs such as the Theory of Planned Behaviour, Norm-Activation, Public Good Theory, and Perceived Response Efficacy are found to significantly affect Willingness-to-Pay (WTP). The results of this study might be of use to policymakers to help with both priority setting and fund allocation.
Making sure of students attendance and the system of mark management by using mobile applications

Marking attendance in the class meeting session and recording the marks of the students are the prime tasks of the subject handlers, but current methods are time consuming and hectic. To avoid these problems, this paper aims to present a mobile application for student attendance and mark management system. This application is mainly designed for the faculties and other staff members of the organization who maintain attendance and marks regularly. Using this system, the subject handlers, staff or the authorities can verify the number of students present or absent in the class meeting sessions. This application allows the users to mark attendance through mobile devices and to keep in touch with students. It also gives a prior intimation to students as soon as their attendance goes below the specified percentage through an alert message. Keywords: attendance; mobile applications; manual; student management, student attendance