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HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON's development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade. [ABSTRACT FROM AUTHOR]

The three‐body recombination reaction, or ternary association, is a termolecular reaction leading to a molecule after a three‐body encounter that plays a vital role in many relevant scenarios in chemical physics. Here, we introduce the Python 3‐Body Recombination program, which is dedicated to the computation of atomic three‐body recombination rate coefficients. The software is based on a classical trajectory approach in hyperspherical coordinates after mapping the three‐body problem as a single particle in a higher‐dimensional space. This theoretical approach is fully general and applicable to any ion‐atom‐atom or atom‐atom‐atom three‐body process. The predictive power of the methodology has been tested in several different experimental scenarios, reaching a good description of every system. The code structure is presented alongside examples and tests to ensure the software's capacity. In addition, the performance of the software after parallelization is shown. [ABSTRACT FROM AUTHOR]

March algorithms are widely used in Memory Built-In Self-Test (MBIST) on-chip memory testing, providing linear test complexities that reduce the test time and cost. However, studies show that March algorithms with complexities lower than 18N have poor coverages of faults that have emerged with the advent of the nanometer process technologies and are more relevant to nowadays memories. New March AZ1 and March AZ2 algorithms, with 13N and 14N complexities, respectively, were introduced to provide optimum coverage of those faults and to produce a shorter test than an 18N-complexity test algorithm with a lesser area overhead, thus reducing chip manufacturing costs. This paper presents the implementation and validation of MBIST controllers that applied the March AZ1 and March AZ2 algorithms in a Field-Programmable Gate Array (FPGA) device. They were implemented in the Intel Max 10 DE10-Lite FPGA Development Board. A test generator was built in FPGA, as an alternative to the external tester, to provide test vectors required in initiating the test on the memory model using the implemented MBIST. The FPGA experimental tests demonstrated that they function correctly as the expected test sequences were observed. In addition, their fault detection abilities were also validated through tests on a fault-injected memory model, which shows that the implemented March AZ1 and March AZ2 provide 80.6% and 83.3% coverage of the intended faults, respectively, which outperform any other existing 14N-complexity March algorithms. [ABSTRACT FROM AUTHOR]

Security is an essential attribute of high‐quality software. However, effectively incorporating security practices into different phases of the software development life cycle (SDLC) remains challenging. Owing to less mature secure testing processes, organizations are prone to ineffective testing practices for defect detection, including severe security‐related failures. Thus, in this study, we present a maturity model for secure software testing (MMSST) to assist software development organizations in improving the secure testing of software applications. We conducted a multivocal literature review and identified 68 primary studies from the formal and gray literature. Then, based on the available evidence, 27 process areas were identified to develop the proposed MMSST. The MMSST includes five main categories: governance, contrive and design, execution, deployment and configuration, and mature. The MMSST was subsequently evaluated using case studies related to practical environments. Results demonstrate that the proposed MMSST is useful for estimating the maturity level of an organization with respect to the secure testing phase of the SDLC. The participants of the case studies also agreed that the proposed MMSST is useful in terms of structure, user satisfaction, and ease of use. We believe that the proposed MMSST can help organizations evaluate and improve software security testing practices. In addition, the proposed MMSST is expected to provide researchers and industry practitioners with an effective foundation for developing new secure testing approaches and tools. [ABSTRACT FROM AUTHOR]

When it comes to software development, precise planning, proper documentation and proper process control, some errors are inevitable in the software environment. These software flaws can lead to quality deterioration, which can be the main reason behind system failure. As the whole world especially developing countries is dependent upon software systems, it is very important to focus on its reliability aspect. Nowadays sophisticated systems require concerted efforts for managing and reducing the shortcomings in software engineering. But, these efforts require more cost, more money and more time. Software error prediction is the most helpful step in the testing stage of the software development life cycle. It identifies components or parts of the code where an error may occur and requires broad testing, so the test resources can be efficiently used. Software error assessment reduces efforts of testing the software by helping the software testers locate the actual problem and classify different classes of errors in the system. Error estimators are majorly used in various organizations to evaluate the software to save time, improve the quality of software and testing and optimize resources to meet timelines. Machine learning provides support in fault projection by collecting the training data from various edge devices and thus helps in escalating the reliability of the software available on Kaggle. The multilayer perceptron shows better results in precision, recall, F1 score and accuracy as compared to decision tree and Gaussian Naive Bayes as it achieves an accuracy of 96.8%. [ABSTRACT FROM AUTHOR]

The efficiency and performance of a software application largely depend on the testing strategy adopted by the firm. Apart from the tools, techniques, and skills used for testing, the duration also plays an important influence in establishing software reliability. This defines the operational performance of the software. The testing duration decision is dependent on the failure behavior depicted by the software during the process of testing and the cost spent at various phases of development. In this paper, we study the multi-release model whose fault removal process is affected by random irregular fluctuations (white noise) and error generation phenomenon and determine the optimal time of testing for a multi-release-based software. The fault removal process is governed by the phenomenon of testing coverage which is affected by random fluctuations. The model shows encouraging results as it handles the stochastic property of the fault detection process. The optimal testing time is determined with the goal to minimize the expected development cost related to software while achieving the desired reliability levels of software for that release using a Genetic Algorithm. A real-life four-release fault dataset of tandem computers has been used to numerically demonstrate the methodology. It is observed through sensitivity analysis that the presence of white noise directly affects the cost and optimal testing duration. The potential to improve sensitivity, flexibility, early detection, discovering unsuspected patterns, and boost fault diagnosis is enhanced by collecting irregular fluctuations in the fault detection rate. This technology, by going beyond existing methodologies, has distinct advantages for detecting faults and can contribute to more dependable and efficient systems in a variety of domains. [ABSTRACT FROM AUTHOR]

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes. [ABSTRACT FROM AUTHOR]

Motivation Oxford Nanopore Technologies (ONT) sequencers enable real-time generation of sequence data, which allows for concurrent analysis during a run. Adaptive sampling leverages this real-time capability in extremis , rejecting or accepting reads for sequencing based on assessment of the sequence from the start of each read. This functionality is provided by ONT's software, MinKNOW (Oxford Nanopore Technologies). Designing and developing software to take advantage of adaptive sampling can be costly in terms of sequencing consumables, using precious samples and preparing sequencing libraries. MinKNOW addresses this in part by allowing the replay of previously sequenced runs for testing. However, as we show, the sequencing output only partially changes in response to adaptive sampling instructions. Here we present Icarust, a tool enabling more accurate approximations of sequencing runs. Icarust recreates all the required endpoints of MinKNOW to perform adaptive sampling and writes output compatible with current base-callers and analysis pipelines. Icarust serves nanopore signal simulating a MinION or PromethION flow cell experiment from any reference genome using either R9 or R10 pore models. We show that simulating sequencing runs with Icarust provides a realistic testing and development environment for software exploiting the real-time nature of Nanopore sequencing. Availability and implementation All code is open source and freely available here— https://github.com/LooseLab/Icarust. Icarust is implemented in Rust, with a docker container also available. The data underlying this article will be shared on reasonable request to the corresponding author. [ABSTRACT FROM AUTHOR]

In order to construct an evaluation index system suitable for tourism management classroom teaching, this article evaluates the teaching effectiveness of teachers and improves the teaching quality of tourism management courses. This article is based on developmental evaluation theory, using Analytic Hierarchy Process, Project Response Theory, and CIPP model to construct an indicator system suitable for tourism management classroom teaching. Then, based on the collected data of 5763 students, the reliability and effectiveness of the tool and indicator system were first verified. Then, the variable of teacher teaching style was introduced to construct an OLS regression model for empirical research. The research will summarize teacher and student data collected through the platform and conduct reliability analysis in SPSS 22.0 software, using Cronbach α The credibility of coefficient testing and evaluation tools. Cronbach in Environmental Fundamentals α The cβoefficient value is 0.8350. Cronbach for resource allocation α The coefficient is 0.735, and the Cronbah of the implementation process α Cronb Bach with a coefficient of 0.7 47 for teaching performance α The coefficient is 0.7240, indicating that rat ings has high reliability. Research has found that among the four specific types, the holistic type has the greatest impact on the specific situation, the holistic type has the greatest impact on the environmental foundation and resource allocation, and the legislative type has the greatest impact on the implementation process and teaching performance. [ABSTRACT FROM AUTHOR]

Regression testing is an essential and expensive process in software testing. However, there may be insufficient resources for the execution of all test cases during regression testing. Test case prioritization (TCP) techniques improve the efficiency of regression testing by adjusting the test case execution sequence. Traditional TCP techniques usually rely on the historical execution information of the software under test for more efficient results. String distance‐based TCP (SD‐TCP) avoids these limitations; it uses only the textual difference information of the test cases themselves for prioritization. However, the time overhead on the sorting process of this method is not ideal, and the extreme test case inputs have an impact on the stability of the method. To address these problems, we propose a novel test case prioritization strategy, it first classifies the test cases more finely using the K‐medoids algorithm and then transforms the set into subsequences and improves the early diversity by greedy sorting within clusters. Finally, the test cases are selected through a polling strategy to compose the execution sequence. Extensive experimental results demonstrate that the proposed approach outperforms SD‐TCP in better time efficiency on test case prioritization; it also has a higher average percentage of fault detected (APFD) value than random prioritization (RP) and SD‐TCP. [ABSTRACT FROM AUTHOR]

Test smells are considered bad practices that can reduce the test code quality, thus harming software testing goals and maintenance activities. Prior studies have investigated the diffusion of test smells and their impact on test code maintainability. However, we cannot directly compare the outcomes of the studies as most of them use customized datasets. In response, we introduced the TSSM (Test Smells and Structural Metrics) dataset, containing test smells detected using the JNose Test tool and structural metrics (test code and production code) calculated with the CK metrics tool of 13,703 open‐source Java systems from GitHub. In addition, we perform an empirical study to investigate the relationship between test smells and structural metrics of test code and the relationship between test smells on a large‐scale dataset. We split the projects into three clusters to analyze the distribution of test smells, the co‐occurrences among test smells, and the correlation of test smells and structural metrics of test code. The ratio of smelly test classes with a specific test smell is similar among the clusters, but we could observe a significant difference in the number of test smells among them. The test smells Sleepy Test, Mystery Guest, and Resource Optimism rarely occur in the three clusters, and the last two are strongly correlated, indicating that those test smells are more severe than others. Our results point out that most test smells have a moderate correlation with high complexity, large size, and coupling of the test code, indicating that they can also negatively affect its quality. To support further studies, we made our dataset publicly available. [ABSTRACT FROM AUTHOR]

Locating software faults during debugging is crucial yet challenging. Automated techniques, such as spectrum-based fault localization (SBFL), aid developers in efficiently localizing faults by analyzing program execution information and utilizing statistical approaches to rank program entities according to their suspiciousness. SBFL is also known as lightweight fault localization because of its scalability and minimal computational overhead. While essential, there has been limited research on how test suites affect fault localization. In this paper, we show how test suites impact fault localization and how they can be optimized for better results. SBFL techniques have some inherent limitations, especially in diagnosing faults within loop bodies or iteration statements. Additionally, identical suspiciousness levels can result in ties. While SBFL techniques effectively rank faulty program entities among the Top-N suspicious entities, they might not consistently position the faulty entity within the initial few positions. To address these research gaps, this paper proposes a hybrid approach that combines test suite optimization, statement execution frequency, and fault context concepts to enhance the performance of existing SBFL techniques in single fault scenarios. We evaluate our approach using three popular SBFL methods (Ochiai, Jaccard, and DStar) on Siemens benchmarks and four large real-world programs (flex, grep, sed, and space) with their test suites. The results demonstrate a significant enhancement in fault localization performance when applying our proposed approach to existing SBFL methods. For example, when applied to Ochiai, it reduces examined statements by 62.76% and 65.23% on average for the two test suites, respectively. Furthermore, it identifies 52% of faults by examining only 1% or less of the code and locates 60% of faults by analyzing only 0.1% or less of the code in Siemens and four large real-world programs, respectively. Similar improvements are observed when our approach is applied to Jaccard and DStar methods on the same test suites. We also show that our results are statistically significant, validating that our approach substantially improves the performance of existing SBFL techniques. [ABSTRACT FROM AUTHOR]

To improve the handling and directional stability of three-axle heavy vehicles, this paper suggests a control strategy that combines direct yaw moment control (DYC) and active front steering (AFS). The control system's structure is divided into three main layers. Based on an online adjustable index, a fuzzy controller acting as a supervised system decides the cooperation of DYC and AFS in the upper layer. In the intermediate layer, the DYC system controller uses a sliding mode controller to calculate the corrective body moment. The AFS system uses a fuzzy controller to generate the corrective steering angle necessary to achieve the three-axle vehicle motion objective. The algorithm for distributing braking force and the slip ratio control (SRC) system comprises the lower layer. The anti-lock braking system (ABS) in the SRC system is built to produce the necessary braking forces at low slip ratios while preventing the wheels from locking up at high slip ratios. Consideration has been given to a heavy, three-axle, 9-DOF nonlinear vehicle with uncertain dynamics. Trucksim software and simulation tests have validated the model. The proposed control system's satisfactory performance is shown through various maneuvers. [ABSTRACT FROM AUTHOR]

One of the popular development trends in diesel engine piston assembly is higher reliability and life of components. These can be ensured by actual engine testing and comparing the appearance of piston assembly. However, testing multiple iterations involves significant cost and development which can be avoided by using software for piston-assembly performance parameters simulation. This paper contains a detailed study conducted using commercially available piston secondary dynamics simulation software and actual testing on a 4-cylinder diesel engine. In this study, the effect of piston profile and clearance on piston secondary dynamics parameters and piston-liner appearance are studied. In this study, three piston skirt profiles are designed by varying piston axial profile (barrel shape), radial profile (oval shape), and nominal clearances. The simulation results are studied in which piston profile 2 has shown minimum piston tilting, radial displacement, radial velocity, tilting velocity, radial acceleration, tilting acceleration, pressure, and force at TS and ATS. Testing and component merit rating for liner bores and piston skirts were also conducted in which piston profile 2 has shown significantly less liner bore polishing. A good correlation was observed between piston secondary motion parameters obtained from simulation and piston and liner bore observations after testing. [ABSTRACT FROM AUTHOR]

In this study, we evaluated extreme climate indicators for precipitation and temperature in the Brazilian Legal Amazon (BLA) from 1961 to 2021. Data from 38 National Institute of Meteorology (INMET) weather stations with a record failure rate less than 12% were used, and RClimDex software and the Mann–Kendall test, Pettitt test, standard normal homogeneity test (SNHT), and Buishand test were employed to analyse the data. The results showed increased extreme rainfall events, including the annual total rainfall divided by wet days (SDII). More frequent discontinuities were observed in the 1980s (SNHT) and 1990s (Pettitt and Buishand tests). The extreme temperature climate indicators also significantly increased, particularly in the 1990s. These increases are likely linked to changes in local climate conditions due to agricultural expansion, intensified industrial activities, and regional urbanization. This study underscores the need for proactive measures to curb illegal deforestation and reduce CO2 emissions and further research to better understand and mitigate the adverse impacts of these changes on the BLA environment and local communities. [ABSTRACT FROM AUTHOR]

PPP_Mansoura is a new software that can process multi-GNSS data work on MATLAB environment and linked with C# in the preprocessing stage. It gives highly accurate results and provides a results file for each epoch, and the users can choose the GNSS system they want to run with the primary systems (GPS or GLONASS) and all this with simple MATLAB Code. For testing the software, we processed the raw data (RINEX 3) from 17 MGEX stations for 24 h data during 1-week with a 30-s interval time and submitted it to the new software and PPPH software. The averaged positioning errors obtained from PPP_Mansoura and PPPH were 5.14 mm and 6.9 mm respectively, for the East direction, 11.6 mm and 14 mm for the North direction, and 14.56 mm and 20.4 mm respectively for the Up direction, the averaged convergence time obtained from PPP_Mansoura and PPPH were 35.3 min and 54.47 min, so the results show that PPP_Mansoura give results with high accuracy can be comparable with PPP standards results and PPP software results. [ABSTRACT FROM AUTHOR]

Fuzzing is an important technique in software and security testing that involves continuously generating a large number of test cases against target programs to discover unexpected behaviors such as bugs, crashes, and vulnerabilities. Recently, fuzzing has advanced considerably owing to the emergence of new methods and corresponding tools. However, it still suffers from low coverage, ineffective detection of specific vulnerabilities, and difficulty in deploying complex applications. Therefore, to comprehensively survey the development of fuzzing techniques and analyze their advantages and existing challenges, this paper provides a comprehensive survey of the development of fuzzing techniques, summarizes the main research issues, and provides a categorized overview of the latest research advances and applications. The paper first introduces the background and related work on fuzzing. Research issues are subsequently addressed and summarized, along with the latest research developments. Furthermore, various customized fuzzing techniques in different applications are presented. Finally, the paper discusses future research directions. [ABSTRACT FROM AUTHOR]

Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers' common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors' knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code's execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users. [ABSTRACT FROM AUTHOR]

According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills. [ABSTRACT FROM AUTHOR]

We present the Python Red Pitaya Lockbox (PyRPL), an open source software package that allows the implementation of automatic digital feedback controllers for quantum optics experiments on commercially available, affordable Field-Programmable Gate Array (FPGA) boards. Our software implements the digital generation of various types of error signals, from an analog input through the application of loop filters of high complexity and real-time gain adjustment for multiple analog output signals, including different algorithms for resonance search, lock acquisition sequences, and in-loop gain optimization. Furthermore, all necessary diagnostic instruments, such as an oscilloscope, a network analyzer, and a spectrum analyzer, are integrated into our software. Apart from providing a quickly scalable, automatic feedback controller, the lock performance that can be achieved by using PyRPL with imperfect equipment, such as piezoelectric transducers and noisy amplifiers, is better than the one achievable with standard analog controllers due to the higher complexity of implementable filters and possibilities of nonlinear operations in the FPGA. This drastically reduces the cost of added complexity when introducing additional feedback loops to an experiment. The open-source character also distinguishes PyRPL from commercial solutions, as it allows users to customize functionalities at various levels, ranging from the easy integration of PyRPL-based feedback controllers into existing setups to the modification of the FPGA functionality. A community of developers provides fast and efficient implementation and testing of software modifications. [ABSTRACT FROM AUTHOR]

Light pollution significantly interferes with animal and human life and should, therefore, be included in the factors that threaten ecosystems. The main aim of this research is to develop a methodology for monitoring environmental and social elements subjected to light pollution in anthropogenic areas. This research is based on yearly and monthly photographs acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite; land cover data from the CORINE Land Cover (CLC) program; and environmental data from the European Environment Agency (EEA) and the World Database on Protected Areas (WDPA). The processing of input data for further analyses, the testing of the methodology and the interpretation of the final results were performed in GIS-type software (ArcGIS Pro). Light pollution in the investigated area was analyzed with the use of maps generated for the years 2014 and 2019. The environmental and social elements were spatially identified in five light pollution classes. The research results demonstrate that the proposed methodology allows for the identification of environmental and social elements that emit light, as well as those that are subjected to light pollution. The methodology used in this work allows us to observe changes resulting from light pollution (decreasing or increasing the intensity). Owing to the use of publicly available data, the methodology can be applied to light pollution monitoring as part of spatial planning in anthropogenic areas. The proposed methodology makes it possible to cover the area exposed to light pollution and to observe (almost online) the environmental and social changes resulting from reductions in light emitted by anthropogenic areas. [ABSTRACT FROM AUTHOR]

The surface reflection pressure sensor installation flatness will directly lead to the change of the explosion shock wave pressure propagation and distribution law, affecting the test results accuracy, and the test data cannot accurately evaluate the ammunition explosion damage power. In this study, the numerical simulation model of the explosion shock wave pressure propagation and distribution law was established by using the explosive mechanics simulation software, and the pressure distribution law was studied when the sensors installation angles were 0°, 4°, 8°, 12°, −4°, −8°, and −12° respectively. Combined with analysis of the pressure peak value and the pressure evolution nephogram at different measuring points, it is clarified that the positive tilt angle of the sensor installation has an enhancing effect on the pressure peak, while the negative tilt angle has a attenuation effect on the pressure peak. Based on the calculation function formula of the surface reflected pressure peak value in the national defense engineering design code, the surface reflected pressure peak value correction function formula is established by introducing the sensor installation angle correction effect. This study results provide a theoretical basis for the design of ammunition explosion shock wave pressure engineering test scheme and the test data validity verification. [ABSTRACT FROM AUTHOR]

In this note, we evaluated the type I error control of the commonly used t-test found in most statistical software packages for testing the hypothesis on $ H_0: \rho = 0 $ H 0 : ρ = 0 vs. $ H_1: \rho > 0 $ H 1 : ρ > 0 based on the sample weighted Pearson correlation coefficient. We found the type I error rate is severely inflated in general cases, even under bivariate normality. To address this issue, we derived the large sample variance of the weighted Pearson correlation. Based on this result, we proposed an asymptotic test and a set of studentized permutation tests. A comprehensive set of simulation studies with a range of sample sizes and a variety of underlying distributions were conducted. The studentized permutation test based on Fisher's Z statistic was shown to robustly control the type I error even in the small sample and non-normality settings. The method was demonstrated with an example data of country-level preterm birth rates. [ABSTRACT FROM AUTHOR]

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. [ABSTRACT FROM AUTHOR]

Mutation testing can evaluate the quality of the test inputs, generate test data, and simulate any test coverage criterion. Genetic algorithms and harmony search have been applied to reduce the cost of generating test inputs. Although hybridizing search algorithms enhances the efficiency of searching the solution domain, there is a shortage of applying the hybrid search techniques in mutation testing. This paper merges the genetic and harmony search algorithms to effectively generate test data to kill higher-order mutants. In addition, the performance of the proposed technique will be evaluated and compared with a stand-alone genetic algorithm and a stand-alone harmony search algorithm through an empirical study using a set of benchmark programs. The experimental study shows that the proposed technique outperformed the compared algorithms, reaching a higher killing ratio, where the proposed approach kills 92.8% of higher-order mutants for all tested programs. In comparison, GA kills 88.7%, and HA kills 86.6%. Besides, the proposed algorithm overcame the compared algorithm in reaching a targeted killing ratio faster than the compared algorithms. HGA reduced the execution time for each program with a reduction ratio ranging from 58.9% to 89.8%. [ABSTRACT FROM AUTHOR]

Thermal energy storage (TES) systems have emerged as a vital solution for addressing the gap between energy supply and demand. While research on solar energy storage has primarily focused on flat‐plate collectors, limited work has been done to explore the potential of Scheffler solar concentrators. This study proposes an innovative approach to thermal energy storage using a Scheffler solar concentrator and phase change material (PCM) to address the energy needs of rural areas with limited access to electricity. Real‐time design and testing were conducted using paraffin wax as the PCM, and computational fluid dynamics (CFD) analysis was performed using the ANSYS Fluent software to determine the optimal charging and discharging times. The results indicated that the required charging time was 17 min according to the CFD analysis, while the experimental results yielded a charging time of 18 min. The results demonstrated that the proposed approach is a feasible and effective solution for meeting the energy demands of rural communities. The charging time was optimized using the response surface method, yielding an optimal charging time of 20.5 min and an optimal discharging time of 26.2 min. Overall, these findings highlight the potential of utilizing Scheffler solar concentrators and PCMs for sustainable and reliable thermal energy storage. [ABSTRACT FROM AUTHOR]

Chaotic maps have been widely studied in the field of cryptography for their complex dynamics. However, chaos-based cryptosystems have not been widely used in practice. One important reason is that the following requirements of practical engineering applications are not taken into account: computational complexity and difficulty of hardware implementation. In this paper, based on the demand for information security applications, we modify the local structure of the three-dimensional Intertwining Logistic chaotic map to improve the efficiency of software calculation and reduce the cost of hardware implementation while maintaining the complex dynamic behavior of the original map. To achieve the goal by reducing the number of floating point operations, we design a mechanism that can be decomposed into two processes. One process is that the input parameters value of the original system is fixed to 2 k by Scale index analysis. The other process is that the transcendental function of the original system is replaced by a nonlinear polynomial. We named the new map as "Simple intertwining logistic". The basic chaotic dynamic behavior of the new system for controlling parameter is qualitatively analyzed by bifurcation diagram and Lyapunov exponent; the non-periodicity of the sequence generated by the new system is quantitatively evaluated by using Scale index technique based on continuous wavelet change. Fuzzy entropy (FuzzyEn) is used to evaluate the randomness of the new system in different finite precision digital systems. The analysis and evaluation results show that the optimized map could achieve the designed target. Then, a novel scheme for generating pseudo-random numbers is proposed based on new map. To ensure its usability in cryptographic applications, a series of analysis are carried out. They mainly include key space analysis, recurrence plots analysis, correlation analysis, information entropy, statistical complexity measure, and performance speed. The statistical properties of the proposed pseudo random number generator (PRNG) are tested with NIST SP800-22 and DIEHARD. The obtained results of analyzing and statistical software testing shows that, the proposed PRNG passed all these tests and have good randomness. In particular, the speed of generating random numbers is extremely rapid compared with existing chaotic PRNGs. Compared to the original chaotic map (using the same scheme of random number generation), the speed is increased by 1.5 times. Thus, the proposed PRNG can be used in the information security. [ABSTRACT FROM AUTHOR]

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate. [ABSTRACT FROM AUTHOR]

Over the last few years, a dynamic increase in the installed capacity of distributed energy sources has been observed, with the largest share being photovoltaic sources. The power grid is a system of connected vessels, and changing the structure of electricity production has a specific impact on the operation of this network, which makes it necessary to study the impact of the sources on the power system. The current and projected increase in the number of connected installations will make the issues of interaction and cooperation of distributed sources with the network extremely important. The article presents an analysis of the impact of a photovoltaic farm on selected parameters of the quality of electricity supply. This analysis was made on the basis of simulation results in a computer program and measurement tests carried out on a real photovoltaic farm with a capacity of 1.8 MW connected to the medium voltage power grid. The impact of the farm-generated power on the values of fundamental indicators of the quality of electricity supply, such as voltage deviations, voltage asymmetry factors, and voltage distortions factors, is presented. These relationships were determined based on the correlation and regression analysis of individual electrical quantities. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

This paper starts with an overview of the main principles used for pressure measurements, focusing on their usage in industrial applications' domains. Then, the importance of calibration procedures, namely, static and dynamic calibration of pressure sensors, is analyzed. Regarding calibration, it is important to note that there are several applications where the pressure signals to be measured can have large variations in short periods of time. In industrial applications, particularly in continuous production processes, generally, dynamic pressure measurements are less common; however, they are still required in several cases, such as control loops that are very sensitive to pressure variations, even if the frequencies of those variations are in the range of a few tens of hertz, or even lower. The last part of the paper presents the hardware and software of a flexible and low-cost static and dynamic pressure calibrator that also presents the capability to generate arbitrary waveform pressure signals for calibration and testing purposes. The proposed calibrator also includes the following advantages: remote pressure sensing capabilities that can be used to minimize calibration errors, such as those associated with capillary effects and pressure leakages; portability; and low cost. The paper ends with some experimental results obtained with the proposed calibrator. [ABSTRACT FROM AUTHOR]

The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program's branch coverage. The proposed method was implemented along with the previous one. The experiments' results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

Background: Overweight and obesity have become serious health problems worldwide, with more than 1.5 billion adults classified as overweight according to the World Health Organization in 2020. Getting overweight after the gestational period is a common phenomenon and getting back to prepregnancy weight is a major challenging concern for mothers. This study aimed to explain the importance of maintaining body mass index (BMI) and evaluate the association of BMI with dietary intake and self-care health behaviors among mothers after the postpartum period. Methods: A mixed-method study was conducted among mothers after the postpartum period having children aged 2 to 10 years old. The current study was performed on a total of 17 mothers who were referred to comprehensive health service centers in Chennai city, Tamil Nadu, India. They were purposefully selected according to the inclusion criteria. Data were collected through face-to-face interviews and analyzed using SPSS software for testing the significance of the P value. Results: The majority of mothers (88.23%) were in the category of overweight and obese. The mean age of mothers was 26.53 ± 5.21 years. BMI was strongly associated with the dietary intake of mothers, hindrance in dietary intake of mothers, and self-care activities of mothers since the P-value was <0.01 level of significance. Conclusion: This study reveals that the BMI of mothers has increased compared to prepregnancy. The adherence to the balanced dietary and self-care activity was very low. It is critically important to pay more attention to how individual, organizational, and societal interventions might assist mothers in attaining the target level of BMI. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present assessment of models and simulation results used in the development process of flight stabilisation system that uses trim tabs for PZL-130 Orlik turboprop military trainer aircraft. Flight test of the system allowed to compare software and hardware simulation results with real flight recordings. Design/methodology/approach: Proposed flight stabilisation system was developed using modern techniques of model-based design, automatic code generation, software and hardware in the loop testing. The project reached flight testing stage which allowed to gather data to verify models and simulation results and asses their quality. Findings: Results of the comparison showed that the trim tab actuator model used in simulation can be improved by adding play. This reduced the difference between simulation and real flight system output – actuator angle. The influence of airloads on the flying actuator angle compared to hardware in the loop simulation in lab is less than ± 0.6°. Originality/value: Proposed flight stabilisation system that uses trim tabs has several benefits over classic automatic flight system in terms of weight, energy consumption and structure simplicity and does not need aircraft primary control modification. It was developed using modern techniques of model-based design, automatic code generation and hardware in the loop simulations. [ABSTRACT FROM AUTHOR]

With the constant growth of software-defined radio (SDR) technologies in fields related to wireless communications, the need for efficient ways of testing and evaluating the physical-layer (PHY) protocols developed for these technologies in real-life traffic scenarios has become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow GNU radio applications to be fed with test traffic in a simple way and through an interoperable interface. This makes the use of any traffic generator possible—existing ones or one that is custom-built—to evaluate a GNU radio application. In addition, this paper proposes an efficient way to collect PHY-specific monitoring data to improve the performance of the critical components of the message delivery path by employing the protocol buffers library. This study considers the entire testing and evaluation ecosystem and demonstrates how PHY-specific monitoring information is collected, handled, stored, and processed as time series to allow complex visualization and real-time monitoring. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet technology has brought about the flourishing growth of social media live streaming. This study employs social presence theory as the primary framework to investigate the impact of Facebook Live's features of sociality, immediacy, and entertainment on users' sense of presence. These features were then considered within the dimensions of awareness, emotion, and cognition. The influence of social presence on user engagement behaviors was divided into browsing behavior, interactive behavior, and creative behavior. Using snowball sampling, an online survey was administered to Facebook Live users, and 416 valid responses were collected. The research team used software to analyze the data, primarily encompassing descriptive statistics, reliability and validity analyses, structural equation modeling, and mediation effects testing. The research findings are as follows. First, the media characteristics of Facebook Live significantly influence the sense of presence. Specifically, sociality, immediacy, and entertainment on Facebook Live have a notable impact on users' awareness, emotion, and cognition. Second, different dimensions of social presence have distinct effects on various user engagement behaviors. Notably, the dimensions of awareness, emotion, and cognition of social presence positively affect users' browsing and interactive behaviors, while emotion influences users' creative behavior. The third finding was that awareness, emotion, and cognition act as intermediates between Facebook Live's media characteristics and user engagement behaviors. Implications are discussed. [ABSTRACT FROM AUTHOR]

Software testing is an efficient way to guarantee the reliability and accuracy of parallel programs. Communication plays a substantial role in parallel program testing. The huge scale of communication within parallel programs poses a great challenge to coverage testing. How to reasonably select the communication to be tested and effectively generate test data to meet the requirements of coverage is a pressing problem. In this paper, a critical communication testing method based on branch transformation is proposed, which can reasonably select part of program communication and generate test data. To be specific, firstly, this paper analyzes the features which easily lead to defects in communication, and then proposes the evaluation method of critical communication based on the above features. Next, we select the critical communication and convert the communication coverage to the coverage of the branch where the communication statement resides. Finally, a method based on genetic algorithm is used to solve the communication coverage problem in different scenarios. The proposed method is applied to the testing of several representative parallel programs. Experimental results show that compared with other methods, this proposed method can effectively reduce the quantity of communication, significantly improve the coverage rate of communication coverage testing and increase the efficiency of test data generation. [ABSTRACT FROM AUTHOR]