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Abstractions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance toward requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form of pairs for better testability, where the "key" helps locate "what to test", and the "value" helps guide "how to test it" by feeding in concrete data. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by a state-of-the-art technique. While the initial findings indicate our abstractions' capabilities of revealing bugs and matching the environmental assumptions created manually, we articulate a new way to perform requirements-based testing by focusing on a software system's changing features. Specifically, we hypothesize that the same feature would behave differently under a pair of opposing environmental conditions and assess our abstractions' applicability to this new form of feature testing. [ABSTRACT FROM AUTHOR]

Particle flow code (PFC) software has been widely used as the general discrete-element modeling (DEM), due to the excellent performance to deal with continuous and discontinuous media. Among them, the mesoscopic parameters can only be acquired to repeatedly debug the experimental data using trial-and-error method, leading to the low efficiency with the high blindness. A set of usable parameters can be inevitable in the dozens of trial and error during calibration, even though the sound experience of experts. Therefore, it is highly urgent to accurately and rapidly calibrate the mesoscopic parameters for the promotion of PFC software and the follow-up test, particularly beyond the manual operation. In this study, the uniaxial creep test model of corn stalk particles was established to combine with the built-in Burgers model of the PFC 2D. An orthogonal experiment was also carried out to verify the improved model. The multivariate analysis of variance was then made to analyze the complex relationship between the macroscopic and mesoscopic parameters of the Burgers model. There was a quite difference in the significance of the influence of each mesoscopic parameter on the macroscopic one. A highly nonlinear relationship was also found between the macroscopic and mesoscopic parameters. Therefore, the regression analysis was inappropriate to obtain the relationship between the macroscopic and mesoscopic parameters for the calibration of the mesoscopic parameters. Fortunately, BP neural network can be expected to serve as these complex relationships, just suitable for the parameter calibration. As such, the BP neural network was established with the 4, 9 and 5 nodes in the input, hidden, and output layer, respectively, according to the number and characteristics of macroscopic and mesoscopic parameters. Then, the resulting BP neural network was trained and calibrated using 150 sets of macroscopic and mesoscopic parameters. It was found that above 92% was achieved in the calibration accuracy of all mesoscopic parameters in the Burgers model, especially with the relatively stable errors. Moreover, the correlation coefficient (R) was greater than 0.96 in the trained BP neural network, indicating the more reliable performance of inversion. The improved calibration of parameters can also be popularized for the mesoscopic parameters. Furthermore, the macroscopic parameters after the uniaxial creep test of corn stalk were introduced into the trained BP neural network for the calibration of the mesoscopic parameters. A better consistence was found in the simulated and measured creep curves with the maximum error of the dependent variable of 2%, indicating the excellent calibration ability of parameters. The finding can also provide a strong reference for the PFC parameter calibration. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid test scheme based on interleaved test applications to increase the efficiency of multisite testing of integrated circuits (ICs) by solving problems related to test coverage, which is an important feature in automotive IC, and test costs, including the number of test pins and the volume of test data. To solve the problems, a hybrid test scheme that combines the merits of broadcast-based scan compression and built-in self-test (BIST) is proposed. The proposed scheme not only generates deterministic test patterns, similar to scan compression, but also requires fewer test pins, similar to BIST, thereby increasing the efficiency of multisite testing. With appropriately designed linear-feedback shift registers (LFSRs) and seeds periodically inserted through the reduced test pins, the test application process combines deterministic testing and random testing. The random testing stage can compensate the seed-insertion time for the subsequent deterministic testing stage and reduce the number of required deterministic patterns by detecting easy-to-detect faults. For further reduction of the volume of test data and hardware overhead, some compatible scan chains (SCs) are grouped and receive test data from an LFSR. The experimental results show that the proposed method increases the efficiency of multisite testing with a reduced pin count. [ABSTRACT FROM AUTHOR]

The nine papers in this special section focus on new developments in agile software and reports on applications for its use. A key aspect for the extensive use of agile software is that it supports developers with coping with the growing uncertainty and turbulence in technological and market environments. Feedback and change are at the core of Agile for a dynamic, evolving, and organic, rather than static, predefined, and mechanistic development process advocated by waterfall management. To create timely, high-quality, cost-efficient, and innovative solutions, Agile developers organized in small, colocated, autonomous teams, build and test software in rapid iterative cycles, actively involving users to gather feedback, updating the project scope, and plan "on-the-fly," using face-to-face communication as opposed to documentation. These papers contribute to the state-of-the-art of agile research by offering a rich, up-todate account of the dynamics occurring when expanding Agile into "not-just-software" contexts of the key challenges and perils related to the scaling and of the possible solutions to them. [ABSTRACT FROM AUTHOR]

Based on a popPK model of meropenem for critically ill adult patients using probability target attainment (PTA), prolonged infusion or a high-dosage regimen of meropenem is proposed, particularly when treating critically ill patients with increased renal clearance or those infected with pathogens with decreased in vitro susceptibility. One of the manuscripts focuses on building a popPK model to establish MIPD algorithm to optimize the dosing of linezolid in patients with multidrug and extensively drug-resistant tuberculosis and propose three sampling occasions to derive an individualized dose that results in effective and safe dosage regimens [[1]]. Population pharmacokinetics (popPK) and dose optimization in different types of patients constitutes a useful working tool within the framework of the MIPD approach. Model-informed precision dosing (MIPD) is an advanced quantitative approach focusing on individualized dosage optimization, integrating complex mathematical and statistical models of drugs and disease combined with individual demographic and clinical patient characteristics. [Extracted from the article]

Cloud testing is gaining much attention in both academia and industry as an emerging concept in the field of software testing. Cloud testing implies leveraging the resources of the cloud computing environment to overcome deficiencies of the traditional testing approaches. As a result, testing‐as‐a‐service (TaaS) is introduced as a service model that conducts all testing activities in a fully automated manner using cloud‐based resources. Performance testing is a type of software testing that validates the performance characteristics of the application under test (AUT) when subjected to different workloads during its operation. Performance characteristics include throughput, response time, and resource utilization of the AUT under a certain workload. This paper focuses on reviewing the literature related to the provision of performance testing as a service (P‐TaaS). In this study, we survey the previous work related to cloud‐based performance testing. We show the strengths and weaknesses of the current research. Besides, we compare the P‐TaaS with the traditional performance testing methodologies. A detailed discussion of the benefits and challenges of P‐TaaS is introduced along with identifying the research gaps and the future directions that can be adopted. [ABSTRACT FROM AUTHOR]

Background: Multiple processes impact the probability of retention of individual genes following whole genome duplication (WGD) events. In analyzing two consecutive whole genome duplication events that occurred in the lineage leading to Atlantic salmon, a new phylogenetic statistical analysis was developed to examine the contingency of retention in one event based upon retention in a previous event. This analysis is intended to evaluate mechanisms of duplicate gene retention and to provide software to generate the test statistic for any genome with pairs of WGDs in its history. Results: Here a software package written in Python, 'WGDTree' for the analysis of duplicate gene retention following whole genome duplication events is presented. Using gene tree-species tree reconciliation to label gene duplicate nodes and differentiate between WGD and SSD duplicates, the tool calculates a statistic based upon the conditional probability of a gene duplicate being retained after a second whole genome duplication dependent upon the retention status after the first event. The package also contains methods for the simulation of gene trees with WGD events. After running simulations, the accuracy of the placement of events has been determined to be high. The conditional probability statistic has been calculated for Phalaenopsis equestris on a monocot species tree with a pair of consecutive WGD events on its lineage, showing the applicability of the method. Conclusions: A new software tool has been created for the analysis of duplicate genes in examination of retention mechanisms. The software tool has been made available on the Python package index and the source code can be found on GitHub here: https://github.com/cnickh/wgdtree. [ABSTRACT FROM AUTHOR]

Regression testing is essential for continuous integration and continuous development. It is needed to ensure that the modifications have not produced any errors or faults, thereby maintaining the quality and reliability of the software. The testers usually avoid exhaustive retesting because it requires lots of effort and time. The test case prioritization and minimization solve the issue by scheduling the critical test cases and removing redundant ones. Optimization techniques help by improving the efficiency of these techniques while utilizing limited resources. This paper proposed an enhanced discrete novel bat algorithm for the test case prioritization. The algorithm is modified in two ways. First, we have proposed a fix-up mechanism for the discrete combinatorial problem, which conducts the perturbation in the population using the asexual reproduction algorithm. Second, the novel bat algorithm is improved, where the bats hunt in different habitats with quantum behavior using Gaussian distribution and search in the limited habitat with Doppler effect. In addition, we have embedded the test case minimization procedure in the algorithm for redundancy reduction. The experimental results are empirically analyzed using different testing criteria, i.e., fault and statement coverage on three subject programs from the software infrastructure repository. Consequently, test selection percentage, coverage loss, fault detection loss, and cost reduction percentages are deduced for the test case minimization at program and version levels. Empirical results and statistical comparisons with the random search, bat algorithm, novel bat algorithm, birds swarm algorithm, whale optimization algorithm, and genetic algorithm show the outperformance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Considering the characteristic of the outer boundary of the two-region composite reservoir has elasticity, firstly, a new two-region composite reservoir seepage model which considers the wellbore storage and effective well radius is established to describe the seepage laws of the two-region composite reservoir more accurately. Then, by combining the Laplace transform and the similar construction method, the solution of dimensionless bottom hole pressure is obtained in the Laplace space. Meanwhile, the solution is also inverted into real space by means of the Stehfest numerical inversion method. Finally, the characteristic curves of dimensionless bottom hole pressure and its derivative are drawn, and the influence of elastic coefficient and reservoir parameters on the characteristic curves is also analyzed. The results reveal that the characteristic curves have affine similarities, and the characteristic curves under the elastic outer boundary condition are always between the closed outer boundary condition and constant pressure outer boundary condition. It can be seen that the conventional three idealized outer boundary conditions (closed, constant pressure and infinite) of two-region composite reservoirs are actually three special cases of elastic outer boundary condition. The introduction of elastic outer boundary condition not only expands the range of data interpretation reasonably, but also shows the actual state of the outer boundary of the two-region composite reservoir better. The similar construction method can avoid complicated and trivial operation processes, and greatly accelerate the model solving speed. The findings of this study can help for better understanding of the real situation of the outer boundary of the two-region composite reservoir, which improves the matching degree between the theoretical model and the actual reservoir, and provides convenience for the development of related well test analysis software. [ABSTRACT FROM AUTHOR]

Purpose: The corrosion problem of the tank bottom plate is growing more and more obvious as the usage period of the tank increases. The purpose of this study is to explore the specific effects of various factors on the potential distribution of the tank bottom plate and propose reasonable cathodic protection measures for tanks through simulation and indoor tests. Design/methodology/approach: In this paper, several aspects (such as anode conditions, soil resistivity and so on) impacting the potential distribution of the tank bottom plate are explored by means of indoor tests and MATLAB software simulations, and the related results are presented using Origin software plots. Findings: The results show that the potential value of the tank bottom plate is positively shifted with the increase of anode well depth and distance and the decrease of output current, and the overall potential distribution uniformity is higher; the anodic well output current has the greatest influence on potential distribution; to set up regional cathodic protection in the multitank area, the anodic well should be arranged in the central position between multiple tanks. Regional cathodic protection potential distribution for multiple anodes is more uniform, but a reasonable number of anodes should be selected, usually 2–3 anodes. Originality/value: This paper provides solid theoretical and technical support for the future establishment of cathodic protection systems in station yards, as well as the renewal and transformation of cathodic protection systems in old tanks, by investigating the influencing factors on the potential distribution of tank bottom plate and verifying them through indoor experiments. [ABSTRACT FROM AUTHOR]

Spectrum-based fault localization (SBFL) is an automated fault localization technique that uses risk evaluation metrics to compute the suspiciousness scores from program spectra. Thus, risk evaluation metrics determine the technique's performance. However, the existing experimental studies still show no optimal metric for different program structures and error types. It is possible to further optimize SBFL's performance by combining different metrics. Therefore, this paper effectively explores the combination of risk evaluation metrics for precise fault localization. Based on extensive experiments using 92 faults from SIR and 357 faults from Defects4J repositories, we highlight what and which risk evaluation metrics to combine to maximize the efficiency and accuracy of fault localization. The experimental results show that combining risk evaluation metrics with high negative correlation values can improve fault localization effectiveness. Similarly, even though the combination of positively correlated effective risk evaluation metrics can outperform most negatively correlated non-effective ones, it still cannot improve the fault localization effectiveness. Furthermore, low-correlated risk evaluation metrics should also be considered for fault localization. The study concluded that getting highly negatively correlated risk evaluation metrics is almost impossible. The combination of such risk evaluation metrics would improve fault localization accuracy. [ABSTRACT FROM AUTHOR]

This article presents a hierarchical map of analyses subsumed by canonical correlation and a shiny application to facilitate the connections between said analyses. Building on the work of other researchers who used canonical correlation analyses to unify analyses in the general linear model, we demonstrate that the hierarchy is not as flat as some have portrayed. While a simpler hierarchy may seem to be more accessible, it implies a lack of relationship between analyses that may cause confusion when learning the vast majority of univariate and multivariate analyses in the general linear model. Because it is not always intuitive how all the relevant analyses for a given data type can be conducted, we developed the Shiny application canCORRgam to demonstrate the hierarchical path of analyses subsumed by canonical correlation for 15 different models. The canCORRgam application provides emerging researchers evidence of the transitive properties implied in the map. Our work also promotes meta-analytic thinking and practice as we provide the tools, formula, and software to relate test statistics to effect sizes in addition to transforming relevant test statistics and effect sizes to equivalent test statistics and effect sizes. [ABSTRACT FROM AUTHOR]

For maintaining the safe operation of structures, it is necessary to develop SHM methods that can detect not only the presence of cracks in the structure but also any alterations of its fastening conditions. The current paper presents a method for developing an Artificial Intelligent model that can detect if a beam is affected by transverse cracks and at the same time, by improper boundary conditions. To this aim, a cantilever steel beam is considered as the in the current study. The training data for the artificial neural network (ANN) is created using an original analytic method which allows calculating the natural frequency loss caused by the occurrence of transverse cracks even if the beam is improperly fastened. The intelligent model is trained by employing the MATLAB software and tested using data acquired from numerical simulations. The results show very high accuracy in determining the presence of transverse cracks, and the capability of detecting the presence and severity of improper clamping conditions. [ABSTRACT FROM AUTHOR]

This work describes the experience in developing and testing software for oil industry automation control systems based on the simulation of technological processes and control systems combined in virtual reality, this approach is called virtual commissioning and is widely used in the world both to create automated process control systems and to simulate interactions between different systems. [ABSTRACT FROM AUTHOR]

As the number of cyber‐attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber‐security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network‐based cyber‐attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber‐attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber‐crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures. [ABSTRACT FROM AUTHOR]

Model transformation testing has become crucial as model‐driven engineering has raised the abstraction level for developing software systems. Transformation is written to transform models from one level of abstraction to another, for example, model to model or model to code. A major challenge in testing the transformation is the creation of test models, such that (i) they conform to the source meta‐model (i.e., multiplicities and Object Constraint Language [OCL] constraints on meta‐model) and (ii) they provide coverage of the complete transformation (solving branch conditions for traversing all paths). Manual creation of test models requires a lot of time and effort. Still, the validity of the developed test models cannot be ensured. This paper aims to solve the above challenges using an automated search‐based strategy. The proposed approach is two‐stepped. First, valid test models are generated by solving source meta‐model constraints. Second, the generated models are evolved for achieving the structural coverage of the transformation by solving the branch conditions. A toolset model transformation testing environment (MOTTER) is developed to automate the search‐based solution. The proposed work is empirically evaluated on two case studies using four search algorithms. The result reflects that it successfully generates valid test models for achieving desired structural coverage with high performance on both the case studies. [ABSTRACT FROM AUTHOR]

Mutation testing is a technique that changes code instructions to assess the quality of automated software tests. Industry has not broadly adopted the technique because execution and analysis times are too long and not considered worth the effort. To change this, a variation called "extreme mutation testing" emerged, which mutates whole methods instead of instructions. The extreme variant trades accuracy for speed gains and also provides pre‐analyzed results. In this study, we aim to analyze both techniques on their granularity levels, look for benefits when combining them, and find motivations when a developer considers killing mutants. For that, we conducted a case study in a company from the semiconductor industry. We mutated a large Java software project which is tested by more than 11,000 unit tests, analyzed the results, manually inspected more than 1000 mutants, and conducted a focus group with five developers of the software. Among other results, we provide the distribution of traditional across extreme mutants as well as qualitative coding results of our mutant inspection and focus group transcript. We conclude that the traditional approach can be similarly strategically applied as the extreme one and that motivations of developers to target mutants are mostly not code related. [ABSTRACT FROM AUTHOR]

DevOps has become an important set of organisational and technical practices in software development under the agile umbrella. Many efforts are still being made in this field, mainly focused on the inclusion of metrics for monitoring progress. Gathering metrics is a difficult task, but it can provide insight into the performance of the software delivery process. The current status of the definition, application and implementation of metrics in DevOps projects and processes is of interest to software practitioners. Thus, the objective of this article is to analyze documents regarding the impact of metrics in DevOps projects. 103 documents were obtained from the Scopus database to analyze them through the bibliometric method considering several aspects. The bibliometric analysis performed included author analysis, author affiliation, authors’ countries, keyword analysis, citation analysis, and network analysis. The results indicate that DevOps research is not centralized in a specific group of researchers. Moreover, the most significant contributions of DevOps are related to continuous integration, software design, and software testing. The bibliometric analysis presented in this article helps to identify the current state of the DevOps literature and provides an insightful discussion of future research trends. [ABSTRACT FROM AUTHOR]

The software engineers retain the test cases they create for specific software for future usage. This form of test case reuse is called regression testing and this step mainly improves the software testing efficiency. The test case minimization and prioritization for regression testing raise different issues such as higher time consumption and heavier resource utilization. To overcome this problem, this paper presents a Side-blotched lizard optimized AdaBoost Convolutional Neural Network (SBLA-AdaBoost CNN) model. The proposed technique mainly aims to discover the faults initially and minimize the test case execution cost. The proposed model is evaluated using the Defects4J dataset. Our proposed method tends to be cost-effective since it integrates test case selection, prioritization, and minimization. The proposed methodology can be also utilized to arrange the test cases during their initial stages of software testing. The results demonstrate that the proposed methodology is efficient in identifying the changes in different parts of the source code, minimizing resource utilization, and time consumption. The precision and recall score obtained by the proposed methodology is 98.5% and 99% which is relatively higher than the state-of-art techniques. The time taken by the proposed methodology to evaluate a total of 50 test cases is 19.14 s. [ABSTRACT FROM AUTHOR]

Background: States, territories, non-profits, and tribes are eligible to obtain federal funding to implement federally endorsed evidence-based home visiting programs. This represents a massive success in translational science, with $400 million a year allocated to these implementation efforts. This legislation also requires that 3% of this annual funding be allocated to tribal entities implementing home visiting in their communities. However, implementing stakeholders face challenges with selecting which program is best for their desired outcomes and context. Moreover, recent reviews have indicated that when implemented in practice and delivered at scale, many evidence-based home visiting programs fail to replicate the retention rates and effects achieved during clinical trials. To inform program implementers and better identify the active ingredients in home visiting programs that drive significant impacts, we aimed to develop an expert derived consensus taxonomy on the elements used in home visiting practice that are essential to priority outcome domains. Methods: We convened a panel of 16 experts representing researchers, model representatives, and program implementers using a Delphi approach. We first elicited standard practice elements (SPEs) using open-ended inquiry, then compared these elements to behavior change techniques (BCTs) given their general importance in the field of home visiting; and finally rated their importance to 10 outcome domains. Results: Our process identified 48 SPEs derived from the panel, with 83 additional BCTs added based on the literature. Six SPEs, mostly related to home visitor characteristics and skills, were rated essential across all outcome domains. Fifty-three of the 83 BCTs were rated unnecessary across all outcome domains. Conclusions: This work represents the first step in a consensus-grounded taxonomy of techniques and strategies necessary for home visiting programs and provides a framework for future hypothesis testing and replication studies. [ABSTRACT FROM AUTHOR]

Background: The low number of patients suffering from any given rare diseases poses a difficult problem for medical research: With the exception of some specialized biobanks and disease registries, potential study participants' information are disjoint and distributed over many medical institutions. Whenever some of those facilities are in close proximity, a significant overlap of patients can reasonably be expected, further complicating statistical study feasibility assessments and data gathering. Due to the sensitive nature of medical records and identifying data, data transfer and joint computations are often forbidden by law or associated with prohibitive amounts of effort. To alleviate this problem and to support rare disease research, we developed the Mainzelliste Secure EpiLinker (MainSEL) record linkage framework, a secure Multi-Party Computation based application using trusted-third-party-less cryptographic protocols to perform privacy-preserving record linkage with high security guarantees. In this work, we extend MainSEL to allow the record linkage based calculation of the number of common patients between institutions. This allows privacy-preserving statistical feasibility estimations for further analyses and data consolidation. Additionally, we created easy to deploy software packages using microservice containerization and continuous deployment/continuous integration. We performed tests with medical researchers using MainSEL in real-world medical IT environments, using synthetic patient data.Results: We show that MainSEL achieves practical runtimes, performing 10 000 comparisons in approximately 5 minutes. Our approach proved to be feasible in a wide range of network settings and use cases. The "lessons learned" from the real-world testing show the need to explicitly support and document the usage and deployment for both analysis pipeline integration and researcher driven ad-hoc analysis use cases, thus clarifying the wide applicability of our software. MainSEL is freely available under: https://github.com/medicalinformatics/MainSEL CONCLUSIONS: MainSEL performs well in real-world settings and is a useful tool not only for rare disease research, but medical research in general. It achieves practical runtimes, improved security guarantees compared to existing solutions, and is simple to deploy in strict clinical IT environments. Based on the "lessons learned" from the real-word testing, we hope to enable a wide range of medical researchers to meet their needs and requirements using modern privacy-preserving technologies. [ABSTRACT FROM AUTHOR]

The development of computer-aided detection (CAD) using artificial intelligence (AI) and machine learning (ML) is rapidly evolving. Submission of AI/ML-based CAD devices for regulatory approval requires information about clinical trial design and performance criteria, but the requirements vary between countries. This study compares the requirements for AI/ML-based CAD devices approved by the US Food and Drug Administration (FDA) and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. A list of 45 FDA-approved and 12 PMDA-approved AI/ML-based CAD devices was compiled. In the USA, devices classified as computer-aided simple triage were approved based on standalone software testing, whereas devices classified as computer-aided detection/diagnosis were approved based on reader study testing. In Japan, however, there was no clear distinction between evaluation methods according to the category. In the USA, a prospective randomized controlled trial was conducted for AI/ML-based CAD devices used for the detection of colorectal polyps, whereas in Japan, such devices were approved based on standalone software testing. This study indicated that the different viewpoints of AI/ML-based CAD in the two countries influenced the selection of different evaluation methods. This study's findings may be useful for defining a unified global development and approval standard for AI/ML-based CAD. [ABSTRACT FROM AUTHOR]

Aim: The motive of this study is to detect the surface roughness of AA 6063 samples using the AlTiN coated HSS drill and Uncoated drill machined in the (Super Jobber) Novel CNC drilling center. The surface roughness formed on the drilled surface was tested in a surface roughness tester. Materials and Methods: The AA 6063 with a diameter of 25 mm and length of 20 mm was used for this research. The experiments were conducted for two groups namely the samples are drilled using AlTiN coated HSS drill (intervention group) and the samples are drilled using an Uncoated drill (control group). The surface roughness test for machined samples was performed according to ASTM standards in the (SJ410-Mitutoyo) surface roughness tester. The sample count calculated by G-POWER software was 80% and a total of 20 samples per group was derived. Results: According to this study, the obtained Surface roughness for AA 6063 samples machined using AlTiN coated drill was 1.1336 µm and the samples machined using an Uncoated drill were 1.9502 µm. By considering the one-way ANOVA test in SPSS software, the significance value was p= 0.025 (p <0.050) indicating the tool efficiency while drilling. Conclusion: Within the limitations of this research, the samples drilled with AlTiN coated HSS drill has lower surface roughness than samples drilled with an Uncoated drill. [ABSTRACT FROM AUTHOR]

Read-across applies the principle of similarity to identify the most similar substances to represent a given target substance in data-poor situations. However, differences between the target and the source substances exist. The present study aims to screen and assess the effect of the key components in a molecule which may escape the evaluation for read-across based only on the most similar substance(s) using a new open-access software: Virtual Extensive Read-Across (VERA). VERA provides a means to assess similarity between chemicals using structural alerts specific to the property, pre-defined molecular groups and structural similarity. The software finds the most similar compounds with a certain feature, e.g., structural alerts and molecular groups, and provides clusters of similar substances while comparing these similar substances within different clusters. Carcinogenicity is a complex endpoint with several mechanisms, requiring resource intensive experimental bioassays and a large number of animals; as such, the use of read-across as part of new approach methodologies would support carcinogenicity assessment. To test the VERA software, carcinogenicity was selected as the endpoint of interest for a range of botanicals. VERA correctly labelled 70% of the botanicals, indicating the most similar substances and the main features associated with carcinogenicity. [ABSTRACT FROM AUTHOR]

Software testing is a complex and costly stage during the software development lifecycle. Nowadays, there is a wide variety of solutions to reduce testing costs and improve test quality. Focussing on test case generation, Dynamic Symbolic Execution (DSE) is used to generate tests with good structural coverage. Regarding test suite evaluation, Mutation Testing (MT) assesses the detection capability of the test cases by introducing minor localised changes that resemble real faults. DSE is however known to produce tests that do not have good mutation detection capabilities: in this paper, the authors set out to solve this by combining DSE and MT into a new family of approaches that the authors call Mutation‐Inspired Symbolic Execution (MISE). First, this known result on a set of open source programs is confirmed: DSE by itself is not good at killing mutants, detecting only 59.9% out of all mutants. The authors show that a direct combination of DSE and MT (naive MISE) can produce better results, detecting up to 16% more mutants depending on the programme, though at a high computational cost. To reduce these costs, the authors set out a roadmap for more efficient versions of MISE, gaining its advantages while avoiding a large part of its additional costs. [ABSTRACT FROM AUTHOR]

This study aims to examine the behavioral intentions and usage behaviors of consumers in Turkey towards mobile shopping applications and the factors affecting these behaviors. A questionnaire was prepared based on the UTAUT2 (Extended Unified Technology Acceptance and Use Theory) model, frequently used in the literature. Since mobile shopping applications are free, the price value factor is excluded. In the first quarter of 2021, 604 usable data were obtained from the online questionnaire by convenience sampling method. The structural Equation Model was applied through the IBM SPSS AMOS software to test the model. According to the findings, performance expectation, effort expectancy, hedonic motivation, and habit dimensions have a statistically significant effect on behavioral intention, while facilitating conditions, habit, and behavioral intention dimensions have statistically significant effects on use behavior. The impact of social influence and facilitating conditions on behavioral intention was not statistically significant. [ABSTRACT FROM AUTHOR]

The results are presented of a numerical simulation for the motion of a group of geosynchronous objects from positional observations obtained with the unique Zeiss-2000 research facility at the Terskol Observatory, Institute of Astronomy, Russian Academy of Sciences. A description is given of improved high-precision mathematical software designed to work with positional observations of satellites. The results are discussed of testing the software for observations of near-Earth objects obtained with the above-mentioned Zeiss-2000 facility. The modified software helps determine the dynamic state vector of a near-Earth object and its windage parameter. The testing has used observations of geosynchronous objects 90008, 90031, 90214, and 97149 (the object numbers are given in accordance with the numbering in the dynamic database of space objects maintained by the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences). The resulting parameters are used to study the long-term orbital evolution of the objects. It is shown that three objects (90008, 90031, and 90214) are captured in a 1 : 1 resonance with the Earth's rotation velocity and have a stable motion in a rotating coordinate system close to a harmonic oscillator over a 10-year time interval. Object 97149 is not resonant, but its motion remains regular over a time interval of 10 years. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of low data reliability and long generation time of traditional automatic generation methods of software test data, an automatic generation method of software test data based on improved Markov model is designed. Firstly, collect software test data in different stages; Then, by calculating the similarity of the collected software test data, remove the test data with high similarity, calculate the importance of the software test data with the help of entropy weight method, and complete the data preprocessing; Finally, the Markov model is improved with the help of genetic algorithm, generation path and variation factor of software test data are set, and the improved Markov model is used to automatically generate high quality software test data. Experimental results show that when the number of experiments is 50, the generation time of this method is about 2.8 s, the reliability coefficient is always higher than 0.8. [ABSTRACT FROM AUTHOR]

Simple Summary: Addressing a large number of genomic problems requires the comparison of genetic and epigenetic features distributed over the genome (genome tracks). The mutual arrangement of these features determines basic molecular mechanisms related to the dynamics of the genome architecture and gene expression. The analysis of data on the genome tracks stored in numerous databases cannot be performed without suitable bioinformatic tools. A package, the Genome Track Colocalization Analyzer, developed by the authors, is intended for the study of colocalization effects between stretch–stretch and stretch–point genome tracks. In this paper, we describe a method for the study of colocalization effects between stretch–stretch and stretch–point genome tracks based on a set of indices varying within the (–1, +1) interval. The indices combine the distances between the centers of neighboring stretches and their lengths. The extreme boundaries of the interval correspond to the complete colocalization of the genome tracks or its complete absence. We also obtained the relevant criteria of statistical significance for such indices using the complete permutation test. The method is robust with respect to strongly inhomogeneous positioning and length distribution of the genome tracks. On the basis of this approach, we created command-line software, the Genome Track Colocalization Analyzer. The software was tested, compared with other available packages, and applied to particular problems related to gene expression. The package, Genome Track Colocalization Analyzer (GTCA), is freely available to the users. GTCA complements our previous software, the Genome Track Analyzer, intended for the search for pairwise correlations between point-like genome tracks (also freely available). The corresponding details are provided in Data Availability Statement at the end of the text. [ABSTRACT FROM AUTHOR]

Revisited - The Subliminal Channel in Blockchain and Its Application to IoT Security (belongs to the topic of new cryptographic algorithms for blockchain technology in the IoT). Second, in the paper "Toward Data Integrity Architecture for Cloud-Based AI Systems" [[2]], Witanto et al. investigated the potential cybersecurity risks of Artificial Intelligence (AI)-based computing architecture operated with modern innovative technologies, such as Cloud computing and Blockchain. The Internet of Things (IoT) is an emerging paradigm, seamlessly integrating a great quantity of smart objects that are connected to the Internet. [Extracted from the article]

Large-scale outflows driven by active galactic nuclei (AGNs) can have a profound influence on their host galaxies. The outflow properties themselves depend sensitively on the history of AGN energy injection during the lifetime of the outflow. Most observed outflows have dynamical time-scales longer than the typical AGN episode duration, i.e. they have been inflated by multiple AGN episodes. Here, we present a neural network-based approach to inferring the most likely duty cycle and other properties of AGN based on the observable properties of their massive outflows. Our model recovers the AGN parameters of simulated outflows with typical errors |$\lt 25{{\ \rm per\ cent}}$|⁠. We apply the method to a sample of 59 real molecular outflows and show that a large fraction of them have been inflated by AGN shining with a rather high duty cycle δAGN > 0.2. This result suggests that nuclear activity in galaxies is clustered hierarchically in time, with long phases of more frequent activity composed of many short activity episodes. We predict that |$\sim \! 19{{\ \rm per\ cent}}$| of galaxies should have AGN-driven outflows, but half of them are fossils – this is consistent with currently available data. We discuss the possibilities to investigate AGN luminosity histories during outflow lifetimes and suggest ways to use our software to test other physical models of AGN outflows. The source code of all of the software used here is made public. [ABSTRACT FROM AUTHOR]

Technologies came to revolutionize the teaching of technical design; they brought resources that enabled 3D and 2D graphic simulation. The objective of this research is to understand how the implementation of the dynamic software GeoGebra on the topic of cylindrical sections can improve 3D spatial visualization in students because of the challenges in understanding the 3D representations and transporting them to the 2D sheet. This is a quasi-experimental case study in a mixed qualitative and quantitative approach which was conducted in a grade 12 class on the subject of descriptive geometry. The instruments used were a pre-test, post-test and Santa Barbara Solids Test. The students of the experimental group used GeoGebra and obtained better results in the post-test than the students in the control group, which suggests an improvement in learning; however, the Santa Barbara Solids Test results showed that the difficulties in visualizing views of the projected solids in a two-dimensional plane persisted. This study is also intended to test software in a classroom with students who have never experienced a 3D technology in their educational practices and direct future research in the complex content of descriptive geometry with the help of software. [ABSTRACT FROM AUTHOR]

Testing of software is an essential aspect of software development to determine the testing release time. remaining number of faults and software reliability. Testing process includes selection of testing team, estimation of testing effort. and execution of software. Study reveals that maximum cost of software development is utilized in testing of software. Therefore, estimation of testing effort during testing of software is needed to determine. The uncertainty involved in estimated parameters and reliability is necessary to address, which can be done in fuzzy paradigm. Due to complexity of software systems, it is very tough to remove all faults and results into imperfect debugging. Sometimes. the new faults may generate in testing a software. this process is called error generation. In this study, we proposed a software reliability growth model using testing effort function in the presence of error generation and imperfect debugging under fuzzy paradigm. The proposed model segregated the process into two phases considering fault observation and fault removal. Moreover. software release time is discussed with the consideration of uncertainty in reliability. Finally. results are validated numerically on real data set. [ABSTRACT FROM AUTHOR]

Under contemporary conditions of social development, it is necessary to rethink the role of waste produced due to economic activity and, in particular, industrial formations of the ferroalloy industry. In order to evaluate the expediency of involving industrial formations in the production process and substantiate the efficiency of design solutions in the field, a functional approach was proposed and its algorithm created in the Tekhnogen-Invest software. This article continues a study aimed at assessing the efficiency of processing industrial formations of the ferroalloy industry. In this part, the developed software was tested using data from the production of high-carbon ferrochrome. The relevance of the applied methodology for assessing the ecological and economic efficiency associated with processing industrial formations of ferroalloy production is confirmed, taking into account the strategic flexibility of projects. The conducted testing the developed Tekhnogen-Invest software produced positive results. The algorithm used in the software allows the cost of implementing an investment project for processing industrial formations of high-carbon ferrochrome production under current conditions of the Urals to be evaluated and the feasibility of its implementation under changing market conditions to be substantiated. The obtained results demonstrate a high potential for producing marketable products from ferroalloy industry waste, confirming the economic efficiency and feasibility of processing industrial formations. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerability to input perturbation is also known. Aligning with a view from software defect detection, this article aims to develop a coverage-guided testing approach to systematically exploit the internal behavior of RNNs, with the expectation that such testing can detect defects with high possibility. Technically, the long short-term memory network (LSTM), a major class of RNNs, is thoroughly studied. A family of three test metrics are designed to quantify not only the values but also the temporal relations (including both stepwise and bounded-length) exhibited when LSTM processing inputs. A genetic algorithm is applied to efficiently generate test cases. The test metrics and test case generation algorithm are implemented into a tool testRNN, which is then evaluated on a set of LSTM benchmarks. Experiments confirm that testRNN has advantages over the state-of-the-art tool DeepStellar and attack-based defect detection methods, owing to its working with finer temporal semantics and the consideration of the naturalness of input perturbation. Furthermore, testRNN enables meaningful information to be collected and exhibited for users to understand the testing results, which is an important step toward interpretable neural network testing. [ABSTRACT FROM AUTHOR]

In this study, we consider test case prioritization (TCP) problem for regression testing. Regression tests are used to ensure that software updates do not affect other software functionality. In the process of regression tests, the tests previously used for software testing are run again. However, running all existing tests is a costly process as it will cause time and labor loss. Therefore, an optimization can be made by giving priority to the tests according to certain criteria and running the tests with high priority values. This reduces the burden of unnecessary tests and regression tests reach their goals more quickly. In this study, we show that TCP problem is equivalent to the problem of finding dominating sets for the bipartite graphs. We propose 3 algorithms based on dominating sets and then we compare experimental results of the proposed algorithms with the results of the existing algorithms. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, Get Us PPE provided a platform aimed at connecting prospective donors of personal protective equipment (PPE) to prospective recipients of PPE. Requests by donors and recipients were collected over time, and periodically, the PPE matching problem was solved in order to instruct each donor to ship a certain quantity of PPE to a given recipient. The objectives of the PPE matching problem include maximizing the recipients' fill rate, minimizing the total shipping distance, minimizing the holding time of PPE, and minimizing the number of shipments of each donor. This paper presents a software framework to facilitate the development of methodologies to solve the PPE matching problem and their testing on a real-world data set collected by Get Us PPE during the COVID-19 pandemic. Both software and data set are available on GitHub. [ABSTRACT FROM AUTHOR]

Since the traditional multiple-error locating method in software testing is difficult to achieve, and its information analysis is inaccurate, a software multiple-error locating method based on path clustering and failure weighting is proposed in this paper. In an environment with complete test cases, the program execution context information is dynamically captured by running test cases, and a path matrix of execution trajectory information is constructed. The cluster analysis is used to divide clusters and expand the weight of failed execution and added to suspicious in the process of degree evaluation to troubleshoot multiple errors. Experiments are implemented on four benchmark programs. The results show that compared with the five methods based on the equivalent evaluation function, the error location cost of the proposed method was reduced by 19.15% on average and effectively improved the efficiency of error location. [ABSTRACT FROM AUTHOR]

In order to improve the steering characteristics, roll characteristics, and lateral characteristics of heavy commercial vehicles, a multiobjective handling stability comprehensive score optimization model (MHSCS optimization model) was proposed in this study. In this study, the main factors affecting the steering characteristics, roll characteristics, and lateral characteristics are determined by combining the road test method and ADAMS software simulation analysis method. Based on the above road tests and ADAMS software simulation analysis, the MHSCS optimization model was proposed with "understeering degree," "vehicle roll angle", and "rearward amplification (RWA)" as evaluation indexes, and the response surface method combined with genetic algorithm was used to carry out multiobjective optimization of heavy commercial vehicle. ADAMS simulation results show that the comprehensive improvement degree of steering characteristics, roll characteristics, and lateral characteristics of a heavy commercial vehicle after optimization is 15.26%. Finally, the field road test results show that the scoring error between the comprehensive scoring optimization model and the real vehicle test was controlled at 0.4%, which proves the accuracy of the optimization model established in this study and effectively improves the handling stability of heavy commercial vehicles. [ABSTRACT FROM AUTHOR]

To reduce costs and improve organizational efficiency, the adoption of innovative services such as Cloud services is the current trend in today's highly competitive global business venture. The aim of the study is to guide the software development organization for Cloud-based testing adoption. Therefore, the objective is to develop a two-stage Interpretive Structural Model (ISM) and Artificial Neural Network (ANN)-based approach, for analyzing the factors influencing cloud adoption for software testing. This study first identifies the determinants and predictors of Cloud adoption for software testing through systematic literature (SLR) and empirical survey. Based on the collected data, an ANN was incorporated to weight the nonlinear effect of the predictors. Then, based on the results of empirical survey; a panel of ten experts was selected, to explore the multifaceted interrelationships among the influential factors (IFs) through SM. To provide a concise understanding of the facts, Cross-Impact Matrix Multiplication Applied to the Classification (MICMAC) was used for factors classification. To achieve our objective, through SLR this study identifies 70 IFs. To offer a brief understanding of the issue, we distributed the identified IFs into ten predictors and analyzed their nonlinear effect on the predictors through ANN. Based on the ANN outcomes, the foremost significant independent variable in predicting CCA intention of CC are perceived business concerns (100%), followed by feasibility planning and risk analysis (84.4%), perceived level of trust (76.1%). Finally, the key 44 factors, identified through panel review, were priorities through ISM and were distributed into four Quadrants using MICMAC approach. Some studies in the form of survey have been conducted to examine the IFs affecting CCA. However, no attempt was made to explore the multifaceted interrelationships among them. This study concludes that software testing should be carried out in the Cloud [ABSTRACT FROM AUTHOR]

Composite materials are obtained by combining two or more materials with different physical properties, which maintain their properties at the macromolecular level, thus retaining their identity, to obtain a resulting material with characteristics clearly superior to the constituent elements, comparable to conventional materials in terms of performance. The specimens are made either by injection molding, machined from the center sections of a standard test sample (see ISO 20735: 2018), processed from semifinished, finished, rolled, or extruded products. In this sense, the article briefly presents the activity of creating a modeling, simulation. and tensile testing software program that can acquire control and process data, taken from an experimental stand or from a software simulation. Thus, the following elements are realized: software for retrieving data from the software equipment of the Zwick/Roell Z020 traction test machine; taking data from an experimental stand; simulation of tensile testing; creating reports, management of the production logistics chain of the benchmark. The experimental and theoretical results are further input in the design of the presented software. [ABSTRACT FROM AUTHOR]

Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to improve map quality. Therefore, the objective of this study was to implement a ML technique and compare it to IDW and to Ordinary Kriging (OK). The ML algorithm used was the Support Vector Machine (SVM). Software based on the SVM method was developed (Smart-Map) using the Python language. This software was tested in an area of 204 ha cultivated with soybeans. The performance of the SVM method was compared to traditional interpolation methods, IDW and Ordinary Kriging (OK). Based on the analysis of 10 soil attributes, OK had better performance than IDW and the ML method when the Moran's I (Index) values were significant and higher than 0.67. With a low density of points and low degrees of spatial autocorrelation, the ML method performed better than IDW and OK. [ABSTRACT FROM AUTHOR]

This study aims to design a self-levelling platform loaded with a missile that is articulated from 0° to 90°. The platform is not situated rigidly on the ground, but it is placed on a railway track. This creates an unbalanced condition for the platform during the missile articulation because the railway guides are not rigid and act as springs. Consequently, the objective of study was to keep the platform in a steady condition at a particular plane at all times, irrespective of the change in load with respect to position and time. A prototype was designed on CATIA V5 software and tested by using Arduino IDE which replicates the original system on a small scale. The effect of railway track was created by using springs of various stiffness coefficients. The model was manufactured and the balancing of the platform was done using linear actuators coupled with an electronic control system with a feedback mechanism. The accelerometer was used to sense the angular variation in X and Y axes along the reference plane. The electro-mechanical linear actuators were used to nullify the unbalance and hence the levelling was achieved. The various tests were performed on the prototype with variable ground conditions using different combinations of springs to validate the balancing principle. It reveals that the levelling was successful with all combinations for any unbalanced ground condition. [ABSTRACT FROM AUTHOR]

The explosive growth and rapid version iteration of various mobile applications have brought enormous workloads to mobile application testing. Robotic testing methods can efficiently handle repetitive testing tasks, which can compensate for the accuracy of manual testing and improve the efficiency of testing work. Vision-based robotic testing identifies the types of test actions by analyzing expert test videos and generates expert imitation test cases. The mobile application expert imitation testing method uses machine learning algorithms to analyze the behavior of experts imitating test videos, generates test cases with high reliability and reusability, and drives robots to execute test cases. However, the difficulty of estimating multi-dimensional gestures in 2D images leads to complex algorithm steps, including tracking, detection, and recognition of dynamic gestures. Hence, this article focuses on the analysis and recognition of test actions in mobile application robot testing. Combined with the improved YOLOv5 algorithm and the ResNet-152 algorithm, a visual modeling method of mobile application test action based on machine vision is proposed. The precise localization of the hand is accomplished by injecting dynamic anchors, attention mechanism, and the weighted boxes fusion in the YOLOv5 algorithm. The improved algorithm recognition accuracy increased from 82.6% to 94.8%. By introducing the pyramid context awareness mechanism into the ResNet-152 algorithm, the accuracy of test action classification is improved. The accuracy of the test action classification was improved from 72.57% to 76.84%. Experiments show that this method can reduce the probability of multiple detections and missed detection of test actions, and improve the accuracy of test action recognition. [ABSTRACT FROM AUTHOR]

Service-Oriented Architecture (SOA) is a technology for designing software systems. By using this architecture, new challenges appeared for software testing. Functional testing of services assures the quality of service-oriented applications. Herein, we introduce a new test web service named TaaWS (Test as a Web Service), which is a combination of both online testing and testing as a service to overcome SOA testing challenges. Each web service can have its test web service. Thus, the consumer can call TaaWS to assure the accuracy of the web services used in the application. TaaWS consists of semantic and structural test cases extracted from the extended-attribute grammar of the web service called Method-Based Attribute Grammar (MBAG). MBAG is created in five steps, including the definition of the input/output variables, the creation of sections in the web method, and the application of rules to specify conditions and operations on the attributes. In addition, a new coverage criterion is introduced for application on MBAG to extract test cases. Using TaaWS, we have applied semantic and structural test cases on the PersonalIDCode-INFO web method as a case study. In our proposed method, a developed program called Sharif-TaaWS as a tool automates MBAG generation steps, test cases, and TaaWS creation. To evaluate TaaWS, other functional testing techniques have been compared to our method by measuring the effectiveness of the test cases (ETC value). This value shows that TaaWS can find more faults compared to other techniques. We propose that TaaWS has advantages such as creating both semantic and structural test cases through MBAG, detecting changes in web services, running regression tests, and managing test results to inform the consumer. However, TaaWS has a drawback; the response time to the user may increase through calling TaaWS. [ABSTRACT FROM AUTHOR]

Software bug prediction aims to enhance software quality and testing efficiency by constructing predictive classification models using code properties. This enables the prompt detection of fault‐prone modules. There are several machine learning‐based software bug prediction studies, which mainly focus on single view data by disregarding the natural ordering relation among the class labels in the literature. Thus, these studies cause losing each view's own intrinsic structure and the inherent order of the labels that positively affect the prediction performance. To overcome this drawback, this study focuses on integrating ordering information and a multi‐view learning strategy. This paper proposes a novel approach multi‐view ordinal classification (MVOC), which learns from different views (complexity, coupling, cohesion, inheritance and scale) of the software dataset separately and predicts software bugs taking the inherent order of class labels (non‐buggy, less buggy and more buggy) into consideration. To demonstrate its prediction performance, the MVOC approach was executed on the 40 different real‐world software datasets using six different classification algorithms as base learners. In the experiments, the MVOC approach was compared with traditional classifiers and their multi‐view implementations in terms of precision, recall, f‐measure and accuracy rate metrics. The results indicate that the MVOC approach presents better prediction performance on average than the multi‐view‐based and traditional classifiers. It is also observed from the results that the MVOC.RF model achieved the highest classification performance with an average accuracy rate of 85.65%. [ABSTRACT FROM AUTHOR]

Regression testing is a testing method conducted to ensure that improvements do not affect the software's current behavior. Test cases play a significant role in software testing activities since it detects faults in the software. The selection of test cases for execution based on the priority is a considerable decision-making step since testing needs to be carried out with limited computing resources like cost, effort, and time. Prioritized selection of test cases involves considering specific test case parameters or criteria; prioritizing test cases can be conceived as a multi-criteria decision-making problem. In this paper, using the Analytic Hierarchy Process (AHP) method, we suggest an approach for prioritizing the selection of the test cases. AHP provides a rational framework for structuring a decision-making problem. It is used to determine the priority of a set of criteria and to calculate the consistency ratio of those criteria using pair-wise comparisons of various measures. Using a new priority regression test algorithm proposed in this study, regression test prioritization is transformed into a multi-criteria decision-making problem. The suggested strategy has a higher Average Percentage of Faults Detected (APFD) value when many determinants are taken into account. The experimental results indicate that our proposed prioritization approach would improve the probability that faults will be detected early and enhance performance compared to other prioritization strategies. [ABSTRACT FROM AUTHOR]

The development and application of information and communication technology in distribution networks (DNs) brings new and abundant measurement data, and this has inspired many solutions for the monitoring, protection and control application of DNs. Against this background, a novel fault location method based on structure‐preserving state estimation (SPSE) is proposed to enhance the fault location performance of DNs. A SPSE model that reflects the change in the network topology during fault incidents is constructed by combining classical measurement equations with newly introduced topology equations. Then, a unified faulted structure‐preserving topology model that eliminates the influence of uncertain fault location point is presented. According to the calculated measurement residual indices and the equivalent fault current for each state estimation (SE) section, the location of the fault is determined. Considering different fault scenarios in a distribution network, the performance of the proposed method is tested by using MATLAB software. Moreover, the factors of sensor accuracy, the bad data and data missing issues are considered. The test results demonstrate that the method can precisely determine the real location of fault and has good robustness to possible data anomalies. [ABSTRACT FROM AUTHOR]