Computational Tests Research Articles

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning.

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

A function improving tip loss correction of blade-element momentum theory for wind turbines

ABSTRACT Tip loss correction is necessary for all aerodynamic computations based on the blade-element momentum theory (BEMT) that plays an essential role in wind turbine design. Glauert tip-loss correction, which is most widely used, assumes the rotor is lightly loaded and thus lose some accuracy for modern wind turbines that are highly loaded. In the present work, a new g-function involving the rotor’s thrust-coefficient is introduced, which connects the tip loss correction with the rotor’s load condition and reduces the error of the tip loss correction. Computational tests are performed on two rotors that are distinct in size and tip shape. The new g-function results in good consistency between the computed forces and the reference data, exhibiting excellent role in improving the accuracy of the BEMT computations. In general, the new g-function combines good effectiveness and applicability, and thus has great potential for engineering applications.

Cognitive Development and Decision Making in Basketball: A Comparison between Male Players with and without Intellectual Impairment and across Different Age-Groups.

The aim of this study was to investigate the role of age and intellectual impairment (II) in decision-making in basketball. The current study investigated differences in decision making between equally well-trained adult basketball male players with intellectual impairment (players with II) (n = 93), adults without II (senior) (n = 44) and youth basketball players (under-14, n = 31; under-16, n = 25; under-18, n = 30). A computer test was developed composed by 20 photographs displaying various basketball game-situations, and participants had to decide as fast as possible what the player in ball possession should do: dribble, pass or shoot. Decision time and accuracy were recorded for every situation. Players with II had slower decision time (3.8 ± 1.8 s vs. 1.5 ± 0.5 s, p < 0.001) and less decision-making accuracy (15.7 ± 2.8 correct decisions vs. 17.9 ± 1.2 correct decisions, p < 0.001) compared to senior players without II. Discriminant analysis with speed and accuracy as independent variables classified 91.2% (CCA = 0.769) of the players correctly into their group: players with II or players without II. A Spearman correlation revealed that age correlated significantly (p < 0.001) with the number of correct decisions (rs = 0.269) and mean decision time (rs = -0.331). Our findings support that decision making in basketball develops with age and experience, but is significantly deteriorated in experienced adult players who have II. Decision-making should be considered as an important eligibility criterion to participate in competitive basketball events for male players with II.

Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm

Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adaptive Multistage Testing algorithm based on Adaptive Genetic Algorithm (AMST-AGA) for optimisation and scalability problems, in which constraints are successively introduced and dealt with at various evolutionary phases. In this paper, many test cases will aid in finding bugs and meeting completeness goals. Each time test cases are created, these testing scenarios must continue to pass.

Model and heuristics for the multi-manned assembly line worker integration and balancing problem

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.

Knowledge-assisted adaptive large neighbourhood search algorithm for the satellite–ground link scheduling problem

Communications between satellites and the ground supported by satellite–ground links (SGLs) are indispensable to the daily management of large-scale low-orbit communication satellite constellations. The SGL scheduling problem (SGLSP) focuses on determining which nonconflicting SGLs should be activated to maximize the total linking time. To solve this problem, we build an integer programming (IP) model and simultaneously propose a knowledge-assisted adaptive large neighbourhood search algorithm (KA-ALNS) based on this model, unifying the strengths of IP and data mining within the ALNS framework. In this algorithm, IP, which is deeply embedded in the ALNS, is employed to further refine the solution improved by ALNS. Then, a data mining method named frequent pattern mining is employed for extracting knowledge of excellent solutions to construct new solutions. The KA-ALNS algorithm that combines data mining methods and IP modelling can effectively solve the SGLSP. A three-day experimental setting with 20 ground stations in the China region, 40 ground stations in the world region (each station is equipped with 2 antennas), and a constellation containing 168 satellites is conducted. Computational tests with 12 different scale instances indicate that KA-ALNS outperforms two state-of-the-art algorithms given both 10 and 60 min of scheduling time.

Impact of yoga on the central and peripheral vascular function among desk-based workers: A single-centered trial study

Background The aim of this study was to observe and analyze vascular function in ‘prolonged sitting’, followed by a yoga asana routine and pranayama intervention. Participants in this study include those who work from desks in offices. The study required the participants to attend on three separate days at random, and they had to finish a computerized test on each day. On the first day, participants were required to complete a computer test while sitting still for four hours (with the exception of washroom breaks). The next day, they underwent a computerized test along with a pranayama intervention. Finally, on the last day, they underwent a computerized test along with a yoga asana intervention. At the start of the study and after two and four hours, we measured the diameter and velocity of the common carotid artery (CCA) and superficial femoral artery (SFA). Methods The study was a within-subjects prospective single-center trial conducted in the Department of Radio-Diagnosis and Imaging, Kasturba Medical Hospital, Manipal, India, between September 2022 and January 2023. Participants were asked to do one of the following ‘activities’ over successive weeks: Week 1 – Prolonged sitting; Week 2 – Pranayama intervention; and Week 3 – Yoga asana intervention during prolonged sitting. The baseline and follow-up variables of pulse velocity, endothelial thickness, and shear rate were assessed for normality through a Shapiro-Wilk Test. Results Our sample included 11 participants with moderate physical activity, five with high physical activity and one with low physical activity. Yoga asana intervention comprised participants sitting continuously for four hours, with a yoga asana intervention being provided every hour, lasting for 10 minutes. Conclusions Yoga asana improves vascular functions in prolonged sitting conditions. This routine can promote the concept of interrupted sitting and ways to reduce it with efficient yoga asana practice without changing the work culture and provide better physical relief. Trial registration Clinical Trials Registry – India ( CTRI/2022/09/045628), date of registration: 19/09/2022(CTRI/2022/9/045628)https://ctri.nic.in/Clinicaltrials/main1.php?EncHid=16349.27799,

Design and Validation of a Computational Thinking Test for Children in the First Grades of Elementary Education

Computational thinking (CT) has garnered significant interest in both computer science and education sciences as it delineates a set of skills that emerge during the problem-solving process. Consequently, numerous assessment instruments aimed at measuring CT have been developed in the recent years. However, a scarce part of the existing CT measurement instruments has been dedicated to early school ages, and few have undergone rigorous validation or reliability testing. Therefore, this work introduces a new instrument for measuring CT in the early grades of elementary education: the Computational Thinking Test for Children (CTTC). To this end, in this work, we provide the design and validation of the CTTC, which is constructed around spatial, sequential, and logical thinking and encompasses abstraction, decomposition, pattern recognition, and coding items organized in five question blocks. The validation and standardization process employs the Kuder–Richardson statistic (KR-20) and expert judgment using V-Aiken for consistency. Additionally, item difficulty indices were utilized to gauge the difficulty level of each question in the CTTC. The study concludes that the CTTC demonstrates consistency and suitability for children in the first cycle of primary education (encompassing the first to third grades).

A knowledge-guided Estimation of Distribution Algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP.

Patent Technology of Shipborne Stabilized Platform

Background: When a ship is sailing or operating at sea, it is unavoidable to produce sixdegrees- of-freedom movements of rolling, pitching, and yawing under the action of sea wind and waves, which seriously impacts the operation of precision equipment on the ship and the safety of personnel. The ship-borne stabilized platform can isolate the ship's motion and eliminate the impact of sea waves on the personnel and equipment on it to a large extent. With the increasing demand for ship surfaces in marine fields, such as navigation equipment, military facilities, and sea condition simulation, there is an urgent need to study ship-borne platforms with high stability and efficiency to isolate swaying and ensure ship performance. Objective: This study aims to analyse the patents and summarize the characteristics and existing problems of the shipborne platform to provide a reference for the future development of the shipborne stabilized platform. Methods: Various representative patents related to shipborne stabilized platforms and multi-DOF platforms were researched in this study. Results: Through the analysis and comparison of patent technology, the main problems existing in the structure and design principle of the existing shipborne stabilized platform are summarized, and the development trend and direction of the shipborne stabilized platform are discussed. Conclusion: Shipborne stabilization is highly significant in isolating the ship's motion. At present, the ship-borne stabilized platform has produced six degrees of freedom of movement, but due to the improvement of control response requirements, the structure of the platform needs to be further improved. With the development of test technology toward intelligent and big data-driven technology, the shipborne stabilized platform is moving toward cloud computing and large-scale testing.

Trileaflet Mechanical Valves: What Is Already Known? A Systematic Review

Background: Trileaflet mechanical valves were introduced in the late 90’s. Despite promising results, none have made it past preclinical testing. Ample in vitro, in vivo and computational testing have already been done. Methods: This systematic review is intended to summarize all available preclinical and clinical testing regarding trileaflet mechanical valves. A literature search of Pubmed, Embase and Web of Science (Core Collection) (from inception to August 2023) was performed using a search string that was well defined and not modified during the study. An extensive overview of the search terms used in each database can be found in the appendix. 24 publications were included in this review. Results: This systematic review serves as an overview evaluating the rationale behind a trileaflet mechanical valve as well as the computational, in vitro and in vivo testing. We aim to summarize all available knowledge on trileaflet mechanical valves. Conclusions: A trileaflet mechanical valve is a promising concept, with equal or even superior hemodynamics compared to conventional bileaflet valves based on computational, in vivo and in vitro data. First in men trials are warranted to elude the true potential of this design.

THE PROSPECTS FOR THE DEVELOPMENT OF THE COMPUTER TESTING SYSTEM FOR STUDENTS IN THE FIELD OF HIGHER EDUCATION IN THE RUSSIAN FEDERATION

Purpose. The promising directions for transforming scattered electronic testing resources into an innovative training system designed to develop competencies in the field of higher education in modern Russia are considered in this article. The research subject is methodological approaches to the preparation of test databases, the organization of training and pedagogical control in the electronic environment of the university. The main purpose of research is explanation of the basic techniques and methods of practical application of testing for the improvement of students' cognitive activity. Methodology. The methodological basis of the research includes methods such as comparative, monographic and structural-logical analysis, estimating, rating as well as dialectical, didactic and systematic approaches. Results. The algorithm has been developed for the integrated implementation of the instrumental capabilities of computer testing as a methodological basis for an innovative training system at the intra-university level. The authors argue that the variety of test task helps students concentrate their attention on the content of the training material. Alternatively, in the opinion of the authors, regular intra-university and external testing should be used as forms of distance learning and pedagogical diagnostics in combination with traditional forms of organizing independent work and control. It allows us to reveal the creative potential of the individual and motivate students to continue their studies at a higher level of education. Practical implications. The results of the study can be applied in the field of higher education in teaching of economic theory, history and other social and humanitarian disciplines.

Exploring Quaternion Neural Network Loss Surfaces

This paper explores the superior performance of quaternion multi-layer perceptron (QMLP) neural networks over real-valued multi-layer perceptron (MLP) neural networks, a phenomenon that has been empirically observed but not thoroughly investigated. The study utilizes loss surface visualization and projection techniques to examine quaternion-based optimization loss surfaces for the first time. The primary contribution of this research is the statistical evidence that QMLP models yield smoother loss surfaces than real-valued neural networks, which are measured and compared using a robust quantitative measure of loss surface “goodness” based on estimates of surface curvature. Extensive computational testing validates the effectiveness of these surface curvature estimates. The paper presents a comprehensive comparison of the average surface curvature of a tuned QMLP model and a tuned real-valued MLP model on both a regression task and a classification task. The results provide strong support for the improved optimization performance observed in QMLPs across various problem domains.

Using Fluid Curtains to Improve Sealing Performance in Turbomachinery Applications

Abstract The results from an investigation into the physics of how fluid curtains can be applied to improve the aerodynamic performance of conventional turbomachinery shaft and rotor seals are described in this paper. Computational fluid dynamics and testing on two experimental facilities are used in the study. In the first part of the work, computational fluid dynamics simulations validated against experimental test data demonstrate the fundamental mechanism by which the presence of the curtain can act to reduce leakage flow through conventional seals. These results are consolidated into a single performance carpet map, showing how the leakage reduction performance and the curtain supply pressure needed to achieve it vary with changes in values of key geometrical parameters. In the second part of the work the effect of swirl in the seal inlet flow, as is often encountered in turbomachinery applications, on the performance of the fluid curtain is investigated experimentally. Test results show that if the swirl momentum in the inlet flow is greater than the momentum of the curtain flow, the performance benefit from applying the curtain is greatly diminished. Overall, the results provide some fundamental design rules for applying fluid curtains to enhance turbomachinery sealing performance for the general type of leakage path geometry (cylindrical channel, 45-deg jet angle, curtain upstream of a conventional seal) and working fluid type and conditions (air, ambient temperature, subsonic leakage channel flow) used in the study.

Impact of yoga on the central and peripheral vascular function among desk-based workers: A single-centred trial study

Background The aim of this study was to observe and analyze vascular function in ‘prolonged sitting’, followed by a yoga routine and pranayama intervention. Participants in this study include those who work from desks in offices. The study required the participants to attend on three separate days at random, and they had to finish a computerized test on each day. On the first day, participants were required to complete a computer test while sitting still for four hours (with the exception of washroom breaks). The next day, they underwent a computerized test along with a pranayama intervention. Finally, on the last day, they underwent a computerized test along with a yoga intervention. At the start of the study and after two and four hours, we measured the diameter and velocity of the common carotid artery (CCA) and superficial femoral artery (SFA). Methods The study was a within-subjects prospective single-center trial conducted in the Department of Radio-Diagnosis and Imaging, Kasturba Medical Hospital, Manipal, India, between September 2022 and January 2023. Participants were asked to do one of the following ‘activities’ over successive weeks: Week 1 – Prolonged sitting; Week 2 – Pranayama intervention; and Week 3 – Yoga intervention during prolonged sitting. The baseline and follow-up variables of pulse velocity, endothelial thickness, and shear rate were assessed for normality through a Shapiro-Wilk Test. Results Our sample included 11 participants with moderate physical activity, five with high physical activity and one with low physical activity. Yoga intervention comprised participants sitting continuously for four hours, with a yoga session intervention being provided every hour, lasting for 10 minutes. Conclusions Yoga improves vascular functions in prolonged sitting conditions. This routine can promote the concept of interrupted sitting and ways to reduce it with efficient yoga practice without changing the work culture and provide better physical relief. Trial registration Clinical Trials Registry – India ( CTRI/2022/09/045628), date of registration: 19/09/2022(CTRI/2022/9/045628)https://ctri.nic.in/Clinicaltrials/main1.php?EncHid=16349.27799,

Risk-averse two-stage stochastic programming for assembly line reconfiguration with dynamic lot sizes

In this paper, a comprehensive optimization problem is developed for a composite of an assembly line reconfiguration problem with multiple lines and a capacitated lot-sizing problem. Multiple products are considered, whose demand is uncertain and is dynamically forecasted. The production planner is assumed to be risk-averse, and decisions are made contingent upon the risk preference. To model the problem, a stochastic program with two stages is utilized. A solution approach is devised using a divide-and-conquer algorithm, which incorporates a set of valid inequalities. The effectiveness and efficiency of the proposed solution approach are assessed through a series of computational tests. Finally, a case study focusing on an engine production process is presented, leading to the derivation of several valuable insights.

Evaluation of the Aquatic Toxicity of Several Triazole Fungicides.

Fungicides play an important role in crop protection, but they have also been shown to adversely affect non-target organisms, including those living in the aquatic environment. The aim of the present study is to combine experimental and computational approaches to evaluate the effects of flutriafol, metconazole, myclobutanil, tebuconazole, tetraconazole and triticonazole on aquatic model organisms and to obtain information on the effects of these fungicides on Lemna minor, a freshwater plant, at the molecular level. The EC50 (the half-maximum effective concentration) values for the growth inhibition of Lemna minor in the presence of the investigated fungicides show that metconazole (EC50 = 0.132 mg/L) and tetraconazole (EC50 = 0.539 mg/L) are highly toxic, tebuconazole (EC50 = 1.552 mg/L), flutriafol (EC50 = 3.428 mg/L) and myclobutanil (EC50 = 9.134 mg/L) are moderately toxic, and triticonazole (EC50 = 11.631 mg/L) is slightly toxic to this plant. The results obtained with the computational tools TEST, ADMETLab2.0 and admetSAR2.0 also show that metconazole and tetraconazole are toxic to other aquatic organisms: Pimephales promelas, Daphnia magna and Tetrahymena pyriformis. A molecular docking study shows that triazole fungicides can affect photosynthesis in Lemna minor because they strongly bind to C43 (binding energies between -7.44 kcal/mol and -7.99 kcal/mol) and C47 proteins (binding energies between -7.44 kcal/mol and -8.28 kcal/mol) in the reaction center of photosystem II, inhibiting the binding of chlorophyll a to these enzymes. In addition, they can also inhibit glutathione S-transferase, an enzyme involved in the cellular detoxification of Lemna minor.

Topology optimization and 3D printing of a unibody quadcopter airframe

The performance of any unmanned aerial vehicle largely depends upon its weight as this characteristic dictates its payload capacity and flight duration. This paper presents a multiphysics simulation method for designing a unibody quadcopter airframe with optimum weight in SolidWorks. By adopting the computer-aided topology optimization concept, the mass of the frame is decreased by 91% (from 1558.44 to 134.738 grams) after getting rid of the unwanted elements, while not compromising its structural rigidity. The efficacy of the model is assessed by finite element analysis and it is found that the stress would remain within the acceptable limit. The optimized structure shows an operating life of 1.6×105 cycles in the fatigue analysis and experiences significantly less drag force in the computational fluid dynamics test than the original airframe for several angles of attack, thus proving the superiority of the optimized frame over the initial model. Finally, additive manufacturing is performed to fabricate the optimized structure using PLA material. 3D printing through the fused deposition modeling technique is chosen since it is ideal for fabricating intricate engineering prototypes compared to orthodox manufacturing processes. While creating the frame, a 45° overhang angle is maintained to ensure the usage of less support material.

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

Newly discovered clouting interplay between matrix metalloproteinases structures and novel quaternary Ammonium K21: computational and in-vivo testing

Aims and objectivesTo analyze anti-MMP mode of action of Quaternary Ammonium Silane (QAS, codenamed as k21) by binding onto specific MMP site using computational molecular simulation and Anti-Sortase A (SrtA) mode of action by binding onto specific site using computational molecular simulation.Materials and methodsIn silico Molecular Dynamics (MD) was used to determine the interactions of K21 inside the pocket of the targeted protein (crystal structure of fibroblast collagenase-1 complexed to a diphenyl-ether sulphone based hydroxamic acid; PDB ID: 966C; Crystal structure of MMP-2 active site mutant in complex with APP-derived decapeptide inhibitor. MD simulations were accomplished with the Desmond package in Schrödinger Drug Discovery Suite. Blood samples (~ 0.5 mL) collected into K2EDTA were immediately transferred for further processing using the Litron MicroFlow® PLUS micronucleus analysis kit for mouse blood according to the manufacturer’s instructions. Bacterial Reverse Mutation Test of K21 Molecule was performed to evaluate K21 and any possible metabolites for their potential to induce point mutations in amino acid-requiring strains of Escherichia coli (E. coli) (WP2 uvrA (tryptophan-deficient)).ResultsMolecular Simulation depicted that K21 has a specific pocket binding on various MMPs and SrtA surfaces producing a classical clouting effect. K21 did not induce micronuclei, which are the result of chromosomal damage or damage to the mitotic apparatus, in the peripheral blood reticulocytes of male and female CD-1 mice when administered by oral gavage up to the maximum recommended dose of 2000 mg/kg. The test item, K21, was not mutagenic to Salmonella typhimurium (S. typhimurium) strains TA98, TA100, TA1535 and TA1537 and E. coli strain WP2 uvrA in the absence and presence of metabolic activation when tested up to the limit of cytotoxicity or solubility under the conditions of the test.ConclusionK21 could serve as a potent protease inhibitor maintaining the physical and biochemical properties of dental structures.