Applying Actual Development Progress Into Education

In this study we investigate the dependencies between human focus of attention and heart rate variability while performing concentration task in virtual environment, using attention improvement device, and taking into account daily physical activity of the participants. For this purpose, a virtual testing environment – football arena – was developed. A volunteer had to “kick” a ball to the gate. He scores a goal if he has reached high attention focus level. The experiments were performed using NeuroSky device for attention level recording, Polar V800 device for heart rate (RR intervals) recording and ElfEmmit device as a better focus stimulator. At the same time, some virtual environment parameters and human action in it were recorded as well. We have used RMSSD parameter as heart rate variability measure. The results showed some unexpected tendencies. RMSSD parameter was in normal range and outside it for both: volunteers who have regular trainings and who do not have any training at all. A tendency was noticed that ElfEmmit device might increase focus of attention during virtual reality game.

MSIE-Transformer: A novel driving behavior modeling approach for virtual simulation test environment.

This study proposes a new approach to creating a virtual flight test environment by integrating a digital twin of an iron bird test rig with an engineering flight simulator. Generating digital twins for test rigs in aviation is a rare and innovative approach. The engineering flight simulator used in this study incorporates real hands-on throttle and stick to provide pilots with a realistic experience. A validated iron bird of a trainer aircraft, a fully equipped test rig encompassing hydraulic, mechanical, flight control, and landing gear systems, was used as the basis for generating its digital twin. This digital twin was then used to update the mathematical model of an actuator, which is a critical component of the flight model operating within the engineering flight simulator. This method produced a highly accurate simulation of flight characteristics for the selected case, and the Flight Simulation Training Device Guideline of the European Union Aviation Safety Agency has been used to ensure the accuracy of this flight model. To demonstrate this concept, a real-world certification test scenario was selected, and the results of the simulator flight tests were compared with the real flight tests. Finally, the study presents a preliminary roadmap for implementing flight test concepts in virtual environments, emphasizing their potential to revolutionize the certification process.

Modern technologies and methodologies for project management have been considered. It is argued that project management is a complex process that encompasses various stages from initiation to completion and involves the use of diverse methodologies, tools, and approaches to achieve goals, effectively utilize resources, and ensure high-quality results. From the author's perspective, project management methodology should be viewed as a comprehensive approach that defines general principles, processes, rules, and standards to be followed in managing a project. A project management technique is a specific method or process used to accomplish a particular task or phase within the methodology. Techniques can be part of a methodology or applied separately. Project management tools are regarded as specific means or technologies used to support and automate project management processes. A comparative analysis of the main project management methodologies has been conducted, proposing to classify them into four groups: traditional (Waterfall model), agile methodologies (Scrum, Kanban, Extreme Programming - XP), hybrid (Scrumban, Agile-Waterfall Hybrid), methodologies for managing large projects (PRINCE2 – Projects In Controlled Environments, PMI’s PMBOK – Project Management Body of Knowledge) and Lean methodologies (Lean Project Management). This division is arbitrary, but it allows us to systematize the main project management tools and techniques according to the complexity and content of the tasks performed. It is proven that each of the examined management methodologies has its own characteristics, advantages, and disadvantages, which should be taken into account when choosing an approach to project management. The methodology determines the approach to project implementation: planning work processes, interaction with stakeholders, risk management, relevant tools, and so on. The selection of a specific methodology and appropriate tools depends on the specifics and complexity of the project, the field of activity, stakeholder requirements, team size, and other factors.

Digital factory is a new technology to construct a virtual plant environment to implement integration between process planning and manufacturing execution. In the analysis of the application of various software for digital factory technology in digital factory layout planning. It puts forward that Microstation platform as a digital factory planning which planners and designers use to facilitate plant and Microstation as a planning platform in practical applications.

The aim of the current research was to develop students' skills in designing computer circuits among computers and information students through the development of an integrated virtual educational environment represented in (Moodle platform -virtual lab -educational software -YouTube channel). The researcher used the descriptive method and the experimental research method by applying the model of "Abdul Latif Al-Jazzar" ( The research sample consisted of two groups, a control group and an experimental group, numbering (60) students, 30 students for one group on first-year computer students. After applying the research procedures on the experimental and control sample, and using statistical treatments, the results showed that there were statistically significant differences at the significance level (0.01) between the mean scores of the students in the post-application of the achievement test. And the observation card in favor of the post application between the two groups in favor of the experimental group One of the most important recommendations of the research is to create virtual environments in the scientific fields in general and in the field of computers and engineering.

Concrete is a composite material with heterogeneities across multiple length scales. Degradation of concrete due to external loadings starts with diffuse microcracking, followed by damage localization that eventually leads to structural failure. Identification of damage at an early stage of degradation reduces the costs associated with maintenance of the structure. Weak changes can be detected using diffuse ultrasonic waves (so‐called Coda waves). In this contribution, a virtual testing environment for the assessment of concrete damage using coda waves is presented. The virtual test environment combines multiscale computational modeling of concrete damage, modeling of ultrasonic wave propagation, and supervised learning. At the scale of mortar material, microcrack growth is modelled using a combination of continuum micromechanics and linear elastic fracture mechanics. The micromechanics model is incorporated into a reduced‐order Lippmann‐Schwinger based mesomodel for concrete. Synthetic concrete specimens at various damage levels are generated using the multiscale damage model and subsequently these specimens are subjected to wave propagation analysis using the rotated staggered‐grid‐finite‐difference scheme. A convolutional neural network (CNN) based supervised learning framework is further employed to classify damage given the coda signals.

Remote data monitoring system which adopts virtual instrument usually applies data sharing, acquisition and remote transmission technology via internet. It is able to finish concurrent data acquisition and processing for multi-user and multi-task and also build a personalized virtual testing environment for more people but with fewer instruments. In this paper, we’ll elaborate on the design and implementation of information sharing platform through a typical example of how to build multi-user concurrent virtual testing environment based on the virtual software LabVIEW.

Deep reinforcement learning algorithms have demonstrated significant potential in showcasing robotic capabilities within virtual environments. However, applying DRL for practical robot development in realistic simulators like Webots remains challenging due to limitations in existing frameworks, such as complex dependencies and reliance on unrealistic control paradigms like a ‘supervisor’. This study introduces an open-source framework and a novel pattern-based method designed to facilitate the exploration of robot learning capabilities through reinforcement learning algorithms in specialized virtual testing environments built on Webots. Our approach simplifies setup by avoiding burdensome external package installations and, crucially, removes the dependency on an unrealistic ‘supervisor’ entity, offering a more practical and real-world-aligned solution. Designed to leverage Webots’ realistic simulation capabilities, the proposed method and system are validated through various examples, ranging from the classic inverted pendulum scenario to a production robot utilized in an actual assembly line. The developed code and examples are publicly accessible on GitHub for the deep reinforcement learning research community.

This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. The proposed methodology employs a novel virtual test environment that records not only basic flight metrics but also complex mission performance metrics, such as the video quality from a target. A group of Belgian Defence drone pilots were trained using this simulator system, yielding several practical results. These include a human-performance model linking human factors to pilot performance, an AI co-pilot providing real-time flight performance guidance, a tool for generating optimal flight trajectories, a mission planning tool for ideal pilot assignment, and a method for iterative training improvement based on quantitative input. The training results with real pilots demonstrate the methodology’s effectiveness in evaluating pilot performance for complex military missions, suggesting its potential as a valuable addition to new pilot training programs.

Human-cockpit interaction is an innovative and promising field of automotive research. Indeed, automakers need to ensure safety and user satisfaction for their cockpit development concepts, if driving and interacting occurs simultaneously. One suggested approach is to evaluate simple cockpit prototypes within virtual test environments. Hybrid prototyping allows a more realistic experience with the prototype in early stages of development. With our research study we focused on important basic parameters within hybrid test environments (e.g. shutter glasses and virtual projected car model) and evaluated their potential of influence. There are no hints to assume that shutter glasses influence user behaviour. Interestingly, we found significant faster task completion times within a virtual projected car model, which indicate that immersive environments increase user performances. In summary, we can suggest hybrid prototyping within immersive test environments for evaluating human-cockpit interactions.

Teknolojik gelişmeler ve bu zamana kadar biriken bilgilerin ışığında otonom sistemlerde muazzam bir ilerleme kaydedilmiştir. Bu sayede otonom sistemler çarpışmadan kaçınma, trafik işareti tespiti, haritalama vb. sayısız akıllı işlevleri gerçekleştirebilmektedir. Gerçek zamanlı otonom araçların en zorlu problemi aracın kendi kendine haritalandırma ve lokasyon işlemlerini yapabilmesidir. Genetik Algoritma (GA) kullanarak optimize edilmiş lokasyon uygulaması ile otonom araçlar için sürüş güvenliğinin artması beklenmektedir. Bu çalışmada lazer tabanlı bir lokalizasyon ve haritalama tekniğinin üzerine odaklanılmıştır. Gerçekleştirilen sistemde sanal bir test ortamı kurulmuş ve bir otonom araç üzerinde denemeler yapılmıştır. Çalışma kapsamında sanal makineler oluşturularak üzerlerine Linux işletim sistemi kurulmuştur. Sonra bu sanal makinelere ROS ortamında TurtleBot3 kurulmuş ve iç mekân lokalizasyonu yapılarak bir harita elde edilmiştir. Bu harita genetik algoritma ile en kısa mesafelerin bulunmasını sağlamak için kullanılmaktadır. Gözlemler neticesinde simülasyon ortamındaki robot yüksek başarımla istenilen konuma gidebildiği sonucuna ulaşılmıştır.

The purpose of this paper is to present a methodology for circuit and realistic fault extraction, and its implementation in a new tool, lobs, to be included in a virtual test environment, DOTLab. Digital, analog and mixed signal ICs, implemented in CMOS, bipolar or BiCMOS technologies are handled both in Manhattan and 45/spl deg/ geometries. Higher level design data, obtained in the top-down design flow, is used for realistic fault characterization. A sliding window algorithm is extended for fault extraction of non-orthogonal geometries. An accurate critical area evaluation algorithm is proposed to compute the probability of occurrence of the defects. Over 100,000 transistor ICs are analyzed for bridging defects.

Objective Use of teleneuropsychological services has greatly increased since the beginning of the COVID-19 pandemic. The present study aimed to create normative data for a neuropsychological test battery of diverse cognitive domains in a Canadian population. Method A sample (n = 291) of adults aged 19 or older completed a comprehensive neuropsychological assessment (i.e. memory, executive function, etc.) via Zoom. Participants included those with a COVID-19 diagnosis (n = 146) and participants who had not contracted COVID-19 (n = 145). Data were stratified by age group as follows: 19–34, 35–49, 50–64, 65–79. Linear bivariate regression in the entire sample and groups stratified by age was used to test the relationship between age and test scores. Test scores were converted to z-scores using the mean and standard deviation for that group, with z-scores then transformed into normative scores for each test. Results Age was a significant predictor of scores for all tests except for FAS, HVLT-R (Retention, Recognition), and Digit Span (Forwards, Backwards). After raw test scores were regressed onto age for each group, age was no longer a significant predictor for most test scores, with exceptions for each age group. Conclusions This study created normative data for a diverse teleneuropsychological test battery in a Canadian population. Standard­ized scores generally fell within the average range, with the exception of TOPF and JLO scores, which may be explained by high educational attainment and virtual testing environment, respectively. The results suggest that the teleneuropsychological testing environment results in similar performance to in-person assessment.

Reinforcement learning has emerged as a transformative paradigm in retail operations, fundamentally altering how businesses approach dynamic decision-making processes. This article explores the mathematical frameworks, technical foundations, and practical applications of reinforcement learning across various retail domains. The integration of reinforcement learning into retail decision systems enables autonomous agents to learn optimal behaviors through direct environmental interaction, discovering strategies that often exceed human-designed heuristics. Beginning with an examination of the core components—agent architecture, environment modeling, and reward system engineering—the article progresses through the technical underpinnings of Markov Decision Processes, value function approximation techniques, and exploration-exploitation balancing strategies. These theoretical foundations manifest in practical retail applications including dynamic pricing optimization, inventory management, personalized recommendation systems, and store layout design. Implementation hurdles such as massive data needs, exploration uncertainties, black-box decision processes, and technical barriers remain significant, yet can be overcome through virtual testing environments, safety-bounded exploration frameworks, transparent AI methods, and purpose-built technology solutions. As retailers continue adopting these technologies, reinforcement learning is increasingly positioned as a competitive differentiator that enables more responsive, adaptive, and intelligent operations capable of balancing complex multi-objective optimization problems that traditional approaches struggle to reconcile.