Development Of System Research Articles

Mathematical model of work optimisation in the development of automated information systems

The paper presents the main components of the mathematical model represent ing the work process when creating an information system. The development of automat ed information systems requires significant expenditure of both resources and time. The process of developing an automated information system consists of numerous stages that require careful planning and management, namely analyzing system requirements, design, programming, debugging, implementation and maintenance. Estimation of the labor inten sity of programming and debugging stages is based on the approach of M. Halstead and amounts to 60-70% of the total cost of information system development. The tasks solved in this study allow us to make a step in the direction of increasing the level of information systems’ automatization. Implementation of the mathematical model of work optimization will allow increasing the overall level of development automation.

К вопросу о формировании перечня приоритетных задач научно-технологического развития Российской Федерации

Currently, science and practice show that for the formation of high competitiveness and breakthrough efforts in the development of both individual organizations, industries, and the state economy as a whole, the possession of only soft skills is not enough. Current economic conditions, as well as high rates of change in both external and internal conditions of economic activity realization show that knowledge, skills, abilities, technologies are the most important key to the development of the country's economy and improvement of the quality of life. The greatest interest in this article is shown to the study of tools and methods of achieving the target indicators of priority tasks of scientific and technological development of the Russian Federation. And also to the formation of proposals to solve the existing problems in the system of adjustment and expansion of the list of priority tasks of the RF development. The main problems hindering the effective functioning of the system of adjustment and expansion of priority tasks of the RF development were identified. The key definitions of the project approach to the development of the national economy are specified. The interrelation of the main elements of the system of development and realization of priority tasks of the national economy development is established. Approaches and methods of solving the existing problems in the system of development and adjustment of priority tasks of the RF development are offered.

A novel approach for improving formation of soft zone in 22MnB5 and DP600 materials by resistance spot welding

Abstract In this study, 1.5-mm thick Al-coated hot formed boron alloy 22MnB5 with martensitic structure and Zn-coated DP600 with dual phase structures, which are frequently used together in the automotive industry, were used. Instead of traditional alternating current (AC), a new generation medium–frequency direct current (MFDC) resistance spot welding method was utilized in the joining process. The uniqueness of this work is that it aims to improve the properties of the soft zone formed in the heat effect zone (HAZ) of 22MnB5 materials during resistance spot welding (RSW). For this purpose, a fixture was designed and manufactured for regional rapid cooling (RRC) of the HAZ following the welding process. During RSW, 6.5 kA welding current, 3.6 bar electrode force and 500 ms welding time were used. This RRC method aims to reduce defects in the joint strength caused by hardness changes. Welding processes were carried out in molds designed and produced in accordance with tensile-shear and cross-tension tests. Afterwards, tensile-shear, cross-tension, and fatigue tests were applied to the welded samples. In addition, the effects of the developed system on the weld zones of these samples were examined by microstructure and hardness studies. The tensile-shear and cross-tension results revealed that the regional cooling exhibited approximately 4–6% higher strength, respectively. As a result of the hardness tests, it was observed that the hardness of the tempered soft zone in the 22MnB5 HAZ of the joint increased by 6% with RRC. However, it was determined that through this developed system, RRC in fatigue tests decreased in the fatigue life of the samples at all loads.

Advancements in nanotechnology for glaucoma detection and treatment: A focus on biosensors, IOP monitoring, and nano-drug delivery systems

Biosensors are essential tools designed to monitor physiologically significant data for diverse applications, including environmental pollution detection, food safety, medication research, and disease diagnosis. The integration of microfluidics, nanotechnology, and advanced electronics has significantly advanced the development of wearable and implantable biosensors for managing chronic diseases such as cancer, diabetes, and glaucoma. Among these, glaucoma remains a leading cause of visual impairment, predominantly driven by elevated intraocular pressure (IOP), with limited treatment effectiveness due to challenges like suboptimal drug bioavailability and therapeutic inefficiency. Despite advances, unmet needs in early diagnosis and sustained management persist, demanding innovative solutions.Recent advancements in nanotechnology offer transformative potential, addressing these challenges by enhancing both the detection and treatment of ocular diseases. This review explores cutting-edge developments in nano-drug delivery systems and continuous IOP monitoring technologies. Particular emphasis is placed on nanoparticle and nanofiber-based biosensors and contact lenses, which represent a promising leap forward in improving therapeutic precision and patient outcomes in glaucoma care. The manuscript uniquely contributes by synthesizing these advancements and highlighting their potential to bridge existing gaps in glaucoma management.

The Expert System Application to Diagnose Computer Damage Using UML Model (Unified Modeling Language)

The expert system application are developed in line with the existence of information technology. The development of expert systems aims to be a means of assistance to provide solutions in our lives. This expert system can help technicians get solutions quickly and can save time. Expert systems use computer technology to integrate, manipulate and display information or characteristics. Expert systems can also help in making better solutions. With the very rapid technological advances today, an idea or idea has emerged from the author to try to implement one of the expert system application programs into the quality of service activities of computer technicians. The author tries to build an application that will help to facilitate the provision of solutions to computer damage to hardware so that it can make it easier for users or technicians to get solutions quickly. The system to be created is "Designing an Expert System for Diagnosing Computer Problems Using Visual Basic" This system will use the prototype method and tools for modeling using UML (Unified Modeling Language). This system is built using the Visual Basic 6.0 application to process the Microsoft Access database.

The Practice of Large Language Models in Automated Question Generation: A Case Study of ChatGLM in High School Information Technology Curriculum

With the advancement of information technology, the field of education is undergoing transformation. In the teaching of high school information technology, scientific and efficient question formulation is crucial for enhancing the quality of education. Traditional methods of question formulation rely on the experience of teachers, leading to issues such as inconsistent difficulty levels and incomplete coverage of knowledge points. Large language models (LLMs), such as ChatGLM, leverage natural language processing and deep learning technologies to automatically generate questions that align with the curriculum, thereby improving scientific accuracy and precision, enhancing diversity and innovation, and catering to students’ personalized needs. Additionally, LLMs can enhance the efficiency of question formulation and reduce the burden on teachers. This paper explores the application value of large language models in the formulation of questions for high school information technology, through empirical research comparing the performance of human-generated and ChatGLM-generated questions in terms of accuracy, relevance, clarity, and willingness. The study selected two chapters, “Data and Information” and “Fundamentals of Algorithms,” and employed both human and ChatGLM-generated questions, inviting teachers to evaluate them. Through data analysis and statistical testing, we reveal the advantages and limitations of large language models in educational question formulation, providing insights for the intelligent development of educational assessment systems.

Development of a High-power Ultraviolet Laser System and Observation of Fast Coherent Rydberg Excitation of Ytterbium

Development of a High-power Ultraviolet Laser System and Observation of Fast Coherent Rydberg Excitation of Ytterbium

Integrating spatial cognition and SLAM for improved performance of autonomous material handling robots in dynamic stockyard environments

Purpose The purpose of this study is to detail the design and development of a robust and practical perception system for autonomous material handling robots (AMHRs) operating within industrial stockyards. This system aims to support simultaneous localization and mapping (SLAM) while generating large-scale spatial cognition, ensuring accurate, low-latency, and scalable operations in demanding industrial environments. Design/methodology/approach The proposed perception system integrates multimodal perception sensors, efficient algorithms and commercial hardware devices to o provide SLAM-based large-scale spatial cognition for distributed AMHRs. The system’s design emphasizes practicality, efficiency and readiness for real-world deployment, ensuring it meets the stringent requirements of accuracy, latency and scalability. Findings Experiments conducted in a real industrial stockyard environment demonstrate the practicality and robustness of the perception system. The system exhibits high performance in state estimation, stockpile modeling accuracy and motion spatial cognition, confirming its effectiveness for AMHR operations. Practical implications The developed system was practically used at Tianjin Port, demonstrating the potential for widespread industrial application, offering a scalable and efficient solution for AMHR operations. Its integration into diverse industrial settings can lead to significant improvements in material handling processes, contributing to enhanced productivity and operational efficiency. Originality/value This work presents an innovative perception system that combines advanced SLAM-based spatial cognition akin to that of the brain with practical deployment considerations. The system’s design and implementation address the specific challenges of AMHRs in industrial environments, providing a novel solution for enhancing the operational efficiency and adaptability of autonomous robots in stockyards and similar settings.

Sistem Pendukung Keputusan Pemilihan Penerima Bantuan Sosial Menggunakan Metode Simple Additive Weighting

Social assistance (bansos) is one form of government program that has become the main instrument in supporting the welfare of low-income communities in Cilaengka Wetan Village to meet their basic needs. However, the main challenge in implementing this program is the selection process for aid recipients which is often done manually, so that it risks causing inaccuracy and injustice in determining recipients. This study aims to develop a Decision Support System (DSS) for the selection of social assistance recipients in Cicalengka Wetan Village using the Simple Additive Weighting (SAW) method so that social assistance recipients are right on target and ensure that assistance is given to people who really need it. The Simple Additive Weighting (SAW) method was chosen because it is able to perform criteria-based calculations with objective and transparent results. The trial results show that the developed system can provide valid recommendations and is in accordance with the conditions of prospective recipients. This research assistance is expected to be a practical solution in social distribution, not only in Cicalengka Wetan Village but also in other areas with adjustment criteria. As a further development, an integration system with web-based technology or mobile applications can be carried out to improve the accessibility and efficiency of the decision-making process.

Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future

Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with its integration into cardiac surgery offering significant advancements in precision, efficiency, and patient outcomes. However, a comprehensive understanding of AI’s applications, benefits, challenges, and future directions in cardiac surgery is needed to inform its safe and effective implementation. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, Cochrane Library, Google Scholar, and Web of Science, covering publications from January 2000 to November 2024. Studies focusing on AI applications in cardiac surgery, including risk stratification, surgical planning, intraoperative guidance, and postoperative management, were included. Data extraction and quality assessment were conducted using standardized tools, and findings were synthesized narratively. Results: A total of 121 studies were included in this review. AI demonstrated superior predictive capabilities in risk stratification, with machine learning models outperforming traditional scoring systems in mortality and complication prediction. Robotic-assisted systems enhanced surgical precision and minimized trauma, while computer vision and augmented cognition improved intraoperative guidance. Postoperative AI applications showed potential in predicting complications, supporting patient monitoring, and reducing healthcare costs. However, challenges such as data quality, validation, ethical considerations, and integration into clinical workflows remain significant barriers to widespread adoption. Conclusions: AI has the potential to revolutionize cardiac surgery by enhancing decision making, surgical accuracy, and patient outcomes. Addressing limitations related to data quality, bias, validation, and regulatory frameworks is essential for its safe and effective implementation. Future research should focus on interdisciplinary collaboration, robust testing, and the development of ethical and transparent AI systems to ensure equitable and sustainable advancements in cardiac surgery.

Sistem Informasi Perpustakaan Berbasis Client Server di SMK Bintara Rancaekek

SMK Bintara Rancaekek still has an educational support system that has not been computerized, one of which is library management. The process of managing library data is handled by one officer, starting from searching for book data, recording loan transactions, return transactions, calculating fines, and generating reports, all of which are still done manually or have not been computerized. As a result, several issues arise, including difficulties in managing borrowing and returning data, time-consuming recapping, and the risk of recording errors and data loss. The purpose of this research is to assist officers in managing book data, visits, borrowing and returning transactions, as well as minimizing data loss and recording errors, simplifying the calculation of fines, and generating reports quickly. The method used in this research is the Rational Unified Process (RUP). The design uses analysis tools such as flowmaps, Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Data Dictionaries, and Structure Charts. The design and development of this library information system are expected to address the aforementioned problems and provide ease of operational work, especially in managing the borrowing and returning of library books.

Transactive memory systems in supply chain teams: role of lone wolf tendencies, technology ease of use and team collaboration

Purpose This study aims to understand the influence of lone wolf tendencies, technology ease of use and team collaboration on the development of transactive memory systems (TMSs) and, consequently, their impact on the performance of supply chain and operations teams. Design/methodology/approach By using a comprehensive operations management simulation involving 160 business student teams, this research tests five hypothesized relationships using structural equation modeling. Findings Lone wolf tendencies, technology ease of use and team collaboration contribute positively to an efficient TMS. This group cognitive system, in turn, strengthens a team’s competitive position and operational efficiency. This study highlights the need to reevaluate traditional management practices that discourage candidates with lone wolf characteristics. In addition, it shows that technology ease of use not only affects individual productivity as highlighted by the technology acceptance model but also influences the team knowledge system. Originality/value This study deepens understanding of the antecedents and consequences of TMSs in supply chain management. Lone wolf tendencies, characterized by independent work proclivity, are common in supply chain teams due to task complexities. Prior research on the impact of lone wolf behavior has yielded mixed results. Furthermore, although Industry 4.0 technologies are widely adopted in supply chain operations, it remains unclear how ease of using these complex technologies affects TMSs and team performance. To the best of the authors’ knowledge, this research is among the first to investigate the influence of lone wolf tendencies and technology ease of use on group cognitive systems.

Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.

Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern. In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems. Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits. These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.

Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation

Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the security posture of cybersecurity operations centers (defenders) through the development of ML-aided network intrusion detection systems (NIDS). Concurrently, the abilities of adversaries to evade security have also increased with the support of AI/ML models. Therefore, defenders need to proactively prepare for evasion attacks that exploit the detection mechanisms of NIDS. Recent studies have found that the perturbation of flow-based and packet-based features can deceive ML models, but these approaches have limitations. Perturbations made to the flow-based features are difficult to reverse-engineer, while samples generated with perturbations to the packet-based features are not playable. Our methodological framework, Deep PackGen, employs deep reinforcement learning to generate adversarial packets and aims to overcome the limitations of approaches in the literature. By taking raw malicious network packets as inputs and systematically making perturbations on them, Deep PackGen camouflages them as benign packets while still maintaining their functionality. In our experiments, using publicly available data, Deep PackGen achieved an average adversarial success rate of 66.4% against various ML models and across different attack types. Our investigation also revealed that more than 45% of the successful adversarial samples were out-of-distribution packets that evaded the decision boundaries of the classifiers. The knowledge gained from our study on the adversary’s ability to make specific evasive perturbations to different types of malicious packets can help defenders enhance the robustness of their NIDS against evolving adversarial attacks.

Deep learning empowered sensor fusion boosts infant movement classification

BackgroundTo assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors. These approaches are hardly comparable as all models are designed, trained and evaluated on proprietary/silo-data sets.MethodsWith this study we propose a sensor fusion approach for assessing fidgety movements (FMs). FMs were recorded from 51 typically developing participants. We compared three different sensor modalities (pressure, inertial, and visual sensors). Various combinations and two sensor fusion approaches (late and early fusion) for infant movement classification were tested to evaluate whether a multi-sensor system outperforms single modality assessments. Convolutional neural network (CNN) architectures were used to classify movement patterns.ResultsThe performance of the three-sensor fusion (classification accuracy of 94.5%) is significantly higher than that of any single modality evaluated.ConclusionsWe show that the sensor fusion approach is a promising avenue for automated classification of infant motor patterns. The development of a robust sensor fusion system may significantly enhance AI-based early recognition of neurofunctions, ultimately facilitating automated early detection of neurodevelopmental conditions.

Single-cell phenotyping of extracellular electron transfer via microdroplet encapsulation.

Electroactive organisms contribute to metal cycling, pollutant removal, and other redox-driven environmental processes via extracellular electron transfer (EET). Unfortunately, developing genotype-phenotype relationships for electroactive organisms is challenging because EET is necessarily removed from the cell of origin. Microdroplet emulsions, which encapsulate individual cells in aqueous droplets, have been used to study a variety of extracellular phenotypes but have not been applied to investigate EET. Here, we describe the development of a microdroplet emulsion system to sort and enrich EET-capable organisms from complex populations. We validated our system using the model electrogen Shewanella oneidensis and described the tooling of a benchtop microfluidic system for oxygen-limited conditions. We demonstrated the enrichment of strains exhibiting electroactive phenotypes from mixed wild-type and EET-deficient populations. As a proof-of-concept application, we collected samples from iron sedimentation in Town Lake (Austin, TX) and subjected them to microdroplet enrichment. We measured an increase in electroactive organisms in the sorted population that was distinct compared to a population growing in bulk culture with Fe(III) as the sole electron acceptor. Finally, two bacterial species not previously shown to be EET-capable, Cronobacter sakazakii and Vagococcus fessus, were further cultured and characterized for electroactivity. Our results demonstrate the utility of microdroplet emulsions for isolating and identifying EET-capable bacteria.IMPORTANCEThis work outlines a new high-throughput method for identifying electroactive bacteria from mixed populations. Electroactive bacteria play key roles in iron trafficking, soil remediation, and pollutant degradation. Many existing methods for identifying electroactive bacteria are coupled to microbial growth and fitness-as a result, the contributions from weak or poor-growing electrogens are often muted. However, extracellular electron transfer (EET) has historically been difficult to study in high-throughput in a mixed population since extracellular reduction is challenging to trace back to the parent cell and there are no suitable fluorescent readouts for EET. Our method circumvents these challenges by utilizing an aqueous microdroplet emulsion wherein a single cell is statistically isolated in a pico- to nano-liter-sized droplet. Then, via fluorescence obtained from copper reduction, the mixed population can be fluorescently sorted and gated by performance. Utilizing our technique, we characterize two previously unrecognized weak electrogens Vagococcus fessus and Cronobacter sakazakii.

Employment of uncrewed systems in attacks on critical infrastructure: a hybrid threat perspective. Security challenges related to recent developments in technology.

Background The uncrewed systems, known colloquially as drones are nowadays widely used in various roles, including strictly civilian, law enforcement and military. The pace of technological development is high, following modern technologies, like artificial intelligence. Due to the growing threat to critical infrastructure that includes physical attacks, a fundamental question arises: how the development of uncrewed systems transforms the threat to critical infrastructure. Methods Historical analysis of historical cases of uncrewed system development and employment was used to describe evolution of said systems. For assessment of possible threat to critical infrastructure, a scenario – based analysis was conducted. Seven scenarios, divided into three groups were described as typical contemporary scenarios of uncrewed system use. For assessment of influence of new and emerging technologies especially artificial intelligence, three further scenarios were analyzed. Results and Conclusions Scenario – based analysis show that the uncrewed systems has shown that evolution of uncrewed system, especially use of artificial intelligence increases potential threats to critical infrastructure, due to growing capabilities. Therefore, it is recommended that future research work on countering such type of threat is required.

Parameter optimization of the chloride mediator-based electrochemical advanced oxidation process for the treatment of commercial azo dyes and actual dyeing effluent

This work focused on the development of an electrochemical system for direct and indirect electrochemical advanced oxidation process (EAOP) with a chloride mediator by electrogenerated active species for the degradation of unused toxic azo dye molecules of textile wastewater and the connection between cathodic and anodic processes. The experiments were carried out in a batch electrochemical cell for the degradation of Direct Red 1 (DR 1) and Reactive Orange 14 (RO 14) dyes. Eight different combinations of iron (Fe), stainless steel (Ss), copper (Cu), and graphite (Gr) electrodes were utilized as the cathode and anode. The influences of effluent initial concentration, pH, supporting electrolyte concentration, temperature, inter-electrode distance, current density, agitation rate, retention time, and radical scavengers on dye degradation have been critically examined. The optimum conditions were obtained at the monopolar-parallel (MP-P) connected electrodes (Gr- Ss) with 7.0 pH, 7.25 V voltage, 0.4 A current, 353 A/m2 current density, 1 g/L NaCl, and a distance of 3 cm between the electrodes. Under these conditions, the removal efficiency reached 95.6% within 60 min of operation. Additionally, a multiple regression analysis was performed to assess how operating conditions affected the effectiveness of dye removal. The actual textile wastewater was examined as well, and the results revealed significant decolorization. Finally, due to its great capacity, the EAOP system is a promising large-scale technique for treating dye-contaminated effluents.Graphical

Continuous Flow Electrocoagulation System for Enhanced Phosphorous Removal in Decentralized Wastewater Treatment Systems

Decentralized wastewater treatment systems (DWTS) are significant contributors to the eutrophication of surface water bodies due to a lack of treatment mechanisms that target dissolved phosphorus removal. Existing advanced treatment systems are expensive to operate, large in nature, and require frequent maintenance, making them unattractive to DWTS owners. This study aims to investigate the development of a continuous flow treatment system that uses electrocoagulation (EC) to remove dissolved phosphorus from small wastewater streams such as septic tank effluent (STE). Operational parameters, including system hydraulic retention time (HRT), applied current density, and wastewater composition, were optimized to maximize total phosphorus (TP) removal most cost-effectively. Using an HRT of 10 min, an applied current density of 2.0 mA/cm2, and an influent concentration of 20 mg/L, the orthophosphate (OP) and TP removal percentages achieved were 99.9 and 88.1%, respectively. Under these conditions, the average effluent Al3+ concentration in the treated effluent was measured to be 1.0 mg/L while the total suspended solids concentration was measured to be 51 mg/L. The operation cost was estimated to be 0.056 CAD/m3. The results demonstrate that the EC reactor is effective in removing dissolved phosphorus from wastewater and is therefore a viable option in mitigating the risk of downstream eutrophication caused by inadequately treated STE.

What Quantum Can Learn from Classical Computer Engineering

Quantum computing represents a paradigm shift requiring reconceptualization of algorithms, architectures, and software. Although much is new, there is much that quantum computing can learn from traditional classical computer engineering. In this Special Issue, we focus on examples of quantum research inspired by classical computer engineering, ranging from quantum machine learning and image processing to the extension of the “multi-core” concept to quantum computing. Hardware/software co-design is difficult when communities have their unique jargon and disjoint problem domains. For parallel programming on classical computers, an approach for communicating application developer needs emerged around identifying key bottlenecks that limited software. It was difficult to guess features of future applications, but experienced developers knew features of a computer system that would limit software performance. For each feature, a small kernel was developed with the idea that if the kernel ran well, a system would be an effective system for application developers. This set of kernels was called the Parallel Research Kernels . They have been useful to guide design of parallel computers. We hypothesize that an analogous tool for quantum computing, called Quantum Research Kernels , may aid hardware/software co-design of quantum computing systems. In this article, we motivate the Quantum Research Kernels and provide working examples.