American Scientific Research Journal for Engineering, Technology, and Sciences

The Evolution of English Language Teaching Methods in the Information Age

Kulp Katerina — Fri, 06 Jun 2025 00:00:00 +0000

This study traces and analyzes the evolution of English language teaching methods with the emergence of information technologies. The research describes modern teaching methodologies and the opportunities that have become available to educators. It also examines the relationship between the mechanics of learning new information and contemporary methods of its delivery. The findings suggest that technology-driven methods are more effective than traditional approaches. The relevance of the study is driven by the rapid development of information technologies and their integration into educational practices. Special attention is given to the interaction between teachers and students with artificial intelligence. The study outlines AI capabilities and their applications in pedagogy, highlighting the advantages of AI-assisted foreign language learning. The research concludes that the advent of the digital era does not necessarily lead to the adoption of all its benefits by every educator. Additionally, it is argued that while teaching methodology as an algorithm remains largely unchanged due to digitalization, the development of information technologies has significantly influenced the ways material is delivered and reinforced. This article will be useful for both novice and experienced educators seeking to automate certain processes, enhance the efficiency of their lessons, and introduce greater diversity into their teaching methods.

The Impact of Microservices Architecture on System Scalability

Saurav Sharma — Sat, 07 Jun 2025 00:00:00 +0000

This article analyzes the impact of microservices architecture on the scalability of information systems, drawing on theoretical foundations, practical experience from large-scale transitions (with a focus on Netflix as a case study), and contemporary performance optimization methods. The study includes a comparative analysis of monolithic and microservices-based approaches, highlighting the advantages of independent scalability, fault tolerance, and development flexibility. The methodology combines comparative analysis of publicly available research, case studies, and the evaluation of caching, load balancing, containerization, and monitoring tools. The findings show that integrating microservices architecture with modern management technologies significantly enhances the efficiency of distributed systems—an outcome that is increasingly vital to the advancement of the digital economy. The material presented will be of interest to researchers in the field of distributed computing, software architects, and IT infrastructure specialists seeking to improve system scalability through the adoption of microservices. The publication may also appeal to graduate students and professionals aiming to conduct in-depth theoretical and engineering analyses of dynamic, flexibly scalable solutions for modern computing systems.

Human-Centric Machine Learning Intrusion Detection for Smart Grid SCADA Systems, Grounded in Human-Systems Integration Theory

Kelech P. Okpara — Fri, 13 Jun 2025 00:00:00 +0000

Protecting Smart Grid SCADA systems, a vital component of U.S. critical infrastructure demands technical rigor and human-centered design to ensure real-world effectiveness. While prior work has delved into technical performance in threat detection, achieving high accuracy and low false positive rates (FPRs), few studies have systematically evaluated how operator interaction and cognitive load influence actual detection and response workflows. The 2015 Ukraine power grid attack, which disabled electricity for approximately 230,000 residents for several hours and revealed that operators struggled to interpret legacy alarms under duress, underscores the necessity of integrating human factors into machine learning-based intrusion detection systems (ML-IDS). This study develops and evaluates a human-centric ML-IDS pipeline that embeds explainability and interface design principles from Human-Systems Integration (HSI) theory. By comparing standard ML models (Random Forest, XGBoost, SVM) with equivalent models augmented by HSI-guided dashboards, we demonstrate that operators using the human-centric pipeline achieved a 28% reduction in FPR compared to baseline ML-IDS outputs, translating to approximately 7 fewer false alarms per 100 alerts, reducing operator alert fatigue and improving average response times by nearly 20 seconds per incident (mean reduction = 19.8 s, SD = 4.2 s, N = 12). Usability metrics further support these findings: the System Usability Scale (SUS) score of 76.2 (above the 68 thresholds for above-average systems) indicates strong operator acceptance, while a NASA-TLX score of 39.4 (approximately 20 points below the 60–70 range observed in traditional IDS interfaces) suggests substantially reduced cognitive workload. These results confirm our hypotheses: H1, that HSI-informed interfaces improve detection effectiveness, and H2, that reduced cognitive load correlates with lower false alarm rates. We conclude that embedding human-centric design into ML-IDS not only maintains high accuracy (0.96 vs. 0.94 for baseline) but materially enhances operational readiness by aligning technical outputs with real-world human decision-making processes.

Assessing the Nutritional Status and Nutrition Education Knowledge of Patients with Type 2 Diabetes in Bamenda Regional Hospital, North -West Region -Cameroon

Sat, 07 Jun 2025 00:00:00 +0000

Type 2 diabetes (formerly termed non-insulin dependent or adult-onset diabetes) occurs when the body does not produce enough insulin (relative insulin deficiency) or cannot use the insulin it produces effectively (insulin resistance) and it could be due to ?-cell dysfunction and dysregulated hepatic glucose production.Effective management of diabetes mellitus requires comprehensive nutrition education. However, the relationship between patients' nutritional knowledge and their nutritional status remains underexplored in Cameroon, particularly in the Bamenda Regional Hospital. The aim of this study was to evaluate the nutrition education knowledge and nutritional status of diabetic patients at Bamenda Regional Hospital. A cross-sectional study was conducted involving 152 diabetic patients receiving care at the Bamenda Regional Hospital. Data collection was done through the administration of a structured questionnaire to obtain information on sociodemographic characteristics and nutrition education knowledge. Anthropometric measurements( weight and height) was carried out and used to calculate BMI (weight/height²). This was used to assess nutritional status following the standard procedures and compared with the World Health Organisation(WHO) classification standards. Dietary intake was assessed using 24 -hour recall and food frequency questionnaire methods.The data was analysed using SPSS (version 21).The level of significance was set at p-value< 0.05.

The study revealed that 62.2% of the diabetic patients were female, 53.9% were?45years,62.5% had low income .Moreover, 23.3% were overweight(BMI;24.9 - 29.9kg/?) and 58.6% were obese (BMI;? 30kg/?). A significant difference was observed between low income, nutritional status and nutrition education knowledge(p<0.05).: The study revealed that diabetes patients in Bamenda Regional Hospital could be having inadequate knowledge which led to poor food choices and financial limitations could have restricted access to healthy foods. Lifestyle modification is the best approach and this can be achieved through enhancing nutrition education programs tailored to local dietary practices which may improve patients' nutritional status and diabetes management outcomes.

Technologies and Methods for Optimizing Web Application Performance

Anastasiia Perih — Fri, 23 May 2025 00:00:00 +0000

The article reviews modern technologies and methods applied to optimize web application performance, emphasizing the direct impact of site speed on user experience, business performance, and competitive positioning. The authors analyze contemporary approaches for enhancing both front-end and back-end performance. Front-end strategies discussed include code splitting, lazy loading, server-side rendering (SSR), image optimization, and minimizing render-blocking resources. Back-end methods encompass various caching strategies, database query optimization, effective API design—particularly comparing GraphQL and REST—and deployment of Content Delivery Networks (CDNs) alongside edge computing solutions. A structured review methodology was applied, synthesizing recent peer-reviewed literature, expert reports, and empirical case studies from industry settings published within the past five years. Quantitative data are provided, illustrating significant performance improvements, including latency reduction, increased throughput, and enhanced user interaction metrics. The authors highlight practical implementation considerations and trade-offs inherent to each technique. Presented findings contribute valuable insights for developers, system architects, and researchers aiming to deliver faster, more reliable, and user-friendly web applications.

Enhancing Manufacturing Efficiency through the Integration of RPA and Power Automate with Camstar MES

Satish Kumar Nalluri, Varun Teja Bathini — Sat, 07 Jun 2025 00:00:00 +0000

The integration of Robotic Process Automation (RPA) and Microsoft Power Automate with Siemens’ Camstar Manufacturing Execution System (MES) is transforming manufacturing workflows by streamlining operations, enhancing productivity, and reducing operational costs. This study investigates the impact of this integration on manufacturing efficiency, cost savings, and scalability. A mixed-methods approach is employed, combining qualitative case studies and quantitative performance metrics analysis from various industries. The research demonstrates that the integration of RPA and Power Automate with Camstar MES improves data accuracy, reduces manual intervention, and accelerates production processes. Results from case studies indicate significant cost savings, enhanced system scalability, and improved decision-making due to real-time data analytics. While the integration presents challenges, such as system compatibility and employee training, the benefits of streamlined workflows and operational agility outweigh these obstacles. This paper concludes with recommendations for manufacturers seeking to adopt automation technologies, emphasizing the need for careful planning, stakeholder engagement, and continuous monitoring to ensure successful implementation. By adopting RPA and Power Automate, manufacturers can achieve a more agile, efficient, and cost-effective production environment.

Machine Learning-Based Detection of Fake Product Reviews and News Articles

Anand Patel — Tue, 27 May 2025 00:00:00 +0000

With the proliferation of online platforms, detecting fake content such as fake reviews and fake news has become a critical challenge for ensuring the authenticity and reliability of digital information. This paper presents a comprehensive survey of machine learning (ML) techniques and models applied to fake review and fake news detection. By leveraging advanced Natural Language Processing (NLP) methods and hybrid machine learning approaches, the paper evaluates various algorithms including Support Vector Machines (SVM), Random Forests, Long Short-Term Memory (LSTM) networks, and ensemble models for their performance in detecting deceptive content. Key metrics such as accuracy, precision, recall, and F1-Score are analyzed across multiple datasets to determine the effectiveness and robustness of these approaches. Additionally, this study explores domain-specific challenges, including the handling of imbalanced datasets, linguistic nuances, and real-time detection requirements. The paper concludes by outlining future directions, emphasizing the need for enhanced models capable of addressing evolving deception techniques and integrating contextual factors for more accurate predictions.

The Use of Artificial Intelligence in the Nail Industry: from Trend Forecasting to Process Automation

Yana Pidhorna — Tue, 20 May 2025 00:00:00 +0000

This article examines the potential and specific applications of artificial intelligence (AI) tools in the nail industry, focusing on trend forecasting and process automation. The relevance of this topic arises from the growing imbalance in the industry: clients increasingly demand personalized services and fast service delivery, while salons struggle with a shortage of skilled professionals and outdated demand forecasting methods. The purpose of this study is to systematize scholarly and expert perspectives on the integration of AI to address these challenges, identifying key technological and ethical concerns. The main contradiction lies in the tension between the economic efficiency of automation (e.g., reducing service time) and the risks of service dehumanization (potential loss of customer loyalty due to diminished emotional interaction). The findings suggest that AI-driven solutions—such as robotics and predictive trend analytics—can significantly transform and enhance the industry. However, successful implementation requires hybrid models in which algorithms complement rather than replace human professionals. The study also includes an analysis of specific case studies (Umia, Clockwork) that highlight the synergy between intelligent systems and the "creative core" of beauty services. Special attention is given to the challenges associated with AI integration. The insights presented will be valuable to beauty salon owners, developers of beauty technologies, and researchers investigating AI’s impact on niche service sectors.

Application of Artificial Intelligence and Machine Learning in Seismological Studies

Tolulope Esther Awopejo, Peter Oluwasayo Adigun, Nelson Abimbola Ayuba Azeez — Fri, 23 May 2025 00:00:00 +0000

Seismological studies have traditionally relied on classical statistical models and manual interpretation to detect, analyze, and predict earthquake events. However, the growing complexity and volume of seismic data have necessitated more efficient and adaptive approaches. This study explores the integration of artificial intelligence (AI) and machine learning (ML) techniques into seismology. This study highlighted the capacity of AI and ML to revolutionize seismic data processing and interpretation. Majorly, the study reviewed findings on algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), support vector machines (SVMs), and unsupervised clustering methods. Also, AI systems such as WaveCastNet, SCALODEEP, BNGCNN, Cycle-Jnet, SASMEX, and UREDAS were reviewed in areas that improved accuracy in earthquake detection, earthquake predictions, and earthquake analysis.

Leveraging Artificial Intelligence and Data Analytics for Decision-Making in IT Project Management

Ievgenii Lysenko — Fri, 13 Jun 2025 00:00:00 +0000

The article focuses on enhancing the justification of managerial decisions in large IT projects, characterized by persistent instability and escalating budgetary and schedule risks, through the systematic integration of artificial intelligence and advanced data analytics. The objectives of this study are twofold: first, to comprehensively describe the mechanisms for deploying predictive models and generative AI tools at every phase of the IT project life cycle; and second, to empirically validate the claimed effects using the PwC CEE IT practice. The novelty of the work lies in the combination of predictive machine learning, Monte Carlo simulations, AI scoring and the Copilot generative planner in a single decision-making loop, as well as in the fact that the author described in detail his experience of implementing and adapting these technologies in the real PMO process of PwC CEE IT. This article will be helpful to project leaders, PMO analysts, and developers of decision-support systems in IT project management.

Cybersecurity in Autonomous Vehicles: Safeguarding Connected Transportation Systems

Sandeep Dommari — Tue, 27 May 2025 00:00:00 +0000

The increasing integration of autonomous vehicles (AVs) has revolutionized the transport sector, with improved safety, efficiency, and convenience. However, as AVs become more interconnected and integrated into advanced transport systems, the interconnectivity-driven cybersecurity threats present a serious challenge. Current security solutions tend to treat individual systems without taking into account the complexity emanating from interconnected networks, real-time data exchange, and advanced AI-based decision-making systems characteristic of autonomous vehicles. This research tries to fill the crucial gap in autonomous vehicle system cybersecurity frameworks, emphasizing the adoption of a holistic, multi-level approach to secure the vehicle and communication networks. The study explores significant vulnerabilities in AVs, such as vulnerability to remote hacking, data integrity issues, and the risks of system crashes that can jeopardize the vehicle occupants and external stakeholders. It evaluates the effectiveness of current cybersecurity and identifies the loopholes in safeguarding the complex infrastructure behind connected transportation systems. The study also identifies the increasing importance of artificial intelligence and machine learning in identifying and preventing cybersecurity threats in real-time, offering a new direction for proactive threat management. Through an interdisciplinary methodology, the paper proposes a framework for securing AVs and networked transportation infrastructure that uses high-level encryption, AI-assisted anomaly detection, and robust incident response plans. By bridging the cybersecurity gap to the specific autonomous system challenges, this study aims to make it possible to build secure, resilient transportation technology that can scale safely in an increasingly interconnected world. The findings aim to educate policymakers, manufacturers, and researchers on the best practices for securing the autonomous transportation system of the future.

Integration of WebAssembly in Performance-critical Web Applications

Poltavskyi Dmytro — Fri, 23 May 2025 00:00:00 +0000

This article explores the integration of WebAssembly into high-performance web applications as a response to the increasing demands for computational power, scalability, and security in the rapidly evolving landscape of web technologies and the Internet of Things (IoT). The study substantiates the relevance of transitioning from traditional JavaScript to WebAssembly, which allows code written in C/C++ or Rust to be compiled into a compact binary format, delivering near-native execution speed. The article analyzes the architecture of WebAssembly, its advantages, and its integration potential with other technologies, such as WebGPU for accelerated parallel computations. Special attention is given to the current limitations of WebAssembly (e.g., the lack of native garbage collection, debugging difficulties, and challenges in cross-language integration) as well as its promising development directions, including the standardization of WASI and enhancements through multithreading and SIMD support. In comparative experiments on 1024 × 1024 matrix multiplication, the SIMD?enabled WebAssembly module with block?optimized memory access outperformed the optimized JavaScript implementation by 1.64 × and delivered a 4 × improvement over the unvectorized Wasm build, while offloading computations to WebGPU achieved an ~50?fold reduction in execution time for both JavaScript+WebGPU and Wasm+WebGPU configurations. These results substantiate that the integration of WebAssembly and WebGPU brings near?native and GPU?accelerated performance to browser?based applications, laying a quantitatively validated foundation for high?load web and IoT systems.The paper demonstrates a way to accelerate client data processing using a combination of Web Assembly and Web GPU. The results of a comparative experiment are presented. This article will be of interest to professionals in web development and systems architecture who aim to optimize computational workflows and maximize the performance of modern web applications via WebAssembly. Additionally, the material provides valuable insights for researchers engaged in the analysis and development of advanced methodological approaches to optimizing high-load information systems.

Using AI Assistants to Enhance Information Security Efficiency

Sergei Beliachkov — Sat, 14 Jun 2025 00:00:00 +0000

This article explores the potential of deploying virtual AI assistants to strengthen information security in light of the rapid evolution of digital technologies and the growing complexity of cyber threats. The study addresses the organizational aspects of implementing AI-based solutions—including threat notifications, user training, monitoring, and analytics—and the technical integration of such assistants with existing information security systems. This includes practical steps such as registering Telegram bots, leveraging Google Apps Script, and integrating with the OpenAI API. Special attention is given to challenges and limitations, such as technical vulnerabilities, false positives, ethical and legal considerations, and functional constraints in complex scenarios. The methodology is grounded in a review of findings from related studies. Results indicate that the integration of innovative AI-driven solutions holds strong potential for advancing the field of information security. Future research should focus on improving Explainable AI algorithms, enhancing the protection of transmitted data, and developing a regulatory framework to support such systems' safe and ethical use. The insights presented in this article will be of particular interest to cybersecurity professionals, researchers, and systems architects focused on the development and deployment of AI-powered approaches for improving threat detection and mitigation mechanisms in information security.

Riding the Green Rails: Revolutionising Indian Railways with Renewable Energy

Aparajita Rai — Sat, 07 Jun 2025 00:00:00 +0000

The document focuses on the green initiatives of Indian Railways (IR) to transform into a more sustainable and environment friendly mode of transportation by incorporating renewable energy sources (RES) into its electrification processes. The paper emphasises IR's commitment to achieving Net-Zero Carbon Emissions by 2030 and its strategy to integrate RES, such as solar and wind energy, to power railway electrification. It explores the challenges and potential solutions related to achieving this green initiative, including the use of hybrid microgrids or round-the-clock (RTC) power supply and the adoption of energy-efficient measures in rolling stock design and operations. The document also highlights the importance of collaborative efforts from various stakeholders, to successfully achieve IR's goal of becoming an environmentally sustainable rail transport system by 2030. The paper provides a comprehensive overview of IR's initiatives to reduce carbon emissions and embrace sustainable practices, emphasising the potential of renewable energy sources to transform the railway network into a cleaner, greener, and environmentally sustainable mode of transportation. It underscores the significance of these efforts in reducing carbon emissions, promoting energy efficiency, and contributing to India's broader environmental goals of sustainable development with self-reliance.

Adaptability as a Core Leadership Competency: Navigating Change in the Modern Workforce

Olena Derkach — Tue, 20 May 2025 00:00:00 +0000

In contemporary firms dealing with technology upheavals, evolving workplace arrangements, and global uncertainty, adaptive leadership has emerged as a crucial competence. In order to investigate how it affects employee retention, workplace innovation, and crisis management, this study synthesizes empirical data and theoretical models. Regression analysis, structural equation modeling, and case studies are used in the study to identify important mechanisms that help leaders develop resilience and strategic agility. These mechanisms include career adaptability, participative change management, and open communication. The findings show that in hybrid work environments, adaptive leadership boosts employee engagement, improves crisis response, and fortifies organizational learning. By combining several leadership contexts, this research provides a comparative viewpoint and offers insights into useful tactics for developing adaptation. In addition to offering future areas for study on long-term leadership adaptability in changing work environments, the findings highlight the necessity for businesses to incorporate this management style development into training programs. Leaders may more effectively manage difficult situations and promote long-term organizational success by being aware of these dynamics.

Creating a Multi-tier Architecture for Web Applications: Design and Implementation

Danylo Sereda — Sat, 07 Jun 2025 00:00:00 +0000

In today's world, where web applications are becoming more complex and diverse, the need for a layered architecture is particularly relevant. This architecture offers flexibility, scalability and ease of management, which is extremely important in the context of rapidly increasing requirements for information systems. Designing and implementing effective architectural solutions is becoming a crucial factor for the successful development and operation of web applications. Developers often face the limitations of a monolithic architecture, where every change in the code can unpredictably affect the entire system. This leads to difficulties in project management, difficulties in adding new features, and problems scaling the application. As a result, the risk of system failures increases and the rate of implementation of new features decreases. The study shows how a layered architecture can significantly reduce the dependency between system components, improve testability, and simplify the implementation of changes.