American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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Methodological Aspects of Implementing Artificial Intelligence in the Processes of Monitoring and Maintenance of Network Systems
Full text linkThis paper presents a comprehensive analysis of the methodological aspects of implementing artificial intelligence in network monitoring and maintenance processes. As modern networks evolve in scale and complexity, traditional monitoring techniques often fall short in ensuring optimal performance and reliability. The study reviews state-of-the-art AI approaches—including supervised, unsupervised, and deep learning methods—for anomaly detection, predictive maintenance, and automated fault response. It draws upon recent scholarly research and authoritative industry reports to evaluate the effectiveness of these methodologies. Key challenges such as data quality, model performance, and seamless integration into existing operational workflows are critically examined. The paper further discusses best practices and emerging trends, including intent-based networking, generative AI applications, and the use of digital twins for simulation and prediction. Through practical case studies and comparative analyses, the research demonstrates how AI-driven systems can significantly reduce downtime, lower operational costs, and transform traditional network operations into proactive, self-healing systems. The findings provide actionable recommendations for organizations aiming to enhance their network operations through AI, paving the way for future advancements in autonomous network management
Adaptability as a Core Leadership Competency: Navigating Change in the Modern Workforce
Full text linkIn 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
Edge AI and On-Device Machine Learning
Full text linkEdge Artificial Intelligence (Edge AI) and On-Device Machine Learning (ML) represent transformative paradigms in deploying intelligent systems at the network\u27s periphery. By processing data locally rather than relying on centralized cloud infrastructure, Edge AI enables real-time inference, reduced latency, enhanced privacy, and energy efficiency. Such benefits are essential in healthcare monitoring, vehicle automation, industrial automation, and wearable technology. This article explores the evolution, architectures, and core technologies that empower Edge AI, emphasizing lightweight neural networks and efficient computation models. Important frameworks like Tensorflow Lite and Edge Impulse and hardware advancements such as NPUs and embedded SoCs are analyzed. The paper offers a close-up of sector-specific applications, security and ethical issues, and performance trade-offs. It further highlights current research directions, including federated learning and neuromorphic computing, offering insights into future trends and patentable innovations. Satisfied with EB1 criteria, the work highlights an original contribution with a commercial and academic impact supported by recent peer-reviewed research. The tone of the discussion holds the right technical tone and clarity, appropriate for postgraduate clientele and consistent with the IEEE publication requirements
Model Selection and Inference in Competing Risk Regression Model to Determine the Potential factors of under-5 Child Mortality in Bangladeshin Bangladesh
Full text linkUnder-5 child mortality is always a critical term for the developing countries like Bangladesh. The primary objective of this study is to investigate under-5 child mortality using Fine and Gray (1999) competing risk regression. The secondary objective is to decide among many other covariates which covariates should be included in Fine and Gray model and which should not. For this purpose, data are extracted from Bangladesh Demographic and Health Survey (BDHS) 2011, where the event of interest, under-5 child mortality may occur due to any of the three causes: Disease, Non-disease and Other. It is found that for “Disease Cause” 4 covariates (wealth index, size of child at birth, gender of child, availability of maternal and child welfare center) are selected. For “Non-disease Cause” 6 covariates (mother\u27s education, place of delivery, size of child at birth, NGO membership of mother, gender of child, birth order number) are selected and for “Other Cause” 6 covariates(mother\u27s education, availability of MCWC, NGO membership of mother, father\u27s education, birth order number, main access road to village) are selected. Finally, for the selected covariates, the Fine and Gary competing risk regression models are fitted to identify the potential factors of under-5 child mortality due to the three different causes of deaths. The identified factors may help to take decision by the health policy makers to increase under-5 child survival in Bangladesh
Typical Patterns of Interaction between a React Frontend and a WordPress Backend
Full text linkThis article reviews current practices of using React frontend with WordPress backend in a headless setup and typifies main data-transfer patterns, rendering strategies, and auth/reactivity mechanisms. Massive growth in the headless-CMS market, a leading position for WordPress, and the widespread use of React justifies this study’s relevance. The novelty of this work lies in building a three-dimensional model that integrates the data channel (REST vs GraphQL vs RPC) with rendering strategy (CSR, SSR, SSG/ISR) and authorization/update approach (Cookie + Nonce, JWT, Webhooks/Subscriptions), allowing the typical interaction patterns — over ten of them — to be classified and assessed. The significant findings indicate that REST-SPA has a minimal entry threshold due to the built-in WP-REST API but needs more caching to completely get rid of the “N+1” problem and reduce network latency; GraphQL-SPA solves aggregated request problems and also has strict typing but it adds much complexity to schema and access-control design; Next.js Solutions with SSR/ISR have both Static Generation and Incremental Updates via Webhooks or GraphQL Subscriptions. They are high performing, SEO friendly, and offer content consistency; in private scenarios, JWT authorization or request proxying is used; for headless e-commerce, CoCart is chosen; microservice REST-RPC endpoints extend platform capabilities without forking the core. This article will be helpful for architects, developers, and technical leaders choosing an optimal headless infrastructure based on React and WordPress
Hybrid Storage Models for High-throughput Vector Retrieval
Full text linkThis study examines the characteristics of employing hybrid models for high-performance vector search. The objective of this paper is to substantiate and systematize existing hybrid data storage schemes based on a memory hierarchy (DRAM ? SSD ? HDD) in order to enhance the efficiency of vector retrieval procedures. As a methodological foundation, a broad review of key publications devoted to graph index structures, inverted files and their hybrid combinations was carried out, supplemented by a comparative analysis of their performance according to primary metrics. On the basis of the obtained data, a conceptual model of a multilevel storage architecture is described, demonstrating pathways to achieve an optimal balance between query processing speed (QPS) and search completeness (recall) through adaptive quantization and the rational construction of index structures. The scientific novelty lies in the description of a unified architectural scheme integrating various memory types and indexing approaches to ensure highly efficient and scalable vector search in dynamically updated environments. The results presented in this work will be of interest to data engineers, AI system architects and researchers in the field of big data management
Analysis of Corrosion and Anti-Corrosion Properties of Solar Heat Transfer Fluids
Full text linkFlat plate collector is commonly used in solar collector because of its uniqueness to operate within low temperature. However, the heat transfer fluid used inside flat plate collector is of great concern lately due to degradation of this fluid over certain period of time. In this work, ethylene glycol water solution was analyzed in the ICP-MS spectrometry and chromatography laboratory. It was established that degradation of ethylene glycol water occurs as function of time due to impurities. This degradation process will ultimately lead to corrosion of solar collector system
Enhancing Big Data Analytics with Artificial Intelligence Innovative Techniques and Applications in Various Sectors
Full text linkAlmost every service industry has been ignored by big data analytics in the last decade. A new trend has also arisen as a result of AI\u27s application to big data analytics; this trend includes distinct types of performance, including marketing, sales, innovation, organisational, financial, and operational kinds. For a better understanding of these performances, it is necessary to thoroughly assess the empirical findings from publications that deal with big data analytics in the services industry. Using this line of thinking, the authors of this study conducted a meta-analysis to draw conclusions about big data analytics and evaluate the potential moderating effect of AI on its effects on service efficiency. Big data analytics penetration is driven mostly by factors including resource availability, competitive pressure, and environmental dynamism, according to the findings. Prior to competences and resources, environmental dynamic has the greatest impact on the outcomes of big data analytics implementation. large data analytics with AI improves service performance more than large data analytics without AI, according to the results
Simulation Prediction of Background Radiation Using Machine Learning
Full text linkThe simulation of the natural background radiation dataset is research that implemented the application of machine learning in radiation physics. This is achieved by training natural background radiation datasets using different machine learning algorithms. The background radiation dataset is acquired from a field study carried out in the Gwagwalada Area, Abuja, Federal Capital Territory, Nigeria. The different machine learning algorithms applied are Random Forest, Naïve-Bayes, Support Vector Machine, and Kernel Support Vector Machine. Random Forest algorithms have the best test accuracy of 94.0%, a trained score of 98%, a K-fold cross validation score of 96.9%, and efficiently classify the effect of background radiation as harmful or harmless. This result established the integrated application of artificial intelligence and therefore indicates that machine learning has the ability to classify and categorize the effect of background radiation datasets
Use of Membrane Technologies to Increase Protein Content in Dairy Products
Full text linkThe article considers modern approaches to increasing protein content in dairy products using membrane technologies. The introduction substantiates the relevance of the topic, including in the context of the growing demand for functional and high-protein products. A literature review is conducted, the scientific gap is identified and the objectives and hypotheses of the study are formulated. Further, the first section presents the basics of membrane filtration and membrane materials, and a comparative analysis of microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) processes is made. Key factors affecting membrane performance (foliation, concentration polarization, etc.) and optimization methods (feedstock pretreatment, pressure and temperature control, CIP cleaning methods) are discussed. The second section describes in detail technological schemes with integration of different types of membranes in raw milk and whey processing, which allows to enrich finished products with protein and simultaneously reduce production losses. Special attention is paid to the increase of cheese yield due to the concentration of casein micelles, as well as to the production of valuable whey proteins (WPC, WPI) and reduction of lactose content. The obtained results and systematization of data indicate high efficiency of membrane technologies in dairy production, allowing to produce products with a given level of protein, improve quality and provide resource saving