American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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Modern Trends in Automating ETL Pipelines in Azure
Full text linkThe study is aimed at systematizing and analyzing contemporary trends in the automation of ETL pipelines within the Microsoft Azure cloud ecosystem. The objective of the work is to identify key paradigms, toolsets and architectural approaches, as well as to develop a scientifically grounded model for selecting an optimal technology stack depending on the specifics of business tasks. The methodological basis includes a comprehensive analysis of current scientific publications, technical documentation and industry reports, as well as a comparative evaluation of the leading Azure services: Data Factory, Databricks and the newest Microsoft Fabric platform. As a result of the study, the dominant trends have been identified: the shift from classical ETL to ELT, large-scale adoption of serverless architectures, active development of low-code/no-code solutions and the emergence of the Data Lakehouse concept as a universal data repository. Within the framework of the work, a decision matrix for selecting an automation tool is proposed, based on the criteria of transformation complexity and the need for an integrated analytics platform. It is concluded that the evolution of automation tools in Azure is progressing from a set of disparate services toward fully integrated platform solutions, which fundamentally changes the methodology of data lifecycle design and management. The results of the study are of practical value for data architects and engineers, as well as for IT department leaders responsible for developing and implementing data management strategies in a cloud environment
Isolation and Molecular Identification of Bacillus Subtilis in Petroleum-Contaminated Soil
Full text linkPetroleum pollution poses a significant threat to soil health and ecosystem stability, particularly in oil-producing regions. This study aimed to isolate and identify hydrocarbon-degrading bacteria from contaminated soil samples. Four soil samples were collected, preserved, and analyzed for microbial growth. The isolate with the highest colony-forming unit count was subjected to Gram staining, biochemical tests, and molecular identification using 16S rRNA gene sequencing. The isolate with the highest colony-forming unit count was subjected to Gram staining, biochemical tests, and molecular identification using 16S rRNA gene sequencing. The bacterium was confirmed as Bacillus subtilis, showing positive catalase and motility reactions, and negative oxidase activity. BLAST analysis revealed 100% sequence identity with B. subtilis strain 17A. These findings validate the potential of B. subtilis as a native bioremediation agent for petroleum-contaminated soils, offering a sustainable solution for environmental restoration
Detection of Rare Events: The Need to Know the Customer
Full text linkThe prediction of customer complaints based on a time series of invoices is a two-stage process consisting of determining anomalies in the sequence of invoices and assessing the response of the customers to these anomalies. In the telecommunication sector, the average complaint rate is approximately 10?? hence the prediction of customer complaints falls in the realm of rare event detection. Detecting rare events poses a significant challenge when working with unbalanced datasets. In machine learning applications, oversampling of the minority class and under sampling of the majority class in the training set are well-known preprocessing tools for creating a more balanced set. In previous work, [14] we proposed a cluster based under sampling approach as an alternative to random under sampling of the majority class, based on splitting heterogeneous data into homogeneous subsets, using Principal Component Analysis, to reduce variability within clusters. In the present work we propose a method for assessing the response of the customers to anomalies detected in the time series of invoices
Sustainability and Circular Economy in Textile and Apparel Industry in Bangladesh
Full text linkThe textile sector is one of the most important and fifth largest industrial sectors in the world. Textile sector has created Job opportunity for Millions of workers around the world. The apparel industry plays a great role in economy, employment, investment and revenue all around the world. Textile recycling is a procedure that transforms old or unwanted textiles into new products. This helps to decrease the amount of textile wastage and save assets. Over the last few decades the rate of both pre- and post-consumer textile waste generation has increased significantly. Textile recycling involves reprocessing post-industrial or post-consumer textiles into new products, the term recycling refers to the conversion of textile waste into something approximating the same value. Textile recycling generally includes mechanical and/or chemical processes that turn textile fabrics back into their fiber components to then be remanufactured into fabrics. Post-consumer waste results in lower-quality recycled fiber due to degradation during wear, therefore, only pre-consumer waste is typically recycled mechanically. A circular economy can assist by the development of right choices in the design of products, selection of resources, production, retailing and consumption phases and ultimately in the end of life of the products. Worldwide, 75% of textile waste is landfilled, while 25% is recycled or reused. Landfilling of textile waste is a prevalent option that is deemed unsustainable. Promoting an enhanced diversion of textile waste from landfills demands optimized reuse and recycling technologies various textile reuse and recycling technologies are available and progressively innovated to favor blended fabrics. This Paper highlights the process of mechanical and chemical recycling of Textiles. Benefits of circular economy and how to overcome the Challenges of Circular Economy. Also, will presents the sustainability and efficiency of the Bangladeshi textile industry
From Prescription to Performance: A Comparative Analysis of SBC 801 and ADB Fire Codes
Full text linkThis report is intended to compare the main elements of fire safety regulations across the Saudi Fire Code (SBC 801) and the UK’s Approved Document B (ADB). It studies how SBC 801 was first introduced, the logic behind its development, and how it is based on the county specific features of Saudi Arabia while supporting Vision 2030. A detailed comparison with ADB is shown focusing on key areas of fire safety such as occupancy classification, means of warning, and access to the building for fire services. The report summarizes comments from professionals on the simplicity of applying SBC 801, how well it works, its weaknesses and the cost factors involved. This analysis points out the significance of SBC 801 for a safe and environmentally sound built environment in Saudi Arabia and explains the different factors involved in its regulations
Exoplanet Detection Using Kepler Mission Data with Machine Learning
Full text linkThe search for habitable planets beyond our solar system has long captivated the scientific community and remains one of the foremost pursuits in modern astronomy. With the advent of space-based missions, such as NASA’s Kepler telescope, our observational capabilities have expanded significantly, resulting in vast volumes of high-quality astronomical data. This data deluge necessitates the development of scalable, automated methods to support astronomers in efficiently analyzing and interpreting these observations. In recent years, machine learning has emerged as a powerful paradigm for automating complex, human-intensive tasks. This study investigates the application of supervised machine learning techniques to the detection of exoplanets using data from NASA’s Kepler mission. The data set comprises Kepler Objects of Interest (KOIs), including both physical and orbital parameters, along with their confirmed classification. We evaluate a range of supervised classifiers, spanning probabilistic, decision tree-based, and neural network models. Our best-performing model, Histogram Gradient Boosting, achieves a precision of 94.6% and a recall of 94.1% on a held-out test set. These results underscore the promise of machine learning in advancing exoplanet detection and offer a pathway toward automating the discovery of planetary systems beyond our own
Auto-Scaling Techniques for Container Workloads in Kubernetes Clusters
Full text linkThis paper presents a comparative study of automatic scaling mechanisms in Kubernetes clusters. The objective of this study is to conduct a comparative analysis of various techniques for automating the scaling of containerized applications in Kubernetes clusters. The methodological foundation of the research comprises a systematic review and analytical processing of current scientific publications in the field. The work examines the architectural principles, key configuration parameters, and built-in limitations of traditional tools, including the Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler. Particular attention is devoted to advanced solutions designed to enhance the adaptability and predictability of scaling. These include event-driven scaling using KEDA, high-efficiency node management with Karpenter, and the implementation of predictive strategies based on machine learning models. The scientific novelty of the study lies in the description of a comparative classification model of autoscaling techniques, which enables the formulation of clear recommendations for selecting the optimal strategy based on the type of workload: microservice web applications, big data processing pipelines, or resource-intensive machine learning tasks. The analysis suggests that to achieve high performance and resilience, it is advisable to combine various approaches — including horizontal, vertical, and cluster scaling — supplemented by heuristic or predictive methods. The findings will be valuable to DevOps engineers, cloud system architects, and researchers focused on optimizing operational performance and resource management in modern distributed environments
Impact of Strict Building Code Enforcement on Urban Risk Mitigation
Full text linkThe article presents an analysis of the role of strict compliance with building codes as a key mechanism for reducing urban vulnerability to natural and technological hazards. The study is based on an interdisciplinary approach that integrates urban studies, engineering seismology, social geography, and risk management. Particular attention is given to the comparison of empirical data and models that reveal the impact of law enforcement practices on the scale of informal construction, the dynamics of seismic losses, vulnerability to floods, and the probability of urban fires. It is found that strict enforcement of building codes reduces the likelihood of destruction and casualties during earthquakes, limits damage from hydrological events, and strengthens trust in state institutions. Comparative analysis shows that physical resilience depends on engineering solutions, while social resilience is determined by the uniformity of enforcement and the adaptive capacity of the population. It is established that current codes are limited by the absence of multi-hazard scenarios, regional disparities in application, and the gap between formal standards and informal construction practices. Such integration minimizes the cumulative effect of risks and provides a conceptual framework for assessing the role of building codes in threat management, opening prospects for digital monitoring and the implementation of sustainable urban development models. The article will be useful for professionals in urban studies, design, risk research, emergency management, and resilience strategy development
Methodological Approaches to Emergency Recovery of Information Systems in Multi-Contour Environments
Full text linkThe article examines a methodological approach to the emergency recovery of information systems in multi?contour environments. The increasing complexity of digital platforms underscores the relevance of this study, the need to segment infrastructures into autonomous technological contours, and the requirement to ensure high recovery speed while maintaining the logical and physical isolation of trust zones. Downtime losses and more burdensome regulations (NIS2, DORA, ISO 22301, IEC 62443) are new pressures on the art of Disaster Recovery. The paper aims to develop and justify methodological principles and algorithms for synchronizing disaster recovery plans across different contexts, while keeping them isolated in terms of target RTO/RPO metrics. Therefore, it includes a review of industry reports, regulatory documents, and practical guides that integrate Business Impact Analysis with threat-scenario modeling, following the NIST SP 800-30 methodology. It lives in two dimensions of novelty: the comprehensive integration of four architectural pillars as a unified framework for multi-contour environments and multi-level failover orchestration, together with contour-aware replication that enables seamless switching. Results are keyed to the facts that this method enables the attainment of the desired RTO and RPO with complete security, lowers the risk of cascading failure, and maintains all paths within the bounds of regulatory requirements. Inside each contour, there is synchronous replication, while between contours, there is asynchronous, encrypted replication, combined with the use of the 3-2-1-1+ backup approach. The involvement of DRaaS providers guarantees business-process continuity, preserving the logical isolation of segments. This article will find its readership among IT infrastructure managers and specialists, cybersecurity engineers, Disaster Recovery Solution architects, and information security auditors
Human-Centric Machine Learning Intrusion Detection for Smart Grid SCADA Systems, Grounded in Human-Systems Integration Theory
Full text linkProtecting 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