American Scientific Research Journal for Engineering, Technology, and Sciences
The American Scientific Research Journal for Engineering, Technology, and Sciences is multidisciplinary peer reviewed Journal intended to publish original research papers, review articles, short communications and technical reports in all main branches of science (All scientific disciplines) such as Social Sciences , Natural Sciences , Formal Sciences, and Applied science. (but not limited to): anthropology, archaeology, communication, criminology, education, government, linguistics, international relations, political science, sociology, Earth science, Ecology, Oceanography, Meteorology, Life science, Human biology, Decision theory, Logic, Mathematics, Statistics, Systems theory, Theoretical computer science, Applied physics, Computer science, all Fields of engineering, Accounting, , Education, Economics, Medical Technology, Biology, Medicine, Management, History, Mineralogy, Civil Engineering, Marine Technology, Commerce, Chemical Engineering, Animal Sciences, Petroleum & Gas, Energy Resources, Agriculture, Medical Sciences, Machine Learning, Machinery, computer Science, Chemistry, Neural Networks, Physics, Social Science, Geology, Transportation, Waste Management, Control Engineering, Applied Mathematics, Oceanography, Biomedical Materials, Construction, Metallurgy, Neural Computing, Industrial Arts, IT, Astronology, Fire & Fire Prevention, Robotics Marine Sciences, Solid State Technology, Business Administration, Food &Food Industry, Atmospheric Sciences, Artificial Intelligence, Textile Industry & Fabrics, Education science, Physiology, Nano Science, Microbiology, Psychology, Statistics, Pharmaceutical Sciences, Genetics, Botany, Veterinary Sciences, Biotechnology, Biochemistry, Zoology, Oncology, Accounting, Entomology, Parasitology, Evolution, human behavior, Biophysics, Fisheries, Pharmacology, Geography, Cell Biology, Genomics, Plant Biology, Law, Religious Studies, Endocrinology, Dentistry, Infectious Diseases, Toxicology, Immunology, Teacher education, and Neuroscience. This International journal usually will provide the Editor's decision based on the peer review results within 4 weeks (28 days) from the paper submission date. The journal accepts scientific papers for publication after passing the journal's double peer review process. For detailed information about the journal kindly check About the Journal page.
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Analysis of Scaled Agile on Turkcell “Offering Solutions Team” for Faster and More Qualified Production: SAFe & LeSS
Agile methodologies are useful to make organizations adapt to the varying needs and changing market conditions in the world and these methodologies become new generation working methods for organizations. “Offering Solutions Team” is the first team that started to work with agile methodologies in Turkcell and it has been putting different agile methodologies into practice according to the requirements for more than 10 years. On this occasion, it continues to increase its experience day by day. “Offering Solutions Team” works on service, tariff and campaign products of Turkcell and it carries out its operations under the scope of the “Information and Communication Technologies” (ICT) function. Quality of these products, time to market (T2M) and improvements are key process indicators (KPI) for this team. This team having agile maturity has increased its gains by scaling agile and inspired different organizations by sharing its output. SAFe (Scaled Agile Framework) and LeSS (Large Scale Scrum) are the methodologies that “Offering Solutions Team” use for scaling agile. This article describes the reasons for choosing these methodologies related to the requirements at “Offering Solutions Team”, their experiences and outcomes.
Copyright (c) 2025 American Scientific Research Journal for Engineering, Technology, and Sciences https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Evaluation of a Convolutional Neural Network Model for Automated Detection of Diabetic Retinopathy Using Retinal Fundus Photographs
Diabetic retinopathy (DR) is a leading cause of preventable blindness globally, necessitating timely and accurate screening methods. This study presents the design and evaluation of a custom convolutional neural network (CNN) model, LightDR, for automated classification of DR using retinal fundus photographs. The Augmented_resized_V2 dataset, derived from the Eyepacs, Aptos, and Messidor collections from Kaggle, was used to train over 143,000 labeled images. The LightDR architecture was built using TensorFlow and optimized through data augmentation, class balancing, and performance-driven callbacks. Evaluation of the model yielded an accuracy of 84%, with precision and recall metrics indicating strong sensitivity to disease presence and reliable classification of healthy cases. The model demonstrated generalization and interpretability, supported by Grad-CAM visualizations and confusion matrix analysis. These findings suggest that LightDR offers a scalable and effective solution for DR screening, with potential for integration into clinical workflows pending further validation.
Application of machine learning for Managing High-Definition Video Streaming
The paper explores the idea of using machine learning for high-definition video streaming. Formats like 4K, 8K, and VR make this task harder because they need more stable delivery. The purpose is to review the work from 2021 to 2024 and apply different approaches — forecasting models, reinforcement learning, and some unsupervised methods — to see how they affect the stability and quality of adaptive bitrate streaming. The review covers a variety of research: forecasting with BiLSTM–CNN and GRU models, reinforcement learning systems like DQNReg, DeepVR, or GreenABR, and clustering techniques for monitoring QoE. The main results indicate that machine learning solutions outperform older ABR rules: fewer stalls, smoother quality, higher QoE, and in some cases lower power use on mobile devices. At the same time, the models are often heavy to run and may not generalize well outside the training data. The article is meant for researchers and engineers working on video delivery and network optimization, and points to where ML-based streaming could be applied in practice.
Complex Methods of Static Code Analysis in Go: Combination of Classical Approaches and Modern Tools
The paper explores comprehensive approaches to static code analysis for Go, highlighting both foundational theory and advanced practical applications. After examining theoretical constructs—such as abstract syntax trees and rule-based detection—this work presents an overview of current trends, including aggregators like GolangCI-Lint. Attention is given to integrating specialized linters (e.g., misspell, unparam, prealloc, bearer) to bolster detection accuracy and address security vulnerabilities. Through detailed practical examples, the article illustrates how automated reports in pull requests facilitate early bug identification and remediation. Configuration strategies for continuous integration and delivery (CI/CD) pipelines are also outlined, focusing on harnessing multi-layered analysis for improved coverage. Concluding remarks emphasize the importance of combined static analysis tools, domain-specific checkers, and regular inspections to achieve high levels of reliability, readability, and security in Go codebases.
Domain Driven Development — Changing the Philosophy of Working on a Project
The article examines domain-driven development (DDD) as a shift from technology-first delivery to business-first modeling with enforceable boundaries and contracts. The review integrates recent findings on bounded contexts, aggregate consistency, context mapping, and domain events with empirical results from microservice performance studies, event-driven pipelines, reactive execution, autoscaling, and overload control. In addition, the review positions CQRS as a complementary pattern to DDD: commands validate invariants within aggregate boundaries while queries rely on denormalized read models for independent evolution. The paper provides a decision aid for when CQRS improves throughput, traceability, and change isolation versus when a unified model remains simpler. The analysis consolidates a boundary-discovery workflow that couples collaborative modeling with data-assisted decomposition. A practitioner case with DDD reports shorter onboarding and faster delivery after establishing a stable ubiquitous language and context map. The manuscript includes an evidence-based interaction table, a governance table for documentation and operations, and a figure illustrating data-driven decomposition. The results target architects and leads who need reproducible criteria for partitioning, collaboration, runtime control, and team enablement across complex enterprise portfolios.
Managed File Transfer Solutions: Security and Scalability with AWS Transfer Family
The study examines Infrastructure as Code for multi-cloud delivery with Terraform and AWS CloudFormation, focusing on conservative cross-cloud abstraction, policy-as-code enforcement, and AI-assisted configuration. Configuration analysis indicates about a 40% reduction in initial setup time and a ~50% decline in recurrent configuration defects. Economic signals show ~15% cost relief for SME tenants and ~30% faster deployment cycles for volatile workloads through pre-validated modules, drift control, and cost guardrails. The paper documents a governance model that maps automated checks to NIST 800-53 control families and integrates plan-time static analysis, secrets detection, and evidence capture. Generative AI is positioned as a CI-embedded assistant that translates natural-language intents into validated templates while remaining policy-, state-, and cost-aware. The contribution consolidates comparative tool behavior, governance placement in the pipeline, and maturity stages for AI-assisted IaC. The material addresses practitioners designing reliable and economical multi-cloud estates and researchers evaluating NL?IaC evaluation workflows.
Digital Sovereignty and the Reconfiguration of Comparative, Advantage: Toward a New Economics of Global Value
The global economy is moving into a stage where data and algorithms shape production as decisively as land, labor, or capital. Earlier theories of trade—beginning with Ricardo’s idea of comparative advantage and the Heckscher–Ohlin factor-endowment model—were built on the assumption that countries prosper by specializing according to natural resources and industrial skills. In the digital era, however, advantage increasingly depends on who manages the main channels of information: data ownership, cloud systems, and algorithmic design. This study proposes a framework for digital comparative advantage that links the unequal distribution of data and digital capacity to new global disparities. It presents digital sovereignty—the ability of a state to govern and gain value from its data networks—as a key influence on national performance. The paper concludes that a fairer global order will require redefining sovereignty not only as a political concept but also as an economic and informational one, extending classical trade thinking to the realities of artificial intelligence.
Smart Farm Animal Intrusion Detection Using Multi-Modal IoT, Edge AI and Kafka Streaming
Wild animals such as deer or rabbits cause significant crop losses worldwide and create major problems for farmers. Traditional methods, such as fences, are often too expensive, difficult to maintain and not consistently effective. This paper provides details about an animal intrusion avoiding system that uses a combination of sensors, including thermal cameras, microphones, and motion detectors. The sensors work in conjunction with artificial intelligence running on edge devices, sending alerts through a Kafka streaming system. Unlike existing systems that rely on only one type of sensor or fixed models, our method combines several signals and can also learn to recognize new animals with only a few examples. In simulation tests, the system achieved about ninety-four percent accuracy, reduced false alarms by more than a third, and responded in less than two hundred milliseconds. When compared with systems that used only motion sensors or only cameras, our approach proved to be more reliable. The work is still limited because it is based on simulations rather than real-world farm testing, but plans include real-world trials, adding long-range communication such as LoRaWAN, and utilizing advanced techniques like federated learning to make the system even stronger.
Applications of Artificial Intelligence Models in Teletherapy: A Review of Efficacy, and Ethical Implications
This review synthesizes contemporary evidence on the emerging applications of artificial intelligence (AI) models in teletherapy, based on their efficacy and ethical implications. Drawing on 27 peer-reviewed studies published between 2014 and 2025, it integrates diverse perspectives on how AI-enabled systems are increasingly incorporated into mental health care through digital cognitive behavioral therapy (CBT), virtual human (VH) agents, speech recognition platforms, and personalized e-therapy modules. Evidence from clinical studies indicates promising outcomes in patient engagement, symptom reduction, satisfaction, and scalability, particularly in resource-limited settings. Evidence underscores that while AI-assisted teletherapy enhances accessibility, efficiency, and real-time monitoring compared to traditional models, it remains limited in replicating the therapeutic rapport, empathy, and contextual sensitivity that are essential for effective treatment. Key ethical challenges include data privacy concerns, bias, transparency, accountability, and the irreplaceable role of human therapists. The review concludes that the future of teletherapy lies in hybrid care models, substantial data governance, and clinician oversight to ensure safe, effective, and ethically sound integration of AI in mental health practice.
Capacity Distribution in Production Line Design: What Questions Have not been Asked?
There has been significant work published about positive results of production systems with unbalanced capacity against the typical balanced lines. While complementary research suggests sophisticated ways to increase the flow of materials on the production lines by balanced or unbalanced capacity, many practitioners use trial-and-error approaches to solving optimization problems. Although a lot of progress has been made during the last decades, there’s still a gap in organizing scientific production about capacity planning among different areas of knowledge, such as finance, supply chain and production. The purpose of this work is to analyze the different approaches that have been used on capacity planning of production lines and tie a set of important questions to be solved in future research to better apply technological tools maximizing the efficiency of production lines projects. Using a review of literature and listing the problems still unaddressed, this work grouped a major framework of questions to orient future research.
AA Review of the Effectiveness of Symmetric and Asymmetric Cryptography Methods with Multi-Level Key Management in Wireless Networks
Wireless networks have nearly no limitations when it comes to communication systems powered by electricity, which are growing all over the globe. However, it is essential to implement robust security measures as sophisticated cyber attacks evolve. The study mainly focuses on symmetric and asymmetric key cryptography, especially their efficiency. The suggested system makes key distribution easy by not only eliminating complicated setups but also employing a secure photon recorder and strict communication protocols. The research combines theoretical analysis and practical simulations to prove the applicability of the proposed method and to determine its effectiveness in maintaining mobile network environments with high security. Moreover, the technique is adaptable to the demanding constraints of modern wireless communication systems with little difficulty. Moreover, the paper includes a detailed comparison of the existing solutions and a set of future research directions to effectively overcome the current barriers related to key management schemes in the area. The main objective is to enable the wireless domain to have a more efficient, scalable, and secure infrastructure.
Auto-Instrumenting Go Applications: A Study of Compile-Time and Runtime Instrumentation Using Opentelemetry
The study aims to provide a systematic comparison of automatic instrumentation methods for Go applications within a unified OpenTelemetry observability architecture. The research problem is that the Go language, being statically compiled and performance-oriented, lacks built-in mechanisms for dynamic telemetry injection, which makes it difficult to achieve complete observability without modifying the source code. To address this issue, the study applies methods of comparative architectural analysis, engineering modeling, and synthesis of experimental data presented in scientific research. The work compares instrumentation performed at compile time and during program execution. It is shown that the first approach ensures high semantic accuracy and metric predictability, while the second provides flexibility and continuous monitoring without requiring recompilation of the application. The study concludes that creating a hybrid observability model that combines the advantages of both approaches offers an effective balance between data precision and operational reliability. The significance of this research lies in forming conceptual foundations for the development of self-regulating observability systems, which can be applied in the design of telemetry infrastructures, optimization of DevOps processes, and enhancement of the resilience of industrial software systems.
Isolation and Molecular Identification of Bacillus Subtilis in Petroleum-Contaminated Soil
Petroleum 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.
Optimization Plan for Agricultural Enterprises among the Military Personnel
Agriculture is the only form of enterprise military personnel are officially allowed to embark on in addition to defending the Nation and ensuring its national security. While the military are engage in different agricultural enterprises, there is insufficient empirical information on the returns to agricultural enterprises among the personnel. The aim of the study was to investigate economic analysis of agricultural production enterprises among the Nigerian military personnel. The objectives were to: (i) assess the level of capacity for implementing optimal plan for agricultural enterprises; and (ii) determine the optimal plan for the enterprises.Based on survey as the research design, 275 military personnel from 10 out of all military formations across Nigeria used were selected through a two-stage sampling technique. A structured questionnaire with a reliability coefficient of 0.86 was used for the study. Descriptive statistics, linear programming, and logistic regression were used for data analysis. Tests of significance were carried out at 0.05 alpha level. The results shows that optimal plan for crop production was 3.25ha of yam/ maize enterprise with a total gross margin of N811,040.00 per season, that of livestock was 12.53 tropical livestock unit (tlu) of cattle and 2.728 tlu of layers, giving a total gross margin of N1,173,070.00 per annum; Commissioning status, marital status, and farming experience were the factors that affect the capacity to implement optimal plan for crop production while farming experience and farm income were the factors that affect the capacity to implement optimal plan for livestock production.
Key Challenges for Entrepreneurship in Nigeria’s Fish Farming Sector
The fish farming sector in Nigeria hold significant potential for contributing to the country’s food security and economic growth and development. However, entrepreneur’s in this sector face numerous challenges/constraints or limitations that obstruct the full realization of this opportunities. This paper explore the key challenges confronting entrepreneurship in Nigeria’s fish farming industry, focusing on areas such as Financial, Technical, and Ecological challenges. Additionally, issues related to Product, Market, Business, Regulatory/Policy and Climatic challenges were revealed. The study highlights how these challenges impact the growth and profitability of fish farming enterprises and suggest some strategic recommendations that could mitigate these challenges. Addressing these challenges is critical for enhancing the productivity and competitiveness of Nigeria’s fish farming sector, which in turn could stimulate broader economic development and poverty alleviation in the country.
Model Selection and Inference in Competing Risk Regression Model to Determine the Potential factors of under-5 Child Mortality in Bangladeshin Bangladesh
Under-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's 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's education, availability of MCWC, NGO membership of mother, father's 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.
Designing Interoperable Microservices for Public-Private Collaboration in U.S. Biomedical Research
Public-private collaboration in biomedical research necessitates secure, scalable, and interoperable software systems that facilitate data exchange and coordinated workflows. This paper proposes a microservice-based architecture leveraging Node.js/.NET Core APIs, AWS API Gateway, and Kafka for event-driven communication to enhance interoperability across biomedical research platforms. It incorporates role-based access control RBAC to ensure secure data governance and enforces site-level data standardization for harmonized integration across research institutions. A layered approach to microservice orchestration enables seamless data and workflow coordination between government agencies, academic centers, and private sector partners. We present architectural patterns, implementation strategies, and deployment models that support collaborative research while meeting regulatory and operational constraints. A case study illustrates the practical application of this architecture in a multisite genomics research initiative.
AI-Augmented Data Modeling: Enhancing Star Schema Design for Modern Analytics
The star schema remains a foundational dimensional modeling approach in business intelligence, valued for its simplicity, performance, and compatibility with OLAP queries. However, manual schema design is labor-intensive and error-prone in large-scale or rapidly evolving data environments. This study investigates the application of Artificial Intelligence (AI), particularly large language models (LLMs), in automating and optimizing star schema generation. Models such as OpenAI’s GPT-4, Google Gemini, and Meta’s LLaMA 3 were evaluated for their ability to infer schema structures, enforce relational integrity, and enhance semantic alignment. Experimental results demonstrated that AI-assisted modeling can reduce development time by over 80%, while increasing accuracy and consistency. These findings highlight the growing potential of AI in streamlining enterprise data modeling processes.
Methodology for Managing High-Urgency Projects in Conditions of National Importance
The study is aimed at the theoretical substantiation of an adaptive hybrid methodology combining predictive principles and elements of iterative delivery to optimize the full lifecycle of national projects with high urgency. The relevance of the study is determined by the acute need for reliable tools for managing critically important projects and the existence of a theoretical gap between the capabilities of known methodologies and the specific requirements for national-scale projects. As a result of a systematic analysis of key scientific publications and analytical reports in recent years, a multi-level management architecture is proposed, integrating strategic planning and control (characteristic of Waterfall/PRINCE2) with iterative execution mechanisms and operational adjustments at the tactical level (borrowed from Agile/Scrum). The aim of the study is to substantiate an adaptive hybrid methodology focused on effective management of high-urgency and nationally important projects. The scientific novelty consists in integrating cascade and agile approaches into a single adaptive model aimed at improving the efficiency of implementing urgent projects of national importance. The obtained results will be of practical interest for leaders of governmental bodies, top managers of state-participated enterprises and researchers in the field of project management in the public sector.
Core Concepts of Financial Reporting Automation in Corporations
The article presents a theoretical overview of the core concepts of financial reporting automation in corporations, with a focus on RPA, AI, ML, and NLP technologies. The study is conducted within an interdisciplinary paradigm that integrates digital finance, corporate governance, accounting, information technology, and regulatory compliance practice. The methodological basis is a qualitative comparative content analysis of domestic and international publications addressing the application of intelligent systems in financial modeling, variance analysis, planning, and report preparation. Automation approaches are identified and classified according to cognitive complexity, system architecture, and the degree of human involvement. Three analytical tables are provided: examples of AI use in auditing, a comparative review of the benefits and risks of RPA implementation, and the challenges and opportunities of AI in the financial domain. Based on empirical and conceptual data, the article demonstrates the effectiveness of a comprehensive “RPA + AI + Human-in-the-loop” model, ensuring interpretability, resilience, and regulatory compliance of financial reporting. The study highlights limitations related to the insufficient cognitive flexibility of RPA, algorithmic bias risks in AI, and the shortage of digital competencies among personnel. The article will be of interest to professionals in corporate finance, accounting process digitalization, auditors, developers of automated reporting systems, and executives responsible for financial function transformation in the digital economy.
Auto-Scaling Techniques for Container Workloads in Kubernetes Clusters
This 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.
Methodological Approaches to Emergency Recovery of Information Systems in Multi-Contour Environments
The 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.
Methodological Foundations of Risk Register Development for Large-Scale Projects
The article examines the methodological foundation for developing a risk register for large-scale projects, including the key principles of its formation, integration, and application in managing uncertainty. The urgency of the topic is justified by the scale of systemic deviations in megaprojects: the average budget overrun reaches 62?percent, while in 91.5?percent of cases projects experience cost overruns, delays or both simultaneously, which underscores the need to embed a risk?management framework at the investment justification stage, when the work breakdown structure and preliminary budget are refined. The study aims to conduct a systematic analysis and comparison of methodologies for developing a risk register, drawing on PMI, USACE and ISO standards and practical regulator recommendations, to identify novel approaches to consolidating information in a single source of truth, linking risks to WBS elements and explicitly declaring residual risk; the novelty of the research lies in combining empirical data from more than sixteen thousand megaprojects, the results of a survey of four hundred companies and software?market forecasts to produce cohesive methodological recommendations. Key findings demonstrate that integrating the risk register with the project baseline during the investment justification phase transforms it from a passive catalogue into an active driver of schedule and cost forecasts. Additionally, four methodological principles form a dynamic system capable of supporting decision-making and enhancing the adaptability of project teams. This article will be valuable to project managers, risk managers, and researchers in the field of large?scale initiative management.
Analysis of Corrosion and Anti-Corrosion Properties of Solar Heat Transfer Fluids
Flat 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.
The February 2025 Mwambashi River Acid and Toxic Spill: Comparative Insights from Global Environmental Disasters
The Mwambashi River disaster of February 2025, caused by the collapse of a tailings dam at a Chinese-owned mine, released over 50 million liters of acidic effluent into the river system. The immediate effects included mass fish kills, farmland destruction, and drinking water contamination. The long-term implications are serious, affecting national food security, biodiversity, and public health. The evacuation of U.S. citizens from the area underscored the global importance of the disaster and revealed some challenges in Zambia's environmental governance.This study uses a comparative case study approach to analyse the Mwambashi Acid and Toxic spill alongside similar international events like Love Canal (USA), Bhopal (India), Chernobyl (Ukraine), Minamata (Japan), Exxon Valdez (USA), Côte d’Ivoire (2006), and the Flint water crisis (USA). The paper examines immediate response measures, long-term remediation tactics, and governance outcomes across these cases by reviewing peer-reviewed literature, reports from international organizations, media accounts, and policy analyses. The findings indicate that rapid evacuation, medical monitoring, and containment strategies are vital in reducing immediate harm. Long-term success relies on enforceable liability frameworks, ongoing ecological rehabilitation, and clear public engagement. Lessons drawn from these comparisons show that weak accountability in cases like Côte d’Ivoire and Dzerzhinsk leads to lasting health and ecological issues. In contrast, the United States and Japan's strong liability and monitoring systems have propelled institutional reforms. For Zambia, the Mwambashi disaster is both a crisis and a chance for improvement. Recommendations for policy changes include enhancing regulatory capacity, establishing liability and compensation mechanisms, restoring water safety and ecosystem health, and increasing community involvement in recovery plans. Taking these steps could turn the Mwambashi spill from a serious failure into a critical moment for reform in environmental governance and sustainable resource management.
Automation of Research Master Data Management for Dataset Consistency
The article addresses the automation of master-data management in research organizations as a key prerequisite for dataset consistency and result reproducibility. The problem is pressing because of the growing volumes of heterogeneous data, the reproducibility crisis acknowledged by most biomedical researchers, and the considerable economic losses associated with manual cleansing and duplicated experiments. The study aims to justify and experimentally confirm the effectiveness of integrating FAIR principles with a multi-layer architecture for data intake, normalization, and golden record creation. The novelty is a holistic method that joins together cloud reference architectures, Data Mesh, and Landing Zone, probabilistic record linkage, graph embeddings, and active learning for dynamically adjusting confidence thresholds, thus reducing the burden imposed on experts while delivering continuous quality metrics. Automated MDM removes 37% data redundancy, reduces researchers’ time spent on cleansing to just 26%, and accelerates integration into machine-learning pipelines by close to one third; besides, it proves an actual economic effect visible already from the estimated annual cost reduction of at least EUR 10.2 billion in the EU. Some known shortcomings about the risk of wrong joins, old records, and people's pushback against using machines will guide further research into changing thresholds, fixing past data issues, and improving human-machine links. This paper is for data-management workers, bioinformaticians, research project bosses, and information-system builders.
Reliability Models in Automated Release Cycles
This article presents an analysis of existing reliability assurance models within automated release cycles (CI/CD), covering a spectrum from classical rule-based and monitoring-oriented approaches to modern AI-accelerated AIOps solutions. Based on a comprehensive literature review, the study outlines a conceptual architecture for an AI orchestration layer that integrates data collection, predictive analytics, automated self-healing, and continuous retraining of ML modules. It is demonstrated that implementing the proposed model reduces mean time to detection (MTTD), decreases mean time to recovery (MTTR), and increases release frequency compared to traditional practices. The paper also discusses key aspects of ML model version management, Explainable AI, and potential directions for future research. The insights regarding reliability models in automated release cycles will be of interest to DevOps engineers and software reliability specialists applying stochastic methods and formal verification techniques to minimize risks during continuous deployment. The material will also be valuable for researchers and graduate students in the field of distributed microservice architecture resilience, particularly those working on integrating Bayesian predictive models with the formalization of service level agreements (SLA) within DevSecOps processes.
Impact of Strict Building Code Enforcement on Urban Risk Mitigation
The 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.
The Imperative of Holistic Optimization in Solar-PoweredSystems
Solar energy is considered to be the safest technology as regard green energy generation. This study assessed several techniques adopted for distributed iterative algorithm and intelligent control of distributed solar-hybrid microgrids. The study reviews the hybrid renewable system and multi-objective optimization, it also analyses machine learning and AI-driven forecasting by examining the complexity in energy system optimization. This paper explains the integration into existing Infrastructure and dual-use approaches as it integrate solar energy into building envelopes like windows. Solar energy storage and integration were explained alongside with hybridization and System Compatibility. This study further narrates how smart technologies are used for optimization especially with AI-driven forecasting and demand response. Findings reveal that microgrids provide communities or industries with a reliable energy source by combining battery storage. The study concludes that artificial intelligence (AI) is highly efficient in advancing basic development in contemporary microgrid technology.
Bridging Zero-Shot and Fine-Tuned Performance in Text Classification through Retrieval-Augmented Prompting
Large Language Models (LLMs) have shown promise in zero-shot and few-shot classification. Yet, their performance often falls short of classic fine-tuned encoders, especially in fine-grained or domain-specific settings. This study compares fine-tuned BERT-family models with zero-shot and few-shot prompting of LLMs (GPT-4o, Llama 3.3 70B, and Mistral Small 3) on two benchmarks: AG News (coarse-grained topic classification) and BANKING77 (fine-grained intent classification). Baseline results confirm that fine-tuned models outperform zero-shot LLMs by ~10-25 points in accuracy, with a larger gap on the fine-grained task. We then test training-free methods to improve LLM performance, focusing on retrieval-augmented few-shot prompting, example ordering, and Chain-of-Thought (CoT) reasoning. Our results show that retrieval-augmented prompting consistently boosts accuracy, especially on the BANKING77 dataset with many semantically similar examples, where GPT-4o even slightly surpasses the best fine-tuned encoder. Ordering demonstrations from least to most similar further improves accuracy, reflecting the impact of recency bias in in-context learning. By contrast, CoT prompting decreases accuracy, suggesting that explanation-based prompting is not universally helpful for classification. These findings demonstrate that careful example selection and ordering can substantially narrow the gap between zero-shot LLMs and fine-tuned encoders, offering a practical, training-free alternative in data-scarce scenarios.
Analysis of the Relationship between University Total Quality Management Requirements and the Deming Cycle for Continuous Improvement, An Exploratory Study of Faculty Members' Opinions at Zakho University
Through the current study, we address two highly significant topics that many organizations in general, and higher education institutions in particular, strive to adopt for their role in achieving desired success and distinction. These topics are the requirements of Total Quality Management for higher education institutions and the Deming cycle for continuous improvement. The study involves investigation and analysis through surveying the opinions of faculty members in various colleges at Zakho University, using a questionnaire prepared for this purpose. The aim is to ascertain the presumed relationship between these two variables. Through description, diagnosis, and analysis of correlation and influence relationships, the study arrived at results that formed the basis for important conclusions. These conclusions served as a genuine starting point for providing a number of necessary recommendations for the researched university and all its colleges, as well as for similar universities and colleges.
Physico-Mechanical Impact of Different Cem Ii 42.5 MPA Cement Brands On Hardened Concrets in Cameroon
Cement is defined as a hydraulic binder composed of finely ground powdered materials which, when mixed with water or a saline solution, form a plastic paste capable of binding various substances as it hardens. This study aims to highlight the impact of different class II cements (strength class 42.5 MPa) available on the Cameroonian market (ROBUST, CIMAF, DANGOTE, and MEDCEM) on the compressive strength of hardened concrete at different curing ages. To determine which cement brand provides the best compressive strength at maturity, a series of tests were carried out on samples from each brand, including consistency, setting time, density, compressive strength, and water absorption. The results support our hypothesis: cements are often used indiscriminately with identical dosages, based solely on their mass, in an attempt to achieve similar compressive strength outcomes. The study revealed that the finer the cement, the higher its compressive strength. Additionally, less dense cements produce lighter concretes but require more water for optimal workability, which affects the water-to-cement ratio. DANGOTE cement exhibited the highest compressive strength (29.09 MPa), followed by MEDCEM, and all the studied cements exceeded the target strength of 25 MPa at 28 days.
Deep Learning-Based Diagnosis of Brain Cancer Using Convolutional Neural Networks On MRI Scans: A Comparative Study of Model Architectures and Tumor Classification Accuracy
Brain tumor diagnosis using magnetic resonance imaging (MRI) is essential for timely intervention and treatment planning, yet manual interpretation is often time-consuming and subject to observer variability. Deep learning, particularly convolutional neural networks (CNNs), has shown considerable promise in automating tumor classification with high accuracy. This study developed and evaluated a LightBT-CNN model using the Brain Tumor MRI dataset, consisting of 7,023 images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. The model was trained, validated, and tested using Python and TensorFlow, with performance evaluated through classification metrics and Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretability. The LightBT-CNN model achieved an overall classification accuracy of 98% accuracy, with strong precision, recall, and F1-scores across tumor types. Grad-CAM visualizations confirmed that the model focused on tumor-specific regions, strengthened the reliability of predictions, and enhanced clinical interpretability.These findings align with existing research, such as Ait Amou and his colleagues (2022), who reported 98.70% accuracy with CNN and Bayesian optimization, Haq and his colleagues (2023), who achieved 99.89% with a multi-level CNN, and Sun (2021), whose customized CNN attained 96% accuracy with an AUC of 0.99. The results demonstrate the feasibility of integrating CNN-based approaches into brain tumor diagnostics, with explainable AI tools like Grad-CAM further supporting clinical accuracy and adoption.
Modern Trends in Automating ETL Pipelines in Azure
The 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.
Global Influence of Transcendentalism on Social and Political Movements
This article explores the lasting impact of Transcendentalism, a nineteenth-century American philosophical and literary movement, on global social and political change. Rooted in the works of Ralph Waldo Emerson, Henry David Thoreau, and Margaret Fuller, Transcendentalism emphasized self-reliance, individual conscience, nonconformity, civil disobedience, and a deep reverence for nature. Though these ideas originated in America, they quickly transcended national boundaries, inspiring reform movements around the world. Emerson’s emphasis on spirituality and nature helped shape modern environmental ethics, while Thoreau’s essay “Civil Disobedience” became a cornerstone for nonviolent resistance movements led by figures such as Mahatma Gandhi, Martin Luther King Jr., and Nelson Mandela. Fuller’s feminist writings laid a foundation for gender equality movements, which influenced global conversations about women’s rights. Through case studies in abolitionism, civil rights, feminism, environmentalism, and anti-colonial struggles, the article illustrates how Transcendentalist values have shaped both historical and contemporary activism. The article also highlights gaps in current scholarship, particularly in understanding the movement’s role in modern ecological and human rights movements. Ultimately, it argues that Transcendentalism remains a powerful and relevant moral framework for social and political reform in the present day.
A Smart Traffic Noise Prediction Model for Nairobi City, Kenya
Road traffic flow produces an undesirable externality since it distorts the ambient environmental noise, especially in cities. Such nuisance noise poses a risk to the health of the inhabitants. Globally, the combined concert of the forces of urbanization and road transport motorization has intensified the noise pollution challenge; yet, locally adapted predictive tools remain limited. In Nairobi, the capital city of Kenya, Road Traffic Noise (RTN) remains a less understood environmental nuisance. To date, no predictive RTN models have been developed, while established models such as CoRTN and RLS-90 lack applicability to Nairobi’s traffic and environmental conditions. This study aimed to develop an accurate smart model leveraging artificial neural networks (ANNs) to forecast RTN levels using traffic information data [22]. Traffic data, including audio recordings using a Samsung Galaxy A12 Model SM-A127F/DS Android Smartphone, equivalent noise levels (Leq) using a Lutron SL-4033SD Class 1 Sound Level Meter (SLM), vehicular volume using a manual tally form, and speed using a speed gun, was collected across 42 locations within Nairobi. Using this data, an Artificial Neural Network (ANN), Multi-Layer Perceptron (MLP) model, was developed with two hidden layers. Hyperparameter tuning via grid search was done to optimize model performance. The model achieved a Mean Absolute Error (MAE) of 0.97 dBA and an R2 value of 0.90, outperforming traditional statistical models like CoRTN with an MAE of 5.0 dBA and RLS-90 with an MAE of 11.0 dBA. These results highlight the model’s high accuracy in predicting Nairobi’s RTN. The model’s deployment on a web-based dashboard enables real-time noise monitoring and stakeholder engagement. This pioneering smart predictive model for Nairobi offers a scalable solution for urban noise management [25], with potential applications in traffic planning and policy implementation.
Testing Methods for Machine Learning Systems: From Data Validation to Model Evaluation
As machine-learning systems penetrate domains with tangible human and economic consequences, conventional specification-driven software testing proves inadequate for artefacts whose behaviour is stochastic and tightly coupled to data distributions. Quality, therefore, requires a multi-axis conception: not merely point estimates of predictive accuracy but an integrated appraisal that spans nominal performance, resilience to input degradation, and measures of group-level parity. This study employs a mixed-methodology approach, combining a structured literature review with empirical case analysis. The empirically taken dataset used is UCI Adult. It has a baseline for logistic regression implemented (Python 3.10; scikit-learn 1.3) under five scenarios: Baseline, Typos — 5% random character replacement noise in categorical fields, Noise — numerical feature perturbed by Gaussian distribution where ? = 0.5, Drift — 10% of test examples replaced with instances from another demographic subgroup, Bias-Mitigation — post-processing with Calibrated Equalized Odds (AIF360 0.5.0). Predictive quality is measured based on Accuracy and ROC-AUC; fairness on two simple metrics: Demographic Parity Gap DPG and Equalized Odds Gap EOG. All five scenarios are run five times to average out possible sampling variation in results. The model gets an accuracy of 0.835 and ROC-AUC of 0.918 under clean conditions with a fairness deficit that is demonstrably measurable by group inequity when aggregate discrimination-agnostic performance is high? DPG = 0.029, EOG = 0.040. Typographical noise does not change accuracy; it stays at 0.835 with the same small but consistent gap remaining (EOG = 0.039), thereby showing one ‘surface-metric’ failure mode where unaccounted ethical risk goes into the metrics reported, say as Accuracy. Applicative noise and distributional shift reduce predictive competence (Accuracy = 0.781 and 0.801; ROC-AUC = 0.869 and 0.876) while drift magnifies between-group error imbalances such that vulnerability is asymmetric on protected groups (EOG rising to 0.065). Calibrated Applying Equalized Odds removes the measured Equalized Odds gap (EOG back to zero) with only a minimal reduction in maximal accuracy, decreasing from the baseline by just one basis point to now be one less than the maximum possible. However, it also leads to increased demographic parity gaps and rising DPG, which continues to grow further. In conclusions; they call for the embedding of multidimensional automated testing regimes that jointly gate correctness, robustness, and fairness within the MLOps pipelines (CI/CD/CT). Calibrated Equalized Odds is good as a way of neutralizing imbalances in error rates -but by reallocation of selection rates and with a modest reduction of nominal accuracies- meaning that fairness targets and tolerances have to be chosen explicitly regarding legal constraints and operational priorities as well as stakeholder values.
Disputes as Risks: A Transformative Approach
Traditional approaches to dispute resolution, such as arbitration and litigation, are often reactive, costly, and untimely. These methods often focus on contract variations, rather than addressing the root causes of disputes. In contrast, this study proactively addresses underlying issues. The purpose of this study is to analyse the effectiveness of treating potential disputes as risks to prevent conflicts in construction projects from evolving into disputes. This study highlights the negative impacts of disputes in construction projects, presents case studies in which potential disputes are treated as risks, and compares the outcomes with those of previous similar projects performed under the same scope and circumstances. By identifying potential disputes in the Risk Register and addressing them as risks or opportunities, disputes are integrated into a project’s risk management framework. By identifying potential conflicts early in the project lifecycle, this proactive approach allows teams to manage conflicts before they escalate into formal disputes, thereby avoiding costly delays and budget overruns. Case studies from the Gulf Cooperation Council (GCC) region, including those recognised as ‘Building Project of the Year’ and ‘Small Project of the Year’ at the MEED Projects Awards 2024, illustrate the practical application and effectiveness of the proposed approach.
Features of Integration of UAV Photogrammetry and GNSS in Digital Mapping of Construction Sites
In this study a comprehensive analysis of the methodological foundations for the integration of unmanned aerial vehicles (UAV) equipped with photogrammetric systems and global navigation satellite systems (GNSS) for digital mapping and monitoring of construction sites was conducted. The objective of the work is to develop a systematization of integration approaches, including traditional methods based on ground control points (GCP) and modern direct georeferencing technologies such as real?time kinematics (RTK) and post?processed kinematics (PPK). Based on a review of current scientific literature and processing of empirical data, a comparative evaluation of the considered methodologies was performed according to criteria such as accuracy characteristics, operational efficiency and economic feasibility. It was determined that the implementation of RTK/PPK ensures a reduction in field work time and a decrease in labor costs compared with the classical GCP scheme while achieving comparable or higher georeferencing accuracy. As a result of the study a classification of integration methodologies and an algorithm for selecting the optimal technology, taking into account the specifics of construction monitoring tasks, terrain features and the presence of factors negatively affecting GNSS signal reception, have been proposed. It is emphasized that the choice of the preferred approach must be based on a comprehensive assessment of required accuracy, budgetary constraints and operating conditions on site. The materials of this work will be of value to surveying engineers, construction project managers and researchers engaged in the automation and digitalization of processes in the construction industry.
Performance Analysis of Fan Configurations for Greenhouse Cooling under Abu Dhabi Climatic Conditions,A Case Study from Al Kuwaitat Research Station, Al Ain, UAE
This short communication presents an evaluation of cooling efficiency in an agricultural greenhouse located at Al Kuwaitat Research Station, Al Ain, UAE, under the climatic conditions of Abu Dhabi. The study focused on comparing fan configurations, with particular attention to the performance of upper fans. Among the tested scenarios, running only the upper fans (Scenario 2) demonstrated the most effective cooling performance, as indicated by significant temperature differences recorded across multiple sensors. This enhanced cooling effect is likely due to improved airflow patterns and heat dissipation facilitated by the strategic placement of the upper fans. To ensure the accuracy and continuity of temperature data, sensors were regularly maintained and calibrated, and a redundant system was implemented to prevent data loss. These measures helped avoid gaps in the dataset, which are critical for reliable analysis. The findings highlight the importance of fan configuration and data integrity in optimizing greenhouse climate control under arid conditions.
Cross-Blockchain Interoperability Mechanisms: Bridges, Light Clients and Relayer Protocols
The article is devoted to the study and comparative analysis of mechanisms for enabling cross-chain interaction, including bridges, light clients, and relayer-based protocols. The relevance of the work is determined by the growing fragmentation of the blockchain ecosystem, where isolated networks cannot efficiently exchange data and assets, which restrains their widespread adoption. The novelty lies in a comprehensive approach to the classification and evaluation of existing solutions according to criteria such as security, decentralization, and efficiency. Within the work, the fundamental problems of interoperability and the architectural principles of various mechanisms are described. Centralized and decentralized bridges, protocols based on light clients (for example, IBC), and relayer networks are examined. Special attention is paid to the analysis of their trust models and attack vectors. The study aims to conduct a comparative analysis of these approaches to identify their strengths and weaknesses. To achieve this goal, methods of systematic analysis of the scientific literature, comparative analysis, and synthesis are used.The article will be useful for developers of blockchain systems, researchers, and architects of decentralized applications working on the creation of integrated and scalable solutions.