American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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A Cost-Optimized, Edge-Based Smart CCTV Surveillance System for Sustainable Security in Resource-Limited Environments
Full text linkThis paper provides the design and implementation of a smart CCTV surveillance system, which combines the use of Internet of Things (IoT) connectivity, edge computing, and wireless communication to resolve the constraints of the conventional surveillance systems. The suggested architecture uses a mathematical model in optimal positioning of cameras, which involves field-of-view, tilt angle, and spacing formulae, and uses low-power elements to improve energy efficiency and scalability. Such functionalities as real-time motion detection, automated alert generation and hybrid cloud-edge storage (consuming less bandwidth) are key. A pilot was used in a controlled office scenario, and its performance compared to a baseline system based on traditional DVR. Quantitative findings reveal that there was a 40% reduction in manual monitoring interventions and also the amount of energy used during the normal working situation was reduced by 30%. The motion detection accuracy of the system was 92% in the field of view that the camera was configured to cover. Nevertheless, the execution of the system is determined by the stability of the network connection when operating remotely, and the edge-processing latency rises as the video stream is of high resolution. Despite these shortcomings, the framework is a cheaper and modular method of sustainable security check in urban and limited resource settings
Isolation and Biochemical Analysis of Microorganisms Found in Stored Maize
Full text linkStored maize is susceptible to microbial contamination, which poses significant risks to food safety and public health. This study investigated the isolation, enumeration, and biochemical characterization of microorganisms found in stored maize samples. Serial dilutions of the samples were inoculated onto Nutrient agar, MacConkey agar, and Sabouraud dextrose agar using spread techniques. Bacterial counts ranged from to CFU/ml, while fungal counts ranged from to CFU/ml. Biochemical and morphological analyses identified Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa among the bacterial isolates, while fungal isolates included Aspergillus fumigatus, Aspergillus niger, and Rhizopus microsporus/stolonifer. The presence of both pathogenic and spoilage organisms highlights the potential health hazards of improperly stored maize and underscores the importance of effective storage practices and microbial monitoring to ensure food safety
Biogenic Nanomineral Formation: Exploring the Role of Microbial Metabolism in Shaping Subsurface Geochemistry
Full text linkThis study explores the mechanisms, pathways, and geochemical implications of biogenic nanomineral formation driven by microbial metabolism within subsurface environments. Microorganisms—including bacteria, archaea, and fungi play a crucial role in generating nanominerals through biologically controlled and biologically induced mineralization processes, mediated by complex metabolic reactions such as iron and sulfate reduction, sulfur oxidation, and redox cycling. These microbial activities give rise to structurally and chemically distinct nanominerals such as magnetite, greigite, pyrite, manganese oxides, carbonates, and metal sulfides, which differ significantly from their abiotic counterparts in crystallinity, morphology, and reactivity. At the nanoscale, extracellular polymeric substances, cell wall functional groups, and metabolic by-products act as catalytic interfaces for nucleation, growth, dissolution, and stabilization of mineral nanoparticles. The study highlights how microbial redox transformations regulate metal mobility, influence geochemical gradients, and shape the mineralogical evolution of sediments, soils, and aquifers. Furthermore, it demonstrates that biogenic nanominerals serve essential functions as redox buffers, contaminant immobilizers, nutrient reservoirs, and biosignatures in both modern and ancient environments. These nanoscale products also contribute to larger processes such as bioremediation, biomineral-based energy cycling, and the long-term stabilization or mobilization of metals. Overall, the findings underscore microbial nanomineral formation as a major driver of subsurface geochemistry, emphasizing the need to integrate microbiology, mineralogy, and geochemical modeling to understand elemental cycling, environmental transformation processes, and the evolution of Earth’s mineral diversity
A Sustainable Integration of Green IoT for Smarter and Greener Cities
No full textAs urban areas expand, the need for intelligent, energy-conscious infrastructure becomes urgent. The Internet of Things (IoT) has revolutionized urban environments by enabling smart systems that enhance efficiency, public services, and daily life. However, traditional IoT can be energy-hungry and ecologically taxing. Green IoT emerges as a solution that marries technological advancement with environmental sustainability. This paper delves into the convergence of Green IoT and smart cities, outlining how the fusion can lead to more sustainable urban futures. It examines current technologies, real-world applications, ethical concerns, challenges, and potential pathways for innovation. Ultimately, the paper provides a comprehensive look at how Green IoT can serve as a backbone for building resilient, responsive, and environmentally friendly cities
Agentic AI Security & Autonomous Red-Teaming
Full text linkRecent progress in foundation models and multi-agent orchestration systems has increased their capabilities and also their attack surface. Cyber-physical systems and edge devices serve both as a target of deployment and as an enabler of operation. The security issues surrounding these enabling mechanisms are already becoming a reality, but the implications of these issues on AI-driven ecosystems are under-researched. In contrast to traditional security areas, threats in agentic AI environments are difficult to anticipate due to their dynamic execution contexts, lack of standardized operational baselines, and the unpredictable behaviors arising from autonomous and emergent agent strategies. This paper examines these challenges and proposes a forward-looking security approach centered on continuous model verification, alignment assurance, and transparency tooling tailored to agentic systems. The framework emphasizes early, automated, and lifecycle-integrated security validation, augmented by autonomous red-teaming to proactively surface weaknesses. The findings suggest that embedding self-assessing security mechanisms into agentic AI pipelines enables more resilient, adaptive, and accountable intelligent systems
Gray Hair and Hair Strengthening: A Professional Guide for Hairdressers and Colorists
Full text linkGray hair has become an increasingly common concern among clients seeking professional coloring services, not only due to aesthetic considerations but also because of changes in hair strength, density, and fiber quality. As clients’ awareness of hair biology grows, professionals are expected to understand the trichological mechanisms underlying depigmentation and structural alterations of gray hair. The purpose of this study is to analyze the biological and structural factors associated with hair depigmentation and to address the problem of maintaining hair fiber quality during coloring procedures. The research problem focuses on the gap between scientific knowledge of gray hair formation and its practical application in professional coloring practice. The study was conducted using a structured analysis of peer-reviewed scientific literature on hair follicle biology, oxidative stress, and melanogenesis, combined with applied professional coloring and care practices. The methodology integrates biological mechanisms of graying with practical diagnostic and cosmetic strategies. The results of the analysis demonstrate that gray hair differs significantly from pigmented hair in terms of follicular activity, oxidative balance, and fiber structure, which directly affects its behavior during coloring procedures. These findings support the need for adapted coloring techniques and targeted care strategies when working with gray hair. This work is significant as it provides an evidence-based framework that helps professionals understand the biological processes behind hair graying, improve coloring outcomes, and support hair fiber strength and quality through informed cosmetic and care decisions
Deep Learning-Based Diagnosis of Brain Cancer Using Convolutional Neural Networks On MRI Scans: A Comparative Study of Model Architectures and Tumor Classification Accuracy
Full text linkBrain 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
Application of machine learning for Managing High-Definition Video Streaming
Full text linkThe 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
Database Backup and Recovery for Near-Perfect Availability
Full text linkThis paper looks at how database backup and recovery fit together to keep systems running with as little downtime as possible. Most of the examples come from recent work between 2023 and 2025, covering backup types, recovery tools, automation, and a bit on security. The purpose is to find workable ways to mix redundancy, orchestration, and compliance in both hybrid and cloud setups so that uptime stays close to 99.999% and data is not lost. The study compares results from academic and industry cases, including Oracle Cloud, SAP hybrid setups, and AI-based tools like ChronoBak and NetBackup. The results show that smarter incremental backups, automatic failover, cross-region replication, and versioned storage resistant to ransomware can cut recovery to under an hour, drop manual work by about 70%, and save around 25% in cost. In modern infrastructure, backup and recovery cannot remain an afterthought. They have to evolve together with container orchestration, forming part of the system’s core design rather than a later addition. Clear RTO and RPO targets guide these processes, while regular reviews help confirm that both security and compliance requirements are met. The discussion will be most relevant to architects, DBAs, and IT managers who work under strict regulatory standards, particularly in finance, healthcare, and government sectors
The Use of Artificial Intelligence in the Nail Industry: from Trend Forecasting to Process Automation
Full text linkThis article examines the potential and specific applications of artificial intelligence (AI) tools in the nail industry, focusing on trend forecasting and process automation. The relevance of this topic arises from the growing imbalance in the industry: clients increasingly demand personalized services and fast service delivery, while salons struggle with a shortage of skilled professionals and outdated demand forecasting methods. The purpose of this study is to systematize scholarly and expert perspectives on the integration of AI to address these challenges, identifying key technological and ethical concerns. The main contradiction lies in the tension between the economic efficiency of automation (e.g., reducing service time) and the risks of service dehumanization (potential loss of customer loyalty due to diminished emotional interaction). The findings suggest that AI-driven solutions—such as robotics and predictive trend analytics—can significantly transform and enhance the industry. However, successful implementation requires hybrid models in which algorithms complement rather than replace human professionals. The study also includes an analysis of specific case studies (Umia, Clockwork) that highlight the synergy between intelligent systems and the "creative core" of beauty services. Special attention is given to the challenges associated with AI integration. The insights presented will be valuable to beauty salon owners, developers of beauty technologies, and researchers investigating AI’s impact on niche service sectors