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99 articles for “complex variables”
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The Tapper Approach: An Integrated Framework for Land Degradation, Restoration, and Climate-Conflict Dynamics
Abstract: Land systems across the globe are increasingly exposed to multiple and interacting pressures, including land degradation, climate change, biodiversity loss, unsustainable land-use practices, rapid population growth, and socio-economic conflicts. These challenges not only reduce ecosystem productivity and resilience but also threaten food security, water availability, rural livelihoods, and long-term environmental sustainability. Despite the growing recognition of these interconnected issues, most existing conceptual and analytical frameworks continue to address them in …
Published in Research & Reviews : Journal of Ecology · Vol. 15, Issue 2, 2026 Read article
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Comparative Analysis of Processing-Property Relationships in Metal and Polymer Matrix Composites: A Unified Statistical Framework for Hardness Characterization
Abstract: Composite materials, encompassing both metal matrix composites (MMCs) and polymer matrix composites (PMCs), exhibit complex processing-property relationships that fundamentally govern their mechanical performance across diverse applications. This study presents a unified statistical framework for analyzing hardness characteristics in composite systems, using aluminum-tungsten carbide (Al-WC) metal matrix composites as a representative model system while establishing connections to polymer matrix composite behavior. The investigation employed comprehensive processing parameter optimization, microstructural characterization, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 419–430 Read article
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A Meta-Analysis of the Role of Serverless Computing Models in Modern e-Healthcare Systems
Abstract: The integration of serverless computing models in e-healthcare systems represents a paradigm shift in healthcare technology infrastructure. This meta-analysis examines the role, benefits, and challenges of serverless architectures in modern healthcare applications, focusing on studies published between 2019 and 2025. Serverless computing offers unprecedented scalability, cost-efficiency, and operational flexibility, making it particularly suited for healthcare applications handling variable workloads such as medical imaging processing, real-time patient monitoring, and electronic health …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 49–58 Read article
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Drug Utilisation and Cost Analysis in End-Stage Renal Disease Patients: A Prospective Observational Study
Abstract: Chronic Kidney Disease (CKD) is a condition in which the kidneys are damaged and unable to efficiently remove waste and excess fluid from the blood. Dialysis serves as a treatment for CKD by artificially carrying out the kidney’s filtering functions. This prospective observational cross-sectional study was conducted to evaluate drug utilization patterns, economic burden, and health-related quality of life (HRQoL) among patients with end-stage renal disease (ESRD) undergoing dialysis. Over …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 · pp. 1–12 Read article
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Land Tenure, Farming Constraints, and Rural Livelihoods in India: An Integrated Assessment
Abstract: Land remains the central asset shaping agricultural productivity, socio-economic security, and rural development in India. However, the complex interaction between land tenure systems, farming challenges, and livelihood patterns continues to hinder sustainable agricultural growth. This review provides an integrated assessment of the key issues influencing land use dynamics, including ownership inequalities, insecure tenancy arrangements, land fragmentation, and the slow implementation of land reforms. These structural limitations restrict farmers’ access to …
Published in International Journal of Land · Vol. 2, Issue 2, 2025 · pp. 11–16 Read article
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Graphs and Their Real-Life Applications in Pharmaceutical Research
Abstract: This study provides a comprehensive overview of the role of graphs in pharmacy research, emphasizing their significance as powerful tools for data visualization, interpretation, and communication. In the field of pharmacy, research often generates large volumes of complex data related to drug development, pharmacokinetics, pharmacodynamics, medication safety, and patient outcomes. Graphs serve as an essential medium to translate these data into accessible, interpretable, and actionable insights, supporting evidence-based practice and …
Published in Research & Reviews : Journal of Statistics · Vol. 14, Issue 3, 2025 · pp. 01–08 Read article
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Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques
Abstract: The use of polymers in biomaterials was now key to designing the next generation of medical implants, which need to be strong and also compatible with living tissue. Tests for biocompatibility, such as those done in the laboratory and by doing experiments on animals, require much time and many resources, so the need for computer-based approaches becomes clear. An artificial intelligence approach was provided in this study to determine how …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 665–692 Read article
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Challenges faced by upper indus basin (UIB) communities due to climate change
Abstract: The Upper Indus Basin (UIB) communities are heavily dependent on water resources for their sustenance. The region relies on glacial and snowmelts for irrigation water, energy, domestic and irrigation uses. Climate change poses threat to vulnerable region all around the world, and Upper Indus Basin (UIB) is no different. The variability in climatic conditions such change in precipitation pattern and temperature fluctuations have disrupted the agriculture practices of the region …
Published in International Journal of Environmental Planning and Development Architecture · Vol. 2, Issue 1, 2024 · pp. 11–20 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
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Integrating Atmospheric Science: Understanding Greenhouse Gases, Aerosols, and Air Quality Dynamics
Abstract: Atmospheric science investigates the Earth’s atmospheric systems to understand their composition, dynamics, and the implications for climate, weather, and air quality. This review explores five primary areas within the field: atmospheric composition, atmospheric modeling, remote sensing, air pollution, and boundary layer dynamics, highlighting critical challenges and advancements. Rising levels of greenhouse gases (GHGs), including carbon dioxide and methane, continue to drive global warming, while feedback mechanisms—like cloud interactions and surface …
Published in International Journal of Atmosphere · Vol. 1, Issue 1, 2024 · pp. 32–35 Read article
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Optimizing Sampling Techniques Using Fuzzy Set Theory: A Comprehensive Approach
Abstract: Sampling is a critical process in statistics, used to estimate population parameters without needing to examine the entire population. Traditional sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, face limitations when applied to complex or heterogeneous populations with imprecise boundaries. These methods often fail to accurately represent populations with overlapping characteristics or missing data, resulting in sampling bias and reduced accuracy. To address these challenges, this …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 29–43 Read article
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Integrated risk assessment framework for mixed use real estate development: Retail–office configuration
Abstract: Mixed-use real estate development is a multifaceted challenge that offers significant potential benefits, yet developers and urban planners face skepticism and uncertainty. Despite the promise of enhancing property values, promoting secure neighborhoods, stimulating economic vitality, and creating synergies, the associated risks demand a critical examination. This research seeks to address this gap by constructing a comprehensive risk assessment framework for mixed-use real estate projects. The literature review underscores the substantial …
Published in International Journal of Rural and Regional Development · Vol. 2, Issue 1, 2024 · pp. 22–36 Read article
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Design and Development of an Automated Robotic Algorithm for Blasting, Painting, and Barnacle Cleaning of Offshore Structures
Abstract: Marine surface maintenance tasks such as barnacle removal, abrasive blasting, and protective painting are traditionally carried out using manual methods that are labor-intensive, hazardous, and prone to variability in quality. These challenges are particularly significant for offshore structures, where harsh environmental conditions and restricted accessibility increase operational risks and maintenance costs. This paper presents the design and evaluation of an autonomous robotic system for automated surface preparation and coating of …
Published in Journal of Offshore Structure and Technology · Vol. 13, Issue 1, 2026 · pp. 1–12 Read article
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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Precision Medicine for Neurofibromatosis Type 1: Progress and Prospects in Drug Discovery
Abstract: Objective: The development of neurofibromas, café-au-lait spots, and other neurological problems are the hallmarks of neurofibromatosis type 1 (NF1), a hereditary disorder. The dearth of efficacious pharmaceutical therapies underscores the need for novel therapeutic approaches, even in the face of clinical variability. Through very accurate prediction of the binding affinity of possible therapeutic drugs with the target protein, the computational technique known as “molecular docking” has become a potent tool …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 2, Issue 1, 2024 · pp. 01–15 Read article
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Development of a Novel Analytical Framework for Investigating Non-Symmetric Deformation Behavior in Strip Rolling
Abstract: In recent years, the asymmetrical rolling process has attracted considerable research attention due to its ability to induce non-uniform deformation characteristics within metallic workpieces. In this context, the present study introduces a novel analytical framework for asymmetrical cold rolling based on an enhanced slab method, specifically designed to overcome the inherent limitations of existing analytical models when applied to a wide range of asymmetric rolling conditions. A newly developed mathematical …
Published in Journal of Experimental & Applied Mechanics · Vol. 17, Issue 1, 2026 · pp. 1–21 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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An Analysis of Machining Parameters for Metal Matrix Composite
Abstract: The electro-discharge machining analysis of hybrid Composite is presented in this work. Variables are chosen for the input process parameters. The material removal rate is acknowledged as an output parameter, together with the current, graphite and silicon carbide percentage and pulse on time. We used the Taguchi Method to conduct our experiments. To create the theoretical model and look into how process parameters affect the rate at which material is …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 76–85 Read article
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Impact of Total Quality Maintenance Parts on The Operation of Electric Motor in Thermal Desorption Unit
Abstract: Thermal desorption Unit been a vital equipment with complex applications in the sections of waste and minerals treatments, suffers intensively in the managerial section as a whole. This paves a way towards the aim of this research work which covers the type of the impact of Total Quality maintenance electric motor parts on the Operation of the Thermal Desorption Unit [TDUs] using that of the Halden Nigeria Limited, located within …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 1, 2024 · pp. 14–25 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article