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344 articles for “Gene prediction”
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Application-Driven Rule-Based Framework for Lubrication Failure Modes in Industrial Systems
Abstract: Modern lubricants increasingly rely on polymer-based composites, integrating synthetic base oils, polymer thickeners and solid additives like MoS₂ and PTFE for high-performance applications. These formulations not only enhance thermal and mechanical stability but also enable low-friction operation across diverse industrial conditions. Lubrication-related failures represent a critical cause of unplanned downtime and reduced reliability in industrial machinery. This paper presents an application-driven, rule-based framework designed to assess and mitigate lubrication failure …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 522–531 Read article
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Geo AI-Powered Urban Footprints
Abstract: In the contemporary era, building footprints are of paramount importance for accurate and current inventories in the development of infrastructure and geospatial analysis. Traditional methods, relying on manual digitization, were largely unsustainable as the urban regions were growing rapidly. Manual digitization was expensive and lacked geometric precision. This paper introduces an automated, end-to-end GEO AI-powered framework for high-end fidelity building footprint extraction from Google Satellite Data. Our approach for this …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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A Novel Approach for Power Generation from Ocean Waves Using Ratchet and Pawl Mechanism
Abstract: Renewable energy sources collectively contribute around 29% to 30% of global electricity generation. Solar and wind power’s intermittency and weather dependency pose challenges for unreliable energy generation, requiring storage solutions like batteries and environmental considerations. By considering the above-mentioned factors, ocean wave energy stands out as a promising and consistent renewable source. Ocean wave energy is highly predictable and consistent, offering a dependable power supply with minimal greenhouse gas emissions …
Published in Recent Trends in Fluid Mechanics · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
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Experimental Validation and Implementation Framework for Optimized Methane Yield Prediction in Anaerobic Digestion
Abstract: The correct validation and realistic application of optimized anaerobic digestion (AD) models are essential steps in transferring biogas production systems to real-life. This paper outlines an experimental validation and deployment pipeline of an AI-optimized model of the methane yield prediction model based on the application of more advanced machine learning and Bayesian optimization methods. Others The validated surrogate-assisted optimization model was tested with controlled laboratory-scale AD experiments at optimized operating …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 25–32 Read article
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Optimization of Pesticide Requirement Calculations for IoT-Operated Hexacopter Delivery Systems
Abstract: The integration of Internet of Things (IoT) technology into precision agriculture has transformed pesticide application strategies, enabling resource-efficient and environmentally sustainable practices. This study presents a computational methodology for optimizing pesticide requirements in an IoT-operated hexacopter system, designed for dynamic, data-driven pesticide delivery. Leveraging a fusion of real-time telemetric data from onboard LiDAR, multispectral imaging sensors, and environmental monitoring modules, the system employs predictive analytics and edge computing to calculate …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 08–14 Read article
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Role of Fatigue and Creep in Structural Damage: A Comprehensive Review
Abstract: Fatigue and creep are two critical degradation mechanisms that significantly influence the structural integrity and longevity of engineering materials. These phenomena arise due to different loading and environmental conditions but often coexist in various industrial applications, leading to severe material degradation over time. Fatigue primarily results from cyclic loading, where repeated stress variations induce microstructural damage and crack initiation, ultimately causing catastrophic failure. Conversely, creep occurs under sustained stress at …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 3, Issue 1, 2025 · pp. 22–26 Read article
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A Theoretical Model for a Fermi–Boson Hybrid Particle in Nuclear and Particle Physics
Abstract: We present a theoretical model for a Fermi–Boson Hybrid Particle (FBHP) that unifies fermionic half-integer spin matter fields with bosonic integer-spin force fields within a single quantum framework. By extending conventional quantum field theory, a hybrid creation operator is formulated that combines fermionic and bosonic operators through a continuous mixing parameter, allowing smooth interpolation between Fermi–Dirac and Bose–Einstein statistical behaviour. A generalized statistical mechanics formalism is developed, leading to quantitative …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 10–18 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 Read article
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Optimization of Turning Process Parameters by Genetic Algorithm Approach
Abstract: In this research, turning parameters were optimized through a genetic algorithm for the purpose to minimize surface roughness and to maximize the material removal rate. High finish quality is guaranteed through minimum surface roughness, and efficient process planning is facilitated through maximum material removal rate optimization. For predicting surface roughness and material removal rate with respect to spindle speed, feed rate, and depth of cut, the empirical models were developed …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 33–41 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article
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Cybersecurity of AI and IoT Integrated for Mechanical Industries
Abstract: By facilitating the concept of Industry 4.0, the intersection of artificial intelligence (AI) and the Internet of Things (IoT) has changed the mechanical industries. When combined, these technologies are advancing process optimization, predictive maintenance, real-time condition monitoring, and smarter automation. In order to give proactive system control and intelligent decision-making, AI algorithms mine large datasets generated via IoT devices for relevant patterns. In the meanwhile, IoT guarantees smooth communication between …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 2, 2025 · pp. 27–33 Read article
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Computational Fluid Dynamics and Composite Material Study on Scoop-Type Savonius Turbine for Train-Based Energy Generation
Abstract: This study investigates the feasibility of integrating a scoop-type savonius vertical-axis wind turbine (VAWT) on the rooftop of a moving train to generate renewable onboard power. The motivation stems from increasing demands for sustainable energy solutions and reducing reliance on fossil fuels, particularly in transportation. A two-blade savonius turbine, with dimensions of 0.4 m in diameter and 0.5 m in height, was modeled in PTC Creo Parametric 3.0 and analyzed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 566–580 Read article
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Solar Thermal Energy Utilization: Design Innovations and Performance Enhancement Techniques
Abstract: The utilization of solar thermal energy has become increasingly significant in the pursuit of sustainable and low-carbon energy solutions. Unlike photovoltaic technologies that directly convert sunlight into electricity, solar thermal systems focus on harnessing solar radiation to generate heat, which can then be applied to diverse sectors such as water heating, space conditioning, industrial process heating, and power generation. In recent years, substantial research efforts have been directed toward enhancing …
Published in International Journal of Energy and Thermal Applications · Vol. 3, Issue 2, 2025 · pp. 1–5 Read article
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Evaluation of Selected Common Wheat (Triticum aestivum L.) Genotypes for Diverse Traits at Kokate and Hossana, Southern Ethiopia
Abstract: During the 2018/19 cropping season, a field trial involving 49 bread wheat (Triticum aestivum L.) genotypes was conducted at the Kokate and Hossana research sub-stations in Southern Ethiopia. The aim was to assess various characteristics within these genotypes. A simple lattice design was employed, and data on 11 quantitative traits were gathered and analyzed using SAS statistical software. The analysis of variance (ANOVA) revealed that there were notable differences in …
Published in Research & Reviews : Journal of Botany · Vol. 14, Issue 1, 2025 · pp. 1–11 Read article
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Trends and Applications of Artificial Intelligence in Mechanical Engineering: A Review
Abstract: Artificial Intelligence (AI) has become a revolutionary force across various fields, including mechanical engineering, where it is redefining traditional approaches to design, manufacturing, maintenance, and overall system optimization. This review aims to provide a comprehensive introduction to AI and explore its diverse applications within the domain of mechanical engineering. The study begins with a foundational overview of AI, including key concepts such as machine learning, neural networks, deep learning, and …
Published in Journal of Mechatronics and Automation · Vol. 12, Issue 3, 2025 · pp. 30–35 Read article
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AI-Driven Precision Nutrition: Advancing Personalized Dietary Systems for Public Health Equity in Resource-Constrained Environments
Abstract: The dual burden of malnutrition and diet-related non-communicable diseases (NCDs) represents a growing global public health challenge, particularly in low- and middle-income countries. Traditional dietary guidelines are largely population-based and fail to account for individual variability in genetics, metabolism, lifestyle, and environmental exposure. This limitation has led to the emergence of precision nutrition, an evolving field that integrates biological data and computational intelligence to deliver personalized dietary recommendations. This paper …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 Read article
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Industrial Prognostics via Ensemble Machine Learning: An Uncertainty Aware Framework for RUL Estimation on NASA FD004 Telemetry
Abstract: Estimating the Remaining Useful Life (RUL) of industrial machinery in real-time is now vital for both operational safety and smart resource management. In the aviation industry, turbofan engines deal with constantly shifting flight conditions, making traditional, scheduled maintenance both expensive and prone to error. This paper addresses the flaws in common “point-prediction” AI models, which offer a single failure date without any margin for error, by introducing a new, uncertainty-aware …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Predictive Learning Powered by AI and Sophisticated Student Engagement Techniques
Abstract: The contemporary landscape of education has witnessed a paradigm shift in integrating advanced technologies that have revolutionized the learning experience. Innovative methodologies have emerged to address longstanding challenges, such as enhancing student engagement, accurately predicting academic performance, and personalizing the learning journey. However, despite the numerous benefits that technology brings to education, there remains a crucial hurdle - sustaining student motivation and engagement. Traditional teaching methodologies often struggle to generate …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 1, 2026 · pp. 127–140 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article