Anthropogenic and Climate Change Impacts on Diwaniya River Water Quality DOI Open Access
Shafaqat Ali, Nadhir Al‐Ansari, Mohammed S. Shamkhi

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 19, 2025

This study aims investigates the calibration and validation of HEC-RAS model to simulate critical water quality parameters in Iraq’s semi-arid environment, focusing on its application for sustainable resource management. Using a robust dataset observed simulated values, research examined biochemical oxygen demand (BOD₅), total dissolved solids (TDS), (DO), electrical conductivity (EC), nitrate (NO₃⁻), phosphate (PO₄³⁻), calcium (Ca), magnesium (Mg). The results demonstrated strong alignment between data, with high R² values key such as NO₃⁻ (R² = 0.94 validation) PO₄³⁻ 0.96 calibration), affirming model’s reliability predicting nutrient dynamics. identified variations accuracy, TDS exhibiting percentage errors ranging from 1.70% 8.73% challenges simulating DO, where negative exceeded 12%. These discrepancies reflect complexity modeling organic matter decomposition dynamics under fluctuating climatic flow conditions. Additionally, pollution hotspots characterized by elevated EC levels were detected, underscoring significant impact anthropogenic activities quality. By providing validated framework indicators, this contributes arid regions. findings offer valuable insights policymakers, emphasizing integration advanced hydrological models management practices. advocates adaptive strategies mitigate degradation, addressing posed climate change increasing population pressures.

Language: Английский

Predictive Maintenance and Energy Optimization with AI-Driven IoT Framework in Textile Manufacturing Industry DOI Open Access

Mathivanan Kathirvel,

M. Chandrasekaran

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 19, 2025

The textile industry is rapidly automating, yet frequent machine failures and excessive energy consumption continue to impede efficiency. Predictive analytics AI-driven management are critical in overcoming these challenges. This study presents an Adaptive Deep Reinforcement Learning with Bayesian Optimization (ADRL-BO) model, integrating predictive maintenance IoT-based control enhance operational reliability. framework aims reduce unexpected equipment optimize using real-time AI analytics. Data collected from major hubs India, including Surat, Coimbatore, Ludhiana, covering 500+ industrial machines. Key parameters, such as acoustic signals, thermal fluctuations, vibrations, monitored through IoT sensors. ADRL-BO model utilizes deep reinforcement learning (DRL) for adaptive fault detection, while optimization refines scheduling. Additionally, IoT-driven smart grid dynamically manages power distribution, adjusting motor speeds compressor loads based on demand. Blockchain technology ensures secure, transparent data logging of usage. Ultra-fast 5G communication supports seamless exchange Evaluation results demonstrate a 45% reduction downtime 35% savings, validating ADRL-BO’s effectiveness over conventional methods achieving more sustainable intelligent manufacturing ecosystem.

Language: Английский

Citations

1
Automating Compliance In Devops Pipelines DOI Open Access

Ramreddy Gouni

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 9, 2025

The expanding popularity of DevOps techniques revolutionized the software delivery pipelines through quick efficient code deployment methods. research Field automated compliance detection within workflows has become essential for solving this problem. This develops a new conceptual model which ensures regulatory criteria flow naturally throughout every stage pipelines. approach performs detailed theoretical evaluation reveals multiple potential benefits including prompt miscon figuration_errors identification as well standard policy enforcement cloud settings and better conditions developers. We identify two forthcoming enhancements methodology comprise artificial intelligence systems development along with multi-cloud network connectivity capabilities. Our proposal delivers blueprint upcoming experimental testing although we prioritize uncovering unified architecture instead practical implementation. analyzes modern industry while establishing strategic strategy to place functions directly results in security risk reduction accelerated compliant solutions. helps communities practitioners reframe into an integrated dynamic factor current practices develop more dependable systems. Organizations achieve by integrating their pipeline

Language: Английский

Citations

0
Artificial Intelligence-Based color Reconstruction of Mogao Grottoes Murals Using Computer Vision Techniques DOI Open Access
Yi Zhang,

Thirawut Bunyasakseri

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 9, 2025

The Mogao Grottoes murals have deteriorated over centuries due to environmental exposure, pigment degradation, and natural ageing, making cultural heritage preservation difficult. AI computer vision can identify, classify, reconstruct faded pigments, revolutionizing color restoration. This reconstructs mural sections using deep learning, image processing, data implemented through TensorFlow, PyTorch OpenCV. study uses high-resolution Digital Dunhuang database images of 50 pigments categorized by color, stability, chemical composition. CNNs learning-based mapping algorithms detect fading suggest restorations pigments. reconstructions along with history accuracy expert evaluations records. Artificial intelligence-driven conservation detects precisely missing sections, matches restored colors historical authenticity, improving accuracy, efficiency, scalability. Scientifically, AI-based digital outperforms manual preserves faithfully sites artworks global learning-driven restoration models. first reproducible scientific model (CNN, GAN algorithms) analysis in was created.

Language: Английский

Citations

0
Anthropogenic and Climate Change Impacts on Diwaniya River Water Quality DOI Open Access
Shafaqat Ali, Nadhir Al‐Ansari, Mohammed S. Shamkhi

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 19, 2025

This study aims investigates the calibration and validation of HEC-RAS model to simulate critical water quality parameters in Iraq’s semi-arid environment, focusing on its application for sustainable resource management. Using a robust dataset observed simulated values, research examined biochemical oxygen demand (BOD₅), total dissolved solids (TDS), (DO), electrical conductivity (EC), nitrate (NO₃⁻), phosphate (PO₄³⁻), calcium (Ca), magnesium (Mg). The results demonstrated strong alignment between data, with high R² values key such as NO₃⁻ (R² = 0.94 validation) PO₄³⁻ 0.96 calibration), affirming model’s reliability predicting nutrient dynamics. identified variations accuracy, TDS exhibiting percentage errors ranging from 1.70% 8.73% challenges simulating DO, where negative exceeded 12%. These discrepancies reflect complexity modeling organic matter decomposition dynamics under fluctuating climatic flow conditions. Additionally, pollution hotspots characterized by elevated EC levels were detected, underscoring significant impact anthropogenic activities quality. By providing validated framework indicators, this contributes arid regions. findings offer valuable insights policymakers, emphasizing integration advanced hydrological models management practices. advocates adaptive strategies mitigate degradation, addressing posed climate change increasing population pressures.

Language: Английский

Citations

0