EPH-International Journal of Science And Engineering
Journal Information Journal Name: EPH - International Journal of Science and Engineering (IJSE) ISSN: 2454-2016 Starting Year: 2015 Publication Frequency: Quarterly Publication Format: Online Subject Area: Science, Engineering, Technology and Interdisciplinary Research Language: English Open Access: All articles are freely accessible. Peer Review: Double Blind Peer Review
Satellite-Integrated Water Quality Trend Prediction Using Multisource Data and Time-Series Forecasting Models
This study suggests a satellite integrated water quality related environmental condition forecasting framework in Tamilnadu, India. The chlorophyll-a concentration was chosen as the main forecasting target, and land-surface temperature and precipitation were chosen as supporting environmental variables. NASA MODIS-Aqua chlorophyll-a product, NASA MODIS MOD11A2 land-surface temperature product, and CHIRPS precipitation product were used to collect data via Google Earth Engine. The observations were quality screened, time harmonised into monthly time series and assessed through chronological walk-forward validation. The performance of ARIMA, Prophet and LSTM models were evaluated and compared with naïve and seasonal-naïve baseline models by using MAE, RMSE, MAPE and R2. Prophet performed best with an MAE of 0.71 µg/L, RMSE of 0.92 µg/L, MAPE of 10.60%, and R2. Prophet has the best forecasting performance with an MAE of 0.71 µg/L, RMSE of 0.92 µg/L, MAPE of 10.60%, and R2 of 0.78. Further processing of the forecasted chlorophyll-a, temperature and precipitation data was done using a threshold-based alert mechanism and presented on an interactive web dashboard. The main engineering achievement is the integration of multi-source satellite data acquisition, processing, comparative forecasting, alert generation and dashboard visualization in a single environmental decision-support framework. It is a research prototype due to the small number of monthly time series, the different spatial resolution of the satellites, the limited in-situ validation and the necessity of local threshold calibration.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Data-Driven Analysis of Solar Energy Harvesting Systems: Performance Assessment, Environmental Influences, and Future Perspectives
Solar photovoltaic (PV) systems have emerged as an important part of the global energy transition in the face of increasing demand for sustainable and clean energy solutions. But the environmental conditions greatly affect the performance of solar energy harvesting systems, which require detailed analysis with the support of data for better efficiency and reliability in the operation of such systems. This research introduces a data-driven evaluation of solar energy harvesting systems based on a combined solar PV generation and weather sensor information from two solar power plants. Key operational factors such as the DC power, AC power, daily yield and total yield were analysed together with the environmental factors such as solar irradiation, ambient temperature and module temperature. Performance evaluation of the system was carried out using descriptive, comparative and correlation analysis to study the impact of environmental factors on photovoltaic energy generation. The findings showed that solar irradiation is the most important factor influencing power output as it has a high positive correlation with the photovoltaic (PV) generation. Also, it was determined that the ambient and module temperature had an influence on the system efficiency – which might lead to a decrease in actual performance if the module temperature rises. It was observed that the operational characteristics of both solar plants are similar and environmental conditions play a significant role in determining the energy harvesting results. The results show the potentials of data-driven solutions in photovoltaic performance assessment and renewable energy optimization
A Data-Driven Investigation of Factors Influencing Compressive Strength in Ultra-High-Performance Concrete
Ultra-high-performance concrete (UHPC) exhibits exceptional mechanical performance, durability, and long-term serviceability; however, its compressive strength is governed by numerous interacting mixture-design and curing variables. Conventional experimental optimization is often time-consuming and resource-intensive, creating a need for interpretable data-driven approaches capable of supporting material design and performance prediction. This study investigated the factors influencing UHPC compressive strength and developed a predictive framework for identifying the dominant variables affecting strength development. A cleaned secondary dataset containing 626 UHPC mixtures, 24 predictor variables, and one compressive-strength target variable was analyzed. Exploratory analysis and Pearson correlation were used to examine variable relationships. A Random Forest regression model was developed using an 80:20 train-test split and evaluated using the coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), and five-fold cross-validation. Feature importance and partial dependence analyses were subsequently employed to interpret the contribution of individual variables. The Random Forest model achieved strong predictive performance, with a test R² of 0.9041, MAE of 7.79 MPa, RMSE of 11.09 MPa, and a mean five-fold cross-validation R² of 0.9178. Curing time emerged as the most influential variable, contributing 57.4% of the total feature importance. Sand content showed the strongest negative influence, while silica fume content, cement content, steel fiber content, and superplasticizer content also contributed to prediction accuracy. The findings demonstrate that interpretable machine learning can accurately predict UHPC compressive strength while revealing the relative influence of key mixture-design variables, providing useful guidance for future UHPC design and optimization.
Explainable Hybrid Ensemble Learning for Sustainable Concrete Compressive Strength Prediction and Mix Design Optimization
Accurate prediction of concrete compressive strength is essential for ensuring structural reliability while promoting sustainable construction practices through the efficient utilization of supplementary cementitious materials. This study proposes an explainable machine learning framework for predicting concrete compressive strength and optimizing sustainable concrete mix designs using a dataset comprising 1,030 concrete mixtures. Following data preprocessing and feature engineering, several machine learning algorithms, including Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, Extra Trees, LightGBM, and XGBoost, were evaluated. The results demonstrated that XGBoost achieved the highest predictive performance, attaining an R² value of 0.9205, MAE of 3.0829 MPa, and RMSE of 4.4687 MPa. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were employed to quantify the influence of input variables on compressive strength predictions. The explainability analysis revealed that curing age, water-binder ratio, and binder content were the most influential factors governing strength development. Furthermore, a sustainability-oriented optimization framework was implemented to identify environmentally favorable concrete mixtures with reduced cement consumption and increased utilization of fly ash and blast furnace slag. The optimal sustainable mixture achieved a sustainability score of 0.736 while maintaining a predicted compressive strength of 33.22 MPa. The findings demonstrate that explainable machine learning provides a reliable and interpretable decision-support tool for sustainable concrete design, contributing to low-carbon construction and resource-efficient infrastructure development.
Data-Driven Analysis of Environmental Sustainability Practices in Modern Engineering Applications
The current focus on energy consumption, carbon emissions, material consumption, waste production, and resource efficiency has made environmental sustainability a key concern in engineering applications today. The present study reviews the environmental sustainability practices by adopting an information-based approach and analyzing the impact of selected engineering practices on the overall engineering sustainability practices. The study utilizes a structured quantitative dataset with 220 observations from various engineering application domains such as civil/construction, manufacturing, energy systems, transportation, environmental engineering, mechanical systems and electrical systems. The sustainability practices discussed are: energy-efficient design, renewable energy measures, sustainable materials, waste management, water management, environmental monitoring, and green technology. To analyze the relationship between sustainability practices and environmental performance, descriptive statistics, Pearson correlation analysis, multiple linear regression analysis and comparative sector-wise analysis were used. The results show that each of the selected sustainability practices has positive and significant relationships with overall sustainability performance. The regression model accounts for 91% of the variation in sustainability performance, meaning that the model has high predictive value. The highest-ranked contributions were from energy-efficient design and waste management practices, followed by sustainable materials use and renewable energy adoption. The analysis by the sectors revealed that transportation engineering and environmental engineering had improved sustainability performance compared to other sectors. Based on the above study, the conclusion can be drawn that data-driven sustainability assessment can be used to support engineering decisions with evidence, enhance environmental performance, and foster sustainable engineering practices.
Machine Learning-Based Water Pollution Prediction for Sustainable Environmental Monitoring
Pollution of water and the decreasing supply of clean drinking water have now become one of greatest environmental and human health issues in the world. The conventional methods of water quality assessment are usually costly, time-consuming and ineffective in continuous environmental monitoring. Here, the current paper examines application of machine learning techniques in predicting water potability using physicochemical water quality indicators. The authors used a publicly available dataset of 3276 water samples with nine environmental factors, including conductivity, organic carbon, trihalomethanes, pH, hardness, solids, sulphate, chloramines, and turbidity. Several monitored ML algorithms, which included; LR, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbor, were comparatively tested in terms of predictive performance. Mean imputation, feature scaling, and exploratory data analysis methods were applied to the data to prepare it before the development of the model. The results showed that SVM had highest accuracy, whereas the Decision Tree model had the most balanced classification results in terms of the F1-score. The most influential predictors of water potability were found to be pH, sulfate, solids, and hardness through feature importance analysis. The findings emphasize the suitability of ML methods in assisting intelligent environmental surveillance, sustainable water resources, and systems of early contamination. The research has a contribution to the increasing role of artificial intelligence to the environment and offers meaningful implications to policy-makers, smart cities, and health agencies.
Machine Learning for Predicting Hydrogen Storage Capacity: Uncovering Key Material Descriptors Governing Metal Hydride Performance
Amongst the various means of hydrogen storage, metal hydride storage with solid-state is considered the most promising way for safe and efficient hydrogen utilization. But, identifying the storage capacity governing descriptors remains a major challenge simply because of complex thermodynamic and compositional effects. This study aimed to predict hydrogen storage capacity in metal hydride materials and identify the key descriptors influencing material performance. A cleaned dataset of 741 observations representing 713 unique metal hydride compositions was analyzed. Hydrogen storage capacity was modeled using Linear Regression, Random Forest, and Gradient Boosting. Predictor variables included material class, heat of formation, entropy of formation, logarithmic equilibrium pressure, and H/M ratio. Model performance was evaluated using MAE, RMSE, and R², while Random Forest feature importance was used to interpret descriptor contributions. Gradient Boosting achieved the best predictive performance, with MAE = 0.1457, RMSE = 0.2400, and R² = 0.8634. Random Forest also performed strongly, with R² = 0.8590, while Linear Regression achieved R² = 0.7978. Feature importance analysis identified the H/M ratio as the dominant predictor, accounting for 47.0% of total importance, followed by the Mg-based material class at 36.7%. Together, these variables contributed approximately 83.7% of total predictive importance. Interpretable machine learning can effectively predict hydrogen storage capacity and reveal descriptor-level insights for metal hydride screening. The findings highlight the importance of hydrogen accommodation capacity and material class in guiding future hydrogen storage material design.
Advances in Applied Science and Engineering Technologies for Sustainable Innovation and Industrial Development
Renewable energy management has become an important applied engineering pathway for advancing sustainable innovation and industrial development. This study analyzed a secondary Excel-based dataset on daily energy management, including photovoltaic generation, electricity consumption, energy price profiles, seasonal energy behavior, and battery state-of-charge records. A quantitative, data-driven research design was used to evaluate renewable contribution, demand–generation balance, price-sensitive sustainability opportunities, and storage utilization. After preprocessing, 8,760 hourly observations from 2023 were retained, with no missing values in the analytical fields. The results showed that annual photovoltaic generation reached 6,263,310 kWh, while annual electricity consumption reached 22,024,941 kWh, producing a renewable contribution index of 28.44%. Seasonal analysis showed the highest renewable contribution in summer at 51.53%, followed by spring at 43.06%, while winter and autumn showed lower contributions of 14.05% and 11.48%, respectively. Renewable surplus occurred during 770 hours, indicating opportunities for storage charging, load shifting, or grid export. Correlation analysis showed a weak positive relationship between photovoltaic generation and consumption and a negative relationship between energy price and photovoltaic generation. Battery state-of-charge profiles confirmed active storage behavior. Overall, integrated photovoltaic generation, demand monitoring, pricing analysis, and battery storage supported sustainable industrial energy planning and data-driven engineering innovation
Impact of Seasonal Variability on Photovoltaic-Based Energy Management Systems
The study investigates the impact of seasonal variability on photovoltaic-based energy management systems by analyzing hourly photovoltaic generation, electricity consumption, demand, and electricity price data across winter, spring, summer, and autumn. A quantitative secondary-data approach was employed using descriptive statistics, one-way ANOVA, effect size analysis, Pearson correlation, and seasonal energy performance assessment. The results revealed significant seasonal differences in all investigated variables. Photovoltaic generation was highest in summer and spring, with average values of 1075.05 kW and 1046.98 kW, respectively, while winter recorded the highest electricity consumption at 2839.99 kW. ANOVA confirmed statistically significant seasonal effects on photovoltaic generation, consumption, demand, and electricity prices (p < 0.001). The highest generation-to-demand ratio was observed in summer (0.515), followed by spring (0.431), indicating greater renewable energy utilization during high solar-resource periods. Economic analysis showed that spring achieved the lowest total electricity cost (€423,822.27), whereas winter recorded the highest cost (€558,311.18). Correlation analysis further indicated significant relationships among photovoltaic generation, consumption, and electricity prices. Overall, the findings demonstrate that seasonal variability strongly affects the technical and economic performance of photovoltaic-based energy management systems. The study highlights the need for season-aware energy management strategies to improve renewable energy utilization, reduce grid dependency, and enhance operational cost efficiency.
Global Trends in Renewable Energy Adoption: A Data Analytics Approach for Sustainable Energy Planning
Environmental and climate change issues, energy security and sustainable economic development are pushing the shift to renewables to the top of the global energy agenda. Although there have been considerable advances in renewable energy deployment, there are significant differences between countries. This study is focused on the worldwide developments of renewable energy consumption as a data analytics approach towards sustainable energy planning. To explore the connections between the adoption of renewable energy and relevant economic, environmental, technological and investment factors in various countries between 2018 and 2025 a quantitative analytical approach was used. Descriptive statistics, trend analysis, correlation analysis, multiple regression analysis, cluster analysis and forecasting analysis were used in the analysis to assess the level of adoption and the key factors. The results show that there is a steady rise in the use of renewable energy, with 39.42% of renewable energy being used in 2018 and increasing to 53.88% by 2025. The regression results show that the variables of renewable energy investment, policy support, technological readiness, grid readiness, and GDP per capita significantly and positively affect renewable energy adoption, while policy support has a negative effect. The study highlights the importance of a coordinated strategy that integrates data-driven decision-making, technological advancements, and investment planning to accelerate global energy transition. The results are meaningful for policy makers, energy planners, stakeholder groups that want to support sustainable development of energy and sustainable environmental and economic outcome in the long-term.
Unsupervised Detection of Abnormal Operating Regimes in Industrial Centrifugal Pumps Using Multivariate Condition Monitoring Data
Industrial centrifugal pumps are critical assets in industrial facilities, where abnormal operating regimes can adversely affect reliability, maintenance efficiency, and process continuity. This study investigated the detection and characterization of abnormal operating regimes in industrial centrifugal pumps using multivariate condition monitoring data and an unsupervised learning framework. A dataset comprising 173,730 observations collected from three centrifugal pumps was analyzed through a workflow that included data preprocessing, sensor-data quality assessment, descriptive statistical analysis, correlation analysis, Isolation Forest-based anomaly detection, and statistical validation. Five operational variables—motor vibration velocity, motor peak acceleration, pump vibration velocity, pump peak acceleration, and outlet pressure—were used for anomaly detection modelling. The results revealed that anomalies were concentrated within specific operating periods rather than being uniformly distributed across the dataset. Pump B on 30 October 2024 exhibited the highest anomaly percentage (59.09%), followed by Pump A on the same date (41.11%), whereas Pump C on 30 October 2024 showed no detected anomalies, indicating a stable inactive operating state. Correlation analysis further demonstrated strong relationships among vibration, acceleration, and pressure measurements, reflecting interconnected mechanical and hydraulic behavior. Anomalous operating conditions were primarily characterized by substantial increases in motor peak acceleration, pump peak acceleration, and outlet pressure. These findings demonstrate that unsupervised anomaly detection can effectively identify abnormal operating regimes in centrifugal pumps without requiring labelled fault information. The proposed framework offers a practical and scalable approach for industrial condition monitoring and supports maintenance decision-making in data-rich operational environments.
An Explainable AI Framework for Circular Bioeconomy Systems: Predicting and Optimizing Biogas Production from Waste Biomass
The increasing emphasis on renewable energy generation, sustainable waste management, and circular bioeconomy development has created a growing need for intelligent approaches capable of optimizing biomass conversion processes. This study proposed an Explainable Artificial Intelligence (XAI) framework for predicting and optimizing biogas production from waste biomass. Using the Biogas Production Analysis dataset, machine learning models were developed to evaluate the relationships between biomass characteristics and methane generation performance. The analytical framework integrated predictive modeling, explainability analysis, and sustainability assessment to improve decision-making and resource utilization within waste-to-energy systems. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, and Artificial Neural Network models, were evaluated using R², Mean Absolute Error, and Root Mean Square Error metrics. The results demonstrated that ensemble learning approaches achieved superior predictive performance, with XGBoost producing the highest prediction accuracy. Feature importance and SHAP analyses identified the critical variables influencing methane production and enhanced model transparency by providing interpretable insights into prediction outcomes. Furthermore, the optimization framework improved resource efficiency, process performance, and methane yield while reducing resource consumption and environmental impacts. The findings highlight the capability of explainable artificial intelligence to support sustainable bioenergy production, efficient biomass utilization, and data-driven optimization within circular bioeconomy systems. Overall, the proposed framework provides a transparent and effective approach for enhancing biogas production and advancing renewable energy development through intelligent waste-to-energy conversion strategies.
An Explainable Artificial Intelligence Framework For Sustainable Biotechnology Predictive Modelling, Process Optimization, And Resource Efficiency
Artificial intelligence (AI) has emerged as a transformative technology for enhancing efficiency, sustainability, and decision-making across biotechnology applications. However, the increasing complexity of machine learning models has created challenges related to transparency and interpretability, limiting their practical adoption in biotechnology environments. This study proposed an Explainable Artificial Intelligence (XAI) framework for sustainable biotechnology that integrates predictive modeling, process optimization, and resource efficiency assessment within a unified analytical approach. The framework employed advanced machine learning techniques to evaluate relationships among biotechnology process variables and predict performance outcomes while maintaining model transparency through explainability mechanisms. Data preprocessing, feature engineering, predictive model development, explainability analysis, and sustainability assessment were incorporated to support comprehensive evaluation and optimization. The findings demonstrated that AI-based models effectively captured complex interactions among operational variables and achieved strong predictive performance. Feature importance and explainability analyses successfully identified key factors influencing biotechnology outcomes, thereby enhancing model interpretability and supporting informed decision-making. Furthermore, the sustainability assessment indicated opportunities for improving resource utilization and operational efficiency through AI-driven optimization. The proposed framework contributes to the growing integration of artificial intelligence and sustainable biotechnology by providing a transparent, data-driven approach for process improvement and resource management
Assessment of Seasonal Renewable Energy Integration Using Real-WorldConsumption Data
The integration of renewable energy into modern power systems is essential for achieving sustainability goals; however, seasonal variability in renewable generation and electricity demand continues to present operational and economic challenges. This study assesses seasonal renewable energy integration using real-world photovoltaic (PV) generation, electricity consumption, battery storage, and dynamic electricity pricing data. A quantitative data-driven approach was employed to evaluate seasonal variations in renewable generation, electricity demand, storage performance, and economic outcomes. Renewable penetration, energy balance, battery utilization, and cost-saving indicators were calculated, while the Kruskal–Wallis test and Spearman correlation analysis were applied to examine seasonal differences and inter-variable relationships. The results revealed significant seasonal variations in renewable generation, electricity consumption, and electricity prices (p < 0.001). Summer achieved the highest renewable penetration rate (51.53%) and the greatest economic benefit, with renewable energy offsetting 43.26% of seasonal electricity costs. In contrast, winter and autumn exhibited lower renewable penetration rates and greater dependence on grid electricity. Battery storage analysis demonstrated substantial contributions to system flexibility, with storage utilization ranging from 40% to 75% across representative operating profiles. Furthermore, renewable generation was consistently negatively correlated with electricity prices, particularly during spring and summer, indicating its potential to reduce market price pressure. The findings highlight the critical role of battery storage and adaptive energy management strategies in enhancing renewable energy integration under seasonal variability. This study provides valuable insights for policymakers, utilities, and energy planners seeking to improve renewable energy utilization and support the development of resilient and sustainable energy systems.
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MULTIDISCIPLINARY ADVANCES IN SCIENCE AND ENGINEERING FOR SUSTAINABLE TECHNOLOGICAL INNOVATION
Multidisciplinary advances in science and engineering for sustainable technological innovation examined the role of collaboration, emerging technology adoption, sustainability integration, renewable energy awareness, and industrial application in strengthening sustainable technological development. A quantitative primary research design was adopted, and data were collected from 150 respondents drawn from academic, research, industrial, and engineering-related environments. A structured questionnaire using a five-point Likert scale was used to obtain measurable responses from participants across mechanical, civil, electrical, electronics, computer science, data science, artificial intelligence, chemical, materials, environmental, energy, and industrial engineering fields. The data were coded, cleaned, and analyzed using Python, with descriptive statistics, Pearson correlation, and multiple regression applied to evaluate the relationships among the study variables. The results showed high agreement across all variables, with sustainable technological innovation recording the highest mean score. Correlation analysis revealed positive and statistically significant relationships between all independent variables and sustainable technological innovation. Regression analysis showed that the predictors explained 61.0% of the variance in sustainable technological innovation, with emerging technology adoption having the strongest effect, followed by sustainability integration and multidisciplinary collaboration. The findings concluded that sustainable technological progress depends on digital transformation, interdisciplinary cooperation, environmental responsibility, renewable energy awareness, and practical industrial implementation.
ENGINEERING-BASED ASSESSMENT OF FOREST FRAGMENTATION AND LANDSCAPE CONNECTIVITY FOR SUSTAINABLE ENVIRONMENTAL MANAGEMENT
Forest fragmentation is a threat to continuity in the ecosystem, causes greater edge effect and decreases forest landscape structure. Engineering analysis of fragmentation indices will help in sustainable environmental planning by recognizing landscape connectivity influences. This study aimed to assess how forest fragmentation indicators influence landscape connectivity and to evaluate regional variation in forest structural continuity. A quantitative secondary-data design was applied using landscape-level forest fragmentation metrics. Descriptive statistics were used to summarize major variables, Pearson correlation analysis examined relationships among fragmentation indicators, and multiple linear regression evaluated the influence of patch density, edge density, aggregation index, and forest cover on landscape connectivity. Country-level comparison was also performed to identify regional differences. Landscape connectivity showed moderate structural continuity across the analyzed forest systems. Aggregation index had the strongest positive association with connectivity, while patch density and edge density negatively influenced connectivity. The regression model explained 57.3% of the variation in landscape connectivity, confirming that spatial configuration is a major determinant of forest structural cohesion. Country-level results showed clear regional variation, with some countries maintaining stronger connectivity and others showing greater fragmentation pressure. Forest connectivity depends more strongly on patch arrangement than forest cover alone. Sustainable environmental management should prioritize aggregated forest structures, ecological corridors, and the reduction of excessive edge formation to improve landscape continuity.
ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS FOR SUSTAINABLE POWER GENERATION
Artificial intelligence-driven optimization of renewable energy systems offers a practical approach for improving sustainable power generation under variable operating conditions. Renewable energy systems often face challenges related to fluctuating solar irradiance, changing temperature conditions, unstable load demand, storage limitations, and grid dependency. Primary data were collected from a renewable energy setup consisting of solar photovoltaic modules, battery storage, an inverter, load monitoring units, smart meters, and environmental sensors. Python software was used for data preprocessing, AI model development, training, validation, testing, and performance evaluation. The AI model was designed to forecast renewable energy output, optimize energy distribution, improve battery scheduling, and balance power supply with load demand. The results showed that the model achieved strong forecasting performance, with a testing prediction accuracy of 95.24%, a Mean Absolute Percentage Error of 4.76%, and an R² value of 0.938. After AI optimization, average power output increased from 3.48 kW to 3.92 kW, energy efficiency improved from 78.35% to 87.42%, and renewable energy utilization increased from 74.80% to 88.60%. Energy loss decreased by 42.99%, while grid dependency declined by 45.44%. The findings indicate that AI-driven optimization can improve energy efficiency, storage performance, system reliability, cost effectiveness, and environmental sustainability in renewable energy systems.
MACHINE LEARNING-DRIVEN THERMODYNAMIC PREDICTION OF HYDROGEN STORAGE PERFORMANCE IN METAL HYDRIDES FOR SUSTAINABLE ENERGY APPLICATIONS
Efficient hydrogen storage remains a major challenge in the development of sustainable energy systems. Metal hydrides have emerged as promising solid-state hydrogen storage materials because of their favorable thermodynamic stability and reversible hydrogen absorption behavior. This study aimed to predict hydrogen storage performance in metal hydrides using machine learning techniques and to identify the variables most strongly influencing Hydrogen Weight Percent. A quantitative computational framework was implemented using secondary thermodynamic data containing 772 observations and 13 variables. Data preprocessing included missing-value treatment, categorical encoding, and feature scaling prior to model development. Three regression algorithms, namely Linear Regression, Random Forest, and Gradient Boosting, were evaluated using R-squared, mean absolute error, root mean square error, and five-fold cross-validation. The results showed that Gradient Boosting achieved the highest predictive performance with an R squared value of 0.9088, a mean absolute error of 0.1182, and a root mean square error of 0.2194. Random Forest also demonstrated strong predictive capability, whereas Linear Regression produced comparatively lower accuracy. Cross-validation results confirmed the robustness and generalization capability of the ensemble learning models. Feature importance analysis revealed that HtoM was the most influential predictor, contributing 57.77% of the total model importance, followed by Material Class, Temperature oC, and Pressure Atmospheres Absolute. The findings demonstrate that machine learning provides an efficient computational approach for hydrogen storage prediction and supports accelerated screening of metal hydride materials for sustainable energy applications.
DEEP LEARNING-DRIVEN SMART GRID OPTIMIZATION FOR SUSTAINABLE ENERGY EFFICIENCY IN SMART CITIES
With the rising need for sustainable energy management and intelligent city infrastructure, the use of artificial intelligence and deep learning techniques in smart grid systems has been gaining traction. Energy optimization frameworks in smart cities need to be adaptive and data-driven, with the ability to optimally manage the dynamic electricity consumption patterns and to achieve sustainability goals. The present study was aimed at creating a deep learning based smart grid optimization framework to enhance sustainable energy efficiency and predictive energy management in smart city environment. The large-scale smart grid load forecasting dataset with 5 years of hourly electricity consumption data and environmental variables was used to perform the research quantitatively with data-driven research approach. Short term load forecasting and energy optimization using a deep learning model namely, Long Short-Term Memory (LSTM) is done after data preprocessing, feature engineering, and time-series analysis. The developed framework was tested with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to measure the predictive performance and forecasting accuracy of the developed framework. It was found that the proposed deep learning model was able to capture the temporal energy consumption patterns and delivered good forecasting performance under different operational and seasonal conditions. It substantially enhanced the energy scheduling, energy load balancing efficiency, and intelligent resource allocation in the smart grid environment by utilizing predictive analytics. The findings also showed that AI forecasting models could aid in the integration of renewable energy, decrease operational losses and improve the sustainability performance of smart city systems. The study revealed that deep learning-based predictive analytics is a viable and scalable solution to optimize smart grid for intelligence and promote sustainable urban energy management. The framework proposed here will be helpful in the future development of intelligent urban infrastructure and will provide valuable inputs to the current research on applications of artificial intelligence, sustainable engineering and smart city energy systems.
PERFORMANCE ANALYSIS OF SOLAR PHOTOVOLTAIC SYSTEMS UNDER VARIABLE ENVIRONMENTAL CONDITIONS
In this study, the performance of a solar photovoltaic crystalline silicon system is evaluated under different environmental conditions. The primary aim was to study the effect of solar irradiance, ambient temperature, PV panel temperature, relative humidity, wind speed and dust condition on PV power output, PV power conversion efficiency and PV performance ratio. The outdoor operating conditions were recorded, such as the voltage, current, power output, irradiance, temperature, humidity, wind speed, and dust level. The results indicated that solar irradiance was found to be the most significant factor among the various factors studied for PV power generation. The maximum irradiance obtained was 910 W/m2, recorded at 01:00 PM and when the maximum power output was obtained at 199.12 W. The maximum conversion efficiency of the panel was however obtained at 09:00 AM when the panel temperature was low of 14.67%. The PV efficiency decreases with the excessive heating as the highest panel temperature of 58.3°C was recorded with the lowest efficiency of 13.68%. The increases in dust accumulation, relative air humidity also contributed to the loss in performance and the wind speed had a moderate cooling effect. The highest energy generation and highest performance ratio was observed during clear sky conditions as observed on a daily basis. The study concludes that to achieve the best performance of solar PV under real environmental conditions regular cleaning, appropriate ventilation, installation angle and constant monitoring are necessary.
DIGITAL TWIN TECHNOLOGY FOR PREDICTIVE ANALYSIS AND OPTIMIZATION OF ENGINEERING SYSTEMS
Digital twin technology was examined for predictive analysis and optimization of engineering systems using a quantitative primary research design. Primary operational data were collected from a selected engineering system through sensors, performance monitoring devices, and operational records. The measured variables included temperature, vibration, pressure, load, energy consumption, operating speed, fault frequency, downtime, maintenance cost, and system output. A digital twin model was developed through data preprocessing, virtual modeling, predictive model integration, and optimization analysis. The model was trained and tested using the collected dataset, and its performance was evaluated through prediction accuracy, mean absolute error, root mean square error, fault detection rate, and response time. The results showed that the digital twin model achieved 93.80% testing accuracy and a 91.70% fault detection rate. Optimization improved system efficiency from 78.40% to 89.60%, reduced energy consumption from 148.62 kWh to 124.38 kWh, reduced downtime by 54.41%, and decreased maintenance cost by 29.96%. Statistical analysis confirmed significant differences between baseline and optimized conditions at the 0.05 significance level. Digital twin technology enhanced predictive decision-making, operational reliability, maintenance planning, and sustainable performance improvement in engineering systems across data-driven industrial and applied engineering environments, while supporting more accurate monitoring, fault prevention, and resource-efficient operation over time.
EXPLAINABLE ENSEMBLE MACHINE LEARNING FRAMEWORK FOR PREDICTING DRUG RELEASE BEHAVIOR IN POLYMERIC LONG-ACTING INJECTABLE SYSTEMS
Predicting drug release from polymeric long-acting injectable systems remains challenging because release behavior depends on nonlinear interactions among formulation, physicochemical, and temporal variables. This study developed an explainable ensemble machine learning framework for predicting drug release behavior and identifying the most influential release-governing variables. A secondary experimental dataset containing 3,783 observations and 20 variables was analyzed. Data preprocessing included quality inspection, categorical encoding, exclusion of identifier variables, and train-test splitting. Linear Regression, Random Forest, and XGBoost models were developed and evaluated using R², mean absolute error, and root mean square error. Feature importance and residual analyses were additionally performed to assess interpretability and model reliability. XGBoost achieved the best predictive performance with an R² of 0.9849, MAE of 0.0276, and RMSE of 0.0406, outperforming Random Forest and Linear Regression. The strongest predictors were T = 1.0, Drug NHA, SE, Drug Mw, and Time. Residual analysis demonstrated low prediction bias and stable error distribution, supporting the robustness of the optimized model. The findings indicate that explainable ensemble learning can accurately model complex release behavior in polymeric long-acting injectable systems and may support efficient formulation screening, optimization, and data-driven pharmaceutical engineering.
PREDICTIVE ASSESSMENT OF CONCRETE COMPRESSIVE STRENGTHUSING MIX DESIGN PARAMETERS AND ENGINEERED CEMENTITIOUSFEATURES
Concrete compressive strength is a key indicator of concrete quality, structural performance, and suitability for construction applications. This study presents a predictive assessment of concrete compressive strength using mix design parameters and engineered cementitious features. The analysis was based on 1,030 concrete mix records containing cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, curing age, compressive strength, and derived features including water-cement ratio, total binder content, aggregate-to-cement ratio, cement-water interaction, and age-strength proxy. Descriptive analysis showed that compressive strength ranged from 2.33 MPa to 82.60 MPa, indicating broad variation across low-, medium-, and high-strength concrete mixtures. Correlation analysis revealed that total binder content, cement content, age-strength proxy, superplasticizer, and curing age were positively associated with compressive strength, while water-cement ratio, aggregate-to-cement ratio, and water content showed negative relationships. Five regression models were evaluated, including Linear Regression, Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, and Support Vector Regression. The results showed that non-linear models outperformed Linear Regression, with Random Forest Regression achieving the highest predictive performance with an R² value of 0.908. Feature importance analysis identified total binder content, curing age, age-strength proxy, and water-cement ratio as the most influential predictors. The findings demonstrate that engineered cementitious features can improve strength interpretation and support preliminary concrete mix assessment, quality control, and construction decision making.
SURVIVAL ANALYSIS OF BREAST CANCER PATIENTS USING CLINICAL RISK PARAMETERS: A STATISTICAL APPROACH BASED ON TIME-TO-EVENT MODELLING
Background: Breast cancer continues to be a major health concern, where patient survival is influenced by multiple clinical and physiological factors. Understanding the relationship between these factors and survival outcomes is essential for improving patient management and early intervention strategies. Objective: The present study aims to evaluate the survival patterns of breast cancer patients and to examine the influence of selected clinical parameters on time-to-event outcomes using appropriate statistical modelling techniques. Methods: The study is based on secondary clinical data collected from 500 breast cancer patients who underwent routine health assessment. Key variables considered include age, body mass index (BMI), heart rate (average), position score (average), systolic and diastolic blood pressure (average), oxygen saturation (average), medical progression score (average), and symptom severity score (average). Survival analysis was carried out using the Kaplan–Meier estimator to estimate survival probabilities, the Cox proportional hazards model to identify significant predictors, and the Nelson–Aalen estimator to assess cumulative hazard. Model performance was evaluated using the concordance index and likelihood-based measures. Results:The median survival time was estimated to be 2.2 hours, indicating early occurrence of events within the study period. The Cox regression analysis revealed that BMI, systolic blood pressure, position score, medical progression score, and symptom severity score have a statistically significant effect on survival outcomes. Among these, medical progression score showed a strong positive association with risk, while symptom severity score demonstrated a protective effect. The concordance index value of 0.66 indicates moderate predictive performance of the model. The cumulative hazard analysis showed a steady increase in risk over time, with a noticeable rise in later intervals. Conclusion: The findings confirm that survival outcomes in breast cancer patients are significantly influenced by selected clinical parameters. The application of survival analysis techniques provides meaningful insights into disease progression and risk patterns. The study supports the importance of early monitoring and the integration of statistical modelling in clinical decision-making for improved patient care.
SEISMIC ANALYSIS OF SKEW CURVED BRIDGES FOR ZONE 4 SEISMOLOGY
Due to the complex behavior of bridges at this level of seismic failure, skewed-curved bridges constitute a severe challenge to structural engineering. They are different from straight bridges in that the combined form of these two curves and angles results in a clear gap between the center of mass and the center of rigidity. So that gives the rock a "pinball" effect in an earthquake. In zones like Zone 4, which experience much severe ground movement, these bridges encounter issues such as excessive deck rotations, pounding between decks and abutments, and risk of falling off their supports. Using Nonlinear Time History Analysis (NLTHA), a new approach is proposed for understanding how such structures react dynamically, with particular emphasis given to their curvature radius and effect on performance. Curvature and skew angles push up the shear forces on piers and cause bigger movement in expansion joints. Following IS 1893:2002 guidelines, the study identifies some key failure modes, such as the "kick" effect at abutments. Its focus is to draw attention to demand for some type of the latest seismic isolation or restraint system to prevent collapse in earthquake-prone regions. With the transitions (if it doesn't sound boring): the author alters styles and breaks from established patterns.
FABRICATION AND TESTING OF PLASTIC SAND BRICKS
The exponential growth of non-recyclable plastic waste presents a problem for the environment, as the increasing non-recyclable plastics disposal or thermal recycling process frequently exacerbates the effects of climate change and ecological degradation. In this paper, we examine yet another environmentally friendly method, that of plastics for building-grade bricks. In a bid to meet or exceed traditional masonry standards, we synthesized high-density polyethylene (HDPE), quartz sand, and bitumen additives into plastic-sand bricks in a set of bricks. The process was waste to material transition from collection, to decontamination and shredding of PET and HDPE waste. The material was thermally processed at 180°C up to 220°C, mixed with river sand with a weight ratio of 1:2, 1:3 and 1:4. These units differ from regular clay bricks in that they are cold pressed in oiled molds and cured at room temperature, bypassing the carbon-intensive kiln-firing part of the construction process. Empirical testing - compressive strength, water absorption, efflorescence test etc - demonstrated the composite bricks to be structurally better than a clay type. Furthermore, it's worth noting that the plastic binder is non-porous providing negligible water absorption leading to excellent weather resistance. Results, for plastic-sand brick, indicate a combined twofold response: It is a strong and energy-saving construction material, and has useful plastic rubbish recycling utility.
COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR PREDICTING CONCRETE COMPRESSIVE STRENGTH
Concrete compressive strength is a basic parameter in civil engineering that relates to the quality and longevity of structures. Traditional techniques of measuring compressive strength are based on laboratory tests which are usually time and resource consuming. The paper examines how machine learning methods can be used to estimate compressive strength of concrete in terms of mixture composition and curing age. They used a dataset of 1030 samples reflecting eight input variables; cement, water, aggregates and age. Three machine learning models, all regressions, were implemented: Linear Regression, Decision Tree Regressor, and Random Forest Regressor. The data was split into 80:20 training and testing data. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and coefficient of determination (R2) were used to gauge model performance. The Regressor model that performed better was the Random Forest Regressor which had a R2 of 0.882 with an RMSE of 5.510. The analysis of importance of features showed that the most important factors in the compressive strength prediction are the curing age and cement content. The findings denote that the machine learning models, especially the ensemble-based models, may be useful in forecasting the compressive strength of concrete and provide an effective alternative to conventional experimental techniques.
AN EFFICIENT SCHEDULING ALGORITHM FOR LOAD BALANCING IN CLOUD COMPUTING ENVIRONMENTS
The problem of load balancing has become one of the primary aspects of cloud computing because of the asymmetry of the allocation of duties, which may lead to an inefficient use of resources and breakdown of performance. Despite the various scheduling techniques that have been expounded, there is a desire to continue and have simpler and adaptive techniques, which can be able to operate effectively with huge and dynamic workloads. This study evaluates a load-aware scheduling approach designed to improve workload distribution across virtual machines in a cloud environment. A large-scale task scheduling dataset was preprocessed to construct job-level workloads, resulting in 80,000 jobs for analysis. Jobs were ordered by submission time and assigned to virtual machines in a simulation framework. The proposed method dynamically assigns each incoming job to the virtual machine with the minimum current load. Its performance was compared with First-Come-First-Serve (FCFS) and Round Robin scheduling using load variance, standard deviation, and fairness index. The results show that the load-aware approach significantly reduces workload imbalance, achieving approximately 99.9% reduction in load variance and over 96% reduction in standard deviation compared to both baseline methods. It also attains the highest fairness index, indicating a more uniform distribution across virtual machines. These findings suggest that simple load-aware scheduling can effectively improve resource distribution without introducing additional computational complexity, making it a practical solution for large-scale cloud environments.
PREDICTION OF RESIDENTIAL HOUSE PRICES USING MACHINE LEARNING REGRESSION TECHNIQUES
The precise forecasting of residential house prices is a significant issue in the real estate industry as it helps in informed decision making by buyers, sellers, and investors. The purpose of the study is to create a machine learning-based model to predict house prices on the basis of a structured housing data. The dataset in this study comprises 545 records with an array of properties, such as area, number of bedrooms, bathrooms, stories, and other housing characteristics. The dataset was prepared by data preprocessing methods like encoding categorical variables and feature selection, to use in modeling. Three machine learning regression algorithms - Linear Regression, Random Forest Regressor and Decision Tree Regressor - were deployed and compared. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R2) were used to evaluate the performance of these models. Results show that the Linear Regression model performed better than the rest with R2 of around 0.65 which shows moderate predictive power. The results indicate that machine learning methods can be effective in predicting the prices of residential properties, and it can be used as a valuable tool in the analysis and decision-making of real estate.