EPH-International Journal of Science And Engineering
EPH - International Journal of Science and Engineering (IJSE) EPH - International Journal of Science and Engineering (IJSE) is an international peer-reviewed, open-access journal dedicated to publishing high-quality research in science, engineering, technology, and interdisciplinary domains. The journal serves as a platform for researchers, academicians, scientists, and industry professionals to share innovative findings and contribute to scientific and technological advancement. The journal welcomes original research articles, review papers, and case studies that address contemporary challenges and emerging developments across scientific and engineering disciplines. Journal Information ISSN 2454-2016 Review Process Double Blind Peer Review Publication Frequency Quarterly ```
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
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.
ADVANCES IN BUSINESS PROCESS AUTOMATION: LOW-CODE PLATFORMS AND ENTERPRISE APPLICATIONS
The automation of business processes (BPA) is among the key contributing factors of digital transformation that will assist in making an organization more efficient, cost-effective and maximize the value of decision-making. With the appearance of low-code solutions, the world of automation systems has shifted once again, since these systems can now be developed quickly and with little knowledge of programming. This research paper tries to explain in details how BPA has evolved especially focusing on how low-code development platforms are becoming relevant and their utilisation in enterprise systems. Reviewing will be done systematically using existing literature on automation systems, workflows models and intelligent systems. The current research will touch upon the background theories and automation plans, along with discussing real-life uses of platforms in industries. Moreover, intelligent automation and artificial intelligence will be discussed in order to shed light on the latest technological trends. As a result, the findings show that low-code development can significantly improve the flexibility and accessibility of automation systems despite the challenges associated with integration, governance and complexity of the system. Moreover, research gaps identified throughout the investigation are crucial for further research in this field.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
A COMPARATIVE STUDY ON INTERPRETABLE MACHINE LEARNING BASED SURVIVAL MODELS FOR CVD WITH CLINICAL DATA
Cardiovascular disease stands as a major cause of death around the globe, thus stressing the need for proper prediction of prognosis and survival in the clinical setting. In classic diagnostic methods the disease presence is treated as a main factor but survival analysis provides the possibility of predicting the time-to-event by including the model information about the censored observation and disease progression. This study presents an interpretable survival modeling framework to understand cardiovascular disease outcomes using clinical data. Classical survival models, ensemble machine learning–based survival models, and deep learning–based survival methods are developed and tested systematically. Performance on the part of the model is examined using concordance index (C-index); interpretability through hazard ratios, coefficient analysis, and feature relevance is stressed. The experimental results show better predictive performance in ensemble survival models, and the requirement for clinical interpretability in classical survival models. These findings provide support for incorporating transparent survival models into clinical decision-support systems for cardiovascular disease prognosis.
A SYSTEMATIC REVIEW OF DATA VISUALIZATION TECHNIQUES AND STORYTELLING WITH DATA
In today’s data-driven environment, organizations produce vast amounts of data every day. However, without effective analysis and clear communication, this information does not translate into meaningful insights. Data visualization and data storytelling play a crucial role in converting complex datasets into clear, actionable knowledge. This paper examines the core principles, methods, tools, and practical applications of data visualization and storytelling. It highlights how combining strong analytical insights with well-structured narratives enhances decision-making across sectors such as business, healthcare, education, and public policy.
EXPERIMENTAL STUDY ON HEAT TRANSFER ENHANCEMENT USING NANOFLUIDS
Heat transfer enhancement is a critical requirement in modern engineering systems such as power plants, automotive cooling, and electronic devices. Conventional heat transfer fluids, including water and ethylene glycol, have limited thermal conductivity, which restricts their performance in high-efficiency applications. This study experimentally investigates the potential of nanofluids to improve heat transfer characteristics. Aluminum oxide (Al₂O₃) nanoparticles were dispersed in distilled water at different volume concentrations (0.1%, 0.3%, and 0.5%) to prepare nanofluids using ultrasonic agitation for uniform stability. Experiments were conducted in a heated tube under controlled laminar and turbulent flow conditions. The convective heat transfer coefficient and Nusselt number were evaluated and compared with those of the base fluid. The results showed a significant enhancement in heat transfer performance, with up to 30% improvement at higher nanoparticle concentrations. This enhancement is attributed to increased thermal conductivity, Brownian motion, and micro-convection effects. However, an increase in viscosity and pressure drop was also observed, indicating a trade-off between heat transfer improvement and flow resistance. Overall, nanofluids demonstrate strong potential as advanced heat transfer fluids for efficient thermal systems.
PERFORMANCE EVALUATION OF DATA HANDLING ALGORITHMS (DT, KNN, RF AND DNN) UNDER THE HANDLING OF TWO DATASET OF DIABETES
Diabetes is a condition that affects people of all ages these days, as everyone is aware. This paper discusses the implementation of algorithms based on two separate diabetes datasets. The algorithms were chosen based on the highest value and accuracy. In this paper analyze the four algorithms such as DT, KNN, RF and DNN. The main objective of this paper is to handle data with different and large datasets because data is the most difficult to handle. In the previous research, diabetes was predicted in different cases and their conclusions were also different. In this paper, the DNN algorithm is used and compared with other algorithms under two different datasets. In this paper, compare the DNN algorithm with the proposed algorithms such as DT, RF and KNN on large and two different datasets of diabetes. The first dataset is taken from Pima Indians Diabetes Database (PIDD) and the second dataset is collected through survey from Manjira Devi Hospital Uttarkashi (MDHU). On the basis of these two datasets, algorithms are compared.
INTEGRATING GRAPH DATABASE MODELING INTO AGILE SYSTEM ANALYSIS AND DESIGN: A CONCEPTUAL FRAMEWORK FOR SCALABLE INFORMATION SYSTEMS
Relational database modeling is still the most popular in system analysis and design (SAD), but has structural constraints in relationship-intensive domains like social networks, fraud detection, recommendation systems, and knowledge graphs (Angles & Gutierrez, 2008; Robinson et al., 2015). These constraints are aggravated in agile systems with iterative delivery and emergent design (Beck et al., 2001; Fowler, 2002; Schwaber & Sutherland, 2017), in which architectural decisions that are susceptible to traversal are often deferred. This study presents the Graph-Agile Framework, a design science artifact that brings the concepts of graph database modeling into agile SAD using a three-tier architecture: Strategic Alignment, Tactical Integration, and Operational Adaptation. Grounded in design science research and theory synthesis (Gregor & Hevner, 2013; Hevner et al., 2004; Jaakkola, 2020a), the framework institutionalizes traversal-aware reasoning, schema optionality, and sprint-level scalability validation into the iterative workflows. An expert-based evaluation (N = 40) assessed structural clarity, theoretical grounding, agile alignment, scalability support, and adoption feasibility. Findings indicate consistently high expert-rated internal coherence and compatibility with agile practices, providing preliminary perceptual support for the framework’s utility. The study advances SAD theory beyond relational assumptions and extends agile methodology by formalizing traversal-aware architectural governance for scalable graph-centric system development.
NYLON-6: POLYMERIZATION CHEMISTRY, PROPERTIES, AND INDUSTRIAL APPLICATIONS –AN INDUSTRIAL REVIEW
Nylon-6, a widely used polyamide, stands out among synthetic polyamides as a versatile, high- performance fibre owing to its unique macromolecular structure, balanced physicochemical properties, and broad processability. It is synthesized by the ring-opening polymerization of ε- caprolactam through hydrolysis, polyaddition, and polycondensation under controlled thermal and pressure conditions. The polymerization process is critical in defining the final polymer’s characteristics, including molecular weight distribution, residual monomer content, and relative viscosity, which directly influence fiber-forming capability. Advanced process control systems regulating temperature, pressure, level, and flow are indispensable for ensuring stability, reproducibility, and energy efficiency at industrial scale. The structural property relationship of Nylon-6 highlights its superior tensile strength, elasticity, abrasion resistance, dyeability, and thermal stability (Tm ≈ 220°C),with crystallinity levels around 50–55%. Post-polymerization treatments, such as hot washing and solid-state aging, further improve viscosity control and spinnability. Modified formulations with additives or resin blends enhance resistance to heat, solvents, and fire, expanding its range of applications. Due to this combination of mechanical robustness and processability, Nylon-6 is extensively employed in technical textiles (tire cords, carpets, fishing nets, parachutes, and sutures), apparel fabrics, packaging films, and engineering plastics processed via injection molding, extrusion, and blow molding. This Industrial Case study consolidates polymerization principles, control strategies, material properties, and applications of Nylon-6, providing a comprehensive perspective for advancing both research and industrial practices.
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A THEORETICAL FRAMEWORK FOR CLOUD AND DATA ANALYTICS CONVERGENCE IN SCALABLE MANAGEMENT INFORMATION SYSTEMS
With the growing use of data-intensive environments in the operations of an organizations and the higher education institutions, Management Information Systems (MIS) must shift away from the known-data-processing-environment to the higher analytical support and timely decision-making environment. Despite the fact that data analytics and cloud computing are recognized as crucial agents of organizational intelligence and scalability, a large portion of the literature currently in publication views the two technologies as distinct disciplines. These has been the focus on elaborating on how cloud and analytics capabilities interact in systematic relations to serve scalable MIS and this is specifically in complex and multidisciplinary academic settings. The paper proposes a theoretically grounded conceptual framework that clarifies how data analytics and cloud computing can be integrated to provides a platform for scalable MIS. The study incorporates cloud computing, data analytics, distributed system, and information system literature using a theory synthesis approach. It is designed based on three construct domains, including cloud infrastructure elements, data analytics capabilities and MIS scalability requirements and operationalized by five convergence mechanisms: dynamic resource orchestration, analytics-support scalability, bidirectional feedback loops, data-compute co-location, and adaptive optimization. Seven theoretical propositions are developed to inform future empirical research. The research work also adds to MIS theory by explaining structural functional relationship between cloud infrastructure and analytics capabilities in scalable system design. Furthermore, it provides viable guidelines to the institutions that seek to achieve the development of analytics-enabled MIS designs that can withstand the increasing levels of data, changing decision needs, and available resource limitations.
BIOTECHNOLOGICAL APPROACHES TO PLASTIC WASTE DEGRADATION
Plastic pollution is a persistent global environmental problem driven by massive production of recalcitrant polymers such as polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET). Biotechnological strategies—microbial degradation, enzyme catalysis, fungal decomposition, engineered microbial consortia, and enzyme engineering—offer promising, potentially sustainable routes to degrade or upcycle plastic waste under controlled conditions. Key advances include discovery of PET-degrading bacteria (Ideonella sakaiensis) and PETases, development of engineered PET hydrolases with enhanced activity and thermostability, and evidence that fungi and mixed microbial consortia can attack diverse polymers. However, the intrinsic chemical inertness of polyolefins (PE/PP), variable additives, and inconsistent experimental standards complicate translation from lab demonstrations to scalable processes. Integrated approaches that combine pretreatment (oxidation, UV, thermal), enzyme cocktails, synthetic biology (pathway engineering, secretion systems), and process engineering (bioreactors, immobilized enzymes, coupled upcycling pathways) may overcome these hurdles and produce value-added products (monomers, oligomers, biopolymers). Life-cycle impacts, CO₂ release, and techno-economic feasibility require careful assessment. Continued discovery of novel enzymes, rational enzyme design, and development of robust consortia optimized for mixed-waste streams are essential next steps toward industrially relevant biotechnology solutions for plastic waste management.
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IN-VITRO AND IN-VIVO STUDY OF SELF-MICROEMULSIFYING DRUG DELIVERY SYSTEM FOR HYPERTENSION
Aim: The aim of the study was to formulate and evaluate self-micro emulsifying drug delivery system (SMEDDS) of nifedipine for hypertension. Methodology: A series of formulations with different compositions were selected in the microemulsion region for assessment of self-emulsification time and droplet size. The optimized SMEDDS formulation was used for in vitro dissolution and pharmacokinetic studies in rats. The optimized formulation was further used for the preparation of various Solid SMEDDS(S-SMEDDS) formulations. formulations were evaluated for stability, robustness to dilution and self-emulsification efficiency. The in vitro release was almost similar for the S-SMEDDS as well liquid within 5 min. Results: The optimized formulation had a particle size of 11.48nm, a transmittance of 98.2%, a zeta potential of -11.86 mV, and a polydispersibility index of 0.243. The solid SMEDDS tablet exhibited improved drug release (99.5% in 60 minutes) compared to the marketed tablet (67.09.75%) and pure drug (26.17%). This study demonstrates the potential of the SMEDDS approach to enhance the solubility and in-vitro drug release of drugs. Conclusion: studies illustrated that adsorption to solid carrier technique could be a useful method to prepare the solid SMEDDS tablets from liquid SMEDDS, which can improve oral absorption of nifedipine, nearly equivalent to the liquid SMEDDS, but better in the formulation stability, drugs leakage and precipitation, etc.
PLANT DISEASE RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND TRANSFER LEARNING
Global food security and agricultural output are seriously threatened by plant diseases, especially when diagnosis is erroneous or delayed. For large-scale farming, manual crop inspection is laborious, subjective, and unfeasible. This work proposes a Plant Disease Recognition System based on Convolutional Neural Networks (CNNs) and transfer learning to address these issues. The suggested solution makes use of a pretrained MobileNetV2 architecture that has been refined using the publicly accessible PlantVillage dataset, which includes more than 54,000 tagged leaf images from various crop species and disease categories. In order to enhance generalization under various visual situations, the methodology places a strong emphasis on robust image preprocessing, such as scaling, normalization, and data augmentation. Users can contribute leaf photos and get real-time illness predictions by accuracy of about 98%. Confusion matrix analysis shows very little misdiagnosis, mostly in cases of early-stage or visually comparable diseases. The results verify that transfer learning dramatically shortens training times without sacrificing classification performance, which qualifies the system for real-world use. The work offers further extensions toward mobile deployment and real-world field validation while highlighting the promise of CNN-based solutions for early plant disease diagnosis.
Copyright (c) 2022 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
ADVANCED LANGEVIN DYNAMICS FOR PREDICTING SPIN DISTRIBUTIONS IN HEAVY-ION FUSION
The angular momentum (spin) distribution of compound nuclei formed in heavy-ion fusion reactions is a key quantity governing fission probability, gamma-ray multiplicity, and evaporation-residue survival. Traditional approaches based on sharp cutoff formulas, coupled-channel barrier penetration, or mean-field dynamics often fail to reproduce experimentally observed spin widths, especially near the Coulomb barrier and in reactions forming very heavy or superheavy systems. In this study, I developed and applied an advanced multi-dimensional Langevin framework to predict spin distributions in heavy-ion fusion, treating the reaction as a stochastic transport process in a collective coordinate space, including radial separation, deformation, mass asymmetry, orientation, and rotational degrees of freedom. Dissipation is modelled using one-body wall-and-window friction, whereas random forces obey the fluctuation-dissipation theorem. Deformation-dependent inertia and realistic nucleus-nucleus potentials were consistently incorporated. Simulations for representative reactions such as , , and reproduce the observed broadening of spin distributions with increasing beam energy, enhanced angular momentum damping in mass-asymmetric systems, and separation of fusion and quasi-fission events. The results demonstrate that advanced Langevin dynamics provides a robust and physically transparent tool for predicting spin distributions and for guiding the design of fusion reactions aimed at producing heavy and superheavy nuclei.
FORMULATION AND CHARACTERIZATION OF HARD, GELATIN-LIKE CAPSULES BASED ON CORN STARCH, AGAR-AGAR, SODIUM ALGINATE AND HPMC FOR ORAL DELIVERY OF SMALL MOLECULES
This study develops and evaluates plant-based hard capsules formulated using corn starch, agar-agar, sodium alginate, and hydroxypropyl methylcellulose (HPMC) as an alternative to traditional gelatin capsules. The polymers were optimized by adjusting viscosity, polymer ratios, and plasticizer concentration to achieve dip-coating compatibility. Physicochemical characterizations included viscosity, swelling index, moisture content, capsule wall thickness, tensile strength, and disintegration time. Structural confirmation was performed using SEM, FTIR, and DSC. The optimized formulation exhibited uniform film morphology, improved thermal stability, inter-polymer hydrogen bonding, and mechanical strength comparable to gelatin. Dissolution studies revealed rapid and reproducible drug release in simulated gastric and intestinal fluids. Compared with literature-reported gelatin, HPMC, alginate, and starch capsules, the proposed composite formulation showed balanced mechanical properties, controlled hydration, low moisture sensitivity, and faster disintegration. The study demonstrates that a synergistic blend of starch, agar, alginate, and HPMC can serve as a robust, low-cost, plant-based alternative for oral drug delivery systems.
Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
CONTINUAL YOLO-BASED DETECTION FOR LONG-TERM MONITORING OF THE GREATER ONE-HORNED RHINO
YOLO-based object detectors support fast wildlife monitoring across diverse habitats. Static training limits these detectors in the field because ecological conditions shift over time. Continual learning provides an incremental update process that preserves earlier knowledge while absorbing new information. This article reviews recent progress in continual learning for YOLO detection, with emphasis on experience replay, self-distillation, and human-in-the-loop supervision. These approaches protect past knowledge, reduce annotation demands, and deliver more stable predictions during long-term deployments. A focused case study on Greater One-Horned Rhino monitoring shows how adaptive learning pipelines strengthen detection reliability during seasonal changes, altered terrain, and new camera placements. The review outlines methods with strong potential for long-term conservation work and highlights future directions for resilient wildlife monitoring systems.
MESTA FIBER (HIBISCUS SABDARIFFA): A REVIEW OF ITS PROPERTIES, APPLICATIONS, AND ROLE IN PROMOTING SUSTAINABILITY
The increasing world demand for eco-friendly natural fibers and products is gaining interest in sustainable fibers as alternatives to man-made fibers. Bast fibers such as linen, banana, jute, bamboo, sisal, kenaf, and Mesta have been used for a long time. Bast fibers are the raw materials of the present and the future, not just for textiles but also for modern eco-friendly composite materials, medicine, cosmetics, food, biopolymers, agro-fine chemicals, and energy. The retting is the biggest problem with extracting the fibers. Dew retting as well as stagnant water and running water retting are the conventional methods to separate the long bast fibers. Due to higher tensile property, mechanical performance, environmental benefits, and economic viability, Mesta has shown significant promise compared to synthetics. They can be grown in numerous climates, and, under the right conditions, they don't hurt the ecology too much or at all. The study looks into the important physical and mechanical properties and benefits of Mesta fibers, as well as their possible uses. It focuses on how they might help promote sustainable consumption and green industrial practices. The review talks on Mesta's environmental, technical, and social-economic importance, showing that they are still crucial for accomplishing ecologically sustainable development goals and coming up with novel bio-based materials.