EPH-International Journal of Applied Science
EPH – International Journal of Applied Science (ISSN: 2208-2182) disseminates high-quality research across a broad range of disciplines, including natural sciences, engineering, health sciences, social sciences, and business management. The journal welcomes interdisciplinary work and innovative studies that bridge multiple fields. Journal Information Journal Title: EPH – International Journal of Applied Science ISSN: 2208-2182 Founded: 2015 Language: English Publication Frequency: Quarterly (4 times per year) Publisher: Southern Cross Academic Press
DATA-DRIVEN PREDICTION OF BUILDING ENERGY LOADS
In the modern building world, energy-load prediction is a vital aspect of energy optimisation, demand management and sustainable energy use. This study developed and compared interpretable machine-learning models for predicting hourly building energy consumption across two buildings with different operational characteristics. Multi-year hourly observations were preprocessed through temporal feature engineering, incorporating historical energy consumption, meteorological conditions, and calendar-related variables. The four supervised regression models, namely Linear Regression, Random Forest, Histogram Gradient Boosting, and Extreme Gradient Boosting (XGBoost), were evaluated using an 80:20 chronological train-test split. Predictive performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²), while SHapley Additive exPlanations (SHAP) were applied to interpret feature importance. Histogram Gradient Boosting consistently achieved the highest prediction accuracy, obtaining an MAE of 8.063, RMSE of 14.151, MAPE of 5.131%, and R² of 0.9661 for Building A, and an MAE of 3.574, RMSE of 5.481, MAPE of 10.303%, and R² of 0.9752 for Building B. SHAP analysis identified one-hour, daily, and weekly historical energy consumption as the dominant predictors, whereas meteorological variables contributed comparatively less. The results presented indicate that temporal feature engineering and gradient-based ensemble learning can provide an accurate, interpretable and efficient framework for short-term building energy-load forecasting and intelligent energy management.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
EXPLAINABLE BREAST CANCER DIAGNOSIS USING TUMOR MORPHOLOGY
Concrete compressive strength is a critical indicator of structural performance and material quality, but Breast cancer remains a leading cause of cancer-related morbidity and mortality, emphasizing the need for accurate and interpretable diagnostic approaches. This study developed an explainable machine learning framework for breast cancer diagnosis using quantitative tumor morphology features derived from the Breast Cancer Wisconsin (Diagnostic) Dataset. Following data preprocessing and exploratory analysis, five machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, were trained and comparatively evaluated using standard classification metrics. Model interpretability was achieved through SHapley Additive exPlanations (SHAP), enabling both global and local interpretation of feature contributions. Among the evaluated models, Logistic Regression demonstrated the highest diagnostic performance, while SHAP analysis identified radius, perimeter, concavity, concave points, and texture as the most influential morphological predictors of malignancy. The explainability framework enhanced transparency by illustrating how individual features affected model predictions, thereby improving the reliability and clinical interpretability of automated diagnostic decisions. The proposed framework demonstrates that combining robust machine learning with explainable artificial intelligence can provide accurate, transparent, and clinically meaningful breast cancer diagnosis. These findings support the integration of explainable diagnostic models into clinical decision-support systems and establish a foundation for future research involving multicenter datasets and multimodal medical information.
EXPLAINABLE PREDICTION OF HEART DISEASE USING CLINICAL INDICATORS
Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for accurate and transparent prediction models that can support timely clinical decision-making. Although machine learning has significantly improved the prediction of cardiovascular disease, the limited interpretability of many predictive models restricts their clinical adoption. This study proposes an explainable machine learning framework for predicting heart disease using routinely collected clinical indicators from the UCI Heart Disease dataset comprising 303 patient records and 13 predictor variables. Following data preprocessing, five supervised machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost), were developed and comparatively evaluated. Model performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve (ROC–AUC). Explainability was incorporated through SHapley Additive exPlanations (SHAP) to quantify the contribution of individual clinical variables to prediction outcomes. Among the evaluated models, XGBoost achieved the highest predictive performance with an accuracy of 92% and an ROC–AUC of 0.96. SHAP analysis identified chest pain type, number of major vessels, ST depression, maximum heart rate, age, and thalassemia as the most influential predictors of heart disease, providing clinically meaningful explanations for model decisions. The findings demonstrate that integrating explainable artificial intelligence with machine learning can improve both predictive performance and model transparency, thereby supporting trustworthy clinical decision support and facilitating early cardiovascular risk assessment. Future research should validate the proposed framework using larger multicentre datasets and real-world clinical data.
PREDICTING GAS TURBINE EMISSIONS THROUGH OPERATIONAL ANALYTIC
The performance of gas turbines and minimize their environmental effects in contemporary power generation systems, it is crucial to accurately predict the emissions produced by these machines. Based on the data gathered from an industrial gas turbine, this study suggests a framework for predicting carbon monoxide (CO) and nitrogen oxides (NOx) emission from the gas turbine based on operational parameters. The data set contains 36,733 hourly measurements over a period of 5 years, from 2011 to 2015, of ambient conditions, turbine operating conditions and emission measurement. Data preprocessing, exploratory analysis, and multiple machine learning regression models were used for assessing the predictive performance based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). The results showed that ensemble based models performed better in predicting the accuracy of the prediction as they were able to capture the nonlinear relationship between the operational variables and the emissions characteristics effectively. The turbine inlet temperature, compressor discharge pressure, exhaust pressure in gas turbine and energy yield in turbines were determined as the main parameters affecting the emission behavior through the feature importance analysis. It is proposed that the framework can be used for reliable predictive modeling to enable operational analytics to support real-time emission monitoring, intelligent process optimization and sustainable gas turbine operation.
DATA-DRIVEN PREDICTION OF CONCRETE COMPRESSIVE STRENGTH
Concrete compressive strength is a critical indicator of structural performance and material quality, but conventional laboratory testing is time-consuming and unsuitable for rapid preliminary assessment. This study developed an interpretable data-driven model for predicting concrete compressive strength. The UCI Concrete Compressive Strength Dataset initially contained 1,030 observations; after removing 25 duplicate records, 1,005 unique observations were analysed. Descriptive, correlation, and multiple regression analyses were conducted, followed by an 80:20 training-testing validation. The model was statistically significant and explained 60.4% of the variation in compressive strength. Cement was the strongest positive predictor, followed by blast-furnace slag, curing age, and fly ash, whereas water showed a significant negative effect. The testing model achieved an R2of 0.580, a mean absolute error of 8.90 MPa, and a root mean squared error of 11.19 MPa. The findings demonstrate that concrete strength can be estimated with moderate accuracy using readily available mixture variables. However, the remaining unexplained variation indicates that curing conditions, cement grade, aggregate characteristics, and testing factors should be incorporated in future models. The proposed approach can support preliminary mix evaluation but should complement rather than replace laboratory testing.
INTELLIGENT CONDITION MONITORING OF HYDRAULIC SYSTEMS
In industrial settings, hydraulic systems are used extensively, and their reliability and early detection of faults is crucial for efficient performance and reduced maintenance costs. Intelligent condition monitoring has become a successful method to continually evaluate the health of the system based on multisensory operational data and data-driven analytical techniques. The main goals of this study were to assess the operating condition of hydraulic systems through multisensory measurements, to explore the relationship between hydraulic sensor parameters and the system's operating condition, and to create an intelligent condition monitoring system to determine various operating conditions. The operational data were secondary, and a quantitative research design was used involving 2,500 operating cycles. Descriptive statistics and Pearson correlation analysis, one-way analysis of variance, multiple linear regression, multinomial logistic regression and feature importance analysis were used to assess hydraulic system performance and to determine important predictors of hydraulic system health. Results showed that the vibration level and oil temperature measurements were the best predictors of hydraulic system degradation, and measurements of pressure, flow, and efficiency-related parameters also helped to differentiate between healthy and faulty operating states. Multisensory analysis was able to effectively classify different operating conditions and facilitate the accurate evaluation of the health of a hydraulic system. The presented framework shows the importance of combining several sensor measures for intelligent fault identification and predictive maintenance. The research underscores the potential of intelligent condition monitoring techniques for enhancing the reliability of industrial hydraulic systems, minimizing unplanned downtime, and aiding in proactive maintenance decision-making.
DATA-DRIVEN FORECASTING OF STEEL INDUSTRY ENERGY CONSUMPTION
The steel industry is energy-intensive and requires accurate short-term electricity forecasting for efficient production scheduling, managing peak demand, and energy management. Data-driven modelling has a lot of potential for increasing the accuracy of forecasts at the operational level, especially with high-frequency data. This study developed and evaluated an interpretable forecasting framework for short-term steel industry energy consumption using temporal and electrical operating variables. A dataset containing 35,040 observations recorded at 15-minute intervals over one year was analysed. Following data-quality assessment, leakage-prone variables were excluded, and lagged consumption, rolling statistics, electrical measurements, and cyclical temporal features were generated. Persistence, Seasonal Naïve, Linear Regression, Random Forest, and XGBoost models were evaluated using chronological training, validation, and independent test partitions. Forecasting performance was assessed using MAE, RMSE, MAPE, sMAPE, and R2, while permutation importance was used to interpret predictor contributions. Random Forest achieved the best overall performance with an RMSE of 8.4778 kWh and an R2of 0.9265, whereas XGBoost produced the lowest MAE of 4.5443 kWh. Both ensemble models outperformed Linear Regression and baseline approaches. Lagged energy consumption, lagged reactive power, and one-hour rolling statistics were identified as the most influential predictors. The proposed framework offers accurate and interpretable short-term energy forecasts and proves to be of practical use for industrial load scheduling, production planning and energy-efficiency management purposes.
SENSOR-BASED ROOM OCCUPANCY ESTIMATION FOR SMART BUILDINGS
Reliable room occupancy is crucial to energy efficiency, comfort of its inhabitants, and automated operation in smart buildings. In this study, a sensor-based machine learning system framework for binary room occupancy classification based on the measurements of the environment and human activities was developed. Data from temperature, light, sound, carbon dioxide, carbon dioxide slope, and passive infrared motion sensors were recorded on a time-stamped basis every ~30 seconds for a total of 10,129 observations. First, the original occupancy count was split into occupied classes and unoccupied classes, and then the data were split chronologically into 80% training and 20% testing. Five supervised classification algorithms (Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors) were trained and evaluated using accuracy, precision, recall, F1-score and confusion matrix statistics. All sensor variables were significantly different for the occupied and unoccupied conditions, with light intensity, temperature, sound and carbon dioxide-related variables exhibiting the greatest discrimination. The logistic regression model has the highest accuracy of 99.85%, precision of 99.28%, recall of 99.64% and the F1 score of 99.46%. It correctly identified 274 occupied observations and 1,749 unoccupied observations with just two false positives and one false negative. The results show that by integrating several environmental sensors with an interpretable machine learning model, highly accurate, practical and computationally efficient occupancy estimation of smart building energy management and automation systems is achieved.
LEARNING ANALYTICS FOR STUDENT SUCCESS PREDICTION
Learning analytics enables higher education institutions to use demographic, academic, assessment, registration, and Virtual Learning Environment data to identify students who may need early support. This retrospective predictive modeling study analyzed 32,593 student–module–presentation enrollments representing 28,785 unique students from the Open University Learning Analytics Dataset. Student success was defined as Pass or Distinction, while Fail or Withdrawn outcomes were classified as unsuccessful. Only information available during the first 30 course days was used to reduce data leakage. Predictors included early assessment scores, assessment completion, Virtual Learning Environment clicks, active learning days, unique resources accessed, educational background, credits studied, and module characteristics. The data were split into 26,122 training and 6,471 independent test observations using student-grouped partitioning. Logistic regression and random forest models were evaluated using accuracy, balanced accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristic area under the curve, precision-recall area under the curve, and Brier score. The random forest achieved the best performance, with 76.03% accuracy and a receiver operating characteristic area under the curve of 0.843, while logistic regression produced comparable results. Academic module, early assessment score, educational qualification, active learning days, and total platform activity were the leading predictors. These findings support transparent, ethically governed early-warning systems linked to timely, student-centered interventions in online higher education.
DECIPHERING URBAN AIR QUALITY THROUGH MULTI-POLLUTANT ASSESSMENT
The increasing urbanisation, traffic, industrial activities, construction and energy usage has further exacerbated air quality issues in urban areas of India. The study adopted a quantitative, observational and longitudinal approach to assess the multi-pollutant pattern in Delhi, Mumbai, Kolkata, Chennai and Bengaluru from 2015 to 2024. The concentrations of PM₂.₅, PM₁₀, NO₂, SO₂, CO and ground-level O3 were analysed in conjunction with Air Quality Index categories. Spatial and temporal variations were interpreted using descriptive statistics, city-wise comparison, annual and seasonal assessment, correlation analysis and principal component analysis. The findings showed that PM being the most predominant pollution concern in the urban environment, the pollution burden in Delhi was the highest, while the levels of particulate matter were comparatively lower in the cities of Chennai and Bengaluru. Strong association with PM₁₀ and moderate correlation with NO₂ and CO indicated the common effect of traffic and other sources of combustion, dust, and construction activities on PM₂.₅. The particulate accumulation was the highest in winter and post-monsoon season while monsoon had lowest particulates. Overall, there was a decreasing trend in annual AQI values; however, in certain cities improvement was uneven, and unhealthy conditions were observed for significant periods of the assessment period. To achieve this, comprehensive traffic and road and construction dust, industrial combustion, municipal waste burning, household fuel consumption emission control is required. The multi-pollutant assessment provides a better foundation for air-quality management, exposure reduction, public-health protection and long-term urban environmental planning on a city-by-city basis.
SPATIOTEMPORAL PREDICTION OF WATER QUALITY ACROSS MONITORING NETWORKS
Water quality prediction is a fundamental part of environmental monitoring, helpful in early detection of water quality shifts and better management of water resources. This work sought to create a spatiotemporal prediction model for predicting the water quality of the next day for the different monitoring networks in the study area based on previous physicochemical observations. The secondary dataset consisted of daily measurements from the various monitoring stations in Georgia, USA and was analyzed with machine learning methods. Key water quality variables included in the dataset were: pH, dissolved oxygen, water temperature, and specific conductance. Standard statistical and machine learning techniques were used to perform data preprocessing, exploratory spatiotemporal analysis, predictive model development and performance evaluation. The developed framework captured the spatial variability in water quality between monitoring stations and temporal changes in water quality, and yielded predictions that were accurate with good agreement between the actual and the predicted values. Dissolved oxygen, specific conductance and temperature were determined as primary contributors for prediction performance and identified through the feature importance analysis. The results show that the incorporation of spatial dependency and temporal information significantly enhances the ability to predict water quality, thus offering a valuable decision support system for environmental monitoring, pollution monitoring and sustainable water resource management.
EXPLAINABLE MACHINE FAILURE PREDICTION FOR SMART MANUFACTURING
Machine failures remain a major challenge in smart manufacturing because unexpected equipment breakdowns reduce production efficiency, increase maintenance costs, and interrupt operational continuity. This study developed an explainable machine learning framework for predicting machine failures using the AI4I 2020 Predictive Maintenance dataset comprising 10,000 machine operating records. A quantitative research design based on secondary data analysis was adopted. Data preprocessing included feature encoding, standardisation, class imbalance handling, and training–testing data partitioning. Multiple supervised machine learning algorithms were developed and compared, while Explainable Artificial Intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were employed to interpret prediction outcomes. The analysis showed that machine failures represented a small proportion of observations, whereas operational variables such as rotational speed, torque, tool wear, and temperature exhibited distinct patterns associated with equipment failure. Heat dissipation, overstrain, and power failures were identified as the most frequent failure mechanisms, indicating the importance of monitoring multiple operational factors simultaneously. The explainability framework improved transparency by identifying the variables contributing most strongly to prediction decisions, thereby supporting more reliable maintenance planning and operational decision-making. The proposed approach demonstrates that combining predictive modelling with interpretable artificial intelligence can strengthen predictive maintenance strategies, minimise unexpected equipment downtime, optimise resource utilisation, and improve the practical adoption of intelligent decision-support systems within smart manufacturing environments.
PREDICTING STUDENT DROPOUT AND ACADEMIC SUCCESS THROUGH EDUCATIONAL ANALYTICS
Students drop out is one of the major concerns in higher education as it impacts learners, institutions and educational resources. The objective of this study was to use educational analytics and supervised machine learning for predicting the academic outcomes: dropping out, continued enrolment and graduation. The following variables were analyzed: demographic, socioeconomic, admission-related, semester-level academic, and macroeconomic. Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbours algorithms were used for classification and compared. Performance of the models was assessed by accuracy, balanced accuracy, precision, recall, F1-score, confusion matrices and multiclass ROC-AUC. Random Forest had the highest class balanced accuracy (70.8%) and highest macro F1 score (0.708), and the highest accuracy (75.9%). The overall accuracy of XGBoost was 76.2% with the highest macro-ROC-AUC of 0.890. The most dominant predictors were identified through explainable artificial intelligence analysis, and were approved curricular units in semester 2, course, approved units in semester 1, tuition-fee status, age at enrolment and semester grades. Both the graduations and the dropouts were better classified than the enrolled students. The results show that interpretable machine learning models can be used to assist early-warning systems and guide the implementation of specific academic, financial and counselling services and support to students identified as being at risk.
TRADITIONAL KNOWLEDGE AND ETHNOMEDICINAL APPLICATIONS OF MENTHA IN MUZAFFARPUR DISTRICT
Mentha (mint), a prominent genus in the Lamiaceae family, holds significant cultural and medicinal importance in Muzaffarpur District, Bihar, India. This study explores the traditional knowledge and ethnomedicinal uses of Mentha, focusing on its historical applications in local healthcare for treating various ailments. Through field surveys, practitioner interviews, and ethnobotanical data collection, the use of Mentha to address respiratory problems, digestive disorders, skin infections, and stress was documented. Despite its widespread use, comprehensive documentation and scientific validation of Mentha’s medicinal properties in this region are lacking. The existing literature recognizes the value of traditional plant remedies, but the specific uses of Mentha in Muzaffarpur have been underexplored, underscoring the need for further research. This work aims to fill the gap by integrating traditional knowledge with modern health practices, with an emphasis on sustainable cultivation and use. The findings highlight the potential of Mentha-based remedies to connect traditional wisdom with modern scientific advances in healthcare.
Comparison the Antibacterial and Antibiofilm Activity of Citrullus Colocynthis Seeds Aqueous Extract against the Bacteria Isolated from human and sheep Oral cavity
The current study was comparison between the bacteria isolated from oral cavity of animal and human against Citrullus colocynthis seeds extract, bacterial isolates were obtained from Al-Muthanna University, College of Veterinary Medicine, the isolates were isolated and from sheep's mouth that located in the Salman area, same isolates were isolated from human's mouth such as Raoultella ornithinolytica, Citrobacter freundii, Pseudomonas aeruginosa, Enterobacter cloacae, The MIC of C.colocynthis seeds extract was ranged from 100 to 66 mg/ml ,while the antibacterial activity results showed generally the isolates from sheep was more resistance than human with significant difference at p ≤ 0.05 in different concentration, However the higher antibacterial activity noted in 400 mg/ml , in addition E.cloacae showed more susceptibility toward the extract in 200 mg/ml, 300 mg/ml and 400 mg/ml, the anti biofilm formation activity showed the bacteria from sheep had biofilm formation stronger than human's isolates with significant differences at p≤0.05 but after exposure to the extract the biofilm formation was decreased with no significantly between human and sheep isolates at p≤0.05, from the other side the findings reported a there was a significant difference before and after the treatment at p ≤ 0.05 in both animal's isolates and human's isolates. the C. colocynthis seedsextract showed antibacterial and antibioilm formation activity against the isolates from human and sheep source but the bacteria from sheep showed resistance to the extract higher than the bacteria from human source .
PHENOTYPIC TRAIT FREQUENCIES IN AN UNDERGRADUATE STUDENT COHORT FROM THANE DISTRICT, MAHARASHTRA
The phenotypic landscape of the Thane District student population (N=500) reveals significant genetic heterogeneity within this cosmopolitan Indian subcontinent hub. In this study eleven variables were recorded per participant: weight (kg), height (cm), double chin (yes/no), thumb cross dominance (left/right), ABO/Rh blood group (self-reported from medical records), earlobe type (free or Unjointed / joined), widow's peak (yes/no), Body colour (Skin Pigmentation) (fair/brown/dark), hair structure (straight/curly), hair colour (black/brown/hazel), and eye colour (black/brown/hazel) respectively. Dietary behaviour was also studied among vegetarians and non-vegetarians (both). This cross-sectional study identifies Blood group A+ as the most prevalent phenotype (34.2%), while the Rh-negative frequency (20.4%) notably exceeds national averages, suggesting localized allele frequencies or ancestral admixture. Morphological analysis of earlobe morphology (51% joined) and presence of widow’s peak (43.8%) aligns with South Asian benchmarks, though their distribution challenges strict Mendelian inheritance models in favor of polygenic or oligogenic patterns. Pigmentation traits remain dominated by ancestral alleles, with black hair (80.6%) and black eyes (59.8%) being near-universal. Anthropometric data confirmed significant sexual dimorphism in height (p < .001), while thumb-crossing dominance showed a near-even split (50.8% left), reflecting the trait's developmental ambiguity. This dataset, integrated with statistical analysis such as Chi-square tests and linear regression, provides a vital baseline for population genetics. Furthermore, it serves as a curated resource for outcome-based education aligned with the NEP-2020 framework, bridging the gap between theoretical evolutionary biology and observable human phenotypic traits in a diverse regional cohort.
DESIGN OF A USER-FRIENDLY BICYCLE REGISTRATION AND TRACKING SYSTEM
Bicycle registration and tracking systems are also gaining significance to aid in sustainable urban mobility, ownership verification, theft deterrence, and recovery support. Most current solutions are however disjointed, complex, expensive or poorly linked with overarching transport and security systems. This review analyzes how to design an easy-to-use bicycle registration and tracking system by synthesising the literature regarding cycling behaviour, bike-sharing systems, Internet of Things architecture, mobile usability, data security, tracking technologies and smart mobility integration. The review points out that to be effective, good systems need more than just technical tracking; they need to integrate both easy registration processes, successful bicycle identification, trustworthy data management, privacy conscious location management, and open user interfaces. Comparative analysis reveals that manual registries, web-based, IoT-enabled trackers and community-based reporting systems have their own benefits, yet also significant limitations. It is advisable to have a hybrid model, where digital registration, passive identification, optional active tracking, alert notification, and authorized recovery support are incorporated into it. The review also points out the major issues that are associated with interoperability, adoption, cost, privacy, and institutional coordination. On the whole, the paper offers a systematic basis on how to design scalable, secure and user-friendly bicycle registration and tracking systems in urban settings.
Copyright (c) 2017 https://creativecommons.org/licenses/by/4.0/
PHYTOREMEDIATION: A GREEN APPROACH FOR THE SUSTAINABLE ENVIRONMENTAL POLLUTION CONTROL
Life without plants is unimaginable on the earth. Starting from sustaining life on the earth to mitigation of various environmental problems, plants are inevitable and irreplaceable. Phytoremediation is a technique for remediation of air, water and soil where plants via various mechanisms can render the harmful pollutants less harmful or completely degrade the pollutants. In recent times phytoremediation has come out as a cost effective, environment friendly and sustainable approach technique of bioremediation. The article gives a comprehensive overview of various primary mechanisms of phytoremediation like Rhizofiltration, Phytovolatilization, Phytostabilization, phytoaccumulation and Phytodegradation for remediation of air, water and soil contaminated with heavy metals, organic pollutants, air pollutants and other contaminants. It further discusses the pros and cons of this mechanism and how modern emerging techniques of nanotechnology, microbial assistance, genetic engineering and biomass valorization are transforming the phytoremediation efficacy. By integration of various technology and environmental policies, phytoremediation can offer powerful and robust pathway to remediate ecosystem for a sustainable future.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
A COMPARATIVE STUDY ON CANCER DETECTION ACROSS DIFFERENT STAGES OF CARCINOMA DEVELOPMENT USING MOLECULAR BIOLOGY TECHNIQUES
Carcinoma, a malignancy arising from epithelial cells, is the most common cancer globally and is linked to significant morbidity and mortality, especially due to late diagnosis and disease advancement. The goal of this study is to compare the usefulness of molecular biology approaches including polymerase chain reaction (PCR), next-generation sequencing (NGS), and biomarker analysis in finding cancer at different stages of development. The study concentrates on evaluating stage-specific diagnostic accuracy, expression patterns of molecular markers, and the sensitivity and specificity of different diagnostic methodologies. The results show that PCR-based methods, notably droplet digital PCR, are very good at finding known genetic alterations and circulating tumor DNA in the early stages of cancer. NGS offers extensive genomic profiling and is essential for detecting new mutations, tumor heterogeneity, and drug resistance, rendering it particularly successful in intermediate and later stages. Biomarker analysis, which looks at proteins, microRNAs, and epigenetic markers, is a way to screen for diseases, keep an eye on them, and figure out how likely they are to get better without having to do any intrusive tests. The combination of various molecular diagnostic technologies greatly improves the accuracy of detection and helps with planning individualized treatment. Nevertheless, significant obstacles to wider clinical adoption persist, including prohibitive costs, the necessity for extensive technical proficiency, and the unpredictability in biomarker expression. The study's conclusion is that a multi-modal diagnostic technique that combines PCR, NGS, and biomarker analysis is a promising way to improve early detection, treatment decisions, and survival rates in people with carcinoma.
A PATH TO COMFORT AND CONNECTION – A PROSPECTIVE RESEARCH DESIGN
Palliative care is dedicated to improving the quality of life (QoL) of patients facing life-limiting illnesses. The growing recognition of holistic care in medicine has led to an increased emphasis on integrative therapies such as music therapy in palliative care settings. Research suggests that music therapy interventions can evoke positive emotions, improve sleep quality, and enhance overall QoL. Despite the promising evidence, structured research evaluating the impact of music therapy in palliative care, particularly within the Indian context, remains limited. The current research is a prospective methodology to evaluate the role of Music Therapy in enhancing emotional & spiritual well-being, and QoL in patients under Palliative Care. The designed intervention consists of structured phases aimed at addressing emotional well-being, relaxation, and overall quality of life. The FACIT-PAL-14 and FACIT - SP-12 which are validated tools designed to measure QoL and emotional and spiritual well-being of patients receiving palliative care, will be incorporated in this research study. As, to the best of our knowledge, there are only limited studies on music therapy in palliative care, particularly in the Indian context, this research offers a structured approach to evaluating its impact. This research design contributes valuable insights for future integration of music therapy into palliative care practices.
PHYSICOCHEMICAL CHARACTERIZATION AND STORAGE STABILITY ASSESSMENT OF DIETARY FIBRE-ENRICHED FOOD PRODUCTS DEVELOPED FROM BANANA PSEUDOSTEM POWDER
The increasing demand for functional foods with enhanced nutritional profiles has prompted investigation of novel ingredients derived from agricultural byproducts. Banana pseudostem, traditionally discarded after fruit harvest, represents an underutilized biomass resource with potential applications in food fortification. This study investigated the physicochemical properties and storage stability of three food product categories (biscuits, crisps, and sweets) fortified with banana pseudostem powder at substitution levels of 10%, 20%, and 30%. The study proposed the objectives, to comprehensively characterize the physicochemical properties of banana pseudostem powder and evaluate the impact of varying substitution levels on functional properties, quality parameters, and storage stability of developed food products over a 28-day period under ambient conditions (25 ± 2°C, 65 ± 5% relative humidity). In the study, banana pseudostem powder was characterized for water absorption capacity (WAC), oil absorption capacity (OAC), bulk density, and pH. Three product types were developed using composite flours with 0% (control), 10%, 20%, and 30% banana pseudostem powder substitution. Products were evaluated for moisture content, peroxide value, free fatty acid content, and microbiological quality (total plate count) at 7-day intervals during storage. Physicochemical changes were analyzed using standard AOAC methods, and statistical significance was determined using ANOVA and Tukey's HSD test (p < 0.05). The result indicated that banana pseudostem powder exhibited high water absorption capacity (4.50 ± 0.12 g/g), moderate oil absorption capacity (1.20 ± 0.08 g/g), bulk density of 0.42 ± 0.03 g/mL, and pH of 5.8 ± 0.2. Moisture uptake during storage increased proportionally with substitution level across all products, with biscuits showing increases from 3.0% (day 0) to 4.6% (day 28) for control and 4.5% to 7.3% for 30% substitution. Peroxide values remained below 5 meq/kg for all formulations throughout storage, indicating adequate oxidative stability. Free fatty acid content increased marginally but remained within acceptable limits (<2.0%). Total plate counts remained below 4 log CFU/g for all products. Crisps demonstrated higher moisture uptake rates (5.8% to 10.2% at 30% substitution) compared to biscuits and sweets due to higher surface-to-volume ratio and oil content. Statistical analysis revealed significant differences (p < 0.05) between substitution levels for all quality parameters. The study concluded that banana pseudostem powder incorporation significantly influences physicochemical properties and storage stability of food products in a dose-dependent manner. While all substitution levels maintained microbiological safety and oxidative stability within acceptable limits during 28-day ambient storage, higher fibre content increased hygroscopic nature, necessitating appropriate packaging solutions. The 10% substitution level demonstrated optimal balance between functional property modifications and storage stability maintenance. Enhanced packaging materials or modified atmosphere packaging may enable successful commercialization of products with higher substitution levels (20-30%) offering superior nutritional benefits.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
THE ROLE OF LOGICAL BELIEFS IN PREDICTING CHATGPT ADOPTION AND AVOIDANCE IN HIGHER EDUCATION
This conceptual research paper examines how logical beliefs—defined here as cognitive, evidential, and epistemic judgments about an innovation—shape both the adoption and avoidance of ChatGPT in higher education. While current debates emphasize ethical risks, skill impacts, and institutional policy, less attention has been paid to how faculty and students form reasoned beliefs about ChatGPT’s reliability, usefulness, and limits, and how those beliefs translate into behavioural intentions. Integrating Theory of Planned Behavior (Ajzen, 1991), the Technology Acceptance Model (Davis, 1989), and scholarship on epistemic beliefs (Hofer & Pintrich, 1997), this paper develops a theoretical model in which logical beliefs (accuracy beliefs, transparency beliefs, evidential beliefs, and boundary beliefs) influence perceived usefulness, perceived ease of use, normative pressure, and perceived behavioral control, and thereby predict both adoption and conscious avoidance. The paper proposes an empirical mixed-methods design to test the model in multiple higher-education contexts, outlines measurement approaches, and discusses implications for policy, instructional design, and faculty development. Practical recommendations for institutions to reduce unwarranted avoidance and to ensure responsible uptake are provided. Limitations and future research directions are discussed.
DEVELOPMENT AND NUTRITIONAL EVALUATION OF FOOD PRODUCTS INCORPORATING BANANA PSEUDOSTEM POWDER: A SUSTAINABLE APPROACH TO DIETARY FIBRE FORTIFICATION
Banana pseudostem, an abundant agricultural byproduct generated during fruit harvest, represents a significant source of dietary fibre and minerals that is traditionally discarded as waste. This research investigated the potential of dried banana pseudostem powder as a functional ingredient for nutritional enhancement of conventional food products. The study aimed to develop and comprehensively evaluate food products (biscuits, crisps, and sweets) incorporating banana pseudostem powder at varying substitution levels (10%, 20%, and 30%) and to assess their nutritional composition, physicochemical properties, sensory acceptability, and storage stability. In the study, the banana pseudostem powder was prepared through systematic drying and grinding procedures. A comprehensive nutritional and physicochemical characterization were conducted by standard AOAC methods. Nine food products were developed at three substitution levels alongside control formulations. Products were evaluated for nutritional composition, sensory attributes using a 9-point hedonic scale with 30 trained panelists, and shelf-life stability was evaluated over 28 days under ambient storage conditions. The results indicated that banana pseudostem powder demonstrated exceptional nutritional properties with 39.0 g/100g crude fibre (13-20 fold higher than wheat flour), 1200 mg/100g potassium (9-fold higher), and 250 mg/100g calcium (12-17 fold higher). Water absorption capacity was 4.50 ± 0.12 g/g. Substitution resulted in systematic increases in dietary fibre content across all products, with biscuits showing fibre increases from 2.5 g/100g (control) to 11.0 g/100g (30% substitution), representing a 340% enhancement. Sensory evaluation revealed that 10% substitution maintained high acceptability (overall scores 7.6-7.9), while 20% substitution approached threshold acceptability (scores 6.8-7.1). The 30% substitution level provided maximum nutritional benefits but showed marginal consumer acceptance (scores 5.9-6.1). Shelf-life studies demonstrated adequate stability at lower substitution levels with appropriate packaging. The study concluded that banana pseudostem powder represents a viable functional ingredient for developing fibre-enriched food products that address dietary fibre inadequacy while promoting agricultural waste valorization. The 10% substitution level is optimal for commercial applications, balancing substantial nutritional enhancement (125-250% fibre increase) with high consumer acceptability and adequate shelf stability. This research provides evidence-based guidance for food manufacturers seeking sustainable ingredients for nutritional fortification.
BIOMASS AND CARBON STORAGE POTENTIAL OF TREES IN AGROFORESTRY SYSTEMS PRACTICED IN THREE DISTRICTS OF ARUNACHAL PRADESH (INDIA) LYING ALONG THREE DIFFERENT ALTITUDINAL GRADIENTS
Agroforestry is an effective strategic approach for carbon sequestration and plays a significant role in addressing climate change. Additionally, agroforestry offers various ecological and economic advantages, tackling issues such as forest degradation and soil infertility. This study highlights the carbon storage capabilities of woody species within agroforestry systems across three distinct altitudinal zones. The total biomass was observed to be highest in the tropical zone's agroforestry systems, gradually decreasing with higher altitudes. Species such as Tectona grandis, Terminalia myriocarpa, and various Pinus species were identified as major contributors to carbon storage potential. Promoting agroforestry systems should be emphasized, recognizing them as an important strategy in the fight against global warming and climate change challenges.
MEDICINAL PLANT: GLYCYRRHIZA GLABRA AND ITS EFFICACY IN IN VITRO AGAINST FASCIOLA GIGANTICA LARVA
Fascioliasis is an economically important disease, which caused by Fasciola (liver fluke) among cattle and human. Class trematoda has Fasciola hepatica and F. gigantica which are worldwide. The intermediate host of Fasciola is Lymnaeidae snail. Fasciola is a hermaphrodite and their larval stages (sporocyst, redia and cercaria) are completed within the host snail. Infection of this disease can be control by destroy the larval stages. The aim of this study is to use of different preparation of Glycyrrhiza glabra plant in in vitro against sporocyst, redia and cercaria larva of F. gigantica. Each experimental setup of Petri dish contains 10 larvae. The dried root powder, organic extracts and column purified fractions were exposed in 10 ml dechlorinated water against sporocyst, redia and cercaria within 2h, 4h, 6h and 8h separately. The column purified fraction were shows after 2h LC50 51.15, 48.42 and 55.69 mg/ml and 8h LC50 45.95, 40.15, and 45.62 mg/ml efficacy against sporocyst, redia and cercaria, respectively.
SUSTAINABLE ENTREPRENEURSHIP AND ITS IMPACT ON LONG-TERM BUSINESS PERFORMANCE
Sustainable entrepreneurship has emerged as a critical business approach that integrates economic performance with environmental stewardship and social responsibility. Unlike traditional entrepreneurship, which primarily focuses on short-term profitability, sustainable entrepreneurship emphasizes long-term value creation for multiple stakeholders. This research paper examines the impact of sustainable entrepreneurial practices on long-term business performance using quantitative indicators such as revenue growth, cost efficiency, market share, employee productivity, and brand equity. The study is based on a survey of 120 small and medium-sized enterprises (SMEs) across manufacturing, services, and technology sectors that have adopted sustainability-driven strategies for at least five years. Statistical tools including descriptive analysis, correlation analysis, and regression modeling were employed to evaluate the relationship between sustainability practices and business performance outcomes. The findings reveal that firms with high sustainability adoption reported an average revenue growth rate of 14.8%, compared to 8.2% for conventional firms. Cost savings through resource efficiency averaged 12– 18% annually, while customer retention rates increased by nearly 22%. Regression results indicate that environmental innovation, ethical governance, and social engagement significantly influence long-term profitability (p < 0.05). The study confirms that sustainable entrepreneurship not only enhances financial performance but also improves organizational resilience and competitive advantage. These findings contribute to the growing body of literature by providing empirical evidence that sustainability-driven entrepreneurship is a strategic pathway for achieving long-term business success in an increasingly competitive and resource-constrained global economy.
ANTIHISTAMINIC PROPERTY OF SOLANUM XANTHOCARPUM AND ITS PHYTOCONSTITUENTS
Now a days there has been steady increase in the prevalence allergic immune response involving immune cells and mediators. The key role in the immune cascade is taken by histamine. It is natural component of the body, which in the allergic inflammatory response is released by the mast cells and basophils. Compound Sx-7 isolated from alcoholic extract of S. xanthocarpum as white crystals (m.p. 164ºC) U.V., 1H NMR and mass spectroscopy of this compound is similar to spectral data of stigmasterol. Active phytoconstituents Solasodine also extracted from alcoholic extract which play an important role for altering concentration in human body. In comparison with standard drug DSCG alcoholic extract protect degranulation of mast cell at dose of 50 mg & 100 mg/kg b.w. 74.39% and 78.26% respectively. Solasodine protect 79.48% and 84.38% at a dose of 1.0mg & 2.0 mg/kg b.w. significantly. Histamine causes branchoconstriction by activating H1-receptors. Eosinophilia is abnormal increase in the peripheral eosinophil count especially in the development of allergic asthma. Stabilization of mast cell by alcoholic extract of S. xanthocarpum at a dose of 50mg & 100 mg/kg b.w., i.p. shown percentage protection of 74.39% and 78.26% respectively as compared to standard drug DSCG 83.81%. Active constituent Solasodine protection percentage was found 79.48 and 84.38 at dose of 1.0mg and 2.0 mg/kg b.w. significantly.
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HARNESSING MICROALGAL DIVERSITY FROM DHOSI HILL, MAHENDERGARH, HARYANA, INDIA FOR RENEWABLE BIODIESEL APPLICATIONS
Microalgae are recognized as hopeful sustainable feedstocks for biodiesel production due to their fast growth rate, high photosynthetic efficiency, and ability to accumulate considerable lipid content. The present study focuses on the isolation, purification, and molecular identification of freshwater microalgae from Shiv Kund Dhosi Hill, Mahendergarh, Haryana, India, and the evaluation of their biodiesel production potential. From the water source one algae was isolated and named as MKI01, it was successfully established in sterile culture. Biodiesel extraction using a solvent-based method revealed that MKI01 yielded a higher lipid-derived biodiesel output, producing approximately 10.0 ± 0.1 mL from 20 g of dried biomass. Molecular identification using 18S rRNA gene sequencing confirmed MKI01 was Chlorella sp. showing 100% similarity with reference sequences in the NCBI database. Phylogenetic analysis further supported the taxonomic placement. The results focous on Chlorella sp. from the Dhosi Hill region as a promising candidate for biodiesel production.
EVERYDAY NUTRITION ENHANCING AMATEUR SPORTS PERFORMANCE
Everyday nutrition empowers amateur athletes to enhance performance in activities like weekend soccer, jogging, and cycling through accessible, food-first strategies without elite supplements. This conceptual paper synthesises evidence on balanced macronutrients, micronutrients, and timing/hydration to optimise glycogen, recovery, and resilience. The Periodized Nutrition Model adapts intake across preparatory, competition, and recovery phases, preventing low energy availability common in non-elites. Cultural adaptations for Indian amateurs leverage ICMR guidelines, emphasising regional staples. Findings highlight 15-30% gains in endurance, body composition, and motivation, fostering lifelong activity. Barriers, such as busy schedules and vegetarianism, are mitigated through fortified atta and monsoon spice recoveries. Future RCTs in diverse cohorts and mobile apps are recommended, positioning everyday nutrition as equitable for India's recreational participants.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
ACORUS CALAMUS L. IN FOCUS: AN INTEGRATIVE BOTANICAL REVIEW OF TAXONOMY, MORPHOLOGICAL PLASTICITY, AND HABITAT-DRIVEN EVOLUTION
Acorus calamus L. is a very intriguing botanical, ecological and evolutionary perennial wetland monocot. Although having a long and rich history of medicinal and cultural use, there is a relative lack of comprehensive integrative analysis of its taxonomy, morphological variability and ecological adaptation. The review is a synthesis of literature both classical and modern to discuss the taxonomic position of A. calamus, morphology, and the degree of phenotypic plasticity that is manifested in the various wetland environments. The importance of habitat heterogeneity, hydrological gradients, and ecological pressures in the morphological diversification and possible evolutionary pathways (cytotypic variation and habitat-induced differentiation) are placed. The review provides the contribution of environmental factors to the adaptive strategies in A. calamus by looking at the integration of taxonomic, morphological, and ecological perspectives. The conservation issues are also discussed in the paper and areas of future research directions are identified which are necessary to gain understanding of the evolutionary ecology of wetland plants.