EPH-International Journal of Applied Science
type: channel
description: 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
publisher: Southern Cross Academic Press
language: en
publication name: EPH-International Journal of Applied Science
issn: 2208-2182
items: INTELLIGENT ELECTRICAL POWER PREDICTION AND OPERATIONAL RESPONSE O. PTIMIZATION FOR RELIABLE MICRO GAS TURBINE ENERGY GENERATION
items: LINKING NUTRIENT ENRICHMENT AND OXYGEN DYNAMICS TO ECOLOGICAL WATER QUALITY IN FRESHWATER SYSTEMS
items: EARLY DETECTION OF OPERATIONAL INSTABILITY IN COLLABORATIVE ROBOTS THROUGH JOINT-LEVEL THERMAL AND ELECTRICAL SIGNATURES
items: FLORISTIC DIVERSITY OF CAMPUS OF GOVERNMENT COLLEGE FOR WOMEN M.A. ROAD, SRINAGAR (UT JAMMU & KASHMIR): A CHECKLIST
items: DECODING MOLECULAR PROPERTY SIGNATURES ASSOCIATED WITH CHEMICAL TOXICITY FOR SAFER COMPOUND SCREENING
items: DATA-DRIVEN PREDICTION OF BUILDING ENERGY LOADS
items: EXPLAINABLE BREAST CANCER DIAGNOSIS USING TUMOR MORPHOLOGY
items: EXPLAINABLE PREDICTION OF HEART DISEASE USING CLINICAL INDICATORS
items: PREDICTING GAS TURBINE EMISSIONS THROUGH OPERATIONAL ANALYTIC
items: DATA-DRIVEN PREDICTION OF CONCRETE COMPRESSIVE STRENGTH
items: INTELLIGENT CONDITION MONITORING OF HYDRAULIC SYSTEMS
items: DATA-DRIVEN FORECASTING OF STEEL INDUSTRY ENERGY CONSUMPTION
items: SENSOR-BASED ROOM OCCUPANCY ESTIMATION FOR SMART BUILDINGS
items: LEARNING ANALYTICS FOR STUDENT SUCCESS PREDICTION
items: DECIPHERING URBAN AIR QUALITY THROUGH MULTI-POLLUTANT ASSESSMENT
items: SPATIOTEMPORAL PREDICTION OF WATER QUALITY ACROSS MONITORING NETWORKS
items: EXPLAINABLE MACHINE FAILURE PREDICTION FOR SMART MANUFACTURING
items: PREDICTING STUDENT DROPOUT AND ACADEMIC SUCCESS THROUGH EDUCATIONAL ANALYTICS
items: TRADITIONAL KNOWLEDGE AND ETHNOMEDICINAL APPLICATIONS OF MENTHA IN MUZAFFARPUR DISTRICT
items: Comparison the Antibacterial and Antibiofilm Activity of Citrullus Colocynthis Seeds Aqueous Extract against the Bacteria Isolated from human and sheep Oral cavity
items: PHENOTYPIC TRAIT FREQUENCIES IN AN UNDERGRADUATE STUDENT COHORT FROM THANE DISTRICT, MAHARASHTRA
items: DESIGN OF A USER-FRIENDLY BICYCLE REGISTRATION AND TRACKING SYSTEM
items: PHYTOREMEDIATION: A GREEN APPROACH FOR THE SUSTAINABLE ENVIRONMENTAL POLLUTION CONTROL
items: A COMPARATIVE STUDY ON CANCER DETECTION ACROSS DIFFERENT STAGES OF CARCINOMA DEVELOPMENT USING MOLECULAR BIOLOGY TECHNIQUES
items: A PATH TO COMFORT AND CONNECTION – A PROSPECTIVE RESEARCH DESIGN
items: PHYSICOCHEMICAL CHARACTERIZATION AND STORAGE STABILITY ASSESSMENT OF DIETARY FIBRE-ENRICHED FOOD PRODUCTS DEVELOPED FROM BANANA PSEUDOSTEM POWDER
items: THE ROLE OF LOGICAL BELIEFS IN PREDICTING CHATGPT ADOPTION AND AVOIDANCE IN HIGHER EDUCATION
items: DEVELOPMENT AND NUTRITIONAL EVALUATION OF FOOD PRODUCTS INCORPORATING BANANA PSEUDOSTEM POWDER: A SUSTAINABLE APPROACH TO DIETARY FIBRE FORTIFICATION
items: BIOMASS AND CARBON STORAGE POTENTIAL OF TREES IN AGROFORESTRY SYSTEMS PRACTICED IN THREE DISTRICTS OF ARUNACHAL PRADESH (INDIA) LYING ALONG THREE DIFFERENT ALTITUDINAL GRADIENTS
items: MEDICINAL PLANT: GLYCYRRHIZA GLABRA AND ITS EFFICACY IN IN VITRO AGAINST FASCIOLA GIGANTICA LARVA

INTELLIGENT ELECTRICAL POWER PREDICTION AND OPERATIONAL RESPONSE O. PTIMIZATION FOR RELIABLE MICRO GAS TURBINE ENERGY GENERATION
type: item
description: The accurate prediction of electrical power coupled with identification of the operating conditions that maintain high and stable output is essential for reliable operation of a micro gas turbine. This study characterized voltage-dependent electrical power behaviour, evaluated predictive models, and identified a balanced operating condition for reliable energy generation. Eight experimental runs with micro gas turbines were analysed, and six were used for the development of the model, while two were left for independent testing. Spearman correlation and linear/quadratic regression were used to study the voltage-power relationship, and the models Linear Regression, Random Forest and Gradient Boosting were compared based on the parameters MAE, RMSE and R². Predictor contributions and operating stability were also assessed. Electrical power showed a strong positive association with input voltage (ρ = 0.9163, p < 0.001), while the quadratic voltage-response model achieved R² = 0.8831. Linear Regression provided the strongest independent-test performance, achieving MAE = 8.0342, RMSE = 10.6132, and R² = 0.9998. Recent power-history variables contributed most strongly to predictive accuracy. Operational optimization identified 9.6316 V as the preferred condition, producing mean electrical power of 3101.5303 with a CV of 1.5943%. Through these findings, the advantages of using short-horizon power prediction in conjunction with operating-point optimization in order to ensure reliable energy generation for micro gas turbines are shown.
subject: Articles
  topic: section
subject: micro gas turbine
  topic: keywords
subject: electrical power prediction
  topic: keywords
subject: operational optimization
  topic: keywords
subject: voltage response
  topic: keywords
subject: reliability
  topic: keywords
creator: Vanshika Jain
rights: Copyright (c) 2026 Vanshika Jain https://creativecommons.org/licenses/by/4.0/
date: 2026-04-27
publication date: 2026-04-27
starting page: 84
ending page: 93
doi: 10.69980/0s4fdy73

LINKING NUTRIENT ENRICHMENT AND OXYGEN DYNAMICS TO ECOLOGICAL WATER QUALITY IN FRESHWATER SYSTEMS
type: item
description: The integrated assessment is important to detect emerging changes in the ecological condition of freshwaters, which is dependent on interacting nutrient, biological, physicochemical and oxygen-related processes. This study aimed to characterize freshwater water quality and examine temporal and statistical relationships among total phosphorus, chlorophyll-a, turbidity, dissolved oxygen, and supporting physicochemical indicators. The data from 23 monitoring observations between October 2022 and April 2023 were analyzed quantitatively to examine the changes over time. Total phosphorus was available for 12 observations. The following descriptive statistics, Shapiro–Wilk testing, Spearman rank correlations, temporal trend analysis and HC3-robust univariable regression models were used. Chlorophyll-a and turbidity increased significantly over time (ρ = 0.859 and 0.823; both p < 0.001), whereas dissolved oxygen declined (ρ = −0.622, p = 0.002). Chlorophyll-a was strongly associated with turbidity (ρ = 0.856, p < 0.001), while dissolved oxygen was inversely associated with chlorophyll-a (ρ = −0.490, p = 0.018) and turbidity (ρ = −0.532, p = 0.009). The turbidity accounted for 37.67% of the variation in DO, whereas chlorophyll-a accounted for 24.85%. There was no significant relationship found between total phosphorus and chlorophyll-a, dissolved oxygen, or BOD. The results suggest that ecological and oxygen indicators provided better temporal information than the available phosphorus measurements and should be routinely used in combination with phosphorus in freshwater water-quality assessments.
subject: Articles
  topic: section
subject: freshwater quality
  topic: keywords
subject: nutrient enrichment
  topic: keywords
subject: dissolved oxygen
  topic: keywords
subject: chlorophyll-a
  topic: keywords
subject: turbidity
  topic: keywords
creator: Amaan Mohammad
rights: Copyright (c) 2026 Amaan Mohammad https://creativecommons.org/licenses/by/4.0/
date: 2026-07-30
publication date: 2026-07-30
starting page: 80
ending page: 89
doi: 10.69980/dd2bgv46

EARLY DETECTION OF OPERATIONAL INSTABILITY IN COLLABORATIVE ROBOTS THROUGH JOINT-LEVEL THERMAL AND ELECTRICAL SIGNATURES
type: item
description: Operational instability in collaborative robots can interrupt production and reduce the reliability of human–robot manufacturing environments, creating a need for effective condition-monitoring approaches. This study investigated joint-level thermal and electrical signatures for identifying unstable cobot operation. A dataset comprising 7,409 observations from 240 operational cycles was analyzed, with 518 observations classified as unstable based on protective-stop or grip-loss events. Temperature, motor current, joint speed, and tool-current measurements were evaluated using statistical analysis and predictive modelling. Electrical signatures showed clearer differentiation between operating states than thermal measurements, with significant current differences observed in five of the six joints. J3 exhibited the strongest electrical change during instability. Current–temperature correlations were generally weak, indicating that thermal and electrical measurements represented complementary aspects of joint behavior. Predictive modelling showed stronger discrimination with Random Forest than Logistic Regression, while J3 current emerged as the most influential predictor. The findings demonstrate the potential of joint-specific multisensor analysis for recognizing operational instability and provide a basis for developing condition-monitoring strategies for collaborative robotic systems.
subject: Articles
  topic: section
subject: Collaborative robots
  topic: keywords
subject: Operational instability
  topic: keywords
subject: Condition monitoring
  topic: keywords
subject: Thermal signatures
  topic: keywords
subject: Electrical signatures
  topic: keywords
creator: Dr.S.ALAGU
rights: Copyright (c) 2026 Dr.S.ALAGU https://creativecommons.org/licenses/by/4.0/
date: 2026-04-30
publication date: 2026-04-30
starting page: 76
ending page: 83
doi: 10.69980/e5fypj84

FLORISTIC DIVERSITY OF CAMPUS OF GOVERNMENT COLLEGE FOR WOMEN M.A. ROAD, SRINAGAR (UT JAMMU & KASHMIR): A CHECKLIST
type: item
description: The UT of Jammu and Kashmir is known for its rich biodiversity. The college campus covers an area of 8.5 acre and is situated at an important place in the heart of Srinagar city. Although, green audit is being carried out annually in the college campus but complete inventorization and documentation of college flora has not been done till date. Therefore, the present study has been undertaken to identify and document the floristic diversity of the college campus. A total of 234 plant taxa were collected and identified during the present floristic survey. The recorded taxa are belonging to 197 genera and distributed over 72 families. The updated scientific nomenclature, family name, growth form and ecological status for each species growing in the campus is provided.
subject: Articles
  topic: section
subject: Diversity
  topic: keywords
subject: documentation
  topic: keywords
subject: identification
  topic: keywords
creator: Suzana Bashir
creator: Chesfeeda Akhter
creator: Nusrath Sayeed
rights: Copyright (c) 2026 Suzana Bashir, Chesfeeda Akhter, Nusrath Sayeed https://creativecommons.org/licenses/by/4.0/
date: 2026-01-30
publication date: 2026-01-30
starting page: 82
ending page: 90
doi: 10.69980/yjjbjc96

DECODING MOLECULAR PROPERTY SIGNATURES ASSOCIATED WITH CHEMICAL TOXICITY FOR SAFER COMPOUND SCREENING
type: item
description: Chemical toxicity screening is important in detecting potentially hazardous compounds prior to a detailed experimental evaluation. In this study, molecular property signatures related to chemical toxicity were analyzed, and their use as indicators of safer compound screening was assessed. The molecular descriptors of 171 compounds, divided into toxic and non-toxic, were subjected to statistical comparisons, correlation, principal component analysis and classification models. MDEC-23 exhibited the highest individual correlations with toxicity, while a few structural and physicochemical descriptors provided complementary discriminatory information. Principal component analysis revealed that the variation in the molecular structure was multidimensional with the two first eigenvectors accounting for 45.35% of the total variation. Random forest had higher classification accuracy (0.673) and ROC-AUC (0.659) than logistic regression, but sensitivity to toxic compound was not high enough. The results show that small molecular property signatures are useful as a initial filter of toxicity information for prioritization. This could be useful for more efficient early stage toxicity evaluation and compound selection for safer compounds when adequately validated externally.
subject: Articles
  topic: section
subject: Chemical toxicity
  topic: keywords
subject: Molecular descriptors
  topic: keywords
subject: Compound screening
  topic: keywords
subject: Molecular signatures
  topic: keywords
subject: Random forest
  topic: keywords
creator: Dr. Tapan Kumar Sankar
creator: Dr. Kailash Chandra Das
creator: Dr. Mukesh Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-08-27
publication date: 2026-08-27
starting page: 69
ending page: 79
doi: 10.69980/wc92rx52

DATA-DRIVEN PREDICTION OF BUILDING ENERGY LOADS
type: item
description: 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.
subject: Articles
  topic: section
subject: Building energy prediction
  topic: keywords
subject: machine learning
  topic: keywords
subject: gradient boosting
  topic: keywords
subject: SHAP analysis
  topic: keywords
subject: load forecasting
  topic: keywords
creator: Meghna Modi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-07-16
publication date: 2026-07-16
starting page: 56
ending page: 68
doi: 10.69980/pe563v71

EXPLAINABLE BREAST CANCER DIAGNOSIS USING TUMOR MORPHOLOGY
type: item
description: 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.
subject: Articles
  topic: section
subject: Breast cancer diagnosis
  topic: keywords
subject: Tumor morphology
  topic: keywords
subject: Explainable artificial intelligence
  topic: keywords
subject: Machine learning
  topic: keywords
subject: SHAP analysis
  topic: keywords
creator: Mr. Jitu Murmu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-07-15
publication date: 2026-07-15
starting page: 45
ending page: 55
doi: 10.69980/4qjfds31

EXPLAINABLE PREDICTION OF HEART DISEASE USING CLINICAL INDICATORS
type: item
description: 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.
subject: Articles
  topic: section
subject: Breast cancer diagnosis
  topic: keywords
subject: Tumor morphology
  topic: keywords
subject: Explainable artificial intelligence
  topic: keywords
subject: Machine learning
  topic: keywords
subject: SHAP analysis
  topic: keywords
creator: Shashanka Shekhar Panda
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-07-15
publication date: 2026-07-15
starting page: 36
ending page: 44
doi: 10.69980/tp9k1h29

PREDICTING GAS TURBINE EMISSIONS THROUGH OPERATIONAL ANALYTIC
type: item
description: 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.
subject: Articles
  topic: section
subject: Gas turbine emissions
  topic: keywords
subject: Operational analytics
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Carbon monoxide prediction
  topic: keywords
subject: Nitrogen oxides prediction
  topic: keywords
creator: Dr. Pawan kumar Mahato
rights: Copyright (c) 2026 Dr. Pawan kumar Mahato https://creativecommons.org/licenses/by/4.0/
date: 2026-07-15
publication date: 2026-07-15
starting page: 25
ending page: 35
doi: 10.69980/c1we7y89

DATA-DRIVEN PREDICTION OF CONCRETE COMPRESSIVE STRENGTH
type: item
description: 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.
subject: Articles
  topic: section
subject: Concrete compressive strength
  topic: keywords
subject: data-driven prediction
  topic: keywords
subject: multiple regression
  topic: keywords
subject: concrete mix design
  topic: keywords
subject: model validation
  topic: keywords
creator: Dr.M.Leena Chandrika
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-07-15
publication date: 2026-07-15
starting page: 12
ending page: 24
doi: 10.69980/6w4tee68

INTELLIGENT CONDITION MONITORING OF HYDRAULIC SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: Hydraulic systems
  topic: keywords
subject: Intelligent condition monitoring
  topic: keywords
subject: Predictive maintenance
  topic: keywords
creator: Prashant K.Mate
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-07-15
publication date: 2026-07-15
starting page: 01
ending page: 11
doi: 10.69980/snerb823

DATA-DRIVEN FORECASTING OF STEEL INDUSTRY ENERGY CONSUMPTION
type: item
description: 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.
subject: Articles
  topic: section
subject: steel industry
  topic: keywords
subject: energy forecasting
  topic: keywords
subject: machine learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: industrial energy management
  topic: keywords
creator: Dr.S.ALAGU
rights: Copyright (c) 2026 Dr.S.ALAGU https://creativecommons.org/licenses/by/4.0/
date: 2026-04-30
publication date: 2026-04-30
starting page: 57
ending page: 67
doi: 10.69980/enxr1t24

SENSOR-BASED ROOM OCCUPANCY ESTIMATION FOR SMART BUILDINGS
type: item
description: 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.
subject: Articles
  topic: section
subject: smart buildings
  topic: keywords
subject: occupancy estimation
  topic: keywords
subject: environmental sensors
  topic: keywords
subject: machine learning
  topic: keywords
subject: Logistic Regression
  topic: keywords
creator: Dr Valliappan S
creator: Dr. Pankaj Sahu
rights: Copyright (c) 2026 Dr Valliappan S, Dr. Pankaj Sahu https://creativecommons.org/licenses/by/4.0/
date: 2026-04-27
publication date: 2026-04-27
starting page: 46
ending page: 56
doi: 10.69980/ax758279

LEARNING ANALYTICS FOR STUDENT SUCCESS PREDICTION
type: item
description: 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.
subject: Articles
  topic: section
subject: Academic performance
  topic: keywords
subject: Early-warning systems
  topic: keywords
subject: Learning analytics
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Virtual Learning Environment
  topic: keywords
creator: Manoj Kumar K S
rights: Copyright (c) 2026 Manoj Kumar K S https://creativecommons.org/licenses/by/4.0/
date: 2026-04-27
publication date: 2026-04-27
starting page: 34
ending page: 45
doi: 10.69980/2qeq8n66

DECIPHERING URBAN AIR QUALITY THROUGH MULTI-POLLUTANT ASSESSMENT
type: item
description: 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.
subject: Articles
  topic: section
subject: urban air quality
  topic: keywords
subject: multi-pollutant assessment
  topic: keywords
subject: particulate matter
  topic: keywords
subject: Air Quality Index
  topic: keywords
subject: seasonal variation
  topic: keywords
subject: Indian cities
  topic: keywords
creator: Dr.Pooja sahu
rights: Copyright (c) 2026 Dr.Pooja sahu https://creativecommons.org/licenses/by/4.0/
date: 2026-04-27
publication date: 2026-04-27
starting page: 22
ending page: 33
doi: 10.69980/zb1d2y80

SPATIOTEMPORAL PREDICTION OF WATER QUALITY ACROSS MONITORING NETWORKS
type: item
description: 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.
subject: Articles
  topic: section
subject: Water quality prediction
  topic: keywords
subject: Spatiotemporal analysis
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Environmental monitoring
  topic: keywords
subject: Monitoring networks
  topic: keywords
creator: Amaan Mohammad
rights: Copyright (c) 2026 Amaan Mohammad https://creativecommons.org/licenses/by/4.0/
date: 2026-04-25
publication date: 2026-04-25
starting page: 10
ending page: 21
doi: 10.69980/7shh2c51

EXPLAINABLE MACHINE FAILURE PREDICTION FOR SMART MANUFACTURING
type: item
description: 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.
subject: Articles
  topic: section
subject: Smart manufacturing
  topic: keywords
subject: predictive maintenance
  topic: keywords
subject: machine failure prediction
  topic: keywords
subject: explainable artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
creator: Parimal Trivedi
rights: Copyright (c) 2026 Parimal Trivedi https://creativecommons.org/licenses/by/4.0/
date: 2026-04-25
publication date: 2026-04-25
starting page: 01
ending page: 09
doi: 10.69980/gqyk9b44

PREDICTING STUDENT DROPOUT AND ACADEMIC SUCCESS THROUGH EDUCATIONAL ANALYTICS
type: item
description: 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.
subject: Articles
  topic: section
subject: educational analytics
  topic: keywords
subject: Agroforestry systems
  topic: keywords
subject: academic success
  topic: keywords
subject: machine learning
  topic: keywords
subject: explainable artificial intelligence
  topic: keywords
creator: Dr Ramaswami Subramony
rights: Copyright (c) 2026 Dr Ramaswami Subramony https://creativecommons.org/licenses/by/4.0/
date: 2026-01-30
publication date: 2026-01-30
starting page: 68
ending page: 81
doi: 10.69980/tndfzk62

TRADITIONAL KNOWLEDGE AND ETHNOMEDICINAL APPLICATIONS OF MENTHA IN MUZAFFARPUR DISTRICT
type: item
description: 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.
subject: Articles
  topic: section
subject: Ethnomedicinal
  topic: keywords
subject: botanists
  topic: keywords
subject: gastrointestinal disorders
  topic: keywords
subject: Decoctions
  topic: keywords
subject: carminative
  topic: keywords
creator: Arunima
creator: Dr. Neelam Kumari
rights: Copyright (c) 2026 Arunima, Dr. Neelam Kumari https://creativecommons.org/licenses/by/4.0/
date: 2026-04-25
publication date: 2026-04-25
starting page: 68
ending page: 75
doi: 10.69980/ceff7s73

Comparison the Antibacterial and Antibiofilm Activity of Citrullus Colocynthis Seeds Aqueous Extract against the Bacteria Isolated from human and sheep Oral cavity
type: item
description: 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 .
subject: Articles
  topic: section
subject: Citrullus colocynthis seed extract
  topic: keywords
subject: anti-bacterial
  topic: keywords
subject: anti-biofilm
  topic: keywords
subject: oral cavity
  topic: keywords
subject: MIC
  topic: keywords
creator: Mhmood Abed Hamzah
creator: Faiza Jabbar Joda
creator: YONIS AHMED KITAN
creator: Saif Mazeel Abed
rights: Copyright (c) 2026 Mhmood Abed Hamzah, Faiza Jabbar Joda, YONIS AHMED KITAN, Saif Mazeel Abed https://creativecommons.org/licenses/by/4.0/
date: 2026-01-30
publication date: 2026-01-30
starting page: 62
ending page: 67
doi: 10.69980/txfpm975

PHENOTYPIC TRAIT FREQUENCIES IN AN UNDERGRADUATE STUDENT COHORT FROM THANE DISTRICT, MAHARASHTRA
type: item
description: 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.
subject: Articles
  topic: section
subject: Phenotypic traits
  topic: keywords
subject: Population genetics
  topic: keywords
subject: Thane District
  topic: keywords
subject: ABO blood groups
  topic: keywords
subject: Allele frequencies
  topic: keywords
subject: Anthropometry
  topic: keywords
creator: Wanule Dinesh Daujirao
creator: Nagare Kantilal Hiralal
creator: Totewad Narayan Dattatraya
creator: Daund Jayashri Ramnath
rights: Copyright (c) 2026 Wanule Dinesh Daujirao, Nagare Kantilal Hiralal, Totewad Narayan Dattatraya, Daund Jayashri Ramnath https://creativecommons.org/licenses/by/4.0/
date: 2026-01-30
publication date: 2026-01-30
starting page: 52
ending page: 61
doi: 10.69980/a2azj993

DESIGN OF A USER-FRIENDLY BICYCLE REGISTRATION AND TRACKING SYSTEM
type: item
description: 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.
subject: Articles
  topic: section
subject: Bicycle registration
  topic: keywords
subject: bicycle tracking
  topic: keywords
subject: user-centered design
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: data security
  topic: keywords
subject: smart mobility
  topic: keywords
subject: theft prevention
  topic: keywords
creator: Varun Anish Reddy Yeruva
creator: Chinthapally Vunnath Reddy
creator: Yugandhar Garapati
rights: Copyright (c) 2017 https://creativecommons.org/licenses/by/4.0/
date: 2017-01-23
publication date: 2017-01-23
starting page: 49
ending page: 60
doi: 10.69980/14hk6p43

PHYTOREMEDIATION: A GREEN APPROACH FOR THE SUSTAINABLE ENVIRONMENTAL POLLUTION CONTROL
type: item
description: 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.
subject: Articles
  topic: section
subject: Phytoremediation
  topic: keywords
subject: Phytoextraction
  topic: keywords
subject: Phytostabilization
  topic: keywords
subject: Heavy Metals
  topic: keywords
subject: Sustainable Development
  topic: keywords
creator: Sharmistha Basu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-10-28
publication date: 2025-10-28
starting page: 130
ending page: 136
doi: 10.69980/jxjfkd49

A COMPARATIVE STUDY ON CANCER DETECTION ACROSS DIFFERENT STAGES OF CARCINOMA DEVELOPMENT USING MOLECULAR BIOLOGY TECHNIQUES
type: item
description: 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.
subject: Articles
  topic: section
subject: Carcinoma Detection
  topic: keywords
subject: Molecular Biology Techniques
  topic: keywords
subject: Polymerase Chain Reaction (PCR)
  topic: keywords
subject: Next-Generation Sequencing (NGS)
  topic: keywords
subject: Biomarker Analysis
  topic: keywords
subject: Circulating Tumor DNA
  topic: keywords
subject: Molecular Diagnostics
  topic: keywords
subject: Personalized Cancer Therapy
  topic: keywords
creator: Soumyaranjan Mahapatra
creator: Asmita Mitra
rights: Copyright (c) 2026 Soumyaranjan Mahapatra, Asmita Mitra https://creativecommons.org/licenses/by/4.0/
date: 2026-01-27
publication date: 2026-01-27
starting page: 36
ending page: 51
doi: 10.69980/2c2v2c56

A PATH TO COMFORT AND CONNECTION – A PROSPECTIVE RESEARCH DESIGN
type: item
description: 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.
subject: Articles
  topic: section
subject: Palliative care
  topic: keywords
subject: Music Therapy
  topic: keywords
subject: emotional well-being
  topic: keywords
subject: spiritual well-being
  topic: keywords
subject: quality of life
  topic: keywords
subject: sleep quality
  topic: keywords
creator: ABINANDH.H
creator: Ms. Caren Rajakumari Jenita
creator: Dr. Sobana.R
creator: Dr.Annie Sheeba J
rights: Copyright (c) 2026 ABINANDH.H, Ms. Caren Rajakumari Jenita, Dr. Sobana.R, Dr.Annie Sheeba J https://creativecommons.org/licenses/by/4.0/
date: 2026-01-27
publication date: 2026-01-27
starting page: 28
ending page: 35
doi: 10.69980/hw0jfr21

PHYSICOCHEMICAL CHARACTERIZATION AND STORAGE STABILITY ASSESSMENT OF DIETARY FIBRE-ENRICHED FOOD PRODUCTS DEVELOPED FROM BANANA PSEUDOSTEM POWDER
type: item
description: 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 a…
subject: Articles
  topic: section
subject: Banana pseudostem
  topic: keywords
subject: physicochemical properties
  topic: keywords
subject: storage stability
  topic: keywords
subject: shelf-life
  topic: keywords
subject: water absorption
  topic: keywords
subject: oxidative stability
  topic: keywords
subject: food fortification
  topic: keywords
creator: Eti Chawla
creator: Neelam Meel
creator: Komal Chauhan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-26
publication date: 2023-12-26
starting page: 21
ending page: 28
doi: 10.53555/5pjhwn49

THE ROLE OF LOGICAL BELIEFS IN PREDICTING CHATGPT ADOPTION AND AVOIDANCE IN HIGHER EDUCATION
type: item
description: 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.
subject: Articles
  topic: section
subject: ChatGPT
  topic: keywords
subject: logical beliefs
  topic: keywords
subject: technology adoption
  topic: keywords
subject: avoidance
  topic: keywords
subject: higher education
  topic: keywords
subject: epistemic beliefs
  topic: keywords
subject: TAM
  topic: keywords
subject: TPB
  topic: keywords
creator: Gopal Singh
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-09-27
publication date: 2023-09-27
starting page: 36
ending page: 40
doi: 10.53555/dv2gy534

DEVELOPMENT AND NUTRITIONAL EVALUATION OF FOOD PRODUCTS INCORPORATING BANANA PSEUDOSTEM POWDER: A SUSTAINABLE APPROACH TO DIETARY FIBRE FORTIFICATION
type: item
description: 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 (score…
subject: Articles
  topic: section
subject: Banana pseudostem
  topic: keywords
subject: dietary fibre
  topic: keywords
subject: functional foods
  topic: keywords
subject: agricultural waste valorization
  topic: keywords
subject: food fortification
  topic: keywords
subject: sensory evaluation
  topic: keywords
creator: Eti Chawla
creator: Neelam Meel
creator: Komal Chauhan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-09-26
publication date: 2023-09-26
starting page: 29
ending page: 35
doi: 10.53555/t9zzq938

BIOMASS AND CARBON STORAGE POTENTIAL OF TREES IN AGROFORESTRY SYSTEMS PRACTICED IN THREE DISTRICTS OF ARUNACHAL PRADESH (INDIA) LYING ALONG THREE DIFFERENT ALTITUDINAL GRADIENTS
type: item
description: 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.
subject: Articles
  topic: section
subject: Biomass
  topic: keywords
subject: Carbon storage
  topic: keywords
subject: Agroforestry systems
  topic: keywords
subject: Altitudinal gradients
  topic: keywords
subject: Arunachal Pradesh
  topic: keywords
creator: Toku Bani
creator: Silikta Manchey
creator: Tonlong Wangpan
creator: Sumpam Tangjang
rights: Copyright (c) 2026 Toku Bani, Silikta Manchey, Tonlong Wangpan, Sumpam Tangjang https://creativecommons.org/licenses/by/4.0/
date: 2026-01-25
publication date: 2026-01-25
starting page: 15
ending page: 27
doi: 10.69980/kpmgw078

MEDICINAL PLANT: GLYCYRRHIZA GLABRA AND ITS EFFICACY IN IN VITRO AGAINST FASCIOLA GIGANTICA LARVA
type: item
description: 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.
subject: Articles
  topic: section
subject: Glycyrrhiza glabra
  topic: keywords
subject: Fascioliasis
  topic: keywords
subject: Sporocyst
  topic: keywords
subject: Redia
  topic: keywords
subject: Cercaria
  topic: keywords
creator: A. K. Vishwakarma
creator: Pradeep Kumar
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-26
publication date: 2023-12-26
starting page: 14
ending page: 20
doi: 10.53555/42gsbh61