Scientiarum: A multidisciplinary journal
Scientiarum: A multidisciplinary journal is an Open Access journal devoted to the rapid publication of high-quality articles from every field of science. We publish articles that follow ethical and scientific publication standards and report on worthwhile and scientifically sound research. Scientiarum: A multidisciplinary journal divides papers and articles into various divisions according to the subject that is being studied. Internal editors, an external academic section, section editors, and a specialized in-house editorial office team oversee the publication process and handle your paper, providing the editorial support and quality control your study deserves.
Cyber-attack detection on maritime autonomous surfaceships: python based predictive analysis
Maritime Autonomous Surface Ships (MASS) that are used for navigation, cargo handling and communication on a digital system. It improves operational efficiency and maximizes cyber risk threats. This project is based on the framework that incorporates cybersecurity methodology and examines dynamic data such as geo-location coordinates, timestamp, navigation map status, performance, efficiency, cargo type, data source and destination port that are grounded in the operating unit. By understanding these analytics based on Python, machine learning (ML) and deep learning techniques that use the system to easily analyze both real-time and Automatic Identification System (AIS) datasets that helps to detect any anomalies present in the patterns that are linked to potential cyber threats. Anomalies are detected in an early phase and the model supports the risk mitigation and strengthens the autonomous maritime operations. Informed decision making can support proactive monitoring of maritime which is supported by AIS data feature which also improve accuracy and reliability. This paper aim is to propose the model which is more reliable, expandable, and accurate prediction through the use of hybrid model which also incorporates the concept of long short-term memory (LSTM) and random forest which is also helpful in further classification and extraction. The model aims to enhance the detection accuracy with techniques such as deep learning and ML.
Copyright (c) 2026 Scientiarum: A multidisciplinary journal
Detection of distributed denial of service attacks in NFV environments using machine learning-based anomaly detection
Objectives: The rise of Network Function Virtualization (NFV) has created a scalable, flexible alternative to traditional hardware-based networking infrastructures. However, due to the nature of NFV, there is a potential for an increased occurrence of Distributed Denial of Service (DDoS) attacks in a dynamic NFV environment and the need for adaptive security mechanisms capable of identifying DDoS attacks with high levels of accuracy. Through this research, the author intends to develop a machine learning (ML)-based anomaly detection framework that uses ML techniques to detect DDoS attacks accurately within an NFV environment. Methodology: A ML model with the use of CICDDoS2019 (2020): Data (a dataset) includes data preprocessing, feature scaling, and principal component analysis (PCA), which are techniques for reducing the dimensions of the dataset and maintaining important features for analysis of traffic on the network in this article. Five ML models were developed from the training dataset: Isolation Forest, Autoencoder, One-Class Support Vector Machine (SVM), Random Forest (RF) and XGBoost (XG). An ensemble approach was also created to improve detection performance by combining three or more of these (or other) ML techniques. Findings from this research demonstrate that PCA can facilitate a reduction in dimensionality from 29 unique features to 24 unique principal components while preserving almost 95% of the variability in the original feature set. In addition, RF and XG were identified as the two highest-performing modeling algorithms, with each scoring a perfect “100” on all measures of performance (including accuracy, precision, recall, and F1) calculated. One-Class Support Vector Machine (OSVM) scored an average of 96%, and the composite of all three models (ensemble model) averaged 93% with a very limited ability to detect each of the different types of DDoS attacks with the same degree of confidence. Therefore, the results of this study support the use of the developed system for detection purposes for both previously known and unknown forms of DDoS attacks as well as increasing the overall level of reliability and resiliency against attacks targeting NFV infrastructure. Novelty: This research presents a novel hybrid framework for anomaly detection in NFV by integrating supervised, unsupervised, and semi-supervised ML methods within a single NFV security architecture. In addition, the proposed method employs PCA for dimensionality reduction and ensemble learning to improve detection accuracy, adaptation to evolving attack patterns, and computational efficiency of detected anomalies, thereby providing a more effective means of defeating DDoS attacks within virtualized network environments.
A Study on Assessing the Financial Stability of Banks in Sultanate of Oman Using Z- Score Analysis
The Z-Score model, a crucial predictor of possible bankruptcy risk, is used in this study to evaluate the financial stability of commercial banks in the Sultanate of Oman. From 2018 to 2022, it evaluates five major banks: Bank Muscat, Bank Dhofar, National Bank of Oman (NBO), Ahli Bank, and Bank Nizwa. Utilizing secondary data from the Muscat Securities Market, a quantitative methodology is applied. Working capital to total assets, retained earnings to total assets, EBIT to total assets, and market value of equity to total liabilities are the four main ratios that the Z-Score model assesses. Financial security is indicated by scores above 2.7, whereas high risk is indicated by scores below 0.9. The results show that, despite having sufficient liquidity and market equity, Bank Muscat (Z-Score: 1.329) is in the "Gray Zone" because of its declining profitability. Low retained earnings and erratic liquidity are Bank Dhofar's (1.0343) weaknesses. NBO (1.251) has comparatively higher market equity but lower working capital and profitability. Despite modest gains in 2022, Ahli Bank (1.11) exhibits distressing signs, indicating problems with earnings and liquidity. On the other hand, Bank Nizwa exhibits sound financial standing, as evidenced by its high Z-Score, which shows steady increases in liquidity and profitability. The study offers insightful information about the banking industry's advantages and disadvantages in Oman. Some banks exhibit financial resilience, but others are exposed to risks that could compromise their long-term viability. For investors, regulators, and legislators, the findings are important because they provide a foundation for better risk management and financial strategies that promote sustainable economic growth.
Architecting patient safety: a narrative review of the evolution of a clinical governance framework at a regional hospital (2022–2025)
Structured clinical governance (CG) is the key element that contributes to delivering qualitative patient care, particularly within the public sector hospitals. This narrative review gives the complete picture of the transformation of patient safety framework that occurred at Kulhudhuffushi Regional Hospital (KRH) during the years 2022–2025. The hospital has diligently taken steps to transform itself from a committee-oriented governance model to a more centralized organization with proactive involvement in addressing the findings of inspections, thereby resulting in the establishment of the Quality Improvement Department (QID). This article explores the key role of governance policies and strategies that have served as the foundation for setting up this system. The key implementations include a meticulous documentation framework that sets standards for the entire clinical and administrative process. The process is designed to follow a two-tiered audit process that stabilizes the system, contributing to continual improvement with qualitative outcomes. The system includes monitoring the Key Performance Indicator (KPI) to relentlessly support and streamline key clinical areas in the hospital. The system acts as an integrative approach that incorporates data such as clinical incidents, patient complaints, and mortality review in a learning-based approach to quality and patient safety. The critical lesson learned along the way is that a systematic and architected approach to develop or enhance quality and patient safety governance in a healthcare system, especially in a resource-constrained and regional geography, is essential. The KRH model has potential implications for others seeking to establish robust governance structures to deliver qualitative patient care and safety.
Copyright (c) 2025 Scientiarum: A multidisciplinary journal
Transfer learning methods for classifying medical images and identifying illnesses with minimal annotated data
Medical image classification is often constrained by the unavailability of large, annotated data-sets owing to the unreasonable cost of expert labeling and class discrepancy in rare conditions. One of the interesting techniques to overcomethesedrawbacksistheprocedureoftransfer learning (TL) which utilizes the knowledge gained from deep learning (DL) models that were formerly trained. DL has achieved significant success in feature representation from complex data, but it generally involves a considerable amount of annotated information for effective training. Using small and imbalanced data-sets, this paper investigates the performance of TL for medical image classification tasks using VGG16, ResNet50, and EfficientNet. A scratch-built baseline Convolutional Neural Network (CNN) is adopted as a reference to compare TL models. COVID-19 chest X-ray databases are utilized in experiments. Accuracy, F1-score, and training efficiency are applied to measure performance. While TL has illustrated impressive performance, often similar to or exceeding diagnosis, its configuration in numerous studies has been defined as arbitrary. Previous studies have revealed that TL is better than scratch-built models on tiny medical datasets, resulting in a COVID-19 detection precision of >96% using ResNet50. Also pointed out the usefulness of ensemble TL approaches. This paper shows that TL is a practicable and effective means to expand medical image solutions in cases with limited data. That makes it probable to quickly deploy artificial intelligence (AI) tools into real-world healthcare applications
DEEP LEARNING-BASED CYBER ATTACK PREDICTION USING DNN-LSTM
In this research, new hybrid feature selection framework is proposed, also that can be used to detect Distributed Denial of Service (DDoS) attacks in Software-Defined Networks (SDN). The framework consists of two different approaches: filter-based and wrapper-based. The filter-based feature selection methods use both a local and global subset of features to classify the attack, while the wrapper-based technique uses the Sparrow Search Optimization Algorithm to select performance-based subsets of features from the training data. Both techniques provide a list of common features that will be used for training Machine Learning (ML) and Deep Learning (DL) models. In the performance evaluation of the feature selection phase, the features from the selected subset were used to train the following ML algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest Classifier (RF), Convolutional Neural Network (CNN) and Ensemble-Based models. The experimental results demonstrate that the hybrid feature selection framework contributes to a reduction in the feature set dimension while achieving high detection accuracy (99.85% accuracy with RF) as compared to the other classifiers tested with 5-fold and 10-fold Cross Validation. The Precision-Recall analysis and ROC analysis performed on the 5-fold and 10-fold cross-validation set support the robustness and generalization capabilities of the proposed hybrid feature selection process for real-time SDN security applications.
Cyber-attack detection on Maritime Autonomous Surface Ships : Python based predictive analysis
Maritime Autonomous Surface Ships (MASS) that are used for navigation, cargo handling and communication on a digital system. It improves operational efficiency and maximizes cyber risk threats. This project is based on the framework that incorporates cybersecurity methodology and examines dynamic data such as geo-location coordinates, timestamp, navigation map status, performance, efficiency, cargo type, data source and destination port that are grounded in the operating unit. By understanding these analytics based on Python, machine learning and deep learning techniques that use the system to easily analyze both real-time and AIS datasets that helps to detect any anomalies present in the patterns that are linked to potential cyber threats. Anomalies are detected in an early phase and the model supports the risk mitigation and strengthens the autonomous maritime operations. Informed decision making can support proactive monitoring of maritime which is supported by AIS data feature which also improve accuracy and reliability. This paper aim is to propose the model which is more reliable, expandable, and accurate prediction through the use of hybrid model which also incorporates the concept of LSTM and random forest which is also helpful in further classification and extraction. The model aims to enhance the detection accuracy with techniques such as deep learning and machine learning.
CIRA: CYBER INTELLIGENT RISK ASSESSMENT USING RNN FOR PREDICTIVE THREAT ANALYSIS
As cyber-attacks become more frequent and sophisticated, harmful systems will be needed to help predict when an attack may occur so that appropriate proactive measures can be adopted. The “Cyber Intelligence Risk Assessment Using RNN for Predicting Threat Analysis (CIRA)” project aims to create a more intelligent method for detecting cyber-attacks by combining both historical data and real-time network activity data to develop an intelligent risk assessment framework. A number of different machine learning models such as Linear Regression, Gaussian Naive Bayes, support vector machine, decision tree, random forest, and convolutional neural network have been tested as predictive technologies, and the convolutional neural network demonstrated the highest level of predictive accuracy such as precision, recall, F1, area under the curve than all other methods in predicting and detecting potentially malicious acts. After using these predictions to assess the level of risk posed by each predicted threat, CIRA will generate suggested preferences for mitigation, thus enabling organizations to improve their security and better protect themselves against the potential consequences of a cyber-attack. The ultimate goals of this initiative include increasing the accuracy of threat detection, assisting with rapid decision-making, mitigating damage as a result of cyber-attacks, and improving the overall security of networked systems.
Predicting rainfall patterns in Palakkad district of South India based on time series forecasting approaches
Rainfall prediction is inevitable for managing agricultural activities, water resources, and mitigating the risks of droughts and floods, particularly in regions like the Palakkad district, Kerala, which is known for its agricultural significance. Time series forecasting plays a vital role in identifying patterns, trends, and seasonal variations in rainfall data. In this study the Holt-Winters exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model are considered for predicting the rainfall in Palakkad district, Kerala. The forecast accuracy metrics root mean squared error (RMSE) and mean absolute percent error (MAPE) are utilised to evaluate the performance of the above models. While the Holt-Winters exponential smoothing model effectively captured simple seasonal patterns, the SARIMA model excelled in handling complex seasonal structures and trends. The SARIMA model was identified as the most accurate, with minimal forecast errors, adherence to assumptions, and low residual correlations.
seasonal autoregressive integrated moving average (SARIMA)
Holt-Winters exponential smoothing model
The role of post-market surveillance in monitoring of artiﬁcial intelligence enabled medical devices after deployment - a comprehensive review
Transformative advances in diagnostics and treatment can be achieved when Artificial Intelligence (AI) is integrated into medical devices. The unique challenge with the introduction of adaptive nature of AI and ML models are safety and performance especially during longer period. Therefore, pre-market evaluation of the medical devices is not sufficient. This highlights the role of Post-market surveillance, which ensures the safe monitoring of these technologies. The performance of the AI-enabled medical devices may show variation due to changes in clinical practice, progressive disease, and unexpected change in data in real-world population unlike traditional devices. AneffectivePost-marketsurveillanceforAIenableddevicesmustbeproactiveandmustbedatadriven.Toalleviate any risks which may arise, post-market surveillance (PMS) systematically collects and analyzes the data from real world. Essential elements of PMS include setting performance standards, maintaining a watch for model instability, executing user feedbacks and enforcing stringent guidelines for clear incident reporting.
An optimized fuzzy c-means clustering algorithm for efﬁcient social media text analysis
The volume of data produced via social media platforms is staggering, with users generating an enormous volume of text through posts, comments, and interactions that occur every day. Analyzing this data is incredibly demanding work due partially to the fact that it is so unstructured, noisy, and contains many overlapping topics. Clustering techniques play an important role in classifying social media data by grouping together similar pieces of information. However, traditional clustering methods are limited in their ability to characterize social media data because they assign each data point to one cluster only (as with the k-means algorithm) and are very sensitive to the initial conditions being used when performing clustering operations. In this research we propose an optimized fuzzy means clustering algorithm to group social media text data, which will permit data points to belong to multiple clusters with multiple membership values, which is a more appropriate way to model how individuals participate and interact with one another online. Optimization techniques are employed in our method to reduce time complexity, improve convergence rates, and improve clustering accuracy. Finally, social media text data will be pre-processed using traditional text mining methods prior to clustering using the proposed algorithm. Experimental results show the optimized fuzzy means clustering produces more meaningful clusters than the traditional fuzzy c-means method and demonstrates the capability to handle large quantities of real-time social media data for analytical purposes.
Synergistic antibacterial activity of honey–spice combinations against Staphylococcus aureus and Klebsiella pneumoniae
The increasing prevalence of antibiotic resistant microorganisms led to renewed interest in exploring natural substances as alternative antimicrobial agents. Honey and commonly available spices have long been used in traditional medicine because of their antimicrobial and antioxidant potential. The present study reveals the medical uses of honey and there combination along with the chosen spices — clove (Syzygium aromaticum), black pepper (Piper nigrum), turmeric (Curcuma longa), and cinnamon (Cinnamomum spp.), against two bacterial pathogens, Staphylococcus aureus and Klebsiella pneumoniae. Aqueous extracts of honey and honey–spice combinations were prepared in a 1:1 ratio and tested at concentrations of 40, 60, 80, and 100 mL by disc diffusion method on Mueller Hinton agar. Standard antibiotics, tetracycline and norfloxacin, served as positive controls. Results indicated concentration dependent bactericidal action for most honey–spice combos. Honey alone demonstrated minimal efficacy against S. aureus and no efficacy against K. pneumoniae. Among all the spices combination examined, honey–cinnamon showed the significant antibacterial activity against S. aureus, compared to the standard antibiotic at higher concentrations. Conversely, honey–clove combination showed stronger inhibitory effects against K. pneumoniae, particularly at 100 mL. Statistical analysis was carried (ANOVA) shows that there is a notable difference between diverse treatments (p < 0.05). These results highlight that honey-based combinations could serve as efficient antimicrobial agents.
Evaluation of Bengal gram (Cicer arietinum) as a cost-effective nitrogen substitute for the optimized growth of probiotic Lactobacillus spp.
ABSTRACT Probiotic Lactobacillus species are nutritionally fastidious microorganisms that require protein-rich nitrogen sources for optimal growth, making conventional culture media such as Luria-Bertani (LB) and Nutrient Agar economically inappropriate for large-scale and industrial applications. The present study analyzes the potential of Bengal gram (Cicer arietinum) powder as a low-cost, plant-based nitrogen source for cultivating Lactobacillus spp., In order to determine the protein concentration per gram of substrate, Bengal gram powder was quantitatively estimated using Lowry’s method. Based on this estimation, Bengal gram powder was added into the culture medium in the place of peptone and tryptone in the culture medium formulation, thereby serving as the primary nitrogen source. The basal medium was consists of beef extract, sodium chloride, agar and distilled water, while standard Luria medium and Nutrient Agar served as control media. The growth performance of selected probiotic Lactobacillus strains was analyzed by biomass yield and observing growth during incubation. The findings highlights that the Bengal gram–based medium effectively promoted the bacterial growth, yielding growth curves and biomass production compared to those of traditional control media. Lactobacillus species nutritional requirements were sufficiently satisfied by Bengal gram’s natural micronutrient profile and high protein content. This study reveals that Bengal gram powder can effectively replace peptone as a nitrogen source, cost-effective, sustainable, and scalable alternative culture medium for the fermentation and industrial productions.
Exploring borrower awareness and adoption of CLSS in semi-urban housing finance: a case study approach
This study explores the knowledge and application of the Credit Linked Subsidy Scheme (CLSS) among borrowers of housing loans located in the semi-urban regions of the Dakshina Kannada District. Using a case study strategy, primary and secondary sources were integrated via a questionnaire and an interview process. While CLSS helps in housing loans with interest burdens, borrowers understanding of eligibility, documentation, and the disbursal of the subsidy remains gaps, especially with women, informal, and financially illiterate workers. Borrowers are moderately aware of CLSS as many depend on a self-appointing bank official. CLSS aids in housing loans with interest burdens. Given these findings, opportunities exist regarding understanding the gaps and the poorly formed procedures around the CLSS. Of direct importance are the poorly conceived advertising campaigns that need form to meeting target. Focusing on administrative perceptions and institutional participation aids in developing understanding and an approach to policy formations based on inclusive semi-urban housing development.
Effect of speciﬁc conditioning training on selected physical components of football players
Football (soccer) has been recognized as the "beautiful game" for its simplicity, rapid pace and various forms of scoring. In recent years, football has grown rapidly in terms of its worldwide audience and popularity. This rapid growth has caused an increased amount of research and discussion about ways to improve a player’s ability to play the game effectively. The purpose of this study is to investigate the effectiveness of a conditioning program designed to improve both aerobic and anaerobic endurance as well as the agility of high school level players. A total of 40 male high school players aged 14–17 years of age from the state of Kerala, India, were selected for the study using random assignment to either control or experimental groups. The Experimental group of players were placed through a 6-week conditioning program using circuit-based conditioning exercises while the Control group players followed their normal training. Two performance tests were used to measure the players’ performance; the Arrowhead Agility Test was used for measuring agility, and the Sprint Fatigue Test was used for measuring anaerobic endurance. Both test methods provided numerical data that were easily reduced to statistical values and subjected to both paired sample and independent t-tests at the.05 level. Results showed that while the just acuity and anaerobic endurance measures improved during the experimental group’s post-test, no improvement in agility and Level III anaerobic endurance measures was observed in the experimental group post-test compared to the control group. The results of this study show us the need to incorporate sport-related conditioning drills into football training programs as means of enhancing the major physical fitness parameters needed by footballers. The study plays a key role in expanding our current understanding of sports science with regards to both sportrelated drills, as well as scientifically designed sport specific drills as a way of increasing the level of an athlete’s performance. Thus, these results may prove beneficial to both football practitioners and coaches.
Aesthetic smile makeover with direct resin composite veneers: a case report
Direct Composite veneers allow clinicians to deliver a beautiful result with the possibility of being minimally invasive. The ability to be as conservative as possible is important for longevity of existing tooth structure and reduces the risk of failure of teeth in future and need for more invasive treatments. The successful completion of a smile designing case with direct composite veneers is extremely rewarding and humbling for the clinician while it provides the patient with outstanding results.
Data-driven business decision making: leveragingpredictive analytics and BI dashboards
In today’s world of Big Data, organizations are beginning to rely on data-driven decision-making (DDDM) and business intelligence (BI) to increase operational performance, improve strategic planning, and gain a competitive advantage with the help of predictive analytics and dashboards. This paper focuses on the fundamental role of DDDM in business through the use of cutting-edge analytics that make predictions about upcoming outcomes using traditional data integrated with data modelling, data mining, and machine learning. In addition, BI dashboards, such as BI tools, can be used to explore data from basic data sets to improve business decisions. This study also explores the DDDM and BI core concepts and identifies the research gaps and difficulties by applying the methodology of reviewing peer journals related to data-driven business decisions through the use of predictive analytics and BI dashboards from 2018 to 2025, where 50 journals were selected. This paper describes how predictive analytics and BI dashboards can be used for DDDM in the future. An overview article is read that explains the method and presents the results.
Data-driven business decision making, Predictive analytics, Business intelligence, BI tools and dashboards, Big data, Artificial intelligence, Data visualization
Intra-operative navigation systems: advancing precision and safety in oral and maxillofacial surgery
Since their initial introduction in neurosurgical applications approximately two decades ago, computer-assisted surgical navigation systems have progressively been adopted within the domain of craniomaxillofacial surgery. The intricate anatomy of the oral and maxillofacial region is characterized by its proximity to vital adjacent structures, necessitates heightened precision during operative procedures. Surgical navigation technologies facilitate real-time intraoperative localization and enable accurate translation of preoperative planning into the surgical field, thereby enhancing procedural accuracy and patient safety. Surgical navigation enable surgeons to navigate complex anatomical regions while assessing the accuracy and effectiveness of preoperative planning, intraoperative execution and postoperative outcomes. Within the field of oral and maxillofacial surgery, these systems have emerged as indispensable adjuncts, enhancing both the safety and precision of surgical interventions. Clinical applications include management of maxillofacial trauma such as complex midfacial fracture repair and reconstruction of orbit, removal of foreign bodies, complex dentoalveolar procedures, skull base and temporomandibular joint surgery, orthognathic surgeries and dental implant placement. Collectively, these applications underscore the proven efficacy of navigation systems in optimizing outcomes across a broad spectrum of oral and maxillofacial surgical procedures. Literature reveals numerous studies with significant statistical data which highlight the significant role of navigation in improving the overall surgical outcome. This review provides a comprehensive overview of navigation technologies and their diverse applications within the field of oral and maxillofacial surgery.
Efﬁcacy and outcomes of ﬁxation methods in bilateral sagittal split ramus osteotomy: a comprehensive review of stability, neurosensory disturbances and TMJ disorders
Introduction: Bilateral sagittal split ramus osteotomy (BSSO) is a commonly performed procedure for correcting mandibular prognathism and retrognathism. Numerous modifications to the technique along with the use of bicortical and monocortical screw fixation systems, have been developed to enhance stability, aesthetics and function. This review explores the stability, relapse rates, neurosensory disturbances (NSD) and temporomandibular joint (TMJ) dysfunction associated with different fixation methods in BSSO. Methods: A comprehensive literature review of studies evaluating the outcomes of BSSO was performed. Factors such as skeletal stability, NSD, TMJ symptoms and the impact of fixation methods were examined. Comparative analyses of bicortical and monocortical screws were conducted to assess their efficacy and complications. Results: Bicortical screw fixation demonstrated better long-term stability and cost-effectiveness compared to monocortical systems with relapse rates varying based on fixation type and surgical scenarios. NSD particularly inferior alveolar nerve (IAN) injury were the common complications. Recovery rates were influenced by patient age, surgical duration and nerve manipulation. TMJ dysfunction outcomes indicated improvement in symptoms for most patients postoperatively, though pre-existing conditions and specific anatomical factors increased risk. Conclusion: BSSO remains a reliable procedure for addressing mandibular discrepancies. Bicortical screws offer enhanced stability but are associated with higher risks of neurosensory complications. Multidisciplinary approaches and individualized treatment planning can optimize functional and esthetic outcomes while minimizing complications such as relapse, NSD and TMJ dysfunction.
Aegeline from Aegle marmelos as a dual-target agonist for managing type II diabetes mellitus
Type II Diabetes Mellitus (T2DM) is a multifactorial metabolic disorder requiring therapeutics that act on multiple pathways. A prominent therapeutic target is the Peroxisome Proliferator-Activated Receptor (PPAR) family, specifically the PPARg and PPARa subtypes. The mechanism of action of Aegeline, a natural compound extracted from Aegle marmelos leaves, is examined in a computational study and it is evaluated against synthetic PPAR agonists Pioglitazone, Rosiglitazone, and Fenofibrate. Full agonistic activity requires binding within the Ligand Binding Domain (LBD) and forming specific hydrogen bond interactions, which was found to be crucial through molecular docking analysis. The binding pose of Aegeline in the PPARg LBD was unique, as it did not have the conserved hydrogen bonds with His323 and Tyr473, which are characteristic of full agonists, suggesting that it acts as a partial agonist. In contrast, Aegeline showed a binding mode that was comparable to Fenofibrate, as it bonds with Tyr334 and Ala333. Based on these findings, Aegeline is believed to act as a PPARa agonist and a partial PPARg agonist, providing both antihyperglycemic and antilipidemic benefits.By using Aegeline, a natural compound that offers a promising lead, dual-target therapies that may have fewer side effects may be possible. In the future, the aim of research should be to verify these findings in vivo and explore their effect on diabetic complications.
ON THE LENGTH-BIASED OLA DISTRIBUTION: PROPERTIES AND ITS APPLICATION TO CANCER DATA
This paper is concerned with the development of a novel length-biased distribution called the Length-Biased Ola Distribution (LBOD). The LBOD is a new modification of the Ola distribution. Various statistical properties of the LBOD, such as the moment generating function, moments, distribution of order statistics, reliability analysis, and maximum likelihood estimation, are presented. In addition, Fisher’s information, entropies, and stochastic ordering are derived. The Bonferroni and Lorenz curves are also discussed. The effectiveness of the new distribution is demonstrated through an analysis of cancer data. In this application, the new distribution provides a better fit than the other distributions considered in the study.
Enhancing Physics Education through Artificial Intelligence Tools
The integration of Artificial Intelligence (AI) in education has opened new avenues for enhancing the teaching-learning process, particularly in subjects like physics, which often involve complex concepts and abstract reasoning. This research explores the application of AI tools in the domain of physics education and evaluates their effectiveness in improving student engagement, conceptual understanding, and performance outcomes. As traditional teaching methods frequently struggle to meet the diverse needs of 21st-century learners, AI offers promising alternatives through adaptive learning platforms, intelligent tutoring systems, and interactive simulations. This study investigates ten widely-used AI tools—including PhET, Labster, ChatGPT, and Squirrel AI—by analyzing their roles in a structured 60-minute virtual physics class model. The methodology includes a mixed-method approach combining pre- and post-test evaluations, student surveys, and teacher interviews across five educational institutions. Quantitative results indicate a significant increase in student scores (average 28% improvement), while qualitative feedback highlights increased motivation, self-paced learning, and better concept retention. The research also presents implementation strategies and acknowledges challenges such as digital inequality, high software costs, and the need for teacher training. Despite these hurdles, the findings support the transformative role of AI in modern physics education. The study concludes that a well-integrated AI teaching model can democratize access to quality science education and support deeper cognitive engagement among students.
AI in education, Physics teaching, Adaptive learning, Simulation tools, Virtual classrooms
Forecasting Agricultural Production Using ARIMAX model
Maize (Zea mays), commonly known as corn, is one of the world's most important cereal crops, playing a vital role in global food security, livestock feed, and industrial applications. As a highly adaptable crop, maize is cultivated across diverse climatic conditions, from temperate to tropical regions. This study investigates the impact of rainfall on agricultural production and forecast the agricultural production using ARIMAX (p,d,q) time series model, by considering rainfall as the exogenous variable. Further, ARIMA (p,d,q) time series model is also identified for the same and a comparative study has been carried out.
MOBILE LEARNING ATTITUDE OF POST GRADUATE STUDENTS IN SRI VENKATESWARA UNIVRSITY: A STUDY.
This study investigates the attitudes of postgraduate students towards mobile learning at Sri Venkateswara University. The research examines the relationship between students' demographic characteristics, mobile device usage, and their attitudes towards mobile learning. The findings provide insights into the benefits and challenges of mobile learning, shedding light on how universities can leverage mobile technology to enhance postgraduate education. The study's results can inform the development of mobile learning policies and strategies that cater to the needs of postgraduate students
Mobile, Learning, Attitude, Towards, Technology, Post Graduate, Sri Venkateswara University.
AWARENESS AND UTILIZATION OF CHATGPT BY THE S.V. UNIVERSITY STUDENTS: A STUDY
Today’s artificial intelligence is one of the important and newest concepts to learn and research. In artificial intelligence one of the most important and latest innovations is ChatGPT. The objectives of this research paper is awareness and utilization of ChatGPT by the university students. This study used survey method and randomly collected students responses from with google forms. A sample size of 50 representing different responses to these interactions with ChatGPT. And from the result there was a positive response about using of ChatGPT. The study suggests that the usage of ChatGPT can increase students motivation to learn. These findings highlight the potential benefits of AI tools like ChatGPT in improving students learning experience and motivating them to learn.
Predicting Rainfall Patterns in Palakkad District of South India Based on Time SeriesForecasting Approaches
Rainfall prediction is inevitable for managing agricultural activities, water resources, and mitigating the risks of droughts and floods, particularly in regions like the Palakkad district, Kerala, which is known for its agricultural significance. Time series forecasting plays a vital role in identifying patterns, trends, and seasonal variations in rainfall data. In this study the Holt-Winters exponential smoothing model and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model are considered for predicting the rainfall in Palakkad district, Kerala. The forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) are utilised to evaluate the performance of the above models. While the Holt-Winters exponential smoothing model effectively captured simple seasonal patterns, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model excelled in handling complex seasonal structures and trends. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was identified as the most accurate, with minimal forecast errors, adherence to assumptions, and low residual correlations.
Robotic Process Automation to Autonomous Bots: A Survey on the Integration of Artificial Intelligence into Robotic Process Automation
Robotic Process Automation (RPA) has established its position as an innovative framework for simplifying repetitive and rule-based procedural tasks throughout the industrial sector. Traditional RPA systems face limitations because they cannot adjust or exhibit cognitive capabilities. The combination of Artificial Intelligence (AI) technology with RPA systems produced Intelligent Automation, which gives bots the ability to detect, think, learn, and make independent decisions. A review paper examines how RPA systems progressed into AI-powered RPA systems by examining major technological developments, obstacles, and modern patterns. The review paper dedicates specific attention to cognitive abilities in RPA through discussions about machine learning, natural language processing, computer vision, and reinforcement learning frameworks. The study analyzes the constraints of contemporary solutions and sets research priorities for creating self-healing bots with complete autonomy. The paper merges academic research with industrial approaches to deliver an extensive guidance system that benefits researchers and practitioners who aim to advance present-day intelligent automation systems.
Robotic Process Automation (RPA), Artificial Intelligence (AI), Intelligent Automation, Cognitive RPA, Machine Learning, Reinforcement Learning, Self-healing Bots, Autonomous Systems
Evaluating the Impact of the Three-Language Formula under NEP 2020 on Language Education and Cultural Integration in Tamil Nadu: Challenges and Opportunities
The introduction of a third language, particularly Hindi, as proposed under the National Education Policy (NEP) 2020, has sparked significant debate in Tamil Nadu. Rooted in a historical resistance to Hindi imposition dating back to the Anti-Hindi Agitations of the 20th century, the issue reflects deep-seated cultural, political, and educational concerns. Tamil Nadu has consistently upheld a two-language policy—Tamil and English—and views the inclusion of Hindi as a threat to linguistic and cultural identity. Politically, it is perceived as an infringement on state autonomy and a move toward centralization, which has intensified opposition from regional parties and civil society groups. Pedagogically, there are challenges related to the shortage of qualified Hindi teachers, lack of localized curriculum resources, and concerns about academic burden on students. Moreover, socio-economic disparities between urban and rural schools could further widen if Hindi proficiency becomes linked to future opportunities. While NEP 2020 promotes flexibility and emphasizes the importance of mother tongue education, ambiguity in the policy's implementation framework has fueled apprehension that Hindi may eventually be imposed indirectly. This paper explores the multifaceted issues surrounding the introduction of Hindi as a third language in Tamil Nadu, highlighting the need for a sensitive, region-specific approach that respects linguistic diversity, protects cultural identities, and ensures educational equity. The resolution of these challenges will be critical to achieving the NEP’s broader goals of national unity and inclusive education without undermining regional autonomy.
Exploring Molecular Interactions of Britanlin E: Targeting DNA Topoisomerase in Breast Cancer and p53 Binding Protein 1 in Head and Neck Cancer
In this study, we employ AutoDock, widely-used molecular docking software, to investigate the molecular interactions of Britanlin E in the context of breast cancer and head and neck cancer. Britanlin E has shown promising anticancer properties, and understanding its binding mechanisms to specific target proteins is crucial for elucidating its therapeutic potential. Our computational approach involves docking simulations of Britanlin E with DNA Topoisomerase, a vital enzyme involved in DNA replication and repair pathways implicated in breast cancer progression, as well as with p53 Binding Protein 1, a critical regulator of cell cycle and apoptosis dysregulated in head and neck cancer. Through AutoDock simulations, we aim to elucidate the binding affinities, binding modes, and key amino acid residues involved in the interactions between Britanlin E and these target proteins. The insights gained from this study will contribute to a deeper understanding of Britanlin E's molecular mechanisms of action, paving the way for the development of novel therapeutic strategies for breast cancer and head and neck cancer.
Objective: The term, “Metaverse” has gained attention from academicians and the corporate companies recently. Metaverse is the term used to denote the virtual-reality environment through which the users’ interaction takes place. The attention on this topic has increased especially after the announcement given by the major tech giants on their new investments in the field of augmented reality (AR), virtual reality (VR), blockchain technology, latest 5G network and many more. Methods: Even though these technologies are growing faster, the companies and the target audience are prone to many privacy issues. Though there are minimal number of articles published, there are not many studies available regarding this. We will explain the traits of Metaverse in detail and will offer a framework on blockchain technology to arrest the privacy issues. Results: We are making enough research on the security issues and will provide our results from the point of view of resolving malicious attacks, privacy issues, decentralized systems, issue with real-world communication and many more. We will also summarize the problems with regards to the security needs. Conclusion: This research paper is to give you an insight on the privacy issues related to the use of metaverse. This is also to provide an idea on the projects that are happening and their related issues in addition to the recent technologies and the solutions that can improve the security. In this paper, we also propose an idea for the usage of blockchain technology for improving the trustworthiness and the security of using Metaverse.