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.
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.
Copyright (c) 2026 Scientiarum: A multidisciplinary journal
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.
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.
Copyright (c) 2025 Scientiarum: A multidisciplinary journal
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.
ISO 27001: All-inclusive framework for information security management
Information security is significant one for attaining the organisation’s success, which gave to the rise of cyber threats and more reliable on digital infrastructure. The International Standard ISO 27001 offers a valuable key framework for Information Security Management System (ISMS). This system enables the organisation to manage their security, confidentiality and availability in a systematic manner. This study focuses on the comprehensive implementation of ISO 27001 and also explores about its advantages like reducing data breaches, improving the reputation of business, limitations challenges in integration with existing process, resource allocation, employee training and some strategies for the business to strengthen their security posture. For this, we are identifying the companies that are ISO 27001 certified. For this, we have chosen the companies, BSI, Cyber sapiens, Consilium Labs, Bureau Veritas and TÜV SÜD America. We have analyzed how ISO 27001 has helped the companies for protecting the data, risk management and its compliance. The study concentrates on key components of ISO 27001 which covers security controls, risk assessment and continuous development cycles. It will help to address the impact created in the organisational culture, costs and confidence gained by stakeholders. This paper will provide a comprehensive guide of ISO 27001 which not only a security standard and framework but also a strategic tool to build a risk aware organisation. By evaluating the steps involved in implementing the ISO 27001 framework and covering the role of leadership, this analysis acts as a roadmap for organisations who are seeking to improve their information security and achieve sustainable security development.
The Role of Picroliv in Wound Healing: A Mini-Review
Picroliv, a standardized extract derived from Picrorhiza kurroa, has garnered attention for its potent antioxidant, anti-inflammatory, and hepatoprotective properties. While traditionally utilized for liver ailments, emerging research underscores its potential in enhancing wound healing processes. This review delves into the multifaceted mechanisms by which Picroliv facilitates wound repair, encompassing its antioxidant capabilities, modulation of inflammatory pathways, promotion of angiogenesis, and cellular proliferation. By synthesizing current scientific findings, this article aims to elucidate Picroliv's therapeutic promise in wound management. Wound healing is a vital physiological process involving complex biological pathways. Natural products with antioxidant and anti-inflammatory properties are increasingly explored as therapeutic agents in enhancing wound repair. Picroliv, a standardized iridoid glycoside fraction obtained from Picrorhiza kurroa, has demonstrated significant potential in modulating oxidative stress and inflammation. This review synthesizes experimental findings, particularly from in vivo studies involving hemorrhage-resuscitation (H/R) injury models, to elucidate Picroliv’s mechanisms of action and its potential role in promoting wound healing and tissue repair.
The hypolipidemic activity of ferulic acid isolated from hordeum vulgare through ppar- γ /sirt1/ foxo1/ lxr- α mediated signaling pathway: a biochemical study on high fat diet induced obese male sprague dawley rats
Introduction: Whole grains are known for its medicinal properties for millennia. Numerous whole grains with antihyperlipidemic qualities have drawn attention for researchers as potential therapeutic adjuncts in lowering the incidence of cardiovascular disease. According to certain folklore and preliminary evidence, barley can lower blood body fat levels. The main goal of this study is to present scientific evidence of the lipid-lowering properties of hulled barley grain powder (HBGP) and Ferulic acid (FA), its active ingredient. Materials and methods: Male Sprague Dawley (SD) rats were provided with high fat diet (HFD) to develop hyperlipidemia. Groups 1 and 2 rats were given the standard feed. Rats in groups 4, 5, 6, and 7 were fed a HFD for fourteen weeks. While Group 6 rats received 200 mg/kg body weight of FA, Group 5 rats started receiving 50% of hulled barley flour mixed into their feed in the 3rd week in addition to 50% of regular and HFD meals. Furthermore, rats in Group 7 were given 10 mg/kg body weight of Rosuvastatin. Rats were euthanized right after fourteen weeks, and mRNA expression of peroxisome proliferator activated receptor gamma (PPAR-γ), silence information regulator 1 (SIRT1), forkhead box factors (FOXO1), and liver X receptor alpha (LXR-α) was assessed in liver and adipose tissue (AT) samples. Results: It had been discovered that liver and AT samples of rats supplied with HFD had fewer mRNA expressions of SIRT1, PPAR-γ, FOXO1 and LXR-α than the liver and AT samples of experimental animals. In HFD+HBGP and HFD+FA fed groups, HBGP and FA co-administration significantly (P=0.001) stimulated SIRT1, PPAR-γ, FOXO1 and LXR-α expression therefore reducing adipocyte differentiation in obese conditions. Conclusion: From our current results, it is highly evident that HBGP and its major compound FA have hypolipidemic effect via increasing the mRNA expression of SIRT1, FOXO1, LXR-α, and PPAR-γ. The hypolipidemic outcome caused by HBGP may be explained by the presence of prominent phytochemicals like FA. FA and HBGP are undoubtedly promising drugs for the management of health issues associated with hyperlipidemia in the general human population.
A Conceptual Study on Employee Satisfaction among various sectors
The modern business environment is rapidly evolving because of technological advances and structural reorganizations. The organization must address the issue of people who play a vital role in innovation and adaptability. The employee satisfaction index is an important parameter to measure management effectiveness, prediction of absence, and ultimately increase in productivity of the organization. Standardization of the survey tool is not less important for reliable data conclusive to the research enterprise. Each of the sectors requires further statistical analyses. These analyses shall reveal the industry-specific determinants of job satisfaction. A comparative analysis of different industries could come up with economically efficient practices that can make a very big difference on key matters. Aspects related to the mental attributes also need consideration, as job satisfaction has a close relationship with the psychology of the employee.
Innovative Insights into Job Satisfaction Among Construction Professionals in Tamil Nadu: A Data Driven Approach
Job satisfaction is very important in determining employee performance, productivity, and retention; dynamic factors in a heavy-stress construction work-related environment. This study aims at establishing factors influencing job satisfaction amongst construction professionals in Tamil Nadu; namely, remuneration, work environment, organizational culture, and the opportunities for career advancement. A standard survey was conducted with respect to the information to be gathered, depending on industry-validated measures like the Minnesota Satisfaction Questionnaire, this was a standardized survey. Where standard tools were absent, pre-testing was conducted, and the survey instrument was modified accordingly for data collection purposes. The analysis employed the advanced statistical techniques of structural equation modeling (SEM) and hierarchical regression modeling to go beyond mere descriptives. The study identified that working conditions did not contribute much to employee satisfaction when remuneration is present in the prospect. Therefore, this study's findings relate to motivating employees to improve satisfaction and, hence, organizational effectiveness.
Implications of Potassium bromate in our day- to- day usage of commercial products
The increased fear of the presence of chemicals in food has received much attention in recent years. This paper aims to examine the use of one such chemical in food. The use of potassium bromate in food items, especially bakery products, due to its effectiveness in enhancing gluten strength and improving dough texture, has been a subject of controversy for over a few decades. The health risks of potassium bromate are beyond doubt, with many studies having highlighted the concerns about the chemical's ability to induce tumors in rats, caused by the chemical's carcinogenic and mutagenic properties. Despite this, many countries have reported the presence of potassium bromate in their bakery items. This research work investigates the presence of potassium bromate in bakery products located in Chennai, India, by implementing qualitative and quantitative analyses. The results indicated the use of potassium bromate in a few commonly used brands with increased concentrations. Additionally, for a multifaceted approach, we integrated insights from zebrafish (Danio rerio) since they share a similarity of 70% genes with humans. The outcome demonstrated cellular damage to the embryos. The results of this study shed light to the presence of potassium bromate in bakery products in the perimeter of Chennai, India, indicating that residents could be exposed to significant health risks associated with potassium bromate.
Enhancing The Performance of Beam-Column Joint Using Artificial Fibers
In recent seismic events, numerous moment-resisting reinforced concrete frame buildings have experienced collapse due to shear failure at beam-column connections. Evidence from these earthquakes indicates that compromised beam-column joints significantly undermine the overall structural integrity of the buildings. The overall ductility of structures is also reduced considerably by brittle joint shear failure; As a result, dangerous failure mechanisms emerge. A variety of techniques have been developed to strengthen beam-column joints. Steel and concrete jacketing are examples of these techniques. A novel technique for enhancing structural components has been recognized for over a decade, utilizing fiber-reinforced polymer (FRP) as externally bonded reinforcement in critical areas of reinforced concrete (RC) elements. This paper investigates the impact of glass fiber-reinforced polymer wrapping on corner beam-column joint frames subjected to ultimate loads. The specimens prepared for this study will measure 700 mm x 700 mm, with a cross-sectional dimension of 200 mm x 100 mm. Twelve concrete specimens will be integrated with glass fiber-reinforced polymer to ensure adequate reinforcement.