Studies in Science of Science | ISSN: 1003-2053
Studies in Science of Science is a premier international journal dedicated to advancing scientific research and innovation. Our publication features cutting-edge studies in science, technology policy, and technology management, offering valuable theoretical insights, research papers, survey reports, and case studies. With a focus on promoting innovation, fostering academic exchanges, and driving technology development, our journal serves as a platform for sharing knowledge and advancing scientific discourse. In addition to our main sections covering scientific theory, technology development strategy, and policy, we also feature columns on technology management, book reviews, academic trends, and more. Join us in exploring the forefront of scientific inquiry and shaping the future of technology.
ASSESSMENT OF THE IMPACT OF TRAINING PROGRAMS ON THE EMPLOYEES’ PRODUCTIVITY – A STUDY ON ANDHRA PRADESH STATE SKILL DEVELOPMENT CORPORATION
Skill development refers to the systematic process of identifying, acquiring, and enhancing the abilities required to perform tasks effectively in personal and professional contexts. It encompasses both technical (hard) skills and behavioral (soft) skills, enabling individuals to adapt to changing economic and organizational environments. The present study examines the socio-economic profile of the respondents in the study area and the relevancy of training programs that synchronizes the trainees job roles. The study further analyzes the relationship between the types of training programs attended by the trainees and their satisfaction with regard to development initiatives. The study adopts Descriptive Research Design . Both primary and secondary data were utilized for drawing conclusions from the study. Convenience sampling method was adopted for the research study and interview schedule method was employed to extract the primary data from the selected self-employed youth respondents. The sampling area selected for the study was Krishna District of Andhra Pradesh. The sample size comprises 120 respondents. The nature of the sample respondents was that of the self-employed youth who had undergone training programs under various schemes of APSSDC and those involved in production and service sectors from Micro and Small enterprises . The statistical tools like ANOVA and Pearson Correlation Coefficient Test were applied for drawing the research inferences. The study result shows that there exists a significant relationship between training outcomes and the relevancy of training programs to trainees job role and it further reveals that there exists a significant relationship between the types of training programs attended and the satisfaction levels of the trainees with regard to their development initiatives.
Skill Development, Training Programs, Employee Productivity
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A VALIDATED SPECTROPHOTOMETRIC APPROACH FOR THE CONCURRENT ESTIMATION OF PARACETAMOL AND METHOCARBAMOL IN COMBINED TABLET FORMULATIONS WITH PRACTICALITY AND GREENNESS ASSESSMENT
equation (Vierordt’s) approach was developed and validated for the simultaneous estimation of paracetamol (PTM) and methocarbamol (MCM) in combined tablet dosage forms. The method utilizes absorbance measurements at 250 nm and 225 nm for PTM and MCM, respectively, eliminating the need for prior separation. The method was validated in accordance with ICH guidelines for specificity, linearity, accuracy, precision, limit of detection (LOD), and limit of quantification (LOQ). Specificity studies confirmed the absence of interference from excipients at the selected wavelengths. The method exhibited good linearity over the concentration ranges of 4–24 µg/mL for PTM and 16–32 µg/mL for MCM. Accuracy was assessed by recovery studies, yielding values in the range of 99.49–109.24% for PTM and 94.59–113.79% for MCM. Precision studies demonstrated good repeatability and reproducibility, with %RSD values less than 2% for both intraday and interday analyses. The LOD and LOQ values indicated adequate sensitivity of the method. The method was successfully applied to the analysis of a marketed formulation, with assay results of 95% for PTM and 98.2% for MCM. Ruggedness and robustness studies confirmed the reliability of the method under small variations in analytical conditions. Furthermore, greenness evaluation using GAPI, AGREE, and BAGI tools demonstrated that the method is environmentally sustainable, simple, and suitable for routine quality control applications. This study reports a novel simultaneous equation-based UV method for this drug combination, providing an efficient alternative to more complex analytical techniques.
Simultaneous equation method (Vierordt’s method), UV spectrophotometry, Paracetamol, Methocarbamol, Method validation (ICH guidelines), Green analytical chemistry
THE IMPACT OF ECONOMIC POLICIES ON EQUITY AND SOCIAL PROGRESS IN VIETNAM
The Vietnamese and global economy are undergoing a period of significant transformation, driven by technology, political developments, and national institutional and policy frameworks. The living standards of people in each country differ, yet Vietnamese citizens are increasingly cared for, enjoying a prosperous and happy life, achieving equity and social progress. This is the positive outcome of major economic decisions and policies implemented by Vietnam during 2015-2025. In the following article, after reviewing the theoretical foundations of equity and social progress, the authors assess how Vietnam’s economic policies in the 2015-2025 period have impacted equity and social progress, and from there proposed solutions to adjust and improve economic policies in the future.
Impact; Economic Policy; Equity; Social Progress; Vietnam
INFLAMMATORY CYTOKINES AS POTENTIAL BIOMARKERS FOR MONITORING THERAPEUTIC RESPONSE IN BLADDER CANCER
Background: Bladder cancer is the most frequent cancer in the world and the most common malignancy of the urinary system where different cytokines that are important for fighting tumors, are produced during this response and have effectively a treatment works. Objective: This study aimed to evaluate the role of inflammatory cytokines as non-invasive biomarkers for monitoring therapeutic response in patients with bladder cancer. Methods: A total of 80 patients with bladder cancer were enrolled and classified into pre-treatment and post-treatment groups, in addition to 30 healthy controls. Serum levels of interleukin-2 (IL-2), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were measured and statistically analyzed according to treatment status, type, and duration. Results: The results showed a significant increase in IL-2 and IL-6 levels in the post-treatment group compared with controls, while TNF-α levels were significantly elevated in bladder cancer patients irrespective of treatment status. IL-6 exhibited a progressive and statistically significant increase across all groups and was positively associated with longer therapy duration. No significant differences were observed between different treatment modalities, which may be explained by the predominance of intravesical (local) therapies that mainly induce a local, rather than systemic, immune response. Conclusion: These findings suggest that inflammatory cytokines, particularly IL-2 and IL-6, may serve as useful biomarkers for assessing immune activation and monitoring therapeutic response in bladder cancer, whereas a combined cytokine panel could provide a more accurate evaluation of treatment outcome and disease progression.
Bladder cancer, inflammatory cytokines, IL-2, IL-6, TNF-α, biomarkers, therapeutic response, immune response, cytokine profile, disease monitoring
IMPACT OF FINANCIAL STATEMENT INFORMATION ON STOCK PRICES: EVIDENCE FROM LISTED COMPANIES ON THE HO CHI MINH CITY STOCK EXCHANGE
Stock prices are influenced by various factors, among which information from the financial statements of listed companies plays a crucial role. Investors frequently rely on financial statement data to make decisions regarding buying, holding, or selling stocks; consequently, the relationship between accounting information and stock prices remains a significant subject of interest for researchers. This study utilizes a sample of 356 companies listed on the Ho Chi Minh City Stock Exchange (HOSE) over the 2020–2024 period, comprising a total of 1,780 observations. The empirical results indicate that Earnings Per Share (EPS) has a strong positive impact on stock prices, with a coefficient of 1.725, significant at the 1% level (p < 0.01). This suggests that an increase in a company's EPS tends to drive its stock price upward, reflecting investors' expectations of the firm's future profitability
Financial statements, accounting information, stock prices, stock market, value relevance, Vietnam
EVALUATION OF ANTIOXIDANTS AND ANTIBACTERIAL ACTIVITIES OF LACTUCA SERRIOLA (L.) LEAVES EXTRACTS AGAINST BACTERIA ISOLATED FROM DIABETIC FOOT ULCERS
Background: Natural products remain crucial as a foundation for drug discovery. Lactuca serriola (L), a member of the family Astereceae, is one of such plants used in traditional medicine. Objective: The present research aims to evaluate the biological activity of L. virosa leaf extract as antioxidant and antibacterial agent, Methods: Different concentrations of ethanol (Solvent A (100%), Solvent B (75%), Solvent C (50%), and Solvent D (25%)) and distilled water (Solvent E) were used for extraction of Lactuca serriola (L.) leaf parts by maceration method. Extracts at concentrations of 100, 75, 50, and 25 mg/ml were prepared for each solvent to detect antibacterial activity against Pseudomonas aeruginosa and Staphylococcus aureus using agar well diffusion method. The antioxidant activity was assessed using the DPPH assay. Results: Solvent A achieved the highest antioxidant activity, while Solvent D recorded the lowest effect. All concentrations of extracts inhibited bacterial growth, and the inhibition effect increased with increasing extract concentration. The highest inhibition zone against S. aureus was recorded at Solvent D with 100 mg/ml (Mean ± SD = 26.00±2.65 mm), while the highest inhibition zone against P. aeruginosa was recorded at Solvent I with 100 mg/ml (Mean ± SD = 26.00±2.65 mm). Conclusion: the possibility of using ethanolic extracts as antibacterial and antioxidant agent
Lactuca serriola (L.), Ethanol extraction, Antimicrobial activity, Antioxidant asctivity
FACTUALITY: BERT TRANSFORMER FOR SEMANTIC ANALYSIS AND FAKE NEWS CLASSIFICATION
The Advancements in technology have led to difficulty in finding authentic news and fake news. The BERT-based transformer model enables the identification of fake news and authentic news across the internet. BERT enhances the model through complex operations, which it can perform, such as deep semantic analysis, pattern matching techniques and rich contextual understanding. The model is trained with authentic news and fake news to understand the patterns between real news and fake news to identify warning signs of misinformation, including internal contradictions, peculiar linguistic patterns, and structural inconsistencies. Leveraging BERT in this model enabled the ability to analyse patterns and detect fake news on various platforms such as healthcare, finance, media, articles, websites. Another major advantage of BERT is that it enhances the accuracy and outperforms the speed of traditional machine learning techniques. Therefore offering a optimized solution toward less misinformation online
BERT, Fake news detection, Misinformation, Transformer models, Semantic analysis, Contextual embeddings, NLP, Article classification, Deep learning, Bidirectional language model
Dementia is a progressive neurological disorder that significantly affects memory, cognitive abilities, and daily functioning, making individuals increasingly dependent on caregivers. Existing dementia care solutions are often limited by lack of personalization, minimal interactivity, and reliance on manual monitoring. To address these challenges, this research proposes an AI-based Dementia Assistant, developed as a cross-platform mobile application using the Flutter framework, enabling deployment on both Android and iOS platforms.The system utilizes speech recognition and natural language processing to facilitate intuitive interaction, helping users perform daily activities independently. It also incorporates a voice-based emergency detection mechanism that identifies critical keywords and alerts caregivers in real time. Additionally, behavioral analysis of user interactions enables the system to adapt responses based on individual cognitive patterns.Experimental results demonstrate reliable performance in speech processing, reminders, and emergency alert generation. Overall, the proposed system offers a scalable, cost-effective, and user-friendly solution that enhances safety, independence, and quality of life for individuals with dementia.
AI Dementia Assistant, Dementia Care, Speech Recognition, Natural Language Processing, Emergency Detection, Behavioral Analysis, Flutter Application, Cross-Platform Mobile App, Caregiver Support, Assistive Technology
MEDISCAN: DOCTOR HANDWRITING RECOGNITION WITH PRESCRIPTION SUMMARIZATION, DISEASE DETECTION, DRUG INSIGHTS, AND SMART REMINDERS
Poor handwriting of medical prescriptions is a significant contributor to the occurrence of medication errors since medical prescriptions are read by hand and thus there is poor legibility. In this paper, I am going to introduce you to MediScan, which is a smart medical helper device, one that combines the handwriting recognition, prescription summary, disease diagnosis, drug analysis, and smart reminder services into one platform. The system uses Optical Character Recognition (OCR) to scan the text on images of a handwritten prescription and a large language model to summarize the context of diagnosis, medications and dosage instructions. According to extracted clinical data, the system detects possible illnesses and conducts drug interaction analysis to increase medication safety. There is also the emergency risk assessment module which notifies the health care providers in case of emergencies and a smart reminder module which reminds the patients to take their medication by sending automatic alerts. The suggested system has a great potential in minimizing the medical interpretation error rates, enhancing patient safety, and providing real-time decision support to clinicians and patients. The system processes text and disease identification correctly, and the analysis of drug compatibility effectively, as experimental assessment demonstrates the effectiveness of the system in practice in healthcare settings.
Optical Character Recognition, Medical Image Processing, Disease Detection, Drug Interaction Analysis, Smart Healthcare Systems
HIRECAST ENGINE - AN AI-POWERED TOOL FOR PREDICTING FUTURE JOBS
The ever-changing nature of the information technology job market is a challenge for students and professionals to predict what kind of job opportunities will arise in the near future. Conventional job search sites are limited to presenting existing job openings. In this context, we propose a novel intelligent analytics framework for predicting future job demand in the IT field. The framework is named HireCast Engine. It uses APIs from Adzuna and JSearch to fetch job postings and form a structured time series dataset based on job demand in different roles and cities. The framework uses a hybrid Prophet-LSTM model to predict future job demand. In addition to forecasting, a graph-based skill gap analysis module is implemented using the Neo4j graph database to analyze skill gaps and recommend appropriate career paths. Experimental results on 31,200 job postings from 12 cities in India proved that the hybrid Prophet-LSTM model is more accurate in predicting job demand, achieving 96.7% accuracy. The proposed framework provides an effective decision-support system for career planning and job market analytics
Time-series forecasting, Facebook Prophet, LSTM, skill-gap analysis, job recommendation, career planning, job market analytics, hybrid models
THE MEDİATİNG ROLE OF QUİET QUİTTİNG İN THE EFFECT OF LEADERSHİP STYLES ON JOB SATİSFACTİON WİTHİN THE CONTEXT OF THE MULTİ LEADERSHİP MODEL
In this study, nine leadership styles—transformational, principled, servant, transactional, entrepreneurial, strategic, empowering, visionary, and authoritarian—were examined to determine whether they are associated with quiet quitting and how they affect job satisfaction. The study was designed using a quantitative research approach, and data were collected via surveys from 300 employees working in different sectors in Turkey through the LinkedIn platform. Confirmatory factor analysis, correlation, and regression analyses were employed to analyze the data. Additionally, structural equation modeling and bootstrap tests were utilized. The results revealed that only authoritarian, empowering, and servant leadership styles had statistically significant effects. According to these findings, authoritarian leadership triggers quiet quitting behaviors, thereby reducing job satisfaction. On the other hand, servant leadership was found to have a positive impact on job satisfaction. Furthermore, quiet quitting was shown to negatively affect job satisfaction. In conclusion, considering the effects of leadership styles on quiet quitting and job satisfaction, it becomes evident that avoiding authoritarian management behaviors and adopting a participative and supportive leadership approach that acknowledges employees’ needs is crucial.
Leadership styles, quiet quitting, job satisfaction, multi-leadership model, mediation analysis
INTEGRATING GREEN ACCOUNTING AND INTELLECTUAL CAPITAL FOR SUSTAINABLE DEVELOPMENT: INSIGHTS FROM ETHIOPIA'S MANUFACTURING INDUSTRIES
This integrative review explores the intersection of green accounting and intellectual capital as drivers of sustainable development in Ethiopia's manufacturing sector. Drawing from international experiences and the Ethiopian context, the study synthesizes peer-reviewed literature from 2000 to 2026 to analyze prevailing practices, barriers, and enablers. It finds that while global manufacturing industries increasingly integrate environmental management into accounting and leverage intellectual capital for innovation, Ethiopian firms face significant internal barriers, including inadequate technical skills, limited awareness, and resistance to organizational change. External barriers—including weak regulatory frameworks, insufficient government incentives, and market forces focused on short-term profitability—further constrain progress. Despite these challenges, the review highlights the critical role of intellectual capital, particularly human and structural dimensions, in facilitating the adoption of green accounting and driving sustainable performance. Theoretical frameworks such as the Resource-Based View and Integrated Reporting Framework provide a foundation for understanding how these elements synergistically enhance competitiveness and value creation. The findings offer practical recommendations for policymakers and industry stakeholders, emphasizing the need for targeted incentives, capacity building, and stronger regulatory enforcement. The review also identifies research gaps and suggests future empirical investigations into the impacts of integrated approaches within the Ethiopian context. Overall, the study concludes that the convergence of green accounting and intellectual capital is essential for the sustainable transformation of Ethiopia's manufacturing sector and for aligning it with international sustainability standards. Therefore, the deliberate and simultaneous cultivation of these integrated practices is not merely beneficial but indispensable for Ethiopia to achieve a globally competitive and environmentally responsible manufacturing future.
Green accounting, intellectual capital, sustainable development, manufacturing, Ethiopia, barriers, enablers, Resource-Based View, Integrated Reporting Framework
THE ROLE OF ANALYTICS ON HRM PRACTICES WITH REFERENCE TO IT SECTOR, TELANGANA
With the fast changing Information Technology (IT) industry, organizations are relying more on analytics to streamline Human Resource Management (HRM) practices. This paper explores how HR Analytics can be used to enhance HRM practices including recruitment, training, performance management, and employee retention. The research design adopted was quantitative and 370 employees who work in the IT organizations were sampled using a structured questionnaire. Exploratory Factor Analysis (EFA) has discovered that there are four significant dimensions of HR Analytics: Data Utilization, Analytical Skills, Technology Infrastructure, and Strategic HR Integration. The measurement model was confirmed by Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) demonstrated that HR Analytics makes a significant contribution to the enhancement of HRM practices in the IT sector. The results indicate that HR practices powered by analytics help to improve decision-making, efficiency, and performance among employees. This paper finds that HR Analytics is a strategic requirement of IT organizations in order to be competitive.
HR Analytics, IT Sector, HRM Practices, Data-Driven HR, EFA, CFA, SEM, Organizational Effectiveness
FLUORESCENT DETECTION OF IODIDE IONS USING COUMARIN DERIVATIVES: INSIGHTS INTO ELECTRON TRANSFER MECHANISM
Sensitive and reliable detection of iodide ion (I⁻) is of considerable importance due to its vital role in environmental, biological, and industrial systems. In the present study, the iodide ion sensing capabilities of three the structurally different coumarin derivatives namely, 6-chloro-4-(4-methoxyphenoxymethyl)-chromen-2-one (S1), 1-(4-methoxyphenoxymethyl)-benzo[f]chromen-3-one (S2), and 6-methoxy-4-(4-methoxyphenoxymethyl)-chromen-2-one (S3) are systematically investigated in dimethyl sulfoxide (DMSO) at room temperature. Steady-state and time-resolved fluorescence spectroscopic techniques are employed to evaluate these interactions. Fluorescence experiments through incremental addition of iodide ions revealed the quenching of coumarin derivatives fluorescence, and analysis was carried out using Stern–Volmer plots to examine the mechanism. The change in Gibbs free energy change (ΔG0), is estimated to explore the role of photoinduced electron transfer (PET). In addition, the capabilities of investigated coumarin derivatives for rapid and visual detection of iodide ion is examined by solution mode detection method. This study provides a comprehensive assessment of coumarin-based fluorophores as sensing platforms for iodide ion detection.
Iodide ion sensing; Coumarin derivatives; Fluorescence quenching; Photoinduced electron transfer (PET); Stern–Volmer analysis
LAPLACE TRANSFORM ANALYSIS OF UNSTEADY MAGNETOHYDRODYNAMIC AL₂O₃–CU/WATER HYBRID NANOFLUID FLOW OVER A STRETCHING SHEET
Nanofluids and, more recently, hybrid nanofluids have emerged as powerful working media for advanced thermal systems where high heat transfer performance is required while still maintaining acceptable pumping power and flow controllability. In this study, the unsteady magnetohydrodynamic (MHD) boundary-layer flow and heat transfer of an Al₂O₃–Cu/water hybrid nanofluid over a linearly stretching sheet is investigated. The sheet velocity varies linearly with the streamwise coordinate and decays with time, and a uniform transverse magnetic field is imposed. The governing unsteady boundary-layer equations for momentum and energy are derived under standard boundary-layer assumptions, including the incorporation of effective density, viscosity, heat capacity, thermal conductivity, and electrical conductivity for the hybrid nanofluid. After introducing an appropriate similarity variable in the wall-normal direction and dimensionless fields for velocity and temperature, the problem is reduced to a pair of unsteady diffusion-type equations with a reaction term in the momentum equation due to the applied magnetic field. To obtain analytical insight into the transient behavior, the Laplace transform with respect to time is applied to the dimensionless equations. The transformed problems are linear ordinary differential equations in the similarity variable, which are solved explicitly. The inverse Laplace transform is then evaluated, yielding closed-form expressions for the dimensionless velocity and temperature profiles in terms of the complementary error function. From the analytical expressions, symbolic formulas are derived for the wall shear stress and wall heat flux, and hence for the skin-friction coefficient and local Nusselt number. The influence of the magnetic parameter, Prandtl number, nanoparticle volume fractions, and time on the flow and temperature fields is discussed qualitatively using these expressions.
Laplace Transform, MHD, Hybrid Nanofluid, Al₂O₃–Cu/Water, Stretching Sheet, Transient Flow
A SEMI-SUPERVISED HYBRID MODEL FOR ANOMALY DETECTION: LEVERAGING AUTOENCODER FEATURE EXTRACTION AND RANDOM FOREST CLASSIFICATION
Anomalies, also known as outliers, are data points that deviate significantly from the norm. Detecting such anomalies is crucial in many real-world applications such as fraud detection, fault diagnosis, cybersecurity, and system health monitoring, where even a single undetected anomaly can lead to critical failures or losses. However, real-world datasets often suffer from extreme class imbalance and overlapping distributions, where anomalies are camouflaged within dense clusters of normal data. Popular anomaly detection algorithms like Isolation Forest, Local Outlier Factor (LOF), and clustering-based methods often struggle in these scenarios, leading to high false negatives. To address this, we propose a hybrid model combining an Autoencoder for reconstruction-based anomaly scoring with a Random Forest classifier trained specifically on ambiguous regions—where reconstruction errors for normal and anomalous instances overlap. This two-stage approach improves decision boundaries by leveraging both unsupervised reconstruction and supervised classification. Experiments on a labeled benchmark dataset demonstrate that our method significantly outperforms traditional models in terms of F1-score and detection accuracy, making it more effective for detecting subtle, disguised anomalies in imbalanced datasets
Anomaly Detection, Outlier Detection, Autoencoder, Random Forest, False Negatives
PLANT DISEASE ANALYSIS AND PREDICTION USING DEEP LEARNING
At present, The agricultural sector plays a critical role in ensuring food security and supporting rural livelihoods, but plant diseases remain a major obstacle to achieving high crop yield and quality. Early and accurate detection of these diseases is essential for timely treatment and prevention of further damage. In many cases, farmers depend on manual observation or experience-based judgement, which can lead to misdiagnosis, delayed action, and unnecessary pesticide usage. This project introduces a plant disease detection system that applies Machine Learning and image classification techniques to identify diseases from leaf images. The system is built using TensorFlow, with a deep learning model trained on a diverse dataset containing images of healthy and diseased leaves from multiple plant species. By learning to recognize patterns in texture, shape and color variations, the model is capable of accurately classifying plant health conditions. The system is deplyed through a user-friendly desktop website where users can upload images of plant leaves. Once uploaded, the image is processed by the TensorFlow model, and the system outputs the detected disease along with suggested preventive or corrective measures. This eliminates the need of constant expert consultation and ensures quick, data-driven diagonsis. By combining image analysis with an accessible web interface, the solution enables faster and more reliable plant disease detection. It promotes targeted pesticide application, minimizes crop loss, and supports sustainable farming practices, ultimately contributing to improved agricultural productivity and economic stability for farmers.
Plant Disease Detection, Machine Learning, Deep Learning, Image Classification, TensorFlow, Leaf Image Analysis, Agricultural Technology, Early Disease Diagnosie
ASSESSING MIRNA-21 EXPRESSION AND CHECKPOINT PROTEIN POLYMORPHISMS IN ULCERATIVE COLITIS PATHOPHYSIOLOGY
Background: Ulcerative Colitis (UC) Is A Chronic Autoimmune Disease With Intestinal Mucosal Inflammation And Ulceration. It Is Associated With An Annual Increase In Incidence, Underscoring The Need For Valid Biomarkers For Proper Diagnosis And Therapeutic Management. Objective: The Present Case-Control Study Was Planned To Evaluate The Diagnostic And Prognostic Value Of Serum Mirna-21 Expression And The Correlation With The CTLA-4 (-318C/T, Rs5742909) Gene Polymorphism In The Population With UC. Methods: One Hundred Eighty Were Recruited, Including 120 Patients With UC And 60 Ostensibly Healthy Controls. Serum Levels Of CTLA-4 And Perinuclear Anti-Neutrophil Cytoplasmic Antibodies (Panca) Were Analysed By Enzyme-Linked Immunosorbent Assay (ELISA), And Mirna21 Expression Was Analysed By Real-Time Quantitative Polymerase Chain Reaction (RT-Qpcr). CTLA-4 Genotyping Was Done Using The Tetra-Amplification Refractory Mutation System Polymerase Chain Reaction (T-ARMS-PCR). Results: The Results Indicated A Significantly Higher Serum Mirna-21 Concentration In UC Patients Compared To Controls (8.59 ± 2.45 Vs 1.02 ± 0.21; P = 0.0001). Receiver Operating Characteristic (ROC) Curve Analysis Showed Excellent Diagnostic Performance For Mirna-21, With 100% Sensitivity And Specificity. Moreover, Mirna-21 Levels Increased With Disease Severity, With The Highest Concentrations In Severe Cases, And Were Significantly Elevated In Patients With Aggressive Disease Compared With Those With Inactive Disease. Higher Mirna-21 Levels Were Also Observed In Panca-Positive Than In Panca-Negative Patients (P=0.0001), Suggesting A Possible Association With Immune Activity. In Contrast, No Significant Correlation Between Serum Mirna-21 Expression And CTLA-4 Genotypes (CC, CT, TT) Was Found. In Addition, The CTLA-4 (-318C/T) Polymorphism Was Not Associated With UC Susceptibility Under Any Genetic Model (Codominant, Dominant, Or Allelic). Conclusion: Serum Mirna-21 Is A Highly Specific And Sensitive Diagnostic Biomarker Of Ulcerative Colitis, Reflecting Disease Severity And Activity. However, Mirna-21 Expression Seems To Be Independent Of Genetic Variation At The CTLA-4 Rs5742909 Locus.
Ulcerative Colitis, -318 C/T CTLA-4 Rs5742909, Immune Checkpoints, Mirna-21 Disease Activity
THE USE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZING FITNESS PLANS AND PROVIDING REAL-TIME FEEDBACK IN PHYSICAL EDUCATION SETTINGS
The field of physical education is getting changed dramatically by Artificial Intelligence (AI). We make customized fitness and training plans with the help of AI. Using advanced tech like machine learning and neural networks AI, Artificial Intelligence can offer other competitive plans that further enhance human made training and fitness plans and which evolve according to user adaptations. Fitness equipment or similar hardware with AI can measure your physical activity levels, provide feedback in real-time and adjust the workout regime to improve performance and avoid injury. As an example, AI apps can see what you do in your exercise and correct you instantaneously. Robots powered by AI offer an enhanced user experience by creating personalized routines and selecting the next task keeping the performance and difficulty level in mind as per info coming from outer sensors. AI programs can check your movements in a workout and give you feedback instantly. With AI, equipment routines would develop and select the next task keeping in mind the fitness and difficulty rate.
Artificial Intelligence, Personalized Training, Real-Time Feedback, Physical Education, Companion Robots
AI-BASED SMARTHEAL SYSTEM: A VOICE AND VISION ENABLED FIRST-AID ASSISTANT FOR REAL-TIME INJURY DETECTION
SmartHeal is a system implemented in MATLAB and designed to detect, identify, and analyze wounds in real time, in low-resource and emergency setting, with the optimization to optimality of the reporting. The system detects the type of wounds including abrasions, burns, lacerations, and ulcers with the help of artificial intelligence and deep learning and also determines the degree of the wound using texture, structure, color, and other features. The hybrid type of segmentation isolates the wound site to enable consistent measurements between different forms of light and equipment. SmartHeal aids sustained monitoring through neighbour epididymis through sequential image comparison to measure healing, identify infection threats, based on clinical indicators rule, including persistent redness, swelling and slowing of closure. Care guidance is also produced as a result of the system in order to guide the non-experts to act accordingly. SmartHeal is fully functional without the use of the internet and makes use of transfer learning, model optimization and MATLAB Runtime to deploy effectively. Its scalable architecture displays an economical approach to remote wound assessment, enhancing prompt responses and aid in medical decisions with less clinical knowledge.
SmartHeal, Wound Detection, Offline Healthcare, Healing Analysis, Infection Assessment, Emergency Support, Medical Accessibility
EXTRACTION, ISOLATION AND CHARACTERIZATION OF BIOACTIVE FROM BERGENIA CILIATA AND CAESALPINIA BONDUCELLA
Introduction: India is very rich in biodiversity. From the ancient times our ancestors have focused on various traditional medicinal herbs to treat various ailments. This research focuses on two important medicinal plants namely Bergenia ciliata and Caesalpinia bonducella. Various bio-actives are reported to have some medicinal effects before, like flavonoids namely 3-O-galloylcatechin and 3-O-galloylepicatechin isolated from Bergenia ciliata has shown anti Alzheimer activity. This research focuses on isolation of such bio-actives which can lead to future potential drugs. The authors strongly believe that such isolated bio actives can be a source of potent medication in the future of pharma innovation leading to good health and wellbeing of individuals. Methods: Powdered seeds of Caesalpinia bonducella and powdered rhizomes of Bergenia ciliata were first extracted separately through Soxhlet extraction which was then followed by preliminary TLC, column chromatography, UV, FT-IR, NMR and Mass to isolate and characterize bio-actives. Results: The percentage yield of Caesalpinia bonducella extract was found to be 4% and that of Bergenia ciliata was found to be 15.10%. In UV spectroscopy the wavelength of isolated fraction (E) of Bergenia ciliata extract were found to be two peaks at 297 and 414 nm and isolated fraction (e) of Caesalpinia bonducella extract was found to be one peak at 316 nm. These isolated fractions were obtained from column chromatography and were then further characterized using FT-IR, NMR and Mass. Conclusion: The authors have successfully isolated and characterized 2-(2,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one and 17-(5-Ethyl-6-methylheptan-2-yl)-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopentaphenanthren-3-ol as bio actives from Bergenia ciliata and Caesalpinia bonducella respectively.
Bergenia ciliata; Caesalpinia bonducella; Bio actives; Traditional medicines; Good health and wellbeing; Industry, innovation and infrastructure
ENVIRONMENTAL ACCOUNTING IN ETHIOPIAN MANUFACTURING INDUSTRIES: OPPORTUNITIES AND CHALLENGES FOR SUSTAINABLE DEVELOPMENT
Environmental accounting is a strategic tool for sustainable development in developing economies like Ethiopia, where manufacturing and mining pressure natural resources. This study, using data from 2025, assessed the awareness, opportunities, challenges, and governmental roles in promoting environmental accounting within the MIDROC Investment Group. A descriptive cross-sectional survey was conducted among 347 employees from manufacturing and mining units, yielding 316 valid responses (91.07% response rate). Data from a structured questionnaire were analyzed using descriptive statistics. Results showed a high awareness of environmental accounting (Mean > 3.80). Respondents strongly agreed it delivers significant benefits: cost savings (M=4.62), improved regulatory compliance (M=4.57), enhanced corporate image (M=4.59), and better risk management (M=4.40). Key challenges were managerial difficulties (M=4.17) and a lack of standardized frameworks (M=3.99). Governmental roles were deemed essential, with strong support for mandatory environmental reporting (M=4.41), financial incentives (M=4.06), and support for empirical research (M=4.30). The study concludes that environmental accounting holds high strategic value for firms like MIDROC and underscores the urgent need for stronger institutional frameworks and government mandates to support its effective implementation.
Environment, Accounting; Sustainability; Ethiopia; Manufacturing; Mining
GREEN MARKETING MIX ELEMENTS EFFECT ON THE ENVIRONMENTAL SUSTAINABILITY PERFORMANCE OF MANUFACTURING FIRMS
Environmental deterioration is generally the fault of organizations of all shapes and sizes. Manufacturing firms, on the other hand, are under pressure to implement measures to slow environmental deterioration, since they are seen as major sources of pollution. Hence, the study sought to assess the effects of the green marketing mix elements (product, price, promotion, and place) on the environmental sustainability performance of Manufacturing firms in two regions of Ethiopia. To achieve the intended purpose, information was collected from 350 respondents of these firms using a self-administered, closed-ended questionnaire. The study was conducted using the Partial Least Squares Structural Model (PLS-SEM). The study finds positive, statistically significant results for all predictive variables, with varying degrees of effect on environmental sustainability. Also, the study showed a substantial predictive relevance. Among them, green products have the strongest effect, while green place has the weakest effect on environmental sustainability. This study suggests that manufacturing firms should invest in green marketing practices, as this approach helps enhance environmental sustainability.
Green marketing mix, environmental sustainability, manufacturing firms, Partial Least Squares, Structural Equation Model
NERF FOR 3D RECONSTRUCTION USING DEEP LEARNING TECHNIQUES
3D scene reconstruction has become an essential component of applications in virtual reality, augmented reality, gaming, and digital content creation. This project presents a novel approach to 3D scene reconstruction leveraging Neural Radiance Fields (NeRF), a state-of-the-art deep learning framework capable of synthesizing photorealistic views from multiple 2D images. The proposed system takes a set of scene images along with their camera poses and trains a neural network to represent the scene as a continuous volumetric function. To enhance reconstruction quality, the approach integrates advanced image preprocessing, optimized ray sampling, and depth aware volume rendering, leading to improved spatial detail preservation and more accurate depth perception. Unlike conventional NeRF based methods that suffer from inconsistent details and reliance on synthetic data, this work focuses on real-scene reconstruction with improved fidelity and reduced artifacts. Implemented using TensorFlow and evaluated on synthetic LEGO scene data, the system effectively produces high-quality 3D reconstructions and generates novel viewpoints with realistic visual consistency. The final output offers interactive visualization and rendered video, demonstrating its potential for immersive digital content applications.
Depth-aware volume rendering, Neural Radiance Fields (NeRF), Novel view synthesis, Optimized ray sampling, Volumetric scene representation
A SITUATIONAL ANALYSIS OF FEMALE DOMESTIC WORKERS AT SINHGAD ROAD AREA, PUNE, INDIA
Demand for the house helpers / domestic helpers is on a rise in India and outside India as well. There are huge demand and supply of domestic work and workers respectively. With poor, unskilled women’s looking for employment and increasing demand for house helpers preciously in urban areas, the employment opportunities are growing in the segment of domestic work. Majority of the times the house helpers are females who have migrated due to various reasons like poverty, male unemployment, sudden death in the family and debt bondage. Thus, to support the family financially these females – by and large unskilled or semi-skilled take-up house helpers / domestic helpers’ work. This research paper targets at understanding the situation of these female house helpers / domestic helpers. For this research secondary information is collected from literature and for primary information data is collected from 50 domestic workers working at Sinhgad Road area in Pune city, India.
House helper / Domestic Helpers / Maids, living conditions of domestic workers, vulnerable conditions of domestic workers, India
NEUROTIC PERFECTIONISM AMONG UNIVERSITY STUDENTS: A COMPARATIVE STUDY ACCORDING TO GENDER AND ACADEMIC SPECIALIZATION VARIABLES
The current study aimed to reveal the variation in levels of neurotic perfectionism among university students according to their gender and academic specialization variables. The study relied on the descriptive analytical approach, as it is one of the most appropriate approaches to achieve its objectives. The study sample consisted of (295) male and female students from the Department of Psychology, Faculty of Social Sciences, including (160) males (54.2%), and (135) females (45.8%) with an average age of 22.54 and a standard deviation of 1.36. The neurotic perfectionism scale prepared by Yamani (2018) was applied to them. The results showed that the level of neurotic perfectionism among university students was generally low, and the results also showed no statistically significant differences in the level of neurotic perfectionism due to the gender variable or the specialization variable. The study also reached many recommendations, including: expanding counseling services in universities to diagnose the cases of students with neurotic perfectionism and work to find appropriate solutions for them, as well as applying tests and scales that target the neurotic perfectionism variable periodically to know the level of neurotic perfectionism among university students and provide the best ways to reduce its negative harm.
neurotic perfectionism, university students, gender, specialization, descriptive approach
CEREBROVASCULAR ACCIDENT PREDICTION USING DEEP LEARNING TECHNIQUES
Stroke or cerebrovascular accident (CVA) is a major cause of mortality and chronic disability in the entire world with an early and precise detection being a key to saving lives of patients. The provided work offers a medical image classification system based on deep learning to predict stroke in real-time with the help of convolutional neural networks (CNNs). The proposed model is trained on the medical images of the brain classified into normal and stroke and the images are resized to 256 x256 and normalized to learn the features with strength. The CNN model is made up of successive convolution, max-pooling, flattening and sigmoid-activated dense layers that are optimised through Adam optimiser with binary cross entropy loss. A batch size of 32 and 10 training epochs with an 80:20 training -validation split are used. To achieve the real-time clinical usability, the trained model is launched with the help of the web-based application with Streamlit, which facilitates the interactive upload of medical images, stroke prediction based on probabilities, choosing specific decision thresholds, and visualizing the confidence levels with bar charts. The created framework shows that it is possible to incorporate the stroke prediction system based on deep learning into real-time medical decision support systems and improve diagnostic support.
Cerebrovascular Accident, Deep Learning, Convolutional Neural Network, Medical Image Classification, Stroke Prediction
THE ROLE OF CONTINUOUS PROFESSIONAL DEVELOPMENT IN THE ETHIOPIAN BANKING SECTOR
This study investigates the significance of Continuous Professional Development (CPD) within the Ethiopian banking sector, focusing on its impact on employee performance, customer satisfaction, and overall organizational effectiveness. In a rapidly evolving financial landscape, the necessity for ongoing education and skills enhancement among banking professionals has become paramount. This research aims to provide a comprehensive evaluation of how CPD initiatives influence the performance of 17 selected banks across Ethiopia. Utilizing a mixed-methods approach, this study combined quantitative data gathered from distributed questionnaires and qualitative insights obtained through interviews with banking professionals. The questionnaires aimed to assess participants' perceptions of CPD programs, their frequency, and the perceived benefits regarding individual career advancement and organizational success. Additionally, interviews with bank managers and employees offered deeper insights into the implementation and relevance of CPD in addressing current challenges faced by the banking sector. Preliminary findings indicate a strong correlation between effective CPD programs and enhanced employee competence, which in turn leads to improved customer service and increased operational efficiency. Employees who engage in consistent professional development report higher job satisfaction and a greater sense of preparedness to meet the evolving demands of their roles. Moreover, banks that actively invest in CPD initiatives appear to experience lower employee turnover rates and higher levels of customer loyalty. Despite these positive outcomes, the study also identified several challenges that hinder the full realization of CPD benefits. Key issues include limited resources allocated for professional development, varying levels of commitment to ongoing training among bank management, and insufficient alignment of CPD programs with the specific needs of the workforce. Many employees expressed concerns about the lack of structured CPD pathways and the need for more tailored training programs that address both industry standards and individual career aspirations. The research reveals a significant gap in the literature regarding the specific impact of CPD on the Ethiopian banking sector, particularly concerning how these initiatives can be optimized to enhance both employee and organizational outcomes. While previous studies have focused on the importance of professional development in various sectors, there is a lack of targeted research that examines its role within the unique context of Ethiopian banking. To address these gaps, the study recommends a more structured and strategic approach to CPD within the banking sector. This includes developing tailored CPD programs that cater to the diverse needs of employees, enhancing collaboration between banks and educational institutions, and establishing robust metrics to evaluate the effectiveness of CPD initiatives. By fostering a culture of continuous learning and improvement, Ethiopian banks can not only enhance employee performance but also improve customer satisfaction and organizational resilience in a competitive market. This research underscores the critical importance of CPD in shaping a capable and adaptable workforce in the Ethiopian banking sector. As the industry continues to face challenges and opportunities, investing in continuous professional development will be essential for sustaining growth and achieving long-term success.
Continuous Professional Development, Ethiopian Banking Sector, Employee Performance, Customer Satisfaction, Organizational Effectiveness
A REVIEW OF THE IMPACT OF SOCIAL MEDIA MARKETING ON ONLINE PURCHASE BEHAVIOUR OF FMCG AND PERSONAL CARE PRODUCTS
Purpose: This article examines how digital platforms effect online fast-moving consumer goods and personal care purchases. It examines how e-commerce, influencer marketing, social media marketing, and platform-based advertising affect customers' buying habits and probability to purchase again. Methods: Reviews and synthesis of literature from diverse contexts are used in the study. Previous study has examined digital marketing, trust, convenience, mobile commerce, quick-commerce, omnichannel tactics, and post-COVID consumer movements. The evaluation also considers Krishna and Guntur district data from Andhra Pradesh to identify local research requirements. Result: According to the study, social media postings, platform-specific marketing, influencer promotions, and e-commerce features affect customers' online purchase behaviors for fast-moving consumer goods and personal care products. Consumers' cognitive and emotional responses, trust, ease, and technology usage affect buy intention and recurring purchasing. Omnichannel retailing, mobile commerce, and quick-commerce are also boosting online buying, according to the poll. The study also states that district-level and platform-specific empirical research in Krishna and Guntur, two semi-urban regions, is inadequate. Conclusions: Digital platforms strongly influence FMCG and personal care purchases, according to the study. Social media, convenience of e-commerce, influencers, and technology affect online product research, appraisal, and purchase. The review uses frameworks like the Stimulus-Organism-Response model to explain this link and emphasizes the need for further empirical research on regional consumer behavior, particularly in semi-urban and emerging markets.
Social Media Marketing; FMCG; Personal Care Products; Online Purchase Behaviour; Facebook Advertising
AI CREATORS HUB: ARTIFICIAL INTELLIGENCE CONTENT CREATION PLATFORM
We are introducing a new social media app, focused on AI and especially made for Generation-Z. It has a lot of cool and interactive features all in one place. Unlike typical platforms where you yourself create and share everything, this app uses generative AI technology, somewhat like that provided by OpenAI, to create videos, music, web series, stories, and custom digital posts based on users interests, trends, and real- time suggestions. If you dont want to be on camera, you can create personalized AI avatars for short films and videos, where your creativity can be showcased without you necessarily being in the limelight. Safety is another big concern. The app has a smart security system which analyzes and blocks any 18 or other inappropriate content. It relies on AI to analyze and filter out offensive comments, which also supports making the space safe. It deals with harmful language, bullying, explicit content, and abuse in one go to make everyone comfortable with expressing themselves. It offers a Gen Z-friendly interface with an effortless and interactive social environment driven by AI. Through a combination of content creation, avatar features, multimedia production, security measures, and community interaction, the project has high potential to shake the ground within the social media world and establish a unique space for AI-generated content.
AI Generated Content, Generative AI Social Media, AI Video Generation, Virtual AI Avatars, Personalized Content Generation, Real-Time AI Recommendations