South Eastern European Journal of Public Health (SEEJPH - Universität Bielefeld)
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"Stress Levels Among Hypertensive Employees in a Corporate Company in Qatar: Influence of Demographic, Occupational, and Health Factors"
Full text linkBackground: Stress is a significant contributing factor to hypertension, particularly in occupational settings. This study assesses stress levels among hypertensive employees in a corporate company in Qatar and examines the influence of various demographic and occupational factors.Methods: A cross-sectional study was conducted among 347 hypertensive male employees from Crompton International Trading & Contracting WLL Company. Data on stress levels were collected using the Perceived Stress Scale (PSS-10), while demographic factors such as age, educational status, employment type, marital status, and years of residence in Qatar were analyzed. Statistical tests, including ANOVA, t-tests, and chi-square analysis, were used to determine significant associations.Results: The overall mean stress score of participants was 19.35 (SD = 6.39). No statistically significant differences were found in stress levels based on age (p = 0.704), educational status (p = 0.324), employment type (p = 0.497), marital status (p = 0.077), or years of residence in Qatar (p = 0.679). However, chi-square analysis revealed that employment type and marital status were significantly associated with stress levels (p < 0.05), indicating their potential impact on hypertensive employees\u27 stress.Conclusion: While most demographic variables did not significantly affect stress levels, employment type and marital status emerged as key factors influencing stress among hypertensive employees. These findings suggest the need for targeted stress management interventions in corporate workplaces, particularly addressing occupational stressors and social support systems for married and working individuals
Efficient Energy Management System Using IoT
Full text linkEnergy conservation is a crucial aspect in both residential and industrial sectors. The rising energy demand necessitates smart monitoring and optimization of power consumption. This paper presents the development of an IoT-based energy management system utilizing an ESP32 (Node MCU) microcontroller, current and voltage sensors, and an I2C display module. The system efficiently calculates and monitors electrical parameters such as voltage, current, power, and watts of various household and industrial appliances like fans, light bulbs, and mixer grinders. The collected real-time data is displayed on an I2C LCD module and remotely monitored via the Blynk IoT platform on mobile and system interfaces. The primary goal of this project is to identify high- energy-consuming appliances and optimize their usage for better energy efficiency. The system provides real-time insights to help users take appropriate measures to reduce unnecessary energy consumption and promote cost-effective power utilization
The Effect of TiO2 Loading on Nanocellulose (NC)/TiO2 Solid Composite and pH on Paracetamol Photodegradation
Full text linkThese days, organic contaminants from the pharmaceutical industry are a constant global issue that pollute water supplies. This study examined the effects of pH on the photodegradation of paracetamol and TiO2 loading on the synthesis of NC/TiO2 solid composites (NCT) as a photocatalyst. Low concentration of acid is used on hydrolysis to extract nanocellulose from empty palm oil bunches (EPOB), and SEM analysis shows that the impregnation approach produced NCT effectively. The optimal conditions for photodegradation of waste containing 15 ppm paracetamol with a concentration reduction of up to 60% for 4 hours are provided by NCT with TiO2 loading of 70% on nanocellulose (NCT 30-70) as much as 1 g/L at pH 6. Reusing photocatalyst demonstrates a 13% reduction in photocatalyst ability up to 4 cycles
AI-Powered Suspicious Activity Monitoring and Detection System Using CNN Architecture
Full text linkThe rapid advancement of artificial intelligence (AI) and deep learning has revolutionized security surveillance systems by enabling real-time detection and monitoring of suspicious activities. This study presents an AI-powered Suspicious Activity Monitoring and Detection System leveraging a Convolutional Neural Network (CNN) architecture for enhanced accuracy and reliability. The proposed system utilizes a hybrid deep learning approach, integrating CNNs with feature extraction techniques to classify and detect anomalous behaviors in real-time surveillance footage. A large-scale dataset comprising diverse human activities, including normal and suspicious actions, was utilized for model training and validation. The CNN model was optimized using transfer learning and hyperparameter tuning, achieving a detection accuracy of 98.3% on benchmark datasets. The system is further integrated with an edge computing framework, ensuring real-time processing and reduced latency in security-critical environments. Performance evaluation metrics such as precision, recall, F1-score, and inference time validate the model’s effectiveness against existing state-of-the-art methods. The results demonstrate that the proposed CNN-based system significantly enhances the efficiency of automated surveillance by reducing false positives and improving threat response mechanisms. This study contributes to the development of robust AI-driven security solutions, fostering safer public and private spaces through intelligent video analytics
The Relationship Between General Attitudes of Sports Managers in Fitness Centers Towards Artificial Intelligence and Their Intrinsic Motivation: The Case of the Marmara Region
Full text linkThis study aims to examine the relationship between the general attitudes of sports managers in fitness centers towards artificial intelligence and their intrinsic motivation. The research was conducted using the relational survey model. A total of 394 participants, including 133 women and 264 men, were voluntarily selected using the convenience sampling method among sports managers working in fitness centers in the Marmara Region.The data for the study were collected using the "General Attitude Towards Artificial Intelligence Scale", developed by Schepman and Rodway (2020) and adapted into Turkish by Kaya et al. (2022), the "Intrinsic Motivation Scale for Employees", developed by Bardak and Nihal (2023), and a "Personal Information Form".For data analysis, t-tests, ANOVA, and Pearson correlation analysis techniques were used. The findings revealed that attitudes towards artificial intelligence showed significant differences based on gender, marital status, age, education level, and income level, whereas intrinsic motivation only showed a significant difference in relation to age. No significant differences were found in intrinsic motivation concerning other demographic variables.Additionally, a low-level negative significant relationship was identified between attitudes towards artificial intelligence and intrinsic motivation. As a result, it can be stated that intrinsic motivation negatively influences attitudes towards artificial intelligence, albeit at a low level
Girish Kasaravalli’s Three Women : Reflecting Women’s Suffering, Redefining Cultural Spaces
Full text linkDrawing inspiration from Italian neo-realism and the Navya literary movement, Filmmaker Girish Kasaravalli has made remarkable contribution to the Indian New Wave Cinema, both in thematic concerns and aesthetic sensibility. Girish’s work bear ample testimony to his indomitable spirit of experimentation with the film form and content. Further, Girish’s works have women-centric narratives that expose the negative shades of patriarchal system. This paper will explore the characterisation of three of his women protagonists from films - Thayi Saheba, Hasina and Gulabi Talkies, and their struggles against patriarchal order
DESIGN AND CHARATERIZATION OF VAGINAL MUCHOADHESIVE FILMS CONTAINING VALACYCLOVIR HYDROCHLORIDE: A FACTORIAL STUDY
Full text linkDesigning and characterizing the mucoadhesive vaginal film of valacyclovir hydrochloride is the primary goal of this work. 23 factorial designs are used in the formulation of the vaginal film. PEG 400, 2-Mercaptocthanol, and Ethyl Cellulose concentration were selected as independent factors, and thickness, tensile strength, and dissolution were selected as dependent variables. The improved batch was tested using FTIR and DSC, and its thickness, tensile strength, folding endurance, surface PH, percentage of drug release, and swelling index were further assessed. The F-OPT formulation and the pure drug\u27s FTIR and DSC data show that the drug and polymer are compatible. The F-OPT formulation, out of all the formulations created, has remarkable results, with the highest percentage of drug release (96.92%) in 12 hours and the lowest swelling index (45.62%). Studies on the F-OPT formulation\u27s short-term stability showed that it was stable. Therefore, valacyclovir HCL mucoadhesive vaginal films may be an appropriate formulation for treating genital herpes with a practical administration method
A COMPREHENSIVE ANALYSIS OF MODERN FINANCIAL MARKETS TRENDS RISK AND OPPORTUNITY IN THE GLOBAL ECONOMY
Full text linkThis research explores the basic questions that stand behind the evolution of financial markets, like the emergence of digital assets, the growth of sustainable investing, and the ongoing fintech revolution. It explains how these trends reshape risk and opportunity in the global economy by putting accent on the messages they send to markets and investors. Contemporary finance is a complex socio-technical environment generated by global processes, technological change and regulatory shifts. The current global instabilities, such as slow economic growth and geopolitical risks, require an analysis of market trends by investors and policymakers. Potential trends are derived from cross-sectional global financial data, composite market indices and select economic indicators. While risks and opportunities are determined from Constellation by using the scenario approach and risk management models. New trends that are applicable, sustainable and growth models like blockchain adoption, ESG investment and cryptocurrency market fluctuation have been analyzed. A survey of institutional investors gives further perspective to these data in the international environment. This analysis draws upon the positive affordances of current modern financial market trends combined with acknowledging a high level of potential negative effects, including heightened market volatility and regulatory complexities. The mostly country in the world has plenty of untapped potential when it comes to innovation and its economy, which easily realized in the field of green finance as well as the increased use of fintech solutions. The paper concludes with a set of best practices for the stakeholders on how to foster the increased efficiency of financial operations and effectively manage the risks and opportunities arising from the growing economic interdependence
Hybrid Machine Learning and Deep Learning Approach for Heart Attack Prediction Using Clinical, Lifestyle, and Time-Series Data with Enhanced Feature Selection and Classification
Full text linkCardiovascular diseases (CVDs), especially heart attacks, remain a leading cause of death globally. Early prediction and intervention are crucial to improving survival rates. This research proposes a hybrid machine learning and deep learning framework for heart attack prediction, utilizing real-time data collected through Internet of Things (IoT) sensors. The framework integrates Random Forest and Long Short-Term Memory (LSTM) models to analyze both structured health data (age, blood pressure, cholesterol) and time-series ECG signals. The hybrid approach enhances predictive accuracy by combining the strengths of ensemble learning and deep learning. Furthermore, privacy concerns are addressed through federated learning and secure data transmission. The model outperforms traditional methods, achieving high accuracy, recall, precision, and AUC, demonstrating its potential for real-time heart attack detection. This system offers a scalable, secure, and interpretable solution for cardiovascular disease prediction in diverse healthcare settings, ultimately contributing to better patient outcomes