Volume 13 | Issue 5 |

 Abstract

Mariama Bâ, one of the prolific women novelists of the African Literature, emerges as a harbinger and torchbearer for women’s identity by fighting against social injustices, particularly those faced by women in the predominantly Muslim and male-dominated society of Senegal. Focused on female emancipation, Bâ challenges entrenched patriarchal values and urges women to seize control of their destinies. In Mariama Bâ’s two novels, So Long a Letter and Scarlet Song. Bâ’s focus on the ills of polygamy is analysed. Her novels highlight the progression of the African educated women. So Long a Letter attempts to examine how an educated, middle-aged and abandoned wife achieves self-realisation and emerges as a confident woman fully in control of herself. It also shows how women are able to accept life on their own terms and live independent lives. This paper explores Mariama Bâ's mission to use narrativization as a weapon for gender empowerment, shedding light on the multifaceted challenges faced by women in post-independence Senegal and emphasizing the transformative journey towards gender equality.

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 Keywords

Narrativization, Female emancipation, patriarchal challenges, societal values, gender dynamics, transformative journey.

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: In the modern era, fire hazards pose significant threats to human lives and infrastructure. Traditional firefighting methods often expose individuals to dangerous conditions, necessitating intelligent automation to enhance safety and efficiency. RoboRescue is an innovative robotic system designed to detect and extinguish fires using Arduino-based control mechanisms. Leveraging advanced sensors such as flame detectors, temperature sensors, and gas sensors, RoboRescue autonomously identifies fire outbreaks and assesses environmental risks. The system incorporates real-time processing and decision-making algorithms, enabling swift responses and precision in firefighting operations. Equipped with mobility features, the robot can navigate hazardous environments, avoiding obstacles while delivering targeted fire suppression using water or fire-retardant chemicals. The integration of IoT capabilities ensures remote monitoring and control, allowing firefighters to manage operations from a safe distance. This research highlights the potential of intelligent robotics in revolutionizing fire prevention and mitigation strategies, reducing human intervention in life-threatening scenarios, and paving the way for safer and more effective emergency response systems.

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 Keywords

firehazard,IoT,flame detectors,temperture sensor

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Inventory control and management at Automobile industry play a crucial role in ensuring efficient supply chain operations, cost optimization, and seamless production processes. The company employs advanced inventory management techniques such as EOQ, ABC analysis and VED analysis automated tracking systems, and data analytics to maintain optimal stock levels while minimizing holding costs and reducing wastage. By integrating modern ERP solutions and real-time monitoring, it ensures accurate demand forecasting, timely procurement, and effective warehouse management. These practices not only help in avoiding stockouts and overstock situations but also enhance the overall efficiency of production and distribution processes.

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Inventory management, Tracking, decision making.

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ABSTRACT The field of public administration has seen a dramatic transition from the traditional Weberian model, which focused on Western philosophies, to the neoliberal model, which is based on a universal theory supported by the World Bank. Yet the field is particularly context-driven and dependent on the society in which it is practiced, and none of these models are good for it. The subject was first taught in India as a sub-discipline of political science and was available in only a few institutions before the country's independence. But after independence it was Appleby who established it as a separate course of study in a number of universities. The discipline gained a lot of attention when it was added as a compulsory item in the Indian Civil Services UPSC syllabus. However, the limited scope of the study and excessive concentration on technical details prevented it from attracting the best minds. An examination of the research inquiry in this area reveals a fundamental weakness: it is a goal-oriented activity that ignores the socio-economic and political environment in which it is situated. In order to combat administrative corruption and preserve the spirit of ethics in administration, the recommendations of the Administrative Reforms Commission resulted in the establishment of several organizations and the adoption of certain legislation. Maintaining public administration's methodological diversity, context-specific approach and public character is therefore essential to the survival of the field as a practice in the face of an ever-changing global environment.

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Public Administration, Western methodology, Political Science, Governance, Re-conceptualization, Ethics, Neo-liberal perspective.

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: Oral cancer remains a critical health issue globally, with more than 360,000 new cases diagnosed annually. Despite advances in medical treatments, survival rates are significantly hampered by late-stage diagnoses. Early detection is paramount, as it can greatly enhance patient survival and reduce treatment costs. This study introduces an innovative framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the detection of oral cancer using multimodal data fusion. CNNs are employed to extract spatial features from clinical images, while LSTMs analyze the temporal dependencies present in medical imaging data, particularly in the case of longitudinal scans or patient records over time. By fusing spatial and temporal information, the model can detect early-stage lesions, subtle changes, and abnormalities often overlooked by traditional diagnostic methods. To assess the effectiveness of this framework, we conducted extensive experiments on a diverse oral cancer dataset, demonstrating the model's exceptional sensitivity, specificity, and area under the curve (AUC). These results emphasize the robustness and generalization capability of the model across various patient demographics and imaging conditions. The proposed deep learning model offers significant promise for clinical implementation, providing healthcare professionals with a powerful tool for early screening, diagnosis, and improving patient care outcomes. The successful application of CNNs and LSTMs in oral cancer detection underscores the transformative role of deep ABSTRACT: Oral cancer remains a critical health issue globally, with more than 360,000 new cases diagnosed annually. Despite advances in medical treatments, survival rates are significantly hampered by late-stage diagnoses. Early detection is paramount, as it can greatly enhance patient survival and reduce treatment costs. This study introduces an innovative framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the detection of oral cancer using multimodal data fusion. CNNs are employed to extract spatial features from clinical images, while LSTMs analyze the temporal dependencies present in medical imaging data, particularly in the case of longitudinal scans or patient records over time. By fusing spatial and temporal information, the model can detect early-stage lesions, subtle changes, and abnormalities often overlooked by traditional diagnostic methods. To assess the effectiveness of this framework, we conducted extensive experiments on a diverse oral cancer dataset, demonstrating the model's exceptional sensitivity, specificity, and area under the curve (AUC). These results emphasize the robustness and generalization capability of the model across various patient demographics and imaging conditions. The proposed deep learning model offers significant promise for clinical implementation, providing healthcare professionals with a powerful tool for early screening, diagnosis, and improving patient care outcomes. The successful application of CNNs and LSTMs in oral cancer detection underscores the transformative role of deep learning in advancing medical image analysis and healthcare diagnostics. Keywords: Oral Cancer Detection, CNN, LSTM, Multimodal Data Fusion, Deep Learning, Medical Imaging, Early Diagnosis.

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Oral Cancer Detection, CNN, LSTM, Multimodal Data Fusion, Deep Learning, Medical Imaging, Early Diagnosis.

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Employee engagement plays a crucial role in determining the success and effectiveness of organizations, especially in dynamic and labor-intensive industries like construction. Successful organizations know that employee performance and employee engagement are crucial. This study explores the impact of employee engagement on organizational effectiveness within construction companies. Employee engagement is basically a level of commitment and involvement that an employees has towards their organization and its values. It examines key engagement factors such as job satisfaction, motivation, leadership, workplace culture, and communication, assessing their influence on productivity, project performance, and employee retention. The research highlights the correlation between highly engaged employees and improved efficiency. Using qualitative and quantitative analysis, the study provides insights into best practices for fostering engagement in the construction sector. The findings suggest that investing in employee engagement strategies leads to enhanced organizational performance, reduced turnover, and increased profitability. The study concludes with recommendations for construction firms to improve engagement levels, thereby driving overall effectiveness and sustainable growth.

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Employee engagement, job satisfaction, organizational performance,

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 Abstract

Kabir Das Ka Shaikshik darshan, Shiksha mein yogdan aur uski vartman prasangikta

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Kabir Das Ka Shaikshik darshan, Shiksha mein yogdan aur uski vartman prasangikta

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This research explores the formulation and development of a herbal energy drink utilizing Moringa (Moringa oleifera) and Ginger (Zingiber officinale) as key ingredients. Moringa is known for its rich nutritional profile, including proteins, vitamins, and antioxidants, while Ginger is valued for its bioactive compounds that enhance metabolism and provide energy. The study focuses on optimizing the formulation for taste, stability, and bioactive retention while evaluating its functional and energy-boosting properties. The formulation process involves optimizing the ratio of Moringa and Ginger extracts, selecting natural sweeteners, and evaluating physicochemical stability. The drink's nutritional profile, sensory acceptability, and functional properties were analyzed through laboratory testing and consumer evaluation. Results indicate that the herbal energy drink is a viable alternative to commercial energy drinks, offering enhanced metabolism, antioxidant benefits, and improved overall wellness. This study highlights the potential of herbal-based functional beverages in promoting a healthier lifestyle.

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Formulation, Development, Nutritional Analysis, Antioxidant Properties, Phytochemicals, Functional Beverages , Caffeine-free Energy

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This study examines the impact of health, safety, and welfare measures on employee productivity. The research investigates the relationship between employee well-being and productivity, highlighting the importance of a supportive work environment. The findings reveal that effective health, safety, and welfare initiatives significantly enhance employee job satisfaction, engagement, and productivity, ultimately benefiting the organization. In sample size for this study is based on margons sapling table which specifies the sample size is 181 when the population is 350. so, the optimal sample size is 181 employees at various level. The study's results provide valuable insights for organizations seeking to improve employee well-being and productivity. By prioritizing health, safety, and welfare measures, companies can create a positive work environment that fosters employee engagement, motivation, and productivity, driving business growth competitiveness

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Key words: (Health Measures in the Workplace, Safety Measures to Prevent Workplace Hazards, Welfare Measures for Holistic Well-Being, Well-being as a Catalyst for Productivity)

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This paper presents the design and implementation of an AC motor speed control system using a Variable Frequency Drive (VFD). The system utilizes an Arduino microcontroller to adjust the motor speed by controlling the frequency and duty cycle of the AC supply. The custom-designed VFD modulates both the frequency and the voltage, enabling accurate control over motor speed. The speed of the motor is directly related to the PWM duty cycle, where the motor's rotational speed increases with the increase in duty cycle. A resistive touchscreen and Bluetooth communication module (HC-05) are used for user interaction, providing both local and remote control options. Additionally, a potentiometer is used for manual speed adjustment. The system is designed for optimal motor performance, ensuring low power consumption and reduced mechanical wear. Experimental results show that the motor speed can be precisely controlled by adjusting the duty cycle of the PWM, making the system suitable for various industrial and automation applications.

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 Keywords

AC Motor, Variable Frequency Drive (VFD), Pulse Width Modulation (PWM), Duty Cycle, Arduino, Speed Control, Touchscreen Interface, Bluetooth Communication, Potentiometer, Motor Control System

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