Volume 11 | Issue 9 |

 Abstract

This study focuses on the challenges posed by the increasing prevalence of senility or age-related cognitive decline among the elderly population in Bangladesh. The objectives of the study include identifying age-related physiological changes in various systems such as cardiovascular, central nervous, and musculoskeletal systems among the elderly. The research also aims to recognize common health problems associated with aging and to explore strategies for preventing or reducing risk factors contributing to functional decline, impaired quality of life, and disability in older adults. The research questions guiding this study inquire about the physiological changes occurring in various systems among the elderly, the roles and functions of nurses in geriatric care, and the need for further research to improve the care provided to the elderly population. This study contributes to the understanding of the unique challenges faced by elderly individuals dealing with senility in the Bangladeshi context and seeks to inform nursing strategies, interventions, and frameworks that can enhance the quality of life and healthcare outcomes for this population. The analysis delved into demographic features, health conditions, caregiving, and relationships of the elderly in the study. These insights shed light on aging, health challenges, and social dynamics. The age distribution skewed towards 60-70, especially 60-65, indicating a focus on younger elderly. A logistic regression model predicts dementia well. Graphs highlight factors limiting elderly activities, family relationship trends, prevalent health conditions, and demographic summaries. Key findings include the need for broader age inclusion, economic support, nurturing relationships, better healthcare, addressing lifestyle issues, and focusing on mental health. These findings hold implications for policies, healthcare planning, and elderly well-being. Recommendations involve diversified age representation, economic empowerment, support for relationships, enhanced healthcare, awareness campaigns, and ongoing research.

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 Keywords

Senility, Old Age, Dementia, Nursing, Demographic

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Herein, we report the biological activity of novel 1-cyclopropyl-6-fluoro-8-methoxy-7-susbstituted-4-oxo-1,4-dihydroquinolin-3-carboxylic acids. Almost all the series of compounds 3a-i showed moderate to excellent activity towards the bacteria and fungi under investigation. Some of them, particularly 3h and 3i can be exploited for formulation of bactericide and fungicide after detailed study by disc method and agar disc diffusion method. Chloroamphenicol and Fluconazole are used as standard positive control.

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antifungal and antibacterial activity, disc method, agar disc diffusion method, hydroquinolin.

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One of the main reasons for cancer-related fatalities worldwide is lung cancer. Patient survival rates and treatment outcomes are greatly enhanced by early identification. The development of computer-based systems for precise and effective lung cancer detection has advanced significantly thanks to developments in artificial intelligence (AI) and deep learning techniques. This research suggests a deep learning-based AI method for the early diagnosis of lung cancer.Convolutional neural network (CNN) architecture is used by the system to automatically analyse medical images, such as computed tomography (CT) scans or chest X-rays, and spot potentially malignant areas. The deep learning algorithm can understand intricate patterns and features indicating malignant tumours because it was trained on a vast dataset of annotated lung cancer photos. The suggested approach includes a number of steps in the detection of lung cancer. The first step in pre-processing medical photographs is to improve their quality and remove noise. The CNN-based model then carries out feature extraction and classification to find questionable areas in the images. The results are then refined in a post-processing stage to produce a final prediction. Extensive tests are run on a wide range of lung cancer imaging datasets to determine the viability of the proposed approach. To evaluate the model's diagnostic accuracy, performance metrics like sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC) are generated. The outcomes show the promising potential of deep learning-based AI-based lung cancer diagnosis. The suggested method successfully detects lung cancer with high accuracy and sensitivity, assisting radiologists and clinicians in early identification and decision-making. Additionally, the system's automated nature makes efficient and reliable analysis possible, potentially lowering the workload placed on medical practitioners. Deep learning methods are used in an AI-based method for finding lung cancer. The suggested system exhibits the capacity to enhance early diagnosis and support improved patient outcomes.The proposed framework can be refined and incorporated into clinical practise as AI and deep learning technology continues to evolve, opening the door for more effective lung cancer detection and treatment methods.

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 Keywords

Artificial Intelligence, Deep Learning, Deep Convolutional Neural Network, Lung Cancer Detection,

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Background: Preterm babies are often subject to many complications in the first few weeks of life due to immature body system. Objectives: The purpose of the study is to assess the pre and post test level of physiological parameters among preterm infants and to evaluate the effectiveness of KMC on level of physiological parameters among preterm infants. Moreover to correlate between the physiological parameters among preterm infants after KMC. Methods: It was a Quantitative research approach one group pre and post test design conducted among preterm babies in NICU selected by using convenience sampling technique. Results: Kangaroo Mother Care is not significantly improves pulse (F = 6.13, P <= 0.01) and temperature (F = 7.46, P <= 0.01) of preterm babies. The respiration (F = 2.90, P > 0.05) and oxygen saturation (F = 1.97, P > 0.05) has no significant changes. There is a significant improvement in the weight gain (F = 5.69, P <= 0.01) of the preterm. The KMC intervention was continued in the preterm babies even after discharge, so can able to see the difference in physiological parameters in preterm's during the follow up care. Conclusion: There is a significant difference in the physiological parameters in preterm babies with the KMC intervention. KMC has emerged as a non-conventional low cost method for newborn care that provides warmth, touch, and security to the newborn and is believed to coner significant survival benefit. .KMC is one of the most effective interventions for preventing mortality of LBW infants.

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Kangaroo Mother Care, Physiological parameter, Preterm babies, Prevalence

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The stability of a transient IEEE 9 bus system with regard to voltage maintenance, generators, and the incorporation of distributed energy resources (DER) is examined in this research. The term "transient stability" describes a power system capacity to maintain synchronization in the face of difficult circumstances such a sudden loss of generation or substantial load swings. It is difficult to incorporate renewable energy sources (RES) within the constrained transmission line capacity, which has an impact on generator capacity. The reduction of system inertia caused by RES integration may have an effect on transitory situations. In order to solve this, we built a wind turbine generator (WTG) inside the point of connection (PoC) and tested the stability of transients by adding faults to the bus.

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DERs (Distributed Energy Resources), wind turbines, Synchronous Generator (SG), the Doubly-Fed Induction Generator (DFIG), Squirrel-Cage Induction Generator (SCIG), Power System Stabilizer (PSS) controller

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The article explores the thrilling world of Goria folk songs, highlighting their importance to culture and lyrical depth. It examines the lyrical themes, historical settings, and ethnomusicological aspects of these songs using a multidisciplinary approach. The article highlights the significance of Goria folk songs in maintaining Jamatia culture and identity.

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Mystic Reverberations: Goria Folksongs of the Jamatia Tribe.

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The use of Artificial Intelligence (AI) in the healthcare industry has been increasing at a very rapid pace in the last few years. In India, there has been a growing interest in the adoption of AI tools and techniques in hospitals to improve patient care and outcomes. This paper presents a literature review on the adoption of AI tools and techniques in Indian hospitals from 2013 to 2023. The review uses bibliometric analysis to identify the trends, patterns, and gaps in the existing research. The Scopus database was used to search for the relevant articles. The findings specify that there has been a noteworthy surge in the amount of publications on AI in Indian hospitals over the past decade, with the majority of the research focusing on the development and application of AI tools and techniques in specific areas of healthcare, such as diagnosis, treatment, and monitoring. However, the adoption of AI in Indian hospitals is still in its early stages, with many challenges and barriers to overcome, such as regulatory issues, data privacy and security concerns, and limited funding and resources. The paper concludes with an argument of the suggestions of the findings for future research and practice in this area. The results of the analysis indicate a growing interest in AI in Indian hospitals, with an increasing number of publications on the topic in recent years. The most commonly researched areas include medical imaging, diagnostic decision-making, and predictive analytics. However, there is still a need for further research to investigate the potential influence of AI on patient outcomes and healthcare costs. Overall, this review highlights the necessity for continued investigation in this area to fully realize the potential of AI in Indian healthcare system..

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artificial intelligence, machine learning, healthcare, Indian hospitals, medical

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 Abstract

DISEASE DETECTION ON MANGO LEAVES USING MACHINE LEARNING ALGORITHMS

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DISEASE DETECTION ON MANGO LEAVES USING MACHINE LEARNING ALGORITHMS

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This study investigates how nutrition impacts the physical fitness of women, specifically focusing on mothers with different numbers of children. It explores the dietary habits of women during various stages of motherhood and examines their correlation with physical fitness levels. The research emphasizes the significance of regular physical activity and a balanced diet in promoting overall well-being and to understand how the number of children influences women's nutrition intake, particularly in terms of protein, fat, carbohydrate, and energy consumption. Additionally, it assesses the physical fitness levels of women in different motherhood categories, taking into account heart rate and the Physical Fitness Index (PFI). The findings suggest that an increase in the number of children is associated with a decline in physical fitness levels among mothers. To improve physical fitness, especially for mothers with multiple children, interventions should focus on maintain a nutritious diet and incorporate regular physical activity. The study also highlights that women tend to consume higher levels of protein and fat than recommended, and these nutritional habits vary based on the number of children. Moreover, the study shows that heart rate and PFI scores differ among women with varying experiences of motherhood. Mothers with more children generally exhibit lower physical fitness levels, likely due to the increased demands and responsibilities of motherhood, the research underscores the importance of adopting a balanced diet and engaging in regular physical activity to enhance the physical fitness of women, particularly those with multiple children. Educating and supporting women in maintaining a healthy lifestyle during motherhood can contribute to their overall well-being and that of their families.

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Physical Fitness Index (PFI), nutrition, heart rate, motherhood, physical fitness. Physical Fitness Index (PFI), nutrition, heart rate, motherhood, physical fitness.

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Generation Z (Gen Z), which includes those born between the middle of the 1990s and the middle of the 2010s, is a separate generation that has been profoundly touched by the digital age. Social justice, economic inequality, and mental health are the three social issues that Gen Z is most enthusiastic about. In attaining their specialized objectives, they give top attention to corporate social responsibility (CSR), diversity, equality, as well as environmental responsibility. To accomplish their goals of personal in addition to professional advancement, individuals require a work-life balance and a adaptable schedule. They place a great importance on by means of cutting-edge technology in addition to holding themselves then their work to high standards. According to a report, there is a strong relationship between CSR, work-life balance, technology, and CSR among Gen Z employees. Three subjects dominated the survey on CSR, work-life balance, and technology. This is achieved by the application of a solid method that includes factor analysis and also these elements impact on how to raise employee serenity as well as productivity. If governments and corporations want to employ and keep members of this workforce, they must be aware of their preferences. Businesses must adjust to Gen Z's attitudes and expectations if they wish to compete in the changing labor market in addition to provide employees with a work environment that fulfills their needs.

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Mental health, Corporate social responsibility (CSR), Diversity, Work-life balance, Flexible schedule, Employee well-being.

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