Volume 12 | Issue 7 |

 Abstract

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Abstract: Introduction: Artificial intelligence (AI) has emerged as a transformative tool in healthcare, particularly in enhancing patient safety measures. By leveraging AI algorithms and data analytics, healthcare systems can proactively detect, prevent, and mitigate clinical-level patient safety concerns. This introduction outlines the significance of AI in revolutionizing patient safety practices, promising improved outcomes and reduced adverse events. Aim and Objectives: To utilize artificial intelligence (AI) to advance patient safety within healthcare systems by implementing innovative solutions and practices.This systematic literature review had the objective to find and evaluate quantitative research that addressed and reported clinical-level patient safety issues by integrating or using AI. Summary: Artificial intelligence revolutionizes patient safety by leveraging advanced algorithms for early detection of adverse events, personalized medication management, and clinical decision support, though challenges such as data privacy and algorithm bias require ongoing attention for effective implementation. Conclusion: According to this systematic review, if utilized appropriately, AI-enabled decision support systems can help to improve patient safety by facilitating better medication management, error detection, and patient stratification. To learn further about how effectively AI can predict safety outcomes in healthcare settings, further work is still required for these systems' strong validation in hypothetical and actual clinical scenarios.

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Artificial intelligence, Patient safety, Clinical-level, Quantitative studies, Systematic literature review

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The fear of persecution, physical harm or death from the warring parties, combined with insecurity owning to conflict forced Sri Lankan Tamil to leave their homes from 1970s to 2000s to India, a closet neighbour. Principally, the civil war between Sri Lankan security forces and the Liberation Tigers of Tamil Eelam (LTTE) impelled the Sri Lankan Tamil to seek refuge in the southern Indian state of Tamil Nadu. From around three lakhs refugees at one point of time, currently more than 52,000 Tamil refugees live in camps spread all over Tamil Nadu. Although Sri Lankan Tamil refugees are entitled to liberal welfare schemes of Tamil Nadu government and work permits, prolonged life in the camps does not lead to resilience and empowerment, and these refugees consider themselves in limbo, belonging neither to Sri Lanka nor to India and unable to get on with their lives. Elder Tamil refugees eager to return - wish to be disassociated with the label of 'refugee', while younger generation feel desire to settle in India or move to abroad as it would improve quality of life for them. On the whole, there are two sustainable options available to Sri Lankan Tamil refugees in India: repatriation or local integration. Resettlement option is the preferred one but the existing divisions and resentment between the two main ethnic communities and perceived anti-Tamil prejudice of Sinhalese leaders are obstacle in repatriation or return to their home. Other options is get citizenship and settle down in India, which some refugees wish to take it up but given the absence of official mechanism will force them to live further in appalling and subhuman condition as refugees. This paper examines issues related to Sri Lankan Tamils refugees in India and the humanitarian dilemma faced by them. The aim is to contribute to the understanding of how Sri Lankan Tamil refugees coping with post-war Sri Lanka and options available before them.

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Refugees, Sri Lanka, Tamil, India, Tamil Nadu

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This study demonstrates the potential of deep learning models to significantly enhance the prediction of plant growth and yield in greenhouse environments. By integrating image analysis and environmental sensor data, our approach provides accurate, timely predictions that can optimize greenhouse operations. The success in predicting key growth metrics and yield outcomes underscores the utility of deep learning in precision agriculture. This predictive capability supports more informed decisions in irrigation, nutrient management, and pest control, contributing to increased efficiency and sustainability. Future research will aim to broaden the dataset, refine model accuracy, and explore application across diverse crop types and growing conditions. The promising results affirm the transformative impact of deep learning in modern agriculture

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Plant growth prediction, CNN, Deep CNN, Data Set, Environmental sensor data, Agricultural productivity, Deployment.

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Abstract:-Aim of the study to assess the prevalence of low birth weight babies and risk factors among postnatal mothers in selected hospital of Patiala (Punjab). An Exploratory research design,the study conduct on 100 Postnatal mothers of LBW babies in Rajindra Hospital Patiala.The inclusion criteria by using convenient sampling techniques under non probability sampling. The results showed that the 80 (80%) were moderate low birth weight , 17 (17%) were very low birth weight , and 2 (2%) were extreme low birth weight. The chi - square value were less than table value at the 0.05 So, the hypothesis H2 was rejected.

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Prevalence, low birth weight, risk factors, postnatal mother

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Expanding educational opportunities is a global priority, underscored by the United Nations' Education for All (EFA) initiative, a crucial component of the Millennium Development Goals. Recognized globally as a means to achieve fairness, justice, and high-quality education for all children, inclusive education seeks to ensure that historically marginalized groups, whether due to disability, ethnicity, gender, or other factors, have equal access to mainstream educational opportunities. While many countries have made significant strides in implementing inclusive education, others are still working towards this objective. The evolving role of teachers and the importance of their preparedness are pivotal in this context. Over the past few decades, significant changes in educational sectors, including curricular reforms and legislative shifts, have impacted the definition and approach to learning challenges, shifting the focus from diagnosing deficits to identifying support needs. This paper emphasizes the critical role of teacher training and professional development in fostering inclusive classrooms. By exploring various components of teacher preparedness and the impact of educational programs, this study underscores the necessity of equipping educators with the knowledge and skills to support diverse learners effectively. Additionally, the paper highlights the recommendations of the National Education Policy (NEP) 2020 in India and the provisions of the Rights of Persons with Disabilities (RPWD) Act 2016, emphasizing their role in promoting inclusive education. A global perspective is also presented, with a focus on the Salamanca Statement and its impact on inclusive education policies worldwide.

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Teacher Preparedness, Inclusive Education, Teacher Attitude, National Education Policy-2020, Rights of Persons with Disabilities (RPWD) Act, 2016

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In the contemporary world, the major threat posed to sustainable living is pollution. This research work aims to combat ways to remediate water pollution using Calcium Oxide- activated carbon nanocomposite. The nanocomposite used in this study was derived from Chicken eggshell waste as it is a rich source of Calcium. Calcium oxide nanoparticles were synthesized for enhanced performance due to its higher surface area to its volume ratio; it imparts size-dependent properties like higher adsorption tendency. CaO nanoparticles have proven to eradicate harmful metals in polluted water but on the other hand, CaO along with activated carbon is found to be more effective in treating bio pollutants in contaminated water. Hence, in the present study, Calcium oxide was combined with commercially procured activated carbon, and this nanocomposite was used as a potent adsorbent in the bioremediation of Tetracycline hydroxide in polluted water. The nanoparticles were characterized by XRD, FTIR, DLS, HR-SEM AND EDAX. Then the water sample synthesized with the bio pollutant Tetracycline hydroxide was tested for bioremediation efficiency using Calcium oxide - Activated carbon nanocomposite. The degradation of Tetracycline Hydroxide (TCH) was calibrated using a UV spectrophotometer and the degradation percentage was calculated. The UV spectrophotometer results validated that there is 83.12% degradation in the harmful pollutan from the water sample for a period of 5 hours of treatment. This research is an eyeopener as no other experimental work has been done on bioremediation of harmful bio pollutant Tetracycline hydroxide using Calcium oxide-Activated carbon nanocomposite synthesized from chicken egg shell waste using green synthesis.

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Chicken Eggshell, Nanocomposite, Bioremediation, Green Synthesis, Tetracycline Hydroxide

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The entire universe is a fascinating creation by the Brahmā through a proper route but what is not possible to create by the Brahmā is possible by the poet. A poet creates a flower in the sky through imagination to represent poetry's delightfulness, which is a necessity of poetry. Everyone is searching for happiness and poetry is a way of happiness but how it is applied in poetry is an amazing factor. Words, meaning and their combination are called poetry when they reflect delightfulness. From the word to meaning there has a way is vṛtti where the third one is Vyañjanā which is a source of charming meaning. Through this charming meaning, a virtuous reader can feel instant cheerfulness from the poetry. So, how poetry would be considered a source of happiness and how happiness comes from fascinating meanings through the Vyañjanā will be discussed in this paper. To build this hypothesis analytical methods are followed by the researcher and it depends on primary and secondary data sources of Sanskrit poetic literature.

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Fascination, Words, Meanings, Vyañjanā, Happiness, Dvani, Sanskrit Poetics.

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This research paper focuses mainly on the analysis, development and investigation of numerical methods for solving the further generalized time- fractional diffusion equation, a mathematical model with diverse applications in the fields of physics and engineering. A hybrid method combining finite difference and spectral techniques for representing the spatial and temporal domains, addressing the challenges presented by fractional -order derivatives and nonlinearity is suggested. Analysis of convergence, stability, and asymptotic behavior of the proposed numerical approach is the main target. By the first theorem, the convergence order of the proposed numerical method which shows that the numerical solution converges to the exact solution with a specified order as the spatial and temporal discretization parameters approach zero is established. The stability , boundedness, and error analysis of the method, providing insights into the method's behavior across a range of parameters is directed by the second theorem. The third theorem analyses the asymptotic convergence, stability in time, and uniform convergence in time, shedding light on the long-term behaviour and accuracy of the numerical solution. This exploration of asymptotic convergence, stability in time and uniform convergence in time has further strengthened the credibility of the proposed numerical approach. The proposed numerical method provides stability and converges accurately to the exact solution, providing a robust framework for solving further generalized time- fractional diffusion equations. The finding results supply to the knowledge and development of effective computational methods for a broach class of fractional partial differential equations which will cover the way for applications in different scientific fields. Thus this my critical analysis work gives foundation for advancing the understanding and application of numerical techniques in the context of time- fractional diffusion equations

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Asymptotic behavior, stability, time- fractional diffusion, Fractional Hybrid numerical methods, finite difference, error analysis, robust framework, convergence analysis, spectral methods, computational efficiency, fractional calculus, further generalized models, boundedness

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Cotton is one of the major crop in India which plays a dominent role in both industrial and agricultural sector of the country. It provides direct livelihood to 6 million farmers in the economy and about 40 to 50 million people are directly employed in cotton production and trading.The Central zone of the country which includes the states such as Gujarat, Maharashtra and Madhya Pradesh was the biggest producer of cotton in India and the Southern Zone which includes the states like Telangana, Andhra Pradesh, Karnataka and Tamilnadu is the second largest producer of Cotton in India. According to the records all Indian cottons in the 13th century were perennials and they are cultivated recently. The bushy perennial forms are grown in remote villages of Rajasthan, Uttar Pradesh and Andhra Pradesh. The Rozi cotton belonging to G.arborrem was supposed to the ancient commercial type. Further the perennial Nadan cotton of the southern most part of Tamilnadu is in all probability, the progenitor of Karunganni variety. On this background the study focus on the cotton cultivation and yield in Tamilnadu. it is concluded that the cultivation of cotton and production of cotton has a negative growth rate. The coefficient of variation of the area was low compared to the production of cotton in Tamilnadu. .

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Keywords: Area, Commercial crop, Cotton, Area, Production, Yield, Tamilnadu

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