Volume 11 | Issue 5 |

 Abstract

The connection between religious affiliation and individuals' spiritual, psychological, and overall health has been established for a long time. The purpose of this study is to investigate the influence of religiosity on the health and well-being of young adults. Those with higher levels of religiosity tended to have higher levels of spiritual well-being, including a sense of purpose and connection to something greater than themselves, and reported lower stress, anxiety, and sadness, as well as improved self-esteem and resilience. Religious practices were linked to lower rates of substance abuse, higher adherence to healthy lifestyles, and lower rates of chronic diseases. However, religiosity can also have negative consequences, such as religious guilt and shame, as well as strict adherence to religious norms. Additionally, the impact of religiosity varies by religious tradition and cultural context. Healthcare providers and policymakers should consider the role of religiosity in young adults' health and well-being and offer resources to those struggling with negative aspects of religiosity.

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 Keywords

Religion, Spirituality, Wellbeing, Health, Young Adults

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 Abstract

IOT PRISon BREAK MONITORING AND ALTERING SYSTEM

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IOT PRISon BREAK MONITORING AND ALTERING SYSTEM

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Now a days, documentary has become very famous to know and feel the poets. The most important components of documentary are poems of the poet, prose, evaluation of the poet by the other poets and so on. In making scene in the light of the poet's life symbols, detailing, cinematic presentation make documentary artistic. Although the inspiration of documentary is the life of poet but it is actually the director's own realization.

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 Keywords

documentary, life, literature, poet

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In order to generate good revenue, every business needs to make proper data-oriented decisions. These decisions lead to the emergence of successful business strategies, which themselves are data driven. This is even more relevant in the world of banking, where the stakes are quite high. The huge number of customers increase the risk of a massive default on credit cards if banks are not being careful. Further, it becomes difficult for banks to cater to the individual needs of such a large number of customers. Therefore, we propose the usage of these machine learning practices to predict the next step, sort your data, and apply multiple algorithms to your dataset to find your target audience. Using these practices, we will be able to predict whether a customer will default their next credit card bill or will be able to repay their loan and organising the customers so that bank can manage their resources to cater to the right group of people who will buy their different schemes.

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 Keywords

Machine learning, Datasets, Customer Segmentation, Loan predictions, Credit card defaulters

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This review article explores the effects of nocebic language in patient communication, especially how it relates to the nocebo effect, in which patients' unfavourable perceptions of their therapies result in subpar results. Nocebic language, which is characterised as words or expressions that might stir up or exacerbate these unfavourable expectations, has been linked to serious negative effects on patient outcomes, altering elements like pain perception and side effect experiences. Research on the nocebo effect, the impacts of nocebic language, and mitigation techniques are examined in this review. Nocebic language's ethical conundrums and how they affect clinical practise are also covered. In order to minimise negative effects while preserving patients' access to information and reducing the nocebo effect, the review emphasises the need for further study on ethical and effective communication tactics.

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 Keywords

Nocebic Language, Nocebo Effect, Patient Communication, Pain Perception, Side Effects, Ethical Dilemmas, Clinical Practice, Communication Strategies

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 Abstract

The advent of concrete buildings in the tropical state of Kerala in India, from the last 100 years have predominantly resulted in an increase of atmospheric temperature inside residences. The ancient housing in the state would have leaf roofing which would disintegrate the heat because of the large surface area each leaf has, and it would also prevent water leakage. However concrete households and Mysore tile roofing also to an extend did increase the interior temperature far from the optimal temperature requirement of 24 c, to as high as 42 degree Celsius, in the summer. This journal will discuss how the optimal temperature can be attained by use of solar panels and how it will help the average monthly budget of each house owner.

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The advent of concrete buildings in the tropical state of Kerala in India, from the last 100 years have predominantly resulted in an increase of atmospheric temperature inside residences. The ancient housing in the state would have leaf roofing which would disintegrate the heat because of the large surface area each leaf has, and it would also prevent water leakage. However concrete households and Mysore tile roofing also to an extend did increase the interior temperature far from the optimal te

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The main aim of our project work is to develop a MATLAB based Simulation model for analyzing 3- phase symmetrical and unsymmetrical faults. Generally, these faults are occurred in the long transmission line system such as single line to ground fault (L-G), double line to ground (2L-G), triple line to ground fault (3LG) and Line to line fault (L-L). Fault analysis for different sorts of faults has been done and it impacts are appeared in simulation output e.g. voltage, current. This research approaches to the MATLAB software in which transmission line modal is simulated. The analysis will be based on a 110KV, 100km, 50HZ 3 Phase Transmission Line deigned for 500KVA. The system behavior in case of fault and fault current limitation techniques are also studied and developed in this project.

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Transmission Line, MATLAB, 110KV, Fault Analysis

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Parkinson's disease (PD) is one of the more common neurodegenerative disorders, with two stages of progression: normal and severe. With all of the shortcomings in clinical settings, identifying the stage of PD severity and predicting its progression course can be difficult. As a result, it appears that there is an increasing need to use supervised and unsupervised artificial intelligence and machine learning methods on clinical and paraclinical datasets to accurately diagnose Parkinson's disease, identify its stage, and predict its course. MRI-related data are regarded as useful in detecting various pathologies in the brain in today's neuro-medicine practises. Furthermore, the field has recently seen an increase in the use of deep learning methods in image processing, often with excellent results. In this study, we used Artificial Neural Networks (ANN) to develop a model for distinguishing different stages of Parkinson's disease. The results demonstrated that our current MRI-based CNN model could potentially be used as a suitable method for distinguishing PD stages with a high accuracy rate.

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Parkinson's disease, Artificial Neural Network (ANN), MRI images, Convolutional Neural Network (CNN)

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Since it is a branch of general pragmatics, clinical pragmatics that deals with identifying, evaluating and treating speech impairment is supported by the principles and theories of the general pragmatics such as speech acts and relevance theory as a domain or framework within which many researchers can build their concepts to distinguish different groups of language disorders. Since communication is the ability to send and received information, express and understand feelings and thoughts, any barrier prevents people to socialize verbal or nonverbal cues impairs or break down the process of communication. In case meaning that is conventionally encoded in the used linguistic form is disordered or not understood, turns during conversation can't be taken correctly and joining verbal communication with nonverbal techniques is difficult, pragmatics is disordered

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 Keywords

Clinical, pragmatics, cognitive and neurophysiology

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Data sharing is an important functionality in cloud storage. In this paper, we show how to securely, efficiently, and flexibly share data with others in cloud storage. We describe new public-key cryptosystems that produce constant-size cipher texts such that efficient delegations of decryption rights for any set of cipher texts are possible. The novelty is that one can aggregate any set of secret keys and make them as compact as a single key, but encompassing the power of all the keys being aggregated. In other words, the secret key holder can release a constant-size aggregate key for flexible choices of cipher text set in cloud storage, but the other encrypted files outside the set remain confidential. This compact aggregate key can be conveniently sent to others or be stored in a smart card with very limited secure storage. We provide formal security analysis of our schemes in the standard model. We also describe other application of our schemes. In particular, our schemes give the first public-key patient-controlled encryption for flexible hierarchy, which was yet to be known.

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 Keywords

Data sharing, public key Cryptosystem, Encryption, Decryption, Aggregate key

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