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Novel approach for nonlinear time-fractional Sharma-Tasso-Olever equation using Elzaki transform
In this article, we demonstrated the study of the time-fractional nonlinear Sharma-Tasso-Olever (STO) equation with different initial conditions. The novel technique, which is the mixture of the q-homotopy analysis method and the new integral transform known as Elzaki transform called, q-homotopy analysis Elzaki transform method (q-HAETM) implemented to find the adequate approximated solution of the considered problems. The wave solutions of the STO equation play a vital role in the nonlinear wave model for coastal and harbor designs. The demonstration of the considered scheme is done by carrying out some examples of time-fractional STO equations with different initial approximations. q-HAETM offers us to modulate the range of convergence of the series solution using ?, called the auxiliary parameter or convergence control parameter. By performing appropriate numerical simulations, the effectiveness and reliability of the considered technique are validated. The implementation of the new integral transform called the Elzaki transform along with the reliable analytical technique called the q-homotopy analysis method to examine the time-fractional nonlinear STO equation displays the novelty of the presented work. The obtained findings show that the proposed method is very gratifying and examines the complex nonlinear challenges that arise in science and innovation. 2023 Balikesir University. All rights reserved. -
A new computational technique for the analytic treatment of time-fractional EmdenFowler equations
This paper presents the study of fractional EmdenFowler (FEF) equations by utilizinga new adequate procedure, specifically the q-homotopy analysis transform method (q-HATM). The EF equation has got greater significance in both physical and mathematical investigation of capillary and nonlinear dispersive gravity waves. The projected technique is tested by considering four illustrations of the time-fractional EF equations. The q-HATM furnish ?, known as an auxiliary parameter, by the support of ? we can modulate the various stages of convergence of the series solution. Additionally, to certify the resolution and accurateness of the proposed method we fitted the suitable numerical simulations. The redeem results guarantee that the proposed process is more convincing and scrutinizes the extremely nonlinear issues emerging in the field of science and engineering. 2021 International Association for Mathematics and Computers in Simulation (IMACS) -
Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique
Cardiovascular diseases (CVDs) pose a significant global health challenge. Early and accurate diagnosis is crucial for effective treatment. This research focuses on developing a robust classification system for CVDs using machine learning techniques. This study proposes an enhanced Random Forest (RF) model optimized for big data environments and explore the potential of CNN-based classification. By leveraging medical imaging data and employing these advanced algorithms, we aim to improve the accuracy and efficiency of CVD diagnosis. 2024 IEEE. -
Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency
This research paper examines the transformative influence of Artificial Intelligence (AI) and Machine Learning (ML) on tumour diagnosis within clinical settings. The advent of AI and ML technologies has revolutionised the field of oncology, offering the unprecedented potential for more accurate, timely, and personalised cancer detection. By leveraging vast datasets of medical images, genomic information, and patient records, these intelligent systems enable the early identification of tumours, classification of cancer types, and prediction of patient outcomes with remarkable precision. This paper delves into the mechanisms through which AI and ML algorithms analyse complex data, highlighting their ability to detect subtle patterns and anomalies that may escape human perception. Moreover, we examine the successful integration of these technologies into clinical workflows, their potential to reduce diagnostic errors, and the implications for patient care and outcomes. As AI and ML continue to emerge, the synergy between technology and clinical expertise promises to enhance tumour diagnosis, ultimately contributing to more effective and personalised cancer treatments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods
Super-resolution has advanced significantly in the last 20years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Gut-Skin Axis: Role in Health and Disease
The human microbiome includes microorganisms and their cumulative genetic details that reside in the human body. Skin, the bodys most external organ and exposed to the external environment, is an ecosystem with 1.8 m2 area. It has a varying epidermal thickness, folds, and appendages in different areas including along with varying moisture and temperature level on the skin surface. Microbial colonization on the skin surface starts from the time of birth. The mode of delivery affects the colonization process to a considerable extent. The group of microbes colonizing the skin surface is determined by physical and chemical features of it, which applies to microbes inhabiting the gut and other ecological niches in the body as well. There is several common important characteristics shared commonly by gut and skin, where both are (1) heavily vascularized, (2) richly perfused, (3) densely innervated, (4) integrated to the immune system, (5) highly associated with the endocrine system, (6) extensively colonized with recognizable microbiota, and (7) both helps our body to communicate with its external environment. It has variously been reported that a close and bidirectional association within the gut and skin in maintaining the homeostasis and allostasis of skin and also gastrointestinal (GI) health. Therefore, numerous intestinal pathologies have been linked to skin comorbidities. It has been found that skin is directly impacted by the various circumstances that principally affect the intestine. Similarly, various gastrointestinal disorders could be linked to distinct dermatological entities. In the same context, a growing body of proof proposes an association of intestinal dysbiosis with many regular inflammatory skin pathologies including atopic dermatitis (AD), psoriasis, rosacea, and acne vulgaris. And the realization of this interconnected association between skin and gut has resulted in a new concept of the Gut-Skin Axis. An intimate bidirectional engagement between the gut and the skin has been well established by growing research evidence in this domain. Recent reports have indicated that the administration of specific Lactobacilli strains to mice can significantly alter the overall skin phenotype. Despite increasing research efforts in this domain, a systematic investigation of the Gut-Skin Axis remains ill explored by both gastroenterology as well as dermatology researchers. And in this context, here we are discussing various aspects of the Gut-Skin Axis and its role in the general well-being of individuals. The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022. -
Artemisinin: A potent antimalarial drug
Artemisinin is known to be a potent antimalarial drug which is naturally obtained from the plant Artemisia annua L. Malaria is a global health problem with nearly 1.2 billion people at high risk. In 2001, WHO recognised artemisinin based combination therapies (ACTs), as the frontline drugs to fight against malaria and therefore, artemisinin is the most effective anti-malarial drug. It appears to be a safe drug with no adverse reactions or noticeable side effects, even for pregnant women. However, access to ACTs by malarial patients, especially in poor countries, is inadequate due to high volatility in price, unpredictable demand and low yield from A. annua. The huge gap in demand and supply has motivated researchers to explore artemisinin production in alternative systems like bacteria, yeast and tobacco. Scientists have been successful in producing this wonder molecule in heterologous hosts. Challenges associated with large-scale production and drug resistance against artemisinin has also been discussed to present a comprehensive picture of artemisnin production, application and limitations. 2019 Scrivener Publishing LLC. All rights reserved. -
Saccharomyces - eukaryotic probiotic for human applications
Probiotics are viable microorganisms which are meant to confer health benefits to host after ingestion. Any probiotic strain has a special characteristic to survive in the extremely acidic and hostile conditions of stomach and intestine. Among all the commercially available probiotic strains, prokaryotes constitute the bulk of it, with quite a few belonging to eukaryotic yeasts. Eukaryotic probiotics are very limited and currently there are only two yeast strains (Saccharomyces boulardii and Kluyveromyces sp.), which are approved for human consumption and are available commercially in market. S. boulardii has been reported to have tremendous therapeutic potential. The main mechanism of action for S. boulardii includes strong antagonistic effect against a number of enteric pathogens, trophic effects on the intestinal mucosa, neutralisation of bacterial toxins as well as modification of host cell signaling pathways involved in inflammatory and non-inflammatory intestinal disease. Pertaining to these advantages, S. boulardii have been reported to be exceptionally effective against diarrheal diseases and intestinal inflammatory conditions including inflammatory bowel disease (IBD). Increasing scientific reports confirming the therapeutic potential of eukaryotic probiotics and their advantages over prokaryotic probiotic strains have dramatically increased the worldwide interest in these probiotics. 2019 Scrivener Publishing LLC. -
Gut Microbiota and Cancer Correlates
The human microbiota is a concoction of bacteria, archaea, fungi, and other microorganisms. It is necessary to maintain a partnership between the host and the microbiota in order to maintain the different aspects of human physiology, such as nutrient absorption, immune function and metabolism. The microbiota can contribute to both progression and suppression of the disease, including cancer. A disturbance in this interspecies balance called microbiome dysbiosis becomes a reason for the host to be more prone to issues such as immunodeficiency and cancer. Gut microbiota could potentially influence the factors that govern cancer susceptibility and progression through mechanisms such as immunomodulation, by producing metabolites, such as, bacteriocins, antimicrobial peptides involved in tumor suppression, and short-chain fatty acids (SCFA), and through enzymatic degradation. It is now an established fact that the host physiology as well as risk of diseases such as cancer could be greatly modulated by these commensal microbes and the regulation of cancer development, progression as well as response to anticancer therapy is greatly dependent on the host microbiota. Therefore, it is being envisaged that by the involvement of microbiome in augmenting antitumor responses to therapeutic approaches, potentially a new era of research with potentially broad implication on cancer treatment could be established. Better cancer treatment responsiveness can be achieved by understanding the role of the tumor microbiome in shaping the tumor microenvironment. This will help us to develop personalized anticancer solution with the goal to discover a bacterial species or a combination of species that decreases systemic toxicity and helps in anticancer therapy. This chapter is written in same context, which focuses on the association of the gut microbiome with the suppression and progression of cancers, the role of the immune system in this interaction, the utilization of these organisms for the treatment of cancers, and future perspectives. Springer Nature Singapore Pte Ltd. 2021, corrected publication 2021. -
A synbiotic composition and application thereof /
Patent Number: 202041045417, Applicant: Alok Kumar Malaviya. -
Emotional intelligence, job satisfaction and psychological well-being among nurses in a tertiary care hospital
Background: Emotional intelligence helps in preservation of mental health because of their effective emotional regulation skills. Objectives: We aimed to evaluate the impact of emotional intelligence on nurses job satisfaction and psychological well-being. Methods: This cross-sectional study was conducted in a tertiary hospital and included 120 nurses. Wong and Law Emotional Intelligence Scale, Psychological General Well-being scale and Job Satisfaction Survey questionnaires were used. Results: The study showed a low positive correlation between emotional intelligence and psychological wellbeing (r=0.313) and a low correlation between emotional intelligence and job satisfaction (r= 0.122). The emotional intelligence was significantly correlated to their psychological well-being (9.8%). Conclusion: Nurses with higher emotional intelligence experience greater psychological well-being. We did not find a link between emotional intelligence and job satisfaction. Implementing interventions to enhance emotional intelligence in nurses is crucial for improving psychological well-being and reducing burnout risk. The Author(s). 2024. -
Agriculture as a means of alleviating rural poverty: Pursuant to the sustainable development goal-1
Poverty is one of the worst problems prevailing in the world. The poorest in the world are often without food, have little or no access to education, basic amenities of life, and lack health facilities. Eradication of Global Poverty eradication is a herculean and complex task. The origination of 2030 Agenda to eradicate poverty was done after the successful completion of the anti-poverty Millennium Development Goal, but still, a vast number of people were living in poverty and a great number among them were living in extreme poverty. So, the 2030 Agenda for Sustainable Development called for the eradication of poverty in all poverty in forms from every corner of the world by almost half. In backward and developing nations, poverty is more rampant in rural areas. The economies of most of these nations are predominantly based on Agriculture and therefore progress in agriculture is viewed as a potent tool to eradicate rural poverty. However, there are serious issues that are required to be addressed in this regard. This chapter explores some vital issues related to agriculture which require the attention of the policymakers, to achieve the objective of reducing rural poverty through advancement in agriculture. 2023 Nova Science Publishers, Inc. All rights reserved. -
A device for caregiver wellbeing assessment and a method thereof /
Patent Number: 202111033343, Applicant: Dr. Ruchi Tyagi.A system and a method for wellbeing assessment to assess psychological and mental needs of caregivers. The method comprising the steps of identifying categories of psychological need, wherein said categories comprises competence, results doubting, self-esteem and fears of failures, criticism, and expectations; plotting category theme on the basis of the identified categories; determining factors affecting the psychological needs of caregivers in COVID 19 situation on the basis of the plotted category theme, where said factors comprise depression, anxiety and/or stress assessment. -
Emotional Abuse and the Pandemic in India: Implications for Policy, Research, and Practice
During the COVID-19 outbreak, cases of violence and abuse have increased significantly around the world, necessitating a reevaluation of our relation-ships. Both violence and abuse seek to control and instill fear in the individ-ual, gradually disrupting their overall well-being. Emotional abuse does not receive the same level of attention and social response as other forms of abuse due to its subtle nature. Its effects are as harmful as physical and sexual abuse, with serious consequences for the mental health of individual and their families. The COVID-19 pandemic has brought to light the importance of mental health. With the imposition of lockdown in India, the number of helplines for domestic violence and abuse has skyrocketed. Abuse experien-ces can be seen to be bidirectional; women are not alone in such instances. Many cases, however, go unreported and never reach formal institutions. The National Family Health Survey (2019-2021) reveals the current state of Indian health and nutrition, but emotional abuse (also referred to inter-changeably in this article as emotional violence) only includes responses from women and is no longer included under spousal violence in the most recent edition. This article also includes recommendations and attempts to highlight existing shortcomings in addressing the issue of emotional violence. The articles cited in this article were obtained from electronic databases. Other secondary data sources mentioned include newspaper articles, magazines, census reports, and periodicals. 2024 Springer Publishing Company. -
Predictive Analysis of Sleep Disorders Using Machine Learning: A Comprehensive Analysis
The diagnosis of sleep disorders often relies on subjective patient reports, sleep diaries, and potentially cumbersome polysomnography (PSG) tests. However, these methods have limitations such as subjectivity, sleep diaries require meticulous effort, and expensive PSG tests are expensive, resource-intensive, and may not accurately capture sleep patterns in a non-clinical setting. Sleep disorders pose significant health risks and can impair overall well-being. Predictive analysis plays a crucial role in identifying individuals at risk of developing sleep disorders, enabling timely interventions and personalized treatment plans. In this paper, a comparative analysis of regression and classification models for sleep disorders prediction using machine learning (ML) techniques on insomnia and sleep apnea are discussed. Through extensive experimentation and comparative analysis, XGBoost and AdaBoost demonstrated as the most effective predictive models for insomnia and sleep apnea. AdaBoost and XGBoost classifiers are displaying 93.49% and 92.73% respectively. It is therefore possible to draw the conclusion that AdaBoost and XGBoost are doing well based on the findings as a whole, as indicated by the results. Our findings contribute to advancing the understanding and application of ML techniques in sleep disorder prediction, paving the way for more accurate and timely diagnosis based on ML techniques and personalized interventions in clinical practices. 2024 IEEE. -
Enhancing Security and Resource Optimization in IoT Applications with Blockchain Inclusion
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data-driven applications. However, optimizing the allocation of resources within IoT networks is a pressing challenge. This research explores a novel approach to resource optimization, combining blockchain technology with enhanced security measures, while addressing the critical concerns of time and energy consumption. In this study, we propose a resource allocation framework that leverages the transparency and immutability of blockchain to enhance data integrity and security in IoT applications. The blockchain-based method is utilized to identify the malicious users in the IoT applications. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. To further optimize this process, we introduce a Hybrid Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), a powerful optimization technique designed to minimize time delays and energy consumption in IoT environments. Our findings demonstrate the effectiveness of the proposed approach in achieving resource efficiency, reducing time and conserving energy within IoT networks. 2023 IEEE. -
STOCHASTIC BEHAVIOUR OF AN ELECTRONIC SYSTEM SUBJECT TO MACHINE AND OPERATOR FAILURE
A stochastic model is developed by assuming the human (operator) redundancy in cold standby. For constructing this model, one unit is taken as electronic system which consists of hardware and software components and another unit is operator (human being). The system can be failed due to hardware failure, software failure and human failure. The failed hardware component goes under repair immediately and software goes for upgradation. The operator is subjected to failure during the manual operation. There are two separate service facilities in which one repairs/upgrades the hardware/software component of the electronic system and other gives the treatment to operator. The failure rates of components and operator are considered as constant. The repair rates of hardware/software components and human treatment rate follow arbitrary distributions with different pdfs. The state transition diagram and transition probabilities of the model are constructed by using the concepts of semi-Markov process (SMP) and regenerative point technique (RPT). These same concepts have been used for deriving the expressions (in steady state) for reliability measures or indices. The behavior of some important measures has been shown graphically by taking the particular values of the parameters. 2024, Gnedenko Forum. All rights reserved. -
A Comparative Study of LGMB-SVR Hybrid Machine Learning Model for Rainfall Prediction
Weather forecasting is a critical factor in deter mining the crop production and harvest of any geographical location. Among various other factors, rainfall is a crucial determining component in the sowing and harvesting of crops. The aim and intent of this paper is to analyze various machine learning algorithms like LightGBM and SVR, and develop a hybrid model using LightGBM and SVR to accurately predict rainfall The hybrid model implements both LightGBM and SVR on a preprocessed dataset and then combines the predicted values of the results through an ensemble model which considers the average of these values based on a predefined weight. The weight of the model is determined by considering various combinations, and the result with the least error is taken into consideration for that particular dataset. The study shows that the hybrid model performed better than LightGBM and SVR individually, and produced the least root mean square error yielding a more accurate prediction of rainfall. 2021 IEEE. -
Broadband Spectral Properties of MAXI J1348-630 using AstroSat Observations
We present broadband X-ray spectral analysis of the black hole X-ray binary MAXI J1348-630, performed using five AstroSat observations. The source was in the soft spectral state for the first three observations and in the hard state for the last two. The three soft state spectra were modeled using a relativistic thin accretion disk with reflection features and thermal Comptonization. Joint fitting of the soft state spectra constrained the spin parameter of the black hole a * > 0.97 and the disk inclination angle i = 32.9 ? 0.6 + 4.1 degrees. The bright and faint hard states had bolometric flux a factor of ?6 and ?10 less than that of the soft state respectively. Their spectra were fitted using the same model except that the inner disk radius was not assumed to be at the last stable orbit. However, the estimated values do not indicate large truncation radii and the inferred accretion rate in the disk was an order of magnitude lower than that of the soft state. Along with earlier reported temporal analysis, AstroSat data provide a comprehensive picture of the evolution of the source. 2022. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.