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Sensitivity analysis of thermal optimisation within conical gap between the cone and the surface of disk with particle deposition
This work examines the thermal and flow characteristics of TiO2+AgBr+GO/EG trihybrid nanofluid in the conical gap that exists between a disc and a cone. Effect of thermophoresis and particle deposition are examined to perceive the mass dissipation change on the surface. The governing equations of the problem are in the form of partial differential equations which are converted to nonlinear ordinary differential equations by applying proper scaling similarity transformations, and then the resultant equations are approximated numerically by using RKF45 technique. The interesting part of this research is to discuss the impact of various pertinent parameters on three cases namely: (1) rotating cone/disk (2) rotating cone/stationary disk and (3) stationary cone/rotating disk. The flow field, heat and mass transfer rates were analysed using graphical representations. Additionally, sensitivity analysis is performed on derived rate of heat transfer as a response function for input factors for different parameters. From the graph, it is perceived that flow field increases significantly with increase in the values of Reynolds numbers for both cone and disk rotations. Also, it is seen that temperature upsurges significantly for ascendent values of solid volume fraction of nanoparticles. It is also noticed that the sensitivity of the Nusselt number towards n is more for all the values of source/sink and for middle level values of n. Akadiai KiadZrt 2024. -
Nonlinear Dynamics in Distributed Ledger Blockchain and analysis using Statistical Perspective
More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB. 2024, International Publications. All rights reserved. -
IDENTITIES AT THE DINNER TABLE: COMMENSALITY, SELF-PERCEPTION, AND RELATIONSHIPS IN ANNE CHERIANS A GOOD INDIAN WIFE
Food studies is rapidly gaining ground as a multidisciplinary area of research. Within it, literary food studies brings an interdisciplinary perspective as works of literature are viewed through the lens of food that is informed by frameworks and concepts that are rooted in a variety of fields including cultural anthropology, sociology, and more. one such concept that is in focus here is that of commensality that is associated with food and food practices. Commensality, drawing from notions of conviviality, refers to the practice of sharing a table and consuming food together. Deeper meanings of communal identities come to the fore in this social practice, leading it to shape how identities are understood and projected. Commensality can be a complex site of belonging and alienation depending on the context, and this paper seeks to explore the representation of the same in Anne Cherians A Good Indian Wife (2008). Leila, the titular Indian wife in the novel, moves to the US from India after her marriage to Neel and grapples with finding her place in the foreign land. With this displacement comes the endeavor to reaffirm her new identity, which now includes the role of being a wife and the aspect of being an immigrant. Neel also deals with complicated feelings towards the projection of his identity. With food playing a crucial role in the everyday experiences of their lives, commensality becomes a point of enquiry into how they see themselves and how their relationships with each other and themselves evolve through the course of the narrative. 2024 Nayana George. -
RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification
Lung cancer remains the leading cause of cancer-related deaths worldwide. Pulmonary nodules, indicative of tumor growth, present significant diagnostic challenges due to their varying sizes and shapes. Computed Tomography (CT) is commonly used for lung cancer screening due to its high sensitivity and efficacy in detecting these nodules. However, differentiating between benign and malignant nodules can be difficult due to their overlapping characteristics. To address this challenge, we propose a Rebuild Convolution Block Attention Module-based Convolutional Neural Network (RCBAM-CNN) designed to accurately classify lung nodules from CT scans. The RCBAM-CNN integrates a Rebuild Convolution Block Attention Module (RCBAM), which includes reshaped layers and redefined spatial attention mechanisms to enhance the networks focus on relevant features while minimizing noise. The performance of the proposed method is evaluated using the LIDC-IDRI dataset. Data augmentation techniques, including rotation, rescaling, and both vertical and horizontal flips, are applied to improve the models robustness and generalization. Subsequently, U-Net is employed for precise image segmentation, ensuring accurate delineation of nodule regions. The proposed RCBAM-CNN demonstrates exceptional performance, achieving an accuracy of 99.72%, surpassing existing methods such as adaptive morphology with a Gabor Filter (GF) and Capsule Network-based CNN. This approach represents a significant advancement in lung nodule classification, offering improved diagnostic accuracy and reliability. 2024 River Publishers. -
Damaged Relay Station: EEG Neurofeedback Training in Isolated Bilateral Paramedian Thalamic Infarct
Stroke is a major public health concern and leads to significant disability. Bilateral thalamic infarcts are rare and can result in severe and chronic cognitive and behavioral disturbances - apathy, personality change, executive dysfunctions, and anterograde amnesia. There is a paucity of literature on neuropsychological rehabilitation in patients with bilateral thalamic infarcts. Mr. M., a 51 years old, married male, a mechanical engineer, working as a supervisor was referred for neuropsychological assessment and rehabilitation with the diagnosis of bilateral paramedian thalamic infarct after seven months of stroke. A pre-post comprehensive neuropsychological assessment of his cognition, mood, and behavior was carried out. The patient received 40 sessions of EEG-Neurofeedback Training. The results showed significant improvement in sleep, motivation, and executive functions, however, there was no significant improvement in memory. The case represents the challenges in the memory rehabilitation of patients with bilateral thalamic lesions. 2024 Neurology India, Neurological Society of India. -
Revolutionizing Biodegradable and Sustainable Materials: Exploring the Synergy of Polylactic Acid Blends with Sea Shells
This study explores the mechanical properties of a novel composite material, blending polylactic acid (PLA) with sea shells, through a comprehensive tensile test analysis. The tensile test results offer valuable insights into the materials behavior under axial loading, shedding light on its strength, stiffness, and deformation characteristics. The results suggest that the incorporation of sea shells decrease the tensile strength of 14.55% and increase the modulus of 27.44% for 15 wt% SSP (sea shell powder) into PLA, emphasizing the reinforcing potential of the mineral-rich sea shell particles. However, a potential trade-off between decreased strength and reduced ductility is noted, highlighting the need for a delicate balance in material composition. The study underscores the importance of uniform sea shell particle distribution within the PLA matrix for consistent mechanical performance. These results offer a basis for additional PLA-sea shell blend optimization, directing future efforts to balance strength, flexibility, and other critical attributes for a range of applications, including biomedical devices and sustainable packaging. This investigation opens the door to more sustainable and mechanically strong materials in the field of additive manufacturing by demonstrating the positive synergy between nature-inspired materials and cutting-edge testing techniques. 2024 The Authors. -
We wear multiple hats: Exploratory study of role of special education teachers of public schools in India
The role of special education teachers (SETs) is multifaceted. A gap was recognised in the literature in the lack of studies on the roles and responsibilities of SETs in India and the field realities of carrying out the role. The aim was to explore to what extent the special education teachers fulfil their roles and responsibilities. The following is an exploratory study, using open-ended questions that interviewed 12 SETs from five public schools in Delhi, India. The policy documents shared that the SETs were responsible for direct instruction to special needs students, parentteacher collaboration and documentation, including IEPs for students with special needs. But in practice, there were not any clear-cut boundaries, the SETs played multiple rolesSubject teacher, taking substitution periods, para teachers, these were keeping the SETs away from their core responsibilities. The results of the study demonstrated an undervaluation of the work of SETs and lack of support from the principal and regular teachers. The results concluded with recommendations for policy proposal with regards to defining the role of all stakeholders in an inclusive education school and improvements for the teacher education program. 2024 National Association for Special Educational Needs. -
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
Supply chain management is a multi-dimensional approach. Growing eco-consciousness has forced businesses to optimize operations and incorporate green practices across all the stages of supply chain in manufacturing and service sectors. Reviewing the past research literature propels us to understand its current and future prospects. Employing a systematic analysis, this research explores the intellectual structure of green supply chain practices and their connection to performance outcomes in various industries. This study covers a systematic literature review, content analysis, and bibliometric analysis on green supply chain management using VosViewer. It utilizes a PRISMA-guided screening method for identification, screening, eligibility and inclusion of literature from the literature available since 1999. The bibliometric analysis reveals key contributors, thematic clusters, prevailing theoretical frameworks, and emerging research trends in the domain of green supply chain management. China, followed by the United States and the United Kingdom, emerged as leading contributors to research in this area, driven by rapid economic growth, heightened environmental concerns, and well-established academic and industrial infrastructures. The study identifies eight thematic clusters within green supply chain management, including the triple bottom line, circular economy, and carbon emissions. The most highly cited papers within these clusters were examined for their methodologies, tools, and key findings, highlighting the prominent theories utilized in this field. Moreover, the research discusses how advanced technologies such as AI, blockchain, and big data analytics are poised to transform supply chains by enhancing decision-making and mitigating risks, thus playing a pivotal role in the future of green supply chain management. Copyright 2024 CA Rajkiran, Shaeril Michel Almeida. -
Employee relations: a comprehensive theory based literature review and future research agenda
This study aims to conduct a systematic and integrative literature review to consolidate the extensive information on employee relations accumulated over the past century, thereby offering new insights into domain-specific phenomena. The research followed a four-phase search strategy in accordance with the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The keyword search utilized terms such as 'employee relations,' 'employee relation,' 'employment relation,' and 'employment relations' in the Scopus and Web of Science databases. By employing an integrative approach along with specific inclusionexclusion criteria, the researchers synthesized articles from leading journals in the field of employee relations, categorizing them based on geographical region, article types, prominent authors and their affiliations, and the most cited research articles. In the final stage, the researchers presented new insights through a conceptual framework utilizing the ADO-TCCM approach, which encompasses antecedents, outcomes, theories, context, methodology, mediators, and moderators of employee relations. This study synthesizes findings and reorganizes key themes into innovative frameworks, providing fresh perspectives on various aspects of employee relations. Ultimately, it offers valuable insights into the critical factors that strengthen long-term employee-employer relationships. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Fluorescence bioimaging applications of europium-doped strontium aluminate nanoparticles
Fluorescence bioimaging is widely used for physiological studies to visualise intercellular molecular events due to its highly selective, sensitive, and non-destructive nature. However, its application in in vivo live imaging is often limited by the scarcity of biocompatible fluorescent probes possessing optimal properties. Our study focuses on developing europium-based nanoparticles for in vivo bioimaging, especially imaging of plants. Eu-doped strontium aluminate nanoparticles were synthesised through a conventional solid-state reaction. Structural characterisation of samples using XRD confirmed the prevalence of SrAl2O4 as the prominent phase. The FTIR spectrum, SEM and TEM images were recorded for further characterization. Photoluminescence studies showed orange red emission of sample. The antibacterial activity of the nanophosphors was studied, demonstrating no antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. Furthermore, in vitro cytotoxicity studies conducted using Neuro-2A cells showed no indications of cytotoxicity associated with europium doped strontium aluminate nanoparticles. When incorporated into the plant tissue culture medium, these nanoparticles were found to have no effect on seed germination and plant growth, and it demonstrated no phytotoxicity. Imaging studies have shown the uptake of nanoparticles by plants and their subsequent transport through the vascular system. Our results emphasise the direct integration of nanophosphors into plant tissues from the growth medium, eliminating the necessity for traditional staining methods in fluorescence bioimaging. Incorporation of nanophosphors into living organisms holds promise for non-invasive and long-term fluorescence imaging, with potential applications in biological studies and diagnostics. The outstanding fluorescence properties and biocompatibility of europium doped strontium aluminate nanoparticles broaden its potential for various applications in fluorescence bioimaging. 2024 Elsevier Ltd and Techna Group S.r.l. -
Extended Slash Modified Lindley Distribution to Model Economic Variables Showing Asymmetry
This article introduces a novel probability distribution to model economic variables with high kurtosis and heavy tails showing a decreasing trend. From a mathematical viewpoint, it corresponds to the distribution of the ratio of two independent random variables, one with the modified Lindley distribution and another with the beta distribution. In some sense, it can be described as an extended three-parameter version of the Lindley distribution that has the ability to model data with high kurtosis. After presenting this distribution in more in-depth details, a comprehensive analysis is given, including its associated functions, moments, skewness, and kurtosis characteristics. Furthermore, a parametric estimation work is carried out. A simulation approach is used to validate the performance of the obtained estimates. The applicability of the proposed distribution is demonstrated by fitting real-world data into various socioeconomic scenarios. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm
UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This studys principal objective is to further develop UAV network routing decisions by using CNNs ability for design recognition and the Modified SBOs optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAVs, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms. 2024 River Publishers. -
Inter-State Migration, Footloose Labour and Accessibility to Health Care: An Exploration among Metro Workers of a Camp in Bengaluru
The neoliberal political economy that India adopted in 1991 has brought in huge Foreign Direct Investments, which has led to a perceptible increase in the number of migrants in the major cities of India due to various structural reasons in their place of origin and rapid developmental activities in the cities. Bengaluru has the second largest migrant population after Mumbai, and as per the labour department of the government of Karnataka; there are more than 65 lakh migrant workers in Karnataka, who are involved in various developmental projects, including the metro railway project in Bangalore. Even though the Karnataka Building and Other Construction Workers Welfare Board (KBOCWWB) offers certain social security, including health care for registered migrants, they must wait more than a year to get these benefits. With privatisation and increased out-of-pocket expenditure for health related issues, the migrants face a major hurdle in surviving at the migrated workplaces. Many of them are unaware of welfare boards, and the number of migrants who are registered with them is very small. This paper aims to understand the accessibility of health facilities for migrant workers working in the Bengaluru Metro Project. This research will understand the legal, economic and psychological aspects related to the health status of migrant workers through qualitative study. The study used in-depth interviews to elicit responses from selected inter-state migrant workers to understand their access towards health facilities. The thematic analysis of the interview transcripts revealed a substantive gap in workers access to health facilities. The unregulated working conditions have added more stress to the workers, and due to poverty and unemployment back home, these hurdles are not forcing them to go back. More awareness creating interventions from the government can transform their lives. (2024), (University of Duisburg). All rights reserved. -
Intention to use fintech services: An investigation into the moderation effects of quality of internet access and digital skills
This paper aims to investigate the moderating influence of the quality of access to internet and digital skills on the factors that influence the intention to use fintech services among the young working population in India. We use the Theory of Planned Behavior to examine the intention to adopt financial technology in a rapidly technologically transformative Indian landscape. We conducted an empirical investigation on 324 young workers in India using the survey method. The TPB model's relevance in an Indian context is validated. Attitude, perceived behavioral control, and subjective norms together accounted for 48.7% of the variation in the intention to use fintech services. The quality of internet access significantly moderated the positive effect of young workers' attitudes on their intention to use fintech. Digital skills significantly moderated the positive effects of attitude and perceived behavioral control on intentions to use fintech services. India is considered a very fast adopter of digital technology. In India, the use of electronic channels in financial service delivery is on the rise. With the wide geographic dispersion and huge population, the quality of internet access and digital skills can influence the intention to use fintech services. There can be vast differences in the behavioral mindset of people in a developing country like India compared to that of a developed one regarding the use and adoption of digital platforms for accessing financial services. Developers and regulators must adopt approaches and policies that consider these behavioral factors. This paper examines the Theory of Planned Behaviour in the context of a rapidly transforming behavioural context in India with the adoption of technology-based financial services. The importance of quality internet access and digital skills as factors moderating the adoption of technology is examined in this paper, unlike many previous studies. 2024 Conscientia Beam. All Rights Reserved. -
The complexities of home and belonging in the Gulf-Malayalee experience: a close reading of Salim Ahameds Pathemari (2015)
This paper explores the interaction between home, belonging, and migration by closely reading Salim Ahameds 2015 Malayalam film, Pathemari. The paper briefly traces migration history from Kerala to the Gulf and its impact on Keralas housing boom, influencing its socioeconomic and cultural landscape. Through this, the paper examines how Gulf Malayalees navigate the multifaceted and contested concept of home despite being physically and emotionally displacedthe paradox of belonging and unbelonging, in their attempts to secure a material home while working as blue-collared Malayalee migrants in the Gulf. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Predicting Consumers' Usage Intention Towards User-Generated Content: A Hybrid SEM-ANN Approach
With the change in the communication pattern, end-users are engaging in creating content and refer-ring to the content created by other users while making purchase decisions. This research aims at modelling factors affecting consumers' usage intention (UI) towards user-generated content (UGC) using Need for Cogni-tion (NfC) as a moderator of the proposed relationships. The factors affecting consumers' UI involve perceived usefulness (PU), source credibility (SC), information quality (IQ) and NfC. Further, a novel attempt has been made by using the neural network approach to assess the predictive accuracy of the model. A structured ques-tionnaire was used to collect data from 298 consumers through a survey. Data were analysed using two-stage structural equation modelling (SEM) and artificial neural network (ANN). All the independent variables viz., PU, SC, IQ and NfC significantly affect attitude towards UGC, which in turn affects UI. Results of multi-group anal-ysis and a series of chi-square difference tests reveal that a NfC significantly moderates the relationship be-tween PU and attitude, as well as that between SC and attitude. The root mean square error values from the neural network analysis suggest that the models show good predictive accuracy. This study provides a novel assessment of the usage of a hybrid SEM-ANN approach for understanding of UGC by incorporating NfC as a moderator in shaping consumers' attitudes and intentions to use UGC. 2024 World Scientific Publishing Co. -
Design of automatic follicle detection and ovarian classification system for ultrasound ovarian images
Polycystic Ovary Syndrome (PCOS) is a common reproductive and metabolic disorder characterized by an increased number of ovarian follicles. Accurate diagnosis of PCOS requires detailed ultrasound imaging to assess follicles size, number, and position. However, noise often needs to be improved on these images, complicating manual detection for radiologists and leading to potential misidentification. This paper introduces an automated diagnostic system for integration with ultrasound imaging equipment to enhance follicle identification accuracy. The system consists of two main stages: preprocessing and follicle segmentation. Preprocessing employs an adaptive Frost filter to reduce noise, while follicle segmentation utilizes a region-based active contour combined with a modified Otsu method. Unlike the conventional Otsu method, where the threshold value is selected manually, the modified Otsu method automatically selects initial threshold values using an iterative approach. After segmentation, features are extracted from the segmented results. An SVM classifier then categorizes the ovarian image as normal, cystic, or polycystic. Experimental results demonstrate that the proposed methods Follicle Identification Rate is 96.3% and the False Acceptance Rate is 2%, which significantly improves classification accuracy, highlighting its potential advantages for clinical application. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database
In the rapidly evolving field of education, a semantic search engine is essential to efficiently retrieve knowledge experts data. Universities and colleges continuously generate a vast amount of educational and research data. A semantic search engine can assist students and staff in efficiently searching for required information in such a big data pool. The existing systems have limitations in providing personalized recommendations that align with the individual learning objectives of students and scholars, thus hindering their educational experience. To address this, this paper proposed a KMSBOT. This novel recommendation system effectively summarizes academic data and provides tailored information for students, research scholars, and faculty, enhancing educational experiences. This paper meticulously details the development of KMSBOT, which comprises a neo4j-based knowledge graph technique, the NLP method for data structuring, and the KNN machine learning model for classification. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses traditional recommendation systems limitations and contributes to a brighter future, improving user satisfaction and engagement in academic environments. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Trusted explainable AI based implementation for detection of neurodegenerative disorders (ND)
The potential of explainable artificial intelligence (XAI) in detection of neurodegenerative disorders (ND) holds great promise in the field of healthcare. These diseases interfere with the daily functioning and independence of a person. The current studies lack in highlighting the aspect of explainability in their predictions and the various algorithms cannot provide any plausible explanations for their predictions making it difficult for medical professionals to place trust in their findings. Thus, the proposed framework aims to bridge this gap by exploring the development of a trustworthy framework for XAI-based ND detection, focusing on key aspects that can significantly impact its effectiveness and acceptance. The framework makes use of Trust-based SHAP (SHapley Additive exPlanations) values in classification. By computing trust values, the framework ensures more reliable predictions and increases interpretability, instilling confidence in clinicians and patients. The results show that with the inclusion of the trust-driven framework, the accuracy of the algorithm increased from 93.33% in the normal circumstances to 98.21%, highlighting the efficacy of the framework as compared to the other works. This shows that a trustworthy framework for XAI-driven ND detection can reshape care by enabling early detection, personalized treatment plans and enhancing decision-making process. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
The Impact of Computer-Mediated Communication on Relationships and Social Interactions
Computer-mediated communication (CMC) has profoundly changed how we express or connect in the modern world. Various virtual platforms, like Instagram, WhatsApp, and online games, have transformed how we communicate, and there is an overlap between the virtual and the physical world. This reflective study uses a comprehensive literature synthesis to examine the transforming nature of CMC on relationships and socialization patterns. The findings emphasize the importance of a holistic approach to understanding technology in interpersonal communication. Through this study, we attempt to mitigate the potential harms of excessive internet use through digital literacy, reflecting on online interactions and mindfulness in using the medium, especially for school-age children. The main takeaway from this reflective research is that when using technology for communication, one should practice equality and fairness across the board. Both the real and virtual worlds operate on the same principles of similarity and social exchange to create relationships, even though these theories are based on traditional offline relationships. 2024 Taylor & Francis Group, LLC.