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Thermal optimisation through the stratified bioconvective jetflow of nanofluid
Bioconvection is a fascinating phenomenon observed in various biological systems, where the motion of motile microorganisms generates fluid flow patterns. This article explores the occurrence and characteristics of bioconvection within the context of a jet flow. The study of bioconvection in jet flow involves the interaction between motile microorganisms and the fluid dynamics of the surrounding medium. Microorganisms such as bacteria and algae are known to exhibit directed swimming behavior, which can lead to the formation of dynamic flow structures. Investigating the mechanisms underlying bioconvection in jet flow requires a multidisciplinary approach encompassing fluid dynamics, microbial ecology, and mathematical modeling. Experimental techniques, such as microscopy and particle image velocimetry, along with computational simulations, are employed to analyze the complex interactions between microorganisms and the fluid flow. In this regard, a supportive mathematical model is designed using partial differential equations (PDEs) which are later transformed into ordinary differential equations using similarity transformations. The resulting system of equations is solved using the RKF-45 method and the outcomes are recorded in tables and graphs. The consideration of thermophoresis has shown a significant impact on the heat and mass transfer of the jet flow and both these profiles are observed to increase with thermophoresis. Meanwhile, the Schmidt number decrease their respective mass profiles. Furthermore, the porosity is found to create a drag force which tends to oppose the fluid flow. 2023 Taylor & Francis Group, LLC. -
Perception to Control: End-to-End Autonomous Driving Systems
End-to-end autonomous driving systems have garnered a lot of attention in recent years, and researchers have been exploring different ways to make them work. In this paper, we provide an overview of the field with a focus on the two main types of systems: those that use only RGB images and those that use a combination of multiple modalities. We review the literature in each area, highlighting the strengths and limitations of each approach. We also discuss the challenges of integrating these systems into a complete end-to-end autonomous driving pipeline, including issues related to perception, decision-making, and control. Lastly, we identify areas where more research is needed to make autonomous driving systems work better and be safer. Overall, this paper provides a comprehensive look at the current state-of-the-art in end-to-end autonomous driving, with a focus on the technical challenges and opportunities for future research. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Transforming Teletherapy: Using Transfer Learning and NLP for Improved Mental Health Care
The increasing reliance on tele-therapy for mental health support highlights the need for advanced methodologies to improve diagnostic precision and patient outcomes. This study explores the transformative potential of transfer learning in natural language processing (NLP) to enhance the detection of mental health conditions during tele-therapy sessions. Leveraging a dataset sourced from mental health-related subreddits, which includes conversations mapped to five target categories (Stress, Depression, Bipolar Disorder, Personality Disorder, and Anxiety), we fine-tuned a pre-trained BERT model for multi-class classification. Our study's results highlight significant performance enhancements achieved through the implementation of transformer-based models. The proposed framework achieved an accuracy of 83%, with macro average precision, recall, and F1-score values of 0.84, 0.83, and 0.83, respectively. Class-specific analysis further underscores the model's robustness, with precision ranging from 0.75 to 0.92 and recall values exceeding 0.80 for most categories. These outcomes significantly outperform traditional machine learning models such as Random Forest (accuracy: 72.65%) and Support Vector Machines (accuracy: 69.71%), demonstrating the superior capacity of BERT to capture complex linguistic patterns and semantic nuances in patient interactions. This research underscores the transformative role of transfer learning in NLP applications for tele-therapy, offering a scalable and precise solution for mental health assessment and paving the way for personalized, AI-driven interventions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Text Summarization Techniques for Kannada Language
Text Summarization is summarizing the original text document into a shorter description. This short version should retain the meaning and information content of the original text document. A concise summary can help humans quickly understand a large original document better in a short time. Summarization can be used in many text documents, such as reviews of books, movies, newspaper articles, content, and huge documents. Text summarization is broadly classified into extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). Even though more research works are carried out using extractive methods, meaningful summaries can be attained using abstractive summary techniques, which are more complex. In Indian languages, very few works are carried out in abstract summarization, and there is a high need for research in this area. The paper aims to generate extractive and abstractive summaries of the text by using deep learning and extractive summaries and comparisons between them in the Kannada language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Novel Framework for Integrating Machine Learning in CSR to Accelerate Sustainability in the Indian Automobile Sector
This paper aims at determining the suitability of using machine learning in CSR in enhancing sustainability of the Indian automobile industry. Prominent automobile companies are known to be major sources of environmental pollution together with wastage of various natural resources. There is a challenge of incorporating sustainability policies in the sector due to the rising regulation and consumers' awareness. Machine learning contains new approaches to managing resources more effectively, minimizing emissions and providing transparency of goods to clients. This study scrutinizes the previous literature, outlines the machine learning-based framework system of CSR activities, and validates the applicability of the system using case studies and qualitative data. The results show that learning from data can improve sustainability at an extensive scale and that the changes are sustainable and financially advantageous. 2025 IEEE. -
Parametric Study on Compaction Characteristics of Clay Sand Mixtures
The behaviour of fine-grained soils can be attributed to their mineral composition and the amount of fines present in them. The present study aims to determine the effect of mineral composition and quantity of fines on the Atterberg limits and compaction characteristics and to determine the correlation between them. Two types of fine-grained artificial soil mixtures were prepared in the laboratory representing kaolinitic and montmorillonitic mineral compositions.The amount of fines was varied at 10% intervals, from 50 to 100%. The Atterberg limits like liquid limit, plastic limit, shrinkage limit, and compaction characteristics like maximum dry density (MDD) and optimum moisture content (OMC) for two compaction energy levels, i.e. standard proctor (SP) and modified proctor (MP) tests, were determined. The correlations were developed between percentage fines and Atterberg limits and similarly between percentage fines, Atterberg limits, and compaction characteristics for artificial mix proportions. The developed correlations were used to predict the properties of natural soil samples, and the predicted and actual values are compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Investigating Key Contributors to Hospital Appointment No-Shows Using Explainable AI
The healthcare sector has suffered from wastage of resources and poor service delivery due to the significant impact of appointment no-shows. To address this issue, this paper uses explainable artificial intelligence (XAI) to identify major predictors of no-show behaviours among patients. Six machine learning models were developed and evaluated on this task using Area Under the Precision-Recall Curve (AUC-PR) and F1-score as metrics. Our experiment demonstrates that Support Vector Classifier and Multilayer Perceptron perform the best, with both scoring the same AUC-PR of 0.56, but different F1-scores of 0.91 and 0.92, respectively. We analysed the interpretability of the models using Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP). The outcome of the analyses demonstrates that predictors such as the patients' history of missed appointments, the waiting time from scheduling time to the appointments, patients' age, and existing medical conditions such as diabetes and hypertension are essential flags for no-show behaviours. Following the insights gained from the analyses, this paper recommends interventions for addressing the issue of medical appointment no-shows. 2024 IEEE. -
In Silico Screening of Medicinal Plant-Derived Compounds Against Spodoptera litura
Spodoptera litura (Lepidoptera: Noctuidae) is a major agricultural pest in the Asia-Pacific region, causing significant crop damage. Current pest control strategies heav-ily rely on chemical pesticides, leading to environmental concerns and rapid resistance development. Molecular docking and molecular dynamics studies were used to investigate bio-compounds from three medicinal plants-Vitex negundo, Artemisia nilagirica, and Portulaca oleraceaas potential eco-friendly pest management al-ternatives. Gas chromatography-mass spectrometry (GC-MS) analysis identified 28 phytochemicals, of which 14 conformed to Lipinskis Rule of Five, which were selected as ligands. Molecular docking simulations were conducted to evaluate ligand interactions with four key target proteins in Spodoptera litura: acetylcholinesterase (AChE), carboxylesterase (CES), ecdysone receptor (EcR) and juvenile hormone (JH). Among the tested compounds, oxalic acid, 6-ethyloct-3-yl hexyl ester, and (11Z)-13-methyl-11-tetradecenyl acetate exhibited the highest binding affinities (-8.4 to-6.5 kcal/mol), suggesting their potential as inhibitory agents. Normal mode analysis (NMA) revealed low eigenvalues of the complexes, ranging from 9.6992260-5 to 3.0715890-4, indicating flexibility and requiring minimal energy for conformational changes. Deformability was highest in hinge regions, while var-iance analysis confirmed inverse proportionality across the complexes. The B-factor graph highlighted stable mobility and the root mean square (RMS) of the 3D con-former structures. Elastic network graphs displayed residue interactions as dots, with darker grey areas signifying greater stiffness. ADME/T analysis showed that these compounds possess favorable pharmacokinetic properties, including efficient ab-sorption and metabolism, while exhibiting no significant risks of mutagenicity or cardiotoxicity. These findings further support the suitability of Oxalic acid, 6-ethyloct-3-yl hexyl ester, and (11Z)-13-Methyl-11-tetradecenyl acetate as promising candidates for advancing sustainable and eco-friendly pest management approaches. Furthermore, the potential of identified plant-derived compounds as novel biopesti-cides contributes to sustainable and environmentally responsible pest management strategies. 2025, Brawijaya University. All rights reserved. -
Alternative Worldview: The Naga Weretiger, an Ecolegend in When the Millet Fields Flower
This paper analyses the Naga Eco-legend tekhumevi to introduce an alternative worldview through Indigenous communities' philosophy and lived experience. In the context of contemporary environmental discourses, literature plays a significant part in highlighting the affective folklore guiding ethical, environmental practices in regions that are considered ecologically rich areas. Foregrounding the vibrant tapestry of North East Indian Indigenous cultures, it aims to discuss the impact of extraordinary stories on the lives of Nagas and how they shape the community's worldviews. This includes their relationships with the non-human world and their cultural identity. The paper also discusses the vitality of the traditional ecological knowledge of the Indigenous communities and its potential to offer alternative ecological sustenance ethics through holistic worldviews. The oral tradition of the Naga community has re-emerged time and again as a potent tool in offering ecological solutions and abiding by the ethics of sustenance and co-existence. The paper discusses an example of such a toolthe Naga weretiger, or tekhumevi's colonial imagery in the Naga oral histories and lore. However, the perception of such philosophical instruments sees a change because of social and ideological shifts that may be attributed to the intervention of scientific technologies, religion, worldviews, and rationale. Similarly, the accelerated climate health crisis has shifted the focus to an inclusive approach in the 'literature of nature', especially towards the more-than-human, as an alternative to this crisis. The paper reinforces the importance of folk literature and its relevance in the contemporary Naga community, reaffirming Indigenous cosmovision and epistemologies as spaces of resistance and representation. Avinuo Kire's "When the Millet Fields Flower" from The Last Light of Glory Days (2021) intersects magic, terror, community, spiritualism, and ecological ethics. The tekhumevi narrative reinstates the Naga ecological wisdom of bridging the gap and promoting a liminal existence/relationship between the Naga people and non-human entities and spirits. 2026, Knowledge Hub Publishing Company Limited (Hong Kong). All rights reserved. -
MAGIC AND TERROR IN EASTERINE KIRES ECOLOGICAL FICTION: Indigenous Naga Ecofeminism and Conservation Ethics
Indigenous women across the globe are front-line environmental activists implementing sustainable living practices and conservation through their activism and narratives. Indigenous women writers from Nagaland dominate published creative work from the region, making creative writing a space of resistance and representation. Native or Indigenous knowledge systems revolve around ecocultural practices of sustainability and conservation ethics. The Tenyimia worldview of the Angami Nagas of Nagaland opens up possibilities of ecological ethics and sustainable living through its knowledge systems. A minority Indigenous community in the Northeast region of India, the Angami Nagas represent a worldview that offers sustainable living practices and means of forest conservation through narratives that incorporate magic and terror. Easterine Kire, a renowned writer from Nagaland, has revived the eco-culture of the community through her representation of the Tenyimia worldview, offering insights into Indigenous ecofeminist views through her narratives, which she terms Peoplestories.' The present chapter investigates how magic and terror in Easterine Kires fiction represent forms of Indigenous knowledge that help define ecological ethics. The study applies an Indigenous ecofeminist approach to Easterine Kires work which invokes magic and terror through forest spirits, river spirits, and environmental legends such as the Tekhumevi, or were-tiger, to offer a re-imagination of the ecological spaces traditionally reflected through the communitys oral narratives. 2025 selection and editorial matter, Ina C. Seethaler and Tripthi Pillai; individual chapters, the contributors. -
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Machine Learning based Plant Disease Identification by using Hybrid Nae Bayes with Decision Tree Algorithm
Artificial intelligence or machine learning as a domain started as a distinct domain marketplace for enthusiasts. Over an extended period of time, this has evolved into an industry with boundless potential. This is the focal point of a plethora of technologies like real-time analytics, deep learning in computer science. It's inherent to various customer needs such as fault detection, home automation, health monitoring devices as well as appliances, and multiple RPM devices Artificial intelligence which has been tested and trained to recognize and determine a plethora of flaws and inaccuracies. This could be intriguing procedures in day-to-day applications. An unimaginable number of prediction models, packages, libraries as well as sensors are utilized to sieve through flaws with the aid of mobile app development and other multispectral sensors. These trendy devices have become ever present and a part of our extensive routine. The demand for dependable and efficient algorithms is satisfied while implementing these devices. The objective primarily dictates emphasis on the prediction of plant diseases in the agricultural arena in reality by providing aid in the field of agriculture, and industry. In this case, the device incorporates a database which stores and keeps track of previously detected flaws or defects. In addition, the history of detected plant infections is maintained in an online repository. This can help with the forecast of the defects within the gadgets that are to be enhanced. Furthermore, the suggested approach of this text inculcates the invigilation of every leaf in the plant via machine learning model. Hence, this approach of implementation limits interaction of humans with the interface and it detects disease ridden plants efficiently with accuracy. The plant disease identification problem is to solve the proposed hybrid Nae Bayes with Decision Tree algorithm. The proposed model provides higher accuracy level compare to the regular model. 2023 IEEE. -
Photocatalytic seawater splitting for hydrogen fuel production: impact of seawater components and accelerating reagents on the overall performance
The future fuel, hydrogen, is a clean, sustainable energy source with a substantial density of energy per unit volume/weight. Breakthroughs in hydrogen production, storage, and transportation are essential to meet the sustainable global energy demands. Solar-to-hydrogen conversion through water-splitting reactions (via photo/electro/photoelectro-processes) is a promising strategy for producing green hydrogen fuel. Specifically, the photocatalytic hydrogen generation reaction, mimicking artificial photosynthesis, is a simple and cost-effective method adopted for solar-hydrogen production. Various semiconductor photocatalysts and hybrid photocatalytic systems have been developed to address the sluggish kinetics and selectivity of pristine water/seawater splitting reactions. Recently, seawater has been used as feedstock for large-scale hydrogen production to advance the field and alleviate the scarcity of freshwater sources. This review article, therefore, aims to highlight the importance of seawater splitting reactions using different photocatalytic systems. A brief introduction to the fundamentals, historical progress, and mechanism of the seawater splitting reaction is presented. The impact of seawater components and accelerating reagents on the intrinsic performance of water splitting catalysts is discussed in detail, followed by an elaborate discussion of natural water and artificial seawater splitting with emphasis on onerous photocatalyst designs. Finally, the current challenges and opportunities of saltwater electrolysis for sustainable hydrogen fuel generation and applications are discussed. 2023 The Royal Society of Chemistry. -
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Expanding the Notion of Personal Well-Being During COVID-19 Campus Closure in India: Results from a Mixed-Methods Study with Members of Higher Education
The COVID-19 pandemic has challenged lives globally in unprecedented ways. While numerous studies have discussed the impact of this pandemic on human lives, this descriptive study examined how this pandemic affected personal well-being (PW) for members of Indian higher education in the early phase of the pandemic in 2020 when there were no vaccines and remedies available. Research participants (n = 551) were faculty members, graduate students, and non-teaching staff in Indian higher education. At the time of data collection, when all campuses were closed, all participants were functioning in their roles in the academic communities via virtual platforms. This descriptive study, based on a mixed-methods research design with concurrent triangulation strategies, collected data from all regions of India. Resulting data identified and discussed the impact of the pandemic on six domains of PW in the life of participants: (a) self-care; (b) professional growth; (c) quality of interrelationship within the family; (d) relationships with significant others outside of the family; (e) process of experiencing/facing and addressing challenges; and, (f) relationship with spirituality/transcendental dimensions. The relevance of the last domain may be unique to Indian participants socio-cultural context and ethos. The findings and discussion explain how PW is a composite of all these six domains, and the pandemic expanded the notion of PW for the members of Indian higher education. Further, the findings also provided a general orientation on how educational leadership teams and institutions can enhance at least three specific dimensions of their community members and thus increase the likelihood of improving the quality of their professional and personal life. The findings may also have relevance for academic communities worldwide and inform clinicians working with members of academic communities, educational institutions, and policymakers. Penerbit Universiti Sains Malaysia, 2024. -
Implementing strategic responses in the COVID-19 market crisis: a study of small and medium enterprises (SMEs) in India
Purpose: The COVID-19 pandemic presents unprecedented challenges for small and medium enterprises (SMEs) in emerging economies. This paper aims to examine how India's SMEs implement their strategic responses in this crisis. Design/methodology/approach: The study uses dynamic capability theory to explore the strategic responses of SMEs. Strategy implementation theory helps to explain how they implement innovative practices for outcomes. A research model defines the COVID-19 challenges, strategic responses and performance outcomes. The study reports the findings of an initial pilot study of 75 firms and follow-up case study results in the context of COVID-19. Findings: Firms choose their approaches according to their perceived market risks. Case studies illustrate that firms display diverse attitudes depending on their strategic direction, leadership vision and organizational culture. They achieve different outcomes by implementing specific styles of risk management practices (e.g. risk-averting, risk-taking and risk-thriving). Research limitations/implications: Although the study context is Indian SMEs, the findings suggest meaningful lessons for other emerging economies in similar crisis events. The propositions may be extended to future research in broad contexts. Practical implications: Even in the extraordinary COVID-19 market crisis, SMEs with limited resources display their strategic potential by recognizing their unique capabilities, translating them into effective actions and achieving desirable outcomes. Social implications: In the COVID-19 pandemic, top leaders' mental attitude, strategic perspective and routine practices are contagious. Positive leadership motivates both internal and external stakeholders with an enormous level of collaboration. Originality/value: This rare study of Indian SMEs provides a theoretical framework for designing a pilot survey and conducting a case study of multiple firms. Based on these findings, testable propositions are articulated for future research in diverse organizational and national contexts. 2021, Emerald Publishing Limited. -
Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE. -
A survey on various applications of internet of things on blockchain platform
[No abstract available] -
Experimental, FEA, and machine learning studies on wear behavior of LM13 aluminum hybrid composites reinforced with zircon and graphite
This paper examines applied load and zircon reinforcement influence on LM13 alloy composites wear behavior. LM13 was reinforced with 3?wt.% graphite with 3, 6, 9, and 12 weight percent of zircon utilizing a stir casting technique with a chill end to achieve unidirectional solidification. Wear tests were conducted on specimen's chill end using a pin-on-disc apparatus under loads of 30?N, to 70?N in steps of 10?N incremental. The results indicated that when the amount of zircon went up, the wear rate dropped, reaching a minimum at 9?wt.% zircon, then slightly increasing at 12?wt.%. Specifically, wear rate reduced from 4.2?10?3mm/Nm at 3?wt.% zircon to 2.7?10?3mm/Nm at 9?wt.% zircon, before rising to 3.5?10?3mm/Nm at 12?wt.%, establishing 9?wt.% zircon as the optimum reinforcement. Finite Element Analysis (FEA) had been used to simulate wear behavior, and its predictions aligned well with experimental data, with deviations under 5%. Both experimental and FEA results confirmed that wear rate increases proportionally with applied load. Additionally, machine learning techniques were employed to validate the observed trends, enhancing the reliability of the findings. Microstructural analysis through Field Emission Scanning Electron Microscopy showed evidence of plastic deformation and delamination at higher stress levels, compromising material integrity. Notably, the composite with 9?wt.% zircon exhibited reduced wear deformation and minimal microstructural damage, confirming its effectiveness in improving wear resistance. IMechE 2025 -
Subscriber Preference and Content Consumption Pattern toward OTT platform: An Opinion Mining
Introduction: The outburst of the pandemic has paved the way for the immense popularity of over-The-Top (OTT) platforms among viewers. Furnishing an alternate medium to watch favorite shows and making it a new normal, the OTT platform has replaced the traditional entertainment platform. However, migrating from traditional television to an OTT platform is still a challenge in developing countries. Hence, the understanding of subscriber preferences and content consumption patterns becomes essential to planning and strategizing future business models. Purpose: The purpose of the paper is to examine the subscriber preference and content consumption pattern toward the OTT platform. In addition, this paper also investigates the popularity of leading OTT platforms among Indian viewers. Methodology: Data has been collected from the subscribers of three major OTT: Amazon Prime, Netflix Video, and Disney+Disney+Hotstar. A total of 1860 reviews were scraped as textual data and analyzed using the lexicon-based method. The polarity of the sentiments pertaining to the reviews of different platforms was analyzed using sentiment analysis. Furthermore, the topic modeling on the reviews was performed using natural language programming(NLP). Findings: The findings of sentiment analysis showed that Netflix and Disney+Disney+Hotstar had a considerable number of positive sentiments among viewers when compared to Amazon Prime Video. Eventually, the paper also showed negative sentiment towards Amazon Prime Video regarding streaming content, ad pop-ups, interface issue, shows, etc. Our findings help OTT platforms to determine which factors are driving this dramatic shift in viewer behaviour so that better strategies for attracting and retaining subscribers can be developed. Despite the rise in OTT platform popularity, this is the first study to investigate the content consumption pattern of OTT viewers comprehensively. 2022 IEEE.