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                <text>Recent Advances in Nanomaterials Based Molecularly Imprinted Electrochemical Sensors</text>
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                <text>electrochemical sensors; molecular imprinting; Molecularly imprinted polymers; nanomaterials; selectivity</text>
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                <text>Nanotechnology and molecular imprinting both are omnipresent in the modern scientific world. Molecular recognition in the biological systems was mimicked to an extreme extent with its difficulties through molecular imprinting. Solving the problems related to this mimicking was the goal of science and technology. Some challenges like difficulties with the imprinting of protein, poor compatibility with aqueous environments, template leakage, and heterogeneous populations of binding sites in the polymers that contribute to a high level of nonspecific binding sites were addressed with recent advancement in the modern era. These issues were solved later with nano level instrumentations and inventions. Different types of nanomaterials were employed for this research on molecular recognition through MIPs to enhance selectivity, sensitivity and stability to specific systems such as sensors. This review paper attempts to give all the recent advances in molecular imprinting and the potential of nanomaterials in electrochemical sensors.  2021 Taylor &amp;amp; Francis Group, LLC.</text>
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                <text>Ann Maria C.G.; Varghese A.; Nidhin M.</text>
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                <text>Critical Reviews in Analytical Chemistry, Vol-53, No. 1, pp. 88-97.</text>
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                <text>Ann Maria C.G., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India; Nidhin M., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Ann Maria, C.G.; Nidhin, M.</text>
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                <text>Dual ion-imprinted chitosan-stabilized platinum nanoparticles for simultaneous electrochemical detection of Cd2? and Pb2? in water samples</text>
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                <text>Ann Maria C.G., Department of Chemistry, CHRIST(Deemed to Be University), Bengaluru, India; Nidhin M., Department of Chemistry, CHRIST(Deemed to Be University), Bengaluru, India</text>
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                <text>The development of highly selective and ultrasensitive electrochemical sensors for trace-level heavy metal monitoring remains a critical challenge in environmental analysis. In this work, a novel dual ion-imprinted, chitosan-stabilized platinum nanoparticle (PtNP)modified glassy carbon electrode (GCE) is reported for the simultaneous electrochemical detection of Cd2? and Pb2? in aqueous media. The synthesized PtNPs provide a large electroactive surface area and are uniformly stabilized within a chitosan matrix enriched with oxygen- and nitrogen-containing functional groups, which act as selective coordination sites for target metal ions. The incorporation of dual ion-imprinting generates specific recognition cavities that promote selective adsorption of Cd2? and Pb2? through electrostatic and coordination interactions, leading to significantly enhanced sensitivity and selectivity. Under optimized conditions, the sensor exhibits wide linear detection ranges of 44.04pM0.18nM for Pb2? and 79.4pM0.18nM for Cd2?, with remarkably low detection limits of 13.2pM and 23.83pM, respectively. The proposed sensing platform demonstrates excellent anti-interference capability and reliable performance in real water samples, confirming its applicability for practical environmental monitoring. This study highlights the synergistic effect of dual ion-imprinting and chitosan-stabilized PtNPs, offering a robust and efficient strategy for multi-ion electrochemical sensing of toxic heavy metals.  The Author(s), under exclusive licence to Springer Nature B.V. 2026.</text>
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                <text>Cd&lt;sup&gt;2&lt;/sup&gt;? detection; Chitosan-stabilized PtNPs; Differential pulse anodic stripping voltammetry; Dual ion-imprinted sensor; Heavy metal sensing; Pb&lt;sup&gt;2&lt;/sup&gt;? detection</text>
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                <text>Springer Science and Business Media B.V.</text>
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                <text>ISSN: 13880764;</text>
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                <text>Ann Maria, C.G.; Varghese, Anitha; Nidhin, M.</text>
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                <text>Molecularly Imprinted Electrochemical Detection of Atrazine in Water Samples Using Chitosan-Stabilized Palladium Nanoparticles</text>
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                <text>Journal of the Electrochemical Society;Volume;172;Issue;2;Article No.;27510;</text>
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                <text>Ann Maria C.G., Department of Chemistry, CHRIST (Deemed to be University, Bengaluru, 560029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University, Bengaluru, 560029, India; Nidhin M., Department of Chemistry, CHRIST (Deemed to be University, Bengaluru, 560029, India</text>
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                <text>Atrazine, a slow-degrading herbicide needs constant monitoring and control as it causes carcinogenicity and reproductive abnormalities. This study presents a new electrochemical sensor designed to detect atrazine in water with high precision. The sensor uses a special technique called molecular imprinting along with chitosan-stabilized palladium nanoparticles on a glassy carbon electrode. The PdNPs enhance the electroactive surface area and thus the number of interaction sites for the analyte. Different material characterization techniques were used to characterize the formulated PdNPs. Molecularly imprinted nanomaterials were used to provide the selectivity among other interferents of atrazine. Differential pulse Voltammetry was used to detect atrazine with a limit of detection of 10.115 fM and a wide linear dynamic range of 33.72 fM to 3.1 nM. The sensor was efficiently tested in different polluted water samples without interference from structural analogs. This sensor offers a reliable tool for monitoring water quality and protecting human health by ensuring atrazine levels are safely controlled.  2025 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.</text>
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                <text>atrazine; chitosan; electrochemical sensors; molecular imprinting technology; palladium nanoparticles</text>
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                <text>Institute of Physics</text>
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                <text>ISSN: 134651; CODEN: JESOA</text>
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                <text>An extensive review on transition metal catalyzed indole C[sbnd]H activation: Catalyst selection and mechanistic insights</text>
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                <text>Antiviral drugs; Catalyst; CH activation of indoles; Iridium; Palladium; Rhodium; Ruthenium and manganese</text>
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                <text>The present review article explores the expansive synthetic methodologies facilitated by C[sbnd]H activation of indoles using transition metal catalysts. The strategic utilization of catalysts such as palladium, rhodium, iridium, ruthenium, and manganese has revolutionized organic synthesis by enabling selective alkynylation, acylation, and annulation reactions. These transformations are pivotal in pharmaceuticals, particularly in the synthesis of antihistamines and potential antiviral drugs against SARS-CoV-2. Additionally, these catalysts play a crucial role in perfumery and other chemical industries, enhancing the efficiency and precision of compound synthesis. The choice of transition metal catalysts is informed by their affordability and compatibility with both traditional analytical methods and innovative techniques like microwave synthesis and LED irradiation. Furthermore, this review underscores the interdisciplinary impact of transition metal-catalyzed C[sbnd]H activation on indoles, highlighting its significance in advancing both fundamental organic chemistry and applied sciences essential for modern technological advancements and drug discovery efforts.  2024 The Author(s)</text>
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                <text>Ann Mary C.J.; Vasudevan D.; Nagarajan P.; Suhas S.; Vijayan A.; Radhakrishnan K.V.; Sudhakar Y.N.</text>
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                <text>Sleep disorders manifest differently across individuals, making accurate diagnosis and treatment highly complex. Even within the same diagnosis, there can be variation in sleep architecture among patients, which makes generalization across people difficult. Traditional sleep analysis methods rely on manual scoring and fixed diagnostic criteria, which fail to capture subject-specific variability in sleep patterns. To address this, we propose a data-driven Personalized Sleep Signature (PSS) approach that learns individualized sleep behaviour using AI models. This study introduces the PSS framework, combining Transformer-based Attention and Graph Attention Networks (GATs) to model nuanced sleep characteristics. We utilize the Nationwide Childrens Hospital Sleep Data, a paediatric Polysomnography (PSG) dataset containing EEG and physiological parameters such as ocular movements, EMG activity, blood pressure, and respiratory rate. From this, we extract sleep epoch features and demographics to form Sleep Signature Groups that reflect common behavioural patterns. Unlike conventional classification, our method captures personal variability and delivers individualized sleep hygiene guidance. The model achieved 94% accuracy in detecting sleep patterns, outperforming traditional methods. Beyond clinical applications, it can be integrated with wearable sensors (e.g., Fitbit, Oura, Apple Watch) to personalize wake/sleep routines and environments. It also enables early detection of sleep disorders and aligns daily schedules with individual chronotypes to enhance well-being. By focusing on sleep behaviour rather than rigid diagnostic categories, this approach supports non-pharmacological, personalized interventions backed by scientific evidence. Our work opens the door to precision sleep medicine, offering actionable insights for clinicians, researchers, and technology innovators.  Little Lion Scientific.</text>
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                <text>ntegrating molecularly imprinted nanoparticles, environmental contaminants are detected electrochemically on a glassy carbon electrode that caters as the transducer host. The modification of electrodes using different noble nanoparticles resulted in enhanced electrooxidation of analytes. The allocation of chitosan as a reducing and stabilizing agent in the green synthesis of noble metal nanoparticles enhances the sensor's efficiency. Different characterizations like UV-Visible Spectroscopy, Fourier Transform Infra Red Spectroscopy, Transmission Electron Microscopy, and Dynamic Light Scattering analysis further confirm the synthesized nanoparticles' morphology, stability, and size. The different experimental conditions needed for effective detection, like electrolytes, potential window, scan rate, and pH, were optimized with utmost careful examination. The morphological characterization of the electrodes were executed utilizing Scanning Electron Microscopy and Optical profilometry, whereas the electrochemical characterization was performed using Electrochemical Impedance Spectroscopy. The Nyquist plot showcased the low resistance and high charge transfer of modified imprinted electrodes with enhanced surface area. Using Differential Pulse Voltammetry, the sensor was validated with nano to femto-level detection limits and a wide linear range with good sensitivity. The imprinting factor displays the superior electroactivity of imprinted sensors compared to non-imprinted sensors. The molecularly imprinted electrode effectively detected environmental pollutants in different water samples. Hence, all the works point to the exceptional approach of imprinted nanomaterials in electrochemical detection with its simplicity and facile preparation. It may be used to develop a susceptible voltammetric sensor for researching environmental pollutants. </text>
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                <text>Comprehensive strategies of Lignocellulolytic enzyme production from microbes and their applications in various commercial-scale faculties</text>
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                <text>Enzyme applications; Lignocellulolytic enzymes; Microbial enzymes; Plant biomass</text>
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                <text>Activities of anthropological organisms leads to the production of massive lignocellulosic waste every year and these lignocellulolytic enzymes plays crucial role in developing eco-friendly, sustainable and economical methods for decomposing and pre-treating the biomass to produce biofuels, organic acids, feeds and enzymes. Lignocellulolytic enzymes sustainably hydrolyse the biomass and can be utilized in wide range of applications such as personal care, pharmaceutical, biofuel release, sewage treatment, food and beverage industries. Every year a significant ton of biomass waste is released and insight on these crucial enzymes could establish in all the industries. However, due to the increased demand for compost materials, biomass degradation has resulted in composting processes. Several methods for improving compost amount and quality have been explored, including increasing decomposer inoculums, stimulating microbial activity, and establishing a decomposable environment. All of these prerequisites are met by biotechnological applications. Biotechnological procedures are used to improve the activity of enzymes on biomass. It leads to an adequate supply of compost and base materials for enterprises. In terms of effectiveness and stability during the breakdown process, lignocellulolytic enzymes derived from genetically modified species outperformed naturally derived lignocellulolytic enzymes. It has the potential to increase the quality and output of byproducts. This review discussed the development of lignocellulolytic enzyme families and their widespread applications in a variety of industries such as olive oil extraction, carotenoid extraction, waste management, pollution control, second-generation bio-ethanol production, textile and dyeing, pharmaceuticals, pulp and paper, animal feed, food processing industries, detergent, and agricultural industries.  2022 Visagaa Publishing House.</text>
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                <text>Natural Resources for Human Health, Vol-2, No. 1, pp. 1-31.</text>
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                <text>ISSN: 25831194</text>
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                <text>Annadurai Y., Department of Biotechnology, Bharathidasan University, Tamil Nadu, Tiruchirapalli, 620024, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Arumugam V.A., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, 641046, India; Liu W.-C., Department of Animal Science, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China; Pushparaj K., Department of Zoology, School of Bioscience, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, Coimbatore, 641 043, India; Pappusamy M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Bhotla H.K., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Meyyazhagan A., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Easwaran M., Computational Biology Laboratory, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, 641046, India; Piramanayagam S., Computational Biology Laboratory, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, 641046, India</text>
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                <text>SPP1, a potential therapeutic target and biomarker for lung cancer: functional insights through computational studies</text>
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                <text>gene expression analysis; gene network analysis; Lung cancer; molecular docking; molecular dynamics simulation</text>
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                <text>NIH reported 128 different types of cancer of which lung cancer is the leading cause of mortality. Globally, it is estimated that on average one in every seventeen hospitalized patients was deceased. There are plenty of studies that have been reported on lung cancer draggability and therapeutics, but yet a protein that plays a central specific to cure the disease remains unclear. So, this study is designed to identify the possible therapeutic targets and biomarkers that can be used for the potential treatment of lung cancers. In order to identify differentially expressed genes, 39 microarray datasets of lung cancer patients were obtained from various demographic regions of the GEO database available at NCBI. After annotating statistically, 6229 up-regulated genes and 10324 down-regulated genes were found. Out of 17 up-regulated genes and significant genes, we selected SPP1 (osteopontin) through virtual screening studies. We found functional interactions with the other cancer-associated genes such as VEGF, FGA, JUN, EGFR, and TGFB1. For the virtual screening studies,198 biological compounds were retrieved from the ACNPD database and docked with SPP1 protein (PDBID: 3DSF). In the results, two highly potential compounds secoisolariciresinol diglucoside (-12.9 kcal/mol), and Hesperidin (-12.0 kcal/mol) showed the highest binding affinity. The stability of the complex was accessed by 100 ns simulation in an SPC water model. From the functional insights obtained through these computational studies, we report that SPP1 could be a potential biomarker and successive therapeutic protein target for lung cancer treatment.  2023 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Annadurai Y.; Easwaran M.; Sundar S.; Thangamani L.; Meyyazhagan A.; Malaisamy A.; Natarajan J.; Piramanayagam S.</text>
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                <text>Journal of Biomolecular Structure and Dynamics, Vol-42, No. 3, pp. 1336-1351.</text>
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                <text>&lt;a href="https://doi.org/10.1080/07391102.2023.2199871" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/07391102.2023.2199871&lt;/a&gt;
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                <text>Annadurai Y., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Easwaran M., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Sundar S., Department of Biotechnology, PSG College of Technology, Tamil Nadu, Coimbatore, India; Thangamani L., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Meyyazhagan A., Dipartimento di Medicina e Chirurgia, Universitdi Perugia, Perugia, Italy, Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India, Department of Translation Medicine and Surgery, Perugia University, Perugia, Italy; Malaisamy A., Transcription Regulation Group, International centre for Genetic Engineering and Biotechnology, New Delhi, India; Natarajan J., Text Mining Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India; Piramanayagam S., Computational Biology Lab, Department of Bioinformatics, Bharathiar University, Tamil Nadu, Coimbatore, India</text>
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                <text>Wearable Leaf-Shaped Slotted Antenna Including Human Phantom for WBAN Applications</text>
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                <text>Complete ground; human phantom; ISM; Leaf pattern; Slot antenna; WBAN</text>
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                <text>A 5.8 GHz leaf-shaped slotted antenna for Wireless Body Area Network (WBAN) applications is presented in this piece of content. The leaf structure includes tri leaves, having a complete ground plane at the lowest floor and a central circle slot. The suggested antenna is 60 mm by 60 mm by 1.16 mm in total dimensions. The ISM (Industrial Science and Medical) band frequency of 5.8 GHz is covered by this antenna's radiation range of 5.5 to 6.4 GHz. The radiated pattern, efficiency, S11 magnitude and gain were the different attributes of the leaf-patterned slot antenna. The creation of a stylish leaf-shaped antenna that can be incorporated into clothing designs is the main goal of this project. This antenna may be used in difficult situations because of its flexible base and conductive fabric. The method considers the needs of wearable antennas, such as the impact of human interactions on this antenna, as well as the opposite.  2023 IEEE.</text>
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              <elementText elementTextId="171840">
                <text>Annalakshmi T.; Neethu P.S.; Revathi G.; Vanitha L.; Sushanth S.G.; Joy S.</text>
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                <text>IEEE 9th International Conference on Smart Structures and Systems, ICSSS 2023</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICSSS58085.2023.10407069" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSSS58085.2023.10407069&lt;/a&gt;
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                <text>ISBN: 979-835038420-8</text>
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                <text>Annalakshmi T., S.A.Engineering College, Ece Department, Chennai, India; Neethu P.S., School of Engineering and Technology Christ (Deemed to Be University), Ece Department, Bangalore, India; Revathi G., Saveetha School of Engineering, Ece Department, Chennai, India; Vanitha L., S.A.Engineering College, Ece Department, Chennai, India; Sushanth S.G., School of Engineering and Technology Christ (Deemed to Be University), Bangalore, India; Joy S., New Horizon College of Engineering, Ece Department, Bangalore, India</text>
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                <text>A virtual private social network (VPSN) allows a device to communicate securely with a network via the internet. Confidential data may be sent more securely thanks to the encrypted connection; it allows the user to operate remotely and prevents unauthorised parties from listening in. A mobile messaging platform is a text-enabled mailbox on the web. They enable companies and organisations to communicate with clients via text message. Social networking organisations may now use compute resources as a utility instead of creating and managing their computer infrastructures thanks to cloud computing (CC). An example of how a distributed sensor-actor environment might be used in a sociology-technical network is shown in this paper. The results are obtained as regular media access actively is 85.7%, security services in IoT is 85.37%, text attackers is 83.6%, loss of information is 82.8%, and blocking text messages is 91.48%. Copyright  2024 Inderscience Enterprises Ltd.</text>
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                <text>Annamalai A., Amity Institute Information Technology, Amity University Rajasthan, Rajasthan, Jaipur, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Shanmugasundaram S., Department of Engineering, Faculty of Engineering and Technology, Botho University, P.O. Box 501564, Kgale KO, Near Game City, Gaborone, Botswana</text>
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                <text>EFFECTS OF VIRTUAL PRIVATE SOCIAL NETWORKING IN ACADEMIC PERFORMANCE OF STUDENTS</text>
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                <text>CNN; Deep learning; Social network; Students</text>
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                <text>A virtual private social network (VPSN) is generated automatically amongst peers using a social media app to build ties. One of the most significant repercussions of students' excessive usage of social networking sites is a decline in their academic performance. In a study of medical students, social media and the internet were shown to harm students' academic performance and classroom attentiveness. An increasing number of studies link the use of social media to poorer academic performance, such as fewer students doing their assignments and lower test scores. Students who receive specialised training in deep learning will have the superior cognitive abilities needed to succeed in today's more cognitively demanding workplaces. It teaches children to be critical thinkers, productive members of society, and active participants in a democratic society. As a perceptron used in image recognition and processing, a convolutional neural network (CNN) processes pixel data from social networks. A CNN uses multiplayer perception to lessen the processing needs of pupils. Humans and neurons make up the VPSN-CNN network, which the article explains. Neurons generate dendrites and axons to receive and transmit signals, while humans engage with long-reaching telecommunication equipment or biological communication systems. These will help remember, learn, unlearn, and relearn what has already been learned. In courses where social networking sites were utilised in addition to traditional teaching methods, most students reported feeling more socially engaged and more positive about their educational experiences. Students' and instructors' concerns regarding the educational usage of social media are addressed with recommendations for further study and practice in better performance and accuracy for student's data secure and comparison with existing methods.  2023 Little Lion Scientific. All rights reserved.</text>
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                <text>Annamalai A.; Poonia R.C.; Shanmugasundaram S.</text>
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                <text>Journal of Theoretical and Applied Information Technology, Vol-101, No. 4, pp. 1579-1593.</text>
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                <text>Little Lion Scientific</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153087456&amp;amp;partnerID=40&amp;amp;md5=b3d67a8a9a2fe5239c66f7004a3fed7b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153087456&amp;amp;partnerID=40&amp;amp;md5=b3d67a8a9a2fe5239c66f7004a3fed7b&lt;/a&gt;</text>
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                <text>ISSN: 19928645</text>
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                <text>Annamalai A., Amity Institute Information Technology, Amity University Rajasthan, Rajasthan, Jaipur, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560 029, India; Shanmugasundaram S., Department of Engineering, Faculty of Engineering &amp;amp; Technology, Botho University, Gaborone, Botswana, PO Box 501564, Kgale KO, Near Game City, Gaborone, Botswana</text>
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                <text>Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach</text>
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              <elementText elementTextId="98169">
                <text>ARIMA; Holt's exponential smoothing; NNAR models; Time series analysis; Univariate analysis</text>
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                <text>This study uses three distinct models to analyse a univariate time series of data: Holt's exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction.  2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.</text>
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                <text>Annamalai N.; Johnson A.</text>
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                <text>SN Computer Science, Vol-4, No. 2</text>
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                <text>&lt;a href="https://doi.org/10.1007/s42979-022-01604-0" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s42979-022-01604-0&lt;/a&gt;
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              <elementText elementTextId="98176">
                <text>All Open Access; Bronze Open Access; Green Open Access</text>
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                <text>ISSN: 2662995X</text>
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                <text>Annamalai N., CHRIST (Deemed to be University), Pune Lavasa, India; Johnson A., CHRIST (Deemed to be University), Pune Lavasa, India</text>
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              <elementText elementTextId="105766">
                <text>Influence of perceived equity, job enrichment, and burnout among educators in Indian private universities on job satisfaction and the desire to quit</text>
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            <description>The topic of the resource</description>
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              <elementText elementTextId="105767">
                <text>burnout; desire to quit job; higher education; job enrichment; job satisfaction; perceived equity; university faculty members</text>
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                <text>The desire to quit converts into actual attrition if left unaddressed. Additionally, employees job satisfaction strongly influences their desire to stay or leave. Several individual and organizational factors affect job satisfaction levels, all of which must be thoroughly analyzed to curb both the desire to quit and attrition. The current study tests a model associating perceived equity, job enrichment, and burnout with job satisfaction and the desire to quit of educators employed in private universities in India. Data were collected from 272 university faculty members using five scales, namely, job enrichment, perceived equity, employee burnout, job satisfaction, and intention to leave, and were analyzed using AMOS 17. The initial fitness results failed to support the hypothesized framework, but a revised framework yielded a good fit for the data. Results show that perceived equity has a positive influence on job satisfaction (Hypothesis 2), job enrichment positively affects job satisfaction (Hypothesis 3), burnout negatively influences job satisfaction (Hypothesis 4), and job satisfaction negatively affects the desire to leave (Hypothesis 1). Perceived equity, burnout, and job satisfaction were found to mediate the association between job enrichment and the desire to leave. The results indicate that private universities must focus on job satisfaction to reduce employees desire to quit, thereby reducing the attrition level, which is currently a severe problem with both financial and non-financial consequences to universities. From the results, it can be seen that job enrichment has acted as a mediator to influence employees job satisfaction. Future research can explore HR practices contributing to high job enrichment, and this study would have considerable practical implications. Copyright  2022 Annamalai.</text>
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                <text>Annamalai S.</text>
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                <text>Frontiers in Education, Vol-7</text>
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                <text>In India, one in 500 people (Balaji, 2019) are diagnosed with ASD. Around 40 non-governmental organizations cater to Autism Spectrum Disorder (ASD) children; out of that, a few organizations focus on adults providing them vocational training to make them employable. One such exercise was initiated by SAP Labs India, a leading software company, and Enable India, an Indian NGO where they developed a focused training program for people with ASD and placed them in vital technical jobs in SAP Labs India (Karwa, 2016). First, we peek into the lifestyles of a few successful neurodiverse rock stars in India and their journey from becoming aware of their profile to establishing a career and becoming a role model to other people with ASD. Second, we present the autism landscape in India. Third, we explore the organizations that have hired people with ASD, their policies connected to neurodiversity, and the organizations that give training and support. Fourth, we present the potentials and the challenges people with ASD face. Fifth and final, we cover the role of different stakeholders to foster support and up-skill people with ASD for better community development.  2022 by Emerald Publishing Limited. All rights reserved.</text>
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                <text>Annamalai S., School of Business and Management, CHRIST (Deemed to be University), BGR Campus, Bangalore, India; Niranjan L.R., School of Business and Management, CHRIST (Deemed to be University), BGR Campus, Bangalore, India</text>
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                <text>This paper will focus on three experiments performed while teaching undergraduate and graduate students using innovative pedagogy. In all three studies, the purpose was to understand how effective these pedagogical tools were in generating enthusiasm and engaging the students in the learning process. In study 1, teams developed and presented video case studies on the organizational behaviour themes studied in the course, applying them to practical scenarios. Later, the facilitators conducted a modified gamification quiz based on the video case studies, and the quiz results were considered to be the actual assessment of the students learning process. In study 2, student teams were asked to brain-storm and develop case simulations based on actual incidents encountered by those students who had work experience. In study 3, the undergraduate students were shown the classic movie 12 Angry Men as a means of learning about team decision-making processes. They reflected on and discussed the movie in relation to theory. Feedback collected at the end of each study conveyed that students experienced high levels of enthusiasm and engagement with positive learning outcomes.  2020 NeilsonJournals Publishing.</text>
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                <text>Journal of Organizational Behavior Education, Vol-13, pp. 143-168.</text>
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                <text>Purpose: With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines. Design/methodology/approach: We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected. Findings: This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies. Originality/value: This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.  2024, Sumathi Annamalai and Aditi Vasunandan.</text>
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                <text>Annamalai S., School of Business and Management, Christ University, Bangalore, India; Vasunandan A., Goldman Sachs India, Bangalore, India</text>
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                <text>The paper tested a moderated mediation model with social isolation, loneliness, emotional intelligence, and quality of life among Generation Z (Gen Z) employees. Approximately 568 Gen Z employees participated in this study. We used WHOQOL-BREF for measuring quality of life, Schuttes emotional intelligence test, UCLA loneliness scale and social isolation scale from Choi and Noh. We applied the PROCESS macro (model 7) by Hayes for a moderated mediation analysis, using emotional intelligence as a moderating variable and loneliness as a mediating variable between social isolation and quality of life. The results indicate that emotional intelligence moderates the mediating effect of loneliness on social isolation and quality of life and supports hypothesis 2. First, the indirect impact of social isolation on quality of life varies as a function of emotional intelligence moderating the path. Second, both social isolation and loneliness are negative and significant predictors of quality of life. Loneliness is not an individual problem anymore but a public health issue around the world. Individuals who are lonely experience both mental and physical health issues. Strong measures are needed to combat loneliness, and the current research results confirm that emotional intelligence-based interventions will help individuals fight loneliness.  2024 The Author(s). Published by Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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                <text>Annamalai S., School of Business &amp;amp; Management, CHRIST University, Bangalore, India; Vasunandan A., Goldman Sachs, Bangalore, India; Mehta A., Bain &amp;amp; Company, Gurugram, India</text>
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                <text>In todays fast- paced and increasingly complex work environment, organizations are under immense pressure to ensure not just productivity but also the overall well- being of their employees. Through the exploration of real- world success stories, this chapter will illustrate the innovative strategies adopted by organizations to create environments where employees flourish. Additionally, Happiness Wisdom Framework, a practical guide that organizations can adopt to cultivate a culture of well- being, is proposed. Methodology relies on secondary sources and focussed group discussions conducted among Generation Z participants to provide a comprehensive analysis of employee well- being practices and the formulation of the Happiness Wisdom Framework. This chapter will be helpful for HR and organizational leaders looking to implement effective well- being strategies and benefit researchers and consultants interested in understanding how to create sustainable, positive workplace cultures through the adoption of innovative frameworks like the Happiness Wisdom Framework.  2025 by IGI Global Scientific Publishing. All rights reserved.</text>
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                <text>Annamalai S., School of Business and Management, Christ University, Bangalore, India; Deepika S.R., School of Business and Management, Christ University, Bangalore, India; Gunasekaran, School of Business Administration, Penn State Harrisburg, Middletown, PA, United States; De Lange A.H., Open University, Faculty of Psychology, Heerlen, Netherlands</text>
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                <text>The operational world today increasingly considers agricultural sustainability and gender equality as twin requirements for a bright future. In such a scenario, the rise of women agripreneurs from rural India, showing passion towards sustainable agribusinesses, can be considered as a positive and powerful transformation. Sharing their success stories, these women elevate their own voices and contribute to an agricultural ecosystem that has become more inclusive and inspiring for future generations. This chapter presents the journey of women agripreneurs living in various rural areas of Tamil Nadu. The chapter covers how these women agripreneurs are using various technologies in driving innovation, fostering sustainable practices to enhance their productivity. It explores social media platforms, namely YouTube channels, to locate women agripreneurs from remote locations in Tamil Nadu and uses the opportunity sample method for data collection. On receiving consent to participate in the research, nine women agripreneurs were interviewed who are very active in social media in developing their businesses, in the process creating a unique identity for themselves. First, the chapter focuses on understanding their experiences as agripreneurs and the passion, drive, and motivation that has ignited them so far. Second, their approach to sustainable practices in their agri-venture is covered. Third, the chapter presents the challenges and mitigation strategies adopted for sustainable growth. The chapter offers valuable insights for policy makers, stakeholders, and aspiring agripreneurs seeking to leverage technology for agricultural advancement and community empowerment.  CAB International 2026.</text>
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