Unveiling Cutting Edge Innovations in the Catalytic Valorization of Biodiesel Byproduct Glycerol into Value Added Products
Glyceric acid; Glycerol carbonate; Glycidol; Lactic acid; Solketal
The increasing production of biodiesel has led to a glut in the production of glycerol, which is a byproduct. This has resulted in the quest for alternative applications using glycerol as a cheap and readily available starting material. One promising approach is the catalytic valorization of glycerol, which converts glycerol into valuable chemicals such as 1,2-propanediol, lactic acid, and acrolein. The glycerol formed affects the efficiency of the biodiesel, and hence it must be removed. Different processes can convert glycerol to various useful products like glycerol carbonate, glycidol, solketal, lactic acid, and glyceric acid. These different products, the processes used for synthesis, and the various catalysts used have been discussed. The most effective methods for the syntheses, the numerous catalyst systems, mechanisms of the reactions, and applications of these products in different fields are discussed in this review. The paper also discusses the challenges and opportunities of glycerol valorization, including the need for improved catalyst selectivity and activity and the potential for integrating glycerol valorization with other biorefinery processes. Overall, the catalytic valorization of glycerol offers a promising pathway for utilizing this abundantly available resource, and this review provides valuable insights for researchers and practitioners working in this area. 2023 Wiley-VCH GmbH.
Mohan K.; Pai S.D.K.R.; Reghunath B.S.; Pinheiro D.
https://doi.org/10.1002/slct.202204501 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165495210&doi=10.1002%2fslct.202204501&partnerID=40&md5=d27c882557f8739d21d857059d8793dc
Mohan K., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Pai S.D.K.R., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Reghunath B.S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
circular economy; green supply chain management; resilience; reverse logistics; supply chain management; sustainability; sustainable supply chain management
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.
Organizations and Markets in Emerging Economies, Vol-15, No. 11720, pp. 378-417.
https://doi.org/10.15388/omee.2024.15.18 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213409549&doi=10.15388%2fomee.2024.15.18&partnerID=40&md5=e5a049f8d8a2eb646c4178c9bef83ba2
Rajkiran C.A., Christ Deemed to be University, India; Almeida S.M., Christ Deemed to be University, India
Unveiling metaverse sentiments using machine learning approaches
Behavior; Information technology; Neural nets; Qualitative research
Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited.
Natarajan T.; Pragha P.; Dhalmahapatra K.; Veera Raghavan D.R.
https://doi.org/10.1108/K-11-2023-2268 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188192750&doi=10.1108%2fK-11-2023-2268&partnerID=40&md5=214cdd4eeb7dcd452f9391b2d9870084
Natarajan T., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Pragha P., Department of Management Studies, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Dhalmahapatra K., Operations and Quantitative Techniques, IIM Shillong, Shillong, India; Veera Raghavan D.R., School of Business and Management, CHRIST (Deemed to be) University, Bangalore, India
Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
Binomial logistic regression; KNN; Malware; Random Forest; XGB classifier
Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Ravindra Babu S.; Leisha R.; Medows K.J.; Sreekumar K.; Girish G.P.
Lecture Notes in Networks and Systems, Vol-874 LNNS, pp. 277-286.
Springer Science and Business Media Deutschland GmbH
Ravindra Babu S., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Leisha R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Medows K.J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Sreekumar K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Girish G.P., Department of Finance, ICFAI Business School, IFHE University (a Deemed to-be-University under Sec 3 of UGC Act 1956), Hyderabad, India
Unveiling stabilization mechanisms in a chaotic fractional Cryosphere model
chaos control; mass balance; predictor-corrector method; Surface energy balance; time delay
This article investigates the influence of chaos control and time delay on a chaotic surface energy balance-mass balance model of the Cryosphere. This research delves into the effectiveness of employing the active control method to stabilize chaotic systems with different fractional orders. Moreover, the investigation uncovers a noteworthy aspect of system dynamics, highlighting the role of time delay as a stabilizing element in chaotic systems. The generalized predictor-corrector method has been used to solve the fractional delayed and non-delayed systems. Numerical simulations show that the addition of time lag confers stability to the chaotic model for fractional orders ? = 1 and 0.95. Remarkably, for ? = 0.90, 0.85, 0.80 and 0.75, the delayed model transitions into an asymptotically stable state, revealing the significant stabilizing effect of time delay. CSP - Cambridge, UK; I&S - Florida, USA, 2024
Mathematics in Engineering, Science and Aerospace, Vol-15, No. 1, pp. 5-14.
Chakraborty A., CHRIST (Deemed to be University), Department of Mathematics, Bengaluru, 560029, India; Veeresha P., CHRIST (Deemed to be University), Department of Mathematics, Bengaluru, 560029, India
Unveiling the Dual Potential of the MoS2@VS2 Nanocomposite as an Efficient Electrocatalyst for Hydrogen and Oxygen Evolution Reactions
2D materials; density functional theory; electrocatalysts; hydrogen evolution reaction; oxygen evolution reaction
Clean and reliable energy sources are essential amidst growing environmental concerns and impending energy shortages. Creating efficient and affordable catalysts for water splitting is a challenging yet viable option for renewable energy storage. Traditional platinum-based catalysts, while highly active, are quite expensive. Our study introduces two-dimensional (2D) MoS2@VS2 nanocomposites, developed using hydrothermal technique, as a bifunctional catalyst for the electrolysis of water into valuable products. Structural studies revealed the formation of MoS2@VS2 nanocomposites with a nanoflake-like structure, where MoS2 nanosheets grow on the VS2 surface. This 2D-based electrocatalyst demonstrated exceptional reaction kinetics, with low overpotentials of 265 mV for the hydrogen evolution reaction (HER) and 300 mV for the oxygen evolution reaction (OER) at 10 mA/cm2. Furthermore, the electrocatalyst displayed small Tafel slopes of 65 mV/dec and 103 mV/dec for HER and OER, respectively, along with excellent stability. The unprecedented catalytic activity stems from the synergistic effect between semiconducting MoS2 and metallic VS2. Density functional theory calculations confirmed that this synergy enhances the electrical conductivity, facilitating efficient electron transfer during the reaction and providing an abundance of exposed active sites. These results mold MoS2@VS2 nanocomposites as promising electrocatalysts for overall water splitting, paving the way for sustainable energy future. 2024 American Chemical Society.
Anju J.S.; Chacko L.; Sruthi T.; Gopika P.; Mathew V.; Aneesh P.M.
ACS Applied Energy Materials, Vol-7, No. 23, pp. 11184-11194.
https://doi.org/10.1021/acsaem.4c02504 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209723774&doi=10.1021%2facsaem.4c02504&partnerID=40&md5=0bb2251be04ee3ae978d57198621e476
Anju J.S., Department of Physics, Central University of Kerala, Kasaragod, Kerala, Periye, 671320, India; Chacko L., Department of Physics and Electronics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Sruthi T., Department of Physics, Central University of Kerala, Kasaragod, Kerala, Periye, 671320, India; Gopika P., Department of Physics, Central University of Kerala, Kasaragod, Kerala, Periye, 671320, India; Mathew V., Department of Physics, Central University of Kerala, Kasaragod, Kerala, Periye, 671320, India; Aneesh P.M., Department of Physics, Central University of Kerala, Kasaragod, Kerala, Periye, 671320, India
Unveiling the Dynamics of Initial Public Offerings: A Comprehensive Review of IPO Pricing, Performance, and Market Trends
Initial Public Offerings (IPOs); Issue price; Listing day performance; Listing price; Offer price
Initial Public Offerings (IPOs) serve as pivotal moments in the financial markets, representing a company's transition from private to public ownership. The importance of IPOs lies in their capacity to raise substantial capital, facilitating business expansion and development. This paper conducts an in-depth analysis of Initial Public Offerings (IPOs) in India spanning the period from 2018 to 2022, with a particular focus on their listing day performance. The study categorizes IPOs into various issue price ranges, revealing substantial variability in listing day returns across these categories. It underscores the importance of pricing strategy, emphasizing the need for companies to carefully assess their issue prices to align with market demand. Furthermore, the analysis highlights the varying levels of risk associated with IPO investments based on issue price ranges, advocating for diversification and thorough due diligence. In addition, the paper emphasizes the dynamic nature of IPO markets, influenced by factors beyond pricing, and encourages a balanced approach that considers both potential rewards and challenges. This research provides valuable insights for stakeholders, guiding companies, investors, and analysts in making informed decisions in the dynamic world of IPOs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Studies in Systems, Decision and Control, Vol-536, pp. 487-498.
Bavachan K.W., Phd Scholar, Christ Deemed to be University, Bengaluru, India; Muthu Gopala Krishnan M., Associate Professor, Christ Deemed to be University, Bengaluru, India
Unveiling the Dynamics: A Performance Analysis of RPL under Congestion in IoT Network
congestion parameters of RPL; ETX and congestion; RPL metrics in Cooja; RPL Protocol
The Routing Protocol for Low Power and Lossy Network (RPL) is a standardized routing protocol for resource constraint devices deployed in diverse applications in Internet of Things (IoT). RPL is the most efficient protocol which is carefully designed to meet energy efficiency of sensor nodes. However, this protocol is prone to network congestion which is one of most crucial bottlenecks of this protocol. In the current study a thorough analysis of effect of congestion on RPL routing metrics are analyzed. We have designed a congestion scenario using Cooja simulator and analyzed its effects on ETX, Power, Duty Cycle through graphs. The results of the experiments finally outline the critical parameters affected due to congestion in RPL. Grenze Scientific Society, 2024.
15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, Vol-2, pp. 4510-4516.
Srivastava V., Department of Computer Science, Banasthali Vidyapith, Rajasthan, India; Paul A., Department of Computer Science, Banasthali Vidyapith, Rajasthan, India; Sinha S., Department of Computer Science, CHRIST Deemed to be University, Bengaluru, India
Unveiling The Effects of Heavy Metals : A Comprehensive Study On Physiological, Phytochemical, and Anatomical Responses in Jacobaea Maritima (L.) Pelser and Meijden
Modernization and industrialization have been of great importance in the recent past, yet newlinethere are a few disadvantages including the release of harmful effluents into the environment. The medicinal plants that are found in these heavy metal polluted soils can have positive and negative effects as well. In this study the plant Jacobaea maritima (L.) Pelser and Meijden an important medicinal plant in the field of homeopathy was subjected to three different heavy metalscadmium, chromium and lead at a concentration range of 50, 100, 150, 200 and 250ppm.The morphological parameters, shoot length and root length reduced more than 50% and 25% incase of shoot and root respectively. Phytochemical analysis shows significant variation inchlorophyll. newlineThe highest of protein was found in Cr 100ppm (7.47mg) and the least was found inCd 250ppm newline(0.38mg). Proline was found to be high in Pb 200ppm (1.242mg/ml) and least in Cr50ppm newline(0.368mg/ml). The Total Phenolic Content (TPC) was found to be highest in Cr 250ppm newline(3.229mg/g) and least in control (0.57mg/g) and Total Flavonoid Content (TFC) was found to be highest in Cd 100 ppm and least in Control (0.04mg/g) plant which include root, shoot and leaf. The growth of the plant was observed along with a few parameters like net photosynthetic rate (Pn), transpiration rate (E), leaf stomatal conductance (C), and the photosynthetic active radiation (PAR). It has been found that the chlorophyll values in the Cr 150 (5.470.4) concentration are high. The chlorophyll values in Pb100 (9.40.35) and Pb250 (9.80.26) are in close proximity to newlineeach other. The highest chlorophyll was found in Cd100 concentration. The net photosynthetic rate was less affected in Pb150 (30.980.75), and most in Cr100 (4.050.09). The Cr 50 (0.190.02) showed the least. The Leaf stomatal conductance was drastically reduced in all the treated plants only Cr 100 (2298.251.85) showed minimal variation. In morphology shoot length newlineand root length were reduced more than 50% and 25% in case of shoot and root respectively.
Unveiling the Emotions: A Sentiment Analysis of Amazon Customer Feedback
Amazon customer feedback; Confusion matrix; Machine learning; Sentiment analysis; Word cloud
This study explores sentiment analysis in the context of diverse regions and contemporary customer feedback, aiming to address research questions related to consolidation based on polarity scores and sentiments. The research utilizes multinomial regression for a comprehensive analysis of customer feedback worldwide. The investigation incorporates confusion matrices, statistics, and class-specific metrics to evaluate the models performance. Results indicate a highly accurate model with perfect sensitivity, specificity, and overall accuracy. The analysis further includes a breakdown of key metrics such as accuracy, confidence intervals, no information rate, p-value, kappa, and prevalence, emphasizing the models robustness. In conclusion, the multinomial logistic regression model demonstrates exceptional performance in predicting sentiment across diverse classes, highlighting its effectiveness in sentiment analysis on a global scale. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Muthu Ruben V.; VijayaKumar R.; Sateesh Kumar T.K.
Studies in Systems, Decision and Control, Vol-536, pp. 403-410.
Muthu Ruben V., School of Law, Christ University, Bangalore, India; VijayaKumar R., Kristu Jayanti College (Autonomous), Bangalore, India; Sateesh Kumar T.K., Kristu Jayanti College (Autonomous), Bangalore, India
Unveiling the Future: Exploring Stock Price Prediction in the Finance Sector through Machine Learning and Deep Learning - A Comprehensive Bibliometric Analysis
Bibliographic Coupling component; Bibliometric; Co-authorship; Publication trends; R-studio; Vos Viewer
The investigation of predicting share prices is a captivating and beneficial area of study within the realm of economic research. precise projections and findings can potentially benefit shareholders by reducing the risk of making suboptimal investment selections. The objective of this investigation is to examine the present state of research pertaining to the prognostication of share price predictions through the utilization of Machine Learning (ML) and Deep Learning techniques. The present study examined the existing body of scientific works on methods involving DL and ML in the context of predicting the value of stocks. This study presents a comprehensive overview of research trends, methodologies, and applications in a particular field by conducting a bibliometric analysis of publications indexed in the Scopus database. Drawing from the presented data, recommendations for optimal methodologies can be formulated. The data was visually represented through the utilization of the R programming language and Vos Viewer software. The investigation additionally discerns the primary authors, institutions, and nations that are making contributions to this particular field of research. The outcomes of this investigation possess the potential to guide future research trajectories and offer significant perspectives for professionals and policymakers who are keen on utilizing machine learning and deep learning in the financial sector. 2024 IEEE.
Martha Sucharitha M.; Basha M.S.A.; Prabhavathi C.; Christina S.; Varikunta O.
International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICSSES62373.2024.10561389 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197864939&doi=10.1109%2fICSSES62373.2024.10561389&partnerID=40&md5=98fb047ec73bdf84444b07fc0e6876fd
Martha Sucharitha M., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to be University), GITAM School of Business, Bengaluru, India; Prabhavathi C., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Varikunta O., CMRIT Engineering College, Department of MBA, Hyderabad, India
Unveiling the Indian REIT narrative-qualitative insights intoretail investors perspectives
Challenges; India; Perspective; Qualitative study; REITs; Retail investors
Purpose: The present study delves into the causes of relatively lower retail participation in the Indian REIT market. Specifically, it investigates investors' attitudes and perceptions towards REITs as a unique asset class. This paper provides a comprehensive understanding of the perception and factors influencing Indian retail investors' reluctance to participate in the REIT market. Design/methodology/approach: Qualitative research was conducted through semi-structured interviews to gather insights from non-investors in REITs. The data were transcribed and analyzed using content analysis techniques. Finally, coding techniques were used to identify broad study themes. Findings: According to the study results, many retail investors are unfamiliar with REITs. Even among those knowledgeable about REITs and with a favorable view, it is not commonly seen as a feasible investment option due to its early stage, unattractive returns and limited number of REITs. Practical implications: Developed countries have established REIT markets, while it is still in its infancy in developing countries such as India. Financial advisors, fund houses and the media should focus on educating investors to increase awareness. Originality/value: The study is the first qualitative investigation into the perception of retail investors to understand the reasons for lower retail engagement in the Indian REIT market. 2024, Emerald Publishing Limited.
Journal of Property Investment and Finance, Vol-42, No. 6, pp. 539-553.
https://doi.org/10.1108/JPIF-01-2024-0003 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192820721&doi=10.1108%2fJPIF-01-2024-0003&partnerID=40&md5=afddea9c5122d4961ffab04abfe40433
Swathi G.R., Department of Commerce, Christ (Deemed to be University), Bengaluru, India; Uma V.R., Department of Commerce, Christ (Deemed to be University), Bengaluru, India
Unveiling the kinetics of oxygen evolution reaction in defect-engineered B/P-incorporated cobalt-oxide electrocatalysts
Crystalline defects; Electrocatalyst; Kinetic study; OER; Oxygen vacancy; Water splitting
Defect-rich transition-metal oxide electrocatalysts hold great promise for alkaline water electrolysis due to their enhanced activity and stability. This study presents a new strategy that significantly improve the OER activity of Co-oxide nanosheets through incorporation of B and P (B/P-CoOx NS), eventually leading to abundant surface defects and oxygen vacancies. The B/P-CoOx NS demonstrates low overpotential of 220 mV to achieve 10 mA/cm2. The electrochemical and kinetic studies coupled with conventional morphological and structural characterizations, reveal that various crystalline defects like vacancies, dislocations, twin planes, and grain boundaries play crucial roles in promoting the OH? ion adsorption, the formation of intermediates, and the desorption of oxygen molecules. The industrial viability of the developed electrocatalyst is substantiated through assessments under harsh industrial conditions of 6 M KOH at 60 C in a zero-gap single-cell alkaline electrolyzer which achieves 1 A/cm2 at 1.95 V. Chronoamperometry tests (100 h) highlight remarkable robustness of the electrocatalyst. This work establishes a new strategy to fabricate defect-rich OER electrocatalysts, setting a precedent to achieve better OER rates with non-noble materials. 2024
Bhide A.; Gupta S.; Bhabal R.; Patel M.; Bahri M.; Fernandes R.; Patel N.
https://doi.org/10.1016/j.mtener.2024.101638 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197747365&doi=10.1016%2fj.mtener.2024.101638&partnerID=40&md5=33016e03829a85944e93ed73a8b2235d
Bhide A., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India, Department of Materials, Design and Manufacturing Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom; Gupta S., Advanced Materials Department, Joef Stefan Institute, Jamova 39, Ljubljana, 1000, Slovenia; Bhabal R., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Patel M., Department of Materials, Design and Manufacturing Engineering, University of Liverpool, Liverpool, L69 3GH, United Kingdom; Bahri M., Albert Crewe Centre for Electron Microscopy, University of Liverpool, Brownlow Hill, Liverpool, L69 3GL, United Kingdom; Fernandes R., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Patel N., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Deep learning; Geographical features segmentation; Remote sensing; U-Net model; UNet++; UNetFormer
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 325-333.
Antony A., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, 560074, India; Kumar R.G., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, 560074, India
Unveiling the Motivation Drivers in Start-Up Workspace
Efficiency; Employees; Job satisfaction; Startups; Workplace environment
This article delves into the relationship between workplace happiness and productivity in startup settings. Its primary objective is to dissect the multifaceted factors impacting employee well-being, aiming to enhance overall efficiency by customizing the work environment in myriad ways. For this study, a descriptive causal methodology was employed to investigate the impact of workplace happiness on productivity within start-up companies. A carefully structured questionnaire was administered to 256 employees within well-established organisations in Bangalore, India. Participants were selected through a Judgement sampling process to ensure impartial and unbiased representation. Survey respondents preferred the pre-COVID working conditions, acknowledging their advantages. However, the increased autonomy and flexibility in work arrangements have led to enhanced productivity under the new hybrid model. Notably, when employees are entrusted with greater responsibility, their job satisfaction rises, resulting in increased work output. organizations are now tasked with offering additional incentives to remote employees, thereby elevating the satisfaction and job fulfilment experienced by these individuals. Effectively tackling challenges necessitates the alignment of learning and development objectives with the internal business processes that maximize each employee's abilities and potential. This involves meeting the criteria outlined in the balanced scorecard components. 2024, Iquz Galaxy Publisher. All rights reserved.
International Research Journal of Multidisciplinary Scope, Vol-5, No. 1, pp. 232-238.
https://doi.org/10.47857/irjms.2024.v05i01.0197 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185126069&doi=10.47857%2firjms.2024.v05i01.0197&partnerID=40&md5=a331b403e4eb341a09a9a16d587c6a03
Jenifer E.D., School of Business and Management, Christ University (Deemed to be), Bengaluru, India; Rashmi R., School of Business and Management, Christ University (Deemed to be), Bengaluru, India
Unveiling the Necropolitics of Oil on Migrant Bodies in Deepak Unnikrishnans Birds
Energy Humanities; exploitation; Labour; Middle East; petro-capitalism
Oil played a significant role in fuelling the sociopolitical and economic development of Middle Eastern nations, attracting mass migration from South Asian nations. The article draws a nexus between the energy dynamics and labour exploitation within these petroleum-rich nations. It undertakes a close reading of the text Birds from Deepak Unnikrishnans novel Temporary People as it depicts the lives of migrant labourers who navigate an exploitative petro-capitalist system. The fictional text employs a narrative strategy juxtaposing elements of magic and realism, opening up a space for multilayered marginalised voices. The article engages with energy theories and interweaves Mbembes theory on necropolitics to grasp oils sovereign influence in delineating the boundaries between life and death in migrant lives. The surplus energy generated through fossil fuel extraction contributes to notions of boundless growth, coupled with technical and economic progress, which conceals the intensive manual labour underpinning these petrocultures. The magical property alluded to oil and the spectral absence of labour in the socio-cultural imagination co-constructs an exploitative and dehumanising labour regime for migrants. The migrant body is kept alive, and their existence is contingent upon the instrumental value of their labouring body, which constructs them as easily disposable and expendable as they are positioned outside the formal boundaries of citizenship. 2024 South Asian University.
Society and Culture in South Asia, Vol-10, No. 2, pp. 277-296.
Jose B., CHRIST (Deemed to Be University), Karnataka, Bangalore, India
Unveiling the pattern of PhishingAttacks using the Machine Learning approach
Adaptive Learning; Models; Phishing Detection; Real-time Threat Detection; User-Centric Design
This study introduces a unique approach to strengthening cybersecurity by combining advanced models for real-time detection of phishing websites. A classifier is trained to discern patterns associated with legitimate and phishing URLs, leveraging a carefully organized labeled dataset. The model in this paper forms the foundation for a real-time detection system, providing users with real-time information on potential phishing threats. Integrating an adaptive decision-making algorithm improves decision-making adaptability, particularly in scenarios challenging the model's confidence. A user feedback loop ensures the continuous learning and refinement of the system, aligning it more closely with user expectations. The future scope of this research involves exploring advanced models, improving explainability, and incorporating dynamic features for enhanced detection. Adaptive policies, large-scale deployment, and ethical implications are pivotal for real-world applicability. In conclusion, this study contributes to advancing phishing detection methodologies and lays the groundwork for future innovations in cybersecurity. The collaborative efforts of academia, industry, and cybersecurity stakeholders arenecessaryfor realizing the full potential of this paper and ensuring a safer online platform for users. 2024 IEEE.
Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024
Sinha V., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, NCR, Delhi, India
Unveiling the Potential of Bacillus paramycoides, a Halotolerant Endophytic Bacterium with Heavy Metal Tolerance and Plant Growth Promotion Properties
Bacillus paramycoides; bioremediation; endophytes; heavy metals; plant growth promotion
The use of heavy metal resistant plant growth promoting endophytes is an effective method for improving crop yield and cleaning up contaminated sites. In our study, we have isolated thirteen bacterial endophytes from the shoots of Alternanthera philoxeroides, an aquatic plant from Bellandur lake, Bangalore, India. Among the isolates, Bacillus paramycoides showed significant plant growth promotion properties including an extortionate amount of indole acetic acid (IAA) production (144.69 1.01 g/mL) along with other plant growth promoting attributes like ammonia production, nitrogen fixation, phosphate, potassium solubilization, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase and siderophore production. The isolate also demonstrated the ability to resist pathogen attacks by producing extracellular enzymes, which could have potential industrial uses. Furthermore, it displayed resistance to multiple heavy metals like chromium (Cr), copper (Cu), lead (Pb), zinc (Zn) and cadmium (Cd) as well as the ability to tolerate high salt concentrations (up to 7% NaCl). These characteristics make it an ideal candidate for promoting plant growth in stressful environments and as an effective bioremediation agent. 2024 World Researchers Associations. All rights reserved.
Research Journal of Biotechnology, Vol-19, No. 3, pp. 120-126.
https://doi.org/10.25303/1903rjbt1200126 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185577035&doi=10.25303%2f1903rjbt1200126&partnerID=40&md5=61bc31a84ad30b398895fb68a9c76c9d
Indhu P., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, 560029, India; Suma S., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, 560029, India; Soma B., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, 560029, India; Saranya J., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, 560029, India
Unveiling the potential of large language models: Redefining learning in the age of generative AI
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are transforming industries by fostering innovation, automating tasks, and enhancing creativity. By enabling personalized user interactions, sophisticated content creation, and advanced data analytics, they are revolutionizing industries such as healthcare, education, and customer service. As these technologies evolve, they can fundamentally change communication and decision-making processes and incorporate AI into everyday life. The objective of this book chapter is to examine the architecture and components, features, functionality, domain-specific applications, recent advances, and future developments of LLMs. Ongoing research aims to reduce biases, increase energy efficiency, and facilitate interpretation. As LLMs continue to evolve, they have the potential to transform many industries, including education, customer service, content creation, and more. As a result, they will be essential for the development of future AI-powered applications. 2024, IGI Global. All rights reserved.
Intersection of AI and Business Intelligence in Data-Driven Decision-Making, pp. 389-414.
Varghese N., Christ University, India; Ramasamy G., Christ University, India
Unveiling the potential: iodide-infused nickel-enhanced MXene composite for high-performance sodium ion hybrid capacitors
composite materials; MXene; sodium ion hybrid capacitor; sustainable battery materials
2D-MXenes have gained much popularity for energy storage applications such as hybrid capacitors, and they have shown very competitive performance, especially as electrode materials for sodium ion hybrid capacitors. However, they suffer from various problems, such as morphology distortion and fast capacity fading, which results in the poor performance of the battery. As a result, researchers have focused more on MXene-based composite materials to address these issues. In this work, we report a sodium iodide and nickel-decorated MXene-based composite (Ti2C/Ni/NaI) material as an electrode for a sodium ion hybrid capacitor. Ti2C MXene and Ni were able to provide physical and mechanical strength, and iodine was able to produce redox activity. The composite had a rough surface with readily aggregated 2D-MXene sheets and was uniformly covered with Ni, Na, and I atoms. Several vibrational bands and peaks associated with Ti, Ni, Na, C and O in the Raman while XPS spectra confirmed the effective incorporation of dopants into the MXene sheets and successful synthesis of the Ti2C/Ni/NaI composite. The fabricated hybrid capacitor exhibited good capacity retention of 59% after 10,000 cycles at a current density of 0.5 mA g?1; thus, the Ti2C/Ni/NaI composite can be a promising electrode material for sodium-based hybrid capacitors. 2025 The Author(s). Published by IOP Publishing Ltd.
Desai N.; Gurumurthy S.C.; Shilpa M.P.; Sudhakar Y.N.; Shetty V.R.; Selvakumar M.; Waikar M.R.; Sonkawade R.G.
Materials Research Express, Vol-12, No. 1
https://doi.org/10.1088/2053-1591/ada51f https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214813535&doi=10.1088%2f2053-1591%2fada51f&partnerID=40&md5=955c5f1c4b96b8f1bf30a571c79224a1
Desai N., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Gurumurthy S.C., Nano and Functional Materials Lab (NFML), Department of Physics, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Shilpa M.P., Nano and Functional Materials Lab (NFML), Department of Physics, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Sudhakar Y.N., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Shetty V.R., Department of Chemistry, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Selvakumar M., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India; Waikar M.R., Radiation and Materials Research Laboratory, Department of Physics, Shivaji University, Maharashtra, Kolhapur, 416004, India; Sonkawade R.G., Radiation and Materials Research Laboratory, Department of Physics, Shivaji University, Maharashtra, Kolhapur, 416004, India