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Fabrication of bismuth ferrite/graphitic carbon nitride/N-doped graphene quantum dots composite for high performance supercapacitors
Supercapacitors are potential energy storage devices with a broad range of applications. In this study, we are investigating a bismuth ferrite/graphitic carbon nitride/N-doped graphene quantum dots composite as an electrode material for supercapacitor applications. XRD patterns of the composite exhibit the different crystalline phases of the individual component and confirm the rhombohedral structure of the composite. The wafer-like structure of bismuth ferrite is produced via hydrothermal technique supported on 2D structures viz. graphitic carbon nitride and N-doped graphene quantum dots. Compared to bismuth ferrite and bismuth ferrite/graphitic carbon nitride (g-CN) binary composite, the bismuth ferrite/g-CN/N-doped graphene quantum dots demonstrates a superior specific capacitance of 1472 F g?1 at 1 A g?1 current density. After 3000 charging-discharging cycles, the device maintains its cycling stability with 87% capacitance retention. A supercapacitor device is assembled utilizing bismuth ferrite/graphitic carbon nitride/N-doped graphene quantum dots and activated carbon as electrodes. This device shows a significantly improved performance with an energy density of 53.1 Wh kg?1 and a power density of 705.4 W kg?1. As a result, the composite electrode developed in this study is proved to be a potential electrode material for high-performance energy storage devices. 2022 -
Real-time Litter Recognition Using Improved YOLOv4 Tiny Algorithm
Littered roads have become a familiar sight in India. The main reason is the increasing population and inefficient waste disposal system. Since garbage collectors cannot pick litter in all the places, there is a need for an efficient way to detect it. Hence, a machine learning-based object detection model is used. In this, we have applied an improved YOLOv4-Tiny algorithm to detect the garbage, classify it and make the detection process easier on custom datasets. We have improved the algorithm in terms of the object prediction time, this is done by replacing a max pooling layer with one of two layers present in a fully connected layer. When an input is given, the algorithm detects the litter in the image with a bounding box around it along with the label and confidence score. The proposed model reduces the prediction time by 0.517 milliseconds less than the original algorithm employed which concludes that the object is predicted faster. 2022 IEEE. -
Systematic Review on Online Brand Advocacy's (OBA) Antecedents and Consequences
Objective: The primary objective of this study is to discern and synthesize the antecedents and consequences of online brand advocacy (OBA) through a systematic review of empirical studies that employ OBA as a measurable construct. In this study, the gap in our knowledge about the drivers and the effect of OBA is identified, with the synthesis of empirical evidence being made on a systematic level to determine the main antecedents and outcomes of OBA. Methodology: A total of 35 peer-reviewed articles were compiled from Web of Science, Taylor and Francis, Springer, and Sage databases. The search keywords included OBA, Online brand advocacy, and brand advocacy. Inclusion criteria were restricted to quantitative, survey-based empirical studies conducted between 2010 to 2021. Findings: The antecedents, consequences and theories of OBA were categorised into six themes: customer-brand relational factors were brand identification, brand equity, trust factors, brand loyalty, brand authenticity, customer satisfaction, individual factors were intrinsic and extrinsic motivations, personality congruence, brand experience, brand love, cognitive and affective image and social factors were community involvement, brand community attachment, social identity, firm performance, wealth creation. Based on theoretical foundations, we have the social identity theory and the social influence theory as the last theme. Originality/value: This review is novel in compiling and categorising the antecedents, consequences, and theories of OBA as used in empirical studies, providing a solid foundation for advancing research on OBA. The Research Publication,. -
Explainable Artificial Intelligence: Frameworks for Ensuring the Trustworthiness
The growing computer power and ubiquity of big data are allowing Artificial Intelligence (AI) to gain widespread adoption and applicability in a wide range of sectors. The absence of an explanation for the conclusions made by today's AI algorithms is a significant disadvantage in crucial decision-making systems. For example, existing black-box AI systems are vulnerable to bias and adversarial assaults, which can taint the learning and inference processes. Explainable AI (XAI) is a recent trend in AI algorithms that gives explanations for their AI conclusions. Many contemporary AI systems have been shown to be vulnerable to undetectable assaults, biased against underrepresented groups, and deficient in user privacy protection. These flaws damage the user experience and undermine people's faith in all AI systems. This study proposes a systematic way to tie the social science notions of trust to the technology employed in AI-based services and products. 2024 IEEE. -
A Road to Become Successful in The Fashion Industry of China: A Case Study of Zara
In this research, it was found that Zara is facing issues while maintain its profitability and also while maintaining its large stores. Existing information collected from websites and articles show that Zara provides inferior quality products, does not have factory in China, focuses less on e-commerce activities and contributes directly to environment pollution through waste generation in China. These are reasons that the organization is losing its brand image in China. To improve its current condition, it is recommended that Zara should improve its products, focus more on marketing, develop factories in China and reduce environment pollution. The Electrochemical Society -
Hydrogen Sulfide-Induced Activatable Photodynamic Therapy Adjunct to Disruption of Subcellular Glycolysis in Cancer Cells by a Fluorescence-SERS Bimodal Iridium Metal-Organic Hybrid
The practical application of photodynamic therapy (PDT) demands targeted and activatable photosensitizers to mitigate off-target phototoxicity common in always on photosensitizers during light exposure. Herein, a cyclometalated iridium complex-based activatable photodynamic molecular hybrid, Cy-Ir-7-nitrobenzofurazan (NBD), is demonstrated as a biomedicine for molecular precision. This design integrates a hydrogen sulfide (H2S)-responsive NBD unit with a hydroxy-appended iridium complex, Cy-Ir-OH. In normal physiological conditions, the electron-rich Ir metal center exerts electron transfer to the NBD unit, quenches the excited state dynamics, and establishes a PDT-off state. Upon exposure to H2S, Cy-Ir-NBD activates into the potent photosensitizer Cy-Ir-OH through nucleophilic substitution. This mechanism ensures exceptional specificity, enabling targeted phototherapy in H2S-rich cancer cells. Additionally, we observed that Cy-Ir-NBD-induced H2S depletion disrupts S-sulfhydration of the glyceraldehyde-3-phosphate dehydrogenase enzyme, impairing glycolysis and ATP production in the cellular milieu. This sequential therapeutic process of Cy-Ir-NBD is governed by the positively charged central iridium ion that ensures mitochondria-mediated apoptosis in cancer cells. Dual-modality SERS and fluorescence imaging validate apoptotic events, highlighting Cy-Ir-NBD as an advanced theranostic molecular entity for activatable PDT. Finally, as a proof of concept, clinical assessment is evaluated with the blood samples of breast cancer patients and healthy volunteers, based on their H2S overexpression capability through SERS and fluorescence, revealing Cy-Ir-NBD to be a promising predictor for PDT activation in advanced cancer phototherapy. 2024 American Chemical Society. -
An objective function based technique for devignetting fundus imagery using MST
Fundus photography is a powerful imaging modality that is utilized for detecting macular degeneration, retinal neoplasms, choroid disturbances, glaucoma and diabetic retinopathy. As the illumination source in fundus imaging is situated at the center of the fundus camera, the illumination at the peripheral regions of the images would be relatively less than the center, which is termed vignetting. Vignetting adversely affects the performance of computerized methods for analyzing fundus imagery. A devignetting method for fundus imagery based on the Modified Sigmoid Transform (MST) is proposed in this paper. Gain (A) and centering parameter (?) of MST have a crucial influence on its performance. For low values of the gain, local contrast is penalized, and the overall dynamic range is compressed. When the value of gain is very high, the images after the illumination correction will have a washed out appearance. The optimum value of gain is determined in this paper from an objective method based on two statistical indices, Average Gradient of Illumination Component (AGIC) and Error of Enhancement (EME). MST with gain value defined via objective methods is able to correct the uneven illumination in fundus images without penalizing the local contrast. The proposed method is compared with illumination equalization model, homomorphic filtering and Adaptive Gamma Correction (AGC) and was found to be superior in terms of naturality uniformity of background illumination, and computational speed. 2018 -
Export Rhythms in Indian Agriculture: Trend and Seasonal Decomposition of Indian Cereal Products Exports
This study investigates the long-term trends and seasonal dynamics of Indias cereal exports specifically Basmati rice, non-Basmati rice, other cereals, and wheat using trend modelling and decomposition techniques. Drawing on monthly export data from April 2006 to November 2024 (with wheat data beginning in 2013), linear, log-linear, and quadratic trend models were estimated alongside additive, multiplicative, and STL (Seasonal-Trend decomposition using Loess) seasonal models. Results indicate strong linear and exponential growth in Basmati and non-Basmati rice exports, wheat exports exhibited no statistically significant trend and displayed high volatility. Durbin-Watson statistics revealed serial autocorrelation in most models, highlighting the importance of incorporating seasonality and external shocks in trend analysis. Additive decomposition reveals pronounced seasonal effects in Basmati rice exports, STL analysis confirms these patterns. Wheat shows moderate seasonal strength, while non-Basmati rice and other cereals exhibit mild seasonality. These findings underscore the necessity of commodity-specific export strategies aligned with harvest cycles and global demand windows. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN)
Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. 2024 IEEE. -
Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET
In recent years, Traffic management and road safety has become a major concern for all countries around the globe. Many techniques and applications based on Intelligent Transportation Systems came into existence for road safety, traffic management and infotainment. To support the Intelligent Transport System, VANET has been implemented. With the highly dynamic nature of VANET and frequently changing topology network with high mobility of vehicles or nodes, dissemination of messages becomes a challenge. Clustering Technique is one of the methods which enhances network performance by maintaining communication link stability, sharing network resources, timely dissemination of information and making the network more reliable by using network bandwidth efficiently. This study uses bibliometric analysis to understand the impact of Clustering techniques on VANET from 2017 to 2022. The objective of the study was to understand the trends & advancement in clustering in VANET through bibliometric analysis. The publications were extracted from the Dimension database and the VOS viewer was used to visualize the research patterns. The findings provided valuable information on the publication author, authors country, year, authors organization affiliation, publication journal, citation etc. Based on the findings of this analysis, the other researchers may be able to design their studies better and add more perception or understanding to their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Comparative Performance Analysis of Clustering Algorithms for Scalable and Reliable Vehicular Ad-Hoc Networks (VANETs)
Vehicular Ad-Hoc Networks (VANETs), widely used in intelligent transport systems, require effective clustering techniques to maintain network stability, reduce network latency and enhance communication efficiency. This research presents an in-depth analysis of three widely used clustering algorithms: K-Means, Spectral, and Leiden. Efficacy is assessed across different vehicle densities and speeds. The study focuses on examining four primary factors: the modularity of cluster formation, silhouette score, throughput, packet delay and cluster head change rate. The results obtained from the tests indicate that K-Means always sends more data & has the quickest packet delivery which generates the best-shaped clusters to elect CH. This is the best choice for networks with a varying number of cars that change speeds. Leiden does well when there are a lot of cars on the road. It stays stable but changes for huge graphs. Spectral clustering always does worse, with longer delays, less data getting through, and cluster heads that change too much. These findings show that selecting the right algorithm matters when building VANETs that can grow and stay reliable. The study concludes that K-Means is the best choice for cluster formation & electing CH where there is a need for quick responses and lots of data flow. Leiden works well in packed networks that need balanced performance. Spectral clustering does not work efficiently when keeping the network running in real-life vehicle situations at higher density & speed. 2025 IEEE. -
VAST-GCN: An Attention-Driven Graph Convolutional Network (GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks
Vehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. Picking the right cluster head (CH) plays a key role to keep connections stable and reduce routing overhead. This paper presents VAST-GCN (Vehicular Attention-based Spatial-Temporal Graph Convolutional Network), a new model that uses attention to make vehicle grouping and CH selection better across different network sizes. VAST-GCN mixes Graph Convolutional Networks (GCNs) with Spatial, Temporal, and Channel Attention systems. Approach in vehicle settings with 100, 500, and 1000 vehicles has been tested using real-time info like speed and place. The design has transformer blocks to model time-based features and attention modules to improve space and feature relationships leading to better vehicle data. Data have been grouped using the K-Means method and checked with modularity score, silhouette score, and group density. At the time of comparison, it has been observed that VAST-GCN does better than regular GCN and MIXHOP GCN models in cutting down loss making better community structures, and keeping CHs stable when there are few vehicles or theyre moving fast. The proposed VAST-GCN framework exhibits clear advantages over existing spatio-temporal GNNs by delivering superior modularity, silhouette scores, and cluster head stability across diverse vehicular scenarios. Its attention-driven architecture not only improves clustering accuracy but also reduces packet delay and enhances throughput, highlighting its excellence as a robust and scalable solution for dynamic VANET environments. The Author(s) 2025. -
A sustainable approach to cloud computing: A comprehensive analysis of load balancing techniques in cognitive environments
Cloud computing is one of the rising eras in big-scale computing, where information is processed in big information centers. One of the demanding situations with that is to accomplish load balancing of all the nodes. In addition, it's essential for proper utilization of assets and division of the work. This indicates that effective allocation and usage of the computing resources among clients in sharing mode leads to satisfactory users as well as good utilization of resources. Balancing the load in huge data-centric systems and networks is required to achieve the stated goal. The load balancing problem is considered as an optimization problem, and the rules for a load provide the number of compensations (scalability enabling, bottleneck avoidance) and resource consumption. A number of proposed algorithms exist for the load balancing problem in the cloud environment. Efforts have been made in this chapter to examine and review a few of the weight balancing algorithms in cloud computing. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
An AHP-TOPSIS Approach for Optimizing the Mechanical Performance of Natural Fiber-Based Green Composites
Natural fibers have emerged as an effective replacement for synthetic fibers in the fabrication of green composites to be used for producing various components in automotive, aerospace, and other applications. In this proposed study, the mechanical properties of banana and coir fiber-based green composites have been optimized by using a hybrid AHP-TOPSIS approach. Corn starch along with glycerol has been used as the matrix material for fabricating the green composites. The mechanical properties such as tensile strength, flexural strength, and impact strength of the developed green composite have been optimized with a focus on the utilization of this composite in automotive and aerospace applications. Three different weight percentages (0%, 5%, and 10%) of banana and coir fibers was considered for the fabrication of green composites. The constituents of the green composite have been taken as the input variables whereas the mechanical properties of the green composite are considered as the output variables for designing the experiment. The design of the experiment consisted of nine different combinations of input and output variables. Results of the study revealed that 5 wt.% of banana fiber, 10 wt.% of coir fiber, and 85 wt.% of corn starch provide the optimum mechanical performance of the developed green composites. 2022 A. N. Shankar et al. -
Comparative Performance Analysis of Machine Learning and Deep Learning Techniques in Pneumonia Detection: A Study
Pneumonia is a bacterial or viral infection that inflames the air sacs in one or both lungs. It is a severe life-threatening disease, making it increasingly necessary to develop accurate and reliable artificial intelligence diagnosis models and take early action. This paper evaluates and compares various Machine Learning and Deep Learning models for pneumonia detection using chest X-rays. Six machine learning models -Logistic Regression, KNN, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machines - and three deep learning models - CNN, VGG16, and ResNet - were created and compared with each other. The results exhibit how just the model choice can significantly affect the quality and inerrancy of the final diagnostic tool. 2023 IEEE. -
Work from home during covid-19-disequilibrium of mental health and well-being among employees
[No abstract available] -
Hycons Renewable Private Limited: Viable Biogas Production from Paddy Straws: A Capital Budgeting Decision
The case revolves around the decision to be taken by Mr Sashikant Hegde, Managing Director, Hycons Renewable Private Ltd, on the project viability of a business proposal. The proposal was to start a manufacturing plant in Punjab to produce compressed biogas using paddy straws. Hegde firmly believed that the company would do well considering the growth of the CNG market in India, as the oil and natural gas sector in India is among the top 10 core industries in the country and plays an important role in the existence of other important sectors as well. The proposal would benefit Hycons to establish its presence in northern India, but the project viability and funding of the investment remained an unanswered question. 2023 Lahore University of Management Sciences. -
Trends and patterns in fintech research: A bibliometric perspective
The traditional financial industry landscape has changed dramatically due to the quick development of financial technology, or Fintech, which has raised interest in academic study. In order to examine trends and patterns in Fintech research during the given time frame of 1980-2023, this study uses a bibliometric approach. Through a thorough analysis of an extensive library of academic publications, this study seeks to offer a complete grasp of the major topics, significant writers, and developing fields in the Fintech industry. Utilizing bibliometric methods and techniques, the methodology aims to derive meaningful insights from co- authorship patterns, citation networks, and metadata. By identifying the most cited papers, most contributing countries, and prolific writers, the study uses advanced data analytics to illuminate the intellectual framework of Fintech research. The findings of this research contribute to the identification of pivotal research clusters, highlighting the evolution of Fintech themes over time. 2024 by IGI Global. -
Impact of green bonds issuance on stock prices - Evidence from India
Today, with the increasing global warming, many companies are trying to adopt sustainable ways of producing the product and preserve the atmosphere. A green bond is one such financial tool that helps companies to raise the funds for social and eco-friendly projects. Keeping this in view and the Indian market emerging as the second-largest bond market in terms of green bond issuance; this paper aims to identify the impact on stock prices due to the issuance of green bonds by the companies. We conduct an event study to understand how the stock prices are subject to volatility due to green bond issuance during the period 2018-2021. The data is collected from secondary sources like Economic Times, Business Standard, Climate Bond Initiative, and the BSE website. The event window is assumed to be [-30,30], [-15, 15] and [-7, 7] days. Using Cumulative Average Abnormal returns and t-tests we understand the volatility of stock prices due to green bond issuance. The empirical results show that green bonds have a short-term impact on stock prices. Overall, the study can be a great input for the investors to understand the behavior of stocks due to the issuance of green bonds. 2023 Author(s). -
Financial well-being A Generation Z perspective using a Structural Equation Modeling approach
The current pandemic situation in the global economy has urged the need to revolutionize the financial services industry with a keen eye on consumers financial needs for sound financial decisions, which is necessary for financial well-being. The purpose of the study is to assess the financial well-being of Indian Gen Z students in relation to financial literacy, financial fragility, financial behavior, and financial technology. In addition, the study also tries to determine how Gen Z students financial well-being is influenced by other factors such as gender, age, parental education, employment status, and monthly income in India. The study uses the scientific data analysis approach, Partial Least Squares-SEM model to estimate, predict, and assess the hypotheses. A sample of 271 University students from India was surveyed using a self-administered structured questionnaire. Questions were incorporated to understand the effect of financial literacy, technology, fragility, behavior, demographic and parental characteristics on financial well-being. The results indicate that financial behavior is positively related to financial well-being, while financial fragility is negatively associated. However, financial literacy and financial technology do not significantly affect financial well-being. The results also show that financial well-being is significantly influenced by gender, parental education, employment status, and monthly income change. Understanding Indian Gen Z student financial well-being will expand the students understanding of the importance of financial literacy for well-planned financial behavior and informed decisions, hence high levels of financial well-being. Government and financial institutions can more effectively identify gaps and deficiencies in student financial well-being. 2022 LLC CPC Business Perspectives. All rights reserved.