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Alexithymia and Internet Addiction: Mediating Role of Social Connectedness, Impulsivity, and Moderation by Depression
Internet addiction is a mounting concern in current times. Recent studies indicate a link between alexithymia and Internet addiction, but the underlying mechanisms of this association require more investigation. The present study explores the relationship between alexithymia and Internet addiction, with the mediating effect of Impulsivity and social connectedness, and the moderating effect of depression. A convenience sample of 362 participants between the ages of 18 and 25 years participated in this study and completed the Youngs Internet Addiction Test, Toronto-Alexithymia Scale, The Social Connectedness Scale, Barratt Impulsiveness Scale 15, and The Centre for Epidemiological Studies Depression Scale Revised. The results indicate that the direct effect of alexithymia on Internet addiction is partially mediated through impulsivity and social connectedness. Further, the moderating effect of depression is found to be non-significant. The results revealed two possible pathways through which alexithymia influences Internet addiction. Future research and interventions on Internet addiction can use these findings to mitigate the adverse outcomes of Internet addiction. 2025, PsychOpen. All rights reserved. -
Anticancer potential of Ipomoea alba: Induction of apoptosis and cell cycle arrest in MDA-MB-231 cells
Cancer is one of the major global health concerns, which supports the investigation of novel therapeutic agents with enhanced potency and minimal side effects. Ipomoea alba, belonging to the Convolvulaceae family, was selected for this study due to its known anti-inflammatory properties and the presence of secondary medical applicability. In this study, the cytotoxic, apoptotic, and cell cycle arrest effects of the methanol extract of Ipomoea alba were evaluated against MDA-MB-231 human breast cancer cell lines. Cytotoxicity was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) assay across varying concentrations, revealing an IC50 value of 167.02 g/mL after 24 hours of treatment. Apoptosis and necrosis were examined through Annexin V/propidium iodide (PI) staining via flow cytometry, with treated cells showing 58.1% apoptosis (p < 0.001) and 16.71% necrosis (p < 0.01) compared to the control. Cell cycle distribution, evaluated using PI/ribonuclease (RNase) staining, revealed a significant increase in the G2 /M phase (56.31%, p < 0.001) and a mild increase in the Sub G0 /G1 phase (1.12%, p < 0.05), indicating arrest at critical regulatory checkpoints. These findings demonstrate that I. alba methanol extract possesses potent anticancer properties by inducing apoptosis and interfering with cell cycle progression in breast cancer cells. The results suggest its potential as a candidate for anticancer drug development and alternative therapeutic strategies. 2025, Indian journals. All rights reserved. -
GC-MS profiling of metabolites in blue and white varieties of heirloom butterfly pea (Clitoria ternatea L.) seeds
The Butterfly Pea is a tropical legume, a perennial herbaceous plant commonly found in Southeast Asia. The plant and its products are rich in bioactive ingredients, attracting the industrial and biopharmaceutical sectors due to their various applications. In this study, the blue and white flowered variety seeds of Butterfly Pea methanolic extract were comprehensively screened to identify the bioactive compounds and their drug-like properties. The methanolic extract was prepared by the cold maceration method, and the crude dried extract was subjected to GC-MS analysis for seed metabolite profiling. The chromatogram analysis revealed 39 abundant phytoconstituents, demonstrating the diverse chemical composition of the Butterfly Pea seeds. Among the identified compounds, the relatively abundant bioactive components in the blue variety seeds were stearic acid (64.6%), methyl stearate (54.0%), hexadecanoic acid, methyl ester (48.2%), and ethriol (35.9%). the white variety seeds primarily included palmitic acid (71.0%), hexadecanoic acid, methyl ester (53.4%), methyl stearate (42.0%), and hydrocinnamic acid (30.5%). Additionally, both varieties exhibited a diverse array of shared compounds reflecting their phylogenetic proximity. These metabolites are associated with key bioactivities in plant signaling and defense, playing vital roles in growth regulation, stress adaptation, and exhibiting potential antidiabetic properties. The research highlights the potential of the butterfly pea seeds as a valuable resource of active metabolites for vast research and therapeutic applications. 2025, Indian journals. All rights reserved. -
An ethnopharmacological investigation of antidiabetic plants used in Gudibande Taluk, Chikkaballapur District, Karnataka, India
This research offers an ethnopharmacological investigation of the application of medicinal plants for treating diabetes. An ethnobotanical survey was conducted in Gudibande Taluk, Chikkaballapur District, Karnataka, India. Traditional healers were interviewed about 28 plant species belonging to 22 families being used in treating diabetes. Fabaceae was recorded as the most prevalent family with maximum number of plant species. Leaves of 41.9% plant species were noticed as the most frequently plant parts used followed by fruits (12.9%), seeds (12.9%), and root (6.5%) for the treatment of diabetes. The study also comprized molecular docking and molecular dynamics simulations to assess the pharmacological potential of bioactive compounds, focused on interactions with human pancreatic alpha-amylase. Two ligands, metformin and compound 197678, were examined with GROMACS for 200 ns. The findings showed that all protein-ligand complexes maintained structural stability, with RMSD, RMSF, Rg, SASA, and hydrogen bonding metrics indicating the stability and possible effectiveness of these compounds. Conservation issues were also recognized, such as habitat loss and ignorance of younger generations about exposure of traditional knowledge. The results of the study underscored the healing potential of neglected medicinal flora and promote community-driven conservation of plants important for diabetes treatment. 2025, Indian journals. All rights reserved. -
Optimization Enabled Ensemble Learning for Leukemia Classification Using Microarray Data
Leukemia classification involves identification and categorization of various leukemias, a cluster of blood malignancies influencing white blood cells. Proper classification is crucial for selecting the appropriate treatment modalities and predicting outcomes in patients. Historically, leukemia classification was based on clinical and morphological characteristics, but new developments in genomics like microarray and next-generation sequencing tools have facilitated more accurate molecular classifications. Machine learning (ML) and deep learning (DL) methods have transformed leukemia classification by enabling automation of analysis in large and intricate datasets to ensure more accurate and efficient leukemia subtype classification. The primary goal of this research is to suggest a new leukemia classification method using microarray data. Leukemia microarray data first undergoes preprocessing, after which feature selection is performed through Serial Exponential-Secretary Bird Optimization Algorithm (SE-SBOA). SE-SBOA is an optimization method that embeds the exponential weighted moving average concept (EWMA) into Secretary Bird Optimization Algorithm (SBOA). The method helps to find the best feature subset, improving model performance at lower complexity. Lastly, leukemia classification is done using the proposed ensemble method that combines Graph Neural Network (GNN), Multi-Layer Perceptron (MLP) and Random Forest. Utilizing the advantages of GNN, MLP and Random Forest, the model proposed herein attains higher classification accuracy and proves to outperform traditional methods. Experimental results demonstrate that the SE-SBOA-based Ensemble Learning technique outperformed standard methods, attaining an accuracy of 95.9%, a precision of 96.1%, a recall of 96.2%, and an F1-score of 96.2%. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
An Adaptive Scriptless Behavior-Driven Development Automation Framework with Self-Healing Intelligence for Evolving Software Applications
Background: The high rate of user interface (UI) and source code changes in contemporary software development resulted in automated testing failures that augmented maintenance expenses and decreased the usefulness of automated testing. The current tools need regular updating by manual means, which is ineffective and expensive. Purpose: To present the Adaptive Scriptless Behavior-Driven Development (BDD) Automation Framework with Self-Healing Intelligence, which is an artificial intelligence (AI) and machine learning (ML)-driven framework of automatic test failure detection and resolution based on UI drift, broken locators, or timing. Approaches: The framework uses dynamic locator approaches, adaptive test generation, and reinforcement learning to allow updating test scripts in response to application changes. Such a self-healing feature will minimize human intervention and reduce maintenance expenses. An experimental case study was conducted in order to assess the performance of the framework in a practical context. Findings: The framework demonstrated significant advances in automated testing, such as a 30% drop in maintenance speed, reduced number of resources to update tests, a 25 % reduction in total cost of testing since less manual effort is needed, and a 40 % rise in stability of the test suites, which can execute its tests more reliably and with greater accuracy despite the presence of changes to the application. Conclusions: The Adaptive Scriptless BDD Automation Framework with Self-Healing Intelligence goes a long way to improving the flexibility, scalability, and efficiency of automated testing. It enhances the speed of testing, saves costs, and adds confidence in the quality of software, and is therefore valuable for ensuring high-quality standards in dynamic software landscapes. 2026, Innovative Information Science and Technology Research Group. All rights reserved. -
Performance Evaluation of Hybrid Lifi-Wifi Internet Systems
The rapid development of wireless technology made Light Fidelity, or LiFi, leave the laboratory and move onto the list of the next big thing, alongside everyday WiFi. WiFi also provides freedom of movement, but once everyone in a large area is streaming video, the signal becomes like a traffic jam. LiFi is going another way: data is sent via the rays of LED lights, meaning it can push files at light speed. The only problem is that the connection will not last forever, as soon as you leave the arc of the lamp or walk too far. In this paper, we explore what would happen when you combine the wide coverage of WiFi with the high speed of Li-Fi to form a hybrid system that avoids the weaknesses of both. Various test-based classrooms, halls, and open laboratories measured the speed of bit movement, the consistency of the beams, and the responsiveness of the games, and the mixed environment performed better in nearly all tests than the technology alone. It even figured out which to use as people entered and left the building, alternating between LiFi and WiFi, increasing uptime and doubling peak throughput. Hospitals, campuses, airports, and factories in need of constant, high-speed connections now have a better roadmap for using this blend so machines keep running smoothly and users remain engaged. The paper itself contains the unraveling of the hardware, the explanation of the test setup, and the weighing of the numbers, accompanied by some thought on what this combination can offer the future of wireless worlds. The findings justify integrating LiFi with WiFi as an innovative, scalable solution to ensure it keeps up with the massive data volumes generated by modern phones, tablets, and smart devices. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Comparative Study of Wi-Fi 6 and 5G for Residential Internet Services
Whereas Wi-Fi 6 and 5G briefly competed in the home internet space, 5G has since outpaced Wi-Fi 6 in speed and reliability. Wi-Fi 6 and 5G have detailed white papers outlining their protocols and specs. Initial home use and advanced Wi-Fi use should prioritize downloading, as Wi-Fi 6 offers a significant edge in download speed, latency, and efficiency in multi-device environments (most homes have a home intelligence system, phones, and PCs). 5G has no physical PC connection and must be provisioned by a carrier and covered by a cell tower. Hence, its advantages are for rural users and for users who typically work from home. Each has specific target markets, with the home as the primary focus, including streaming media and gaming, multiple smart home devices, and a home office. Install speed and cost, system-wide latency (total system, including devices), data retention and privacy, device lock (data retention), and scaling (to be sound). 5G uses cell towers with a large and covered geographic area and no physical restrictions. Each has target markets where advanced Wi-Fi has outpaced rural users and mobile users. This is where Wi-Fi has outpaced 5G (5G is a better solution for streaming, data retention, and scaling). Findings indicate that Wi-Fi 6 is excellent in environments with high-speed broadband. At the same time, 5G demonstrates its advantages in areas with low-quality broadband or when users require mobile Internet access. Because both technologies offer unique benefits, combining them may yield the best home connectivity. The document enables buyers, Internet Service Providers (ISPs), and community decision-makers to choose cutting-edge internet connectivity options. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Modeling User Movement Patterns for Enhanced Internet Experience
Providing an effective quality of service (QoS) can be very challenging, especially in mobile or dynamic environments. The goal of this study is to improve the Internet in a way that anticipates user mobility allowing for more efficient and responsive resource allocation through real-time resource management and connectivity prognostic. The ability for the model to determine typical paths, times, and transition probabilities between access points is accomplished by exploring historical location data, mobility traces, and user activity in a networked environment. The proposed findings can be integrated into network control algorithms requiring spatial predictive data for future user behaviours such as prefetching, intelligent handover, and load balance to configurable infrastructures. User mobility predictions of future mobility are augmented by the combination of machine learning and Markov Chains. Usability testing conducted utilizing real-world mobility datasets is suggestive of many improvements in terms of connection stability, reduction of latency, and increased efficiency of bandwidth utilization. The supporting evidence obtained from this study is supportive of the hypothesis that network management which aware of the mobile user will enhance performance and experience in urban areas and smart cities. This research is important because it further develops a notion of intelligent, human-centric communication systems towards 5G (and beyond) to reframe spatial and temporal user behaviour to be responsive, anticipatory internet service structures and/or systems. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Impact of Network Virtualization on Application Performance Metrics
The advent of network virtualization has redefined modern networking philosophies by allowing the partitioning of physical networks into virtual networks that are easily scalable, elastic, and efficient. The shift has been markedly critical to the function of applications in virtualized settings. This research examines the impact of network virtualization on key application performance indicators, including latency, throughput, packet loss, and resource utilization. The paper empirically assesses factors related to user experience and system performance by analyzing various virtualization technologies, including Software-Defined Networks (SDN), Network Functions Virtualization (NFV), and virtual overlay networks. The approach used entails deploying controlled benchmark applications on both virtualized and non-virtualized networks and measuring deviations from benchmark performance. Results indicate a strong correlation between the moderation of network performance overhead with virtualization and the presence of adaptive performance improvement schemes tailored to specific conditions. The efficiency of the hypervisor, placement of network functions, and orchestration policies significantly influence the responsiveness of the applications Additionally, the paper addresses the increasing scalability limitations of performance in cloud-native and edge-computing environments. There is a finding that indicates increased need for research 'effective intelligent resource management and adaptive network reconfiguration strategies in order to minimize latency and redundant bandwidth allocation bottlenecks'. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Performance Evaluation of Transfer Learning VGG16 in Handwritten Text Using Word Beam Search and Language Model
This study evaluates the performance of transfer learning using the VGG16 model for handwritten text recognition, integrating Word Beam Search decoding and language modeling techniques. The VGG16 model, pre-trained on large-scale datasets, serves as a feature extractor for handwritten text images, capturing intricate patterns and structures inherent in handwriting. To convert these visual features into textual information, the system employs a Recurrent Neural Network (RNN) trained with the Connectionist Temporal Classification (CTC) loss function, producing a matrix of character probabilities for each time-step. The Word Beam Search algorithm is utilized for decoding these probabilities into coherent text, effectively constructing recognized text by referencing a predefined dictionary and addressing challenges such as arbitrary character strings and varying handwriting styles. The integration of language models incorporates context which further sharpens the output and improves precision and trustworthiness of recognition systems. Experimental results demonstrate that this combined approach significantly improves recognition performance, highlighting the efficacy of transfer learning and advanced decoding strategies in handwritten text recognition. This involves analyzing its effectiveness across various datasets. Transfer learning leverages pre-trained models, like VGG16, to address challenges such as limited labeled data and extensive training times. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
An Energy-aware Dynamic Scheduling Algorithm for Optimizing Workflows Under Budget-constraints
The provision of cloud computing offers an untapping scalability and elasticity which is best suited for the execution of user tasks and complicated scientific workflows. Regardless, the big problem of workflow scheduling under a user-specified budget still prevails as a result of the task inter-dependencies and the resource diversity. This research proposes a hybrid Energy-Aware Enhanced Salp Swarm Algorithm (EA-ESSA), designed to dynamically schedule tasks while adhering to user-specified budget constraints. This technique supports dynamic scheduling using task duplication in idle time spots and integrates APIs for real-time spot pricing. This proposed technique also minimizes makespan and energy consumption by improving resource utilization. The algorithm's performance was exhaustively experimented with using both simulated workloads and actual HPC2N datasets. The simulation results show significant advancements in the makespan, resource utilization and energy consumption compared to existing algorithms like ACO, GA, PSO, and MOTSWAO. This research benefits cloud environments comprising complex, unpredictable workflows by cutting environmental effects and shrinking processing expenses. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Underwater Image Dehazing: A Comprehensive Approach
Underwater imaging in recent times has advanced by trying to correct color distortion, increase contrast, and increase image clarity if the light need is less. The use of deep learning has been effective in enhancing image quality, but challenges persist in the decompression process due to data inconsistencies. In order to do this a new scheme is proposed in this study. Unlike other methods which depend only on the single images captured, here an attempt is made to use images taken in other conditions to overcome this limitation, by using the model to try and improve such underwater images in general irrespective of the water conditions. A key innovation is the disassembly and synthesis of multi-channel illuminance data. Specifically, we decompose the input image into its red, green, and blue frequencies, and then approximate the illuminance component within each channel. By independently manipulating and reconstructing these channel-specific illuminance maps, we can effectively address the non-uniform light scattering and absorption that are characteristic of underwater environments. This allows us to correct for the inherent color casts and haze that degrade image quality. To further refine the enhancement, we incorporate, advanced color correction methods such as image saliency exploration and white balance adjustment to compensate for color attenuation caused by light absorption at different depths. These techniques effectively restore lost colors and enhance contrast, thereby improving image clarity and sharpness. This is helpful in the field of engineering and also forms the foundation for further exploring methods of improving images captured underwater. Investigational outcomes exhibit that the intended method ominously augments image eminence, making it highly effective for underwater detection and exploration tasks, offering an innovative solution for hazy images in various conditions and advancing underwater monitoring and exploration technologies. 2026, Modern Education and Computer Science Press. All rights reserved. -
Classification of Medicinal Plant Leaves using Deep Learning Algorithms
This research explores the automated leaf-based identification of medicinal plants, utilizing machine learning and deep learning techniques to address the crucial need for efficient plant classification. Driven by the vast potential of medicinal plants in pharmaceutical development and healthcare, the study aims to surpass the limitations of existing methodologies through thorough experimentation and comparative analysis. The primary goal is to develop a robust and automated solution for classifying medicinal plants based on leaf morphology. The methodology encompasses acquiring diverse datasets. Specifically, set 1 data is processed by applying resizing, rescaling, saturation adjustment, and noise removal, while Set 2 data is processed by applying resizing, rescaling, saturation adjustment, noise removal, and PCA (Principal Component Analysis). The proposed algorithms include Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), YOLOv8, Vision Transformer (ViT), ResNet, and Artificial Neural Networks (ANN). The study evaluates the efficacy and effectiveness of each algorithm in plant classification using metrics such as accuracy, recall, precision, and F1 score. Notably, the ResNet model achieved 93.8% and 94.8% accuracy in Set 1 and Set 2, respectively. The SVM model demonstrated 56.5% and 56.6% accuracy in Set 1 and Set 2, while the Vision Transformer (ViT) model achieved 84.9% and 74.4% accuracy in Set 1 and Set 2, respectively. The CNN model showcased high accuracy at 96.7% and 94.8% in Set 1 and Set 2, followed closely by the ANN model with 96.7% and 96.6% accuracy. Lastly, the YOLOv8 model achieved 96.0% and 95.1% accuracy in Set 1 and Set 2, respectively. The comparative analysis identifies CNN and ANN as the top-performing algorithms. This research significantly contributes to the advancement of medicinal plant identification, pharmaceutical research, and environmental conservation efforts, emphasizing the potential of deep learning techniques in addressing complex classification tasks. 2026, Modern Education and Computer Science Press. All rights reserved. -
Application of Large Language Models for Data-Driven Analytics in Oncology: Insights and Evidence Generation from Real-World Imaging Data
Breast cancer is one of the most common and serious types of cancer. It can affect people of all ages and genders around the world. The increasing incidence of breast cancer, coupled with its complexity, has placed a significant burden on healthcare systems and patients alike. Traditional diagnostic methods, while effective, often face limitations in early detection and accurate prognosis, which are critical for improving patient outcomes. In recent years, artificial intelligence (AI) and machine learning (ML) are changing the way we solve problems and make decisions in the field of medical diagnostics, enhancing the ability to detect, diagnose and predict breast cancer. However, there are still challenges, such as the need for large and diverse datasets to train these models, making AI tools work smoothly in hospitals, and addressing ethical concerns in healthcare. This paper looks at how AI and ML are used in breast cancer care, especially in analyzing real-world medical data like images, histopathology, and other datasets such as doctor notes & discharge summaries, to identify patterns that may be unnoticeable to medical experts. Large Language Models (LLMs) using embeddings, are highlighted for their capacity to improve the accuracy of image related interpretations, potentially detect early-stage tumours, and predict disease progression and treatment responses. Real-world medical datasets have been collected and analysed using different models. A publicly available Convolutional Neural Network (CNN) and a custom-built Large Language Model (LLM) with embeddings were tested. The Generative AI model achieved 98.44% accuracy, significantly higher than the traditional AI model's 61.72%. Future research can explore how Generative AI can help classify patients based on risk levels. This could lead to personalized treatment plans, reducing unnecessary treatments and improving patients' quality of life. Given the research is primarily focussed on breast cancer, there is an attempt to showcase that by harnessing the power of AI and ML, there is potential to significantly reduce the global burden of breast cancer, offering new avenues for early detection, accurate diagnosis, and tailored therapeutic strategies. Continued research and collaboration among oncologists, data scientists, and policymakers are essential to fully realize the benefits of AI in the fight against breast cancer, ultimately leading to better patient outcomes and a decrease in breast cancer-related mortality. 2025, Modern Education and Computer Science Press. All rights reserved. -
Bayesian and Frequentist Estimation of Stress-Strength Reliability from a New Extended Burr XII Distribution
In this article, we propose and study a new three-parameter heavy-tailed distribution that uni-fies the Burr type XII and power inverted Topp-Leone distributions in an original manner. This unification is made through the use of a simple shift parameter. Among its interesting function-alities, it exhibits possibly decreasing and unimodal probability density and hazard rate functions. We examine its quantile function, stochastic dominance, ordinary moments, weighted moments, incomplete moments, and stress-strength reliability coefficient. Then, the classical and Bayesian approaches are developed to estimate the model and stress-strength reliability parameters. Bayes estimates are obtained under the squared error and entropy loss functions. Simulated data are considered to point out the performance of the derived estimates based on the mean squared error. In the final part, the potential of the new model is exemplified by the analysis of two engineering data sets, showing that it is preferable to other reputable and comparable models. 2025, National Statistical Institute. All rights reserved. -
Ecological and Socioeconomic Triggers of Forest Fires in Uttara Kannada, India, and Their Impact on Biodiversity Conservation
This study studies the complex cause and consequence aspects of forest fire in the western ghats district of Uttara Kannada, Karnataka, India-a region particularly known for biodiversity. It singles out key factors influencing fire dynamics: natural or anthropogenic elements, such as lightning, droughts, and anthropogenic changes induced by socioeconomic change. Through field information and satellite image analysis, research shows how such climate change and increased human activities continue to fuel rising fire frequency and intensity, posing a threat to the ecological and biodiversity balance within the region. The study conclusion calls for collaborative forest management mechanisms that integrate grassroots practices with wider global conservation visions. This study puts forth actionable recommendations that improve the technique of fire management and prevention. These efforts seek to minimize ecological and economic damage brought about by forest fires. These efforts lead to better comprehension of the concept of ecosystem integrity and bring forward the relevance of preserving biodiversity against climatic and human challenges. 2025 The Author(s). -
Combined Antimicrobial Activity of Chromolaena odorata, Azadirachta indica Leaf Extract, against Streptomyces scabiei (Plant pathogen) and Paenibacillus polymyxa, and their interaction
Streptomyces scabiei is a gram-positive soil dwelling actinobacteria, that causes the scab disease in many plants, especially in potatoes. This causes black blotches on the tuber that negatively impacts the economic and market worth of potatoes. Plant species like Chromolaena odorata and neem are known for their antibacterial potential against common human microflora and agricultural pathogens. Aiming to find natural solutions to plant pathogens, the current study analyzes, the antimicrobial effect of these plant extracts at different concentrations using well diffusion, MIC and MBC. Combination study was also performed to test the synergistic antimicrobial effect against Streptomyces scabiei. In addition, another soil dwelling bacteria, Paenibacillus polymyxa, known for its plant growth promoting potential was also tested against the plant pathogen to understand microbe-microbe interactions through cross streak assay. Both plant extracts showed promising antimicrobial effect against Streptomyces scabiei, however, they showed very less antimicrobial potential against the plant growth promoting bacterium. Moreover, cross streak assay showed that both the bacterium coexisted together. Hence, further studies can be conducted to formulate a biofertilizer containing the two-plant extract in optimum concentrations and Paenibacillus polymyxa to prevent scab disease and also enhance plant growth. 2025, Crop Protection Research Centre. All rights reserved. -
Brand Loyalty Drivers among Generation Z Fashion Consumers: A Comparative Analysis
Brand loyalty is crucial in the competitive fashion market, particularly among Generation Z. Although previous studies have investigated what drives loyalty, there is still limited evidence from India, particularly about gender differences. This study adopts a context-specific and exploratory approach to examine brand loyalty and its drivers among Generation Z fashion consumers in Bangalore. The study adopts a quantitative research design with a structured questionnaire using a 5-point Likert scale. A sample of 100 Generation Z students in Bangalore was selected using convenience sampling to collect the data. Further descriptive and inferential statistical analyses were conducted using SPSS. The findings show positive associations among brand loyalty, brand awareness, perceived quality, emotional connection, and social influence. Independent-samples t-tests reveal no significant difference in overall brand loyalty between male and female respondents. However, regression analyses indicate that perceived quality and brand awareness are relatively stronger predictors of brand loyalty among male respondents. In contrast, emotional connection is a stronger predictor among female respondents. These findings suggest differences in motivational pathways rather than loyalty intensity. The study suggests that while overall brand loyalty levels are similar across genders, the motivational drivers underlying loyalty differ. These findings are context-specific and exploratory, and their generalizability is limited by convenience sampling and a restricted geographic scope. 2026 Journal of Computers, Mechanical and Management. -
A STUDY ON CONJUGACY GRAPHS
In this paper, we introduce the notion of an equivalence graph based on equivalence relation defined on a group. Furthermore, restricting ourselves to conjugacy relation, a special type of equivalence graph called a conjugacy graph is also defined. In addition, a graph theoretical expression for the class equation is established followed by related results. 2025, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved.