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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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 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. -
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. -
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. -
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. -
Toward a Kashmiri Cultural Psychology: Integrating Indigenous Knowledge and Mental Health
This paper presents a critical theoretical intervention addressing epistemic imbalance in mental health research and practice related to Kashmir. It (a) develops conceptual frameworks elucidating indigenous healing rooted in Sufi mysticism, communal networks, and culturally specific coping strategies; (b) identifies and theorizes culturally derived constructs essential for contextually appropriate mental health infrastructures and interventions, emphasizing epistemic justice and locally situated knowledge; and (c) demonstrates culturally grounded interventions that foreground indigenous epistemologies on their own terms, addressing the limitations and potential dominance of Western clinical models. By centering Kashmiriyat, the Valleys indigenous cultural ethos that encompasses communal solidarity, shrine-centered spiritual practices, and historically rooted coping strategies guiding everyday communal and spiritual life, this work reconceptualizes resilience as collective and historically situated. The proposed framework enriches global psychological theory and offers innovative models of culturally congruent and socially transformative interventions for conflict-affected societies. 2026, PsychOpen. All rights reserved. -
Within-School Socioeconomic Disparities in Academic Achievement: A Qualitative Case Study of Study-Regulation Supports among Indian Secondary Students
Objective: This study explored how socioeconomic contexts shape students study strategies and how these differences relate to academic achievement within the same school setting. Methods and Materials: A single-site qualitative case study was conducted in a private, unaided English-medium CBSE school in Bengaluru, India, enrolling students from diverse socioeconomic status (SES) groups. Thirty students in Grades 89 (aged 1315) were selected through purposive sampling, representing all achievement levels and residence types (day scholars and residential/hostel students). SES classification was informed by parental education/occupation and the Modified Kuppuswamy Scale (2019). Data were collected through semi-structured individual interviews, audio recorded, transcribed verbatim, and analyzed iteratively using line-by-line and focused coding guided by Charmazs grounded theory approach, leading to theme development. Findings: Three themes explained within-school achievement disparities: (1) parental engagement and access to cultural/social capital varied by SES, shaping monitoring, subject support, and study regulation at home; (2) hostel routines and mentoring provided compensatory structures resembling middle-class concerted cultivation, supporting academic regulation for some low-SES residential students; and (3) for low-SES day scholars, teachers and remedial support served as the primary learning resource, often framed in skill-deficit terms rather than culturally responsive pedagogy. Conclusion: Equal access to school resources does not necessarily produce equal outcomes because study regulation develops within unequal family and institutional support ecologies. Equity-oriented, culturally responsive, and relational school practicesalongside targeted academic mentoringmay help reduce persistent achievement gaps. 2025 the authors. -
In-vitro antioxidant analysis of Aristolochia indica, Ipomoea obscura, Tylophora indica, Glinus oppositifolius and Abroma augustum from Bankura district, West Bengal
Five therapeutic plants that have been utilized traditionally across the Sonamukhi Block of Bankura District, West Bengal, were tested for antioxidant activity using three assays: ABTS radical scavenging activity, FRAP reduction power, and DPPH free radical scavenging. According to the DPPH assay, Glinus oppositifolius (68.4%) and Ipomoea obscura (23.83%) showed moderate radical-scavenging activity, whereas Aristolochia indica (73.07%), Abroma augustum (52.87%), and Tylophora indica (25%) demonstrated the highest levels. While Glinus oppositifolius (0.685) and Ipomoea obscura (0.401) showed moderate activity in the FRAP assay, Abroma augustum (0.459), Tylophora indica (0.637), and Aristolochia indica (0.545) demonstrated significant reducing power. According to the ABTS assay, Aristolochia indica (90.37%) and Glinus oppositifolius (98.7%) had the highest levels of radical scavenging activity. These findings support the traditional medical usage of these plants, especially Glinus oppositifolius and Aristolochia indica, which showed the most antioxidant qualities. The results highlight the importance of these plants in traditional medicine, shed light on their therapeutic potential, and lay the groundwork for further research on natural antioxidant treatments. Authors CC4-NC-ND, ScienceIN. -
Role of Dufour's gland and mandibular gland secretion in Ant Colony organisation and defense mechanisms of Camponotus compressus and Oecophylla smaragdina
Ants are eusocial insects and observed in 3 castes secreting a wide variety of pheromones for surviving in the colony. They can be sex pheromone, trail pheromone and alarm pheromone. These pheromones help in the recruitment of workers, mating, foraging food. These pheromones contain a single compound from a single gland or from two glands. This study analyses the knowledge of these pheromones and their chemical structure. The mandibular gland and Dufours gland of Oecophylla smaragdina and Camponotus compressus were extensively studied to provide a valuable resource in chemical ecology research. Limited research has been done on pheromones released by Oecophylla smaragdina and Camponotus compressus. The Dufour's gland is one of the most well-developed glands, playing vital roles in defense, foraging, information exchange, and reproduction. The chemical components were analyzed using gas chromatography-mass spectrometry. The secretions from the Dufours gland and mandibular gland contained high concentration of n-undecane, which serves as an alarm pheromone, and compositions varied among different castes. This highlights a research gap and the need to investigate the differences in the chemical composition between these two ant species, we analyse the diverse chemicals released from the Dufours gland and the mandibular gland. Authors CC4-NC-ND, ScienceIN. -
Exploring the pharmaceutical potential of discarded ink glands from Amphioctopus aegina
Ocean is called the Natural Medicine Chest of the New Millennium, as it encompasses diverse marine ecosystems that are rich in bioactive compounds. One of the fascinating organisms is cephalopods as they are known for their inking capabilities. The ink, composed mainly of melanin and mucous, has garnered attention for its diverse bioactive properties. In addition to melanin, components like Quinones, 8-hydroxy-4-quinolone, and various amino acids exhibit anti-oxidant and other therapeutic activities. Researches have shown that cephalopod ink can possess anti-inflammatory, anti-cancer, anti-microbial, anti-hypertensive, anti-ulcerogenic, and antiretroviral properties. However, these ink glands are discarded in the seafood industry. Amphioctopus aegina is one of the octopus species found in Indian waters and is less explored. This study was conducted to explore the chemical composition and pharmaceutical properties of the discarded ink glands of A. aegina. The study revealed the gland of this organism to be rich in bioactive compounds like alkaloids, phenols and flavonoids. Antioxidant studies revealed both aqueous and ethanol extracts showed good antioxidant capability, with remarkable radical scavenging activity, with IC50 values-65.76 g/mL and 51.7 g/mL respectively. The extracts also showed moderate inhibition of protein denaturation and were non-toxic to RAW264.7 cell lines. These findings highlight A. aegina ink as a promising source of therapeutic biomolecules and offer a sustainable approach for valorizing cephalopod waste. 2026, ScienceIn Publishing. All rights reserved. -
A concise study on the phytochemistry and antimicrobial efficiency of Artemisia absinthium L.: Phytochemical analysis of plant
This study is meant to elucidate the phytochemical and antibacterial characteristics of wormwood Artemisia absinthium L, a perennial herb from Asteraceae family that has been used extensively in traditional medicine. It has diverse phytochemical composition, including bitter sesquiterpenoid lactones like absinthin, as well as essential oil constituents including camphene, ?-cadinene, guaiazulene, ?-thujone, ?-thujone, and thujyl alcohol esters. Applications of A. absinthium in the past include its ability to treat a wide range of illnesses, from fever to gastrointestinal problems. This study highlights the presence of various phytochemical compounds in plant extract of A. absinthium, such as tannins, saponins, and terpenoids through standardised protocols. Remarkably, this study also reveals its antibacterial capabilities using agar well diffusion method against five different pathogenic bacterial strains, including Escherichia coli (MTCC 443), Salmonella typhi (not sequenced, procured from Chettinad Hospital, Chennai), Staphylococcus aureus (MTCC 3160), Enterococcus faecalis (MTCC 439), and Klebsiella pneumonia (MTCC 109). Testing it against these strains of bacteria highlighted its effectiveness in this area. A. absinthium presents a compelling topic for continued scientific investigation due to its complex phytochemical composition and antimicrobial efficiency. 2026, ScienceIn Publishing. All rights reserved. -
A SYSTEMATIC RESEARCH REVIEW AND META-ANALYSIS OF ENVIRONMENTAL SCIENCES AND MANAGEMENT MODELS
This research advances the comprehension of the processes behind individuals' environmentally friendly behavior using a comprehensive approach. A questionnaire addressed intrapersonal, motivational, relationships, and educational aspects, with environmental science as the primary catalyst for green behavior within a complete theoretical structure.The method is the CADMIACA approach, which is founded on Comprehensive Action Determining Modeling (CADM), together with various Motivational and Interpersonal (MI) theories and the Activity Competence Algorithm(ACA). This framework encompasses various control factors relevant to comprehensively characterizing the factors influencing environmentally friendly behavior, including climate change, energy conservation, recycling, sustainable buying, and contamination.The findings were gathered in the A Coru metropolitan region to experimentally evaluate the causal relationships among the parameters that formed the framework utilizing Structural Equation Modeling (SEM). Findings show that environmentalscience serves as an effective instrument for fostering eco-friendly behavior among residents. The extensive CADMIACA model aligns well with the information since all components incorporated in the framework (intrapersonal, inspiring, social, and institutional) are pivotal in shaping green conduct.Environmental instruction and intrapersonal variables emerged as the primary predictors of green conduct, but social and motivational variables were less prevalent in influencing such behavior. The findings suggest that human conduct plays a vital role in environmental protection. 2025, Rotherham Academic Press Ltd. All rights reserved. -
Determinants of Auditor Choice: Evidence from Sharia Commercial Banks in Indonesia
This research aims to determine the impact of corporate governance, firm complexity, foreign ownership, and ownership concentration towards auditor choice for Sharia commercial banks in Indonesia in 2016-2023. Firm size is also accounted for as a control variable. This research was conducted using a quantitative approach using the logit logistic regression analysis method through the Eviews 13 software. The sampling method was carried out using a purposive sampling method, which produced a sample of 9 Sharia commercial banks in Indonesia with a total of 72 observations. This study aims to provide an overview of the factors that Sharia commercial banks in Indonesia consider in choosing their external auditors, namely between Big 4 and non-Big Four auditors, which differ from other companies and industries. The results show that in partial analysis, corporate governance mechanisms and ownership concentration significantly and negatively affect auditor choice. Meanwhile, firm complexity and foreign ownership do not affect auditor choice. Low demands cause the negative influence of ownership concentration due to the private nature of the banks and efforts to achieve efficiency in audit fees while maintaining the same quality standards. 2025, Creative Publishing House. All rights reserved.