Recent Articles

Shubham Bhatia  ·  International Journal of Communication, Media and Linguistics  ·  14 Aug 2026

The rapid diffusion of generative artificial intelligence (AI) has made synthetic political media deepfakes and cheapfakes an increasingly salient concern for democratic communication. This study examines public awareness, perception, skepticism, and self-reported behavioral response to AI-generated political content among a diverse sample of 404 respondents. Using a structured survey instrument covering demographic profile, awareness, individual perception, decision-making impact, media skepticism, and democratic participation, the study finds near-universal awareness of AI (99.5%) but comparatively lower recognition of specific terminology such as ‘deepfake’ and ‘cheapfake’ (85.6%). One-sample chi-square goodness-of-fit tests confirm that response distributions on every attitude item depart significantly from chance (p < .001 in all but two cases), indicating that respondents hold clear, non-random positions rather than indifferent ones. Two-proportion z-tests show that respondents overwhelmingly agree that AI-generated media undermines the trustworthiness of political information (58.4% vs 31.2%, z = −5.27, p < .001), and that exposure has made them more cautious in decision-making (70.8% agreement, z = 14.40, p < .001). A recurring third-person pattern emerges: respondents report personal resilience to influence (61.9% say their own opinions are unaffected) while simultaneously reporting that AI-generated media erodes trust and discourages public dialogue at a societal level (69.8–73.0% agreement). The findings suggest a public that is highly aware but deeply skeptical, one that is adapting through heightened vigilance rather than blind acceptance, with clear implications for platform labeling policy, media literacy interventions, and electoral regulation.

Sumesh Sabharwal, Dr. Umesh Arya  ·  International Journal of Communication, Media and Linguistics  ·  14 Aug 2026

This study conducts a corpus-linguistic analysis of the representation of rare earth elements (REEs) in Indian web-based news from 2023 to 2026, amid increasing Chinese export restrictions and India’s push for critical-mineral self-sufficiency. Utilizing a corpus of 1,141 articles (701,121 words) primarily from Indian outlets, the analysis employed methods such as frequency counts, collocation profiling, and an inductively developed framing taxonomy influenced by Entman’s framing theory and Baker’s corpus-assisted discourse studies. The term 'rare earth(s)' appeared 7,511 times, indicating a normalized frequency of 106.7 per 10,000 words, with 88.4% of the articles containing this term. Collocational analysis revealed a dominant lexical environment focused on supply-chain and industrial vocabulary, alongside notable mentions of dependency and conflict terms. Framing analysis identified that 65.7% of articles were categorized under an Economic and Industrial Development frame, 15.0% under Geopolitical Rivalry/Weaponisation, and 9.7% under Diplomatic Cooperation, with smaller proportions in National Security, Science and Technology, and Environmental frames. Notably, China appeared in 84.4% and the United States in 72.1% of relevant articles, highlighting the external dependency contextualized in ostensibly domestic discussions. The findings suggest that Indian web news presents a dual narrative regarding REEs: one of vulnerability to Chinese influence and another of aspirational self-reliance, emphasizing the importance of integrating quantitative corpus methods with qualitative framing analysis in the examination of geopolitical narratives in business-centered digital journalism.

Dr. Chandrakant Ramrao Phad, Mr. Bharat Ramchandra Pawar  ·  International Journal of Economics and Business Management  ·  14 Aug 2026

Climate-related uncertainty is becoming an important business risk for rural enterprises because many local firms depend on agriculture, natural resources, seasonal income and geographically concentrated markets. The Marathwada region of Maharashtra provides a relevant setting for examining this issue because recurrent drought, uneven monsoon behaviour, water stress and episodes of extreme heat can affect production systems and the purchasing capacity of rural households. This paper examines how rural entrepreneurs can build resilience when their operating environment is exposed to such risks. The study uses an exploratory secondary-research design and synthesises official government material, development-project documents and research literature. Rather than treating resilience as simple survival after a disaster, the paper conceptualises it as a combination of preparedness, shock absorption, adaptation and enterprise transformation. Six dimensions are proposed: financial preparedness, diversification, resource efficiency, market flexibility, knowledge and technology, and institutional connectivity. The analysis suggests that the resilience of rural enterprises is closely connected with the resilience of the wider rural economy. Business continuity can be improved when entrepreneurs diversify revenue sources, manage liquidity carefully, conserve scarce resources, use climate and market information, strengthen customer networks and establish links with financial and public institutions. The paper develops a conceptual model and offers recommendations for enterprise development programmes in Marathwada. It also identifies the need for future district-level primary research to test the proposed relationships quantitatively.

Binji Seidu Zakaria, Noble Nkrumah-Abraham  ·  International Journal of Arts, Culture and Creative Studies  ·  14 Aug 2026

This study sought to evaluate the animated video on the following themes: Storyline, character(s), setting, costume, plot, sound effects or scores and moral lessons, among other theatrical technicalities, to ascertain if indeed it has achieved its goal of projecting and preserving Ghanaian cultural heroes and heritage, respectively, as well as the purported related moral values. Through a qualitative study and a single-case study design, the 2D animation, which was selected from a secondary source, was evaluated using content analysis. As revealed by Nkrumah-Abraham (2022), foreign animations which do not reflect Ghanaian cultural values, characters and heritage are dominating the Ghanaian media space and therefore pushing the act of traditional storytelling into extinction. Ghanaian folktales, which have been a medium for instilling moral values, revealing and revering cultural heroes or heroines, have seen a dreadful decline over the period. And in effect, the nation as a whole is losing the benefits it derives from traditional Ghanaian mythical stories or folktales. He further posits that an intervention to remedy the situation or mitigate its repercussions is to project indigenous Ghanaian stories through animation and other audio-visual media. To that effect, a 2D animation on the story of Asebu Amenfi the Giant, a traditional hero and founder of the Asebu Kingdom, was produced. This study considers the animation as a tool for preservation of historical and cultural narratives of the Asebu Community as well as a reflection of their cultural values and identity. The study therefore recommends a more deliberate means of distributing the 2D animation in the multimedia space, towards the promotion of indigenous Ghanaian content. Keywords: Theatre; Animation; Culture; Heritage; Identity; Asebu Amenfi; Ghana

Divyansh Mishra, RAJESH KUMAR MISHRA, REKHA AGARWAL  ·  International Journal of Communication, Media and Linguistics  ·  14 Aug 2026

The news media industry has undergone one of the most sustained business model disruptions of any sector in the digital economy, as advertising revenue has migrated to platform intermediaries, audiences have fragmented across social and video platforms, and generative artificial intelligence has begun to reshape both news production and news discovery. This paper presents a systematic, thematically organized review of academic and high-quality industry literature on the digital transformation of news media business models, published or released primarily between 2014 and 2026. Following a PRISMA-inspired identification, screening, and eligibility process, 48 sources were retained for thematic synthesis, spanning peer-reviewed communication and media-management scholarship, and authoritative industry and statistical sources including the Reuters Institute Digital News Report, the Pew Research Center, Statistics Canada, and Press Gazette's journalism job-cuts tracker. The review identifies five interlocking themes: the structural collapse of advertising-based revenue models; the rise, and subsequent plateauing, of reader-revenue and subscription models; growing platform dependency and algorithmic distribution risk; sustained newsroom restructuring and employment contraction; and the accelerating, ambivalent integration of artificial intelligence into news production and distribution. Drawing on this synthesis, the paper proposes a five-stage conceptual model from print-centric models through digitization, platformization, the reader-revenue pivot, to the AI-augmented newsroom that situates these themes within a single explanatory framework. The paper closes with theoretical and practical implications for media managers, journalism educators, and policymakers, and identifies priority directions for future research, including the long-term sustainability of reader-revenue models and the net effect of generative AI on public trust in journalism.

Joseph Kortu, Mohamed Musa Kabba, Mohamed Ambrose Koroma, Lawrence Teteh Kargbo  ·  International Journal of Medical and Health Sciences  ·  14 Aug 2026

Maternal malnutrition remains a major public-health concern in Sierra Leone, yet community-level evidence based on objective anthropometric measurement is scarce. This study assessed anthropometric indicators of maternal malnutrition among pregnant women attending antenatal care (ANC) clinics in Nongowa Chiefdom, Kenema District. A cross-sectional, descriptive design was used, and data were collected from 63 pregnant women using a structured, interviewer-administered questionnaire and standardized measurements of body mass index (BMI) and mid-upper arm circumference (MUAC). Data were analyzed in SPSS using frequencies, percentages, and charts. The findings revealed a clear double burden of malnutrition: by BMI, 14.3% of respondents were underweight, 49.2% were of normal weight, 25.4% were overweight, and 11.1% were obese; by MUAC, 19.0% were malnourished (below 23 cm). Overall, just over half (50.8%) fell outside the normal BMI range, and MUAC detected more acute undernutrition than BMI. The population was highly economically vulnerable, with 65.1% reporting no income and 50.8% unemployed, and more than half experienced household food insecurity. Respondents identified poverty and food shortages, rather than cultural beliefs, as the main factors affecting their nutrition. Although ANC coverage, supplementation, and nutrition education were high (all above 87%), most women (79.4%) attended only one or two ANC visits, far below the WHO-recommended minimum. Maternal malnutrition in the study area is therefore driven principally by poverty and food insecurity, and the routine integration of MUAC screening into ANC, alongside economic and food-security interventions, is recommended.

Kemal Kitaba, Rameto Nura  ·  International Journal of Civil and Environmental Engineering  ·  14 Aug 2026

Among the major crops cultivated common bean (Phasoulus vulgaries L.) is a crucial and widely used for home consumption. It is highly preferred by because of its early maturity, and providing producers with food security. However, the yields obtained from farmers’ fields remain below those achieved at research stations. Among the factors limiting bean production, weeds are the major constraint. Therefore, this experiment was conducted with the objective of determining appropriate weed management practices in bean production. The experiment consisted of 11 treatments, comprising varying rates of two herbicide types combined with different hand-weeding frequencies. The experiment was laid out in a randomized complete block design (RCBD) with three replications. Among the evaluated treatments, S-metolachlor at 1 kg ha-1 supplemented with hand hoeing at 20 and 35 days after sowing (DAS) was the best weed management option, in addition to weed free. This treatment yielded an 81.21% yield advantage beyond the unweeded plots. The weed-free treatment and the S-metolachlor (1 kg ha-1) supplemented with hoeing at 20 and 35 DAS produced the maximum grain yields of 2518.6 kg ha-1 and 2320.7 kg ha-1, respectively. The maximum net benefits 59,845.6 ETB ha-1 was obtained from the application of S-metolachlor at 1 kg ha-1 supplemented with hoeing at 20 and 35 DAS. Therefore, to effectively manage weeds in common bean production within the study area, the using of S-metolachlor at 1 kg ha-1 combined with hoeing at 20 and 35 DAS is recommended until other more preferable alternative management options are developed.

Kefale Adefris Asfaw  ·  International Journal of Civil and Environmental Engineering  ·  14 Aug 2026

Although Ethiopia’s road network has expanded significantly in recent years, maintaining long-term pavement performance remains a major challenge. In response to increasing sustainability concerns, modern road construction practices emphasize the recycling and reuse of existing asphalt materials, an approach that has been successfully applied since the early 1900s and offers both technical and environmental benefits. This study investigates the laboratory performance of hot mix asphalt (HMA) incorporating varying proportions of reclaimed asphalt pavement (RAP) for binder course applications. Comprehensive material characterization tests were conducted on all constituent materials to evaluate their suitability for asphalt mixture production. Marshall mix design and testing were subsequently performed on both conventional (control) and RAP-modified mixtures to assess key mechanical properties. To further evaluate field performance, moisture susceptibility, indirect tensile strength (ITS), and Hamburg wheel tracking tests were carried out on the prepared mixtures. The results indicate that all RAP-containing mixtures satisfied the Marshall design criteria and performance specifications. Furthermore, the mixtures demonstrated acceptable resistance to moisture damage, adequate tensile strength, and satisfactory rutting performance under laboratory conditions. These findings confirm that binder course asphalt mixtures incorporating RAP can be successfully produced while meeting the required engineering and performance standards. The study concludes that incorporating up to 40% RAP in HMA mixtures is technically feasible for binder course applications. Such mixtures can satisfy both Marshall and performance-based requirements while providing economic advantages through reduced material consumption and lower production costs compared with conventional asphalt mixtures. The findings support the broader adoption of RAP as a sustainable and cost-effective solution for pavement construction and rehabilitation.

Divyansh Mishra, Dr. RAJESH KUMAR MISHRA, Dr. Rekha Agarwal  ·  International Journal of Civil and Environmental Engineering  ·  14 Aug 2026

River water quality is governed by the interaction of hydrology, temperature-dependent biochemical kinetics, and episodic pollution loading, making both long-term trend characterization and short-term event forecasting central problems in civil and environmental engineering. This paper (i) reviews the classical Streeter-Phelps oxygen-sag framework and the Water Quality Index (WQI) concept that together underpin most operational water-quality assessment; (ii) reviews the rapidly growing literature applying machine learning (ML) to water quality and dissolved-oxygen (DO) prediction; and (iii) reports two complementary, fully reproducible simulation-based experiments. First, a 20-year daily simulation, grounded in the standard Benson-Krause DO-saturation-temperature relationship, quantifies a thermally-driven decline in DO saturation capacity of 0.072 mg/L per decade under a modest (+0.35 °C/decade) warming trend, with temperature and DO saturation correlated at r = -0.997. Second, an hourly-resolution, 3-year Streeter-Phelps pollution-event simulator, driven by episodic biochemical oxygen demand (BOD) loading pulses analogous in mathematical structure to the injection/decay processes used in companion geophysical forecasting studies, was used to train and evaluate Random Forest, Gradient Boosting, Multilayer Perceptron, and linear baseline models predicting WQI at 6-, 24-, and 72-hour horizons under a chronologically leakage-aware protocol. The best model (Gradient Boosting) achieved RMSE = 3.62 WQI points and R² = 0.441 at 6 hours, degrading to R² ≈ 0.10-0.13 by 24-72 hours, a steeper skill decay than reported for single-variable geophysical indices, attributed to the compounding of independent noise sources across the five WQI sub-indices. Feature-importance and ablation analysis show that recent WQI history dominates short-horizon predictability, while hydrological drivers (temperature, flow) alone are competitive at longer horizons. Because live access to real river-monitoring archives was not available in this environment, both experiments are explicitly disclosed as physically-grounded simulations rather than analyses of observational data, and the paper concludes with a discussion of the sim-to-real gap, operational relevance for water treatment and pollution-control decision-making, and directions for future work.

Manikanta R  ·  International Journal of Philosophy, Ethics and Humanities  ·  14 Aug 2026

Artificial intelligence (AI) is increasingly transforming organizational work by enabling humans and intelligent systems to collaborate in decision-making, problem-solving, and operational activities. Despite rapid adoption, existing research remains fragmented across organizational behavior, human factors, information systems, and AI governance, providing limited guidance on how organizations can effectively design and manage human–AI collaboration. This conceptual paper develops the Collaborative Intelligence Framework (CIF) through an integrative review of peer-reviewed literature published between 2020 and 2026. The framework identifies four essential conditions for successful human–AI teaming: task interdependence, calibrated trust, role clarity, and organizational enablement. It explains how these conditions promote effective collaboration while reducing the risks of algorithmic aversion and excessive reliance on AI recommendations. The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design. The paper contributes to the growing literature on responsible AI by providing a structured framework that can guide researchers and practitioners in designing sustainable human–AI collaboration. It concludes by discussing managerial implications, study limitations, and opportunities for future empirical validation across diverse organizational and occupational contexts.

Salisu Hassan Nahuche, Magaji Ladan  ·  International Journal of Physical and Chemical Sciences  ·  14 Aug 2026

Adsorption is potentially an attractive methodology for removal of hazardous dyestuff from industrial effluents. In this study, the removal efficiency of industrially important dyes methylene blue (MB) and methyl orange (MO) from aqueous solution using jute leaves (JL) and sickle senna leaves (SSL), and activated carbon of jute and sickle senna leaves A C-SSL and AC-JL as Adsorbents had been investigated. The prepared adsorbents were characterized using Fourier Transform Infrared (FTIR) spectroscopy, Scanning electron microscopy (SEM) and Energy Dispersive X-ray, (EDX). The effect of different parameters such as contact time, temperature, initial dye concentration, adsorbent dosage and pH were studied. The results obtained revel that the optimum time at 60 minutes and 50 minutes, acidic pH of 2 and 0.5g and 0.6g respectively, the experimental data were interpreted and studied using. Four isotherm models namely Langmuir, Freundlich, Temkin and Dubenin Redushkebich were tested and adsorption was found to fit well into Langmuir model and Temkin relatively better than others. The maximum loading capacity (qm) of the adsorbent AC-MOJLA is 248 and AC-MBJLB is 245 respectively. Adsorbtion kinetics parameters such as Pseudo first order, Pseudo second order, Intra particles diffusion and Simple elovich were used to described the data, the investigations showed that the kinetic data was well described by Pseudo first order and Pseudo second order kinetic model with the high correlation coefficients (R2). Thermodynamic parameters such as ∆G, ∆H, ∆S, were calculated. The negative values of ∆G, indicate that the process is spontaneous in nature and the positive values of ∆H, shows the endothermic nature of the process. The positive values of ∆S indicated that the randomness of the solid/solution interface increased during the adsorption process. The activation energy also was calculated. The FTIR and SEM analyses of the adsorbent suggest that adsorption of the dyes was through an electrostatic interaction between the functional groups present in the dyes and those on the surface of the adsorbent.

Alfred Ndi  ·  International Journal of Commerce, Accounting and Finance  ·  14 Aug 2026

This paper applies David Teece’s 2010 framework—linking business models, business strategy, and innovation—to a mission-driven Cameroonian startup, CamShield AI, which develops AI-based detection and resilience tools for deepfakes and disinformation while pursuing cultural sovereignty objectives. Using a single-case exploratory design framed by Teece’s three-part business model elements (value proposition, market segments, and value capture) and the dynamic capabilities lens (sensing, seizing, transforming), the paper diagnoses CamShield AI’s current model, identifies gaps, proposes alternative configurations, and recommends an implementation roadmap. Findings address customer pain points, value-creation mechanisms, appropriability and data strategy, complementary assets, pricing and revenue models, governance and ethics, partnerships, and scaling trade-offs in the African context. The discussion grounds each finding in Teece’s theory and in relevant literature on deepfakes, disinformation, and cultural sovereignty. The paper concludes with prioritized recommendations for business-model experimentation, capability development, and future research avenues, including empirical validation, impact evaluation, and comparative studies across African markets.

Dr. Bhukya Devender  ·  International Journal of Education, Pedagogy and Psychology  ·  13 Aug 2026

Education is a tool of human development, social change, and growth of nation. It helps an individual to fight ignorance, utilize his/her full potential, earn economic prosperity, conserve nature and society, and think rationally and logically. Keeping in view the significance of education in 21st-century India, it is necessary that all the tribal groups of India should be provided with quality education. This study focuses on the societal involvement, socialization, perception of society, learning styles, educational schemes of government, and performance of students from Koya and Lambada tribes of Bhadradri Kothagudem District of Telangana state. The current study is restricted to those tribal students who are pursuing their studies in VIII and IX classes in ITDA (Integrated Tribal Development Agency) run schools. A total of 410 tribal students have been taken for the study. Firstly, an ethnographic profile of the Koya and Lambada tribes is to be developed. Secondly, the issues related to the education of the tribes will be identified. Thirdly, the socialization and societal perception of the tribes will be investigated. Fourthly, the effect of school setting and socio-cultural environment on the sociability and social perception of the tribes will be analyzed. Fifthly, the relation between the variables like socialization, societal perception, learning style, government educational scheme and academic achievement will be studied. Findings of the study would give insight into the educational experience of the tribal community and assist in formulating an effective educational policy to improve academic achievement and enhance social integration of the tribal community.

KAMIREDDY SRILEKHA, Dr. J. Rama Krishna Naik, Vamsi Dasi  ·  International Journal of Technology and Emerging Research  ·  13 Aug 2026

Chatbots powered by artificial intelligence are becoming a part of higher education, supplementing teaching and learning as well as student services. Nevertheless, there is only limited evidence regarding the factors influencing students’ intention to use these technologies in the Indian higher education context. The influence of performance expectancy, effort expectancy, social influence and perceived trust on students’ intention to adopt artificial intelligence-based chatbot. This study adopted a quantitative research design using a self-administered, structured questionnaire administered to students of Pondicherry University. For data analysis, 477 valid responses were analyzed through IBM SPSS Statistics and SmartPLS after screening. The results demonstrate that performance expectancy was the strongest predictor of chatbot adoption intention, suggesting students are likely to adopt chatbots when they see obvious academic and learning gains. Social influence, perceived trust, and perceived intelligence also significantly and positively impact adoption intention, whereas effort expectancy does not significantly affect students' behavioral intention, implying that ease of use may be less important for digitally literate learners. This study recommends that educational institutions should focus on developing chatbot system programs with high efficiency, elegance and academic aptness while promoting their usage through institutional initiatives alongside peer influence. Combining the factors of the Technology Acceptance Model with artificial intelligence-specific features provides this study with a holistic view and practical advice to increase student acceptance and intent to engage.

kh altaf hussain  ·  International Journal of Arts, Culture and Creative Studies  ·  09 Aug 2026

A salient weapon that change the society, Politics, make history, spread of ideas, exposes injustice and bring revolution without violence and so on known as Pen. Similarly the salient figure of India Maulana Abul Kalam Azad was one of the most important figure during colonial era, used his salient weapon as a tool to challenge the injustice and inspire a sense of national pride. As a writer and thinker, his important journals like Al-Hilal (1912) and Al-Balagh (1915) had a big impact on shaping political ideas during a key time in the movement for independence, work as powerful voice against British rule. This paper looks at how his journals helped build a spirit of nationalism in mother land, He gave efforts to brought Hindus and Muslims in same thoughts and ideology, and finally he was success in his goal and encouraged resistance against British control. The study shows how Azad changed the idea of journalism from just reporting facts to being a way to wake people up. Through powerful editorials, writing, and commentaries, he brought together Islamic values with democratic principles, stressing freedom, fairness, and self-dependence as both religious and national responsibilities. His understanding of Islam broke down barriers between different groups and offered a message that was relevant to both Muslims and others, countering the British efforts to create divisions in Indian society. Azad's writing also criticized British policies, highlighting issues such as unfair treatment, racial bias, and lack of basic rights. His brave words led to many restrictions, legal actions, and even jail time, showing both the strength of his powerful ideas and how much the colonial government feared their influence. Even with these challenges, his writings reached many people, inspiring young revolutionaries, leaders, and the general public with a vision of working together for a shared goal. The paper also places Azad's contributions within the broader context of nationalist journalism in India, comparing his work with that of other leaders like Bal Gangadhar Tilak and Mahatma Gandhi. It argues that Azad's unique strength was his ability to connect faith with politics, turning journalism into a powerful tool for spreading ideas and mobilizing people. In the end, the research shows that Maulana Azad's writing played a major role in India's freedom struggle and helped create the basis for a diverse, democratic nation. His journalism, filled with deep thought and strong moral beliefs, remains a powerful reminder of how ideas and words can change history.

Shivi, Shaveta Kalsi  ·  International Journal of Technology and Emerging Research  ·  08 Aug 2026

Plant disease is one of the primary issues that directly reduce the quality of agricultural produce. Identification and classification of plant diseases are among the primary tasks to improve the overall quality of crop production for economic development. Numerous approaches for disease detection have been demonstrated by a group of researchers using a digital image dataset of plant leaves. However, due to their intricate algorithms and incapacity to provide an effective method for clearly demarcating boundaries among the provided data classes for the purpose of making the ultimate decisions, such systems fall short of the anticipated perfection. Deep Neural networks, which are multi-layer architecture models whose layers learn to represent the data at several abstract levels and have a less complex algorithm compared to conventional statistical methods, can be suggested as a solution for this problem. In the present context of the problem, a self-designed Multi-Branch Fusion Network model for the multiclass categorization of plant diseases has been proposed in this work. Images from several plant diseases were taken from a publicly accessible dataset and used to simulate the proposed model. It has been discovered that the suggested method works well in the specified situation.

Kevin George  ·  International Journal of Philosophy, Ethics and Humanities  ·  08 Aug 2026

Emily Brontë's Wuthering Heights has traditionally been examined through the critical frameworks of Gothic fiction, Romanticism, psychoanalysis, feminism, and class relations. While these approaches have illuminated the novel's emotional intensity and symbolic complexity, comparatively less attention has been devoted to its recursive narrative architecture. This article argues that repetition functions as the novel's primary structural principle, shaping both character development and narrative progression across successive generations. Employing qualitative textual analysis and close reading, the study examines the recurring emotional, spatial, and relational patterns embodied by characters such as Catherine Earnshaw and Cathy Linton, Heathcliff and Hareton Earnshaw, and the symbolic interplay between Wuthering Heights and Thrushcross Grange. Rather than presenting these parallels as mere instances of psychological doubling or inherited temperament, the article demonstrates that Brontë systematically reconstructs earlier conflicts in modified forms, producing a cyclical narrative that resists linear development. The second generation simultaneously repeats and revises the emotional trajectories of the first, suggesting that recurrence becomes the novel's organizing principle rather than a stylistic device. By foregrounding narrative recursion as a structural phenomenon, this study offers an alternative perspective on Brontë's artistic method and contributes to ongoing discussions of nineteenth-century narrative form, character construction, and the enduring complexity of Wuthering Heights.

Pavankumar M, Dr. B. Charumathi  ·  International Journal of Technology and Emerging Research  ·  05 Aug 2026

The rapid growth of fintech start-ups in India has transformed the financial services landscape by accelerating innovation in payments, digital lending, wealth management, and insurance while promoting financial inclusion. However, this digital expansion has simultaneously exposed fintech start-ups to significant cybersecurity risks due to limited resources, evolving technologies, and complex regulatory requirements. This study examines the cybersecurity challenges faced by Indian fintech start-ups through a regulatory lens, identifying key threats such as data breaches, identity theft, phishing attacks, ransomware incidents, and vulnerabilities arising from third-party integrations. This paper maps the evolving cybersecurity governance structure shaped by regulatory and institutional mechanisms, including those of the Reserve Bank of India, the Digital Personal Data Protection Act, Securities and Exchange Board of India frameworks, and CERT-In directives. Using a synthesized analytical framework, the study categorizes cybersecurity risks into technological vulnerabilities, data privacy concerns, and operational challenges specific to start-ups. It further analyses sector-specific risks while aligning them with regulatory responses such as digital lending norms, data localization requirements, and sandbox-based compliance mechanisms. Drawing on recent developments and governance perspectives, the paper highlights the limitations of fragmented oversight and underscores the need for harmonized, adaptive, and innovation-friendly cybersecurity regulation. The findings contribute to a structured understanding of the interplay between fintech innovation, cyber risk exposure, and regulatory preparedness, offering policy-relevant insights for strengthening resilience in India’s rapidly evolving fintech ecosystem.

Dasi Vamsi, Dr. J. Rama Krishna Naik, K. Srilekha  ·  International Journal of Technology and Emerging Research  ·  05 Aug 2026

The insurance industry is increasingly leveraging artificial intelligence (AI) to enhance operational efficiency, customer experience, and decision-making capabilities. While existing studies have predominantly focused on AI adoption determinants and specific application areas, limited attention has been given to understanding how insurers evolve from digitalization to AI-enabled business models over time. Addressing this gap, the present study examines the AI transformation journey of India's private general insurance sector through a longitudinal content analysis of ICICI Lombard and Bajaj Allianz General Insurance. The study analyses corporate disclosures, annual reports, and publicly available documents from 2017 to 2025. The findings reveal a four-stage transformation pathway comprising digitalization foundation, process automation, AI integration, and intelligent insurance. Both insurers progressively expanded AI applications across underwriting, claims management, customer service, fraud detection, and risk assessment functions. The analysis further indicates a shift from operational automation toward enterprise-wide AI capabilities and Generative AI-enabled solutions, reflecting the emergence of intelligent insurance ecosystems. The study contributes to the literature by providing longitudinal evidence of AI-driven organizational transformation in the insurance industry and extending understanding of how firms develop and deploy AI capabilities over time. The findings offer practical insights for insurers seeking to accelerate their transition from digitalization to intelligent insurance through strategic AI implementation.

Mulugeta Tilahun Bekele  ·  International Journal of Computer Science and Artificial Intelligence  ·  05 Aug 2026

Abstract: The rapid expansion of distance education has increased access to learning but has also intensified challenges related to assessment security, academic integrity, quality assurance, and student trust. Conventional Learning Management Systems (LMS) often rely on centralized architectures that are vulnerable to data tampering, unauthorized access, delayed verification, and limited transparency in grading and credential management. This study proposes a Blockchain–Artificial Intelligence (AI) Hybrid Framework designed to enhance secure assessment, institutional quality assurance, and student trust in distance education through decentralized verification and intelligent analytics. A mixed-methods research design integrating quantitative and qualitative approaches was employed. Quantitative evaluation was conducted using assessment records, blockchain transaction logs, AI prediction outputs, and system performance metrics collected from a distance learning environment. Qualitative data were obtained through interviews and structured questionnaires involving students, instructors, and quality assurance experts. The proposed framework integrates blockchain-based immutable assessment records with AI-driven automated grading, anomaly detection, plagiarism identification, and predictive quality analytics. Performance was compared with conventional cloud-based e-learning systems using assessment integrity, grading accuracy, fraud detection rate, transaction verification time, system latency, user trust, and overall quality assurance effectiveness as evaluation parameters. Experimental results demonstrated that the proposed framework achieved 99.8% assessment data integrity, 98.6% AI grading accuracy, 97.9% academic misconduct detection accuracy, 95.8% quality assurance compliance, and 96.7% student trust satisfaction, while reducing verification time by 64% and assessment processing latency by 42% compared with traditional centralized systems. Qualitative findings further confirmed improved transparency, fairness, accountability, and confidence in online assessment processes. The study concludes that integrating blockchain and artificial intelligence provides a scalable, secure, and trustworthy solution for sustainable distance education, strengthening assessment reliability, institutional quality assurance, and learner confidence while supporting evidence-based educational decision-making.