Volume 21 Issue 7 2026

This essay examines the role of unreliability as a constitutive condition of artistic creation rather than merely a defect of interpretation or judgement. Beginning with the familiar claim that a work of art should be allowed to "speak for itself," it questions the authority traditionally accorded to critics and explores the complex relationship between artistic intention, critical mediation and audience reception. Drawing upon Kevil Melchionne's conception of the "Aesthetics of Unreliability" and Yuriko Saito's theory of everyday aesthetics, the essay argues that aesthetic judgement is necessarily provisional, situated and corrigible. It proposes a distinction between defendable judgement, which can be justified through reasons, and dependable judgement, which acquires credibility through repeated practice, revision and responsiveness to materials, experience and critique. The discussion further examines how unreliability functions as a productive force in creative practice by fostering experimentation, innovation and interpretive openness. Examples drawn from literature, cinema, music and the visual arts—including unreliable narration, fragmented storytelling, improvisation, chance operations, Wabi-Sabi aesthetics and the creative use of accident—demonstrate that uncertainty frequently becomes an enabling rather than disabling condition of artistic expression. Rather than treating unreliability as a failure to attain certainty, the essay proposes it as a dynamic mode of aesthetic engagement through which artists, critics and audiences continually renegotiate meaning. Artistic authority, it concludes, is neither absolute nor self-validating; it remains local, provisional and sustained through an ongoing dialogue between creation, interpretation and lived experience.

The Environmental Kuznets Curve (EKC) hypothesis proposes that environmental degradation follows an inverted U-shaped relationship with economic growth. Although widely examined in empirical research, its theoretical applicability to resource-dependent transition economies remains insufficiently understood. This chapter critically re-evaluates the EKC from the perspective of Central Asia, where fossil-fuel dependence, carbon-intensive production structures, institutional heterogeneity, and incomplete structural transformation challenge the assumptions underlying the conventional EKC framework. Building on insights from environmental economics, institutional economics, resource dependence theory, and innovation studies, the chapter introduces the Central Asian Environmental Kuznets Curve (CA-EKC) Framework, a conceptual model that integrates economic growth, resource dependence, institutional quality, green innovation, energy transition, and structural transformation to explain environmental outcomes. The framework argues that rising income alone is insufficient to generate environmental improvement and that the existence, timing, and shape of the EKC depend on broader structural and institutional conditions. It further identifies four possible development pathways—conventional, delayed, Nshaped, and no-EKC trajectories— highlighting that environmental sustainability is contingent upon policy choices and institutional capacity rather than economic growth alone. The chapter contributes to EKC scholarship by extending its theoretical foundations and offers a comprehensive conceptual framework to guide future empirical research and environmental policymaking in Central Asia and other resource-rich transition economies.

The policies and acts shown a clear direction to the international and national organizations to practice and facilitate as close as possible within the existing educational program. This direction also recommended for reasonable accommodation by providing necessary teaching and learning experiences to the children with diverse learners including special need children. The purpose of study is to understand the capabilities of teachers to facilitate various learners in inclusive education settings. To identify the basic skills and methods needed for understanding the children with special needs, transferring their skill of teaching and to considering the effects of teacher competence on the didactic and other activities of students concern in inclusive classrooms is focused in this study. This study involves a qualitative method using the stratified sampling technique with six hundred teachers working in government schools of various levels. The result shows that there is strong organizational and counseling skills enhance teachers' effectiveness in supporting students with disabilities. Teachers with positive skills were better in facilitating the needs of students with diverse learning. The challenges included inadequate resources and a lack of specialized training in different areas.

The Government policies and acts givens the direction directly to the organizations working at international and national capacity to practice and facilitate as close as possible within the existing educational program. This direction also ascertains for reasonable accommodation by providing necessary teaching and learning experiences to the children with Special Education Needs including the various categories of special need children. The purpose of study is to understand the availability of learning resources to facilitate various learners in inclusive education settings and to examine the availability and utilization of educational resources for children with special needs in general schools as well as to identify gaps and areas for further development of inclusive practices in education. To identify the basic learning needs and the learning resources that enriches the learning of children with Special Needs to facilitate the transaction of teaching and learning in the class rooms the transaction kills and methods needed for understanding the children with special needs, taking external support for transferring the skill of teaching on the didactic and other activities of students concern in inclusive classrooms is focused in this study. Survey method with purposive sampling was used to determine the educational resources accessible to children with special needs. The data was gathered from 600 responders at 300 schools in the public schools with prior permission. It is understood and concluded in this study that, while organizational facilities and manpower resources have a significant impact on the provision of educational resources for children with special needs in the inclusive education program, It was identified significant gaps in the educational resources available for education of children with Special education needs in the inclusive class rooms and these needs to be addressed towards better inclusivity and outcome based learning of children with special education needs in the inclusive education program. It will provide unique insights into the availability and effectiveness of educational resources as well as to identify critical gaps and targeted strategies for increasing inclusivity in general schools.

We present a passive impedance-tracking matching strategy that resolves the critical efficiency drop-off observed in deep subthreshold (-30 to -10 dBm) RF energy harvesting. Conventional rectifiers typically suffer from a "semiconductor sensitivity cliff" due to the exponential divergence of diode impedance at low power. To counter this, we utilize a Differential Evolution (DE) algorithm to synthesize a fixed L-match network that approximates the diode’s complex conjugate trajectory over a 20 dB dynamic range. Unlike active tuning circuits that consume overhead power, our solution is purely passive. Simulation at 915 MHz reveals that this approach sustains >90% transmission efficiency throughout the target window, yielding a 93.5 percentage point efficiency gain over standard fixed-matching baselines at -30 dBm. Monte Carlo analysis further validates the design, showing 100% yield under standard manufacturing tolerances.

The increasing dependence of higher education institutions on digital technologies has heightened the importance of cybersecurity in protecting research assets and sustaining scholarly productivity. Despite growing investments in cybersecurity technologies, limited empirical evidence explains how institutional cybersecurity adoption contributes to research performance within academic environments. This study examined the relationship between cybersecurity technology adoption (CTA) and research performance by integrating the Technology– Organization–Environment (TOE) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT) into a comprehensive path model. Using a quantitative explanatory research design, survey data were collected from 259 faculty members, administrators, researchers, and information technology personnel from a Philippine state university. Path analysis was employed to examine the direct and indirect relationships among technological, organizational, and environmental factors, cybersecurity technology adoption, scholarly publications and dissemination, and research performance. The findings demonstrated that technological readiness, organizational support, environmental pressures, effort expectancy, and performance expectancy significantly influenced cybersecurity technology adoption. Cybersecurity technology adoption, in turn, positively affected scholarly publications and dissemination, which subsequently enhanced research performance in terms of research impact, recognition, innovation, and funding capacity. The proposed integrated TOE–UTAUT model provides empirical evidence that cybersecurity functions not only as a protective mechanism but also as a strategic capability supporting institutional research productivity. The study contributes to cybersecurity and higher education literature by extending technology adoption theories into research management and offers practical guidance for university administrators in developing cybersecurity policies that strengthen institutional research performance.

Chest X-rays are an important tool for diagnosing lung diseases, but interpreting them manually takes time and can lead to errors. VISIOLUNG. AI solves this problem with an automated system based on deep learning, specifically using the Swin Transformer architecture to classify and detect multiple thoracic diseases in chest X-ray images. The model provides probability scores for each disease class, helping radiologists make quicker and more accurate decisions. The experimental results show consistent improvements over training epochs. By epoch 50, the model reached a training accuracy of 75.15% and a validation accuracy of 71.34%. Training and validation losses were reduced to 0.63 and 0.58, respectively. These results demonstrate the model's effectiveness and its potential for use in computer-aided diagnosis.

The study used the Philippine Commission on Women's Enhanced Gender Mainstreaming Evaluation Framework (GMEF) to evaluate the use of Gender-Fair Language (GFL) in the Workplace. In particular, the respondents' demographics and the degree of GFL implementation in the four GMEF entry points—Policy (Institutionalization and Formalization), People (Competence, Awareness, and Culture), Enabling Mechanisms (Tools, Systems, and Budget), and Programs, Activities, and Projects (PAPs)—were determined. Additionally, it examined notable differences in respondents' perceptions across organizational and demographic traits and recommended a course of action to improve the mainstreaming of gender and development (GAD). In this investigation, a quantitative descriptive comparative design was employed. Participants were 285 employees from J.H. Cerilles State College, Local Government Unit, and other agencies from the municipality of Dumingag, Zamboanga del Sur. An Enhanced GMEF-based questionnaire, customized by the researcher, was used to collect data. The data analysis used statistical procedures such as Chi-square tests, weighted means, percentages, frequency counts, and standard deviations. According to the findings, most of the respondents were middle-aged, married, female, held a bachelor's degree, were employed permanently, and had worked for 1 to 5 years. For all four GMEF entry points—People, Programs, Activities and Projects, Enabling Mechanisms, and Policy—the use of gender-neutral terminology was rated as Very High Positive results were consistently obtained. Age and marital status did not differ statistically significantly from sex, education level, employment status, position, years of service, or department or unit. Based on these results, a Gender-Fair Language Action Plan was developed to enable sustainable GAD mainstreaming by bolstering organizational processes, improving staff competencies, strengthening institutional regulations, and promoting gender-responsive communication practices.

Institutions of higher education are increasingly focused on graduate job readiness; in fact, employers now expect not only graduates with the necessary technical skills, but also graduates who possess transferable employability skills. An aspect of this study is whether supervised machine learning can be applied to predict job readiness for graduating Information Technology (IT) students using the CareerEDGE employability framework and selected psychological attributes (self-confidence, self-efficacy, and self-esteem) to develop and test the success of those predictions. For this study, data pre-processing included normalizing the data, evaluating the features and applying a stratified 20-fold cross-validation process. The eight supervised machine learning classifiers that were evaluated were: Naïve Bayes; Logistic Regression; CN2 Rule Induction; K-Nearest Neighbors; Decision Tree; Support Vector Machine; AdaBoost; Random Forest and Neural Network, using the Area Under the Receiver Operating Characteristic Curve (AUC); classification accuracy (CA); F1; Precision; Recall; Matthews Correlation Coefficient (MCC) and; Specificity to measure their predictive performance. Feature importance analysis indicated CareerEDGE was the most significant contributor for job readiness with self-confidence second and self-esteem contributing the least of the three constructs evaluated. In terms of predictive performance among the classifiers, the Neural Network classifier performed the best with a predictive AUC of 0.817, Classification Accuracy of 0.766, F1 of 0.766, Precision of 0.766, Recall of 0.766, MCC of 0.531, and Specificity of 0.765. The pairwise comparison results based on AUC and CA confirm the predictive superiority of the Neural Network classifier. The results of this research confirm that by combining the CareerEDGE employment model and supervised machine learning, it is possible to create a valid and transparent method to predict the level of employment preparedness students will have when graduating. This model outlines how data can be analyzed to allow institutions to identify groups of students who may need targeted employment support.

The study applies fractional calculus operator formulas to the product of the 𝐺∗ −function and the 𝑘 − hypergeometric function. The results of the study produce new identities which researchers can use to extend existing results which they already found in previous research that used the 𝜓-function.

Abuja Electricity Distribution Company (AEDC) often grapples with the challenge of customer dissatisfaction arising from poor supply, estimated billings, poor relationship with customers and poor responses to customer complaints. It is a general believe that the adoption of an effective Customer Relationship Management (CRM) practices could serve as an elixir to many of the challenges that corporate organisations do encounter when dealing with customers. Therefore, the study examined the effect of CRM on customer satisfaction through a quantitative research approach. The aim of the study was to examine the effectiveness of CRM in promoting customer satisfaction at AEDC. To achieve this aim, the study generated primary data using a structured questionnaire from a sample of 316 staff of AEDC across all its regions and clusters. The data generated was analysed using descriptive statistics in the form of tables and percentages while the null hypotheses were tested using inferential statistics in the form of Multiple Regression Analysis which was done using the Statistical Package for Social Science version 23 software. The results obtained from the analysis revealed a model explanatory power of 58% as indicated by the R2. The results also indicated that CRM Technology (β = 0.399, p-value = 0.000) has a significant positive effect on customer satisfaction. Similarly, customer value (β = 0.275, p-value = 0.006) and Customer knowledge (β =0.171, p-value = 0.048) affected customer satisfaction significantly and positively. Consequently, all the null hypotheses of the study were rejected at 0.05 significant level. The study therefore concluded that CRM has a significant positive effect on customer satisfaction and recommended among others that AEDC’s management should tailor CRM strategies towards satisfying customers which invariably boost company's performance.

Postpartum depression (PPD) remains a substantially under-recognised maternal health condition in Northern Nigeria, where cultural, religious, and communal interpretations of illness continue to compete with biomedical understanding. This study examined the extent to which eleven categories of information sources, spanning interpersonal, religious, healthcare, broadcast, print, and digital communication channels, predict postpartum depression knowledge among women of reproductive age in Niger and Yobe States, Nigeria. A crosssectional survey design was employed, and data from 519 women were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The measurement model demonstrated acceptable convergent validity and reliability across all constructs, and the structural model indicated that Family and Friends, Medical Personnel, Radio, Religious Leaders, Social Media, and Websites significantly predicted postpartum depression knowledge, while Indigenous Media, Magazines, Newspapers, Opinion Leaders, and Television did not reach statistical significance. Medical Personnel and Family and Friends emerged as the strongest predictors, jointly underscoring the primacy of trusted, relationally embedded communication over formal or print-based mass media. The findings extend the Health Belief Model by demonstrating that health beliefs are not formed through individual cognition alone but are socially and communicatively mediated. The study recommends that maternal mental health interventions in culturally embedded, low-resource settings be channelled through trusted interpersonal, religious, and clinical communication structures rather than generalised mass-media campaigns.

Textile industries are a major source of industrial water pollution due to the discharge of dyecontaining effluents with high organic and inorganic loads. Bhilwara, known as the textile hub of Rajasthan, generates large volumes of wastewater that pose serious environmental challenges. In the present study, a green nanotechnology approach has been investigated for the treatment of textile wastewater using eco-friendly synthesized zinc oxide (ZnO) nanoparticles. The ZnO nanoparticles were synthesized through a plant-mediated green synthesis method and applied as photo catalysts for the degradation of pollutants present in textile effluents collected from local industries. The raw wastewater exhibited high pollution levels with pH 10.5–12.5, TDS 5400–6700 mg/L, BOD 320–420 mg/L, COD 950–1200 mg/L, and brown-black color. Photocatalytic experiments were conducted using ZnO nanoparticle concentrations ranging from 0.5–1.5 g/L under light irradiation for 45–120 minutes. The optimum treatment conditions were obtained at 1.5 g/L catalyst dosage, pH 7–8, and 120 minutes’ reaction time, resulting in 85–92% color removal, 70–78% COD reduction, and 65– 72% BOD reduction. The results demonstrate that green synthesized ZnO nanoparticles possess high photocatalytic efficiency and can serve as an environmentally friendly and costeffective technology for the treatment of textile wastewater in industrial regions like Bhilwara.

Modern individuals frequently struggle with maintaining self-discipline and adhering to long-term behavioral goals. While conventional habit-tracking software relies primarily on positive reinforcement, behavioral economics demonstrates that humans are fundamentally more responsive to the threat of loss than to equivalent gains—a phenomenon known as loss aversion. This paper presents ‘Do Or Pay’, an AIdriven behavioral contract platform engineered to maximize habit retention through the integration of financial stakes and a sophisticated automated verification pipeline. The platform’s core strength lies in its novel synthesis of behavioral economic incentives with a high-fidelity automated verification system, which fundamentally overcomes the industry-wide challenges of user fraud, manual auditing fatigue, and subjective verification bias. Built on a robust architecture featuring an Expo/React Native frontend, a secure Firebase cloud infrastructure, and advanced image analysis, ‘Do Or Pay’ enables users to commit to behavioral contracts backed by financial deposits. Our technical evaluation and 3-week user study demonstrate that this integration not only drastically improves task adherence (+112.0%) and platform retention (+161.4%) but also provides a scalable, cryptographically transparent paradigm shift for m-Health and productivity solutions.

Electric vehicles have become a key component of sustainable transportation, highlighting the need to understand factors influencing consumers’ perceived value and purchase intention. This study aims to map the knowledge structure, research hotspots, and evolutionary trends of perceived value and purchase intention toward electric vehicles through bibliometric analysis. The dataset includes 145 English-language journal articles published from 2014 to July 2026 and retrieved from the Web of Science. VOSviewer and SCImago Graphica were applied to analyze publication trends, disciplinary distribution, international collaboration, keyword co-occurrence, thematic clusters, and research evolution. The findings reveal that this field has developed into an interdisciplinary domain involving environmental science, transportation, business, and consumer behavior, with China emerging as the leading contributor. Keyword analysis identifies four themes: consumer adoption behavior, purchase intention and environmental sustainability, technology acceptance and perceived value, and green consumption and planned behavior. Overall, this study provides insights into the intellectual development of electric vehicle research, although the findings are limited by the exclusive use of Web of Science and English-language publications, suggesting future studies incorporate broader databases and sources.

This study conducts a reliability analysis of a two-unit cold standby system model comprising dissimilar units. In this model, the cold standby unit replaces the active unit, which subsequently transitions to a cold standby state at random intervals. The failure-time distributions are assumed to follow an exponential distribution with distinct parameters, while the repair time distributions are considered arbitrary. Utilizing the regenerative point technique, various measures of system effectiveness, which are of significant interest to industrial managers, are derived. Finally, the system's behavior is examined graphically by plotting the Mean Time to System Failure (MTSF), availability, and profit curves.This study conducts a reliability analysis of a two-unit cold standby system model comprising dissimilar units. In this model, the cold standby unit replaces the active unit, which subsequently transitions to a cold standby state at random intervals. The failure-time distributions are assumed to follow an exponential distribution with distinct parameters, while the repair time distributions are considered arbitrary. Utilizing the regenerative point technique, various measures of system effectiveness, which are of significant interest to industrial managers, are derived. Finally, the system's behavior is examined graphically by plotting the Mean Time to System Failure (MTSF), availability, and profit curves.

The rapid integration of artificial intelligence (AI) into organizational decision-making, knowledge work, and strategic processes is fundamentally transforming the nature of leadership. Despite significant advances in leadership research, existing theories including transformational, servant, ethical, authentic, and digital leadership—remain predominantly human-centric and provide limited theoretical guidance for organizations in which humans and AI systems function as interdependent actors. Addressing this gap, this conceptual paper develops the Hybrid Intelligence Leadership Theory (HILT) and introduces the Hybrid Intelligence Leadership (HIL) Framework, a novel theoretical perspective for understanding leadership in human–AI organizations. Drawing upon Strategic Management, Organizational Behavior, Dynamic Capabilities Theory, Socio-Technical Systems Theory, and Distributed Cognition Theory, the proposed framework reconceptualizes leadership as the orchestration of complementary human and artificial intelligence capabilities rather than the supervision of human employees alone. The framework identifies five interrelated leadership capabilities—AI literacy, ethical AI governance, collaborative intelligence, adaptive decision-making, and continuous organizational learning—that enable leaders to effectively coordinate distributed intelligence across human and artificial agents. It further explains how these capabilities foster trust in AI-supported decision-making, enhance organizational resilience, stimulate innovation, improve decision quality, and create sustainable competitive advantage. By integrating fragmented streams of AI management, leadership, and organizational research into a unified theoretical model, this study extends leadership theory beyond traditional anthropocentric assumptions and provides a robust conceptual foundation for future empirical research. The paper concludes by outlining theoretical contributions, managerial implications, and a comprehensive research agenda for organizations navigating AI-driven transformation.

This study investigates dealer satisfaction and market perceptions regarding Cement dealers in Uttar Pradesh, evaluating performance differentials across urban, rural, and dual-market segments. Based on empirical survey data (N=53), key operational, commercial, and promotional dimensions were analysed using Descriptive Statistics and Chi-Square (χ2) tests of independence. Findings reveal high general satisfaction with product quality (92.45%), pricing (71.70%), and delivery timelines (79.24%). However, core operational challenges persist, including severe territorial infiltration—reported as high by 45.28% of respondents— and low dealer engagement with digital innovations such as Virtual Reality services (45.28% neutral). Hypothesis testing reveals statistically significant differences between urban and rural segments regarding field team support (χ2 = 29.74, p < 0.001), incentive structure value (χ2 = 14.78, p = 0.007), and customer quality feedback (χ2 = 53.87, p < 0.001), while core terms like pricing and profit margins demonstrate homogenous satisfaction regardless of geography. Strategic recommendations emphasize digital track-and-trace implementation to control cross-border infiltration and re-balancing field sales support to bridge urban-rural service gaps.