Beaconhouse National University

The visual arts industry is important in the cultural growth, socialization, and innovation. Nevertheless, the administration of artistic talents in cultural organizations is a specific issue because of the complicated interaction of creativity, identity, and organizational systems. The present study presents a theoretical framework which incorporates strategic human resource management practices, talent nurturing, the role of narrative identity and commitment to organizations within the framework of visual arts organizations. Based on creativity theory, talent management theory, self-determination theory and organizational identity theory, the research hypothesis is that positive HR practices can develop artistic talents and help artists to create meaningful narrative identities. These processes of identity reinforce organization commitment which in the end leads to sustainable creative performance in cultural institutions. The paper also includes the practical implications of the study to museums, galleries, and other arts organizations that are interested in creating enabling environments among the creative professionals. Making use of the insights of both the human resource management and the research of creative industries, the paper contributes to the literature on reminding the significance of identity-based HR practices in supporting artistic communities and maintaining cultural organizations.

The intricacies surrounding the measurement and modelling of money laundering (ML), corruption (COR), and black-market prevalence (BMP), as well as their effects on socio-economic development (SD), introduce significant challenges to accurately capturing and understanding these phenomena. There is a plethora of theories on individual measurements of each concept, but regrettably, they are not immune to criticism, and no such explicit approach is available to study the nexus between these complex concepts. The current study employs the multiple indicator approach to measure ML, COR, and SD, and utilizes the PLS-SEM approach to explore the relationships between these complex concepts, with a focus on the mediating role of the BMP. The per capita investment (PCI) expenditures, modelled through the multiple indicator approach, have been used as the control variables. The study has adopted a data-driven approach to conduct pre- and post-estimation analysis for the constructs and validate the results for a cross-section of 198 countries in 2022. The results indicate that the impact of corruption on socio-economic development is negative and statistically significant. The black market has a direct, negative, and significant impact on socio-economic development; similarly, the BMP has a positive and significant effect on ML. In addition to the direct impact on socio-economic development, BMP also indirectly affects SED through the ML. The direct effects of ML on SED are adverse, while it has an indirect positive impact on SED through its significant multiplier effect on per capita investment. These findings have implications for anti-money laundering and anti-corruption policies worldwide.

Earthworms are widely used as biological indicators of soil contamination and are often referred to as the 'intestine of the soil'; however, their effectiveness can vary across regions and environmental conditions. The performance of earthworms as indicators of soil pollution may vary among regions, and there is a need to assess their reliability under specific environmental conditions. So, in this study, soil and earthworm samples were collected from 19 locations across Punjab, Pakistan, to assess heavy metal pollution and bioaccumulation using geoaccumulation indices (Igeo) and bioaccumulation factors (BAF). Soil and earthworm tissues were digested and analyzed for heavy metals such as Sr, Zn, Ca, Cu, Fe, Mn, Co, Cr, Pb, Cd and Ni using ICP-MS. According to Igeo, cadmium frequently classified soils as strongly to extremely polluted, with values up to 4.5 in Bahawalnagar, whereas most other metals fell within the unpolluted to moderately polluted classes. Earthworms showed clear bioaccumulation (BAF > 1) for several essential or poorly regulated elements, particularly Ca, Mn, Cu and Sr; the maximum BAF for Ca reached 8.18 in Faisalabad. In contrast, group-1 carcinogenic metals (Pb, Cd and Cr) generally did not exhibit strong bioaccumulation. Relationships between BAF and soil metal concentrations or abiotic properties (moisture, pH, electrical conductivity, total dissolved and suspended solids) were generally weak (R2 typically ≤ 0.32), indicating that these factors explain only a modest fraction of the variation in metal uptake. Overall, the results highlight cadmium as the primary soil contamination risk in the surveyed areas. Because bioaccumulation showed weak relationships with soil metal concentrations and physicochemical parameters, earthworms in this study demonstrated limited effectiveness as bioindicators of toxic heavy metal accumulation. The limited internal accumulation of highly toxic metals suggests possible physiological regulation or tolerance, although the long-term ecological implications require further investigation. Further controlled studies are necessary to better evaluate their ecological role and potential applications in soil remediation, waste management, and vermicompost production.

Artificial intelligence (AI) has played a pivotal role in enhancing the efficiency of financial technology (Fintech), ultimately contributing to the stability of the banking sector. The advancements in Fintech driven by AI tools are significantly improving risk management within the banking industry. This paper investigates the mediating role of AI in the relationship between Fintech and financial stability in the context of Islamic and conventional banks across selected countries in the Organization of Islamic Cooperation (OIC). It employs structural equation modeling (SEM) to explore the causal linkages across time domains. The results of this research identify that AI is a significant mediator, playing a critical role between Fintech and stability. It either mitigates or amplifies risks, depending on the regulatory framework and implementation practices in place. The analysis indicates that AI has a weak mediating effect in the short run, but a strong mediating effect in the long run between Fintech and stability. This research paper emphasizes the importance of developing robust, forward-thinking policies to leverage the benefits of AI. It also addresses the risks to financial stability in both Islamic and conventional banking systems.

This research investigates the transformative role of Artificial Intelligence (AI) in teaching English in Pakistan's private primary schools. The study focuses on AI technologies such as Grammarly, ChatGPT, and Duolingo, examining their benefits and challenges in primary education. International studies have shown that AI aids in writing, feedback, and personalised learning; however, the impact of these tools in Pakistan’s primary schools remains underexplored. This gap is addressed by synthesising findings from global literature and examining how international trends are influencing local educational practices. The research method involves a systematic literature review of 57 articles from databases such as Scopus and ERIC. Using qualitative synthesis and theme classification according to PRISMA guidelines, the study identifies both positive outcomes, including enhanced engagement and improved language performance, and challenges, such as digital inequality, teachers' lack of preparedness, and an over-reliance on automation. The findings underscore the need for tailored ethical guidelines and ongoing professional development for teachers to ensure the effective and equitable integration of AI in primary school education

Background: The global decline in adolescent reading culture, intensified by digital media proliferation, presents a significant pedagogical challenge. School libraries are posited as vital antidotes, yet their efficacy hinges on access, quality, and guided mediation. This is particularly critical in exam-oriented educational systems like Pakistan's. Objectives: This study aimed to (1) explore how secondary students utilize school library reading materials, (2) determine the quantitative relationship between library use and reading culture development, and (3) investigate the mediating role of English Language Teachers (ELTs) in this process. Methods: A convergent mixed-methods design was employed in a private secondary school in Karachi. Quantitative data from 80 students were collected via a closed-ended survey and analyzed using descriptive statistics, Pearson’s correlation, and linear regression in SPSS (v23). Qualitative data from 25 ELTs were gathered through open-ended questionnaires and analyzed using thematic analysis. Results: Quantitative analysis revealed a significant positive correlation between library use and reading culture (*r* = .421, *p* < .000). Regression confirmed library use as a significant predictor (β = .421, *p* < .000), explaining 17.7% of the variance in reading culture. Students primarily used the library for academic tasks (assignments: 85%, research: 78%). Thematic analysis of teacher responses yielded seven core themes: (1) Teachers as Motivators and Guides, (2) Facilitating Physical Access, (3) Student Perception as an Academic Resource, (4) The Digital Disruption, (5) Systemic Constraints, (6) Collaborative Advocacy with Administration, and (7) Pedagogical Integration of Library Resources. Conclusion: While school library use significantly impacts reading culture, its potential is largely confined to academic reinforcement due to systemic constraints: insufficient library periods, outdated collections, and an exam-centric syllabus. ELTs are active, willing mediators but require structured institutional support. Cultivating a genuine, pleasure-based reading culture necessitates a tripartite strategy: administrative investment in relevant, diverse library stocks and schedule allocation; teacher-led pedagogical innovation; and a conscious effort to balance academic and leisure reading within the school ecosystem.

This systematic literature review uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to assess progress in blockchain-based Federated learning (FL) and Machine Learning (ML) for detecting financial fraud over the last five years (2020–2025). An initial pool of 29,274 records identified across IEEE Xplore, ACM Digital Library, and ScienceDirect yielded 1585 peer-reviewed studies that met the inclusion criteria. Both qualitative and quantitative approaches were used. The examined papers were classified according to algorithm type, fraud types, and evaluation measures. Credit card fraud and cryptocurrency fraud dominated the literature, with supervised learning (e.g., XGBoost, 95% accuracy) and federated learning (e.g., FedAvg, 91% accuracy) emerging as dominant methodologies. Centralized ML outperforms FL in latency but poses privacy risks. FL–blockchain hybrids reduce false positives. While precision, recall, and F1-score are commonly used, few studies use cost-sensitive criteria. Future research should prioritize adaptive FL aggregation, privacy-preserving ML, and cross-industry collaboration.

The aim of this study was to investigate the perceptions of undergraduate students about using artificial intelligence for academic writing in the private universities of Lahore, Pakistan. Using a quantitative research methodology, the data were collected through questionnaire responses from 313 students. Key findings indicate that a significant proportion of students (99%) believe AI tools to be useful; however, a prominent concern also surfaced that it hinders their learning and impact their creative thought processes. The findings emphasize that while AI may be a way of helping students in managing their tasks more efficiently and with less stress; it simultaneously erodes critical engagement and creativity. This research contributes to the limited literature on the use of AI tools within Pakistan's higher education offering valuable insights for stake holders. The study recommends AI integration within curricula and provide a structure to safeguard academic integrity and inculcating genuine learning.

This paper presents a novel hybrid copy–move forgery detection method that combines the efficiency of FAST-BRIEF (for rapid keypoint detection and binary descriptors) with the robustness of SIFT (for scale- and rotation-invariant feature matching). The proposed framework employs g2NN matching for accurate feature correspondence, followed by morphological processing and LSC-SSIM superpixel segmentation for precise localization of tampered regions. The method is evaluated on 30 diverse test images from benchmark datasets comprising over 700 images, achieving a 95% F-measure with an average CPU time of 6.02 seconds. It demonstrates strong resilience to geometric transformations (rotation, scaling), photometric adjustments (contrast, brightness), additive noise, and multiple forgeries. The proposed methodology offers a 5–30% improvement in accuracy and computational speed. This approach addresses emerging challenges in deepfake detection and satellite imagery authentication, where localized manipulations threaten media integrity.