This paper investigates the relationship between non-renewable energy consumption, globalization and carbon emissions under the Environmental Kuznets Curve (EKC) hypothesis, based on the observations of ASEAN economies between 1990 and 2023, which are unbalanced panel data. A panel cointegration analysis is used to explore the time-series dependence of the variables both in the short-term and in the long-term. The findings indicate the support of the Environmental Kuznets Curve (EKC) hypothesis which implies the energy consumption – carbon emission curve is inverted U-shaped. The results also confirm that the use of non-renewable energy sources also has a positive correlation with environmental pressure. Globalization, however, has not reached all countries similarly and appears to influence countries in different ways depending on the differences in countries' economic development, institutional environment, and environmental policies. Therefore, this study proposes that the move towards Renewable Energy (RE) should be a priority in ASEAN economies, and should be done in an environmentally sustainable way.
Research Paper, Anum Gul, Faraz et al.· The social science· 0 citations
Digital transformation is transforming the way organisations work; however, it is not just about the technology, it's about how people react to it. The human side of digital transformation is explored in this paper as it looks into the impact of human resource management (HRM) practices on employees' adaptability, and subsequently on employee performance. Based on the Ability-Motivation-Opportunity (AMO) framework and technostress theory five hypotheses are formulated relating digital-oriented training and development, digital leadership, organizational support of technostress management, adaptability of employees, organizational agility and employee performance outcomes. The conceptual framework suggests that adaptability of employees as a mediating factor between HRM practices and performance, and organizational agility as a moderating factor between HRM practices and performance. To illustrate the analytical approach, the paper provides the results of a representative survey data (N = 350) that was analyzed using descriptive statistics, correlation, regression, and bootstrapping mediation and moderation analysis. Results suggest that there are significant relationships between digital-oriented HRM practices and employee adaptability, and between employee adaptability and employee performance, with organizational agility enhancing these relationships. The paper ends with theoretical and practical implications for HR professionals in digital change as well as the limitations and directions for future research.
Research Paper, Mashal Tariq, Muhammad Abdullah et al.· The social science· 0 citations
The crossroads of deep learning and corporate governance is a paradigm shift in financial risk analysis, which goes beyond the conventional ratio models and modifies the multifaceted relationship relationships of board organizations and corporate networks. In this article, the authors provide a robust set of predictors of financial distress and fraud based on transformer-based designs of the board network data. We introduce a new methodological framework that combines graph neural networks with attention mechanisms to model director interlocks, committee structures, and measures of governance quality as high-dimensional relational features. The framework employs advanced econometric methods such as difference-in-differences with continuous treatment, propensity score weighting with neural network propensity estimation, and panel VAR with impulse response functions to create a causal identification. Empirical evidence on a decade of board-level data shows that transformer models have better predictive accuracy than conventional methods and that area under the curve (AUC) gains are 12-18 points in predicting financial distress and 22-28 points in predicting fraud. The cognitive interpretability module establishes the board independence, audit committee expertise and the network centrality of directors as the most important determinants of firm resilience. These results indicate that the application of algorithmic governance based on the use of deep learning can improve transparency, reduce agency risks, and give regulators decision-support systems to conduct active risk monitoring.
Research Paper, Muhammad Usman, Malik et al.· The social science· 0 citations
Saadat Hassan Manto is one of the greatest Urdu short story writer of the twentieth century in the Indo Pakistan Sub-continent. Born in the colonial period, his literary pursuits reflect the culture, customs, language and traditions of the prevalent society. Manto has the singular honour of being renowned short story writer of colonial as well as postcolonial period. Homi K. Bhabha is a well-known postcolonial theorist and has extensively dilated his concepts of Mimicry, Ambivalence and Hybridity etc. Manto’s short story Farishta has been analyzed under the lens of theory of Bhabha s Mimicry, Ambivalence and Hybridity thus exploring yet another dimension of Manto;s literary works. This is a new vista of critique from Bhabha theory and bi-lingual analysis which further enhances significance of this short story.
R. Paper, Dr. Freeha Nighat, Ph.D Pak et al.· The social science· 0 citations
This qualitative research focuses on examining how the Khawaja Sira community in Sheikhupura, Pakistan, has evolved linguistically over the years, shifting from the historically used Persian language in Mughal courts to the modern Punjabi street language. The investigation follows this path in a diachronic sense, which anticipates the historical and sociolinguistic movements of the community throughout the years. The study assumes a dual methodological approach, combining semi-structured interviews with twelve respondents from two generations and a critical examination of historical documents, media images, and other primary sources. Through questioning the ideological underpinnings of language and its indexical uses, the research paper aims to explain how language shift forms the identity construction in this marginalized community. Results show that socio-political side-lining, the instability caused by colonialism, post-decolonization linguistic-nationalism, and economic forces all culminated in a notable replacement of Persian and formal Urdu by Punjabi. This change of language is not only a shift in the repertoire but also a historical shift for the Khawaja ″Sira communities, marking their transition from status in the Mughal Empire to marginalization in modern society. Furthermore, these changes are documented and influenced by media images, including films, television dramas, and social media information, which determine the social and cultural positioning of the community. In the final analysis, this study contributes to a larger body of knowledge on how language practices are used as indicators of social status, resistance, and belonging, thus bringing light to the complex interaction between language, power,, and identity in marginalized groups.
Research Paper, Komal Sarfraz, Khān et al.· The social science· 0 citations
Smart transportation and associated systems are becoming more susceptible as they also apply to cyber threats such as Distributed Denial-of-Service (DDoS), model poisoning, node impersonation, and ransomware lateral movement using more interconnected IoT products (traffic controllers, sensors, cameras, etc.). The current centralized Intrusion Detection Systems (IDS) have structural disadvantages, namely, high latency, bandwidth overhead, privacy exposure, and single-point collision, which limit their applicability in real and safety-critical urban settings. In order to handle these gaps, this research will suggest a Federated Learning Multi-layer Intrusion Detection System (FL-IDS) that is specifically crafted to intelligent traffic infrastructures. The architecture incorporates edge-based anomaly detection, federated collaborative learning, secure aggregation, differential privacy, encrypted communication (TLS 1.3, MQTT-S, SNMPv3), and devices-integrity (Secure Boot and firmware signing). Every intersection does its local detection and transmits the encrypted model updates, which allows them to learn globally and capture the local traffic features. Detection performance, latency, and bandwidth consumption coupled with resistance to poisoning attacks were tested within a conceptual experimental framework comprising of the CICIoT2023 dataset and trafficking simulated variations in the real world. Findings indicate that FL-IDS is better at performance compared to Cloud-IDS and traditional ML-IDS, with a high detection rate of 95% and a false-positive rate of 1.8 percent along with a detection latency of 80 ms and bandwidth consumption of 2.3 MB. With the conditions of model-poisoning, the reduction in accuracy is only as high as 8 percent, proving to be highly resilient with secure aggregation and differential privacy.
Research Paper, Woh Xiang Huai, Lin Peng et al.· International Journal of Eme...· 0 citations
A two-tier hybrid IDS that uses a Random Forest model for quick initial detection and a Neural Network for deeper analysis of suspicious traffic is proposed that provides a balanced and efficient solution that overcomes key limitations of existing IDS models and offers a pathway towards a more robust real-time IoT intrusion detection.
R. Paper, Wong Zoey, Y. Watanabe et al.· International Journal of Eme...· 0 citations
With the growing importance of Artificial Intelligence (AI) in academic life, it is increasingly necessary to understand the impact of this technology on research activities, yet the quantitative relationships between the use of these tools and research productivity in university settings have not been sufficiently quantified. This study aimed to examine the frequency of AI tool use and the relationship between this use and attitudes towards AI and research productivity among university students and faculty. A cross-sectional survey was carried out with 220 university respondents (undergraduate students, post-graduate students, and Faculty/researchers) across five disciplines. Data were analyzed with descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation, chi-square test and multiple linear regression. Nearly half (49%) of those who responded reported using it regularly ('often'/'always'), and the most popular category of tools used was general-purpose AI assistants (44.1%). The frequency of the use of AI was positively correlated with productivity (r = .48, p < .001) and attitude (r = .37, p < .001), but there were no significant differences between gender (p = .77) or discipline (p = .62). The results indicated that together, usage frequency and attitude accounted for 30% of the variance in productivity. The two factors, together, explained 30% of the productivity (regression) variance. AI adoption is associated with higher research productivity for the institution across different groups of researchers by various disciplines and demographic groups, supporting the case for investing in structured training for AI literacy by institutions.
Research Paper, Asma Atta, Hafiz Kosar et al.· The social science· 0 citations
The findings reveal that social media usage exacerbates mental health issues such as depression, anxiety, fear of missing out (FOMO), social and financial comparisons, and educating users on healthy social media habits and the early signs of mental health distress is critical.
R. Paper, Ebrahim Timol, Kebashnee Moodley· 0 citations
The study concludes that rule-based approaches remain highly valuable for mission-critical 5G slicing applications and recommends their integration with adaptive techniques to enhance scalability and performance.
R. Paper, Zayyanu Yunusa, Usman Haruna· International Journal of Eme...· 0 citations
The multi-modal approach improves accuracy, reduces mistakes, and adapts better to new phishing methods, and performs better than single-method systems and has strong potential for future improvement.
R. Paper, Wong Ki Hurn, T. Yan et al.· International Journal of Eme...· 0 citations
To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.
R. Paper, Research Supervisor Prof, Dominique Ferraro· The social science· 0 citations