Rehan Ali

In this paper, we introduce a novel two-step inertial Bregman--Tseng algorithm for solving non-monotone variational inequality problems in reflexive Banach spaces. The proposed method combines a two-step inertial extrapolation scheme with a Bregman forward-backward-forward (Tseng-type) framework, leading to a computationally efficient iterative process that avoids auxiliary projections onto complex sets. Without imposing monotonicity assumptions on the cost operator, we establish strong convergence of the proposed algorithm under standard conditions, including Lipschitz continuity and mild control requirements on the inertial and relaxation parameters. The convergence analysis is carried out using a self-adaptive line search rule and relies on the assumption that the solution set of the corresponding dual variational inequality is nonempty. Furthermore, we provide numerical experiments to demonstrate the effectiveness of the proposed method and highlight its advantages in terms of computational simplicity and efficiency when compared with existing projection-type and extragradient methods. The results presented in this work extend and complement several important contributions in the current literature on variational inequality problems in Banach spaces.

In this paper, we develop modified inertial hybrid projection methods to obtain common solutions of generalized mixed equilibrium problems involving monotone and Lipschitz continuous operators. The use of alternative half-space constructions reduces both projection steps and operator evaluations. Strong convergence is established under standard conditions. The proposed approach is further extended to common solutions of variational inequality problems, and numerical results are provided for illustration.

This study examined the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) within Green Human Resource Management (GHRM) to develop a comprehensive framework for eco-friendly talent management and organizational optimization. The research adopted a qualitative and conceptual approach, systematically synthesizing recent literature to explore how AI-driven analytics and IoT-enabled systems enhance sustainable HR practices. Thematic analysis was employed to identify key dimensions, including green recruitment, digital training, performance management, employee engagement, and environmental monitoring. The findings indicated that AI significantly improves HR efficiency through automation, predictive analytics, and data-driven decision-making, enabling paperless recruitment, personalized e-learning, and objective performance evaluation. Concurrently, IoT facilitates real-time monitoring of workplace environments, energy consumption, and resource utilization, enhancing transparency and accountability. The integration of these technologies, termed AIoT, creates a synergistic effect that significantly enhances green recruitment, intelligent training, sustainable performance management, and employee engagement. This synergy enables organizations to optimize resource allocation, promote pro-environmental behaviour among employees, and achieve long-term environmental and operational goals. However, the study identified critical barriers to effective adoption, including technological complexity, data privacy concerns, high implementation costs, skill gaps, and organizational resistance. The study proposed a structured conceptual framework integrating AI and IoT into GHRM practices, offering practical insights for organizations and policymakers. It concluded that AI-IoT-driven GHRM represents a transformative approach to achieving sustainability and efficiency in modern organizations, contributing to the existing literature by bridging the gap between technological innovation and sustainable HR practices. References Adli, H. K., et al. (2023). Recent advancements and challenges of AIoT application in smart agriculture: A review. Sensors, 23(7), 3752. https://doi.org/10.3390/s23073752 Al-Garadi, M. A., Mohamed, A., Al-Ali, A., Du, X., & Guizani, M. (2018). A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials, 20(4), 2923–2960. https://doi.org/10.1109/COMST.2018.2846401 Alherimi, N., Abdulmaksoud, S., Ahmed, V., & Bahroun, Z. (2025). A systematic literature review of artificial intelligence advancements in green human resource management. Sustainability, 17(22), 10283. https://doi.org/10.3390/su172210283 AlKetbi, A., & Rice, J. (2024). The impact of green human resource management practices on employees, clients, and organizational performance: A literature review. Administrative Sciences, 14(4), 78. https://doi.org/10.3390/admsci14040078 AlSuwaidi, M., Eid, R., & Agag, G. (2023). Understanding the link between green HRM and organizational sustainability. Sustainability, 15(9), 7452. https://doi.org/10.3390/su15097452 Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A., Hamdi, M., & Guizani, M. (2021). Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2021.3072172 Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2023). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence. International Journal of Production Research, 61(8), 2525–2545. https://doi.org/10.1080/00207543.2022.2039587 Benitez, J., Ayala, N. F., & Frank, A. G. (2023). Industry 4.0 innovation ecosystems: An integrated framework for sustainable digital transformation. Technological Forecasting and Social Change, 186, 122130. https://doi.org/10.1016/j.techfore.2022.122130 Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2023). The adoption of artificial intelligence in human resource management. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04094-6 Dwivedi, Y. K., et al. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges and opportunities. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Fatima, N., Rafiq-uz-Zaman, M., Arshad, I., Rasheed, I., & Fatima, A. (2025). The role of human resource management in teacher training for inclusive education: A phenomenological study. Indus Journal of Social Sciences, 3(3), 551–564. https://doi.org/10.59075/ijss.v3i3.1921 George, G., Merrill, R. K., & Schillebeeckx, S. J. D. (2024). Digital sustainability and entrepreneurship. Journal of Business Venturing, 39(1), 106274. https://doi.org/10.1016/j.jbusvent.2023.106274 Gupta, M., Srivastava, R., & Sharma, G. D. (2023). Artificial intelligence and environmental sustainability. Environmental Science and Pollution Research, 30, 35678–35695. https://doi.org/10.1007/s11356-022-24591-3 Ibrahim, I., & Abu Eitah, R. (2025). Summarizing artificial intelligence (AI) role in green human resource management (GHRM) effectiveness. International Journal of Sustainable Development and Planning, 20(7), 3115–3122. https://doi.org/10.18280/ijsdp.200736 Jabbour, C. J. C., Jabbour, A. B. L. S., Sarkis, J., & Filho, M. G. (2023). Unlocking the circular economy through new business models based on artificial intelligence and big data. Journal of Cleaner Production, 384, 135638. https://doi.org/10.1016/j.jclepro.2022.135638 Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2024). Understanding the role of internet of things in smart industries. Journal of Industrial Information Integration, 36, 100529. https://doi.org/10.1016/j.jii.2023.100529 Khan, S. A. R., Yu, Z., & Umar, M. (2024). How...

In this work, we present a Krasnosel’skiǐ–Mann-type subgradient extragradient algorithm to solve variational inequalities and hierarchical fixed-point problems for nonexpansive and quasi-nonexpansive mappings in Hilbert spaces. We establish weak convergence of the generated sequences to a common solution and derive several related results. The algorithm is validated through numerical examples, and several applications are discussed to demonstrate the method’s applicability. The proposed approach extends and unifies existing methods and findings in this field.

This work develops and analyzes an iterative method to solve the combined generalized equilibrium and fixed point problems involving two relatively nonexpansive mappings. We establish that the generated sequence converges strongly to a shared solution within a two-uniformly convex and uniformly smooth real Banach space. We also highlight some immediate consequences of the main result. To confirm the algorithm’s efficiency, a numerical example is provided. Furthermore, the practical utility of the proposed algorithm is illustrated using comprehensive tables and figures.

Report I BELIEVE THIS PAPER HAS POTENTIAL FOR PUBLICATION, SUBJECT TO MINOR REVISIONS. The paper introduces scSpecies, a novel deep learning-based approach to align single-cell RNA sequencing (scRNA-seq) datasets across species. The proposed method...

This research work introduces a connection of adjoint Bernoulli’s polynomials and a gamma function as a sequence of linear positive operators. Further, the convergence properties of these sequences of operators are investigated in various functional spaces with the aid of the Korovkin theorem, Voronovskaja-type theorem, first order of modulus of continuity, second order of modulus of continuity, Peetre’s K-functional, Lipschitz condition, etc. In the last section, we extend our research to a bivariate case of these sequences of operators, and their uniform rate of approximation and order of approximation are investigated in different functional spaces. Moreover, we construct a numerical example to demonstrate the applicability of our results.