Lahore Garrison University

PurposeThis research examines the relationship between green intellectual capital (GRIC), blockchain technology (BCT), green manufacturing (GM) and sustainable performance in large manufacturing firms.Design/methodology/approachCross-sectional data were gathered from manufacturers, and partial least square structural equation modeling was employed to examine the proposed hypotheses.FindingsThe outcomes elucidated that GRIC, which includes green human capital (GRHC), green structural capital (GRSC) and green relational capital (GRRC), has enabled the implementation of GM and BCT. Furthermore, GM and BCT have a positive relationship with all three dimensions of sustainability performance.Research limitations/implicationsThe findings provide a policy framework for practitioners, decision makers and legislators to enhance GM in firms as its implementation gives a competitive position, enhances profitability, enable to meet customers' demands and improves societal safety and help preserving natural environment.Originality/valueThe current research is novel in that it discusses in depth mechanic on GRIC, BCT and GM. This study enhances the comprehension of the intellectual capital-based view and dynamic capabilities theory by postulating that GRIC has a significant role in the adoption of GM and BCT, which further affects sustainable performance.

TiO2 has been regarded as the most appropriate candidate for dilute magnetic semiconductors because of its practically observed and theoretically expected room temperature ferromagnetism. Therefore, a series of Fe doped Cu-TiO2 dilute magnetic semiconductors has been synthesized with the general formula FexCu0.15Ti0.85-xO2 (x = 0, 5, 7, 9, 11 and 13 wt

In this paper, we present novel exact traveling wave solutions for the coupled Whitham-Broer-Kaup equation, which includes both odd- and even-order partial derivative terms. These solutions are obtained using the extended generalized Riccati equation mapping method. The resulting traveling wave solutions exhibit diverse forms, including bright solitons, periodic waves, breather solitons, lump solitons, and others. Furthermore, the stability and conservation laws associated with these solutions are analyzed in detail. These findings have significant implications in physics and various applied sciences. In addition, the proposed method proves effective for solving a broader class of coupled nonlinear partial differential equations.

Hydrogen is a promising energy carrier for a sustainable and carbon-free future due to its high energy density and zero emissions. However, developing efficient, safe, and economical hydrogen storage systems remains a key challenge. Biomass-derived carbon materials (BCMs) have garnered significant attention due to their renewable origin, high surface area, and tunable porosity. This review summarizes recent progress in the synthesis and modification of BCMs for hydrogen storage applications. Carbonization techniques, including pyrolysis, hydrothermal treatment, molten-salt treatment, and ionothermal processes, are analyzed for their effects on the physicochemical properties of BCMs. Factors affecting pore structure, surface functionality, and adsorption behavior are systematically discussed, along with surface functionalization methods designed to enhance hydrogen uptake. Uniquely, this review establishes a direct relationship between synthesis parameters, structural characteristics, and hydrogen adsorption performance of BCMs, providing a novel framework for understanding structure–property-performance correlations. This perspective offers new insights into the rational design of cost-effective, sustainable hydrogen storage materials. Remaining challenges related to scalability, structural stability, and cost are identified, and future research directions are proposed to advance the practical implementation of BCMs in hydrogen storage technologies.

Wind turbine reliability is critical for sustainable energy production, yet fault diagnosis faces challenges due to data privacy concerns, heterogeneous operational conditions, and resource constraints in distributed wind farms. Traditional centralized Machine Learning (ML) approaches struggle with these issues, necessitating decentralized solutions. This study introduces the Adaptive Federated Fault Diagnosis (AF2D) framework, a novel Federated Learning (FL) approach for wind turbine fault diagnosis that ensures data privacy while addressing non-i.i.d. data distributions. Using a dataset of 35 uniaxial vibration recordings from six turbines at the University of Mustansiriyah, AF2D leverages two key modules: Adaptive Model Aggregation (AMA) and Lightweight Model Optimization (LMO). AMA employs Jensen-Shannon divergence and cosine similarity to adaptively aggregate local model updates, mitigating data heterogeneity, while LMO applies structured pruning (60% filter reduction) and 8bit quantization to enable deployment on resource-constrained SCADA systems. Results show AF2D achieves 91.3% accuracy (+/- 1.2%, 95% confidence interval), a 3.5% improvement over FedAvg (87.8% +/- 1.4%), with statistical significance (p < 0.05), and outperforms state-of-the-art methods like Clustered FL (88.5%) and Privacy-Preserving FL (87.2%). LMO reduces inference time by 64.44% and memory usage by 53.71%, enhancing edge deployment feasibility. However, the small dataset raises overfitting risks, and scalability tests reveal a threefold communication cost increase (54.5 to 150.6 MB) for 18 clients, mitigated by proposed compression (30%-50% reduction) and asynchronous updates (20%-40% overhead reduction). Privacy is maintained with a differential privacy guarantee of epsilon = 1.0, though advanced techniques like secure multiparty computation could achieve epsilon < 1. Despite limitations in severe fault detection and dataset diversity, AF2D demonstrates robust performance. Future work includes integrating multi-modal data (SCADA, vibration, environmental), testing real-time deployment, and expanding federated datasets to enhance generalizability and scalability.