Nowshad Amin

Braid-reinforced thermosetting pipes (BRTPs) are rapidly displacing metallic and filament-wound composite pipelines, but their mechanical performance is highly sensitive to manufacturing variables. Here, Response Surface Methodology (RSM) was used to map and optimize the processing parameters–mechanical performance space defined by braiding angle (55°, 65°, 75°), reinforcement layer count (1–3), and matrix system (epoxy, vinyl ester, polyester). BRTPs were fabricated for all factor combinations and tested to relevant ASTM standards for axial tensile strength/modulus, hoop tensile strength/modulus, and pipe stiffness at 5% and 10% diametral distortion. Analysis of variance identified braiding angle and layer count as the primary drivers of stiffness and strength, with matrix type governing deformation resistance and load transfer efficiency. Quadratic RSM models demonstrated high predictive accuracy, with coefficients of determination (R ² ) of 0.9944, 0.8969, 0.9981, 0.7422, 0.9993, and 0.9994 for the respective responses, and enabled multi-objective optimization. The optimum configuration (epoxy matrix, three layers, 75° braiding angle; desirability = 0.822) yielded experimentally validated values of 80 MPa hoop tensile strength, 0.66 GPa hoop tensile modulus, 18.5 MPa axial tensile strength, 0.643 GPa axial tensile modulus, 11.86 MPa pipe stiffness at 5%, and 15.46 MPa at 10% deflection, all within <5% prediction error. This provides an actionable processing window for high-reliability BRTPs in harsh oil-and-gas service.

This work systematically derives a broad class of exact cylindrically symmetric spacetime models possessing scaling symmetries of varying dimensions. These models are then used to solve Einstein’s field equations for an imperfect fluid source that yields explicit anisotropic energy-momentum distributions. The solutions include vacuum, anisotropic fluid, and wave-like geometries, with connections to known models such as cylindrical waves, Levi-Civita spacetimes, and Kasner-type expansions. This study extends the catalog of exact cylindrically symmetric solutions and illustrates how scaling symmetry organizes the physics of time-dependent, anisotropic spacetimes.

Decision-making under uncertainty, bipolarity (positive-negative assessments), and periodic/oscillatory behaviors remains challenging for existing fuzzy and soft set models, often resulting in information loss and unstable rankings. To address this gap, we propose the complex fuzzy bipolar soft set (CFBSS) framework, which integrates complex fuzzy sets (amplitude-phase representation for periodicity) with bipolar soft sets. We develop novel operational laws, score/accuracy functions, distance/entropy measures, and three aggregation operators: CFBSSWA, CFBSOWA, and CFBSHWA, along with their mathematical properties (idempotency, boundedness, monotonicity). A complete MCDM algorithm is introduced and applied to solar panel selection for investment strategy optimization. Comparative and sensitivity analyses indicate preliminary improvements in ranking stability, adaptability, and interpretability compared to existing approaches. While the findings are based on a single case study, they suggest promising potential for applications in finance, healthcare, engineering, and other domains involving dual and time-varying uncertainties.

Universities are increasingly recognized as critical institutional actors in advancing sustainability transitions; however, empirical evidence explaining how governance‐driven green infrastructure practices translate into sustainability outcomes in higher education remains limited, particularly in emerging economies. Drawing on ecological modernization theory (EMT), institutional theory, and the resource‐based view (RBV), this study examines how green building practices (GBP), energy conservation (EC), sustainable transportation (ST), and waste management systems (WMS) influence university sustainability (US). The study further investigates the mediating role of resource efficiency (RE) and the moderating effect of technological readiness (TR) in shaping these relationships. Using a quantitative research design, data were collected from 384 respondents across 10 universities in KSA and analyzed using Smart‐PLS 4.0. The findings reveal that all four green infrastructure practices significantly enhance US. RE acts as a key mediating mechanism, indicating that sustainability gains emerge when institutions effectively optimize energy, water, and material resources. The moderating analysis shows heterogeneous effects of TR: it strengthens the positive impact of ST on US, weakens the influence of GBP, and shows no significant moderating effect on EC and WMS. This study contributes to the sustainability and higher education literature by demonstrating that sustainability transformation in universities is not driven solely by green infrastructure investments but by the interaction between institutional governance, resource optimization capabilities, and technological capacity. From a policy perspective, university administrators and policymakers should strengthen governance mechanisms that enhance RE and strategically adopt technological innovations to ensure technologies support, rather than replace, core sustainability practices.

The integration of intermittent renewable energy into smart grids introduces critical vulnerabilities in security, transparency, and real-time resilience. This paper presents a novel blockchain-secured 6G Smart grid framework that synergistically integrates sixth generation (6G) ultra-reliable low-latency communication (URLLC), distributed ledger technology, and ensemble machine learning to establish a secure, scalable, and intelligent energy ecosystem. The proposed architecture leverages adaptive 6G network slicing to support differentiated services-including peer-to-peer energy trading, grid control, and cybersecurity monitoring-while ensuring sub-30 ms latency and robust connectivity. A permission blockchain layer provides decentralized trust, immutability, and automated transaction validation via formally verified smart contracts. An ensemble learning model combining XGBoost, Random Forest, and LightGBM enables real-time multi-dimensional anomaly detection across energy, network, and transaction layers. The framework is evaluated using a synthetically generated dataset of 5000 hourly records encompassing energy generation, consumption, 6G network performance, and blockchain transactions. Experimental results demonstrate a blockchain transaction success rate of 95.16% and sustained network latency below 30 ms across all slices, even under cyberattack conditions. Reported using class-based anomaly-detection metrics, the model achieves a Recall of 0.93 and F1-score of 0.97 for the anomaly class, and a Recall of 1.00 and F1-score of 0.99 for the normal class, with an overall accuracy of 0.98. The proposed system provides a foundational architecture for resilient, autonomous, and secure renewable energy management in next generation decentralized smart grids.