University of Engineering and Technology Lahore

Self-compacting concrete (SCC) is modern concrete which can flow and compact naturally without requiring mechanical vibration. This attribute minimizes the labour and time spent on construction thus SCC is highly efficient. The use of supplementary cementitious materials or SCMs has gained momentum over the recent years to partially substitute cement in conventional concrete to maintain strength without increasing cost and environmental degradation. Nevertheless, the use of SCMs in self-compacting concrete is scarcely being researched, and thus individuals are not eager to apply it due to the uncertainty in its performance. The present work studies the behaviour of SCC on the addition of fly ash instead of some cement to bridge this gap. Cement was substituted with fly ash in weight percentages of 0%, 5%, 10%, 15%, 20%. Standard concrete cubes were made for every mix ratio. To assess the fresh SCC's workability and identify the ideal fly ash content, the slump flow test was performed. A Compressive Testing Machine (CTM) was used to evaluate the samples' compressive strength following 14 days and 28-days water curing period. The results showed that the maximum workability was achieved when fly ash was substituted for 15% of the cement. Furthermore, blends with 10% to 15% fly ash replacement had the highest compressive strength. These results show that fly ash, when used in the right amounts, improves flowability and strength in SCC and is a cost-effective partial substitute for cement.

Breast cancer is a prevalent disease affecting women globally, and early detection and treatment can improve survival rates. Breast ultrasound is a common method for preoperative diagnosis, but its low resolution can lead to inaccurate judgments. Artificial intelligence (AI) can enhance diagnostic accuracy and aid in formulating effective treatment strategies. This research aimed to integrate medical ultrasound image analysis and AI to develop a multimodal nomogram for preoperative prediction of breast cancer. The study utilized a publicly available dataset that included B-mode ultrasound and color Doppler flow imaging (CDFI) scans from 611 patients, supplemented by relevant clinical variables. The study develops a multimodal deep learning radiomic nomogram model (DeepRadix) based on the ResNet50 backbone and channel attention mechanisms to classify breast malignancy. Radiomics features were extracted from lesion ROIs using PyRadiomics and subsequently reduced via minimum Redundancy–Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO)-based feature selection to identify the most informative radiomic predictors; in parallel, deep representations were learned using a ResNet50-based network and summarized as a deep learning score. The proposed multimodal model integrates predictive radiomic features, deep learning, and clinical information to overcome the limitations of existing methods and fully leverage their strengths. The model also introduced a nomogram-based visualization to enhance interpretability and improve clinical understanding of each patient’s characteristics. The research demonstrated strong discriminative ability in preoperative prediction of benign and malignant breast cancer, revealing potential associations among clinical features, ultrasound imaging features, and disease pathology. These findings could aid precision medicine and inform the design of diagnostic and therapeutic strategies for patients with breast cancer.

Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer scales. Optical diffraction and process-induced distortions complicate precise patterning, necessitating advanced techniques beyond traditional Optical Proximity Correction (OPC). Inverse Lithography Technology (ILT) offers a mathematically robust approach to enhance pattern fidelity, yet its high computational complexity limits scalability. We propose Adaptive Reinforcement Learning for Lithography Optimization (ARLO), a U-Net-based framework integrating self-attention mechanisms and reinforcement learning (RL) to iteratively optimize photomasks using real-time lithographic simulations. Evaluated on the LithoBench benchmark, ARLO achieves a 37.8% reduction in [Formula: see text] Loss and a 74.0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14.7% and 9.1% [Formula: see text] Loss reductions and 51.3% and 37.1% PVB reductions versus Deep LithoNet (DLN) and RL-ILT, respectively. Despite a higher shot count (181.4% increase vs. GAN-OPC, 59.0% vs. DLN-1, 29.4% vs. RL-ILT), ARLO maintains a competitive runtime of 0.035 seconds per patch. These results position ARLO as a scalable, efficient solution for next-generation semiconductor manufacturing.

ABSTRACT Catalytic ozonation is an effective technique for the decontamination of organic pollutants from wastewater. This study investigates the use of an iron-coated reduced graphene oxide (Fe-RGO) catalyst. Using SEM-EDS, XRD, and FTIR techniques, the catalyst was characterized. To determine their effect on dye removal efficiency, different conditions, such as catalyst dosage and the role of scavengers, were explored. The catalyst demonstrated good recyclability, maintaining efficiency over five cycles, and the impact of radical scavengers was also analyzed. Results showed that using 0.04 g of Fe-RGO at pH 7 resulted in a 98.1% removal of M.B., compared to 88% with RGO alone. These findings suggest that for wastewater treatment, Fe-RGO significantly improves the efficiency of catalytic ozonation, offering an economical and environmentally friendly solution.

This study presents a critical and structured review of protection challenges and emerging solutions in renewable-integrated microgrids. The proliferation of distributed energy resources (DERs) has introduced complex protection issues, including bidirectional power flow, low fault currents, mode-dependent dynamics, and communication dependencies. This review systematically examines fault detection, classification, and coordination strategies for both grid-connected and islanded operating conditions from 2020 to 2025. A qualitative comparative analysis is conducted across diverse protection approaches, including conventional relay-based methods, artificial intelligence (AI)-based schemes, fuzzy logic systems, time-frequency analysis, phasor measurement unit (PMU)-assisted techniques, and communication-assisted multi-agent frameworks. The analysis identifies dominant trends toward hybrid, adaptive, and data-driven protection strategies while highlighting persistent gaps in scalability, experimental validation, cybersecurity, and real-world deployment. By synthesizing current research and identifying unresolved challenges, this review provides a clear roadmap for future work toward robust, standardized, and practically deployable microgrid protection systems.

Abstract Water scarcity has intensified the demand for efficient desalination technologies, making membrane engineering a crucial research focus. A novel type of thin film composite nanofiltration membrane was developed by doping exfoliated molybdenum disulfide nanosheets as fillers within polysulfone and thin film composite membranes. Thin film composite membranes were achieved by interfacial polymerization using m ‐phenylenediamine and trimesoyl chloride monomers. Exfoliated molybdenum disulfide nanosheets were finely dispersed in a polysulfone matrix as observed by optical microscopy. The synergistic integration of molybdenum disulfide nanosheets with the polyamide layer led to improved hydrophilicity, porosity and mechanical strength, promoting rapid water transport. The optimized membrane containing 1.5 wt% molybdenum disulfide achieved a pure water flux of 118 L m −2 h −1 and salt rejection of 40.1% (NaCl) and 64.4% (MgSO₄), compared with 70 L m −2 h −1 , 19.9% and 32.6% for pristine polysulfone. These results highlight the potential of 2D‐material engineering for high‐efficiency water purification. © 2026 Society of Chemical Industry.