This experimental study investigates the mechanical performance and energy absorption characteristics of materials for a newly developed multilayered impact-resistant panel. The proposed lightweight structure combines fiber-reinforced ceramic (FRC), nickel-titanium shape memory alloy (SMA), and additively manufactured Ti-6Al-4V lattice structures in uniform body-centered cubic (UBCC) and vertical-strut (UBCCz) configurations. Each material was fabricated, heat-treated under controlled conditions, and mechanically tested to assess its suitability for impact-resistant applications. The FRC panels exhibited a compressive strength of 50 MPa and a fracture toughness of 2.8 MPa·m¹ᐟ², supporting their use as a strike-face layer. Heat treatment of the SMA at 700 °C for 30 minutes increased energy absorption by 21% and failure strain by 40%, while reducing tensile strength. For both UBCC and UBCCz Ti-6Al-4V lattice structures, non-heat-treated samples demonstrated superior compressive properties compared with heat-treated samples. Increasing the UBCCz strut diameter from 1 mm to 2 mm substantially improved compressive strength and energy absorption. The experimental results for the multilayered panel were in good agreement with numerical simulation results reported in the literature. At an impact velocity of 100 m/s, the panel distributed the impact energy across its layers: 21% was absorbed by the FRC, 28% by the SMA, and 50% by the titanium lattice, while the aluminum backing contributed minimally. Multilayered impact-resistant panels with the same sequence of material layers were physically fabricated. Further impact testing is recommended to evaluate the panels’ damage and energy absorption behavior.
The Qasimabad Grid Station has lost its efficiency due to increasing electricity demand and the aging of its infrastructure. This has resulted in frequent outages, overloading of transformers, degradation of the power factor, and bus voltages that are close to the minimum allowable values. To evaluate the performance of the grid, a power flow study was performed using the Electrical Transient and Analysis Program (ETAP) in accordance with the regulatory framework of the National Electric Power Regulatory Authority (NEPRA). The main goal of this study was to improve the performance of the grid system through the integration of Static Var Compensators (SVCs) in the modeled network. The first step in the research was to analyze system limitations to determine the reactive power needs of the system for proper SVC location and sizing in the network. This stage required data gathering and analysis during both high load and low load periods, paying particular attention to the number of transformer overloads experienced in June 2023, as well as underloads experienced in December 2023. The results further strengthened the findings of previous studies that SVCs are beneficial in improving grid reliability by maintaining nominal bus voltages, reducing transformer loading, and correcting the power factor.
With rapid industrial expansion, effective wastewater treatment has become an urgent environmental priority. Industrial effluents contain diverse pollutants, including toxic heavy metals, organic and inorganic contaminants, and microplastics. Among these, cadmium (Cd) is a persistent, non-biodegradable heavy metal released through electroplating, mining, and agricultural activities, posing severe ecological and human health risks. This study evaluates the efficacy of chemically unmodified sugarcane bagasse biochar as a sustainable adsorbent for removing Cd from aqueous solutions. Sugarcane bagasse biochar was prepared via slow pyrolysis in a muffle furnace at 550 °C at Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, and surface functional groups were characterized before and after adsorption using Fourier Transform Infrared Spectroscopy (FTIR). Batch adsorption experiments evaluated the effects of solution pH (2–12), initial Cd concentration (5–100 mg/L), adsorbent dosage (0.5–3.0 g/L), and contact time (10–180 min) on Cd removal efficiency and adsorption capacity. Optimal removal was achieved at pH 10, an initial Cd concentration of 25 mg/L, an adsorbent dose of 3.0 g, and a contact time of 90 min, yielding removal efficiencies of 98.6% (at pH 10) and up to 99.8% (at a 3.0 g dose). Equilibrium data were modeled using the Langmuir and Freundlich isotherms; the Langmuir isotherm provided the best fit (R² = 0.999), confirming monolayer chemisorption on a homogeneous surface with a maximum adsorption capacity (qmax) of 24.45 mg/g. Desorption experiments using 0.1 M CaCl2 and 0.1 M HCl demonstrated that CaCl2 achieved higher desorption efficiency (67.98% vs. 60.46% at 100 mg/L) due to the stronger competitive displacement of divalent Ca2+ ions compared with monovalent H+ ions. These findings demonstrate that unmodified sugarcane bagasse biochar is a low-cost, chemical-free, and sustainable adsorbent for cadmium remediation.
Urban flooding poses significant risks to human life, urban systems, and the economies of affected communities. Recent flood-prediction studies increasingly employ deep learning, hybrid modelling, and IoT-based systems; however, their computational complexity, data requirements, and deployment constraints can limit their suitability for resource-constrained edge and IoT environments. This highlights the need for lightweight frameworks that can provide accurate multiclass flood-severity classification with low computational overhead. To address this need, this study proposes UFNet, a lightweight Support Vector Machine (SVM)-based framework for six-class urban flood-severity classification. UFNet uses hydrometeorological variables, including rainfall, temperature, humidity, and water-level measurements, to classify flood conditions into six severity levels. The model was evaluated on historical hydrometeorological data and achieved high class-wise classification performance, with precision ranging from 95.18% to 98.72%, recall from 92.50% to 100%, F1-score from 94.27% to 97.56%, and ROC-AUC values of 0.98 to 1.00, thus demonstrating strong class separability. Additionally, the Critical Success Index (CSI) and Composite Performance Index (CPI) further support the reliability of the obtained classification results, with CSI values from 0.892 to 0.952 and CPI values from 0.966 to 0.985. Computational evaluation shows an inference time of 5 ms and memory consumption of 10 MB, providing low computational cost compared to the tested machine-learning models. The results indicate that the UFNet framework achieves high classification performance with low computational cost and has potential for flood-severity classification in IoT and edge-computing environments.
The rapid growth of unmanned aerial vehicles (UAVs) has raised significant concerns about security, privacy, and unauthorized surveillance. This paper presents a proposed advanced Radio Frequency (RF)-Convolutional Neural Network (CNN) fusion model to improve the accuracy of UAV detection and identification. The proposed framework employs a deep 1D CNN trained on normalized RF amplitude data, with dropout regularization and 10-fold cross-validation to enhance generalization and result stability. The developed model can identify the signature of the UAV communication in the RF signal much better than the traditional detection strategy like radar and acoustic analysis through the application of deep learning tools on RF signal analysis and without the need for interpretation with RF signal analysis software. The suggested RF-CNN model has the highest accuracy and F1-score of 99.67% and 99.7 respectively, and that indicates its reliability and strength. Also, the UAV identification model achieves 88.39% accuracy with an F1-score of 88.4 which is better than the current RF-based UAV detection models. It has a low Root Mean Square Error (RMSE) of 0.003, indicating that the model has few classification errors; hence, it can be used in real-time drone monitoring. In contrast to the conventional techniques, the suggested RF-based deep learning model has scalable implementation, less computation complexity, and environmental adaptability. The results of this study provide practical implications to the airspace security, protection of critical infrastructure, and counter-drone technologies to facilitate the creation of sound AI-based surveillance systems on UAVs.
