Biju Patnaik University of Technology

The Internet of Things (IoT) has significantly raised vulnerability to cyber threats, highlighting the need for effective and scalable Intrusion Detection Systems (IDS). This research presents a hybrid framework that combines Principal Component Analysis (PCA) with an optimized ensemble Random Forest classifier to improve intrusion detection accuracy in high-dimensional and imbalanced IDS datasets. To assess the performance of the framework, a systematic evaluation was made using three benchmark datasets—NSL-KDD, UNSW-NB15, and CICIDS2017. Comprehensive experiments indicate that PCA improves feature selection and model generalization, whereas Random Forest offers significant interpretability and robustness. The proposed method attains a peak accuracy of 99.88

The optimization of reinforced concrete (RC) structural elements remains a fundamental aspect of sustainable and resilient building design. However, traditional methodologies are often constrained by manual heuristics, deterministic load assumptions, and a lack of transparency in computational modeling. This study introduces an integrated framework that combines nonlinear multi-objective evolutionary optimization with interpretable machine learning (ML) to derive and predict the optimal beam and column configurations for mid-rise residential buildings. The structural design space was derived from full-scale STAAD.Pro simulations of two reinforced concrete buildings, incorporating real-world boundary conditions, code-based loading (IS 875 and IS 1893), and ductility requirements as per IS 13920. Pareto-optimal designs were initially identified by minimizing the total cost and material usage while maximizing a safety index that reflects flexural, shear, and axial performance. The resulting configurations were subsequently used to train two predictive models: extreme learning machines (ELM) and elastic net regression (ENR) to estimate the optimal cross-sections and reinforcement requirements under varying design scenarios. ELM consistently outperformed ENR, achieving a higher predictive accuracy with lower mean errors across all target variables. The SHAP analysis further elucidated the structural influence of input variables such as span, end condition, and floor level, ensuring model transparency and physical interpretability. Interaction surface plots derived from the trained models revealed highly nonlinear and position-sensitive relationships between geometric attributes and reinforcement demands, aligned with structural mechanics principles such as moment redistribution, load path variation, and biaxial force interaction. This framework advances the paradigm of data-informed, human-centric design in accordance with Industry 5.0 objectives by integrating AI-driven optimization with rigorous compliance to structural codes. It offers a scalable and deployable path toward real-time, sustainable, and explainable structural engineering solutions.

The current study aims to gain a deeper understanding of the design and optimization of streptokinase-loaded nanoparticle systems using the Box-Behnken Design (BBD). The outcomes can further be applied to improve the oral bioavailability of streptokinase. Thrombolytic therapy plays a vital role in the treatment of thrombotic diseases. Streptokinase, a plasminogen activator, has been widely used for its low cost and efficient thrombolysis. As it is a protein, it is mostly administered parenterally, and the current study focuses primarily on the oral bioavailability of Streptokinase. The nanoparticulate system is prepared using a coacervation process using the polymers chitosan and alginate loaded with streptokinase. The nanoparticles prepared were characterised by using Zetasizer, FESEM, UV Spectrophotometer, and FTIR. The nanoparticulate system was further optimised by Box-Behnken Design (BBD) for different factors like Streptokinase concentration, polymer concentrations, and stirring speed on parameters such as particle size and drug loading. The in vitro release profile of Streptokinase from nanoparticles was examined to estimate its potential for increased bioavailability. The optimized formula projected by the software Design-Expert® is 1.165 mg/mL SK, 0.500 mg/mL sodium alginate, and a stirring speed of 875.42 rpm. The particle size and loading were 282.60 nm and 26.01

The increasing adoption of photovoltaic (PV) systems and electric vehicles (EVs) introduces new challenges and uncertainties like grid instability, higher operational costs, and lower system efficiency in low-voltage unbalanced radial distribution networks (URDNs). The proposed integration of EVs in Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes, in coordination with PV systems, supports the distribution network while also providing economic benefits to EV owners by intelligently timing the charging and discharging of EV batteries. An Artificial Protozoa Optimizer Algorithm (APOA) is used to optimally place PVs and EVs in the network, ensuring voltage compliance while minimizing costs from energy arbitrage, peak demand, voltage regulation, and transformer aging. A novel energy management strategy for grid-connected EV and PV systems is developed to ensure seamless power flow among all interconnected components. Evaluations conducted annually on IEEE 33-bus and 69-bus URDNs, demonstrate that the proposed approach effectively reduces grid stress, improves transformer lifespan, and enhances voltage profiles. The results show that Scenario 2 achieves a 26.19% reduction in annual power losses for the IEEE 33-bus system and 9.19% for the IEEE 69-bus system compared to Scenario 1. The findings highlight that intelligent EV-PV coordination improves grid performance while offering financial advantages to EV owners.

The effect of incorporating Rattan fibers (RFs) into Polylatic acid (PLA) matrix, using varying weight percentages (i.e. 0, 5, 10, 15, 20, and 25 wt.