The rapid adoption of the Industrial Internet of Things (IIoT) in smart manufacturing and critical infrastructure has significantly increased the exposure of industrial networks to sophisticated cyber threats. Ensuring secure communication and reliable threat detection in IIoT environments has therefore become a critical challenge. This study proposes an intelligent Cyber Threat Detection and Response System that integrates a Hybrid Deep Neural Network with the Grey Wolf Optimizer to enhance security in IIoT networks. The proposed framework utilizes CyberTec IIoT Malware Dataset (CIMD‑2024) on Kaggle containing network traffic characteristics, device communication patterns, and anomaly indicators. A comprehensive data preprocessing phase is employed, including noise removal, normalization, and missing value handling, to improve data quality and model reliability. The hybrid deep learning architecture combines Convolutional Neural Networks for spatial feature extraction with Long Short-Term Memory networks to capture temporal dependencies in network behavior. Additionally, a dual-attention mechanism is incorporated to emphasize significant spatial and temporal features, thereby improving the accuracy of cyber threat classification. The Grey Wolf Optimizer is applied to optimize key hyperparameters such as learning rate, dropout rate, and batch size, leading to improved model performance. Experimental results demonstrate that the proposed model achieves an accuracy of 96.5
The seismic performance of high-rise reinforced concrete (RC) buildings with vertical geometric irregularities is a critical aspect of structural engineering. This study evaluates the impact of P-Delta effects on a G+10 RC building with under Nonlinear Time History Analysis (NLTHA) using Bhuj earthquake data. A model was analyzed in ETABS, considering variations in structural configuration and the presence or absence of P-Delta effects. The first order analysis is used to analyze buildings using linear elastic methods. In a first order analysis displacements and internal force are calculated in relation to the geometric undeformed structure. It does not consider buckling and material yielding. In the case of first order elastic analysis, the structure's deflection is not assessed by the first order linear analysis. This kind of geometric non-linearity can be examined by iterative procedures, which can only be carried out with the aid of computer programs.
ABSTRACT Workload pressure, examination stress, family responsibilities, and a variety of other factors all contribute to an increase in stress in the body. Stress weakens the human mind and body by accelerating several health disorders. Therefore, the number of approaches for early projection of stress plays a vital role in the healthcare sector. Stress prediction techniques can be broadly categorised as questionnaire‐based, where a psychiatrist provides a feedback form to the user to identify the status of stress. In sensor‐based stress measurement methods, stress will be measured by some symbolic constraints like heart rate, skin conductance, pupil diameter, and a list of questions. For collecting the crucial data for stress detection, skin temperature (ST), electrocardiogram (ECG), and electrodermal activity (EDA) are keenly monitored during the experiment. In this manuscript, a comparative study was performed using the results of various techniques to predict human stress. This article presents various techniques for stress prediction and various opportunities to improve their performance.
The response spectrum method was used to construct a three-dimensional R.C. frame utilising linear elastic dynamic analysis. A computer program called ETABS is used to evaluate a reinforced concrete building's performance using the dynamic analysis technique. The various retrofitting techniques—such as steel and concrete jacketing and the use of fibre reinforced polymer (FRP) composites—that were employed to increase the load-bearing capacity of individual structural elements are highlighted, as are techniques like shear walls and shear cores that can be used to increase a building's overall stability. The majority of retrofitting methods will result in a minor increase in mass and stiffness, which shortens the duration. The strength and ductility of the retrofitted structure are frequently increased by a shorter vibration time. Therefore, if a suggested retrofit plan increases the structure's strength and ductility capacity to the point where it surpasses the needs of earthquakes, it can be considered effective.
Antibiotic stewardship programs (ASPs) seek to enhance patient outcomes by maximising the use of antibiotics in the fight against antimicrobial resistance (AMR). Artificial intelligence (AI) developments in recent years have showed significant potential for improving these applications. Artificial intelligence (AI) technologies, such as machine learning (ML) and natural language processing (NLP), are revolutionising antibiotic stewardship through enhanced diagnostic precision, customised treatment plans, resistance pattern prediction, and resource allocation optimisation. The article examines the major contributions of AI to antimicrobial stewardship, assesses the state of present applications, and talks about obstacles and future directions. Because AI can analyse enormous datasets, combine data from various sources, and produce actionable insights, it is a critical weapon in the fight against antimicrobial resistance (AMR).
