In recent times, digital transformations and Industry 4.0 have revolutionized real-time bridge monitoring and its inspection. The use of smart Structural Health Monitoring (SHM) techniques is becoming powerful with the competencies of Building Information Modeling (BIM) tools, Artificial Intelligence (AI), Internet of Things (IoT), and Virtual/Augmented (VR/AR) technologies. However, the lack of interconnectivity between these tools limits their functionality. This research has addressed this problem by developing an integrated framework to assess serviceability and implement a smart SHM for a newly constructed extradosed bridge. Using Finite Element Analysis (FEA), the study proposes an integrated SHM system that utilizes various IoT sensors, including Wired Strain Gauges (WSG), Liquid Levelling Sensors (LLS), MEMS accelerometers, and a Weather Monitoring Station (WMS) to monitor concrete deformations, vertical displacements, structural vibrations, and weather conditions. BIM tool is used to develop the virtual replica of the proposed SHM system which is then used in the 3D Game Engine (GE) to develop an AR application. This application is then successfully deployed and tested in the AR headset (HoloLens) where its capabilities for onsite bridge health monitoring are discovered. This approach overcomes the limitations of HoloLens devices by providing real-time access to SHM data through a web platform, enabling on-site or remote AR-based bridge health monitoring. Conclusively, this paper emphasizes the numerical modeling of bridges for the design of a health monitoring system, that highlights the importance of robust SHM techniques in assessing bridge conditions. Moreover, it introduces a novel approach for smart bridge inspection and onsite visualization of structural defects in an AR environment.

The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.

Memory Denial of Service (M-DoS) attacks refer to a class of cyber-attacks that aim to exhaust the memory resources of a system, rendering it unavailable to legitimate users. This type of attack is particularly dangerous in cloud computing environments, where multiple users share the same resources. Detection and mitigation of M-DoS attacks in real-time is a challenging task, as they often involve a large number of low-rate requests, making it difficult to distinguish them from legitimate traffic. Several real-time detection schemes have been proposed to identify and mitigate M-DoS attacks in cloud computing environments. These schemes can be broadly classified into two categories: signature-based and anomaly-based detection. Signature-based detection methods rely on the identification of specific patterns or characteristics of known M-DoS attack techniques, while anomaly-based detection methods identify abnormal behaviour that deviates from the normal pattern of usage. This study presents a hybrid model for real-time detection of cloud and MDOS attacks using SVM-KNN-LR. The dataset used in this study was collected from various sources and pre-processed to extract relevant features for attack detection. A feature selection process was also applied to identify the most important features for attack detection. The hybrid model achieved an accuracy of 96%, outperforming other individual models such as SVM, KNN, LR, Naive Bayes, Decision Trees, Extra Trees, Bagging Trees, and Random Forests. Confusion matrices were also used to evaluate the performance of each model. In the discussion section, we examined the performance of the hybrid model in detecting MDOS attacks and found that it had a high precision score of 0.97. However, the recall score was lower at 0.87, indicating that the model was not able to detect all instances of MDOS attacks.

This paper presents detailed results regarding the physical behavior of Cs₂SnI_6−xBr_x alloys for their potential use in photovoltaic applications. Numerical computations based on density functional theory (DFT) revealed that Br substitution at I sites significantly influenced the electronic structure of Cs₂SnI₆, resulting in an increase in bandgap values from 1.33 eV to 2.24 eV. Additionally, we analyzed the optical properties, including the absorption coefficient, which exhibited high values in the visible light region, highlighting the material’s excellent light-trapping abilities. Moreover, Cs₂SnI_6−xBr_x compounds were employed as absorber materials in an fluorine-doped tin oxide (FTO) TiO₂/Cs₂SnI₆/P3HT/Ag perovskite solar cell (PSC) to investigate its performance. The simulation process consisted of two interconnected steps: (i) the DFT calculations to derive the material properties and (ii) the SCAPS–1D (one-dimensional (1D) solar cell capacity simulator) simulation to model device performance. To ensure reliability, the SCAPS–1D simulation was calibrated against experimental data. Following this, Cs₂SnI_6−xBr_x compound with various ratios of Br content, ranging from 0 to 6, was investigated to propose an efficient solar cell design. Furthermore, the cell structure was optimized, resulting in a development in the power conversion efficiency (PCE) from 0.47% to 3.07%.

Bearings are the backbone of industrial machines that can shut down or damage the whole process when a fault occurs in them. Therefore, health diagnosis and fault identification in the bearings are essential to avoid a sudden shutdown. Vibration signals from the rotating bearings are extensively used to diagnose the health of industrial machines as well as to analyze their symmetrical behavior. When a fault occurs in the bearings, deviations from their symmetrical behavior can be indicative of potential faults. However, fault identification is challenging when (1) the vibration signals are recorded from variable speeds compared to the constant speed and (2) the vibration signals have diverse fault depths. In this work, we have proposed a highly accurate Deep Convolution Neural Network (DCNN)–Long Short-Term Memory (LSTM) model with a SoftMax classifier. The proposed model offers an innovative approach to fault diagnosis, as it obviates the need for preprocessing and digital signal processing techniques for feature computation. It demonstrates remarkable efficiency in accurately diagnosing fault conditions across variable speed vibration datasets encompassing diverse fault conditions, including but not limited to outer race fault, inner race fault, ball fault, and mixed faults, as well as constant speed datasets with varying fault depths. The proposed method can extract the features automatically from these vibration signals and, hence, are excellent to enhance the performance and efficiency to diagnose the machine’s health. For the experimental study, two different datasets—the constant speed with different fault depths and variable speed rotating machines—are considered to validate the performance of the proposed method. The accuracy achieved for the variable speed rotating machine dataset is 99.40%, while for the diverse fault dataset, the accuracy reaches 99.87%. Furthermore, the experimental results of the proposed method are compared with the existing methods in the literature as well as the artificial neural network (ANN) model.