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The mobility applications of laminated composites are constantly expanding due to their improved mechanical properties and superior strength-to-weight ratio. Such advancements directly contribute to a significant reduction in energy consumption in mobile applications. However, the orthotropic nature of these materials results in complex failure modes that require advanced damage detection techniques to prevent catastrophic failures. Therefore, various non-destructive evaluation techniques for structural health monitoring (SHM) of laminated composites are constantly being developed. Moreover, due to the latest advancements in intelligent computational methods, such as machine learning and deep learning, more reliable inspections can be performed. This review discusses current advances in SHM of composite laminates for safety–critical mobility applications such as aerospace, automobile, and marine. A comprehensive overview of the steps involved in SHM of mobility composite structures, such as sensing systems and intelligent computational methods, is presented. Additionally, the review discusses the procedure for developing these intelligent computational methods. The article also describes various public-domain datasets that readers can utilize to create novel, intelligent computational methods. Finally, potential research directions are highlighted that will enable researchers and practitioners to develop more accurate and efficient damage monitoring systems for mobility composite structures.

Recently, energy harvesting from human motion has attracted substantial research into its ability to replace conventional batteries for smart electronics. Human motion exhibits excellent potential to provide sustainable and clean energy for powering low-powered electronics, such as portable instruments and wearable devices. This review article reports on the piezoelectric, electromagnetic, and triboelectric energy harvesting technologies that can effectively scavenge biomechanical energy from human motion such as, walking, stretching, and human limb movement, as well as from small displacements (e.g., heartbeat, respiration, and muscle movement) inside the human body. Furthermore, various recent designs and configurations of human motion energy harvesters are presented according to their working mechanisms, device compositions, and performances. In order to provide insight into future research prospects, the paper also discusses the limitations, issues, and challenges of piezoelectric, electromagnetic, and triboelectric energy harvesting technologies for the development of smart electronics.

The availability of computational power in the domain of Prognostics and Health Management (PHM) with deep learning (DL) applications has attracted researchers worldwide. Industrial robots are the prime mover of modern industry. Industrial robots comprise multiple forms of rotating machinery, like servo motors and numerous gears. Thus, the PHM of the rotating components of industrial robots is crucial to minimize the downtime in the industries. In recent times, deep learning has proved its mettle in different areas, like bio-medical, image recognition, speech recognition, and many more. PHM with DL applications is a rapidly growing field. It has helped achieve a better understanding of the different condition monitoring signals, like vibration, current, temperature, acoustic emission, partial discharge, and pressure. Most current review articles are component- (or system-)specific and have not been updated to reflect the new deep learning approaches. Also, a unified review paper for PHM strategies for industrial robots and their rotating machinery with DL applications has not previously been presented. This paper presents a review of the PHM strategies with various DL algorithms for industrial robots and rotating machinery, along with brief theoretical aspects of the algorithms. This paper presents a trend of the up-to-date advancements in PHM approaches using DL algorithms. Also, the restrictions and challenges associated with the available PHM approaches are discussed, paving the way for future studies.

Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures.

Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems.

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development.

This study investigated the association between temperament and retrospective growth rates in 84 Nili Ravi buffalo heifers aged 18 to 24 months. Temperament was assessed using chute score and exit velocity, measured twice at a seven-day interval, and classified as calm (≤3) or nervous (>3). Retrospective average daily weight gain data were retrieved from farm records, and blood samples were collected to measure cortisol levels. ANOVA was used to analyze data, considering temperament, age group, season, and year of birth as fixed effects, with birth weight as a covariate. Results showed that 48 heifers were calm and 36 were nervous. Calm heifers exhibited significantly higher average daily gains than nervous heifers during the post-weaning period, with an increase of 240 g/day from 4 to 6 months and 190 g/day from 6 to 12 months (p < 0.001). However, this difference was not significant at 18–24 months (p = 0.144). Calm heifers have numerically lower cortisol levels (0.96 vs. 1.27 μg/dL; p = 0.11). These findings suggest that calmer heifers grow faster in early life, emphasizing the importance of temperament in breeding programs aimed at improving growth performance and welfare.

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

This study investigates the impacts of financial development (FMD), GDP per capita (GDP), primary energy consumption (ENG), and trade openness (TO) on total greenhouse gas emissions (EQ) in Tunisia over the period 2000–2021. Using the ARDL approach, complemented by FMOLS, DOLS, and CCR estimators, the results indicate that financial development significantly reduces emissions, reflecting its role in financing green projects and fostering technological innovation. In contrast, GDP per capita and energy consumption exert a positive and substantial effect on emissions, highlighting the carbon-intensive nature of Tunisian growth. Trade openness has a limited short-term effect but contributes to emission reductions in the long term. The negative and significant error-correction coefficient shows a rapid adjustment towards long-term equilibrium. These findings emphasise that promoting sustainable finance, accelerating the energy transition, and implementing well-regulated trade policies are essential levers for mitigating emissions and enhancing environmental sustainability in Tunisia.

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of laminated composite structures. Their favorable strength-to-weight ratio renders them ideal for the automotive, marine, and aerospace industries. Among various non-destructive testing (NDT) methods, ultrasonic techniques have emerged as robust tools for detecting and characterizing internal flaws in composites, including delaminations, matrix cracks, and fiber breakages. This review concentrates on recent developments in ultrasonic NDT techniques for the SHM of laminated composite structures, with a special focus on guided wave methods. We delve into the fundamental principles of ultrasonic testing in composites and review cutting-edge techniques such as phased array ultrasonics, laser ultrasonics, and nonlinear ultrasonic methods. The review also discusses emerging trends in data analysis, particularly the integration of machine learning and artificial intelligence for enhanced defect detection and characterization through guided waves. This review outlines the current and anticipated trends in ultrasonic NDT for SHM in composites, aiming to aid researchers and practitioners in developing more effective monitoring strategies for laminated composite structures.