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Industrial environments face increasing challenges in achieving accurate environmental monitoring and maintaining data privacy. This paper presents the SecureIoT-FL framework, an innovative integration of federated learning and multi-sensor fusion for real-time, privacy-preserving prediction of environmental pollutants in industrial settings. A normalized Federated Averaging (N-FedAvg) as an enhanced version of the conventional FedAvg algorithm, is introduced for global model aggregation to balance the contributions from all clients with varying data volumes, thus stabilizing the learning process. This study contributes a scalable and secure solution for environmental monitoring and risk management in industrial IoT applications. The framework leverages IoT nodes equipped with Raspberry Pi 5 and sensors to detect pollutants (e.g., PM2.5, PM10, CO2, VOCs, CH₂O, CO, O₃, NO₂, H₂S, O2, CH₄, SO₂) across three distinct environments: a PCBA factory, a CNC machining factory, and a plastic injection factory. Over five months, the system collected over 516,000 data points, enabling a multi-source data fusion approach to enhance predictive accuracy. The key outcomes of the proposed work include significant improvements in the model performance over 10 biweekly training rounds, with increasing the accuracy from 70 % to 92 % and 68–89 % for LSTM and CNN models, respectively. The framework effectively predicts the pollutant concentrations over multiple timeframes (10, 30, and 60 minutes), supporting timely interventions to reduce worker exposure to harmful pollutants. The SecureIoT-FL framework employs federated learning with TLS encryption, ensuring data privacy while supporting secure local model training and weight sharing via MQTT over AWS.

Addressing the imperative need for advancements in thermoelectric generation, this study pioneers an analysis of nanofluid-cooled solar segmented thermoelectric generators with non-uniform cross-sections. Insights into thermal management, structural integrity, and economic efficiency in thermoelectric systems are provided, employing a numerical model that accurately represents thermoelectric effects by accounting for temperature dependencies of semiconductor materials. Through research, exploration of diverse coolant strategies, including TiO2, Fe3O4, Al2O3, and graphene, offers key insights into their impact on cooling dynamics. Findings demonstrate that graphene nanofluid, operating at a flow velocity of 2 m/s, outperforms others, achieving optimal power generation of 221.77 mW and an exergy efficiency of 8.99 %. Additionally, at a concentrated irradiance of 595 kWm−2, graphene leads in environmental benefits, with the highest CO2 savings of 0.38 kgyr−1, and demonstrates economic advantages with a cost-effective dollar per mW value of 3.79×10−6 $mW−1. Furthermore, the study verifies the structural integrity of these systems, with graphene achieving an optimal von Mises stress of 1.35 GPa at a semiconductor height of 0.2 mm. These advancements contribute to environmentally friendly and high-performance generators for sustainable energy solutions, paving the way for future innovations in thermoelectric technology.

Leak proof tube-to-tube sheet joints are mandatory for the optimal operation and longevity of the shell and tube heat exchanger. Tube-to-tube sheet joints play a crucial role as fluid barriers, ensuring effective separation between the tube and shell side fluids within shell and tube heat exchangers. Non-conventional friction stir welding, known for imparting less residual stresses, is not commercially used for the fabrication of tube-to-tube sheet joints due to complex joint configuration and geometrical limitations. An extensive study on friction stir welding considering the geometrical parameters of tube-to-tube sheet arrangement is highly demanded. This research examined the mutual influence of radial clearance ranging from 0 mm to 0.5 mm and tube projection lying from 1 mm to 2 mm in friction stir welding of tube to tube sheet configuration on the pull-out strength, extension and, hardness at the stirring and fusion zones. Optimum radial clearance and tube projection for achieving high mechanical weld properties was estimated using multi-objective integrated Taguchi-PCA-GRA optimization. The result proves that the friction stir welded joint is capable of achieving joint strength close to the tungsten inert gas welded joint. The optimum parameters are 2 mm for tube projection and 0 mm for radial clearance to achieve maximum strength and weld penetration, while minimizing hardness at both stir zone and fusion zone. The findings of this research proved the adaptability of friction stir welding for the fabrication of tube-to-tube sheet joints with the right choice of tube projection and radial clearance.

A study was carried out to investigate and enhance the effects of friction stir welding on the mechanical characteristics of a flange composed of 6061-T3 aluminum alloy. The pipes possess an outer diameter and wall thickness of 6 mm, while the plates are sized at 100 x 100 × 6 mm. Nine unique experiments were planned using the Taguchi orthogonal array method, with the welding tool remaining constant. Three independent variables (travel speed, rotation speed, and shoulder diameter) were altered at three different levels for each variable. The research analyzed the influence of various FSW factors on the weld flange joint. Analysis of variance (ANOVA) and grey relational analysis (GRA) were employed to determine the impact of each FSW underwater parameter. Moreover, the statistical Taguchi technique (TM) was used to predict the optimal combination of welding parameters to improve tensile properties, including tensile strength (UTS), yield strength (YS), elongation (EI%), and bending load (BL) of welded joints. Additionally, the actual tests demonstrated a high level of agreement with the results obtained from the proposed mathematical model. The validation results obtained indicate that the optimization method is a dependable tool for enhancing the quality responses of friction stir welding.

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

With the rising demand of electric vehicles (EVs) and hybrid electric vehicles (HEVs), the necessity for efficient thermal management of Lithium-Ion Batteries (LIB) becomes more crucial. Over the past few years, thermoelectric coolers (TEC) have been increasingly used to cool LIBs effectively. This study provides a comprehensive analysis of thermoelectric technologies for improving the thermal management in LIB Systems. The review examines core ideas, experimental approaches, and new research discoveries to provide a thorough investigation. The inquiry starts with analysing TEC Hybrid battery thermal management system (BTMS) Cooling, including air cooled, phase change material (PCM)-cooled, liquid cooled, and heat pipe cooled thermoelectric BTMS. This paper also examines the shape, thickness, and arrangement of heat sink fins in TECs, providing valuable insights for enhancing thermal efficiency. The review additionally focuses on Control in TEC-based Li-ion BTMS, emphasizing the need for effective regulatory systems. Recognizing inherent limits, the study examines issues, stimulating future research possibilities. It encourages a complete grasp of contemporary thermoelectric technologies in Li-ion BTMS by synthesizing theoretical frameworks, experimental data, and literature assessments. The review also examines Future Directions, emphasizing prospects for innovation. A collaborative future is envisioned in which shared information drives long-term advances in energy storage technologies.

Illegal logging poses a significant threat to environmental conservation, contributing to deforestation, ecosystem disruption, and economic losses. Traditional detection methods, such as ground patrols and satellite imagery, are often ineffective, expensive, and time-consuming. This paper introduces a novel Unmanned Aerial Vehicles (UAV)-based IoT system for chainsaw noise detection and prevention. The system uses low-cost IoT nodes with microphones and microcontrollers to detect chainsaw sounds via a Convolutional Neural Network (CNN) model. These nodes reduce power consumption, allowing the battery to last at least 8 years. Detected chainsaw sounds are transmitted to a LoRaWAN gateway and forwarded to a cloud server. The cloud server processes the data and dispatches a drone equipped with a camera to the site. The drone streams live video to the cloud, where AI algorithms analyze the footage for human presence. If detected, the drone activates sound and light alarms to deter illegal logging. Experimental results show that IoT nodes can detect chainsaw sounds up to 100 m, with the CNN model achieving an accuracy of 99.37 % and a loss of 0.0185. Comparisons with Logistic Regression, Decision Tree, Random Forest, and SVM demonstrated the superior performance of CNN. This UAV-IoT system offers a fast, reliable, scalable, and real-time solution for detecting and preventing illegal logging. Leveraging advanced AI and IoT technologies, this system enhances environmental monitoring and conservation efforts. It represents a significant step forward in the fight against illegal logging, providing a practical, effective, and sustainable solution.

Heat pipes (HP) have been extensively used for thermal management in many sectors as a flexible potential heat transfer mechanism, including laptop computer CPUs, projectors, solar collectors, and battery thermal management systems (BTMSs). This study reviews and compiles the latest advancements in using HPs for efficient thermal management of high-performance lithium-ion battery systems. This review examines the most recent BTMS that are based on HPs, with a particular emphasis on the role of artificial intelligence (AI) in optimizing thermal performance. It also addresses significant distinctions from prior research, including AI-driven predictive models and hybrid cooling techniques. A classification is created using various wick topologies, working fluids within a lithium-ion BTMS's temperature range, and their appropriate envelope materials. The instances of each one's application in the BTMS or potential uses in the future have been presented. HPs are divided into several categories depending on their form (tubular, flat, loop, etc.) and each variety is given thorough explanations, illustrations, and data on how it performed in various trials. Furthermore, extensive literature research reveals AI's role in fine-tuning operational parameters, crafting algorithms to predict core temperatures in HP systems, and employing advanced optimization and deep learning techniques for efficient and safe management of HP-cooled reactors under stringent power limitations. Moreover, hybrid cooling strategies, including air-cooled, liquid-cooled, phase change material (PCM) cooled, and thermoelectrically cooled HPs, are also highlighted. Future research work recommendations have been provided for several studies on HPs to cool lithium-ion batteries.

Ni-rich oxides with layered structures are considered promising cathode materials for high-voltage lithium-ion batteries due to their high capacity and wide potential window. However, they suffer from volume expansion and contraction, as well as Ni reactivity with electrolyte components, leading to structural degradation and continuous lithium consumption during cycling. In this study, a highly electrically and ionically layer of Te-doped LiNbO3 is coated onto the surface of LiNi0.8Co0.1Mn0.1O2 (NCM811) to protect the crystal structure from cracks and side reactions with the electrolyte at high voltages (4.3 V vs. Li/Li+). Characterization techniques, including X-ray diffraction (XRD), dynamic light scattering (DLS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and electrochemical impedance spectroscopy (EIS), are employed to analyze the structure, morphology, and electrochemical performance of the coated materials. Results show that the delivered capacity at 0.1 C increases from 192.9 to 210.8 mAh g−1 and the capacity retention at 0.2 C increases from 79.7 to 89.2% after 100 cycles. Moreover, the diffusion coefficient of the coated NCM is 4.6 × 10−13 cm2 s−1, while that of bare NCM is only 1.5 × 10−13 cm2 s−1 due to the reactivity of the coating layer with lithium. These findings provide valuable insights into the design and optimization of cathode materials for next-generation energy storage systems, contributing to the advancement of sustainable and efficient energy technologies.

Soft robotic hands have gained significant attention in recent years due to their simplicity in control, ease of fabrication, and remarkable compliance. However, traditional designs often suffer from limited contact area and insufficient grasping force. In this study, we propose a novel four-finger pneumatic soft robotic hand with wave-shaped contour actuators to address these issues. Inspired by natural grasping mechanisms, the proposed design integrates multiple actuation modes to enhance both adaptability and reliability. An analytical model based on constant curvature bending theory and the Yeoh constitutive model is developed to guide the design and predict the performance of the actuators. The model is validated through extensive Abaqus finite element simulations, demonstrating excellent agreement with experimental results. Our findings confirm that the actuators achieve consistent and reliable bending motion with optimal structural parameters. Experimental evaluation of the soft robotic hand includes bending trajectory analysis and grasping tests with various irregular objects. The results show that the actuators provide a maximum bending angle of 180° under 26 kPa pressure and maintain stable grasps for objects of different shapes and sizes. The soft robotic hand demonstrates superior grasping adaptability and reliability due to its high compliance and optimized design. This study paves the way for practical applications in industrial automation, medical devices, and service robotics, highlighting the potential of wave-shaped contour actuators in enhancing the functionality of soft robotic hands.

This study presents a Bayesian neural network for the comprehensive performance forecast of an advanced thermoelectric system featuring segmented thermocouples with variable area shape for optimal solar energy conversion. The training data is obtained from a three-dimensional numerical model developed in ANSYS software to account for the energy, exergy, and entropy flows in the thermoelectric module to maximize the power output, energy, and exergy efficiencies while minimizing the entropy generation and system irreversibilities. The thermodynamic model optimizes key parameters, including geometry (height, cross-section, and percentage skutterudite content) across realistic diverse heat fluxes and cooling coefficients. To facilitate a faster and more efficient optimization pipeline, various neural networks, leveraging algorithms like Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization, are utilized for efficient data management and accurate performance prediction. The Bayesian neural network emerges as the most accurate and efficient model providing the lowest training and testing loss of ∼10−9. Furthermore, key findings highlight the importance of skutterudite content and solar concentration ratio. For instance, an optimal skutterudite content of 6.2 % at 25 Suns produces maximum power of 29.7 W, while 37 % skutterudite at 75 Suns minimizes exergy destruction. This study's methodology and findings contribute to designing more efficient clean energy systems and advancing sustainable energy goals.

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

In this study, a groundbreaking exploration of a concentrated photovoltaic-thermoelectric (CPV-TE) module employing an unprecedented selection of six thermoelectric materials across diverse temperature ranges is presented. This work innovatively employs a multi-objective optimization framework that combines genetic algorithms and goal attainment methods, aiming to optimize energy, exergy, environmental, and economic (4 E) performance. Notably, this study is the first to construct and evaluate five regression-based machine learning models with an emphasis on minimal root mean squared error and mean absolute error for rapid CPV-TE performance predictions, essential for real-world applications. Our analysis unveils that among the tested materials, the CPV-TE module incorporating lead telluride (PbTe) achieves the highest 4 E performance, demonstrating a peak exergy efficiency of 14.2 %, and annual energy savings and carbon reduction of 13.5 kWh and 6.4 kg, respectively. Furthermore, the Gaussian Process Regression model is identified as the most effective among the machine learning models for forecasting performance across the materials. This study significantly advances the field by providing novel insights into thermoelectric material selection and optimization for CPV-TE modules, and establishing pioneering forecasting tools that catalyze the efficient deployment of these systems.

The present work deals with the effect of penetration depth on welding T-joints through conventional friction stir welding (FSW) as well as underwater friction stir welding (UFSW). Various set of parameters have been used such as tool rotational speeds of 1000, 1400, and 1800 rpm, and depths of penetration, such as 6, 7, and 8 mm. In UFSW, lower heat generation prevents the development of complex intermetallic compounds and defects in welding. Additionally, in the weld region, a rapid cooling rate in UFSW generates fine microstructural particles. Mechanical and microstructural characteristics has been compared in both UFSW and FSW. There was a substantial grain size effect on mechanical properties. The stir zone shows comparatively finer grains with average grain size of 49.76 µm at 1800 rpm and 7 mm depth of penetration. It was seen that the tensile strength of UFSW was 189.9 MPa and the nugget zone hardness was 70 VHN, compared to the FSW that has 175.2 MPa and 62 VHN, respectively, obtained at a rotation speed of 1800 rpm and a travel speed of 60 mm/min. The joints tested at various penetration depths show a significant number of uniform and equiaxed dimples. The presence of ductile rupture and the formation of dimples suggest that the joints were effectively bonded and tested at different strain rates.

Despite offering significant advantages in machining, cutting fluids have contributed to substantial environmental concerns. Dry machining, although considered a more environmentally friendly option, falls short in providing effective solutions for processing difficult-to-cut materials. Consequently, extensive research is underway to explore alternative greener machining solutions such as Minimum Quantity Lubrication (MQL), cryogenics, vegetable-based fluids, and hybrid strategies like Cryo-MQL. Among them, Cryo-MQL lubrication combines two sustainable machining techniques that have been widely adopted to improve the machinability of difficult-to-cut materials. This paper reviews published articles over the last six years to provide a comprehensive analysis of the current literature related to Cryo-MQL techniques, its supply systems, machinability evaluation, and optimization studies compared to other cooling and lubrication methods. Results, summarized based on reviewed articles, suggest that Cryo-MQL is an effective technique for improving the machinability of many difficult-to-cut materials in comparison to other cooling/lubrication environments. A comprehensive discussion on different supply systems; external, internal, and hybrid, for delivering Cryo-MQL mediums has also been presented in this review. It was found that hybrid technique offers favorable lubrication and cooling conditions, and advantageous in reducing the machining heat and enormous temperature. Therefore, its application can enhance the metal cutting process and benefit the manufacturing sector. Lastly, the parametric optimizations of cryogenic and MQL agents in hybrid technology can substantially improve lubrication and cooling effectiveness, leading to better surface finish, tool life, reduced cutting forces, and lower machining temperatures.

Stress Corrosion Cracking (SCC) poses a significant challenge in the oil and gas pipeline industry, jeopardizing pipeline integrity and posing risks to both the environment and human life. This research applies simulative finite element analysis (FEA), based on phase field modeling (PFM), and experimental methods to analyze the influence of applied potential on corrosion rate in simulated near neutral soil solution (NS4), utilizing Slow Strain Rate Testing to investigate the role of electrochemistry in SCC under NS4 solution conditions. Smooth round tension specimens were employed in the experiments, with data acquisition and logging facilitated through a condition-based monitoring system. The obtained results include stress-strain curves observed across various experimental conditions, along with detailed discussions on Tafel plots highlighting the effects of different applied potentials on time-to-failure, and the results showed that the time-to-failure of the material increased when the applied potential is decreased lower than the corrosion potential, which is noted to decrease the time-to-failure from roughly 7.25 days at normal conditions and no corrosive solution to roughly 5.12 days at −2000 mV. And it is noted that an increase of the applied potential to a certain value over the corrosion potential would increase the life of the material to 7.40 days at +400 mV, this increase requires further investigation. An FEA model was developed to predict the electrochemical behavior of the material and assess the impact of applied potential on material properties, aiding in the design of materials and structures resistant to stress corrosion cracking. The PFM model findings showed agreement with the experimental results, in terms of the stress-strain curves, the change in the corrosion rate, and the material time-to-failure. The findings have the potential to inform the development of preventive measures against SCC in pipeline systems, leading to life-saving interventions and environmental damage prevention. Furthermore, the study emphasizes the reliability and importance of utilizing slow strain rate testing as an effective approach for investigating SCC in AISI 4340 steel alloys.

Households in developing countries rely on traditional cooking methods despite low-efficiency and high risks of air pollution and health. Various improved biomass cookstove technologies have been developed to address this issue, but there is still room for improvement in biomass cooking technology. This research aims to enhance the biomass cookstove by investigating its performance under different air inlet conditions using computational fluid dynamics analysis. The impact of air flow rate and the position of the secondary air inlet on the thermal efficiency of the biomass cookstove is examined. The eddy dissipation combustion model and k-epsilon turbulence model are utilized in ANSYS Fluent to simulate the combustion process. The cookstove's performance is assessed at three different air flow rates (0.01, 0.02, and 0.03 kg/s) and at two different secondary air inlet positions. The highest thermal efficiency is observed at secondary air inlet located 50 mm below the outlet at a flow rate of 0.03 kg/s. The computational findings from this investigation show a significant connection between the air inlet conditions and the thermal efficiency of the biomass cookstove. Repositioning the secondary air inlet to a location with more available space for complete combustion improves thermal efficiency compared to traditional designs.

Stress corrosion cracking (SCC) under harsh environmental conditions still poses a significant challenge, despite extensive research efforts. The intricate interplay among mechanical, chemical, and electrochemical factors hinders the accurate prognosis of material degradation and remaining service life. Furthermore, the demand for real-time monitoring and early detection of SCC defects adds further complexity to the prognostication process. Therefore, there is an urgent need for comprehensive review papers that consolidate current knowledge and advancements in prognosis methods. Such reviews would facilitate a better understanding and resolution of the challenges associated with SCC under harsh environmental conditions. This work aims to provide a comprehensive overview of various prognosis methods utilized for the assessment and prediction of SCC in such environments. The paper will delve into the following sections: exacerbating harsh environmental conditions, non-destructive testing (NDT) techniques, electrochemical techniques, numerical modeling, and machine learning. This review is inclined to serve as a valuable resource for researchers and practitioners working in the field, facilitating the development of effective strategies to mitigate SCC and ensure the integrity and reliability of materials operating in challenging environments. Despite considerable research, stress corrosion cracking in harsh environments remains a critical issue, complicated by the interplay of mechanical, chemical, and electrochemical factors. This review aims to consolidate current prognosis methods, including non-destructive testing, electrochemical techniques, numerical modeling, and machine learning. Key findings indicate that while traditional methods offer limited reliability, emerging computational approaches show promise for real-time, accurate predictions. The paper also briefly discusses notable SCC failure cases to underscore the urgency for improved prognosis techniques. This work aspires to fill knowledge gaps and serve as a resource for developing effective SCC mitigation strategies, thereby ensuring material integrity in challenging operational conditions.

In the pursuit of sustainable energy innovations, this study investigates the transformative potential of material segmentation and non-uniform cross-sections in the field of thermoelectric power generation. By analyzing four distinct thermoelectric models under varying optical concentration ratios (OCRs), our research is dedicated to optimizing exergy efficiency, economic viability, and thermo-mechanical performance. Model 3, representing a uniform cross-section segmented thermoelectric generator (TEG), emerges as the frontrunner in our study. The significance of this research lies in its potential to revolutionize thermoelectric power generation, offering a fresh perspective on sustainable energy production. Our primary aim is to elucidate the impact of innovative design strategies on thermoelectric performance. To achieve this, we employ numerical simulations and transient analyses to explore the dynamic behavior of these models under changing OCRs. Our key findings reveal the remarkable superiority of Model 3, which achieves a peak power output of 624.60 mW, exergy efficiency of 10.27 %, and CO2 savings of 1.08 kgyr−1, all under an OCR of 700. Notably, Model 3 outperforms conventional systems by 300.45 % in power output and 75.76 % in CO2 savings. Conclusively, our research provides valuable insights into the capacity of material segmentation and non-uniform cross-sections to reshape the realm of thermoelectric power generation, highlighting their potential in propelling sustainable energy solutions.

PhotoVoltaic (PV) systems are often subjected to operational faults which negatively affect their performance. Corresponding to different types and natures, such faults prevent the PV systems from achieving their nominal power output and attaining the required level of energy production. Regarding the operational optimization of PV systems, this paper aims primarily at surveying and categorizing different types of PV faults, classified as electrical, internal, and external, where each is thoroughly investigated: internal faults occur at the PV cellular level, and can either be short circuit, open circuit, bridging, or bypass diode faults. External faults on the other side are mainly classified as temporary (i.e., clouds shading, snowstorms, etc.) or permanent (e.g., glass breakage, frame defects, etc.) mismatch faults. Lastly, electrical faults involve common circuitry problems, such as short circuits (e.g., line to ground, line to line, etc.), power processing units’ faults (e.g., inverter faults), and arc faults. As for the detection methods, six major fault detection methods are investigated for the AC side of the PV system with twenty-nine total AC based fault detection methods. On the other hand, eleven major fault detection methods are surveyed for the DC side of PV systems with seventy-three total DC based fault detection methods. The investigated methods are critically analyzed, and compared relevantly to each other, within the mutual sub-sets. The resulting tabulated comparative data assessments for PV faults (i.e., cause-effect relationships, impact on the PV system performance), as well as for faults detection methods (i.e., priority for application, etc.) compose a rich background for related PV systems’ performance security fields, where a nexus future work is also suggested.

This paper addresses cylindrical lithium-ion cells' heating and non-uniform cooling by simulating a simple battery pack design with 24 cells and axial airflow. Cooling a battery pack using forced air convection in a rectangular container causes non-homogeneous temperature distribution. The middle cells and the second half of the battery pack (near the exit) get less cooling compared to the cells near the inlet. To solve this issue of non-uniform cooling, in this paper, we propose an optimized Battery Thermal Management System (BTMS) which allows higher amounts of turbulent kinetic energy to be generated near the cells closer to the exit and farther away from the inlet. We attempted to create a narrow region in the middle of the pack which leads to an increase in mixing and turbulence in the airflow. This directs the flow precisely, hence getting rid of dead air flow zones behind the cells. The convergence functions like a nozzle, propelling the air flow into the second half of the battery pack. Using COMSOL Multiphysics, the mixing and turbulence of the airflow, as well as the temperature increase and homogeneity inside the battery pack, are investigated. Three separate investigations are conducted in order to determine the optimal shape for the BTMS control volume as well as to determine the effect of system's inlet pressure and outlet velocity on the cooling performance. Results indicated that the system's temperature decreased significantly by lowering the outlet dimensions and introducing convergence to the BTMS's central area. Increasing outlet velocity increased cooling in a case with a single fan significantly, whereas increasing pressure in a case with two fans increased cooling slightly.

Stress Corrosion Cracking (SCC) is a failure mechanism that occurs when certain materials are subjected to both external or residual stresses and corrosion. This combined effect leads to the development of cracks in the susceptible materials. Submerged steel pipelines in the petroleum sector are built of low-alloy steels having a ferrite-cementite composition, including API 5L X70. Such materials are sensitive to SCC damage in aqueous systems. The film rupture dissolution repassivation (FRDR) process is used in this study to evaluate the cracks and pits growth in oil and gas pipelines in the Gulf area under diverse SCC environmental conditions. The SCC crack propagation and pit growth under near-neutral environmental conditions were analyzed using phase field modelling. X70 steel under NS4 the solution was used for the analysis to represent the anodic dissolution film rupture mechanism. A parametric study was done to study the impact of different electrochemistry and phase field parameters on crack growth behaviour. The study assess the susceptibility to SCC caused by an pit by employing diverse settings to evaluate the impact of corrosion parameters and the interaction among the FRDR mechanism. The corrosion rates are influenced by the interface kinetics coefficient (L), which exhibits an accelerated effect as L increases. This transition from fracture-controlled to dissolution-controlled SCC growth occurs until the system reaches the diffusion limit, beyond which further increases in L do not significantly impact corrosion rates. Moreover, higher values of the kinetic coefficient (k) advance the creation of SCC cracks at the crack front, resulting from corrosion originating from pitting at the crack mouth. This process leads to the refinement of the pit and its transformation into a crack. A comparison analysis was utilized to validate our simulation under a near-neutral NS4 solution for X70 steel by correlating the findings with other numerical methods for crack growth utilizing the same material and environmental parameters. The results show decent agreement with the comparative study.

This study combines experimental and numerical evaluations of Vortex Bladeless Wind Turbines (VBWTs) to understand their potential in renewable energy generation. The methodology employs Two-Way Fluid–Solid Interface (FSI) simulations, alongside real-world data, providing important insights into the turbine’s vibration dynamics and flow interactions during operation. Key findings include identifying optimal vibration frequencies and amplitudes that enhance energy harvesting and a clear advantage in power-generation estimations shown by one of the models used. The study reveals possible applications of VBWT in various settings like airport runways, highways, and buildings, indicating a promising avenue for incorporating such renewable-energy solutions. Discussions on the economic feasibility and environmental benefits of VBWT deployment are also presented, suggesting a need for further research and optimization in this area. A conceptual generator design and business model are introduced as part of a broader discussion on technology integration and energy storage. The research in this study encompasses experimental and numerical analysis, to achieve a broader understanding of the workings of a VBWT, realizing the feasibility of using such systems in lower-wind-speed conditions and upscaling to higher-wind-speed cases.

This research focuses on finding the best surrogate performance prediction model for a solar photovoltaic-thermoelectric (PV-TE) module with different semiconductor materials. The need for a surrogate machine learning model arises due to the inefficiency of the current numerical simulations used to assess the performance of the device. The study introduces several surrogate machine learning models, such as recurrent, time delay, and regular artificial neural networks (ANNs), that are trained using expensive finite element generated data when the operating parameters of the system are altered. These parameters include the optical concentration ratio, cooling coefficient, wind speed, air temperature, glass emissivity, semiconductor dimensions, external load resistance, and thermoelectric current. Despite the time-intensive and costly data generation method, 714 datapoints were produced and utilized to train the surrogate machine learning models for improved performance prediction and optimization. The results indicate that the optimal machine learning model for solar PV-TE performance modelling was the ANN architecture with two hidden layers and five neurons per layer. Furthermore, lithium nitride oxide PV-TE showed a 65% improvement over the popular bismuth telluride PV-TE when tested under 25 Suns. The surrogate ANN also outperformed the conventional numerical simulations by 10,000 times using the same computing resources. Finally, the study suggests the potential of recurrent and time delay neural networks for modelling time series PV-TE data in the future.

A study on the first natural frequency of rotating nanocomposite beams is addressed in this research. The beam has been reinforced with carbon nanotube. With the purpose of incorporating the shearability of the beam, the Reddy's third order shear deformation theory has been assigned. The motion equations are discretized in space employing the well-know Ritz-technique. For the sake of study on the first natural frequency, the nonlinear in time discretized equations of motion are linearized around the static deformation induced by the centrifugal force. The linearized discretized governing equations around the post-buckling state are exploited to find the first natural frequency after the buckling incidence. The outcomes reveal that although a stationary free-clamped(simply-clamped) beam and a stationary clamped-free(clamped-simply) beam treat similarly in the analyses, the associated rotating beams demonstrate a qualitative discrepancy. Moreover, the outcomes based upon the Reddy's third order shear deformation theory deviate from the results in regard of the Timoshenko beam theory approximately below the length to the height of the beam equal to 12 for CS FGX CNTRC beams. Nevertheless, for the FGO CNTRC beams the results in reference to the both mentioned theories approximately are consistent to each other.

Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.

Quantized conduction achieved in layered materials offers a wide range of applications in electronics. A comprehensive analysis of electronic properties of Sr2ZrO4/TiN- and Sr2ZrO4/TaN-layered heterostructure is carried out using plane wave-based first principles calculations. To understand the origin of quantized conduction, the role of oxygen vacancies (Vos) in 2D layered Ruddleson–Popper perovskite (Sr2ZrO4) is analyzed using density of states, isosurface, and integrated charge density plots. The origin of quantized states formed near the Fermi level is proposed in terms of charge conduction layer formed at the interface. The comprehensive insight of Sr2ZrO4/TiN and Sr2ZrO4/TaN heterostructure interface is provided by shedding light on the charge redistribution from charge density and Bader charge analysis. Meanwhile, work function is calculated for the confirmation of charge conducting behavior of the two layered heterostructures. The interface of these two layered heterostructures revealed the quantized conduction phenomena which cannot be achieved with either layer alone. Stable switching achieved withaTaN electrode being an important task for robust RS and solving sneak path related problem is opening roadmap for 2D layered RRAM devices.

The mass production of cost-effective, large area, defect-free and high crystal quality graphene sheets with a high yield is a challenging task. In order to investigate the mechanisms involved, we report on the synthesis of graphene sheets by a homemade atmospheric pressure thermal plasma jet system, which is a single-step and less time-consuming technique. The samples were prepared by using pure Ar gas and a mixture of Ar and N2. The microstructure of the synthesized graphene sheets was characterized with the help of Raman spectroscopy, field emission scanning electron microscopy (FE-SEM) and Fourier transform infrared (FTIR) spectroscopy. The appearance of G and 2D peaks in the Raman spectrum confirmed the formation of graphene. Moreover, we observed that the addition of nitrogen increased the production of the graphene sheets but compromised the quality of those graphene sheets by increasing their structural defects. The morphology of the synthesized samples studied via FE-SEM images showed that the sheets were composed of multilayers. FTIR spectra show the presence of C=C and a hydroxyl group directly bonded to the aromatic hydrocarbon.

The computational modeling of metal oxide clusters for photovoltaic application is carried out by using density functional theory. The structural and electronic properties of heteronuclear (TMFeO3)x molecular clusters (where x = 2, 4, 8 and TM = Sc, Ti, Fe) are investigated in detail. The physical parameters such as energy gap, formation energy, binding energy, and stability are determined. The computed values and trends in electronegativity (χ), chemical potential (μ), hardness (η) and softness (S), positions of highest occupied molecular orbitals (HOMO) and lowest unoccupied molecular orbitals (LUMO), and HOMO-LUMO gap with varying cluster sizes are discussed. The iso-surface plots with relaxed structure related to the frontier MOs are described to shed light on the charge transfer mechanism. In the entire series of the studied clusters, the computed gap of (Fe2O3)8 was found minimal and thus suitable for red light absorption, whereas (TiFeO3)2 exhibited a maximum gap which shows potential for blue light absorption. The clusters exhibiting different values of the gap are found suitable to absorb the solar radiation. HOMO and LUMO position with their energy differences in the clusters are found compatible for applications in photocatalytic and photovoltaic applications. The observed trend in the computed parameters points to the potential of the simulated materials for application in a TiO2-based semiconducting photoanode to harvest sunlight.

The recent advancement in autonomous robotics is directed toward designing a reliable system that can detect and track multiple objects in the surrounding environment for navigation and guidance purposes. This paper aims to survey the recent development in this area and present the latest trends that tackle the challenges of multiple object tracking, such as heavy occlusion, dynamic background, and illumination changes. Our research includes Multiple Object Tracking (MOT) methods incorporating the multiple inputs that can be perceived from sensors such as cameras and Light Detection and Ranging (LIDAR). In addition, a summary of the tracking techniques, such as data association and occlusion handling, is detailed to define the general framework that the literature employs. We also provide an overview of the metrics and the most common benchmark datasets, including Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), MOTChallenges, and University at Albany DEtection and TRACking (UA-DETRAC), that are used to train and evaluate the performance of MOT. At the end of this paper, we discuss the results gathered from the articles that introduced the methods. Based on our analysis, deep learning has introduced significant value to the MOT techniques in recent research, resulting in high accuracy while maintaining real-time processing.

The production of high-performing thermoelectrics is limited by the high computational energy and time required by the current finite element method solvers that are used to analyze these devices. This paper introduces a new concentrating solar thermoelectric generator made of segmented materials that have non-uniform leg geometry to provide high efficiency. After this, the optimum performance of the device is obtained using the finite element method conducted using ANSYS software. Finally, to solve the high energy and time requirements of the conventional finite element method, the data generated by finite elements are used to train a regressive artificial neural network with 10 neurons in the hidden layer. Results are that the power and efficiency obtained from the optimized device design are 3× and 2× higher than the original unoptimized device design. Furthermore, the developed neural network has a high accuracy of 99.95% in learning the finite element data. Finally, the neural network predicts the modified device performance about 800× faster than the conventional finite element method. Overall, the paper provides insights into how thermoelectric manufacturing companies can harness the power of artificial intelligence to design very high-performing devices while saving time and cost.

Through human development and technological expansion, it has become apparent that the potential lies within each individual to have an essential part in the transcendence of society and the community. People less privileged than others may need more strength and determination to surpass their current resources to overcome normal and natural obstacles in order to simulate an environment where productivity and creativity exist. This paper aims to study an approach that will assist the elderly and people of determination in one of the most essential activities practiced by individuals: shopping. The study focuses on facilitating the acquirement of items from shelves and skipping the cashier line. The proposed system is a service robot supported by a robotic arm and a linear actuator as a lifting mechanism, controlled by a remote joystick to help the elderly or disabled people reach items on high shelves. The scanning system is based on barcode detection, using transfer learning. The network was designed using YOLOv2 layers connected to TinyYOLO as feature extraction layers. This network has proven to be the most practical, with 86.4% accuracy and real-time operation with 27 FPS in comparison to using the YOLOv2 layers with DarkNet or VGG19 as feature extraction layers. An anti-theft system is integrated into the robot to improve the reliability of the self-checkout feature. The system uses computer vision GMM and Kalman filter for item detection inside the cart, and the item is validated to be the one that has been scanned, using SURF for structural features, HSV for color, and load-sensors mounted to the base of the cart to measure the item’s weight.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.