Eng, Volume 6, Issue 3 (March 2025) – 20 articles

Glass fiber-reinforced polymer (GFRP) composites are widely utilized across industries, particularly in structural components exposed to hygrothermal environments characterized by elevated temperature and moisture. Such conditions can significantly degrade the mechanical properties and structural integrity of GFRP composites. Therefore, it is essential to utilize effective methods for assessing their hygrothermal aging. Traditional approaches to hygrothermal aging evaluation are hindered by several limitations, including time intensity, high costs, labor demands, and constraints on specimen size due to laboratory space. This study addresses these challenges by introducing a facile and efficient alternative that evaluates GFRP degradation under hygrothermal conditions through surface wettability analysis. Herein, a glass fiber-reinforced benzoxazine (BZ) composite was fabricated using the vacuum-assisted resin transfer molding (VARTM) method and was aged in a controlled humidity and temperature chamber for up to 5 weeks. When analyzing the wettability characteristics of the composite, notable changes in the contact angle (CA) and contact angle hysteresis (CAH) were 21.77% and 90.90%, respectively. Impact droplet dynamics further demonstrated reduced wetting length and faster droplet equilibrium times with the prolonged aging duration, indicating a progressive decline in surface characteristics. These changes correlated with reductions in flexural strength, highlighting the surface’s heightened sensitivity to environmental degradation compared with internal structural integrity. This study emphasizes the critical role of surface characterization in predicting the overall integrity of GFRP composites. Full article

Water body detection from satellite imagery is still challenging due to spectral confusion and the limitation of traditional water indices. This paper proposes a new approach by incorporating Particle Swarm Optimization with a Spectral Multi-Band Water Index for the enhanced detection of water bodies using Sentinel-2 imagery. The proposed approach optimizes the coefficients of seven Sentinel-2 bands (Blue, Green, NIR, NIR-Narrow, Water Vapor, SWIR1, and SWIR2) using an intelligent PSO with adaptive inertia weight and early stopping mechanisms. This work strategy proposes a new fitness function that applies dynamic thresholding and target-based optimization, allowing it to calibrate precisely to the local characteristics of the water body. The performance of the PSO-SMBWI was evaluated against traditional water indices, including the NDWI, MNDWI, and AWEI. The results indicate that the PSO-SMBWI has the highest accuracy, which exactly coincides with the ground truth of water coverage (12.12%), while the NDWI, MNDWI, and AWEI have deviations of +1.24%, +0.53%, and +12.15%, respectively. The proposed method automatically handles multi-resolution band integration in 10 m, 20 m, and 60 m and eliminates manual threshold tuning. Furthermore, our consensus-based validation approach ensures robust performance verification. Its effectiveness is due to its adaptive optimization framework and comprehensive spectral analysis. Hence, it is most suitable for any geographical context on the ground for highly accurate water body mapping. This research contributes a lot to the area of remote sensing by introducing an automated, highly accurate, and very computationally efficient approach to water body detection. Full article

The Cone Morse (CM) implant-abutment junction is designed to improve screw mechanics and minimize bacterial leakage through a process known as “cold fusion”. This research evaluated a clinically stable self-locking CM implant that was retrieved after 30 years of functional loading, focusing on the bone–implant interface. Histological evaluation was conducted to assess the extent of bone-to-implant contact (BIC), identify any tissue reactions, and determine the overall condition of the interface. The analysis revealed a high percentage of BIC in the endosseous portion (56.9%) and at the first contact point (77.4%). Notably, the bone in direct contact with the implant showed healthy integration, indicating no signs of adverse reactions or degradation despite the long duration of functionality. Additionally, osteocyte lacunae were found to be more numerous and larger in the coronal region compared to the apical region. These findings confirmed that the CM implant design sustains a high degree of BIC in humans, even after extended functional loading. The absence of epithelial migration, inflammatory infiltrate, and fibrous tissue at the interface suggests that this type of implant can offer long-term stability and integration. Full article

Accurate segmentation of the sky region is crucial for various applications, including object detection, tracking, and recognition, as well as augmented reality (AR) and virtual reality (VR) applications. However, sky region segmentation poses significant challenges due to complex backgrounds, varying lighting conditions, and the absence of clear edges and textures. In this paper, we present a new hybrid fast segmentation technique for the sky region that learns from object components to achieve rapid and effective segmentation while preserving precise details of the sky region. We employ Convolutional Neural Networks (CNNs) to guide the active contour and extract regions of interest. Our algorithm is implemented by leveraging three types of CNNs, namely DeepLabV3+, Fully Convolutional Network (FCN), and SegNet. Additionally, we utilize a local image fitting level-set function to characterize the region-based active contour model. Finally, the Lattice Boltzmann approach is employed to achieve rapid convergence of the level-set function. This forms a deep Lattice Boltzmann Level-Set (deep LBLS) segmentation approach that exploits deep CNN, the level-set method (LS), and the lattice Boltzmann method (LBM) for sky region separation. The performance of the proposed method is evaluated on the CamVid dataset, which contains images with a wide range of object variations due to factors such as illumination changes, shadow presence, occlusion, scale differences, and cluttered backgrounds. Experiments conducted on this dataset yield promising results in terms of computation time and the robustness of segmentation when compared to state-of-the-art methods. Our deep LBLS approach demonstrates better performance, with an improvement in mean recall value reaching up to 14.45%. Full article

In today’s competitive industrial landscape, effective performance measurement is crucial for achieving operational success. Key Performance Indicators (KPIs) are widely used to track progress, but their implementation often lacks a comprehensive framework that considers both financial outcomes and managerial insights. A quali-quantitative analysis model is introduced to optimize the implementation of KPIs in industrial settings, demonstrated through a case study of a Cambodian charcoal factory. By integrating Cost–Benefit Analysis (CBA) and Fuzzy Analytic Hierarchy Process (FAHP), the model combines both quantitative financial analysis and qualitative managerial evaluations to assess and rank a selected set of KPIs. This dual approach ensures a more comprehensive understanding of KPI impacts, enabling informed decision-making. The results highlight the critical need for balancing measurable financial benefits with qualitative insights, particularly in industries within developing nations that are forced to compromise in constrained environments, and where both economic outcomes and strategic considerations are essential for sustainable growth. Furthermore, the proposed model has universal applicability across different industrial contexts, providing a flexible and adaptable framework for KPI selection beyond the specific case study analyzed. Full article

This study investigates the thermal performance of heat pipes using nanofluids based on silver (Ag), aluminum oxide (Al₂O₃), and multi-walled carbon nanotubes (MWCNTs) at varying concentrations. Heat pipes, recognized for their efficiency in passive thermal management, face limitations with traditional fluids. Nanofluids, engineered by dispersing nanoparticles in base fluids, were explored as alternatives due to their superior thermal conductivity and convective properties. Nanofluids were prepared using ultrasonication, and their thermal conductivity, viscosity, and stability were evaluated. Experimental tests were conducted under controlled conditions to assess the impact of nanoparticle type, concentration, inclination angle, and fluid filling ratio on performance metrics, including thermal resistance (TR) and heat transfer coefficients (HTCs). The results demonstrated that Ag-based nanofluids outperformed others, achieving a 150% increase in thermal conductivity and an 83% reduction in TR compared to deionized water. HTCs increased by 300% for Ag nanofluids at a 0.5% concentration. Inclination angles and filling ratios also significantly affected performance, with optimal conditions identified at a 70% filling ratio and a 30° inclination angle. The findings highlight the potential of nanofluids in optimizing heat transfer systems and provide a framework for selecting suitable parameters in industrial applications. Full article

This study is an experiment that looks at what happens when 18 supported reinforced concrete beams with waste glass inside them are put on fire. All the supported beams were tested under a three-point load. We classified the beams into three groups based on the glass-to-sand replacement ratio. Two sand replacement ratios (10% and 20%) were considered and compared with the control beams (without replacement). Two periods of burning were studied to investigate the mechanical properties of ferrocement and the behavior of simply supported beams. We considered a temperature of 550 °C and gradually increased the burning to reach this degree. Mode failure, mechanical properties, and load–deflection were present in this study. According to this study and its results, it seems that approximately all mode failures were compound flexural and shear failures. The flexural and compressive strength of replacing sand with glass concrete leads to an improvement in the flexural behavior of the reinforced concrete beam incorporating waste glass (brittle failure) that happened when burning the beam element without sand replacement glasses. The replacement ratio (10%) is the best value of the replacement ratio of the glasses; the compressive strength increased by about 10% to 29% by the replacement ratio. When replacing 10% of the sand with glasses, the ratio increases from 1% to 16%, but the compressive strength decreases from 20% to 51% when the burning time increases from one hour to an hour and a half. When 10% of the sand is replaced by glasses by weight, the first crack load capacity goes up by about 8% for one hour of burning and by 16% for one hour and a half of burning compared to beams that are not burning. The ultimate load capacity also goes up by about 17.5% for one hour of burning and by 23.5% for one hour and a half of burning compared to beams that are not burning. Otherwise, sand replacement was 10% by glasses; by weight, the ultimate load strength increased about 6% when the burning was one hour and 12% when the burning was one hour and a half compared with the beams without burning for the same phase. Full article

Given the significant increase in electricity consumption, especially in the industrial and commercial categories, exploring new energy sources and developing innovative technologies are essential. The fourth industrial revolution (Industry 4.0) and digital transformation are not just buzzwords; they offer real opportunities for energy sustainability, using technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT). In this context, this study focuses on improving energy efficiency in smart spaces within the context of Industry 4.0 by utilizing the SmartParcels framework. This framework creates a detailed and cost-effective plan for equipping specific areas of smart communities, commonly referred to as parcels. By adapting this framework, we propose an integrated model for planning and implementing IoT applications that optimizes service utilization while adhering to operational and deployment cost constraints. The model considers multiple layers, including sensing, communication, computation, and application, and adopts an optimization approach to meet the needs related to IoT deployment. In simulated industrial environments, it demonstrated scalability and economic viability, achieving high service utility and ensuring broad geographic coverage with minimal redundancy. Furthermore, the use of heuristics for device reuse and geophysical mapping selection promotes cost-effectiveness and energy sustainability, highlighting the framework’s potential for large-scale applications in diverse industrial contexts. Full article

The expansion of cloud-based storage has intensified concerns about integrity, security, and fair compensation for third-party auditors. Existing authentication methods often compromise privacy with high computational costs, punctuating the need for an efficient and transparent verification system. This study proposes a privacy-preserving authentication framework that combines blockchain-driven smart contracts with an optimized ranked-based Merkle hash tree (RBMHT). Experimental results demonstrated that our approach lowers computational costs by 24.02% and reduces communication overhead by 86.22% compared to existing solutions. By minimizing redundant operations and limiting auditor–cloud interactions, the systems improve reliability and scalability. This makes it well-suited for applications where privacy and trust are critical. Beyond performance gains, the scheme constitutes self-executing smart contracts, preventing dishonest collusions. By bridging security, dependability, and fairness, our findings set a new standard for reliable cloud attestation for a more secure and transparent auditing system. Full article

Magnesium silicate (MgSiO₃) is widely utilized in glass manufacturing, with its performance influenced by structural modifications. In this study, we employ classical and density functional theory (DFT) simulations to investigate the defect and dopant characteristics of MgSiO₃. Our results indicate that a small amount of Mg-Si anti-site defects can exist in the material. Additionally, MgO Schottky defects are viable, requiring only slightly more energy to form than anti-site defects. Regarding the solubility of alkaline earth dopant elements, Ca preferentially incorporates into the Mg site without generating charge-compensating defects, while Zn exhibits a similar behavior among the 3D block elements. Al and Sc are promising dopants for substitution at the Si site, promoting the formation of Mg interstitials or oxygen vacancies, with the latter being the more energetically favorable process. The solution of isovalent dopants at the Si site is preferred by Ge and Ti. Furthermore, we analyze the electronic structures of the most favorable doped configurations. Full article

PSS (Photovoltaic Solar Systems) are a key technology in energy transition, and their efficiency depends on multiple interrelated factors. This study uses a systematic review based on the PRISMA methodology to identify four main categories affecting performance: technological, environmental, design and installation, and operational factors. Notably, technological advances in materials such as perovskites and emerging technologies like tandem and bifacial cells significantly enhance conversion efficiency, fostering optimism in the field. Environmental factors, including solar radiation, temperature, and contaminants, also substantially impact system performance. Design and installation play a crucial role, particularly in panel orientation, solar tracking systems, and the optimization of electrical configurations. Maintenance, material degradation, and advanced monitoring systems are essential for sustaining efficiency over time. This study provides a comprehensive understanding of the field by reviewing 113 articles and analyzing three key areas—materials, application of sizing technologies, and optimization—from 2018 to 2025. The paper also explores emerging trends, such as the development of energy storage systems and the integration of smart grids, which hold promise for enhancing photovoltaic module (PM) performance. The findings highlight the importance of integrating technological innovation, design strategies, and effective operational management to maximize the potential of PM systems, providing a solid foundation for future research and applications across residential, industrial, and large-scale contexts. Full article

In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data over the long term, thereby enabling more accurate environmental analysis and decision-making. Specifically, the effectiveness of SbPPs in Norfolk, Virginia, is assessed by comparing them with observed precipitation data from Norfolk International Airport (NIA) using common bias adjustment methods. The study applies three different methods to correct biases caused by sensor limitations and calibration discrepancies and then identifies the most effective methods based on statistical indicators, detection capability indices, and graphical methods. Bias adjustment methods include additive bias correction (ABC), which subtracts systematic errors; multiplicative bias correction (MBC), which scales satellite data to match observed data; and distribution transformation normalization (DTN), which aligns the statistical distribution of satellite data with observations. Additionally, the study addresses the uncertainties in SbPPs for estimating precipitation, preparing practitioners for the challenges in practical applications. The additive bias correction (ABC) method overestimated mean monthly precipitation, while the PERSIANN-Cloud Classification System (CCS), adjusted with multiplicative bias correction (MBC), was found to be the most accurate bias-adjusted model. The MBC method resulted in slight PBias adjustments of 0.09% (CCS), 0.10% (CDR), and 0.15% (PERSIANN) in mean monthly precipitation estimates, while the DTN method produced larger adjustments of 21.36% (CCS), 31.74% (CDR), and 19.27% (PERSIANN), with CCS, when bias corrected using MBC, identified as the most accurate SbPP for Norfolk, Virginia. This case study not only provides insights into the technical processes but also serves as a guideline for integrating advanced hydrological modeling and urban resilience strategies, contributing to improved strategies for climate change adaptation and disaster preparedness. Full article

A comprehensive understanding of large elastic deformation, characterized by its nonlinear strain and stress properties, is vital for examining tectonic deformation across geological timescales. We employ the PFELAC software 2.2 platform to automate the generation of parallel elastic large deformation finite element codes. By writing only a minimal amount of fundamental finite element language rooted in the principle of virtual work, we significantly enhance the program development efficiency. The accuracy of the finite element method is rigorously validated through comparisons with analytical solutions from two idealized models. Furthermore, we investigate the influences of mesh density and CPU core count on computational performance. As the number of cores increases, the parallel speedup ratio rises, but the parallel efficiency decreases. For 16 cores, the speedup ranges from 11.36 to 12.24, with a parallel efficiency between 0.71 and 0.77. In contrast, for 64 cores, the speedup is between 24.70 and 34.78, while the parallel efficiency drops to between 0.39 and 0.43. The program’s application to simulate crustal fold deformation reveals marked distinctions between large and small deformation theories, emphasizing the critical importance of large deformation theory in tectonic studies. Full article

In the high-pressure environment of dental practice, dentistry burnout syndrome frequently manifests as emotional exhaustion, depersonalization, and reduced professional fulfillment. While traditional methods for assessing dentistry burnout syndrome often overlook the complex dynamics of stress factors, this study specifically aims to predict burnout syndrome utilizing entropy and artificial intelligence to verify whether digital tools can alleviate burnout levels among dental professionals. The methodology used incorporates ideas from thermodynamics to facilitate reasoning and data representation. Data were obtained through a questionnaire exploring four key areas, which integrated job satisfaction, artificial intelligence-powered tools, time and communication, and patient expectations. The cohort included 126 dental professionals aged 25 to 65, with a mean age of 39.2 ± 9.5, comprising both genders. An artificial neural network model is proposed, delivering an accuracy greater than 85% to predict the impact of digital tools on dentistry burnout syndrome. The findings suggest that digital tools hold substantial promise in reducing burnout levels, paving the way for improved early detection, prevention, and management strategies for dentistry burnout syndrome. The study also demonstrates the transformative potential of integrating entropy analysis and artificial intelligence in healthcare to provide more refined and predictive models for managing work-induced stress and burnout. Full article

(1) Background: The purpose of this review is to explore how advanced sensor technologies and AI-driven methods, like machine learning and image processing, are shaping non-destructive imaging (NDI) systems. NDI plays a vital role in ensuring the strength and reliability of composite materials. Recent advancements in sensor technologies and AI-driven methods, such as machine learning and image processing, have opened up new ways to improve NDI systems, offering exciting opportunities for better performance. (2) Methods: This review takes a close look at how advanced sensor technologies and machine learning techniques are being integrated into NDI systems. The review evaluates how effective these technologies are at detecting defects and examines their strengths, limitations, and challenges. (3) Results: Combining sensor technologies with AI methods has shown a clear boost in defect detection accuracy and efficiency. However, challenges like high computational requirements and integration costs remain. Despite these hurdles, the potential for these technologies to revolutionize NDI systems is significant. (4) Conclusions: By synthesizing the latest research, this review offers a comprehensive understanding of how sensor technologies are enhancing NDI. The findings highlight their importance for improving defect detection and their broader impact on research and industry, while also pointing out areas where further development is needed for future growth. Full article

The recent events impacting public health highlight the need for investigating airflow patterns in confined spaces like elevator cabins. It is essential to ensure proper ventilation, prevent the accumulation of contaminants, and ultimately promote a healthy indoor environment for occupants. In this study, an evaluation of the airflow distribution, temperature, and mean age of air control within an occupied elevator cabin is presented. For that, a CFD model that simulated the airflow patterns in an elevator cabin was developed, validated, and used to conduct the study under six air ventilation scenarios, involving mechanical ventilation with air curtains or displacement flows. The proposed ventilation configurations in Cases 2–6 enhanced the airflow circulation within the elevator. Among these configurations, Case 4, a case of displacement flow, exhibited the most favourable conditions, providing an ACH of 27.05, a mean air age of 84.45 s in the breathable plane, an air change effectiveness of 1.478, and a temperature of 25 °C near the doors and around the occupied zone. It is important to highlight Case 3, which had a lower ACH of 21.2 compared to Case 4. Despite this, Case 3 presented a mean average air age of approximately 122.84 s and an air change effectiveness of 1.309. Based on these findings, displacement ventilation (Case 4) is recommended as the most effective configuration, followed by Case 3, which also showed improved air circulation compared to the other scenarios. The results evidence that the ventilation configuration is particularly influential when aiming to promote air ventilation and improve air age conditions in elevator cabins. Full article

Human activity recognition (HAR) is an important field in the application of millimeter-wave radar. Radar-based HARs typically use Doppler signatures as primary data. However, some common similar human activities exhibit similar features that are difficult to distinguish. Therefore, the identification of similar activities is a great challenge. Given this problem, a recognition method based on convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and class activation mapping (CAM) is proposed in this paper. The spectrogram is formed by processing the radar echo signal. The high-dimensional features are extracted by CNN, and then the corresponding feature vectors are fed into the BiLSTM to obtain the recognition results. Finally, the class activation mapping is used to visualize the decision recognition process of the model. Based on the data of four similar activities of different people collected by mm-wave radar, the experimental results show that the recognition accuracy of the proposed model reached 94.63%. Additionally, the output results of this model have strong robustness and generalization ability. It provides a new way to improve the accuracy of human similar posture recognition. Full article

Integrating electric vehicles (EVs) into the transportation ecosystem is crucial for environmental protection. With the increasing demand for sustainable mobility solutions, wireless power transfer (WPT) systems present a promising method to facilitate the adoption of EVs while reducing carbon footprints. This paper presents a control strategy for the primary side of a WPT charger utilizing a genetic algorithm (GA) combined with a feedforward artificial neural network (ANN). The aim is to optimize charging in constant current (CC) mode and enhance energy transmission efficiency. The proposed approach employs a GA to control the WPT charger, enabling real-time adaptation of charging parameters. The ANN estimates the system’s efficiency, ensuring optimal performance during the charging process. The developed control strategy significantly improved energy transfer efficiency and system stability. Simulation results demonstrate the effectiveness of this new approach, achieving an efficiency of 89.32% in challenging situations of loss of communication with the vehicle. To validate the design procedure, an experimental prototype was constructed, operating at an operational frequency of 85 kHz. Experimental results confirm the proposed design methodology. Full article

Direct current (DC) motors are widely used in various applications because of their operational advantages and ease of control compared to those of other rotating machines. This study focuses on regulating the operation of a bidirectional conveyor powered by a permanent magnet DC motor driven by a full H-bridge power converter. The mechatronic system, comprising a conveyor, a DC motor, and a power converter, is modeled using first-order differential equations. For control design purposes, a simplified actuator model is derived, treating the conveyor load as an external disturbance. A linear control scheme, based on classical control theory, is proposed to ensure that the actuator velocity tracks the reference input. To improve the disturbance rejection, particularly against variations in mechanical loads, an extended state observer is incorporated. Simulation tests validated the proposed control scheme, highlighting the functionality and tradeoffs of its internal components. Full article

In order to evaluate the short-term voltage stability of an electrical power grid, it is necessary to employ not only systematic and well-targeted fault simulations, but also an evaluation method that assesses the criticality of the individual scenarios. A binary decision between stable or unstable, or whether a threshold value is exceeded or not, is inadequate, particularly in instances where the modeling of the system is subject to a certain degree of uncertainty. Since systematic deviations are subject to natural principles and an intervention limit can thus be determined deterministically, an evaluation method is therefore required that allows a statement to be made about the proximity to instability or to a threshold value. It is common practice to employ indices for the evaluation of voltage recovery following a fault event in simulations or from real measurements. However, depending on the specific question being analyzed, the requirements for an index may vary. A review of the literature revealed the existence of several indices that have been developed and applied in the context of various problems and analyses. These indices have been shown to be effective in the respective contexts. However, none of them fully satisfy the requisite criteria for addressing the aforementioned issue. This paper presents and discusses a new index that was developed explicitly for the problem at hand in dealing with model uncertainties, derived requirements from it, and compared it with existing indices from the literature. The benefits of this novel index in comparison with the established ones were visualized based on a number of indicative simulations. Subsequently, the uncertainties inherent in the load parameterization and their implications on the voltage recovery were presented via Monte Carlo simulations. The evaluations of these effects in terms of the distance from the permissible threshold value were then analyzed using the various indices. All simulations were executed within the framework of the IEEE 39 bus New England system. Full article