Eng

In this study, we explored the correlation between fluctuated waveform tails under both positive and negative impulse voltages and their corresponding spectral lines during millisecond observations of arc discharge. We examined impulse voltages in ±100, ±125, and ±150 kV across 3, 3.5, and 4 cm gaps using spectroscopic analysis focused on oxygen excitations. Six selected spectra in ±100, ±125, and ±150 kV at 3.5 cm and two negative spectra of −100 kV at 3 and 4 cm were analyzed by identifying spectral lines in the wavelength range of 200–900 nm. The results revealed a correlation between the fluctuated waveform tails and spectral lines in positive voltage discharges, which were almost similar, while in negative voltage discharges, this correlation was found only in −100 kV at 3 and 4 cm. We concluded that during the spark phase for both positive and negative voltage discharges, symmetrical fluctuation in the waveform tails was observed after breakdown time, especially above the voltage level of the recombination phase. This suggested the presence of energetic oxygen excited states in the 200–400 nm range, with higher peak intensity than the O I line at 777.417 nm, observed in most positive impulse voltage discharges and at −100 kV with 3 and 4 cm gaps, contributing to rapid breakdown. Full article

The push for sustainability in nanomaterials has catalyzed significant advancements in the green synthesis of carbon dots (CDs) from renewable resources. This review uniquely explores recent innovations, including the integration of hybrid techniques, such as micro-wave-assisted and ultrasonic-assisted hydrothermal methods, as well as photocatalytic synthesis. These combined approaches represent a breakthrough, offering rapid production, precise control over CD properties, and enhanced environmental sustainability. In addition, the review emphasizes the growing use of green solvents and bio-based reducing agents, which further reduce the environmental footprint of CD production. This work also addresses key challenges, such as consistently controlling CD properties—size, shape, and surface characteristics—across different synthesis processes. Advanced characterization techniques and process optimizations are highlighted as essential strategies to overcome these hurdles. Furthermore, this review pioneers the integration of circular economy principles into CD production, proposing novel strategies for sustainable material use and waste reduction. By exploring innovative precursor materials, refining doping and surface engineering techniques, and advocating for comprehensive life cycle assessments, this work sets a new direction for future research. The insights provided here represent a significant contribution to the field, paving the way for more sustainable, efficient, and scalable CD production with diverse applications in optoelectronics, sensing, and environmental remediation. Full article

The current surge in the deployment of web applications underscores the need to consider users’ individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform’s capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset. Full article

The increasing sophistication of cyberattacks necessitates the development of advanced detection systems capable of accurately identifying and mitigating potential threats. This research addresses the critical challenge of cyberattack detection by employing a comprehensive approach that includes generating a realistic yet imbalanced dataset simulating various types of cyberattacks. Recognizing the inherent limitations posed by imbalanced data, we explored multiple data augmentation techniques to enhance the model’s learning effectiveness and ensure robust performance across different attack scenarios. Firstly, we constructed a detailed dataset reflecting real-world conditions of network intrusions by simulating a range of cyberattack types, ensuring it embodies the typical imbalances observed in genuine cybersecurity threats. Subsequently, we applied several data augmentation techniques, including SMOTE and ADASYN, to address the skew in class distribution, thereby providing a more balanced dataset for training supervised machine learning models. Our evaluation of these techniques across various models, such as Random Forests and Neural Networks, demonstrates significant improvements in detection capabilities. Moreover, the analysis also extends to the investigation of feature importance, providing critical insights into which attributes most significantly influence the predictive outcomes of the models. This not only enhances the interpretability of the models but also aids in refining feature engineering and selection processes to optimize performance. Full article

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Currently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and external factors causing fatigue, and fluctuations in grading standards due to the grader’s mood at the time. This paper proposes the use of fractal analysis for the grading evaluation of beef marbling to achieve automatic grading without the inconsistencies caused by human factors. In the experiments, cross-sectional images of the parts used for visual judgment were taken, and fractal analysis was performed on these images to evaluate them using fractal dimensions. The results confirmed a correlation between the marbling evaluation and the fractal dimensions, demonstrating that quantitative evaluation can be achieved, moving away from qualitative visual assessments. Full article

The challenge of providing reliable electricity during power interruptions, especially in rural and remote regions, has prompted the exploration of Hybrid Renewable Energy Systems (HRESs). This systematic review employs the PRISMA framework to conduct a comparative analysis of HRES configurations, specifically those integrating rooftop solar photovoltaic (PV), diesel generators (DGs), converters, and battery energy storage systems (BESSs). This review assesses the techno-economic performance of these systems in various countries, highlighting the cost efficiency, reliability, and environmental impact compared to traditional single-resource systems. The analysis reveals that HRESs offer significant advantages in managing energy supply during power interruptions, particularly in regions with high solar potential but unreliable grid access. A comparative analysis with other countries demonstrates that while HRES configurations are tailored to local conditions, the integration of solar PV with diesel generators is a consistently effective strategy across different contexts. This review provides essential insights for policymakers and stakeholders, facilitating the optimization of energy solutions tailored to regional needs. Full article

The Combined Cooling, Heat, and Power (CCHP) System is an efficient technology that reduces primary energy consumption and carbon dioxide emissions by generating heat, cold, and electricity simultaneously from the same fuel source. This study developed an economic optimization model using linear mathematical program theory to determine the optimal sizes of different components in a CCHP system. The study found that CCHP systems with internal combustion engines have the largest optimal size due to lower capital expenditure and improved hourly changes in combined energy production by considering electrical and absorption chillers simultaneously. The analysis compared the size determination of CCHP systems with internal combustion engine (ICE), sterling engine (SE), and proton exchange membrane fuel cell (PEMFC) technologies. PEMFC had the highest annual overall cost among the technologies studied. The results of determining the size of the CCHP system are compared with ICE, SE, and PEMFC technologies. It has been noted that PEMFC has the highest annual overall cost among the studied technologies. The usefulness index of the CCHP system increased from 23% to almost 40% when electricity was sold to the grid using internal combustion engine technology. Full article

Discarded rubber tires (DSRTs) have become a significant landfill and environmental problem that needs to be solved to reduce health risks, fires, and other environmental issues. The inclusion of such rubber can enhance the ductility of concrete and increase its resistance to dynamic loads, as well as enhancing the concrete’s durability and lifespan by modifying its impact resistance (IR). However, the smooth surface and low bond strength with cement pastes directly lead to a decrease in the strength of the proposed concrete, restricting its range of use in the construction industry. The inclusion of pozzolanic materials with high hydraulic capacity in the concrete matrix as partial cement replacements, such as granulated blast furnace slag (GBFS), has led to enhanced performance of the modified rubberized concretes (MRCs) in terms of bond strength and other mechanical properties. Based on these facts, this study aimed to evaluate the effects of including 20% GBFS and various levels (5–25%) of metakaolin (MK) as replacements for ordinary Portland cement (OPC), on the engineering properties of newly designed rubberized concretes. For this purpose, twenty-two mixes of MRCs were prepared by replacing the OPC and natural aggregates with various contents of GBFS, MK, and DSRTs. The results indicated that the MRC specimens prepared with a ternary blend of OPC-GBFS-MK illustrated significant improvements in strength performance, wherein the compressive strength (CS) after the curing age of 56 days (46.5 MPa) was higher than that of the OPC control mix (41.2 MPa). Moreover, the mix designed with high amounts of MK-GBFS-DSRTs significantly enhanced the engineering properties of the proposed MRCs by increasing the IR and reducing the total porosity. It can be asserted that, by using MK, GBFS, and DSRTs as renewable resources for construction materials, the environmental problems can significantly be reduced, with excellent benefits in the engineering properties of the designed rubberized concretes. Full article

A typical halogen-free flame-retardant (HFFR) formulation for electric cables may contain polymers, various additives, and fire-retardant fillers. In this study, composites are prepared by mixing natural magnesium hydroxide (n-MDH) with linear low-density polyethylene (LLDPE) and a few types of ethylene–octene copolymers (C₈-POE). Depending on the content of LLDPE and C₈-POE, we obtained composites with different crystallinities that affected the final mechanical properties. The nucleation effect of the n-MDH and the variations in crystallinity caused by the blending of C₈-POE/LLDPE/n-MDH were investigated. Notably, in the C₈-POE/LLDPE blend, we found a decrease in the crystallization temperature of LLPDE compared to pure LLDPE and an increase in the crystallization temperature of C₈-POE compared to pure C₈-POE. On the contrary, the addition of n-MDH led to an increase in the crystallization temperature of LLDPE. As expected, the increase in the crystallinity of the polyolefin matrix of composites led to higher elastic modulus, higher tensile strength, and lower elongation at break. It has been observed that crystallinity also influences fire performance. Overall, these results show how to obtain the required mechanical features for halogen-free flame-retardant compounds for electric cable applications, depending on the quantities of the two miscible components in the final blend. Full article

Unmanned aerial vehicles (UAVs) are currently employed to carry different types of cargoes, such as medical products. Several advantages can be related to the integration of UAVs in health care systems, including the possibility to access remote areas, low costs and high mobility and speed. However, some concerns can arise regarding the payload integrity, especially considering medical products that can be sensitive to vibration and lose their therapeutic effect. This paper presents the flight dynamics of a quadrotor and an attached payload, assuming a flexible attachment between them. Constraint vector representation is used to model the flexible attachment and guarantee a physical distance between them. A dual loop control, formed by a sliding mode control and reduced dimension observer, is developed to improve the trajectory tracking and payload undesired oscillations. The estimated disturbance (DE) is then calculated by the difference between the estimated payload and the desired trajectories. Numerical results have shown that with the use of the DE strategy, the undesired oscillations are attenuated, showing a reduction from maximum peaks of 0.2 m to 0.05 m. Regarding performance index evaluation, a reduction of approximately 84% is observed in terms of payload oscillation. In a second case, with a different payload and external disturbance intensity, the proposed strategy is also able to positively estimate the payload vibration and, consequently, attenuate the undesired oscillation, with an 85% reduction. Therefore, the dual loop control represents an efficient strategy for tracking trajectory with low undesired oscillation intensity. Full article

The unique electromagnetic properties of rare earth elements (REEs) have led to rapid technological advances, creating a sharp increase in demand for these materials. The inherent challenges of separating REEs and the significant drawbacks of existing processes have driven the development of a new method known as field-effect separation (FES). This technology leverages electrical and magnetic fields to achieve separation by exploiting the differences in magnetic moments or effective charges of REEs in solution. Experiments on REEs were conducted using a microchannel based separation device, which confines fluid flow to facilitate separation within a field, with metal cations in solution being transported based on their respective electrostatic or magnetic properties. The results demonstrate that separation based on effective charge or paramagnetic properties is achievable. The confinement of fluid flow to microchannels allowed advective and osmotic forces to be suppressed sufficiently such that a reasonable separation of ions was achieved, though the impact of these forces were not completely removed. This innovative approach promises to improve the efficiency and effectiveness of REE separation, addressing both the growing demand and the limitations of current methods. Full article

Theoretical modeling and numerical simulation of an integrated micro-thermoelectric generator system for thermal power generation are carried out. The system measures 4.2 × 4.2 × 5 mm and consists of a micro-thermoelectric module (bismuth telluride) and two finned heat sinks (aluminum). The system can be used to convert thermal energy to electricity in Seebeck effect-based micro-applications. This work aims to improve an advanced model to effectively predict the thermal performance of the system and to develop thermal and flow simulations to accurately evaluate real micro-thermoelectric generator systems. The advanced model solves the thermoelectric module’s energy equations, incorporating heat balance in the heat transfer calculations. The thermal and flow simulations take into account the dynamic calculations under the thermal loads occurring in the system. This innovative aspect can considered separately for the different materials (ceramics, semiconductors and copper strips) of the micro-thermoelectric module for heat transfer enhancement. The results predicted that when the temperature difference of the thermoelectric module was increased from 18 K to 58 K, the power output and the conversion efficiency of the system increased by about 0.5 W and 50%, respectively. Also, the transfer of useful heat to electrical power was achieved at 83%, with 11% saved heat and thermal losses of 6% W at maximum temperature difference of the module. In terms of overall energy consumption, the integrated micro-thermoelectric generator system has a little environmental impacts. Validation of the model with particular experimental works was accomplished for dependability. Comparisons with different modeling strategies demonstrate that the accuracy and performance of the advanced model can be used to reliably study the thermal performance of real micro-thermoelectric generator systems. Full article

In this work emphasis was given to determine the evolution of the retained austenite phase fraction via X-ray diffractometry technique in the as-hardened AISI 440C martensitic stainless steel surface subjected to cavitation for increasing test times. Scanning electron microscopy results confirmed the preferential carbide phase removal along the prior/parent austenite grain boundaries for the first cavitation test times on the polished sample surface during the incubation period. Results suggest that the strain-induced martensitic transformation of the retained austenite would be assisted by the elastic deformation and intermittent relaxation action of the harder martensitic matrix on the austenite crystals through the interfaces between both phases. In addition, an estimation of the stacking fault energy value on the order of 15 mJ m⁻² for the retained austenite phase made it possible to infer that mechanical twinning and strain-induced martensite formation mechanisms could be effectively presented in the studied case. Finally, incubation period, maximum erosion rate, and erosion resistance on the order of 7.0 h, 0.30 mg h⁻¹, and 4.8 h μm⁻¹, respectively, were determined for the as-hardened AISI 440C MSS samples investigated here. Full article

This paper provides a recent overview of the exact, approximate, and hybrid optimization methods that handle Multi-Objective Mixed-Integer Non-Linear Programming (MO-MINLP) problems. Both the domains of exact and approximate research have experienced significant growth, driven by their shared goal of addressing a wide range of real-world problems. This work presents a comprehensive literature review that highlights the significant theoretical contributions in the field of hybrid approaches between these research areas. We also point out possible research gaps in the literature. Hence, the main research questions to be answered in this paper involve the following: (1) how to exactly or approximately solve a MO-MINLP problem? (2) What are the drawbacks of exact methods as well as approximate methods? (3) What are the research lines that are currently underway to enhance the performances of these methods? and (4) Where are the research gaps in this field? This work aims to provide enough descriptive information for newcomers in this area about the research that has been carried out and that is currently underway concerning exact, approximate, and hybrid methods used to solve MO-MINLP problems. Full article

Bridges are crucial components of infrastructure networks that facilitate national connectivity and development. According to the National Bridge Inventory (NBI) and the Federal Highway Administration (FHWA), the cost to repair U.S. bridges was recently estimated at approximately USD 164 billion. Traditionally, bridge inspections are performed manually, which poses several challenges in terms of safety, efficiency, and accessibility. To address these issues, this research study introduces a method using Unmanned Aerial Systems (UASs) to help automate the inspection process. This methodology employs UASs to capture visual images of a concrete bridge deck, which are then analyzed using advanced machine learning techniques of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to detect damage and delamination. A case study on the Beyer Road Concrete Bridge in Michigan is used to demonstrate the developed methodology. The findings demonstrate that the ViT model outperforms the CNN in detecting bridge deck damage, with an accuracy of 97%, compared to 92% for the CNN. Additionally, the ViT model showed a precision of 96% and a recall of 97%, while the CNN model achieved a precision of 93% and a recall of 61%. This technology not only enhances the maintenance of bridges but also significantly reduces the risks associated with traditional inspection methods. Full article

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression model. A dataset of 110 blasts was divided into 97 blasts for training and testing, whereas a separate set of 13 new, unseen blasts was used to evaluate the robustness and generalization of the model. Hierarchical Agglomerative (HA) and K-means clustering algorithms were used, with HA clustering providing a higher cluster quality. To address class imbalance and improve model generalization, a synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) was employed. Hyperparameter tuning was conducted using HyperOpt by comparing Random Search (RS) with the Advanced Tree-structured Parzen Estimator (ATPE). The combination of ATPE with HA clustering and SMOGN in an expanded search space produced the best results, achieving superior prediction accuracy and reliability. The proposed HAC1-SMOGN model, which integrates HA clustering, ATPE tuning, and SMOGN augmentation, achieved a mean squared error (MSE) of 0.0002 and an R² of 0.98 on the test set. This study highlights the synergistic benefits of clustering, hyperparameter optimization, and data augmentation in enhancing machine learning models for regression tasks, particularly in scenarios with class imbalance or limited data. Full article

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R² and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis. Full article

The conventional utilization of fossil fuels precipitates uncontrolled carbon dioxide and sulfur oxides emissions, thereby engendering pronounced atmospheric pollution and global health ramifications. Within the maritime domain, concerted global initiatives aspire to mitigate emissions by 2050, centering on the adaptation of engines, alteration of fuel compositions, and amelioration of exhaust gas treatment protocols. This investigation pioneers experimentation with marine gas oil augmented by methanol, a practice conventionally encumbered by prohibitively expensive additives. Successful amalgamation of methanol, animal-derived biodiesel, and marine gas oil (MGO) is empirically demonstrated under meticulously controlled thermal conditions, creating a homogeneous blend with virtually zero sulfur content and reduced carbon content, featuring characteristics akin to conventional marine gas oil but with no use of expensive emulsifiers. This new blend is suitable for employment in maritime engines utilizing Delaval technology, yet with significantly lower energy requirements compared to those necessitated using conventional very low sulfur fuel oil (VLSFO) with a maximum sulfur content of 0.5% w/w. Full article

There is an enormous amount of water vapor in ambient air that can be converted into liquid water by several methods. A method that is capable of producing a large amount of water is a vapor compression system. However, this method requires significant power input, which may cause the cost of producing water to be prohibitive. In this paper, it is proposed that a vapor compression refrigeration system that is used to cool air in a combined cycle power plant has the potential to be a viable method of atmospheric water generation. This system produces saturated air by mixing atmospheric air with water, and reduces air temperature and humidity using a mechanical chiller. The reduction in inlet air temperature enables the combined cycle power plant to generate more power output, which is used to operate the air cooling system. Therefore, the air cooling system can harvest atmospheric water without requiring external power input. This concept is proven by simulating system performance in various atmospheric air conditions using system models of mass and energy balances. Full article