Eng, Volume 5, Issue 3 (September 2024) – 41 articles

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

The technology of 3D bioprinting has sparked interest in improving tendon repair and regeneration, promoting quality of life. To perform this procedure, surgical intervention is often necessary to restore functional capacity. In this way, 3D bioprinting offers a scaffold design, producing tendons with precise microarchitectures, promoting the growth of new tissues. Furthermore, it may incorporate bioactive compounds that can further stimulate repair. This review elucidates how 3D bioprinting holds promise for tendon repair and regeneration, detailing the steps involved and the various approaches employed. They demonstrate future challenges and perspectives and provide valuable information on the concept, bioprinting design, and 3D bioprinting techniques for the repair of tendon injuries. Full article

Synthetic nitrogen fertilizer is the backbone of modern agriculture, helping to feed ~50% of the world’s population. However, the current industrial production, distribution, and use of nitrogen fertilizers are built on an unsustainable foundation of fossil resources, and are energy-intensive, environmentally polluting, and inefficient in their usage. With the rapidly declining cost of renewable electricity, such as solar and wind, it is time to develop and implement the decentralized production and application of nitrogen fertilizer with nonthermal plasma technologies. Such locally sourced production at the farm site, using only air and water as feedstock, circumvents the need for the extensive capital investment and infrastructure required for synthetic nitrogen fertilizer production and storage, as well as the complex and costly distribution networks. It will be adaptive to the intermittency of the solar/wind electricity supply, leave no carbon footprint, and also have the advantage of being easily switched on/off, immediately responding to weather changes and local conditions, such as soil, climate, crops, and farming business models, for precision agriculture. Full article

Phononic or acoustic bandgap materials have often been made using a polymer matrix with metal inclusions such as tin and steel, which have high densities compared to the matrix material. Acoustic bandgaps are observed when waves are not transmitted at certain frequencies. These have been applied in cavity resonators, acoustic waveguides, and more. This paper introduces a concept of using cement as the surrounding matrix and carbon nanotubes as the core inclusions to develop phononic materials. The exhibition of a bandgap makes it possible for the cementitious phononic material to be used as a sensor for cement cracking and defects in oil well bores. This paper discusses ways to optimize the characteristics of the carbon nanotube core to develop gaps in transmission spectra. It shows the behavior of the cementitious material with changing filling fraction, location of core cells, and surrounding defects, creating a pathway for paradigm-shifting non-destructive sensing technologies. Full article

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. This paper presents a novel application of artificial intelligence tools for real-time posture estimation specifically designed for cruise machinists. The primary aim is to enhance occupational health and safety by providing precise, real-time feedback on ergonomic practices. We developed a dataset by capturing video recordings of cruise machinists at work, which were processed to extract skeletal outlines using advanced computer vision techniques. This dataset was used to train deep neural networks, optimizing them for accuracy in diverse and constrained shipboard environments. The networks were tested across various computational platforms to ensure robustness and adaptability. The AI model demonstrated high efficacy in recognizing both correct and incorrect postures under real-world conditions aboard ships. The system significantly outperformed traditional ergonomic assessment tools in terms of speed, accuracy, and the ability to provide instant feedback. The findings suggest that AI-enhanced ergonomic assessments could be a transformative approach for occupational health across various industries. Full article

A main topic in mass production of machine parts is how to increase the productivity to produce more parts in a given time while maintaining the prescribed surface quality on the machined surfaces. Novel machining procedures have been introduced to achieve this goal; however, the further development of already established and wide-spread procedures can offer simply accessible solutions. Tangential turning is a rediscovered variant of the traditional turning procedure, where a specially designed cutting tool ensures chip removal with a feed tangential to the workpiece. This process results in low surface roughness even at higher feed rates. In this paper, the achievable surface roughness is analyzed by analytical and experimental steps. In the mathematical analysis, the theoretical surface roughness is determined using the constructive geometric modelling method. The worked-out equations are validated in cutting experiments on 42CrMo4 grade steel workpieces. The theoretical and experimental analyses show that the strictly prescribed surface roughness can be achieved with high feed rates by the application of tangential turning. Full article

The relevance of this research is due to the need to stabilize the composition of the melting products of copper–nickel sulfide raw materials. Statistical methods of analyzing the historical data of the real technological object and the correlation analysis of process parameters are described. Factors that exert the greatest influence on the main output parameter (the fraction of copper in a matte) and ensure the physical–chemical transformations are revealed: total charge rate, overall blast volume, oxygen content in the blast (degree of oxygen enrichment in the blowing), temperature of exhaust gases in the off-gas duct, temperature of feed in the smelting zone, copper content in the matte. An approach to the processing of real-time data for the development of a mathematical model for control of the melting process is proposed. The stages of processing of the real-time information are considered. The adequacy of the models was assessed by the value of the mean absolute error (MAE) between the calculated and experimental values. Full article

This paper proposes a sensorless control strategy for permanent magnet synchronous motors (PMSMs) based on a sliding mode observer (SMO), and high-speed PMSM sensorless velocity control is realized. To solve the serious chattering and phase lag problems of conventional SMOs, the continuous function is used as the control function, and the low-pass filter is improved into a back electromotive force (EMF) observer with an adaptive structure. In addition, the phase-locked loop is combined to perform the SMO-based sensorless control. The simulations and experiments prove the effectiveness of the proposed strategy. Full article

This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while participants imagined executing specific motor tasks. Each participant underwent multiple trials for each motor imagery task, ensuring a diverse and comprehensive dataset for model training and evaluation. A deep neural network model comprising convolutional and bidirectional long short-term memory (LSTM) layers was developed and trained using k-fold cross-validation, achieving a notable accuracy of 98%. The model’s performance was further compared against recurrent neural networks (RNNs), multilayer perceptrons (MLPs), and Τransformer algorithms, demonstrating that the CNN-LSTM model provided the best performance due to its effective capture of both spatial and temporal features. The model was deployed on a Python script interfacing with an Arduino board, enabling communication with two servo motors. The Python script predicts actions from preprocessed EEG data to control the servo motors in real-time. Real-time performance metrics, including classification reports and confusion matrices, demonstrate the seamless integration of the LSTM model with the Arduino board for precise and responsive control. An Arduino program was implemented to receive commands from the Python script via serial communication and control the servo motors, enabling accurate and responsive control based on EEG predictions. Overall, this study presents a comprehensive approach that combines machine learning, real-time implementation, and hardware interfacing to enable the precise and real-time control of servo motors using EEG signals, with potential applications in the human–robot interaction and assistive technology domains. Full article

This research focuses on cylindrical helical springs with circular cross-sections made from carbon steel (SH 0.82% C) and stainless steel (AISI 302). The transformation from a linear bar to a circular spiral involves numerous factors such as material mechanical behavior, stress–strain relationships and residual stresses. This research investigates the spring-back phenomenon, which affects the final diameter of helical springs post-manufacture, using analytical, experimental and numerical methods. An analytical model, derived from the mechanical bending process, was proposed to predict spring-back, and its accuracy was validated against experimental data. This study also employed finite element simulations to analyze elastic recovery, confirming the analytical predictions. Results indicated that the spring-back ratio k could be expressed as an exponential function of the spring index C (the ratio between the final diameter of the spring D₂ and the diameter of the wire D_W), with a maximum error of 4.80% for stainless steel and 3.62% for carbon steel. This study’s findings provide valuable insights into optimizing the spring manufacturing process, enhancing the precision of spring diameter predictions, and potentially reducing production errors and material waste. Full article

The environmental conditions to which dams are exposed play a major role in dictating the progression and manifestation of the alkali–aggregate reaction (AAR). However, in the numerical thermal-mechanical simulation of AAR-affected dams, the solar radiation and its associated shadow effects have received little attention. The spatiotemporal distribution of the solar radiation incidence on the dam surfaces has often been addressed in a simplified way or has just been neglected. Yet, far less attention has been given to shadows cast by the dam’s own geometry or the slopes. The main reasons for these simplifications derive from the fact that contrary to other thermal loads, environmental actions vary in daily and annual cycles, with the added complication that solar radiation also depends on the orientation of the surface with respect to the Sun’s rays. In this way, a conventional thermal finite element code should be modified in order to deal with these two particular issues. Therefore, this article starts with the estimation of the solar radiation distribution by recourse to concepts of astronomy and computer graphics. Then, to illustrate the influence of the nonuniform temperature distribution on dam surfaces due to solar radiation and shading, the analysis of an AAR-affected arch dam is presented in this paper. A comparison of the AAR expansions computed on the dam with or without considering the solar radiation and shading is presented. Full article

Many fatal inhalational accidents occurring during construction typically involving confined spaces and structures that confine the atmosphere continue to defy identification. Very little information is available, principally from accident summaries and government reports. Increasingly, these identify CO₂ (carbon dioxide) as a probable cause. This article discusses situations identified in accident summaries and worldwide databases. CO₂ lacks an odor and other means of identification without the use of monitoring instruments. Emissions typically involve exhaust gases; aerobic and anaerobic respiration in microbiological systems in wastewater and landfills; geological deposits capable of chemical reaction to produce CO₂; and unintended discharge from pressurized systems. Emissions can occur continuously or abruptly subject to the type of system and conditions involved. Anaerobic systems that behave as shear-thinning, pseudoplastic, non-Newtonian fluids emit abruptly on the application of a shear force. A lethal concentration can develop almost instantaneously. Upon cessation of the stress, the ambient condition restores rapidly. Chemical and physical processes provide reservoirs for the storage of gas. Very limited methods are available for the prevention of these accidents because of the infrequency and unpredictability of the emission. Preventive measures include mandatory atmospheric monitoring and ventilation at all times, where hazardous conditions can develop, and sometimes the use of high-level respiratory protection. Full article

Rail traction and resistance play an essential role in the efficient operation of rail systems. The nature of traction is based on the balance between static friction and generated force at the perimeter of the driving wheels. The main objective of this paper is the development of a methodology and a modeling procedure for the design of train speed trajectory diagrams for the suburban railway. The model is applied to the Athens–Chalkida railway line (in Greece). For this purpose, geometric data for the above-mentioned railway line is collected from the Hellenic Railways Organization (OSE) and then recorded and digitized. A code is developed in MATLAB to calculate the total resistance of the railway line at each kilometer position. The traction elements of the trains operating on the Athens–Chalkida–Athens line, as well as other representative trains, and the magnitudes of their mechanical-aerodynamic resistances are recorded. The MATLAB program generates and compiles the train speed trajectory diagrams and the traction-resistance matrices. Finally, a comparison is made between the time, energy, CO₂ emissions, and fuel costs of the rail in relation to the competing mode of transportation, which, for the specific line studied, is the tourist bus. Full article

This paper reports the preliminary experimental studies carried out on an innovative sliding bearing made of polycrystalline diamond, a material with excellent mechanical and chemical characteristics, used mainly in the drilling industry. Bearings crafted from this material do not necessitate lubrication due to their extremely low coefficient of friction and high resistance to wear. For this reason, they are prime candidates for replacing traditional oil bearings, eliminating the need for auxiliary systems and thereby reducing environmental risks. In this regard, an innovative test rig was designed, capable of reaching speeds up to 6000 rpm both in vertical and horizontal configurations thanks to a novel tilting frame. Moreover, with a high modularity it was possible to test three different kinds of radial PCD bearings. Dynamic data were acquired and elaborated to evaluate orbits, acceleration and absorbed torque, to finally compare these different configurations to better understand how dynamic behavior is influenced by bearings’ geometrical characteristics. Full article

Computed tomography (CT), magnetic resonance imaging (MRI), and radiography expose patients to electromagnetic fields (EMFs) and ionizing radiation. As an alternative, Electrical Impedance Tomography (EIT) offers a less EMF-influenced method for imaging by measuring superficial skin currents to provide a map of the body’s conductivity. EIT allows for functional monitoring of anatomical regions using low electromagnetic fields and minimal exposure times. This paper investigates the application of EIT for the morphological and functional assessment of tissues. Using the Finite Element Method (FEM) (Comsol 5.2), both two-dimensional and three-dimensional models and simulations of physiological and pathological tissues were developed to replicate EIT operations. The primary objective is to detect carcinoma by analysing the electrical impedance response to externally applied excitations. An eight-electrode tomograph was utilised for this purpose, specifically targeting epithelial tissue. The study allowed the characterisation of tomographs of any size and, therefore, the possibility to verify both their geometric profile and the ideal value of the excitation current to be delivered per second of the type of tissue to be analysed. Simulations were conducted to observe electrical impedance variations within a homogeneously modelled tissue and a carcinoma characterized by regular geometry. The outcomes demonstrated the potential of EIT as a viable technique for carcinoma detection, emphasizing its utility in medical diagnostics with reduced EMF exposure. Full article

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling task depends on the orthopedist and surgeon’s high level of skill and experience. This paper aimed to provide a summary of previously published review studies in the field of bone drilling. This review paper also presents a comprehensive review of the application of machine learning for bone drilling and as a future direction for automation systems. This review can also help medical surgeons and bone drillers understand the latest improvements through parameter selection and optimization strategies to reduce bone damage in bone drilling procedures. Apart from the review, bone drilling vibration data collected in a university laboratory experiment is also presented in this study. The vibration data consist of three different layers of femur cow bone, which are processed and classified using several deep learning (DL) methods such as long short-term memory (LSTM), convolutional neural network (CNN), and recurrent neural network (RNN). These DL methods are used in the bone drilling lab case study to prove that the layers of bone drilling are associated with the vibration signal and that they can be classified and predicted using DL methods. The result shows that LSTM is outperformed by CNN and RNN. Full article

Environmental comfort involves creating comfortable and healthy indoor environments, taking into account the climate characteristics of the built environment. The novelty herein is to define the challenges of using Building Information Modeling (BIM) to assess the three dimensions of environmental comfort: thermal comfort, visual comfort, and acoustic comfort. This work conducts a bibliometric review, using the VOSviewer software (version 1.6.20) and the GPSV website, and a bibliographic review of recently published articles in the field. This paper aims to identify the dimensions of sustainability with a focus on environmental comfort and the themes associated with these dimensions, recognize the limitations of the research, and propose recommendations for future work. The results of this work define the limitations related to the three dimensions of environmental comfort and recommend establishing a reliable database, integrating BIM with parameters that could interfere with the quality of the indoor environment. Full article

Numerical simulations were generated to investigate the response of a very large floating airport to an airplane takeoff, using the set of nonlinear shallow water equations of velocity potential for water waves interacting with a floating thin plate. We have proposed two methods to reduce persistent airport vibration: reflectance reduction by decreasing the flexural rigidity in airport edge parts and amplification reduction by decreasing the still water depth partially under airport runways. First, when the flexural rigidity is uniformly decreased in an airport edge part, the reflectance of the floating body waves due to a B737 was reduced because of the multiple reflections. However, the wave reflectance for a B747 increased, depending on the conditions. A too-long edge part was not effective in reducing the wave reflectance. Conversely, when the flexural rigidity is linearly decreased in an airport edge part, the wave reflectance was reduced for both airplanes. Second, when the still water depth under an airport runway is partially reduced at the location where floating body waves are amplified, the wave heights of floating body waves tended to decrease as the still water depth in the shallower area decreased. Full article

Anaerobic digestion is a biological process that transforms high-strength organic effluents into biogas with multiple benefits. However, concurrent with organics’ biological transformation, a liquid phase with a high solid content is also derived from this process. Valorizing this fraction is not an easy task if an agronomic application cannot be considered as a suitable option. The thermal valorization of this fraction allows for energy extraction but also gives rise to additional capital investment and increases the energy demand of the global process. In addition, the thermal treatment of digestate has to deal with a mineralized material. The changes in organic matter due to anaerobic digestion were studied in the present manuscript, by evaluating the thermal behavior of samples, activation energy, and organic transformation using Fourier transform infrared (FTIR) spectroscopy. Digested samples of a mixture composed of manure and glycerin (5% v/v) were studied. The stabilization caused a dramatic decrease in aliphatic compounds, greatly increasing the mineral content of the sample. Results from differential scanning calorimetry (DSC) indicated an energy content of 11 kJ/g for the feed material and a reduction to 9.6 kJ/g for the long-term stabilized sample. The activation energy of the feed was 249.5 kJ/mol, whereas this value was reduced to 70–80 kJ/mol for digested samples. If the valorization route selected for digestates is thermal conversion, the lower energy content and more complex structure of these materials (higher content of lignin and protein-type compounds) must be carefully evaluated. Full article

Rolling stock manufacturers are finding innovative structural solutions to improve the quality and reliability of railway vehicle components. Structural optimization processes represent an effective strategy for reducing manufacturing costs, resulting in geometries that are easier to design and produce combined with innovative materials. In this framework, the present paper proposes the development of a design methodology to innovate a railway bolster beam using topological optimization techniques, assessing the effect of different manufacturing constraints oriented to the casting process. A comprehensive numerical testing campaign was conducted to establish an effective testing procedure. Two different designs were obtained and compared, statically and dynamically, evaluating the difference in terms of mass, mechanical performance and manufacturability. Reductions in stress values up to 70% were observed, along with an 8% increase in the first natural frequency of the component, leading to beneficial effects in terms of stiffness. The methodology shows encouraging results to streamline the design of complex casting components, moving to a new generation of structural railway components. Full article

This article presents a systematic review using PRISMA methodology to explore trends in the use of machine and deep learning in diagnosing and detecting Alzheimer’s disease using electroencephalography. This review covers studies published between 2013 and 2023, drawing on three leading academic databases: Scopus, Web of Science, and PubMed. The validity of the databases is evaluated considering essential factors such as the arrangement of EEG electrodes, data acquisition methodologies, and the number of participants. Additionally, the specific properties of the databases used in the research are highlighted, including EEG signal classification, filtering, segmentation approaches, and selected features. Finally, the performance metrics of the classification algorithms are evaluated, especially the accuracy achieved, offering a comprehensive view of the current state and future trends in the use of these technologies for the diagnosis of Alzheimer’s disease. Full article

Additive manufacturing (AM) plays a crucial role in the development of NiTi alloys, enabling the creation of complex and customized structures while optimizing properties for various biomedical and industrial applications. The aim of this paper was to investigate the influence of laser scanning speed on laser-assisted reactive sintering of a mixture of No and Ti powders. The samples were sintered at two different beam speeds, 4 and 5 4 mm/s and their morphological and microstructural characteristics were investigated. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX) and X-ray diffraction (XRD) analyses revealed the presence of intermetallic compounds rich in Ni and Ti for both scanning speeds; however, the scanning speed of 5 mm/s produced a microstructure with greater porosity, leading to a sintered body with poorer consolidation. Thus, employing a slower beam scanning of 4 mm/s seems to be a better alternative in the laser-assisted reactive sintering of NiTi alloys. Full article

More collisions occur at the exit ramps of expressways due to frequent lane-changing behavior and interweaving between vehicles. Young drivers with shorter driving mileage and driving experience, radical driving styles, and worse behavior prediction are likelier to be involved in collisions at the exit ramps. This paper focuses on the correlation analysis between young drivers’ characteristics and their visual and physiological attributes at expressway exit ramps. First, the driver’s gender, driving experience, and mileage are classified. Then, seven expressway exit models are established using the UC/Win road modeling software. The driver’s driving plane vision is divided into four areas using the K-means clustering algorithm. In addition, the driver’s visual and heart rate attributes were analyzed at 500 m, 300 m, 200 m, and 100 m away from an expressway exit. The results show that the visual attributes, gender, and driving mileage of young drivers strongly correlate with the fixation times and average saccade amplitude. In contrast, the driving experience has almost no correlation with the fixation behavior of young drivers. Young drivers’ driving experience and mileage strongly correlate with cardiac physiological attributes, but there is virtually no correlation with gender. The practical implications of these results should be helpful to highway planners and designers. Full article

The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health. Full article

The exploration of alternative energy sources has gained significant traction in recent years, driven by the urgent need to mitigate greenhouse gas emissions and transition towards sustainable energy. Among these alternatives, ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems have emerged as promising options due to their potential for providing clean, renewable energy. This study presents a probabilistic investigation into the sensitivity of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems under various reservoir conditions in the Williston Basin, North Dakota. In addition to elucidating the impact of reservoir conditions on system performance, the study utilizes probabilistic methods to assess energy output of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems. Insights derived from this probabilistic investigation offer valuable guidance for the working fluid selection, systems design and optimization in the Williston Basin and beyond. Results from the sensitivity analysis reveal the profound influence of reservoir conditions on the behavior and efficiency of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems. Our case study on Red River Formation and Deadwood Formations shows a potential of 34% increase and 32% decrease in heat extraction based on varying reservoir conditions. Our investigations in the Beaver Lodge field within the Red River Formation yielded arithmetic mean values for ${\mathrm{C}\mathrm{O}}_2$ best case resources, hydrothermal resources and the ${\mathrm{C}\mathrm{O}}_2$ worst case as 6.36 $\times {10}^{18}$ J, 4.75 $\times {10}^{18}$ J and 3.24 $\times {10}^{18}$ J, respectively. Overall, this research contributes to advancing the knowledge and understanding of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems as viable pathways towards sustainable energy production and carbon sequestration. By highlighting the sensitivity of these systems to reservoir conditions, the study provides valuable insights that can inform decision-making processes and future research endeavours aimed at fostering the transition to a low-carbon energy landscape. Full article