Explore the vast range of titles available.

The current study aims to investigate and compare the effects of waste plastic oil blended with n-butanol on the characteristics of diesel engines and exhaust gas emissions. Waste plastic oil produced by the pyrolysis process was blended with n-butanol at 5%, 10%, and 15% by volume. Experiments were conducted on a four-stroke, four-cylinder, water-cooled, direct injection diesel engine with a variation of five engine loads, while the engine’s speed was fixed at 2500 rpm. The experimental results showed that the main hydrocarbons present in WPO were within the range of diesel fuel (C13–C18, approximately 74.39%), while its specific gravity and flash point were out of the limit prescribed by the diesel fuel specification. The addition of n-butanol to WPO was found to reduce the engine’s thermal efficiency and increase HC and CO emissions, especially when the engine operated at low-load conditions. In order to find the suitable ratio of n-butanol blends when the engine operated at the tested engine load, the optimization process was carried out by considering the engine’s load and ratio of the n-butanol blend as input factors and the engine’s performance and emissions as output factors. It was found that the multi-objective function produced by the general regression neural network (GRNN) can be modeled as the multi-objective function with high predictive performances. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RSME) of the optimization model proposed in the study were 0.999, 2.606%, and 0.663, respectively, when brake thermal efficiency was considered, while nitrogen oxide values were 0.998, 6.915%, and 0.600, respectively. As for the results of the optimization using NSGA-II, a single optimum value may not be attained as with the other methods, but the optimization’s boundary was obtained, which was established by making a trade-off between brake thermal efficiency and nitrogen oxide emissions. According to the Pareto frontier, the engine load and ratio of the n-butanol blend that caused the trade-off between maximum brake thermal efficiency and minimum nitrogen oxides are within the approximate range of 37 N.m to 104 N.m and 9% to 14%, respectively.

Global road safety reports identify human factors as the leading causes of traffic accidents, particularly behaviors such as speeding, drunk driving, and driver distraction, emphasizing the need for autonomous driving technologies to enhance transport safety. This research aims to provide a practical model for the development of autonomous driving systems as part of an autonomous transportation system for inter-building passenger mobility, intended to enable safe and efficient short-distance transport between buildings in semi-open environments such as university campuses. This work presents a fully integrated autonomous platform combining LiDAR, cameras, and IMU sensors for mapping, perception, localization, and control within a drive-by-wire framework, achieving superior coordination in driving, braking, and obstacle avoidance and validated under real campus conditions. The electric golf cart prototype achieved centimeter-level mapping accuracy (0.32 m), precise localization (0.08 m), and 2D object detection with an mAP value exceeding 70%, demonstrating accurate perception and positioning under real-world conditions. These results confirm its reliable performance and suitability for practical autonomous operation. Field tests showed that the vehicle maintained appropriate speeds and path curvature while performing effective obstacle avoidance. The findings highlight the system’s potential to improve safety and reliability in short-distance autonomous mobility while supporting scalable smart mobility development.

In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carried out assisted by surrogate models, which are constructed by adding a local deviation estimated by the kriging method and a global model approximated by a radial basis function. An expected hypervolume improvement is then computed on the basis of the model uncertainty to determine additional samples that could improve the model accuracy. In the investigation, the proposed approach is applied to two-objective and three-objective optimization test functions. Then, it is applied to aerodynamic airfoil design optimization with two objective functions, namely minimization of aerodynamic drag and maximization of airfoil thickness at the trailing edge. Finally, the proposed method is applied to aerodynamic airfoil design optimization with three objective functions, namely minimization of aerodynamic drag at cruising speed, maximization of airfoil thickness at the trialing edge and maximization of lift at low speed assuming a landing attitude. XFOILis used to investigate the low-fidelity aerodynamic force, and a Reynolds-averaged Navier–Stokes simulation is applied for high-fidelity aerodynamics in conjunction with a high-cost approach. For comparison, multi-objective optimization is carried out using a kriging model only with a high-fidelity solver (single fidelity). The design results indicate that the non-dominated solutions of the proposed method achieve greater data diversity than the optimal solutions of the kriging method. Moreover, the proposed method gives a smaller error than the kriging method.

Amid globalization, tourism has increasingly emphasized health and well-being through sustainable, wellness-oriented services. Thailand has been recognized as having strong potential to become a regional hub for wellness tourism, supported by its efficient healthcare system and diverse attractions. This study aims to identify key indicators of wellness tourism based on holistic health principles and to examine their relationships with tourists’ intentions to use wellness services. Data were collected from 1200 wellness tourists in Thailand through stratified random sampling and analyzed using Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM). The results revealed six significant wellness factors, with Environmental Wellness being the most influential. In addition, gender, income, and occupation were found to positively affect wellness tourism behavior. Attitude and subjective norms also significantly influenced tourists’ intentions to engage in wellness services. This study provides policy recommendations to assist tourism and public health agencies in promoting wellness tourism and enhancing health-focused travel experiences.

Powerful computer-aided design tools are presently vital for engineering product development. Efficient global optimization (EGO) is one of the most popular methods for design of a high computational cost problem. The original EGO is proposed for only one additional sample point. In this work, parallel computing is applied to the original EGO process via a multi-additional sampling technique. The weak point of the multi-additional sampling is it has slower convergence rate when compared with the original EGO. This paper applies the multi-fidelity technique to the multi-additional EGO process to see the effect of the number of multi-additional sampling points and the converge rate. A co-kriging method and a hybrid RBF/Kriging surrogate model are selected for the surrogate model in the EGO process to show the advantage of the multi-additional EGO process compared with the single-fidelity Kriging surrogate model. In the experiment, single-additional sampling points and two to four number of multi-additional sampling per iteration are tested with symmetry and asymmetry mathematical test functions. The results show the hybrid RBF/Kriging surrogate model can obtain the similar optimal points when using the multi-additional sampling EGO.

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

Robotic technology has developed in various ways to challenge problems in complex and modern situations. Working in indoor spaces creates many extra complications due to intricacy in spaces and due to signal instability, that tends to make navigation efficiency difficult for mobile robots. Localizing objects correctly and Measurement them is fundamental. This was achieved using a SLAM system with 2D LiDAR to map the environment and added object detection from a monocular camera that identifies and tracks specific items within the view of the robot. It improves the view of the robot by fusing LiDAR spatial accuracy with camera capability of object recognition. After this data fusion within the ROS framework, the system coordinates outputs of both sensors using a publisher-subscriber model for real-time integration of distance measurements and visual detections. Further, the LiDAR data is filtered using a Kalman filter to get rid of noise, thus achieving stable measurements of distances and eventually enhancing localization reliability. Equipped with the proper field of view calibration between the camera and LiDAR, it is enabled to track and position an object steadily and precisely, which is vitally useful when moving about dynamic indoor spaces. The TurtleBot platform is used to implement this system, enabling the robot to autonomously explore and create maps of indoor environments. The YOLO and Deep SORT algorithms applied to the camera data allow for efficient real-time object detection and Measurement, while the SLAM-based mapping from LiDAR ensures a comprehensive view of the environment. This multi-sensor fusion technique thus addresses the challenges of indoor navigation, facilitating reliable obstacle detection, accurate localization, and robust path planning, ultimately allowing the robot to operate autonomously and respond to environmental changes in real time.

The \"Big Knife\" or \"Eto\" is the local name for one of the popular types of knives in Thailand which has many applications. These knives are typically forged from car leaf spring steel that the villagers buy wholesale into the knife forging shop at the scale of a community industry. However, occasionally the materials used in knives are insufficient to meet their needs and there may be uncertainty because of various or unidentified sources. This study investigates the replacement of car leaf spring steel used in knife forging with commercial AISI 1010 low-carbon steel in order to solve the problem mentioned above. The low-carbon steel that was used as the replacement knife forging material was processed by the pack carburizing process with several types of energizers, including calcium carbonates, egg duck shells, cow bone, river snail shells, and golden apple snail shells under different conditions of temperature and time, and the properties in terms of hardness and impact tolerance were investigated. To make it easier to implement, the pack carburizing process conditions were optimized for hardness and impact properties via the NSGA-II multi-objective optimization algorithm with the Gaussian process regression model (GPR), which is one artificial intelligence algorithm, as a surrogate model. After the experiment, results clearly indicated the effect of heat treatment conditions (energizer type, temperature, and time) on the hardness and impact of treated AISI 1010 steel; moreover, the GPR model also shows a relatively high efficiency measured in various terms of performance metrics representing the behavior of hardness and impact that arise from the pack carburized process parameters.

An alloy wheel is generally a symmetrically shaped part integral to a vehicle because its weight and strength can improve driving performance. Therefore, alloy wheel design is essential, and a novel design method should be considered. Currently, the Multi-Additional Sampling Efficient Global Optimization (MAs-EGO) has been proposed and widely implemented in various fields of engineering design. This study employed a surrogate model to maximize Expected Hypervolume Improvement (EHVI) for multi-objectives by increasing multi-sampling per iteration to update a surrogate model and evaluate an optimal point for alloy wheel design. Latin Hypercube Sampling (LHS) was used to generate an initial design of an alloy wheel, including the thickness and width of the spoke wheel. The maximum principal stress according to the dynamic cornering fatigue simulation was then evaluated for risk of failure using Finite Element (FE) analysis. The objectives were to minimize both the principal stress and weight of the symmetric alloy wheel. The Kriging method was used to construct a surrogate model, including a Genetic Algorithm (GA), which was performed to maximize hypervolume improvement to explore the next additional sampling point, and that point was also an optimal point for the process when computation had converged. Finally, FE results were validated through a designed apparatus to confirm the numerical solution. The results exhibit thatMulti-Additional Sampling Efficient Global Optimization can achieve an optimal alloy shape. The maximum principal stress distribution occurs in the spoke area and exhibits a symmetrical pattern around the axis following the cyclic bending load. The optimal design point of the alloy wheel can reduce 20.181% and 3.176% of principal stress and weight, respectively, compared to the initial design. The experimental results are consistent trend in the same direction as FEA results.

Today, artificial intelligence plays a huge role in the mechanical engineering field for solving many complex problems and the problem with fracture mechanics is one of them. In fracture mechanics, artificial intelligence is used to predict crack behavior under various conditions such as mixed-mode loading. Many parameters are used for explaining the crack behavior under various conditions, but those parameters are obtained from destructive testing, in which usually, only one data point is obtained from each test. An artificial problem method requires a large amount of data to train the model to be able to learn crack behavior, which is a disadvantage of applying this method to fracture mechanics. To eliminate the disadvantage of the large amount of experiment data required for modeling, in this study, the small data obtained from the experiment along with data obtained from fracture criteria that were used for elementary prediction of mixed mode fracture toughness were used to create an artificial intelligence model. Data from the experiment was combined with fracture criteria data using the multi-fidelity surrogate model that is described in this study. The mixed mode I/II fracture toughness of the PMMA material was tested in order to primarily propose the data combination technique. After the modeling process, the prediction results indicated that the performance of a model in which the actual test data was combined with the data from the fracture criteria (multi-fidelity surrogate model) was more predictively effective compared to only actual data-based modeling.