Engineering Science Research Articles

Learning-based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation

With the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering, and, latterly, neural style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalized methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation, and development of AI artwork methods face many challenges. This article is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line, and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.

Examination of Research Conducted on the Use of Artificial Intelligence in Science Education

The advancement of artificial intelligence (AI) has been significantly driven by developments in machine learning and neural networks. As AI becomes increasingly pervasive, its applications are diversifying, with notable penetration in sectors such as health, education, social media, robotics, and entertainment. One area in which AI is being deployed is science education. The objective of this study is to examine the research that incorporates AI within the field of science education. By analysing trends in the reviewed studies, this research identifies the countries, institutions, journals and scholars that are the most prominent contributors to this field of enquiry. The findings suggest that the incorporation of artificial intelligence into science education is still in its infancy, with a paucity of widespread implementation. However, there is a discernible increase in the quantity of published works, with an emerging emphasis on the assessment of learning outcomes and the enhancement of academic performance. The findings indicate that the United States is the leading country in terms of publications related to AI in science education, accounting for 38% of the total contributions. Additionally, Türkiye has emerged as a notable contributor in this field, demonstrating a growing presence. The Journal of Science Education and Technology was identified as the preeminent journal publishing research on AI. Furthermore, the findings revealed that GPT was the most frequently utilised tool in this context. In light of these findings, it is recommended that future investigations into the application of artificial intelligence (AI) in science education employ a range of AI tools and explore the development of higher-order thinking skills.

Heat and mass transfer analysis in flow of Walter's B nanofluid: A numerical study of dual solutions

AbstractThis study investigates the behavior of Walter's liquid B fluid over a biaxially stretching/shrinking surface for Homann stagnation point flow, focusing on the rheological properties that are crucial for understanding physical phenomena in engineering and industrial processes. The problem is modeled using Buongiorno's model in the presence of a permeable surface subject to heat generation/absorption, Ohmic heating, and chemical reactions influenced by Arrhenius activation energy. The governing partial differential equations are solved numerically using an appropriate similarity transformation, and the results are graphically analyzed via MATLAB's bvp5c solver. The study highlights the importance of characterizing the intricate properties of viscoelastic fluids, with potential applications in biomedical engineering and materials science. The findings reveal the coexistence of two distinct flow regimes and dual solutions are obtained. It is noted that the activation energy parameter is shown to enhance mass distribution in both solutions, while chemical reactions accelerate reactant conversion but reduce mass distribution. Additionally, an increase in viscoelasticity shifts the fluid's behavior towards elasticity, creating greater resistance to shear deformation and reducing both velocity profiles.

A comparative study on the use of spatiotemporal maps for teaching data visualization in a hybrid learning context

ABSTRACT Data visualization is a complex matter and so is teaching it in the context of engineering and data science. Even more challenging is teaching in a midst of once in a century pandemic. In this paper, we show the results of a workshop conducted with second-year students on a university degree in data science, which took place in the context of pandemic restrictions in a hybrid learning environment. About half of the students were in the classroom and the other half at home. They learned to use STMaps, a tool developed in the scope of the SILKNOW project. We transferred research outcomes to the classroom; similarly, the results of evaluating the tool were transferred to the project. But more importantly, we found insights that the learning process of students that were online was not the same quality as of those that were physically in the classroom. While both groups performed well in terms of correctly fulfilling the tasks proposed during the workshop, the students at home were less motivated and perceived the tasks more cumbersome to achieve. From a research perspective, this activity was perceived as very useful, and similar activities are being planned for each academic year.

Pioneering technologies over time to rehabilitate crude oil-contaminated ecosystems: a review.

The unremitting pollution of our environment induced by crude oil spillage and drilling site accidents has jeopardized every living species in the biological ecosystem. Removing heavy crude oil constituents with the help of traditional and mainstream oil sorbents because of their ingrained raised viscosities is a strenuous venture. Lighter distillates of crude oil, like condensate, do not aggregate with tremulous shine on the aquatic surface nor settle at the bottom sediment of the water bodies like the heavier components do with time. Fabricating optimally designed materials capable of capturing, degrading, or removing toxic chemical constituents of this fossil fuel is critical in this modern era. This review comprehensively discusses the evolution of scientific technologies developed to separate these constituents from land and aquatic bodies. We provide an overview of the latest physical and chemical strategies and prevalent biological remediation schemes for removing these pollutants from soils and water for environmental protection. The article highlights the urgency of preventing oil spill accidents, whose anticipation is challenging to harness. A spectrum of advanced functional methodologies is also discussed to adequately treat discharged hydrocarbon contaminants, establish public safety, and pave the path to enhancing the circular economy metrics linked with oil industries.

Preface: 8th International Conference on Education, Management and Social Science (EMSS 2024)

At the end of 2024, we would like to present an international proceeding that containing of the selected papers from the 2024 8th International Conference on Education, Management and Social Science (EMSS 2024). This conference is an annual important event to publish of research results. We are hoping that the event will become a foothold of thinking in welcoming of global developments. In addition, publications packaged in this seminar will further strengthening existence and reputation as an educational institution that excels in scientific publication at national and international levels. EMSS 2024 is a leading international conference for researchers and practitioners to share their new ideas, original research results and practical development experiences from education research, management engineering, historical research, humanities, sociology, and social science. This conference was held in Manila, Philippines during September 21-22, 2024 with the aim to act as a forum for exchange and dissemination of knowledge and ideas on the latest developments. We hope the international proceedings will be able to encourage students, teachers, lecturers, practitioners, researchers, and higher education community in developing scientific research and publications to support the nation's developments. EMSS Organizing Committees Manila, Philippines

An Integrated Approach for Structural Crack Monitoring: Combining Plastic Tell Tales and Image Analysis for Enhanced Structural Health Evaluation

Structural Health Monitoring (SHM) calls for the development of the ideas of safety and main ability of civil structures. The most critical of the aspects explored in the paper is the safety monitoring of the structural configuration of any civil engineering work. Supervision of any sign of an odd shift from the usual state of structure enables one to prevent and or counter severe loss. It also depends on other environmental parameters such as load, nature of a seasonal parameter and the type of soil. Within this research project the Al-Beruni Academic Block of Sarhad University of Science and Information Technology is taken to monitor the cracks using Glass Tell-Tales, to collect data on cracks of buildings and during seismic activity, observe, study the cracks that are present.

Organizing Scientific Knowledge from Engineering Sciences Using the Open Research Knowledge Graph: The Tailored Forming Process Chain Use Case

Organizing Scientific Knowledge from Engineering Sciences Using the Open Research Knowledge Graph: The Tailored Forming Process Chain Use Case

Are random price generators useful for health policy processes and analysis? effectiveness of random price generators in health policy processes and analysis

This paper discusses why using random price generators in an economic model for medical markets may be useful. It summarizes the different steps for the development of the model and describes an original contribution of the physicians’ choice model with random drug prices applied to Type 2 diabetes in the US. Pharmaceutical markets have been supply-driven to boost life science and medical technologies; however, with the widening inequalities inside national health systems and the global agenda for universal health coverage, more economic research is being done to strengthen the analysis of demand for healthcare services. Research, especially, examines more disaggregated levels of demand systems to understand the heterogeneity of physicians’ choices and better capture patient needs. It can also be an approach to calibrate supply and demand adjustments in medical markets. This paper argues that choice modeling is particularly relevant, using random price generators in a structural model where a demand approach is useful. If validated by additional experimental studies, this first study by Professors Huttin & Hausman (2021) could be used in advanced value assessment frameworks. Random price generators on medical markets could also be tested with additional models that fit oligopolistic market structures (e.g., models for differentiated product markets such as the Berry–Levinsohn–Pakes model). It may also help with a policy analysis process that addresses major disruptive transformations of market dynamics, evolving boundaries in science, fast digitalization, and artificial intelligence-based information systems.

Evolution of CFD as an engineering science. A personal perspective with emphasis on the finite volume method

Evolution of CFD as an engineering science. A personal perspective with emphasis on the finite volume method

Halogen substitution effects in Chlorostannate(II) hybrid material: Insights from DFT study on 2(C4H4FN3O)·SnX6·2(H2O), (X = F, Cl, Br, I)

The development of new materials is crucial for advancing technologies in material science, energy, and biology. In this study, we investigate the effect of Halogen-substitution on the compound 2(C4H4FN3O)·SnX6·2(H2O) (where X = F, Cl, Br, and I). Using extensive theoretical analysis, we examined the impact of halogen substitution on the structural properties, band gap energy (Eg), and chemical behavior. Our results show that the Eg decreases with the increasing atomic number of halogens. The I-substituted compound is the most reactive and exhibits significant intramolecular charge transfer, enhancing its antioxidant properties. The spatial distribution of electron density in the Frontier Molecular Orbitals (FMOs) indicates that the HOMO is localized on the inorganic anion and the LUMO on the organic cation, a pattern consistent across all substituted compounds. These findings highlight the potential of these compounds in electron transfer reactions and provide insights for tailoring them for specific chemical and biochemical applications.

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Abstract The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception. Graphical abstract

Data proficiency in MAE education: Insights from student perspectives and experiences

This paper explores how mechanical and aerospace engineering (MAE) students understand and improve their data proficiency throughout their engineering curriculum. Data is essential for engineering students to be proficient in handling, as it is involved in every aspect of engineering. With the growing ubiquity of data and data analysis in all engineering fields, engineering students need to learn and master data skills to be competitive in the current and future job market. However, there is a lack of research on how non-computer science or software engineering majors perceive data proficiency and how they seek opportunities to develop data skills, especially as it relates to specific subdomains. In this paper, we investigate how students perceive data proficiency and how they develop using interview data from N = 27 MAE students at a research institution in the southeastern United States. Using the How People Learn framework, we analyzed the data through thematic analysis methods with a postpositivist approach, considering the bounded context of this study. The results show that MAE students value data proficiency as a crucial skill for their future careers and recognize its importance in making evidence-based engineering decisions. The study also reveals that, even though data proficiency is often a “hidden competency,” MAE students intuitively find various ways to enhance their data skills. These findings may help engineering educators to tailor their instruction to their students’ needs, address misconceptions about data and data proficiency, and prepare a data-literate future engineering workforce.

Implementation of Sports Science and Technology Integration Infrastructure: A Case Study of Speed Skating Utilizing Web and Mobile Applications, and Information Visualization Technologies

In the field of sports, there is an active discussion on and an attempt to apply technologies that integrate sports with cutting-edge science and technology for the purpose of enhancing athletic performance. In line with this trend, South Korea is supporting research on the fusion of sports and science technology at an interdepartmental level. For the improvement of performance in elite sports, it is important to consider the athlete’s skills, the coach’s information analysis, the scientification of equipment, and the environmental optimization. Accordingly, this study aims to propose web and mobile app technologies to establish an integrated infrastructure of sports science and technology for the three factors of athletes, equipment, and environment to improve the performance of speed skating. Furthermore, it aims to make policy recommendations to activate this integration. The application of such technologies and policy recommendations can be transferred and organically integrated into other sports, including track and field, which are time-based competitions. Additionally, it is expected that this approach will lead to the formation of new theories that consider the emotional aspects occurring in sports situations.

Effect of Variable Gravity Field on Dual Component Convection in a Couple Stress Fluid Saturated Anisotropic Porous Layer With Temperature‐Dependent Heat Source

ABSTRACTThis study examines how gravity fluctuations, the couple stress parameter, anisotropic parameters, and heat source collectively influence dual‐component convection in the porous layer. The linear analysis is conducted utilizing the normal mode technique. The authors proposed three categories of gravity fluctuation, namely: (a) linear, (b) parabolic, and (c) exponential. Expressions for both stationary and oscillatory Rayleigh numbers are derived using the Galerkin approach. Neutral stability curves for both stationary and oscillatory modes are analyzed, with graphical representations to show the effects of various stability parameters, including gravity fluctuations, couple stress, anisotropy, and heat source. The results show that the mechanical anisotropy parameter and Vadasz number lead to system destabilization, while the couple stress parameter, Lewis number, gravity parameter, solute Rayleigh number, and thermal anisotropy parameter help to stabilize the system. Furthermore, the system is more stable with exponential gravity fluctuations and less stable with parabolic gravity fluctuations. This finding offers insights into thermal convective instability in porous media, impacting applications in geoscience, engineering, and environmental science.

Optimization of Dynamic Characteristics of Rubber-Based SMA Composite Dampers Using Multi-Body Dynamics and Response Surface Methodology

The suspension system of a commercial vehicle cab plays a crucial role in enhancing ride comfort by mitigating vibrations. However, conventional rubber suspension systems have relatively fixed stiffness and damping properties, rendering them inflexible to load variations and resulting in suboptimal ride comfort under extreme road conditions. Shape memory alloys (SMAs) represent an innovative class of intelligent materials characterized by superelasticity, shape memory effects, and high damping properties. Recent advancements in materials science and engineering technology have focused on rubber-based SMA composite dampers due to their adjustable stiffness and damping through temperature or strain rate. This paper investigates how various structural parameters affect the stiffness and damping characteristics of sleeve-type rubber-based SMA composite vibration dampers. We developed a six-degree-of-freedom vibration differential equation and an Adams multi-body dynamics model for the rubber-based SMA suspension system in commercial vehicle cabins. We validated the model’s reliability through theoretical analysis and simulation comparisons. To achieve a 45% increase in stiffness and a 64.5% increase in damping, we optimized the suspension system’s z-axis stiffness and damping parameters under different operating conditions. This optimization aimed to minimize the z-axis vibration acceleration at the driver’s seat. We employed response surface methodology to design the composite shock absorber structure and then conducted a comparative analysis of the vibration reduction performance of the optimized front and rear suspension systems. This study provides significant theoretical foundations and practical guidelines for enhancing the performance of commercial vehicle cab suspension systems.

A Study on Functional Requirements and Inspection Items for AI System Change Management and Model Improvement on the Web Platform

The rapid adoption of artificial intelligence (AI) on the web platform across multiple sectors has highlighted not only its inherent technical hurdles, such as unpredictability and lack of transparency, but also significant societal concerns. These include the misuse of AI technology, invasions of privacy, discrimination fueled by biased data, and infringements of copyright. Such challenges jeopardize the sustainable growth of AI and risk the erosion of societal trust, industry adoption and financial investment. This analysis explores the AI system’s lifecycle, emphasizing the essential continuous monitoring and the need for creating trustworthy AI technologies. It advocates for an ethically oriented development process to mitigate adverse effects and support sustainable progress. The dynamic and unpredictable nature of AI, compounded by variable data inputs and evolving distributions, requires consistent model updates and retraining to preserve the integrity of services. Addressing the ethical aspects, this paper outlines specific guidelines and evaluation criteria for AI development, proposing an adaptable feedback loop for model improvement. This method aims to detect and rectify performance declines through prompt retraining, thereby cultivating robust, ethically sound AI systems. Such systems are expected to maintain performance while ensuring user trust and adhering to data science and web technology standards. Ultimately, the study seeks to balance AI’s technological advancements with societal ethics and values, ensuring its role as a positive, reliable force across different industries. This balance is crucial for harmonizing innovation with the ethical use of data and science, thereby facilitating a future where AI contributes significantly and responsibly to societal well-being.

Phase-change composite elastomers for efficient thermal management at contact interface

Given the challenges posed by interfacial thermal transport in domains such as energy science and electronic technology, there is a compelling and timely pursuit to enhance the heat transfer performance of composite elastomers as thermal interface materials. Herein, we propose a method to prepare low-leakage composite elastomers by blending phase change silicon wax and polydimethylsiloxane with fillers. The phase change behavior of silicon wax provides the materials 99.09 % modulus change and a corresponding decrease in contact thermal resistance by 66.91 %, leading to significantly improvement of heat transfer performance. Combining with the thermal performance that evaluated by chip heating simulating system, these findings offer valuable insights into the design of composite elastomers, establishing their suitability for use as thermal interface materials.

Influence and collaboration in library and information science research: A university perspective

Objective. The objective of this study was to examine the influence and collaboration patterns within library and information science (LIS) research, with a particular focus on the contributions of universities over time. Design/Methodology/Approach. This study used bibliometrics and LIS network analysis to examine 14,517 articles indexed in Scopus from 1954 to 2023. The Bibliometrix R-package was used to identify publication trends, influential institutions, authors, and collaboration networks in the LIS field. Findings. The key findings indicated that Wuhan University was the foremost institution in LIS research, with notable contributions from Chinese scholars who were identified as the most influential authors in the field. The keywords “information science” and “information retrieval” emerged as common thematic areas, reflecting core research interests. The content analysis also revealed that LIS research had robust connections with fields such as physics, computer science, and information technology, underscoring an interdisciplinary trend. Originality/Value. This study offered original insights into the evolving landscape of LIS research, identifying both leadership in academic output and potential gaps in current research coverage. It underscored the necessity for future research to expand the scope of its database and refine keyword selection for more comprehensive representation. The findings contributed to the strategic development of LIS research and the fostering of international collaboration.

Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.