Innovative approaches for microfluidics techniques in tissue engineering and revolutionizing sports medicine: Enhancing athletic performance and recovery using finite element and statistical analysis.

ABSTRACT Public research institutes (PRIs) and higher education institutions (HEIs) are important actors in knowledge production in regional innovation systems (RIS), influencing network dynamics and inventions. While their impact on RIS has been widely studied, less attention has been paid to how different types of research affect RIS depending on regional technological characteristics. We therefore analyse the effects of German PRIs and HEIs on patent collaborations, centrality, invention quality and regional technological paths using a spatial vector autoregressive (spVAR) model. Our results show that basic research generates substantial positive regional effects in the long run and is particularly conducive to introducing new technologies. Moreover, mechanical engineering, alongside research and development (R&D)-intensive fields, strongly boosts the effects of PRIs and HEIs.

Purpose : to assess the impact of a border position on trade interaction between Russian and Belarusian regions. Methods : the study used statistical methods for analyzing indicators of socio-economic development of the regions, general logical methods of comparison and generalization, as well as gravity and Dixit-Stiglitz-Krugman models. Results: as a result of assessing indicators of socio-economic development of the Russian-Belarusian border regions, it was found that the influence of the border position on the regions’ foreign trade interaction is heterogeneous and depends on a combination of spatial and sectoral factors. Calculations of elasticity coefficients have shown that for the border regions the volume of industrial production and transport accessibility are significant determinants of foreign trade flows, while the price factor is less stable. Comparison of the industry profiles of the Russian regions bordering the Republic of Belarus (Smolensk, Pskov and Bryansk) made it possible to identify groups of the regions with pronounced industry connectivity, primarily in the chemical industry, mechanical engineering and process manufacturing. Conclusions and Relevance: the results obtained confirm that the presence of a border position itself is not a sufficient condition for formation of stable foreign trade relations and requires taking into account the sectoral structure of the regional economy. The spatial and sectoral interpretation of the results made it possible to identify the complementary specialization profiles of the regions, which allowed substantiating directions of cross-border cooperation development and clarifying the guidelines for spatial development of Russia and Belarus border regions. The practical significance of the study lies in possibility of using the findings in the development of regional policy measures and strategies for the socio-economic development of the border areas.

ABSTRACT Graduate employability is a critical issue in Australian engineering education, where high competition for entry-level roles persists despite industry skill shortages. While Engineers Australia’s Stage 1 Competency Standard provides a benchmark, there is limited empirical evidence defining the specific skill sets required for graduate mechanical engineers. This study addresses this gap by analysing a national dataset of job advertisements to identify and map priority skills. Unlike automated studies producing broad skill inventories, this research delivers high-resolution, discipline-specific mapping of skill interdependencies. By analysing a manually verified dataset through Louvain community detection and Apriori association rule mining, the research reveals the structural architecture of employability in mechanical engineering. Findings indicate that employers seek integrated skill sets rather than isolated technical abilities. Proficiency in CAD and computational software is fundamental but consistently demanded alongside professional skills like communication, teamwork, and project management. Clustering analysis identified distinct role archetypes combining technical, professional, and contextual attributes. Rule mining uncovered strong associations between software expertise, stakeholder communication, and understanding ethical and sustainable practices. This research demonstrates that employability is constructed through holistic integration. Consequently, engineering curricula must evolve to embed these integrated skills, ensuring educational outcomes align directly with contemporary industry expectations.

Engineering education requires pedagogical approaches that integrate sustainability with the development of core technical competencies. This study develops, implements, and evaluates a Sustainability-Integrated Problem-Based Learning (SI-PBL) approach in an undergraduate mechanical vibration course. The approach anchors the learning process in the inherent sustainability characteristics of an engineering problem, requiring students to explicitly negotiate trade-offs between technical performance and sustainability objectives. A quasi-experimental study with 121 mechanical engineering students compared the SI-PBL approach to traditional lecture-based instruction through a compressor redesign project in which students redesigned the balancing system of a single-stage air compressor. Analysis of covariance showed that the SI-PBL cohort achieved significantly larger gains in conceptual understanding (d=0.74, p<0.001), mathematical proficiency (d=0.77, p<0.001), complex problem-solving (d=0.56, p<0.001), and sustainability-oriented decision-making (d=0.61, p<0.001). A positive correlation between gains in complex problem-solving and sustainability reasoning within the SI-PBL group (r=0.41, p=0.001) indicated related competency development. The study provides empirical evidence for using sustainability as an integrating context for developing both technical and sustainability competencies in engineering education.

Material Science and Metallurgy is an introductory yet conceptually deep subject taught in undergraduate engineering programs, and it often poses challenges for students in bridging the gap between abstract theory and material behavior. The present research is a proposed teaching-learning framework in the form of a learning by doing and writing model, which is applied in a third-semester Mechanical Engineering undergraduate course at Guru Ghasidas Vishwavidyalaya (A Central University) in the state of Chhattisgarh, India. The framework incorporates case scenarios and problem-based learning activities grounded in research, collaborative group tasks, and technical writing to improve conceptual knowledge and professional skills, including observation, curiosity, divergent thinking, empathy, and collaboration. Four learning activities were carefully designed to cover fundamental topics, including crystal structures, phase diagrams, strengthening mechanisms, heat treatment, and product development based on material properties. A rubric-based assessment of the assignment was used to quantitatively evaluate the effectiveness of the intervention, based on end-semester examination results across three academic periods (2023–24, 2024–25, and 2025–26). The findings reveal a positive change in average academic achievement, reduced score dispersion, increased learning consistency, and a decline in the rate of failures following the pedagogical intervention. Student feedback further confirms improved engagement and conceptual clarity. The results demonstrate the usefulness of active, writing-based, student-centered pedagogies and offer a scalable model of enhancing the learning processes in materials education and other engineering fields.

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals.

Purpose: Maintenance plays a crucial role in the industrial sector to maintain and restore the optimal performance of machinery and equipment. This activity, conducted as a community service for mechanical engineering students, aims to provide an initial understanding of maintenance concepts and strategies as a preparation for entering the workforce. Methodology/approach: The program used interactive lectures, presentations, discussions, and case studies covering breakdown, corrective, preventive, predictive, and reliability-centered maintenance topics. It also introduced the DMAIC steps (Define, Measure, Analyze, Improve, Control) and the link between maintenance, productivity, and industrial processes. Results: The program improved students’ understanding of basic maintenance concepts, asset management strategies, and the importance of maintenance for smooth production and safety. Students were also able to identify critical factors in maintenance management, such as machines, materials, methods, manpower, and the environment. Conclusions: The seminar effectively increased students’ knowledge and readiness to face the challenges of the workplace. A solid foundation of maintenance concepts enhances efficiency, productivity, and competitiveness in the industry. Limitations: The study was limited to one group of students and short-term training; therefore, further studies involving larger samples and longitudinal evaluations are recommended. Contributions: This program provides practical and theoretical insights, supporting the development of future workers with strong analytical and problem-solving skills in maintenance engineering.

This paper considers a model for the solar cell as a mechanical open-cycle thermodynamic engine where chemical potential is produced in an isochoric process corresponding to the thermalization of electron–hole pairs. Expansion of the beam under one-sun illumination and the current generation are described as lost isothermal work. More generally, voltage produced in an open-cycle process corresponds to availability, leading to a correction to the Shockley–Queisser detailed balance limit.

This study investigated the growth language mindset of undergraduate students in Thailand, focusing on their beliefs about intelligence, aptitude, and age sensitivity in English language learning. A total of 1,174 students from diverse academic disciplines participated in the survey, which employed the adapted Language Mindset Inventory (LMI). Descriptive statistics, multivariate analyses of variance (MANOVA), and confirmatory factor analysis (CFA) were conducted to address four research questions: (1) the overall level of growth mindset in English learning, (2) variations of mindset across disciplines, (3) differences based on the urban–rural divide, and (4) the construct validity of the proposed three-factor model. Results revealed that students generally hold strong growth-oriented beliefs, with the highest mean score for age sensitivity. MANOVA results indicated significant disciplinary differences in intelligence beliefs and age sensitivity beliefs, with students in telecommunication engineering, polymer engineering, manufacturing automation and robotics engineering, and mechanical engineering reporting the strongest growth mindsets. In contrast, bioscience and environmental engineering students showed weaker orientations. A separate MANOVA showed significant urban–rural differences across all three constructs, with urban students consistently reporting higher growth beliefs, particularly in age sensitivity. The CFA provided strong support for the three-factor model, and fit indices confirmed an excellent model fit. These findings highlight that while growth-oriented beliefs about language learning are widespread among Thai undergraduates, disciplinary and geographical disparities remain. The validated three-factor model confirms that intelligence, aptitude, and age sensitivity beliefs are distinct but interrelated dimensions of language mindset. The study highlights the need for tailored interventions to foster growth-oriented language learning environments.

The Mechanical Engineering Cooperation Agency (BKS-TM Indonesia) has established the International Symposium on Advances and Innovation in Mechanical Engineering (ISAIME) as an annual scientific forum for academics, researchers, and practitioners in mechanical engineering. The 6 th ISAIME was convened on 9 October 2025, organized by the Mechanical Engineering Study Program, Faculty of Engineering, Mechanical Engineering Department, Universitas Andalas, Padang, Indonesia, with the theme “Artificial Intelligence and Sustainability in Mechanical Engineering: Smart Solution for a Greener Future.” This symposium served as a platform to present and discuss recent advances, research findings, and innovations across a wide range of topics, including mechanical application design, energy conversion, manufacturing processes, materials engineering, and mechanical engineering education. Distinguished experts delivered a total of two keynote lectures from Universiti Teknologi Malaysia and the Université Laval, Canada, while 112 papers were presented and grouped into four thematic areas: mechanical application design, energy conversion, manufacturing processes, and materials engineering. These contributions reflect the growing role of mechanical engineering in advancing technological innovation, environmental sustainability, and human resource development within Industry 4.0. The Organizing Committee gratefully acknowledges the sponsorship and support of various companies and partner institutions, as well as the commitment of all speakers, authors, and participants who contributed to the success of this event. The knowledge and insights shared during the 6 th ISAIME are expected to foster academic collaboration, strengthen networks between academia and industry, and advance science and technology in mechanical engineering.

ABSTRACT Instruction and educational research on teaching multicomponent phase diagrams (PDs) to engineering students often focuses on visualization tools instead of promoting conditional knowledge, an understanding of when information is useful for a specific task. Decision‐Based Learning's (DBL's) emphasis on scaffolded decision‐making and just‐in‐time learning may offer a more effective approach. This retrospective study examined whether DBL improves student performance during PD instruction compared with traditional methods and investigated how students and instructors perceive its benefits and shortcomings. A mixed‐mode approach was used. Exam performance and survey data were analyzed for five semesters of an introduction to materials science course in mechanical engineering. The exam performance of 520 students was analyzed using analysis of covariance with a covariate to account for prior knowledge. Surveys and interviews provided insight into student and instructor experiences using an explanatory sequential design. Quantitative results showed improved performance during the first semester DBL was implemented, but no consistent effects across the other three DBL‐using semesters were seen. Qualitative results showed students valued structured feedback and expert‐modeled thinking, though some reported technical difficulties. Instructors found classroom walkthroughs of the DBL tree allowed for clearer scaffolding, providing improved engagement and understanding compared with alternative activities. Our findings suggest DBL can improve students' ability to interpret PDs compared with alternative teaching methods when DBL is actively used during class. However, this retrospective, rather than randomized‐controlled, study limits our ability to isolate the effects of DBL on performance and experience.

Abstract Coffee is a significant agricultural commodity, especially in tropical nations, and is among the most consumed beverages globally. Coffee beans’ flavor, aroma, and physical characteristics are all greatly influenced by the roasting process; as a result, maintaining product homogeneity and market value requires precise roasting level classification. This study uses a Convolutional Neural Network (CNN) in conjunction with the Coiflet-based Discrete Wavelet Transform (DWT) to propose an autonomous coffee bean roasting level categorization system. By highlighting textural and edge-related elements in the images, the Coiflet transform lessens the effect of background noise and lighting changes and improves the CNN’s capacity to learn more distinctive patterns. The 1,600 coffee bean images in the dataset utilized in this study were grouped into four roasting levels: Green, Light, Medium, and Dark. With consistently high precision, recall, and F1-scores across all classes, research results show that the proposed Coiflet-CNN model achieves a classification accuracy of 98.12%. These results validate the large improvement in classification performance of wavelet-based preprocessing. With the ability to be integrated into sorting machines and automated inspection systems, the suggested method offers a dependable and scalable option for quality control in agricultural industries beyond coffee processing, which is in line with applications in mechanical engineering and smart manufacturing.

The object of this study is tumbling equipment in which working containers execute a complex spatial motion. Articulated spatial mechanisms are widely used in various branches of industry, particularly in mechanical engineering, including mechanisms that contain passive constraints in their structure. The presence of passive constraints can cause operational problems and negatively affect equipment reliability. Therefore, an important task is the synthesis of articulated spatial mechanisms through modification of existing structures in order to eliminate passive constraints. This paper reports the synthesis of a statically determined seven-link articulated spatial mechanism with revolute kinematic pairs. A technique for eliminating a passive constraint in the structure of an articulated spatial mechanism has been proposed, which allows for its static determinacy. As a result, the need to compensate for inaccuracies in geometric relationships between the links by means of clearances in the kinematic pairs is eliminated, making it possible to improve operational characteristics and prolong service life. The introduction of an auxiliary link into the spatial kinematic chain creates conditions for effective implementation of tumbling technological operations by increasing the amplitude of spatial displacement of the container. Analytical relationships between the main geometric parameters of the seven-link spatial mechanism that determine its operability have been established. The derived mathematical dependences allow a justified selection of rational geometric parameters at the design stage and provide a basis for calculating key geometric characteristics for further engineering application in industrial practice.

This study considers a tool rejection system in mechanical engineering production with a single or small-batch type. The task addressed is to substantiate assessment of the degree of wear of replaceable carbide inserts that are rejected during single and small-scale production based on the results of current diagnostics. The proposed approach could make it possible to track possible failures or premature rejection and unjustified losses based on a qualitative analysis of actual wear using probabilistic-stochastic methods. A method for assessing the condition of rejected carbide inserts has been proposed, underlying which is measuring their wear on the back surface, transition to dimensional wear, their statistical processing and grouping by wear level. Experimental studies were conducted on carbide inserts used in a milling cutter during roughing of steels under conditions of cyclic shock loads. It was found that the magnitude of dimensional wear obeys the normal distribution law with the following characteristics: mean value mm, dispersion of scattering D(h) = 0.00135 mm2, and mean square deviation σ(h) = 0.0375 mm. Dependences were derived and the percentage composition of rejected inserts was determined: 50.91% of inserts can still be used (with different resources); 1.17% of inserts are excessively worn (which could lead to defects); and 47.91% of inserts are correctly rejected during production. The proposed methodology could be practically applied without complex measuring equipment and specialized monitoring systems, which makes it suitable for implementation during single and small-scale production. Implementing the method makes it possible to reduce unjustified rejected tools, increase the efficiency of the diagnostic system, and ensure the economy of material resources of an enterprise.

This paper presents a review of the role of artificial intelligence (AI) and machine learning (ML) in advancing mechanical engineering, with an emphasis on domain-specific implementations that have driven recent technological progress. Applications such as predictive maintenance, structural integrity assessment, intelligent design optimization, automated quality inspection, and renewable energy system enhancement demonstrate the capacity of AI approaches, including deep neural networks and reinforcement learning, to improve performance efficiency, minimize operational costs, and foster sustainable engineering solutions. Beyond individual applications, the review discusses fundamental AI attributes, including model adaptability, interpretability, and the coupling of data-driven techniques with physics-informed frameworks, which collectively enable scalable adoption across mechanical engineering disciplines. Notwithstanding these advances, unresolved issues persist, particularly in terms of model reliability, computational overhead, and the availability of high-quality data. By consolidating recent research outcomes, highlighting existing limitations, and proposing prospective research pathways, this review aims to provide valuable insights for both academic researchers and industry.

In 2018, the Ministry of Education implemented Resolution No. 7, which establishes guidelines for extension in Higher Education and makes it a part of the curriculum. This new model seeks to strengthen the social commitment of Higher Education Institutions by promoting social participation. At SENAI CIMATEC University, university extension was restructured with the creation of the Community Extension Center (NEC) in 2024. The NEC works to develop extension programs that incorporate the principles of Popular Education and Citizen Education, promoting social development and integrating teaching and research. To identify community needs, the NEC adopted the Territory Mapping tool, in which students conduct a sociocultural assessment of the location, understanding its physical, political, and cultural dimensions. The territory mapping was conducted by Mechanical Engineering and Data Science and Artificial Intelligence students between March and July 2025. This work aimed to report on the experience of the NEC team and the students, addressing the main stages of the mapping, student feedback, and the identification of potential projects. The first part of the mapping investigated basic infrastructure and environmental and cultural aspects. The second part addressed community participation, economic issues, security, and cultural manifestations. The most frequently identified cultural manifestations were music, dance, sports, graffiti, and crafts. Through their experience reports, the students demonstrated their involvement and knowledge of local realities. Territory mapping proved to be an effective tool for civic education, allowing students to connect with the realities of their communities and awakening the desire to develop relevant extension projects. The mapping results, including the identification of infrastructure, environmental risks, and cultural manifestations, will serve as a basis for future extension projects, strengthening the relationship between teaching, research, and extension, in line with the objectives of SENAI CIMATEC University.

Enhancing CNC instruction with augmented reality: empirical evidence from mechanical engineering education

Managing learning for mechanical engineering students is a crucial activity to foster changes in their behavior regarding energy use and environmental preservation. The objective is to raise awareness of a greener society. This research employed an experimental design, focusing on a target group of 22 individuals studying a course on industrial energy management at Rajamangala University of Technology Thanyaburi. The activities organized for this study revolve around energy conservation and involve dividing the students into four groups. The results indicate that the assessment scores of mechanical engineering students who completed the course significantly improved. Moreover, the research assessed students' awareness of energy use, contributing to the overall goal of fostering a greener society. All groups rated the quality of energy awareness positively, according to the findings. Furthermore, the satisfaction assessment indicated a high average level. In conclusion, the study demonstrates that mechanical engineering students have enhanced their understanding and awareness of energy usage, leading to more efficient energy consumption and a commitment to sustainable practices.

This study investigates the impact of cavitation phenomena on heat and mass transfer in working fluids. To quantify the intensity of transport processes within cavitation bubble clusters, a numerical analysis of bubble dynamics was carried out with explicit consideration of fluid compressibility. The results demonstrate that physicochemical transformations induced by cavitation are governed not only by shock waves and pressure pulses generated during bubble collapse, but also by extreme thermal effects arising within collapsing cavitation clouds. Under conditions of maximum bubble compression, the vapor inside the bubbles and the surrounding liquid may undergo a transition to a supercritical state. The developed model elucidates the structure of microflows in the interbubble region and provides a quantitative evaluation of local velocity, pressure, and heat flux fields. The systematic assessment of cavitation-enhanced heat and mass transfer offers valuable insights for the advancement of conventional heat and mass transfer technologies and the design of innovative devices in mechanical and chemical engineering.