Engineering Science Research Articles

Analyzing the Dynamics and Friction Forces on Spheres in Fluid Flows: Applications Across Engineering and Sports Science

This study explores the dynamics and friction forces of spherical objects in fluid flows, emphasizing their applications across engineering and sports science. By examining key theoretical concepts such as drag coefficient, Reynolds number, and fluid dynamics equations, the research highlights how these principles influence the motion of spheres in various fluid environments. The analysis encompasses computational fluid dynamics (CFD) and experimental approaches, addressing applications in ocean, aerospace, and vehicle engineering, as well as sports science. The findings demonstrate the critical role of understanding drag and friction characteristics in optimizing designs, improving performance, and driving technological innovation. Challenges such as multi-scale phenomena, nonlinear dynamics, and measurement techniques are identified, suggesting future research directions to refine theoretical models and enhance practical applications. The study not only advances the theoretical understanding of fluid dynamics but also provides essential insights for engineering optimization and sports performance enhancement.

Catalyzing sustainable development: insights from the international workshop on STI policies and innovation systems in Central America.

This article examines the landscape of Science, Technology, and Innovation policies in Central America, focusing on Nicaragua, Guatemala, Honduras, and El Salvador. These nations face significant challenges in leveraging STI for sustainable development, including financial constraints and limited resources. Additionally, Central America struggles with systemic issues such as corruption, violence, and high levels of emigration, further complicating efforts to advance STI. A workshop organized by Georgetown University's Science Technology and International Affairs program brought together scholars to discuss STI policies, resulting in key recommendations. The article highlights critical challenges, including over-reliance on state funding, stagnant researcher numbers, and the pressing need for research diversification. It emphasizes the importance of youth engagement, leadership, and resilience in shaping effective STI policies. Recommendations include investing in science education, establishing governmental scientific advisory bodies, promoting research diversity, and addressing climate change through STI strategies. The findings provide valuable insights for scholars, policymakers, and international organizations working with less developed nations globally.

A next year after the tenth anniversary

The Chimica Techno Acta (CTA) journal was launched in 2014 by a recognized expert in organic chemistry, Prof. Yuri Morzherin, as a response to the various demands of scientists specializing in chemistry, chemical engineering, and materials science. Despite the existence of numerous chemical-related journals in Russia at the time, he believed that a new journal would serve as a platform for facilitating efficient regional communication among scientists, primarily students and postdoctoral researchers from Ural and related regions. Initially published in two languages (Russian and English) and two formats (print and online), CTA has garnered attention from its readership, then transforming gradually from a purely regional edition into the well-known Russian journal.

Formation of a gender-sensitive environment in the innovative transformation of the scientific and educational space: the aspect of STEM education

In the conditions of martial law, the creation of a gender­sensitive environment in higher education institutions based on STEM is an urgent task. An important task of science and education at the moment is to provide favourable conditions for the subjects of education based on the state’s innovation policy (for example, STEM, artificial intelligence, robotics, etc.), as well as to provide feedback between the student and the teacher. Activation of the problem of the gender component, focusing attention on the concept of zero tolerance in the system of training personnel from the economic profile and developing a new methodology for building a gender­sensitive environment in this field will ensure the integrity of the process of forming intellectual potential among women and men, is the main goal of the author’s research. This is aimed at the rationality of the organization of the training of education seekers taking into account gender aspects on the basis of STEM. In the research, the authors outlined contextual levels and contradictions regarding the formation of a gender­sensitive environment, in particular: the context of the needs for social order (taking into account the equality of rights of women and men); the context of needs for science (pedagogical, engineering, technical, legal, physical and mathematical disciplines), taking into account transdisciplinarity and aspects of STEM education; in the context of the needs of pedagogical practice (development and implementation of innovative approaches in a gender­sensitive environment in the context of the formation of soft skills of subjects of education capable of implementing STEM technologies in the educational process). The purpose of the research is scientific substantiation, conceptualization and development of a gender­sensitive environment for institutions of higher education based on STEM. The object of research is the educational process in institutions of higher education. The subject of the research is the theoretical and methodological substantiation of the expediency of the formation and development of a gender­sensitive environment of a higher education institution based on STEM education.

Innovative Uses of Technologies in Science, Mathematics and STEM Education in K-12 Contexts

Innovative Uses of Technologies in Science, Mathematics and STEM Education in K-12 Contexts

An Investigation of University Students' Attitudes Towards Artificial Intelligence Ethics

The increasing complexity and widespread use of artificial intelligence (AI) underscore the importance of its ethical dimensions. Understanding diverse perspectives on AI ethics is crucial, especially among university students who will shape future technological advancements. This study aims to deeply examine university students’ attitudes toward AI ethics, focusing on fairness, transparency, privacy, responsibility, and non-maleficence. A mixed-methods approach was used. In the quantitative phase, 355 students from engineering (E) and education science (ES) programs were evaluated using the AI Ethics Attitudes Scale. In the qualitative phase, semi-structured interviews with 23 students were thematically analyzed to gain detailed perspectives based on gender and discipline. The findings revealed significant gender-based differences in fairness and privacy, with female students scoring higher than male students. Interdisciplinary differences were evident in the transparency dimension, where ES students showed greater sensitivity. Interviews highlighted that female student emphasized legal compliance and data security more, whereas male students focused on financial information privacy. ES students prioritized user-friendly language and feedback and complaints in transparency discussions.

AI-Augmented Finite Difference Methods for Solving PDES: Advancing Numerical Solutions in Mathematical Modeling

This study explores the integration of artificial intelligence (AI) with finite difference methods (FDM) to enhance the numerical solution of partial differential equations (PDEs) in physics, engineering, and data science. Traditional FDM approaches, though effective for approximating solutions to PDEs, face limitations in handling high-dimensional, nonlinear, or computationally intensive problems due to constraints in grid size and stability. AI techniques, particularly machine learning (ML) and deep learning (DL), offer promising enhancements, including adaptive grid refinement, optimized time-stepping, and model selection, which significantly improve accuracy and computational efficiency. Using Python-based implementations, this research investigates AI-augmented FDM for various PDEs, including the heat equation, wave equation, Laplace’s equation, and Burger’s equation. Simulation results demonstrate that AI-enhanced FDM not only achieves robust performance but also reduces computational costs by focusing resources on high-error regions in real time. These findings highlight the potential of AI-driven techniques to revolutionize numerical modeling in applications such as fluid dynamics, climate modeling, and wave propagation. This interdisciplinary approach opens avenues for scalable and efficient solutions to complex PDEs, with implications for diverse fields like healthcare, finance, and geophysics. Future research will focus on extending these methods to more intricate PDEs and exploring their application in real-world, resource-constrained scenarios.

Exploring The Impact of Digital React Applications

This study examines the effects of teaching the "Reproduction, Growth, and Development in Living Organisms" unit from the 7th-grade science curriculum using digital REACT applications on students' academic performance, attitudes towards science and digital technology, motivation for learning science, and knowledge retention. Conducted in a secondary school in Mersin's Akdeniz district during the 2022-2023 academic year, this quasi-experimental research involved 66 seventh-grade students. The students were divided into three groups: one control group and two experimental groups. The first experimental group participated in digital REACT activities, while the second group used the REACT strategy without digital tools. The control group followed the standard curriculum. Data collection instruments included the "Academic Achievement Test," the "Science Course Attitude Scale," the "Attitude Scale Toward Digital Technology," and the "Motivation Scale for Learning Science." The findings showed that digital REACT activities significantly improved students' academic achievement and knowledge retention. However, no significant changes were observed in their attitudes towards science or digital technology and their motivation for learning science. Based on these results, recommendations were made for future research and for consideration by the Ministry of National Education.

A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice.

With the global population increasing and the demographic shifting toward an aging society, the number of patients diagnosed with conditions such as peripheral neuropathies resulting from diabetes is expected to rise significantly. This growing health burden has emphasized the need for innovative solutions, such as brain-computer interfaces. brain-computer interfaces, a multidisciplinary field that integrates neuroscience, engineering, and computer science, enable direct communication between the human brain and external devices. In this study, we developed an autonomous diabetes therapeutic system that employs visually-induced electroencephalography devices to capture and decode event-related potentials using machine learning techniques. We present the visually-induced optogenetically-engineered system for therapeutic expression regulation (VISITER), which generates diverse output commands to control illumination durations. This system regulates insulin expression through optogenetically-engineered cells, achieving blood glucose homeostasis in mice. Our results demonstrate that VISITER effectively and precisely modulates therapeutic protein expression in mammalian cells, facilitating the rapid restoration of blood glucose homeostasis in diabetic mice. These findings underscore the potential for diabetic patients to manage insulin levels autonomously by focusing on target images, paving the way for a more self-directed approach to blood glucose control.

A Review of How AI Affects Education in Marketing Communications in College

The integration of artificial intelligence (AI) in higher education (HE) has expanded significantly in recent years. Colleges and other educational institutions are gradually adopting new technologies in teaching and learning. The development of technology and large model algorithms have greatly enhanced AI-generated content's capabilities, positioning AI as promising generative tools that add convenience and potential to education. While much research has focused on AI's role in disciplines like Engineering and Computer Science, its impact on Marketing Communications (Marcom) education remains under-explored. This review investigates how AI tools enhance Marcom education by simulating real-world marketing environments because Marcom is a career-oriented discipline. This review discusses AI in HE and its applications within the Marcom industry and then bridges the gap between HE and the industry by exploring Marcom education's current situation and future opportunities with AI assistants. The findings highlight the importance of equipping both educators and students with AI competencies. Educators can use AI to improve the teaching process, and they also play an essential role in monitoring students' AI usage to ensure their learning quality. From the students' aspects, mastering AI tools can help them be well-prepared for the evolving Marcom industry.

Role of bio health extract on wheat growth according to Zadoks decimal scale

Combating climate change and promoting sustainability are agricultural experts' priorities in Iraq. Using environmentally friendly fertilizers on strategic crops is considered a milestone for accomplishing that. This study aimed to determine the effect of Bio Health fertilizer spraying on some growth qualities of two wheat cultivars (Triticum aestivum L.). A field experiment was conducted at the Agricultural Research Station, Faculty of Agricultural Engineering Sciences - University of Baghdad, during the winter season 2023-24. A randomized complete block design (RCBD) was used in a split-plot arrangement with three replicates, and the experiment included two factors, the first in the main plots such as two wheat cultivars (Ibaa 99 and Adina), while the second factor in the secondary plots was spraying with Bio Health at concentrations of 800, 1600 and 2400 mg/L, in addition to the control treatment (without spraying). The results showed the superiority of the Ibaa 99 cultivar in plant height, the area of the flag leaf, the number of total branches, chlorophyll content, dry weight of the flag leaf, dry weight of the plant, crop growth rate and biological quotient with an increase of 3.64, 15.77, 5.01, 3.38, 7.33, 3.31, 3.34 and 5.73%, respectively. The results also showed the superiority of spraying with Bio Health at a concentration of 1600 mg/L compared to the comparative treatment, the number of total branches, the dry weight of the plant, the growth rate of the crop and the biological yield, with an increase of 28.39, 48.88, 13.21, 12.10 and 23.19%. As for the study's two factors overlap, the results demonstrated a considerable superiority between the two qualities, the height of the plant as well as its dry weight, and the combination of Bio Health spraying at a concentration of 1600 mg/L for the Ibaa 99 cultivar achieved the highest averages.

The innovation of engineering project management in the context of big data

ABSTRACT With the development of China’s economy and society, the society continues to move forward. Related to this, the scale of China’s engineering construction is also increasing. With the development of the times and the continuous growth of demand, the complexity and specialisation of engineering projects continue to improve. This has put forward more stringent requirements for the construction of engineering projects. As the traditional engineering project construction management in China, the innovation is mainly through the practice of construction projects and the research of the engineering problems that arise. So, in today’s environment of big data background, the innovation of construction project management is to combine with the actual situation of construction projects, from the construction characteristics of the project, using a variety of advanced scientific technology, for the project data collection, calculation and storage applications and other aspects of optimisation and innovation, so that more convenient to achieve The modern construction engineering project management innovation work of diversified needs, for the final completion of the entire construction project can be expected to provide a strong guarantee and support the comprehensive benefits of the goal. The results show that our innovative scheme outperforms other comparative methods and achieves a 23% improvement in project management efficiency.

Boosting transcriptional activities by employing repeated activation domains in transcription factors.

Enhancing the transcriptional activation activity of transcription factors (TFs) has multiple applications in organism improvement, metabolic engineering, and other aspects of plant science, but the approaches remain unclear. Here, we used gene activation assays and genetic transformation to investigate the transcriptional activities of two MYB TFs, PRODUCTION OF ANTHOCYANIN PIGMENT 1 (AtPAP1) from Arabidopsis (Arabidopsis thaliana) and EsMYBA1 from Epimedium (Epimedium sagittatum), and their synthetic variants in a range of plant species from several families. Using anthocyanin biosynthesis as a convenient readout, we discovered that homologous naturally occurring TFs showed differences in the transcriptional activation ability and that similar TFs induced large changes in the genetic program when heterologously expressed in different species. In some cases, shuffling the DNA binding domains and transcriptional activation domains (ADs) between homologous TFs led to synthetic TFs that had stronger activation potency than the original TFs. More importantly, synthetic TFs derived from MYB, NAC, bHLH, and Ethylene-insensitive3-like (EIL) family members containing tandemly repeated ADs had greatly enhanced activity compared to their natural counterparts. These findings enhance our understanding of TF activity and demonstrate that employing tandemly repeated ADs from natural TFs is a simple and widely applicable strategy to enhance the activation potency of synthetic TFs.

Enhanced spray-wall interaction model for port fuel injection under medium load conditions

Abstract This study presents an Eulerian-Lagrangian framework for the numerical analysis of spray dynamics, with a focus on droplet movement, spray-wall interactions, and the effects of varying injection parameters associated with port fuel injection (PFI) system. A grid-independent criterion is introduced to optimize mesh analysis for accurate predictions of fuel penetration length. The size distribution of secondary droplets is described using a probability density function, and statistical optimization is subsequently implemented to estimate their mean size. This probabilistic approach enhances the Lagrangian wall film (LWF) model, leading to accurate predictions of the Sauter mean diameter (SMD) at a given radial width ( $$R_\text{{w}}$$ R w ), with results closely matching experimental data. For $$8.0 ~\text {mm} \le R_\text{{w}} \le 24.0 ~\text {mm}$$ 8.0 mm ≤ R w ≤ 24.0 mm , the maximum SMD of 21.67 $$\mu$$ μ m corresponds to $$R_\text{{w}} = 14.0, \text {mm}$$ R w = 14.0 , mm , while the smallest SMD of 12.68 $$\mu$$ μ m is computed for a radial position of $$R_\text{{w}} = 24.0 ~\text {mm}$$ R w = 24.0 mm . The numerical investigation quantifies the role of spray-wall interactions in determining the trajectory of fuel distribution, particularly in the formation of wall films and the relative spatio-temporal diesel concentration (F/A) %. The study explores aspects such as droplet size variations, heat transfer during evaporation, and film behavior under different injection pressures, providing insights into the multiphysical characteristics of spray-wall systems. Near the impingement site ( $$2.0 ~\text {mm} \le R_\text{{w}} \le 4.0 ~\text {mm}$$ 2.0 mm ≤ R w ≤ 4.0 mm ), the plume height ( $$H_\text{{w}}$$ H w ) slightly decreases with an increase in injection pressure. While the CFD methodology in this current work has been primarily developed for automotive engineering sector (PFI engines), it also has potential applications in areas such as additive manufacturing, hydropower engineering, climate science, and environmental engineering.

Company-university intersections through service-learning (SL): a systematic review

The most relevant intersections in society include the relationship between universities and companies for a projection toward the sustainable employability of future graduates. Among the possible intersections, Service-learning (SL) is an educational proposition that may help university students to develop their personal skills, offering them opportunities to learn and practice civic commitment, improving their sense of social and citizen responsibility, and combining academic and community-service learning in a constructed programme where participants train by working on real needs of the environment to optimize and transform the latter. The development of SL programmes in university departments related to technical areas is posing a challenge to faculty members and students, thus it is important to explore this lack of programmes. The main aim of the present study was to identify SL projects and their topics through a systematic review, following the guidelines of the «Preferred Reporting Items for Systematic Reviews and Meta-Analyses» (PRISMA) declaration in the knowledge areas of Architecture, Computer Science, Environmental Engineering, Software Engineering, Computer Engineering, Artificial Intelligence, and Computer Languages and Systems, from the year 2008 to the year 2023. This review includes 128 articles, which were analyzed with ATLAS. Ti 22. The categorical system employed in this work emerged from the topics of the programmes identified in the selected articles, which were verified by experts in the mentioned fields of knowledge. The agreed categories were: accessibility, learning, social groups, courses, devices, infrastructure, games, environment, landscaping, heritage, software and web. The most relevant conclusions highlight that most of the articles refer to theoretical aspects of SL, showing a lack of data on the practical development of SL programmes and their impact on employability. The largest number of SL programmes are developed in the areas of Architecture, Computer Science and Software Engineering. Regarding the topics that are addressed in research, most of the articles refer to social groups, software, learning and accessibility.

Generative AI Learning Environment for Non-Computer Science Engineering Students: Coding Versus Generative AI Prompting

This paper addresses the need for a positive and effective learning environment for engineering students in non-computer science fields to grasp Generative AI principles while navigating the intricate balance between its application and developmental insights. Drawing from pedagogical theories and cognitive science, especially Leinenbach and Corey (2004)’s Universal Design for Learning, this study proposes a framework tailored to the unique needs and backgrounds of engineering students. The framework emphasizes active learning strategies, collaborative problem-solving, and real-world applications to engage learners in meaningful experiences with Generative AI concepts. The central learning context is a M.Sc. program in management engineering with a course/training opportunity in Machine Learning Fundamentals using Python based on Google Collab. The introduction of Generative AI is based on selected Google libraries for Python. Furthermore, this paper explores various instructional approaches and tools to scaffold students' understanding of Generative AI, including hands-on projects, case studies, and interactive simulations. It also addresses ethical considerations and societal implications associated with Generative AI deployment, encouraging students to critically reflect on the broader impacts of their technical decisions. Through a synthesis of pedagogical best practices and AI development principles, this paper contributes to the ongoing discourse on effective AI education for non-computer science disciplines. By embracing a holistic approach that integrates theory with practical application, educators can empower engineering students to harness the transformative potential of Generative AI while navigating its complexities responsibly and ethically.

Comprehensive Bibliometric Review on the Sustainability and Environmental Impact of Fiber-Reinforced Polymers

Fiber-reinforced polymers (FRPs) are increasingly recognized in sustainable materials research due to their potential environmental advantages. This study presents a focused bibliometric review of the sustainability research on FRPs. An initial search of the Web of Science (WOS) database identified 803 documents, which were refined to 749 relevant articles, reviews, and proceedings. A co-authorship analysis highlights the significant contributions of the USA and India, with European countries forming regional collaborations. The research output has steadily increased since 2011, peaking in 2022 and 2023. The multidisciplinary nature of the research spans materials science, engineering, and environmental sciences, with journals such as *Polymers*, *Sustainability*, and the *Journal of Cleaner Production* emphasizing sustainability themes. This analysis covers key aspects such as keyword co-occurrence, overlay visualizations, co-authorship networks, and the distribution of publications by year, research area, and journal. The findings underscore the evolving research landscape of sustainable FRPs and highlight the ongoing need for life cycle assessments and interdisciplinary collaboration.

The Emerging of 2D Layer‐Blocked Frameworks

Blocked copolymers are a versatile and powerful class of materials, and their properties can be modified by carefully designing the molecular architecture. Their ability to self‐assemble into nanoscale structures and the multifunctionality make them invaluable in advanced materials science, nanotechnology, and biomedical engineering. Compared to traditional blocked copolymer morphologies such as linear, cyclic, and branched, 2D layer‐blocked frameworks provide materials with clear structure, permanent porosity, favorable chemical and thermal stability, larger surface area, ultra‐high carrier mobility, efficient charge separation and migration, and excellent photoexcitation response. In this concept article, we summarized the design concepts and state‐of‐the‐art strategies for the construction of 2D layer‐blocked frameworks and reviewed the development of some 2D‐like blocked frameworks and their applications in semiconductor, catalysis, energy storage, etc. Finally, we also outlook for the challenges of these materials in preparation and applications across different research fields.

Innovative Strategies for Microalgae-Based Bioproduct Extraction in Biorefineries: Current Trends and Future Solutions Integrating Wastewater Treatment

Recently, microalgae have emerged as a promising feedstock for biorefineries, offering significant potential for producing high-value bio-based products in areas such as biofuels, nutraceuticals, and environmental management. This study, therefore, undertook an in-depth bibliometric review of 535 articles out of 736 publications published between 2010 and 2024 and sourced from the Scopus database. With the use of the VOS-viewer software, this work identified the major trends within significant research areas in terms of focus and global collaboration networks that pertain to microalgae-based bioproducts. Also, it explored cutting-edge techniques for bioproduct extraction and processing that are both efficient and eco-friendly. This analysis also showed a remarkable growth in output, with peaks in the year 2022, reflecting an interest in renewable energy and methods of sustainable production. The main keywords identified deal with subject areas such as energy, environmental science, and chemical engineering. The dominant technologies referred to dealing with lipid extraction, bio-crude production, and nutrient recycling. While addressing cost, scale-up, and environmental concerns, there is still a need to improve extraction techniques like ultrasonic treatment, supercritical fluid treatment, and enzymatic treatment. Other emerging areas of research include genetic engineering and integrated biorefinery models, which are expected to provide a roadmap for future advancements in the field. The challenges innate in meeting this through innovation and optimization will be the key to realizing the full potential of microalgae to contribute to the circular bioeconomy.

Revolutionizing Education: Harnessing Graph Machine Learning for Enhanced Problem-Solving in Environmental Science and Pollution Technology

Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.