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Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

The Human Resources Development Service of Korea developed a digital exam for five representative engineering categories and conducted a pilot study comparing the findings with the paper-and-pencil exam results from the last three years. This study aimed to compare the test efficiency between digital and paper-and-pencil examinations. A digital examination was conducted with 93 participants through two rounds of test-status measurements. We collected the following data: early check-out rate, pass rate, and the average total response time per grade. The composition ratio of each question type in the digital exam was determined per the "Exam Operation Regulation" and selected from a databank spanning five years by experienced instructors. The early check-out rate of the digital exam at the Engineer level was 90% (percentage points), approximately 15%p higher than that for the paper-and-pencil exam, but the pass rate was low (9.8%p); the response time per question for the digital exam was approximately 0.2 min shorter. The results for both exams were similar in the Industrial Engineer category. The early check-out rate did not positively affect the exam pass rate, but the time taken depended on the number and characteristics of the subjects. It is necessary to improve the simplification and convenience of digital examinations, optimize test environments, and establish operation and management systems in line with the change in the Korean Technical Qualification Framework. We also suggest a gradual shift to digital examination by developing questions and tools using technologies to accurately measure individual abilities. [ABSTRACT FROM AUTHOR]

An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on "Rigorous Engineering of Collective Adaptive Systems." It presents the 11 contributions of the section categorizing them into five distinct research lines: correctness by design and synthesis, computing with bio-inspired communication, new system models, machine learning, and programming and analyzing ensembles. [ABSTRACT FROM AUTHOR]

His main interests include resource optimization technique, adaptive resource provisioning, multi-objective optimization, computational intelligence, incomplete information processing, cloud computing, and security. The evaluation showed that these apps had an acceptable score on the System Usability Scale (SUS). The paper "Influence of Belbin's Role Theory on Database Design: Experimenting with Software Engineering Students" explores the benefits of using Belbin's role theory of team and individual related tasks in the software development process, particularly for Database Design (DB) Design. The authors argued that the usability evaluation described in this work can serve as a reference for developers seeking to improve learning monitoring and personalization services in software applications. [Extracted from the article]

This editorial article discusses recent findings in the optimization and engineering for sustainable development. The current Special Issue (SI) of the Optimization and Engineering journal stems from the 2022 Sustainable Development of Energy, Water and Environment Systems Conferences. These events included the 5th South East European, 3rd Latin American, and 17th SDEWES Conferences. Being selected from conference presentations, the papers in SI represent the main topics in optimization approaches that integrate various life-supporting systems discussed during these three events. After careful selection, the current SI accepted ten excellent papers summarized here. These contributions use differentiated modeling approaches and supporting tools, such as P-graph and network-flow modeling, computational fluid dynamics, pinch analysis (PA), and the Geographic Information System. The range of optimization methods includes mixed-integer linear and mixed-integer nonlinear programming (MILP and MINLP), dynamic programming, stochastic optimization, and PA and P-graph extensions. [ABSTRACT FROM AUTHOR]

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Experimental Philosophy (X-Phi) is now a fully-fledged methodological project with applications in almost all areas of analytic philosophy, including, as of recently, aesthetics. Another methodological project which has been attracting attention in the last few years is conceptual engineering (CE). Its areas of implementation are now diverse, but as was the case initially with experimental philosophy, aesthetics has unfortunately been left out (or perhaps aestheticians have failed to pay attention to CE) until now. In this paper, I argue that if conceptual engineers are interested in expanding their project to the field of aesthetics, which would greatly benefit the field, then they should rely on the existing experimental work of aestheticians. Experimental philosophers have only recently started to join forces with conceptual engineers in various fields, as well as to explore the methodological implications of such an alliance. This paper goes a step further by not only arguing that CE has potential in aesthetics, but that the way to realize this potential is to piggyback, so to speak, on the work of experimental aestheticians. In other words, instead of building a CE project in aesthetics from the ground up, this paper describes the support that CE can and should derive from current experimental aesthetics, thereby making the former's development more efficiently realizable. Furthermore, I argue that doing so would also be beneficial to experimental aesthetics. Currently, the integration of X-Phi to the wider field of aesthetics is losing ground because certain objections—notably, the objection that X-Phi cannot be of relevance to normative questions—have not been properly refuted. By pairing up with a normative programme like CE, though, experimental aestheticians should finally be able to put these objections to rest. [ABSTRACT FROM AUTHOR]

This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key mechanism to access the transformative capabilities of LLM AI in the learning process. This paper charts the evolving trajectory of LLM AI as a tool poised to reshape educational practices and assumptions. In particular, this paper breaks down the potential of prompt engineering practices to enhance learning by fostering personalized, engaging, and equitable educational experiences. The paper underscores how the natural language capabilities of LLM AI tools can help students and educators transition from passive recipients to active co-creators of their learning experiences. Critical thinking skills, particularly information literacy, media literacy, and digital citizenship, are identified as crucial for using LLM AI tools effectively and responsibly. Looking forward, the paper advocates for continued research to validate the benefits of prompt engineering practices across diverse learning contexts while simultaneously promoting potential defects, biases, and ethical concerns related to LLM AI use in education. It calls upon practitioners to explore and train educational stakeholders in best practices around prompt engineering for LLM AI, fostering progress towards a more engaging and equitable educational future. [ABSTRACT FROM AUTHOR]

The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research. [ABSTRACT FROM AUTHOR]

Copper alloys are at the forefront of alloy innovation, as they possess outstanding strength and unparalleled electrical conductivity. This papers sequentially reviews current strategic approaches that have successfully balanced the attainment of both strength and conductivity in copper alloys in the Cu–Fe–Si system. These approaches involve fine-tuning the composition and utilizing hierarchical multi-scale microstructural templates. This work provides a comprehensive assessment of the important developmental stages of high-strength conductive copper alloys in the Cu–Fe–Si system. This study emphasizes the relationship between composition, microstructure, and mechanical and electrical properties by implementing a cost-effective rapid casting method with alloying procedures and conducting extensive electron microscopy for microstructural characterization. The full research yields valuable insights that may be used to optimize copper alloys for a wide range of applications that demand both strength and conductivity. Furthermore, the present study additionally enhances our understanding of the phase transformation events in the alloy system by including novel microstructural observations. The current paper also presents novel findings about sustainable performance and real-time engineering applications, specifically in connection to creep qualities and their link with microstructure. This paper explores the complex relationship between the composition of alloys and the process of refining their structure. The knowledge acquired provides a foundation for creating customized alloys that are well-positioned to suit the changing requirements of various industries, including aerospace and electronics. [ABSTRACT FROM AUTHOR]

Background: The purpose of this scoping review is to describe how the literature has discussed and studied disability in undergraduate-level STEM courses in the United States. A Critical Disability Studies lens informed our inclusion criteria. Results: We considered extensive lists of disability types and diagnoses and concluded that "disability" as a search term best captured educational experiences rather than medical approaches. After screening nearly 9000 abstracts, we identified a final set of 409 dissertations, articles, conference papers, commentaries, briefs and news items. Sources appeared in discipline-based education research (DBER), STEM disciplinary and education journals as well as DBER conferences. Under 10% of sources included 2-year college settings. The largest groups of sources focused on disability writ large (39%, vs. specific categories) and across STEM (38%, vs. specific disciplines). Students were the main research participants (80%). Instructors were the main target of recommendations (84%). In terms of solutions, the largest group (n = 111) advocated for Universal Design, followed by accommodations (n = 94), and technology developed or tested with persons with disabilities (n = 90). Sources which the authors framed as empirical studies less frequently disclosed positionality as a person with a disability (16%) than non-empirical sources (21%). Quantitative (n = 125), qualitative (n = 99), and mixed methods (n = 64) approaches were well-represented. The most common data collection methods were surveys, assessments or task completions (n = 161 sources), followed by interviews (n = 109), observations (n = 44), document analyses (n = 18), and institutional student records (n = 14). Conclusions: More research is needed that centers the experiences of students with disabilities, focuses on specific disability types, employs critical quantitative and qualitative methodologies, and otherwise avoids implicit deficit views of disabled students. Citations to the qualifying sources are available in a public Zotero library. [ABSTRACT FROM AUTHOR]

Injection molding is one of the most important polymer processing methods, and the traditional injection molding process cannot meet the high repeatability requirements necessary for the production of optical lenses. The impact of melt flow behavior and non-return valve closing behavior on injection molding repeatability is studied in this paper. A strategy of adjusting the closure behavior of the non-return valve is proposed, which can improve the repeatability of injection molding without the need for specific modifications to the injection molding machine. In this paper, variables such as the reversal action, reversal speed, forward movement action, and forward speed of the screw were investigated. It was found that the reversal action and forward movement action had the most significant impact on the closure stability of the NRV. Through the implementation of this process, injection molding repeatability can reach 0.019%. Highlights: The closing behavior of the NRV during the injection molding is investigated. A method for regulating the closing behavior of NRV is proposed. Injection molding repeatability can reach 0.019% under the regulating method. [ABSTRACT FROM AUTHOR]

Memristors offer vast application opportunities in storage, logic devices, and computation due to their nonvolatility, low power consumption, and fast operational speeds. Two-dimensional materials, characterized by their novel mechanisms, ultra-thin channels, high mechanical flexibility, and superior electrical properties, demonstrate immense potential in the domain of high-density, fast, and energy-efficient memristors. Hexagonal boron nitride (h-BN), as a new two-dimensional material, has the characteristics of high thermal conductivity, flexibility, and low power consumption, and has a significant application prospect in the field of memristor. In this paper, the recent research progress of the h-BN memristor is reviewed from the aspects of device fabrication, resistance mechanism, and application prospect. [ABSTRACT FROM AUTHOR]

The paper reveals a small chapter of the history of the development of metallurgical science and technology in the Soviet Union. The point at issue is the development of scientific contacts between the American and Soviet metallurgical scientists in the second half of the 1950s. The successes of the USSR in the space race had demonstrated to the Western countries the real scientific and economic power of the country and thus shown both the unconstructiveness of the policy of isolation and the benefits of economic cooperation. The high level of development of metallurgical science and technology in the USSR had thus surprised the American specialists. The Ural Polytechnic Institute was one of those universities visited by the American guests. This paper is devoted to this chapter of history and continues the material published earlier. [ABSTRACT FROM AUTHOR]

Complexity is often regarded as a "problem" to solve. Instead of attempting to solve complexity, we follow systems engineering practices and switch back to the problem domain, where a major obstacle is the impossibility to universally define complexity. As a workaround, we explored complexity characterization and its existing shortcomings, including: lack of standardization, inconsistent semantics, system-centricity, insufficiently transparent reasoning, and lack of validation. To address these shortcomings, we proposed a compilatory framework to characterize complexity using the Five Ws information-gathering method. The answer to the WHO question proposed four complexity viewpoints; the answer to the WHY question proposed a two-dimensional structure for complexity drivers; and the answer to the WHAT question derived generalized complexity challenges. As a preliminary step to show the potential of the framework to characterize complexity, we used and validated it as a tool to structure general literature related to complexity. In general, our findings suggest that papers with complexity solutions do not frame their research within the complexity problem domain, hindering the contribution evaluation. Through the viewpoints, we identified general research gaps of six solution directions. From the drivers, we noted three observations in the discourse of complexity origins: (1) a system-driven tendency, (2) a preference for concreteness vs. abstraction, and (3) an unclear distinction between origins and effects. Through the challenges' findings we explored two hypotheses: (1) a system-centric preference; and (2) a solution-oriented vision, both of which were supported by the results (most challenges relate to the system viewpoint and challenges are defined based on solution directions). [ABSTRACT FROM AUTHOR]

Developers often encounter challenges with their introductory programming tasks as part of the development process. Unfortunately, rectifying these mistakes manually can be time-consuming and demanding. Automated program repair (APR) techniques offer a potential solution by synthesizing fixes for such errors. Previous research has investigated the utilization of both symbolic and neural techniques within the APR domain. However, these approaches typically demand significant engineering efforts or extensive datasets and training. In this paper, we explore the potential of using a large language model trained on code, specifically, we assess ChatGPT's capability to detect and repair bugs in simple Python programs. The experimental evaluation encompasses two benchmarks: QuixBugs and Textbook. Each benchmark consists of simple Python functions that implement well-known algorithms and each function contains a single bug. To gauge repair performance in various settings, several benchmark variations were introduced including addition of plain English documentation and code obfuscation. Based on thorough experiments, we found that ChatGPT was able to correctly detect and fix about 50% of the methods, when code is documented. Repair performance drops to 25% when code is obfuscated, and 15% when documentation is removed and code is obfuscated. Furthermore, when compared to existing APR systems, ChatGPT considerably outperformed them. [ABSTRACT FROM AUTHOR]

Water hyacinth (Eichhornia crassipes) is considered a prospective free-floating aquatic plant potentially used to address current issues on food, energy, and the environment. It can grow quickly and easily in various tropical and subtropical environments as long as it has access to adequate light and water to support photosynthetic growth. Ecosystems are threatened by their invasive growth and remarkable capacity for adaptation. However, managing this plant can result in valuable products. This paper demonstrates particle technologies that might be used to utilize water hyacinths, including brake pads, fertilizer, bioenergy, animal feed, phytoremediation agents, bioplastics, and adsorbents. This study is accompanied by a discussion based on the conducted experiments and currently available literature, providing readers with a clearer understanding. Water hyacinth's capacity to absorb macro- and micro-nutrients, nitrogen, and phosphorus makes it a good plant for phytoremediation. The prospect of producing cellulose makes it prospective as a biomass energy source and livestock feeding. Further, it can be transformed into high-cellulose content particles for applications in bioplastics, brake pads, and adsorbents. The current reports regarding education of water hyacinth to student also were added. Finally, issues and suggestions for future development related to the use of water hyacinths are discussed. This study is expected to provide comprehensive knowledge on how to turn invasive water hyacinth plants into valuable products. [ABSTRACT FROM AUTHOR]

Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have gained widespread attention in dealing with them. As the objective dimension increases, the error of predicting solutions based on surrogate models accumulates. Existing algorithms do not have strong selection pressure in the candidate solution obtaining and adaptive sampling stages. These make the effectiveness and area of application of the algorithms unsatisfactory. Therefore, this paper proposes a two-risk archive algorithm, which contains a strategy for mining high-risk and low-risk archives and a four-state adaptive sampling criterion. In the candidate solution mining stage, two types of Kriging models are trained, then conservative optimization models and non-conservative optimization models are constructed for model searching, followed by archive selection to obtain more reliable two-risk archives. In the adaptive sampling stage, in order to improve the performance of the algorithms, the proposed criterion considers environmental assessment, demand assessment, and sampling, where the sampling approach involves the improvement of the comprehensive performance in reliable environments, convergence and diversity in controversial environments, and surrogate model uncertainty. Experimental results on numerous benchmark problems show that the proposed algorithm is far superior to seven state-of-the-art algorithms in terms of comprehensive performance. [ABSTRACT FROM AUTHOR]

Dynamic Tensor Inversion (DTI) is an emerging issue in recent research, prevalent in artificial intelligence development frameworks such as TensorFlow and PyTorch. Traditional numerical methods suffer significant lagging error when addressing this issue. To address this, Zeroing-type Neural Dynamics (ZND) and Gradient-type Neural Dynamics (GND) are employed to tackle the DTI. However, these two methods exhibit inherent limitations in the resolution process, i.e. high computational complexity and low solution accuracy, respectively. Motivated by this technology gap, this paper proposes an Adaptive Coefficient Gradient Neural Dynamics (ACGND) for dynamically solving the DTI with an efficient and precise manner. Through a series of simulation experiments and validations in engineering applications, the ACGND demonstrates advantages in resolving DTI. The ACGND enhances computational efficiency by circumventing matrix inversion, thereby reducing computational complexity. Moreover, its incorporation of adaptive coefficients and activation functions enables real-time adjustments of the computational solution, facilitating rapid convergence to theoretical solutions and adaptation to non-statinary scenarios. Code is available at https://github.com/Maia2333/ACGND-Code-Implementation. [ABSTRACT FROM AUTHOR]

Mechanical equipment usually operates in noisy and variable load environments, which presents serious challenges for existing intelligent diagnostic models. In addition, there are few labelled fault samples in real engineering scenarios, which makes it difficult to perform accurate fault identification for mechanical equipment. Thus, to solve the problem of diagnostic model performance degradation under small sample, noisy and variable load environments, this paper proposes a Multiscale Dilated Convolution and Swin-Transformer (MSDC-Swin-T) method for small sample gearbox fault diagnosis. First, we design the Coordinate Reconstruction Attention Mechanism (CRAM), which enhances the capture of impulse information by coordinate reconstruction. In addition, a multiscale convolutional token embedding module is constructed to extract local features at different scales, and its ability for capturing important features is adaptively enhanced by CRAM. Then, Swin-Transformer is utilized for modeling global dependencies, thus mining more subtle fault features. Finally, the effectiveness and stability of the MSDC-Swin-T is proved on two gearbox datasets. The experiments show that MSDC-Swin-T has superior diagnostic performance under small sample with noise and variable load environments. The diagnostic accuracy is better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of finding the necessary and sufficient conditions to be satisfied by the engineering moduli of an anisotropic material for the elastic energy to be positive for each state of strain or stress. The problem is solved first in the most general case of a triclinic material and then each possible case of elastic syngony is treated as a special case. The method of analysis is based upon a rather forgotten theorem of linear algebra and, in the most general case, the calculations, too much involved, are carried out using a formal computation code. New, specific bounds, concerning some of the technical constants, are also found. [ABSTRACT FROM AUTHOR]

This theme section brings together the latest research at the intersection of artificial intelligence (AI) and model-driven engineering (MDE). Over the past years, we have witnessed a substantial rise of AI successfully applied to different domains, including software development and MDE. Dedicated events at the intersection of AI and MDE have been created, too, such as the MDE Intelligence workshop series co-located with the MODELS conference. This theme section covers research contributions integrating AI components into MDE approaches—increasing the current benefits of MDE processes and tools and pushing the limits of "classic" MDE with the goal to provide software and systems engineers with the right techniques to develop the next generation of highly complex model-based systems—and applications of MDE to the development of AI components. In total, nine submissions were accepted in the theme section after a thorough peer-reviewing process. [ABSTRACT FROM AUTHOR]

Due to the influence of the Next Generation Science Standards (NGSS), the National Research Council's (NRC) Framework for K-12 Science Education, and a socio-workforce push to increase diverse representation in STEM fields, engineering education has become more prevalent in K-12 classrooms over the past decade. Although this shift is seemingly optimistic, critical consideration must be given to the ways engineering education tends to be reductive and positivistic, mimicking engineering practices decontextualized from high-stakes socio-political and socio-ecological realities through the adherence of a closed, systematic design process. Teaching and learning that is situated within the confines of normative engineering learning fail to nurture and cultivate students' intellectual health and ontological security, and further promotes destructive settler ecologics. This study took up efforts to disrupt and desettle engineering education, which opened space for children to critically speculate about real-world engineering entanglements deeply connected to their futurities. The aims of this paper are to (1) establish the need for anticolonial engineering, and engineering education, (2) promote approaches for doing so (3) and elevate the just worlding engineering design principles put forward by radically caring children committed to socio-ecological and multispecies justice. The learning shared in this paper suggests not only why science and engineering fields and education should be reformed, but also provides guidance for evolving the designing of shared worlds, for kincentric flourishing. [ABSTRACT FROM AUTHOR]

The background of this Special Issue (SI) of the Optimization and Engineering (OPTE) journal is the 2021 Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conference. This broad field was discussed by the participants of the 16th SDEWES Conference held in Dubrovnik, Croatia. This SI aims to bring together articles that discuss recent advances in optimization methods and algorithms that integrate various life-supporting systems. A decision-making problem often appears as a parameter estimation problem; it can be formulated as an optimization model and solved using different optimization algorithms and techniques. All papers included in this OPTE journal SI consider aspects of numerical analysis, mathematical modelling, and computational methods involved in investigating, planning and implementing sustainable development. In this context, the guest editors have confidence that the selected papers and addressed issues will substantially contribute to the increase of the knowledge body published in the OPTE journal, and the SI will be a powerful platform for researchers to discuss, share, and disseminate new ideas. [ABSTRACT FROM AUTHOR]

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios. [ABSTRACT FROM AUTHOR]

This paper aims to call attention to the potential of using film in engineering ethics education, which has not been thoroughly discussed as a pedagogical method in this field. A review of current approaches to teaching engineering ethics reveals that there are both learning outcomes that need more attention as well as additional pedagogical methods that could be adopted. Scholarship on teaching with film indicates that film can produce ethical experiences that go beyond those produced by both conventional methods of teaching engineering ethics and more arts-based methods such as fiction, as well as connect ethics learning outcomes and issues to the lifeworld of a person. The paper further illustrates the potential of using Miyazaki Hayao’s film The Wind Rises for highlighting a range of ethical issues pertaining to engineering. It also discusses the important role educators play in how film can be used effectively in the classroom. Synthesizing a range of sources from film theory to the use of film in business and medical education, the paper makes the case for using film in engineering ethics education and calls for more research on the use of this method. [ABSTRACT FROM AUTHOR]

In both co-authored and solo-authored work over the past decade, we have developed the idea of "metalinguistic negotiation". On our view, metalinguistic negotiation is a type of dispute in which speakers appear to use (rather than explicitly mention) a term in conflicting ways to put forward views about how that very term should be used. In this paper, we explore four possible dimensions of variation among metalinguistic negotiations, and the interactions among those dimensions. These types of variation matter for understanding the nature, and the potential range, of the phenomenon of metalinguistic negotiation. As an illustration of the latter, we argue in our concluding section that understanding the full range of forms that metalinguistic negotiations can take has implications for debates about the "implementation" of conceptual engineering proposals. [ABSTRACT FROM AUTHOR]

Promoting ethical practice within an organization has been a continuous challenge in the business ethics community. To enrich organizational practices for promoting business ethics across an organization, this paper aims to introduce the voices of practitioners working in organizations that offer exemplary practices. Based on semi-structured interviews with 21 engineers working in the health products industry, we identified 12 pervasive ethical values that we grouped to four categories: fiduciary, economic, engineering, and process values. As ethics has been embraced as a core element of the ethical practice of our study participants regardless of their rankings and roles in their organizations, we investigated organizational strategies for promoting ethical practice. We identified four categories of organizational strategies: symbols, memberships, practices, and systems. Each strategy affected the 12 ethical values in different ways. Lastly, we identified three characteristics of the industry contexts that appeared to influence ethical values: customer impact, economic norms, and highly regulated industry. We discuss how the findings from this paper can potentially promote new discussions and practices in the business ethics community. [ABSTRACT FROM AUTHOR]

It is reported that engineering for play-based settings is a new body of knowledge under debate and contradictory. Many studies show how play-based settings are full of engineering possibilities for young children, whilst others have identified missed engineering teaching possibilities. In order to better understand the research, this paper answers the questions, 'How is engineering knowledge being constructed through research into play-based settings?', and 'What do the results show for practice and future engineering research for preschool settings?' To answer these questions, a critique of those studies which focused on engineering education in play-based settings was undertaken. By examining existing studies in relation to the study designs and the results, this paper reports on how categories of engineering knowledge were established and how knowledge was constructed for engineering education for play-based settings. Using cultural-historical theory, the results show that studies of play-based setting have primarily been guided by practices from only some engineering professions, knowledge construction is based on both pre-defined and emergent categories, and research has tended to focus mostly on constructing (e.g. blocks). Gendered engineering knowledge was also identified, suggesting only partial understandings of engineering education evident. The critique not only identified knowledge construction that positively contributes to scholarship but also found gaps in what was being studied, thereby contributing to research by cautioning against full acceptance of the existing analytical concepts in the literature and suggesting that the building of a broader set of analytical categories for future research is needed. [ABSTRACT FROM AUTHOR]

Nanocellulose, as a native renewable material extracted from cellulosic biomass, has gained increased attention in biomedical applications due to its unique physical features, biocompatibility, low toxicity, and modifiable surface chemistry. However, as a candidate for biomedical uses, native nanocellulose still has some limitations, including mechanical properties that are incomparable to those of natural tissues, a lack of biodegradability in vivo, and insufficient cell adhesion support, all of which prevent the widespread use of nanocellulose in the biomedical field. Recently, surface or interface engineering has been advocated as a promising strategy for tailoring the bioactivities and physiochemical properties of native nanocellulose for specific biomedical applications. This review paper focuses on recent advances in nanocellulose surface and/or interface engineering strategies for potential biomedical applications. The features of native nanocellulose that are similar to those of biomaterials have been emphasized. It also proposed a roadmap for the development of nanocellulose-based biomaterials, along with bottlenecks and prospects. This review paper emphasizes a new concept for nanocellulose-based biomaterials development strategies based on nanocellulose surface and interface properties for potential biomedical applications. [ABSTRACT FROM AUTHOR]

The engineering vehicle detection is a key issue for the raw material warehouse scenes. Through the engineering vehicle detection, the working conditions of engineering vehicles in the raw material warehouse can be intelligently managed to prevent large-scale smoke pollution and the danger of smoke and dust. In this paper, we propose an intelligent method based on the framework of YOLOv4-Tiny for locating and identifying the engineering vehicles. In our detection task, the monitoring scenes are complex with a lot of interference. And the scope of monitoring is large. In order to solve these challenging problems, we introduce the Split-attention module to the network, which can adaptively extract important information of the image and improve the receptive field of detection. In addition, we introduce the Dynamic ReLU function to the network, which allow the network to adaptively learn more suitable ReLU parameters based on the input. We also collect a large number of images obtained from the front-end cameras and create a self-built dataset of engineering vehicles. In this paper, we test our method on the COCO dataset and the self-built engineering vehicle dataset. Experimental results show that our method proposed in this paper can detect engineering vehicles with higher accuracy and faster speed, which can be used for engineering vehicle detection in the scenes of raw material storage warehouses. [ABSTRACT FROM AUTHOR]

In order to achieve an optimal system performance, decision makers are continually faced with the responsibility of making choices that will enhance availability and reduce failures cost. To realize this goal, it is crucial to ensure the timely maintenance of equipment, which often poses a significant challenge. However, the adoption of predictive maintenance (PdM) technology can offer a solution by enabling real-time maintenance, resulting in various benefits such as reduced downtime, cost savings, and enhanced production quality. Machine learning (ML) techniques are increasingly being used in the field of predictive maintenance to predict failures and calculate estimated remaining useful life (RUL) of equipment. A case study is proposed in this research paper based on a maintenance dataset from the aerospace industry. It experiments and compare multiple combination of feature engineering techniques and advanced ML models with the aim to propose the most efficient techniques for prediction. Moreover, future research papers can focus on the challenge of validating this proposed model in different industrial environments. [ABSTRACT FROM AUTHOR]

Complex systems are difficult to manage, operate and maintain. This is why we see teams of highly specialised engineers in industries such as aerospace, nuclear and subsurface. Condition based monitoring is also employed to maximise the efficiency of extensive maintenance programmes instead of using periodic maintenance. A level of automation is often required in such complex engineering platforms in order to effectively and safely manage them. Advances in Artificial Intelligence related technologies have offered greater levels of automation but this potentially pivots the weight of decision making away from the operator to the machine. Implementing AI or complex algorithms into a platform can mean that the Operators' control over the system is diminished or removed altogether. For example, in the Boeing 737 Air Max Disaster, AI had been added to a platform and removed the operators' control of the system. This meant that the operator could not then move outside the extremely reserved, algorithm defined, "envelope" of operation. This paper analyses the challenges of AI driven condition based monitoring where there is a potential to see similar consequences to those seen in control engineering. As the future of society becomes more about algorithm driven technology, it is prudent to ask, not only whether we should implement AI into complex systems, but how this can be achieved ethically and safely in order to reduce risk to life. [ABSTRACT FROM AUTHOR]

Understanding teachers' experiences as they lived them and as they told them is important for conceptualizing a well-rounded view of the state of science education and the role teachers play in it. Journey mapping, a way of visualizing one's processes and experiences as they progress towards a goal, is a tool with the potential to capture teachers' lived experiences. The purpose of this study was to explore elementary teachers' lived experiences as they work to provide science and engineering learning experiences for their students. This paper highlights the use of STEM journey mapping as a way of exploring teachers' lived experiences in their day-to-day practice. This study features the STEM journey map and subsequent narrative of a fourth-grade teacher, Elliot. The data analysis supported the argument that journey mapping was a powerful tool for exploring teachers' lived experiences. [ABSTRACT FROM AUTHOR]

This work is the extended version of a paper presented at the conference HMM2021, about the history of mechanical engineering. First, the initial cultural and industrial steps in the robotic field in Japan are introduced, to display the beginning of this interlaced path, before WW2; then, in the context of the aftermaths of the war, some famous anime heroes are presented as ancestors of the coming mecha anime series. The rising research in the field of robotics and more generally the developments of contemporary Japanese automation industry are then drawn, as a technological substrate for mecha conception. In particular, Masahiro Mori studies are taken into consideration to identify an actual robot design pattern to apply to the following comparative description and examination of the different mecha; finally, thanks to this analysis, the common breeding ground whence robotics and mecha (and their reciprocal influences) arise, is outlined. [ABSTRACT FROM AUTHOR]

Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning). [ABSTRACT FROM AUTHOR]

Densified superwood, a renewable material being developed for lightweight structural applications, requires effective methods for joining to metallic materials like aluminum. While adhesive bonding provides high joint strength, its limited elongation during lap shear tensile testing poses challenges for automotive applications. This paper presents an experimental study on self-pierce riveting (SPR) and riv-bonding (a combination of SPR and adhesive bonding) for joining superwood to aluminum. SPR successfully joins superwood to aluminum alloys, achieving reasonable strength (about 1-kN load) and significantly higher elongation (about 2-mm displacement) compared to adhesive-only bonds. Riv-bonding, which combines the advantages of both methods (about 5-kN load and about 4-mm displacement), offers an effective solution for superwood-to-aluminum joints for structural applications. The study reports the effects of rivet geometry, insertion speed, superwood coating, pre-drilling, and adhesive curing to identify optimal procedures for SPR and riv-bonding in these applications. [ABSTRACT FROM AUTHOR]

Investigating an optimized efficiency of an engine is crucial for minimizing wastage. The simple two level heat engine was introduced to calculate the efficiency at maximum power.In this paper, we explore the optimum efficiency of a simple two-level heat engine that consists of two distinct energy levels coupled with two thermal baths with distinct temperatures. By employing unified energy converter criteria, we determine the optimized efficiency under two optimum operations scenario, situated between the maximum and minimum efficiency values. The minimum efficiency is associated with either zero efficiency or efficiency at maximum power. We further express the optimum efficiency in terms of scaled parameters such as power-wise, period-wise and efficiency-wise as a function of Carnot efficiency. Finally, a figure of merit is introduced to evaluate overall engine performance, reveals that the second optimization criterion exhibits better performance compared to the first criterion with the entire range of Carnot efficiency. [ABSTRACT FROM AUTHOR]

Rockburst prediction has important significance for individual safety in deep geologic engineering. To accurately predict the rockburst-intensity level, this paper proposes a rockburst prediction method via multiscale graph convolutional neural network. In the method, six rockburst indicators of each rockburst are utilized as a rockburst sample, and data segmentation oversampling of rockburst samples solves the unbalanced class-distribution problem of rockburst-data samples. The similarity perception for capturing similarity structure relationships of rockburst samples in Euclidean space is given, and a novel indicator correlation constraint of the similarity relationships with multiple rockburst criteria is designed, which can reduce the structure distortion of Euclidean space. By integrating the perception and the constraint, criterion-constrained graph structure data are constructed under the graph neural network framework. On the basis of the graph data, a multiscale graph convolutional neural network is further proposed, and multiscale information with the topology structure and criterion-constrained relationships can be effectively captured by the network, which can achieve the accurate prediction of the rockburst-intensity level. The effectiveness of the proposed method is demonstrated by extensive experiment analysis. Highlights: Give similarity perception to measure the rockburst similarity for similar structural relationships of rockburst samples in Euclidean space. Design a novel indicator correlation constraint with multiple rockburst criteria to reduce the structure distortion of Euclidean space. Construct criterion-constrained graph structure data by integrating the perception and the constraint. Propose a multiscale graph convolutional neural network based on the data, which achieves accurate prediction of rockburst-intensity levels. [ABSTRACT FROM AUTHOR]

Titanium alloy, as an essential structural material, has gained significant attention across a multitude of applications owing to its exceptional overall performance. To obtain desirable mechanical performance, titanium alloys with lamellar microstructure through multi-step thermomechanical processing are indispensable for acquiring the globular morphology providing the balance between strength and ductility. Although the sluggish globularization process emerges as a critical challenge in recent overview, less related reviews aiming at rapid globularization kinetics has been reported. This paper reviews the novel methods of accelerating globularization kinetics including strain path changes, the addition of second-phase particles, and in situ globularization of advanced technology. In addition, the in-depth reasons such as the crystallographic and geometrical orientation of α phase, deviation angle from Burgers orientation relationship (BOR), slip systems of α phase, and co-evolution of α phase and β phase are exactly investigated. Furthermore, the simulation of globularization behavior is also summarized. Finally, the outlook and conclusion are provided to help point out the innovative directions for accelerating globularization kinetics. [ABSTRACT FROM AUTHOR]

The physical and mechanical properties of steel slag are comparable to those of natural aggregates. Reusing steel slag on a large scale as a substitute for natural aggregates can greatly reduce the need for natural resources and lower environmental harm, resulting in notable economic, environmental, and social benefits. However, the limited volume stability of steel slag remains a major drawback for its use in road construction. Therefore, controlling the volume stability of steel slag is essential for advancing its resource utilization. This paper reviews the properties of steel slag aggregates and mixtures, examines the main factors causing volume expansion, outlines current modification methods to reduce this expansion, and evaluates their respective strengths and weaknesses. Additionally, it proposes using surface modification techniques to improve the hydrophobicity of steel slag coarse aggregates, preventing the conditions that lead to expansion when exposed to water. [ABSTRACT FROM AUTHOR]

The paper presents the results of experimental studies of the effect of capacitive discharge of low-temperature plasma on the main characteristics of medical nonwovens based on polypropylene. The modes of plasma modification of nonwoven materials are established, at which an increase in absorbency of fabrics by 20.1–106.9% is achieved. [ABSTRACT FROM AUTHOR]

The paper compares two methods of the carbon fiber surface cleaning for their following modification and more complete realization of its physical and mechanical properties. The aim of the work is to identify the optimal method of pre-modification of carbon fiber, which in the future will be used as a filler of polymer binder in the composite. The results demonstrate successful surface cleaning, preservation of the level of physical and mechanical properties of fibers, increase of capillarity index. [ABSTRACT FROM AUTHOR]

Aluminum alloy lattice structures have important application prospects in modern engineering, construction, and lightweight manufacturing of key industrial components. The current "dot-by-dot" manufacturing method, using Wire Arc Additive Manufacturing (WAAM) with Cold Metal Transfer (CMT) as the heat source is an important way to achieve integrated manufacturing of large-sized and complex aluminum alloy lattice structure. In this paper, a detailed study is conducted on the design and prototype manufacturing of aluminum alloy dot-by-dot deposition process. Single strut design, "X lattice unit" design and geometric parameter design are explored through characterization of both microstructural and mechanical properties. The porosity, microstructure, and mechanical properties of the lattice strut in different areas were studied. The majority of pores were present between each layer deposition and accumulated in the line bridge area. A detailed discussion was then conducted on the formation mechanism of pores in aluminum alloy struts and the deposition strategy of the so-called X lattice unit. Finally, the manufacturing of a circular lattice structure composed of X lattice unit was achieved through deposition strategies control. [ABSTRACT FROM AUTHOR]

In this paper, new analytic solutions for the free vibration analysis of passive constrained layer damping (PCLD) beams, which are widely used in engineering to suppress vibrations and noise, are shown based on the symplectic method. The Hamiltonian-based governing equations and the new boundary condition expressions of PCLD beams are established by the original vector and its dual vector obtained by variation of the quasi Lagrangian function. The explicit solutions are obtained in the symplectic space in a direct, rigorous way without any trail functions under various boundary conditions. To verify the accuracy of the present method, the frequency parameters and loss factors of PCLD beams are compared with the results available in the literature. Comprehensive results under various boundary conditions are also tabulated for further benchmark use. [ABSTRACT FROM AUTHOR]

T-shunt is one of the commonly used components in gas-liquid two-phase flow pipeline. It has the function of separating, transmitting and carrying fluid. The junction of the T-shunt is the area of diversion and confluence in the pipeline. In this paper, we study the characteristics of the T-shunt in the cross-section of the fluid flow. A visual window is installed at the end of the experimental T-shunt section, and the fluorescence pipe's cross-section image captured through the visual window is illuminated by the laser above the pipe. The fluid velocity field is calculated using the PIV (Particle Image Velocimetry) method and the velocity field is classified and analyzed. The result shows that there are great differences in fluid flow patterns on the cross-sections of the T-shunt at different positions, and great differences in the fluid erosion and corrosion of the pipe wall at different positions away from the T-shunt outlet. [ABSTRACT FROM AUTHOR]

Semi-supervised learning approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the generative model framework with a missingness mechanism for unclassified observations, as introduced by Ahfock and McLachlan (Stat Comput 30:1–12, 2020). We show that in a partially classified sample, a classifier using Bayes' rule of allocation with a missing-data mechanism can surpass a fully supervised classifier in a two-class normal homoscedastic model, especially with moderate to low overlap and proportion of missing class labels, or with large overlap but few missing labels. It also outperforms a classifier with no missing-data mechanism regardless of the overlap region or the proportion of missing class labels. Our exploration of two- and three-component normal mixture models with unequal covariances through simulations further corroborates our findings. Finally, we illustrate the use of the proposed classifier with a missing-data mechanism on interneuronal and skin lesion datasets. [ABSTRACT FROM AUTHOR]

Conceptual Engineering, the practice of stipulating a change in the meaning of a word in order to improve it in some fashion, for some end, has proved a popular topic among philosophers of language in recent times. Deutsch (Philos Stud 177:3935–3957, 2020) has argued that it has received an undue degree of interest since its implementation falls onto one of the horns of a dilemma: either the change to be effected is in the global semantic meaning of the given word/concept, in which case it is infeasible, or it is merely in the local speaker-meaning, in which case it is largely trivial. I suggest, however, that there is a deeper, more troubling, objection to Conceptual Engineering. My criticism does not strictly take the form of a dilemma, since some cases may fall upon both horns at once and others may be able to squeeze through the gap, but it does involve highlighting two potential weaknesses of the project: one of those, as with Deutsch, is the danger of triviality, although it is not concerned only with the division between semantic and speaker meaning. It seems clear that there are cases where a particular authority or legislature has the capacity to alter a word's legal meaning; others where certain groups may discuss the best way to use a technical term, each putting forward their preferred suggestions; and individual speakers are at liberty to choose to use a word with a particular intention. None of this is new, nor controversial, and, thus, is largely trivial. In all cases, but particularly where meanings are manipulated without due authority, inexplicitly, or in order to further a particular theoretical conception, however, there is a strong chance that the engineer will be led to commit one of the fallacies of language to which philosophy is particularly vulnerable (Hinton Evaluating the language of argument, Springer, Cham, 2021). These include fallacies of vagueness, equivocation, and persuasive definition. In this paper, I use examples to illustrate the likely fallaciousness of conceptual redefinitions and highlight in particular the 'Philosophical Fallacy', outlined by Nelson (A theory of philosophical fallacies.Translated by Leal F, Carus D. Springer, Cham 2016). I conclude that, as a method for philosophers, Conceptual Engineering is, in broad terms, either trivial or fallacious. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between conceptual and embodied reasoning in engineering work. In the last decade across multiple research projects on pipeline engineering, we have observed only a few times when engineers have expressed embodied or sensory aspects of their practice, as if the activity itself is disembodied. Yet, they also often speak about the importance of field experience. In this paper, we look at engineers' accounts of the value of field experience showing how it works on their sense of what the technology that they are designing looks, feels, and sounds like in practice, and so what this means for construction and operation, and the management of risk. We show how office-based pipeline engineering work is an exercise in embodied imagination that humanizes the socio-technical system as it manifests in the technical artifacts that they work with. Engineers take the role of the other to reason through the practicability of their designs and risk acceptability. [ABSTRACT FROM AUTHOR]

Date palm (Phoenix dactylifera) is an agricultural crop, which plays an important role as a source of nutrition in the day-to-day life of people. Date palm cultivation, processing industries, and consumption generate tons of wastes. One of these wastes is date seed, which represents 10% of the total weight of date fruit. The utilization of date seed waste is a research topic of interest for many applications such as cosmetic, nutritional, and engineering. In this review, the different methods used for preparing date seeds' powder and extracting its oil are surveyed. A comprehensive review of the physical and chemical properties of the date seed powder and oil is provided in addition to the employed characterization methods. This paper also reviews the utilization of date seed waste in the most common engineering applications including biofuels, composites, and adsorbent applications. The paper also highlights the research limitation and future research potentials of the utilization of date seed for several engineering applications. [ABSTRACT FROM AUTHOR]