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The article is a response to a critique of the author's paper on conceptual engineering (CE)which appeared in the October 2021 issue of Academy of Management Review. The original paper, "Optimizing concepts: Conceptual engineering in the field of management" by P.T. Makowski, outlined a new perspective for theory building by showing how methods of conceptual engineering enable systematic development of conceptual competences. The critique by Oswick, et al. argues that CE is difficult to implement as a tool to optimize concepts especially in precision engineering and CE cannot be a normative enterprise. In this rebuttal, Makowski addresses these two points.

In order to obtain stable and reliable configuration transformation ability, reliability optimization design is regarded as an effective way to reduce the probability of kinematic function failure for the constrained metamorphic mechanism. Based on the structural composition principle of multi-configuration source metamorphic mechanism that can operate in an under-actuated state, the modularized calculation methods are established for the force analysis of augmented Assur groups including metamorphic kinematic joints. According to the equivalent resistance gradient model of metamorphic mechanisms, with considering the uncertainties in the link dimensions, masses, and compliance parameters et al., a probabilistic evaluation method for describing the configuration transformation ability of the constrained metamorphic mechanism is established. Based on reliability evaluation and reliability sensitivity analysis, a reliability optimization design method for improving the configuration transformation ability is proposed, and then the optimization design is carried out for tolerances of random variables focusing on those structural parameters with higher reliability sensitivity, so that the optimized results can satisfy the requirements of both reliability and economic simultaneously. Finally, the feasibility and effectiveness of the proposed method is verified by the illustration of a paper folding metamorphic mechanism. The research provides the foundation of reliability design of metamorphic mechanisms to obtain the high-probability repeated execution ability of configuration transformation, it also has theoretical and practical significance to promote the engineering application of metamorphic mechanisms. [ABSTRACT FROM AUTHOR]

When planning and conducting a Mission Engineering (ME) study, it is important to have a complete, correct, and coherent model of the mission architecture. The Unified Architecture Framework (UAF) has been found to be effective for this purpose. The OUSD (R&E) Mission Integration office is exploring how to use UAF for their ME architectures. This paper will explore some of the required modeling features and constructs that will enable this to occur. The paper "Implementing Mission Engineering with UAF" was presented at a previous conference and this paper will expand on that presentation and will discuss additional work that has been accomplished since then. This paper will also explore the proposed extensions for UAF to better support ME. We created a prototype model using the Battle of Hoth from Star Wars as a proof of concept for these modeling extensions and used the process and ME concepts defined in the Mission Engineering Guide (MEG). Since then there have been several concepts that were explored such as compatibility with the Model‐Based Acquisition (MBAcq) approach, recent initiatives from the Office of the Assistant Secretary of Defense for Mission Capabilities, Enterprise Systems Engineering (ESE) process and methods, detailed resource engagement, use of different modeling languages (e.g., Systems Modeling Language (SysML), SysML v2 and UAF v2), Effects and Outcomes, variety of measures, additional attributes/stereotypes such as differentiation between enemy/friendly/neutral ‐ Blue Force, Red Force, etc., provenance/confidence of enemy resources, and so forth. This paper will summarize the research and modeling done to date and explore these additional concepts as well as new ideas introduced in the MEG v2. [ABSTRACT FROM AUTHOR]

Food information engineering relies on statistical and AI techniques (e.g., symbolic, connectionist, and neurosymbolic AI) for collecting, storing, processing, diffusing, and putting food information in a form exploitable by humans and machines. Food information is collected manually and automatically. Once collected, food information is organized using tabular data representation schema, symbolic, connectionist or neurosymbolic AI techniques. Once collected, processed, and stored, food information is diffused to different stakeholders using appropriate formats. Even if neurosymbolic AI has shown promising results in many domains, we found that this approach is rarely used in the domain of food information engineering. This paper aims to serve as a good reference for food information engineering researchers. Unlike existing reviews on the subject, we cover all the aspects of food information engineering and we linked the paper to online resources built using Open Research Knowledge Graph. These resources are composed of templates, comparison tables of research contributions and smart reviews. All these resources are organized in the "Food Information Engineering" observatory and will be continually updated with new research contributions. [ABSTRACT FROM AUTHOR]

We developed two symmetrical green MR‐TADF emitters, BpIC‐DPA and BpIC‐Cz, based on the rigidification and regulation of molecular orbitals (MO) engineering strategies. Both emitters exhibited pure green emission with narrow FWHM below 25 nm and high PLQY over 84%. Among the fabricated OLEDs, BpIC‐Cz‐device showed maximum external quantum efficiency (EQEmax) of 25.7% with alleviated efficiency roll‐off characteristics (EQE1000nits = 24.3%) and longer device‐lifetime (LT90) of 291 h at the initial luminance of 1000 nits. [ABSTRACT FROM AUTHOR]

Engineers make things, make things work, and make things work better and easier. This kind of knowledge is crucial for innovation, and much of the explicit knowledge developed by engineers is embodied in scientific publications. In this paper, we analyze the evolution of publications and citations in engineering in a middle-income country such as Mexico. Using a database of all Mexican publications in Web of Science from 2004 to 2017, we explore the characteristics of publications that tend to have the greatest impact; this is the highest number of citations. Among the variables studied are the type of collaboration (no collaboration, domestic, bilateral, or multilateral), the number of coauthors and countries, controlling for a coauthor from the USA, and the affiliation institution of the Mexican author(s). Our results emphasize the overall importance of joint international efforts and suggest that publications with the highest number of citations are those with multinational collaboration (coauthors from three or more countries) and when one of the coauthors is from the USA. Another interesting result is that single-authored papers have had a higher impact than those written through domestic collaboration. [ABSTRACT FROM AUTHOR]

This document provides a summary of various research papers covering a wide range of topics. The papers explore subjects such as the properties of glass fiber reinforced polyamide 6, new materials for nuclear reactor fuel cladding, natural fiber composites for noise control, regenerative anti-lock braking systems for electric vehicles, vehicle sensing and localization technologies for autonomous driving, UAV communications, linear profile involute gears, digital marketing capabilities in Indonesian firms, factors influencing confidence in STEM practices among preservice teachers, tangible user interfaces for physical therapy in children, intelligent interfaces for personalized recommendations in tourism websites, paper-based glucose sensors for non-invasive glucose monitoring, top-priority industries in Russian regions, scalable processes for producing analytical grade sodium chloride, functionalization of cotton fabric using silver nanoparticles, catalytic cracking of palm fatty acid distillate for biofuel production, and kinetic models for enzymatic biodiesel production. These papers provide valuable insights into various scientific and technological advancements in their respective fields. [Extracted from the article]

Background: Training both pre – and in-service teachers who are capable of planning and enacting engineering design–based science instruction are critical for integrating engineering into K-12 science curricula. Purpose: This study investigates pre-service teachers' efforts to integrate engineering into their lesson plans. Sample: 13 pre-service chemistry teachers, enrolled in a 13-week engineering-infused integrated STEM course, were the participants of the study. Design and Method: Considering the purpose of the study, qualitative design was deemed to be the best fit including qualitative data collected through lesson plans and reflection papers. To assist pre-service teachers' learning, 13-week engineering-infused integrated STEM was started with the information about the engineering profession and how engineers work. Additionally, pre-service teachers engaged in six integrated STEM activities including engineering design cycle. After each activity, the participants were asked to write a reflection paper on what they learned about engineering, the engineering design process, and integrated STEM. Finally, the participants had three chances for analyzing strong and weak integrated STEM lesson plans and planning integrated STEM lesson planning experience with mentors. The pre-and post-course lesson plans (n=26) were analyzed using existing codes from the related literature, which is a deductive coding process. Later, an inductive approach was used to determine the categories of engineering integration into the lesson plans. Four categories (ranging from a complete engineering lesson to a science lesson with no engineering link) were formed. Reflection papers were used to support the findings. Result: The results showed that none of the pre-plans included engineering. However, after participated in a 13-week integrated STEM course, all post-course lesson plans integrated engineering through the use of engineering design processes to some extent. Conclusion: The training with different experiences seems promising for pre-service teachers to learn engineering integration into STEM lesson plans. [ABSTRACT FROM AUTHOR]

Background and Purpose: As a growing number of instructional units have been developed to promote young children's scientific and engineering practices (SEPs), understanding how to evaluate and assess children's SEPs is imperative. However, paper-and-pencil assessments would not be suitable for young children because of their limited reading and writing skills. To explore the assessments for SEPs available for young children aged 3–8 years, this study reviewed assessments of young children's SEPs reported in empirical studies, and analysed the characteristics of these assessments to delineate how young children's SEPs have been measured. Methods: We followed the procedures of a systematic review proposed by Zawacki-Richter et al. (2020). The EBSCOhost database was used to gather empirical studies in education and psychology. A total of 46 articles published from 2003 to 2020 met the inclusion criteria and were reviewed. Findings: The findings indicated that of the eight SEPs suggested by the National Research Council (2012), Analysing and interpreting data was assessed the most, followed by Using mathematics and computational thinking, Constructing explanations and designing solutions, and Planning and carrying out investigations. A majority of assessments were designed for children of 4, 5, and 6 years old and used paper-based visualizations and real objects to present the tasks and items. Additionally, due to the verbal or performance nature of the SEPs, the assessments collected different types of data as evidence to evaluate children's SEPs. Performance-based assessments were the most common, followed by multiple-choice, ranking, and oral responses. Conclusion: The findings of the reviewed assessments revealed a variety of performance expectations of SEPs and suggested that some SEPs are measurable and developmentally appropriate for young children. Also, the availability of assessments is uneven in different types of SEPs, and more assessments for information communication and modelling practices are needed. [ABSTRACT FROM AUTHOR]

The concept of digital twins in bridge engineering is still vague and even confused with the Bridge Information Model (BrIM). Therefore, this study provides a detailed review of 42 papers related to digital twins in bridge engineering, focusing on a proper definition, key features and creation techniques for bridge digital twin (BDT). The paper also compares BDT and BrIM from the perspectives of their elements, features, fidelity, services provided, and degree of development. The applications of BDT at different life cycle stages are identified, and the related technologies are analyzed in detail. The results show that the research clusters of BDT are divided into geometric model generation, finite element model updating, and management and are focused on the operation and maintenance phase while lacking attention in the design and construction phase. Besides, a reference framework of BDT based on the life cycle of bridges is proposed, and directions for future research are suggested. [ABSTRACT FROM AUTHOR]

Paper‐based strain sensors (PSS) have broad prospects in disposable products due to their low cost and easy degradation as environmentally friendly materials. Herein, a strain sensor made of a laser‐induced carbonization electrode is created by direct laser writing with filter paper. The conductivity and gauge factor (GF) of this strain sensor are improved by adding lignin and applying strain engineering. This enables the sensor to simultaneously satisfy high sensitivity (GF ≈ 408 and 91) for weak tension and compression strain, respectively, and with long‐term reliability. The tensile strain GF factors of up to 201 are possible, even with a weak tensile strain of 0.00088%. Furthermore, the paper‐based sensor for monitoring physiological activities like finger gestures, pulsing, swallowing, and eye blinking is demonstrated. The facile fabrication and superior performance of PSS fabricated by direct laser writing with strain engineering may pave the way for promising applications of flexible, portable, and wearable electronic devices. [ABSTRACT FROM AUTHOR]

With the launch of OpenAI's ChatGPT, large language models have garnered significant attention, and applications based on these models have proliferated. A critical challenge has emerged: how to rapidly enhance the capabilities of general LLMs in specialized domains. Compared to fine-tuning and other methods, prompt engineering has proven to be a cost-effective approach for improving the performance of LLMs on specific tasks, yielding remarkable results. However, current prompt example construction methods are numerous and lack a universally applicable approach that spans different models and tasks. Furthermore, existing research is predominantly tested and evaluated on a limited range of specific datasets, failing to explore the broader impact of these methods on a wider array of tasks. This paper proposes a prompt example construction method based on clustering and semantic similarity, which combines clustering algorithms with semantic similarity techniques to significantly improve the quality of prompt examples. In comparative tests conducted on six LLMs and seven datasets, the overall accuracy and stability of the proposed method significantly outperforms five other common methods, demonstrating broad applicability and the potential to enhance the output performance of all LLMs. Through comparative experiments, this paper also identifies that as the parameter scale of LLMs increases, the improvement effect of the prompt example construction method on LLM output performance tends to diminish. Additionally, diversified prompt example sets provide a more pronounced enhancement in LLM output performance. [ABSTRACT FROM AUTHOR]

This paper discusses two teachers' experiences implementing a phenomenon and problem-driven curriculum for the first time in two kindergarten classes. It describes how teachers shifted their teaching to support students' collaborative sensemaking about phenomenon. It also discusses how the teachers helped students overcome anxiety about uncertainty when figuring out phenomenon and during an engineering design challenge. Throughout the paper teachers offer detailed descriptions of the adjustments they made to their instructional methods and how those changes in pedagogy impacted students during the curriculum. Impacts to students' overall sense of academic agency are also discussed. Finally, the paper addresses real-world concerns facing teachers transitioning to phenomenon and problem-driven instruction, including the amount of class time allocated to science learning and the amount of content required by the Next Generation Science Standards. [ABSTRACT FROM AUTHOR]

Modelica is a simulation language that is able to model systems from many different fields of scientific and engineering contexts, which can coexist in individual models. Modelica Standard Library is an open‐source library that contains many ready‐to‐use open‐source models built in Modelica language but does not have any code to determine transmission line parameters from its geometry, thus limiting its usability for power system studies. This paper first describes important issues arising when modelling power transmission lines, then presents how they can be solved using Modelica code. Readers of this paper will, therefore, be able to model power lines from the geometry of conductors using any of the existing (some freely available) Modelica‐capable simulation tools. [ABSTRACT FROM AUTHOR]

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM's application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM's evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry. [ABSTRACT FROM AUTHOR]

This paper presents simple and intuitive strategies for effective online assessment of a freshman engineering course. The proposed strategies for unproctored online exams include creating multiple sets with identical options, using image-format questions, maintaining short duration of exam, and employing a rotational assignment. Unlike a single-set paper where the class average was found to be disproportionately high, the proposed strategies helped in correctly restoring the class average. Feedback and results from 141 students show a significant statistical difference in the scores obtained using the proposed multi-set quiz framework, as opposed to conventional single-set papers. The paper also presents a simple setup of remote proctoring using dual-video and screensharing to replicate in-person classroom exams without the need of commercial proctoring services. [ABSTRACT FROM AUTHOR]

Introduction: Augmented reality (AR) is a transformative technology that enhances teaching and learning by blending virtual and real environments. While the potential of AR to improve visualization, interaction, and student engagement in engineering education is recognized, its application across various engineering disciplines remains underexplored. This study systematically investigates the use of AR in engineering fields, highlighting its educational impact and identifying gaps for future research. Methods: A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, analyzing 67 peer-reviewed papers on AR in engineering education. Content analysis was employed to assess AR's effectiveness in enhancing visualization, interaction, and motivation. A bibliometric analysis identified key AR tools, research trends, geographic distribution, and interdisciplinary collaboration. Results: AR is extensively applied in civil and mechanical engineering, where tools such as Unity 3D significantly improve visualization and interaction. AR enhances student engagement and comprehension, particularly in complex areas like construction design. However, its adoption in other engineering disciplines remains limited. The analysis also shows the positive impact of AR on motivation and learning outcomes despite challenges such as technical limitations and insufficient training for educators. Conclusion: Although AR is gaining traction in engineering education, broader adoption is hindered by technical challenges and the need for better curriculum integration. Future research should address these barriers and explore AR's potential in underutilized engineering disciplines to maximize its educational benefits. [ABSTRACT FROM AUTHOR]

The era of artificial Intelligence (AI), the landscape of language training, especially in the vicinity of Engineering English, is gift technique a transformative shift. This paper offers a unique data-driven technique to Engineering English training, stronger via modern day algorithms, to address the precise stressful situations and opportunities furnished through AI enhancements. Our approach integrates algorithmic answers with conventional language coaching methodologies to create a dynamic, adaptive gaining knowledge of environment sustainable-made to the unique wishes of engineering students. Primary method is the usage of AI-driven analytics to investigate students' language talent and studying patterns. Using leveraging natural language processing and machine mastering algorithms, we are able to customize the curriculum and offer focused practise that aligns with each student's linguistic and technical level. This consists of the development of specialised vocabulary, comprehension of technical files, and effective verbal exchange in expert engineering contexts. A tremendous element of this study is the collection and evaluation of data on student performance and engagement. This information-driven feedback loop allows non-prevent refinement of coaching techniques and substances, ensuring that the instructional content material cloth remains relevant and effective in the unexpectedly evolving discipline of engineering. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of Augmented Reality Technology (ART) in Science Education and Engineering Technology. Science Education and Engineering Technology go hand in hand as without scientific studies applications cannot exist. The term science is well known for knowledge-based society. It has a vast scope in this world. In every step of human life, the terms Science and Technology are both the two sides of one coin. The main objectives of this review are to - 1. Understand the concept of Augmented Reality Technology (ART). 2. Identify the different types of Augmented Reality Technology (ART). 3. Find out the correlation between Augmented Reality Technology (ART) and Science Education. 4. Identify the various strategies of Augmented Reality Technology (ART) for Science Education. The rising prominence of Augmented Reality (AR) technology stems from its ability to deliver captivating and interactive experiences to users. The current landscape of AR technology is characterized by its dynamic and fast-paced evolution, with numerous applications emerging across industries like gaming, retail, education, and healthcare. By overlaying digital content onto the physical world, AR technology creates a blended reality environment that offers engaging and immersive experiences to users. In particular, AR has the potential to revolutionize the methods through which students comprehend and interact with scientific and engineering concepts. It presents an opportunity for future research to delve deeper into exploring the extensive capabilities of this technology and unlock its full potential in educational contexts. [ABSTRACT FROM AUTHOR]

This article studies the migration and settlement laws of backflow proppants after fracturing tight sandstone. This paper proposes a fitting method based on a multi-task learning network to address the issue of interference from multiple physical parameters during the transport and settlement processes of proppants. This method can effectively handle multi-dimensional interference factors and fit the mapping logic of multiple engineering parameters to transport patterns through the continuous correction of multi-layer networks. We first introduce the characteristics of tight sandstone reservoirs and their important value in mining, as well as the status of current research on the migration and settlement laws of proppants at home and abroad. Based on this, we then deeply analyze the sedimentation rate model of proppants in tight sandstone backflow and the equilibrium height of proppants under multiple factors of interference while considering the distribution characteristics of proppants. In order to more accurately simulate the transport and settlement laws of proppants, this paper introduces a multi-task learning network. This network can comprehensively consider multi-dimensional parameters, learn the inherent laws of data through training, and achieve accurate fitting of the transport and settlement laws of proppants. This study trained and tested the model using actual production data, and the results showed that the proposed model can fit the input–output relationship well, thus effectively supporting the study of proppant transport and settlement laws. [ABSTRACT FROM AUTHOR]

Reconfigurable Manufacturing Systems (RMS) have gained attention in the aerospace industry in the past years, as post-pandemic context shows drastic production capacity changes and new environmental regulations to which it must adapt quicker than before to maintain competitiveness. Nevertheless, current RMS design methods have not thoroughly considered this industry specificities, nor industrial resources requirements in the current concurrent design practice. These limitations have been identified in several recent research works. Ontology-based Engineering (OBE) systems can stand overly complex collaborative design processes involving multidisciplinary stakeholders and various digital tools, integrating different levels of decision. Models for Manufacturing (MfM) is an OBE methodology aiming to enable industrial design and decision-making in manufacturing by preserving the company knowledge in ontology models, usable as knowledge base to generate and integrate the aircraft and manufacturing systems design. This paper presents an MfM application for RMS design in aerospace, introducing innovative design concepts that allow implementing RMS in a collaborative engineering process of an aerospace product. An implementation is shown designing the RMS of a model aircraft family to illustrate the concepts introduced and considerations are given to transfer this knowledge base into an OBE system to support complex real-life applications. [ABSTRACT FROM AUTHOR]

Endo-1,4-β-xylanases belonging to the glycoside hydrolase (GH) 11 family hydrolyze the β-1,4-glycosidic linkages in the xylan backbone to convert polymeric xylan into xylooligosaccharides. GH11 xylanases play an essential role in sugar metabolism and are one of the most widely used enzymes in various industries, such as pulp and paper, food and feed, biorefinery, textile, and pharmaceutical industries. pH is a crucial factor influencing the biochemical properties of GH11 xylanase and its application in bioprocessing. For the optimal pH shifting of GH11 xylanase in industrial applications, various protein engineering studies using directed evolution, rational engineering, and in silico approaches have been adopted. Here, we review the functions, structures, and engineering methods developed for the optimal pH shifting of GH11 xylanases. The various GH11 engineering techniques and key residues involved in pH shifting are discussed based on their crystal and modeled structure. This review provides an overview of recent advancements in the characterization and engineering of GH11 xylanases, providing a guide for future research in this field. [ABSTRACT FROM AUTHOR]

The article provides a summary of the QiR International Conference 2023, which covers a wide range of topics related to sustainability and technological advancements. The papers presented at the conference offer valuable insights and potential solutions in areas such as breast cancer treatment, energy-saving performance of office buildings, waste management, earthquake estimation, underwater pressure sensors, road safety, and plastic waste management. The conference highlights the significance of international collaboration and knowledge exchange in creating a sustainable future for both humans and the environment. [Extracted from the article]

This document is a summary of a special issue of the journal "Materials" focused on the topic of surface engineering for wear, corrosion, and erosion prevention in metallic and ceramic materials. The special issue includes twelve scientific papers covering various aspects of the operational performance and properties of metallic and ceramic materials. The papers discuss topics such as the effects of peening treatments, corrosion resistance, tribological behavior, and wear improvement through microstructural modification and the deposition of wear-resistant coatings. The special issue received significant interest from authors and readers, leading to the announcement of a second volume on the same topic. The summary acknowledges the need for experimental data in surface engineering to understand the wear and performance of engineering materials. [Extracted from the article]

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This paper presents the evaluation of the alpha version of a gamified tool called Call for Papers: The Game (CfP:TG), specially designed for teaching scientific writing in the training of future engineers. A non-probabilistic convenience sampling was carried out with the participation of engineering students from a Peruvian public university. The short version of the user experience questionnaire (UEQ) was applied, and usability was qualitatively evaluated. The main results indicate that the Pragmatic Quality of CfP:TG is in the neutral range with a value of 0.729, and the Hedonic Quality receives a positive evaluation with a value of 1.089. [ABSTRACT FROM AUTHOR]

Cost engineering capabilities are becoming increasingly important for the competitiveness of industrial firms, especially for engineer to order products (ETOPs). Despite this relevance, the literature on the use of advanced data-driven methodologies, such as machine learning (ML), for early cost estimation (CE) of ETOPs is quite sparse. Furthermore, ML has still seen little use in real industrial applications due to several challenges. Accordingly, the objective of this paper is threefold: (a) to develop a solid early CE approach for ETOPs, including feature selection; (b) to investigate the benefits of adopting ML for ETOPs' CE; (c) to identify how ML can be introduced into real industrial context with little knowledge on ML. Long action research has been carried out with a large industrial company that produces Oil & Gas ETOPs. We observed how ML facilitates the exploration of the relationships between the choices of early design stages and the CE. ML algorithms also allowed to both capture the high variability of the data and test different combinations of cost drivers in very effective ways. The project resulted in an accurate CE framework with an iterative feature selection process and an approach for introducing ML into a real industrial context. [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]

In nearly 60 years of research on exoskeleton design, manufacturing, testing, and application, few researchers study what goes into the process of designing these devices that augment human performance. Exoskeleton evaluation and selection, however, remains under--researched and ultimately unclear given the large number of decision criteria with possible interdependencies. In this paper, an analytic network process (ANP) model was structured and used due to its ability to consider multi-levels of interdependencies between decision criteria while encapsulating the advantages of expert opinion. A panel of experts categorized 55 engineering design metrics into five categories. Discussion sessions were conducted to identify the metrics' interdependencies yielding an unweighted, weighted, and limit priority super-matrix from the ANP model. The results of the ANP model provide an analysis of how interdependencies affect the importance of design metrics in exoskeleton design. The results of the proposed model were compared to a previous study where a panel of 40 experts individually ranked the importance of engineering design metrics without considering interdependencies. The use of the ANP can provide a stronger, more holistic approach to the inherently multi-criteria decision-making involved in exoskeleton design. [ABSTRACT FROM AUTHOR]

Classical viscoelastic models usually only consider the motion of chain segments and the motion of the entire molecular chain; therefore, they will cause inevitable errors when modeling self-healing vitrimer materials with many group movements. In this paper, a group-enriched viscoelastic model is proposed for self-healing vitrimers where the group effect cannot be neglected. We synthesize a specific damping vitrimer with many dangling chains, surpassing the limited loss modulus of conventional engineering materials. Due to the dangling chains, the damping capability can be improved and the group effect cannot be neglected in the synthesized damping vitrimer. The group-enriched viscoelastic model accurately captures the experimental damping behavior of the synthesized damping vitrimer. Our results indicate that the group-enriched viscoelastic model can improve the accuracy of classical viscoelastic models. It is shown that the group effect can be ignored at low frequencies since the chain segments have sufficient time for extensive realignment; however, the group effect can become significant in the case of high frequency or low temperature. [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]

In this paper, we present a two-phase multimodal optimization model designed to efficiently and accurately identify multiple optima. The first phase employs a population-based search algorithm to locate potential optima, while the second phase introduces a novel peak identification (PI) procedure to filter out non-optimal solutions, ensuring that each identified solution represents a distinct optimum. This approach not only enhances the effectiveness of multimodal optimization but also addresses the issue of redundant solutions prevalent in existing algorithms. We propose two PI algorithms: HVPI, which uses a hill–valley approach to distinguish between optima, without requiring prior knowledge of niche radii; and HVPIC, which integrates HVPI with bisecting K-means clustering to reduce the number of fitness evaluations (FEs). The performance of these algorithms was evaluated using the F-measure, a comprehensive metric that accounts for both the accuracy and redundancy in the solution set. Extensive experiments on a suite of benchmark functions and engineering problems demonstrated that our proposed algorithms achieved a high precision and recall, significantly outperforming traditional methods. [ABSTRACT FROM AUTHOR]

Featured Application: For industrial-scale extinguishment, natural processes such as acoustic wave propagation can be used. As proven, a technology described in the article using modulated and unmodulated waves can be applied to extinguish flames, as well as to control them. The results presented in the article are limited to extinguishing candle flames, but in the long term, this technique can be used to extinguish flames (especially firebreaks) from various materials (Classes C and B). This inexpensive, versatile, and environmentally safe method can provide a supporting or additional means of fire protection, especially useful in the first stage of fire occurrence. This technique appears to be environmentally friendly (it does not leave stains and does not emit chemicals). Moreover, acoustic waves can be applied to reduce the intensity of smoke and harmful substances in the ambient air, the presence of which is the result of fires (future plans). Due to the consequences of fires, new and environmentally friendly firefighting techniques are constantly being sought. There are many methods of extinguishing flames around the world. One of them is a technique that uses acoustic waves for extinguishing, which can be seen as repeated sequences of molecular compression and dilation (acoustic waves transfer energy due to the movements of molecules and atoms). This research shows a new approach to the extinguishing of flames. In practice, the extinguishing capabilities of low-frequency modulated and unmodulated acoustic waves were tested on a laboratory station, the main component of which was a high-powered acoustic extinguisher (the nominal power was equal to 1700 W). A B&C 21DS115 woofer was applied as a sound source. A Rigol DG4102 and a Proel HPX2800 were used as an acoustic generator with a modulator and as a power amplifier, respectively. In this paper, the presented results are limited to extinguishing candle flames. The tests made it clear that flames can be extinguished using properly generated and directed acoustic waves. As the results indicate, it becomes possible to effectively extinguish flames with both low-frequency modulated and unmodulated acoustic waves, which brings many benefits. [ABSTRACT FROM AUTHOR]

Fiber-reinforced thermoplastic composites (FRTPs) are gaining increasing attention and widespread use in engineering applications due to their high specific strength and stiffness, excellent toughness, and recyclability. The mechanical properties of these composites are closely tied to their crystallization process, making it crucial to accurately describe this phenomenon. Existing theoretical models for analyzing the non-isothermal crystallization of thermoplastic composites often face challenges relating to the complexity of obtaining multiple parameters and the difficulty of achieving a final relative crystallinity of 1. To address these issues, this paper introduces a novel functional form of the crystallization rate parameter K(T), tailored for engineering applications, and proposes an improved Mampel model. This model assumes K(T) to be zero before the onset of crystallization and also to be linearly dependent on temperature thereafter, ensuring that the final relative crystallinity reaches 1. The model requires only two easily accessible parameters: the initial crystallization temperature (Ts) and the linear slope (k). The simplicity of the model makes it particularly well suited to engineering applications. This provides a straightforward and effective tool for describing the non-isothermal crystallization kinetics of fiber-reinforced thermoplastic composites. [ABSTRACT FROM AUTHOR]

It gives me great pleasure to explore and synthesize the unique and synergistic contributions of 14 articles for this important special issue, as well as to set future directions for the field of engineering education. Given that evidence‐based syntheses help shape key research advances in the medical, psychological, and educational fields, we sought papers that used state‐of‐the‐art methods for systematic reviews and meta‐analyses to explore fundamental topics in engineering education. We received many excellent manuscripts, and eventually accepted 14 for this special issue. They cover a range of topics, including reading and writing in engineering, the use of concept maps for assessment in engineering, and other key topics. Most papers used systematic review methods, while three used scoping reviews and bibliometric methods. However, none used meta‐analysis. Although this is surprising, it presents an opportunity to call for more rigorous experimental and quantitative research on key topics in engineering education. Experimental studies can uncover cause and effect relationships and enable researchers to draw inferences more easily. While I advocate for all traditions of rigorous research, the collection of reviews in this special issue points to a pressing need for more experimental studies in the field of engineering education. [ABSTRACT FROM AUTHOR]

Due to the lack of essential understanding of asphalt foaming behavior, engineering problems such as mix design failure and unstable performance of foamed asphalt cold recycling mixtures are prone to occur. This paper develops a novel asphalt foaming performance testing system and proposes new indicators to evaluate the effectiveness of asphalt foaming. The 3D spatial coordinates of different points on the foamed asphalt surface and the height of the foamed asphalt corresponding to these coordinates can be continuously monitored by binocular ranging equipment. By analyzing these dynamic parameters, the application effect of binocular ranging technology, the expansion characteristics and dynamic evolution rules of foamed asphalt in the spatial domain are analyzed. Results showed that by a combined use of the designed binocular ranging system and new evaluation indicators, the foaming ability and post-foaming stability of asphalt can be accurately assessed by comprehensively reflecting the expansion characteristics of foamed asphalt. To achieve a more efficient mix design of foamed asphalt cold recycling mixture, the correlation between asphalt foaming indicators and the road performance of the mixture could be an important focus of the next stage. [ABSTRACT FROM AUTHOR]

In this paper, the unconventional light‐matter interactions between giant atoms and structured baths (i.e., lattices) are studied with either Hermitian or non‐Hermitian next‐nearest‐neighbor coupling terms. Essentially different dynamics of the atoms and the propagating field in the Hermitian and non‐Hermitian cases is revealed, which can be further engineered by tuning parameters such as the atomic transition frequency and the (synthetic) magnetic field associated to the coupling terms. The next‐nearest‐neighbor couplings play an important role in controlling the emission direction and the field distribution in the lattice, thus providing opportunities for tailoring exotic dipole–dipole interactions. The results in this paper have potential applications in, e.g., engineering unconventional quantum networks and simulating quantum many‐body systems. [ABSTRACT FROM AUTHOR]

This paper introduces the Quantum Chimp Optimization Algorithm (QU-ChOA), which integrates the Chimp Optimization Algorithm (ChOA) with quantum mechanics principles to enhance optimization capabilities. The study evaluates QU-ChOA across diverse domains, including benchmark tests, the IEEE CEC-06–2019 100-Digit Challenge, real-world optimization problems from IEEE-CEC-2020, and dynamic scenarios from IEEE-CEC-2022. Key findings highlight QU-ChOA's competitive performance in both unimodal and multimodal functions, achieving an average success rate (SR) of 88.98% across various benchmark functions. QU-ChOA demonstrates robust global search abilities, efficiently finding optimal solutions with an average fitness evaluations (AFEs) of 14 012 and an average calculation duration of 58.22 units in fire detection applications. In benchmark tests, QU-ChOA outperforms traditional algorithms, including achieving a perfect SR of 100% in the IEEE CEC-06–2019 100-Digit Challenge for several functions, underscoring its effectiveness in complex numerical optimization. Real-world applications highlight QU-ChOA's significant improvements in objective function values for industrial processes, showcasing its versatility and applicability in practical scenarios. The study identifies gaps in existing optimization strategies and positions QU-ChOA as a novel solution to these challenges. It demonstrates QU-ChOA's numerical advancements, such as a 20% reduction in AFEs compared to traditional methods, illustrating its efficiency and effectiveness across different optimization tasks. These results establish QU-ChOA as a promising tool for addressing intricate optimization problems in diverse fields. [ABSTRACT FROM AUTHOR]

Image fusion of infrared and visible images to obtain high-quality fusion images with prominent infrared targets has important applications in various engineering fields. However, current fusion processes encounter problems such as unclear texture details and imbalanced infrared targets and texture detailed information, which lead to information loss. To address these issues, this paper proposes a method for infrared and visible image fusion based on a specific dual-attention mechanism (SDAM). This method employs an end-to-end network structure, which includes the design of channel attention and spatial attention mechanisms. Through these mechanisms, the method can fully exploit the texture details in the visible images while preserving the salient information in the infrared images. Additionally, an optimized loss function is designed to combine content loss, edge loss, and structure loss to achieve better fusion effects. This approach can fully utilize the texture detailed information of visible images and prominent information in infrared images, while maintaining better brightness and contrast, which improves the visual effect of fusion images. Through conducted ablation experiments and comparative evaluations on public datasets, our research findings demonstrate that the SDAM method exhibits superior performance in both subjective and objective assessments compared to the current state-of-the-art fusion methods. [ABSTRACT FROM AUTHOR]

The safety of chemical processes is of critical importance. However, traditional fault monitoring methods have insufficiently studied the monitoring accuracy of multi-channel data and have not adequately considered the impact of noise on industrial processes. To address this issue, this paper proposes a neural network-based model, DSCBAM-DenseNet, which integrates depthwise separable convolution and attention modules to fuse multi-channel data features and enhance the model's noise resistance. We simulated a real environment by adding Gaussian noise with different signal-to-noise ratios to the Tennessee Eastman process dataset and trained the model using multi-channel data. The experimental results show that this model outperforms traditional models in both fault diagnosis accuracy and noise resistance. Further research on a compressor unit engineering instance validated the superiority of the model. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of cities, the utilization rate of underground space has gradually increased, and anti‐pull piles have also been widely used in practical engineering. In this paper, the failure surface form of the anti‐pull pile was assumed, the soil parameters of each soil layer in the layered foundation and the Poisson effect generated by the anti‐pull pile under the action of the uplift force was considered, and the calculation formula for the bearing capacity of the anti‐pull long pile in the layered foundation was obtained, which was verified by on‐site test data. Finally, the parameter range of the calculation method was analyzed. The results show that when the aspect ratio of the anti‐pull pile is large, and the elastic modulus of the anti‐pull pile is small, the Poisson effect of the anti‐pull pile is obvious, and the increment of pile side friction caused by the Poisson effect is also large. When the value range of parameter N is 50 ≤ N ≤ 100, the calculation results are more reasonable. The average error of the lower limit N = 50 is 11.7%, and the average error of the upper limit N = 100 is 5.3%. [ABSTRACT FROM AUTHOR]

Over the past decades, Na0.5Bi0.5TiO3 (NBT)-based ceramics have received increasing attention in energy storage applications due to their high power density and relatively large maximum polarization. However, their high remnant polarization (Pr) and low breakdown field strength are detrimental for their practical applications. In this paper, a new solid solution (1−x)Na0.5Bi0.5TiO3–xCaHfO3 (x = 0.04, 0.08, 0.12, 0.16) was constructed by introducing CaHfO3 into NBT, and thus was prepared using a conventional solid-state reaction. With the addition of CaHfO3, the disorder of the structure increased, A-site vacancies formed, and thus oxygen vacancies were suppressed due to the replacement of the Na+ by Ca2+, resulting in the enhanced relaxation behavior and the reduced Pr, the refined grain, and improved breakdown strength. Furthermore, an optimal recoverable energy storage density (Wrec) of 1.2 J/cm3 was achieved in 0.92Na0.5Bi0.5TiO3–0.08CaHfO3 ceramics under the breakdown strength of 140 kV/cm, which is mainly attributed to the resultant defect of Na+ vacancy. [ABSTRACT FROM AUTHOR]