Search

Flexible conductive hydrogels (FCHs) have attracted widespread interest as versatile monoliths that can be intricately integrated with various ingredients boasting multiple functionalities. The chemicophysical properties of FCHs cover a wide range, which significantly vary in their building blocks. However, achieving both favorable mechanical strength and high conductivity simultaneously through a facile approach remains a challenge. Herein, polyvinyl alcohol, dialdehyde cellulose nanofibrils, silver nanoparticles, borax, and tannic acid are readily "one-pot" incorporated into FCHs with great tensile stress (499 kPa), tensile strain (4591 %), and compressive stress (269 kPa) due to abundant hydrogen bonding, dynamic borate-diol bonding, and intermolecular acetalization. They also exhibit desired self-healing, generalized-adhesive, and antibacterial performances. Taking advantage of these, FCHs are further employed to support an epidermal sensor, on which remarkable strain sensitivity (gauge factor = 8.22), high-pressure sensitivity (≥ 0.258 kPa−1), and fast response (≤ 190 ms) are recorded. Its highly adaptive mechano-electric transformability and functions can be well maintained in serving as an array unit and touch screen pen. The results well addressed in this work are anticipated to pave the universal way of engineering FCHs. [Display omitted] [ABSTRACT FROM AUTHOR]

Nature- and society-inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm(IOA), which is inspired by people showing positive-negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the Ru/RuS 2 nanoparticles as dual co-catalysts were self-assembled on the surface of g-C 3 N 4 nanotubes (Ru/RuS 2 /g-C 3 N 4 NTs) for photocatalytic H 2 production. The Ru/RuS 2 /g-C 3 N 4 NTs showed greatly enhanced photocatalytic H 2 production activity (1409 μmol·g−1·h−1), 1.16 times of RuS 2 /g-C 3 N 4 NTs (1212 μmol·h−1·g−1), 10.51 times of Ru/g-C 3 N 4 NTs (134 μmol·g−1·h−1), and 82.88 times of the pure g-C 3 N 4 NTs (17 μmol·g−1·h−1). Besides, the apparent quantum yield value (AQE) of Ru/RuS 2 /g-C 3 N 4 NTs is 3.92% at 370 nm. Ru/RuS 2 as dual co-catalysts are self-assembled on the surface of g-C 3 N 4 NTs and show strong electronic synergistic interaction between the interfaces, reduce Δ G H* of RuS 2 and desorption energy of Ru, and promote the selectivity and activity of HER kinetically and thermodynamically respectively, which exhibits higher photogenerated carrier concentration and lower charge migration resistance than RuS 2 and Ru, showing the synergistic effect to facilitate the generation and migration of carriers and provides active sites for photocatalysis. [Display omitted] • Ru/RuS 2 nanoparticles as dual co-catalysts are self-assembled on the surface of g-C 3 N 4 nanotubes. • Ru/RuS 2 /g-C 3 N 4 NTs show greatly enhanced photocatalytic H 2 production activity (1409 μmol·g−1·h−1). • Ru/RuS 2 show strong electronic synergistic interaction between the interfaces. • Ru/RuS 2 promote the selectivity and activity of HER kinetically and thermodynamically respectively. [ABSTRACT FROM AUTHOR]

Microbial electrosynthesis (MES) is a promising technology that mainly utilizes microbial cells to convert CO2 into value-added chemicals using electrons provided by the cathode. However, the low electron transfer rate is a solid bottleneck hindering the further application of MES. Thus, as an effective strategy, genetic tools play a key role in MES for enhancing the electron transfer rate and diversity of production. We describe a set of genetic strategies based on fundamental characteristics and current successes and discuss their functional mechanisms in driving microbial electrocatalytic reactions to fully comprehend the roles and uses of genetic tools in MES. This paper also analyzes the process of nanomaterial application in extracellular electron transfer (EET). It provides a technique that combines nanomaterials and genetic tools to increase MES efficiency, because nanoparticles have a role in the production of functional genes in EET although genetic tools can subvert MES, it still has issues with difficult transformation and low expression levels. Genetic tools remain one of the most promising future strategies for advancing the MES process despite these challenges. [ABSTRACT FROM AUTHOR]

This paper examines the seismic response of buckling restrained braced frames (BRBFs) under near-field (NF) ground motions to obtain the interrelationships among different engineering demand parameters (EDPs). Several case study BRBFs with varying span lengths and heights are designed based on relevant US design standards. These study frames are numerically developed in the OpenSees platform, incorporating the applicable nonlinear characteristics of the pertinent structural components. Nonlinear time response analyses are conducted on the adopted frames under a suite of NF ground motion records, and the results are post-processed to obtain structural responses in the form of EDPs such as inter-storey drift ratio (IDR), residual drift ratio (RDR), maximum ductility demand (μ max), and cumulative ductility demand (μ cum). Regression relationships among EDPs are also explored. Subsequently, probabilistic seismic demand models are developed, and the resulting fragility curves are generated. The results from the study can display the approximate regression relationships among the adopted EDPs under NF records. Identifying RDR as the critical EDP that governs the BRBF response under NF seismic excitation, distinct damage state (DS) values are also proposed for the IDR, μ max, and μ cum to facilitate fragility curve development. The study also shows that the increase in span length makes the BRBFs more vulnerable to permanent damage under NF disturbances. • Examines near-field seismic response of buckling-restrained braced frames factoring in bay span length & frame height. • For the accounted scenario, the inter-relation among the different engineering demand parameters (EDPs) of BRBFs, namely, inter-storey drift ratio (IDR), residual drift ratio (RDR), maximum buckling-restrained brace (BRB) ductility (μ max)and cumulative BRB ductility (μ cum) are derived. • Additionally, the associated median estimate of capacity (Sc) of IDR, μ max and μ cum are obtained based on the code given RDR median capacity value and the nonlinear time response analyses results. • Finally, the resulting fragility curves for RDR, IDR μ max and μ cum are generated and discussed. [ABSTRACT FROM AUTHOR]

There is a pressing requirement to design a simple battery thermal model that balances simulation accuracy regarding battery thermal characteristics and engineering application feasibility. The paper carries out the experimental study and model characterization of thermoelectric coupling characteristics of ternary lithium batteries with different shapes under ambient temperature conditions. The paper designs a characterization method of the battery thermoelectric coupling model. It conducts experiments to analyze the battery's charging and discharging characteristics and extracts essential modeling parameters. It reveals the differences in the modeling parameters of ternary lithium batteries with different shapes and constructs the thermoelectric coupling models of the square and 21700 cylindrical ternary lithium battery respectively. At the same time, the paper applies the model to carry out the simulation and experimental verification of the battery thermal behavior. The experimental data monitored by the infrared thermal imager and temperature sensors are compared with the simulation results. The errors are less than 5 %. It can provide a reference for related researches and engineering applications of battery thermal behavior and thermal management. • Designing a model characterization method for batteries with different shapes. • Revealing differences in modeling parameters of batteries with different shapes. • Constructing a simpler battery thermal model with low calculation volume. • Balancing the accuracy of simulation with the feasibility of applications. [ABSTRACT FROM AUTHOR]

The traditional approach to formulating building codes often is slow and labor-intensive, and may struggle to keep pace with the rapid evolution of technology and domain findings. Overcoming such challenges necessitates a methodology that streamlines the modernization of codal provisions. This paper proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical natures. In this approach, a codal provision (i.e., equation) is analyzed to trace its properties (e.g., engineering intuition and causal logic). Then a ML model is tailored to preserve the same properties and satisfy a collection of similarity and performance measures until declared equivalent to the provision at hand. The resulting ML model harnesses the predictive capabilities of ML while arriving at predictions similar to the codal provision used to train the ML model, and hence it becomes possible to use in lieu of the codal expression. This approach was examined successfully for seven structural engineering phenomena contained within various building codes, including those in North America and Australia. The findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes. [ABSTRACT FROM AUTHOR]

• A novel framework (MCMD) to classify days based on traffic state similarities measured across multiple traffic datasets. • MCMD outputs scale-invariant and scale-variant similarity-based classification labels and outliers. • A novel multi-view feature engineering algorithm is proposed for joint dimensionality reduction of traffic datasets. • Missing data is effectively handled by leveraging shared information across datasets. A common task in traffic data analysis and management is categorizing different days based on similarities in their network-wide traffic states. Given the multifaceted nature of traffic, it is essential to consider multiple attributes for a comprehensive quantification. However, challenges arise when combining these attributes to achieve consistent day-to-day classification across datasets. While various data-driven classification algorithms have been proposed in traffic literature, challenges persist. These include a) applicability limited to univariate datasets, b) incompatibility with datasets containing missing values, c) distance concentration problem in high-dimensional clustering, d) inability to classify outliers, and e) computationally expensive hyperparameter optimization. This research introduces the MCMD (Multi-view Classification based on Consensus Matrix Decomposition) framework, a novel approach for the joint classification of multi-view traffic data. MCMD treats multiple traffic datasets with varying geographical coverage as complementary views of the entire network's traffic state. It then extracts shared hidden features across these datasets and assigns each day classification labels that are consistent across views. MCMD consists of three key modules: the novel Multi-view Uni-orthogonal Non-negative Matrix Factorization (MUNMF) algorithm, an outlier removal module, and the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm. A logical integration of the above-stated modules enables MCMD to a) output scale-invariant (SI) and scale-variant (SV) classifications and b) identify outlier days based on the shape and scale of multi-view traffic-state profiles. Compared to existing clustering methods, the design of the MCMD algorithm offers greater versatility in handling both single- and multi-view datasets for SI and SV clustering, computational robustness to missing data, and resilience to the "distance concentration problem" associated with the curse of dimensionality. These advantages stem from its ability to extract relevant cross-dataset features, reduce dimensionality, and eliminate redundancy. Although the primary motivation of the framework is derived from the need to develop a traffic pattern repository to support reliable prior Origin-Destination (OD) selection for online dynamic OD demand adjustment, the paper, through extensive experiments on real-world and synthetic traffic datasets, demonstrates the effectiveness of MCMD from several generic standpoints. These include a) demonstration of the meaningfulness of SI and SV labels, b) assessment of the robustness toward missing information, c) evaluation of its effectiveness in classifying days with special events, d) benchmarking properties against alternative joint day-to-day clustering algorithms, and e) demonstrating the efficacy of the proposed hyperparameter selection method for efficient joint classification of multiple large-scale traffic datasets. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main principles and methods for measuring acoustic cavitation are discussed. • Acoustic wave and system parameters affecting US process efficiency are discussed. • US for agri-food applications improved product quality and extraction efficiency. • US can be combined with conventional agri-food processes to improve operations. • Innovative designs for large-scale US assisted food processing are described. Acoustic cavitation, an intriguing phenomenon resulting from the interaction of sound waves with a liquid medium, has emerged as a promising avenue in agri-food processing, offering opportunities to enhance established processes improving primary production of ingredients and further food processing. This comprehensive review provides an in-depth analysis of the mechanisms, design considerations, challenges and scale-up strategies associated with acoustic cavitation for agri-food applications. The paper starts by elucidating the fundamental principles of acoustic cavitation and its measurement, delving then into the diverse effects of different parameters associated with, the acoustic wave, mechanical design and operation of the ultrasonic system, along with those related to the food matrix. The technological advancements achieved in the design and set-up of ultrasonic reactors addressing limitations during scale up are also discussed. The design, engineering and mathematical modelling of ultrasonic equipment tailored for agri-food applications are explored, along with strategies to maximize cavitation intensity and efficiency in the application of brining, freezing, drying, emulsification, filtration and extraction. Advanced US equipment, such as multi-transducers (tubular resonator, FLOW:WAVE®) and larger processing surface areas through innovative designing (Barbell horn, CascatrodesTM), are one of the most promising strategies to ensure consistency of US operations at industrial scale. This review paper aims to provide valuable insights into harnessing acoustic cavitation's potential for up-scaling applications in food processing via critical examination of current research and advancements, while identifying future directions and opportunities for further research and innovation. [ABSTRACT FROM AUTHOR]

The statistical study of droplets in spray evaporation tower has guiding significance for understanding spray evaporation and designing drying tower. The key to spray evaporation simulation is establishing a suitable single droplet evaporation model through experiment. In this paper, a visualized single droplet evaporation experiment platform was established. The mass, diameter, and temperature during the evaporation of 10 % NaCl solution droplets were obtained. The Reaction Engineering Approach model was developed and validated to describe the single droplet evolution process in saline water spray. A comparison calculation between the spray evaporation of pure and saline water was executed, and the distributions of residence time of droplets were obtained. It reveals that droplets with initial diameters less than 117 μm can fully evaporate, and for larger droplets, pure water droplets represent a higher evaporation ratio. About 50 % of the fully evaporated saline droplets stay in the backflow area. Contrast numerical experiments of saline water spray evaporation were performed, concluded that similar performance can be attained with the same gas-liquid ratio, and excessive inlet air will reduce the residence time of droplets. The influence of other operating parameters on the performance was also discussed, providing a comprehensive understanding of the regularity of spray drying. • A visualized single droplet evaporation experiment platform was established. • Effect of flow-induced vibration of cantilever was mitigated by low pass filtering. • REA model for NaCl solution droplets was first established and verified. • Salt particles trapped in circulation was observed in spray drying tower. [ABSTRACT FROM AUTHOR]

Navigating the complex pavement system to the circular economy is a multi-dimensional and time-consuming endeavour. Obstructed by lock-in mechanisms of the prevailing "take-make-dispose" linear mode, the current circular economy studies in pavement infrastructure often focus only on one certain type of practice, featured by fragmentation from a system perspective. This study presents the first systematic literature review on the historical development trend of the circular economy concept in the pavement sector based on the collected 59 studies from 2006 to 2022. This review also identifies the soft (regulatory, social/environmental) and hard (technical, market/economic) drivers of- and barriers to the paradigm shift, aiming to coordinate the existing fragmented findings and facilitate the future systemic implementation of the circular economy. The synthesis of the results indicates that the underdeveloped technical guidance and legal system are inhibiting the paradigm shift to the circular economy. Furthermore, the authors argue that the importance of transparent and standardized data along the whole supply chain should be highlighted. Ultimately, this paper suggests that a welcoming environment set by the road authorities is crucial for innovation and entrepreneurship, as well as for constructing a system where stakeholders can confidently take responsibility and initiative. • First systematic review of circular economy transition in pavement engineering. • Identify the soft and hard drivers of- and barriers to the sustainable transition. • Guide towards the future adoption of material passports in the pavement sector. • Call for decentralization and multi-stakeholder engagement in the decision-making process. [ABSTRACT FROM AUTHOR]

In this paper, a polarization-independent chain Huygens' metasurface is proposed. It is polarization multiplexing of two chain structures. Owing to the polarization-independent property, it can be conveniently applied to design double-layer dual-polarized transmitarray antennas (TAAs) with low profile. The measured results show that, for the designed low-profile dual-polarized TAA, its peak gain is 29.7 dBi at 28 GHz with aperture efficiency of 48.9 %, and the 3-dB gain bandwidths is 8.8 %. With the F/D ratio of only 0.36, the proposed low-profile dual-polarized TAA based on the chain Huygens' metasurface has critical applications in antenna engineering. [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]

This paper presents and evaluates a role-play simulation called 'Theatrical Technology Assessment', in which students learn about complex stakeholder dynamics around emerging technologies. This role-play combines insights from Constructive Technology Assessment, improvisational theatre and educational role-play designs. It was implemented in an online setting with bachelor students of an interdisciplinary engineering programme. Based on collected chat conversations and reflection reports in seven different simulations, we qualitatively analysed what students learned about stakeholder dynamics. The study shows that students gained novel and relevant insights regarding stakeholder perspectives on technologies, the effects of uncertainties on decision making, collective stakeholder dynamics, and strategies to steer technology development. Students considered the role-play simulations to be engaging learning experiences. The dual set-up of the role-play, with students as players and co-designers, and the integral use of improvisational theatre are novel elements of educational role-play designs. [ABSTRACT FROM AUTHOR]

The later Carnap is usually cited as the first conceptual engineer – someone who argues that we can and should revise our concepts in order to make them fit for their purpose. This paper shows that there is an earlier, pragmatist, account of conceptual engineering and that today's conceptual engineers would do well to turn to it, rather than to Carnap. [ABSTRACT FROM AUTHOR]

Pragmatists are interested primarily not in representing a purportedly unchanging Reality but in articulating prophetic future possibilities on the basis of the values most venerated by a culture/society in the present. This makes pragmatism sound a little like 'Conceptual Engineering'. Conceptual engineers too are interested in transforming our ways of talking, which implies some notion of how such improvements are to be evaluated. Nevertheless, this paper argues that accounts of conceptual engineering that regard it as key to the project of elucidating an externalist semantics find it difficult to accommodate on their own terms the problem-phenomena taken to be of shared interest: regions of discourse where the demand for change are at their most pressing culturally. This difficulty takes the form of what I call the Conceptual Engineering Dilemma (CED), an inability to accommodate simultaneously the ethical and ameliorative dimensions of contestation. I argue that acknowledging the formalist commitments that undergird CED should make pragmatism more appealing to the practically-orientated analytic philosopher and conclude with a brief survey of some of the tools that it makes available for the pressing work of changing minds for the better. [ABSTRACT FROM AUTHOR]

The School of Engineering and Technology of Central Queensland University focuses on the continuous development and innovation of best learning and teaching practices to increase student retention and improve their learning experiences. Face to face and distance learning (teaching delivery models) are fundamental aspects of providing quality support to the students' learning. One incredibly important aspect of students' learning is to provide relevant industry-related projects and applications of relevant simulation software to mimic the systems. This paper develops students' essential problem-solving skills and control strategies of fluid power systems in mechanical engineering through the Master of Engineering programs by employing Simulink, SimScape Fluid applications in Matlab software and Energy Plus with Design Builder. The main focus is to ensure that the students achieve the required skills of building fluid circuit models based on physical connections that directly integrate with appropriate symbols and modelling paradigms in fluid power applications to model the appropriate physical models to mimic the industrial fluid power projects selected. The basic ideas and content are to be delivered through weekly lectures and tutorial sessions, and the students' skills in fluid power systems and software are developed and monitored through weekly workshops scheduled for all projects. The key outcomes of this study are the level of understanding of fluid power systems, development of simulation skills using the software indicated, interpretation of the results the students have obtained and validations of those results. The students must show they have developed appropriate problem-solving skills using simulation software, professional presentation and effective team-building skills. As the students develop appropriate problem-solving and engineering practice skills, their satisfaction and feedback rates improve significantly. [ABSTRACT FROM AUTHOR]

Background: Despite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K‐12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to sustainable learning outcomes. Objectives: The current study examines how a series of Arduino‐based robotics learning activities capture the fuller essence of concepts related to CT. Methods: College students (n = 50) completed a series of six robotics learning activities. Think‐alouds, student reflections and performance scores were used to assess students' CT through a robotics challenge in virtual and physical learning environments. Results and Conclusions: Students verbalized CT concepts related to algorithmic thinking much more than abstraction, problem decomposition and testing and debugging. Students exposed to active learning performed better in a virtual robotics challenge compared to their peers in a traditional‐oriented classroom. Students' scores on the physical robotics challenge increased as a function of the number of references they made to CT concepts during the think‐alouds. It is possible to design pedagogical experiences that tap into various dimensions of CT at incremental levels of complexity through a series of Arduino‐based robotics activities. With the integration of an online simulation, students can visualize and transfer their CT skills between a virtual and physical learning environment, thus leading to more sustainable learning outcomes. Lay Description: What is already known about this topic: Computational thinking (CT) is the conceptual foundation for problem‐solving.CT shifted from unplugged to plugged activities.CT concepts can be fostered through educational robotics. What this paper adds: A multi‐dimensional approach to assessing CT.Applications of CT provide insights into skill transfer.An integrative theoretical approach to study the 'how' and the 'what' of CT. Implications for practice: Educational robotics activities can cater to a fuller range of CT skills.Virtual simulations with constructive feedback are beneficial for students in traditional settings.Mixed learning environments contribute to making links between lectures and labs. [ABSTRACT FROM AUTHOR]

With constantly changing political landscapes affecting the ability of college students with minoritized identities of sexuality and/or gender (MIoSG; Vaccaro et al., 2015) to thrive on campus, higher educators need to understand student reported stressors to design more inclusive learning environments. Building from minority stress theory and using data from a grounded theory study with 56 collegiate STEM students with MIoSG, this paper documents stressors that students reported as contributing to diminished wellbeing. We used constant comparative grounded theory analysis to identify stressors shared by all participants, which included lack of belonging and invisibility in competitive STEM cultures as well as exclusionary interactions with STEM peers. Recommendations include the design and delivery of holistic education and support services on campus. [ABSTRACT FROM AUTHOR]

Solving a multiple-criteria optimization problem with severe constraints remains a significant issue in multi-objective evolutionary algorithms. The problem primarily stems from the need for a suitable constraint handling technique. One potential approach is balancing the search in feasible and infeasible regions to find the Pareto front efficiently. The justification for such a strategy is that the infeasible region also provides valuable information, especially in problems with a small percentage of feasibility areas. To that end, this paper investigates the potential of the infeasibility-driven principle based on multiple constraint ranking-based techniques to solve a multi-objective problem with a small feasibility ratio. By analyzing the results from intensive experiments on a set of test problems, including the realistic multi-objective car structure design and actuator design problem, it is shown that there is a significant improvement gained in terms of convergence by utilizing the generalized version of the multiple constraint ranking techniques. [ABSTRACT FROM AUTHOR]

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]

Conceptual engineering is the enterprise of evaluating and improving our representational devices. But how should we conduct this enterprise? One increasingly popular answer to this question proposes that conceptual engineering should proceed in terms of the functions of our representational devices. In this paper, we argue that the best way of understanding this suggestion is in terms of normative functions, where normative functions of concepts are, roughly, things that they allow us to do that matter normatively (for example, things in virtue of which we have normative reasons to have these concepts). Not only does this introduce a novel view about functions to the literature. This proposal also fits more naturally than the alternatives with conceptual engineering as a normative enterprise, and it allows functions to play all of the explanatory roles assigned to them in the conceptual engineering literature. Our discussion of the explanatory advantages of normative functions also advances the understanding of the ways in which concepts can be authoritative, what this means for conceptual engineering, and highlights the importance of political philosophy for thinking about conceptual engineering in practice. Furthermore, the paper explicates what kind of role considerations about function can and should actually play in conceptual engineering. [ABSTRACT FROM AUTHOR]

• ENEN creation: historical context. • The nuclear engineering academic offer today. • Education in nuclear in an international perspective. • ENEN contributions to nuclear education (international mobility and EMSNE). • Nuclear applications spectrum and related ENEN prospects. The European Nuclear Engineering Network Association (ENEN), later named European Nuclear Education Network, was created 20 years ago after concerns had been expressed by the nuclear industrial sector about the recruitment of qualified workforce. Networking between European universities and research centers appeared as a tool to attract more students in nuclear engineering by offering them various harmonized curricula and enhanced learning opportunities in terms of knowledge, skills and attitudes (KSA). This was achieved by developing new programs in areas such as radiochemistry, radiation protection, nuclear safety engineering, geological disposal and radioactive waste management, and by offering important mobility opportunities for engineering education and training. Through its successive Framework Programs, the European Commission supplied an important financial support to ENEN projects: besides the mobility actions, around 40 thematic projects enabled to develop new products such as well-coordinated programs, learning platforms, and distance learning courses. The paper concludes with suggestions for the future. A suggested area for new inter-university co-operations is the strengthening of the academic programs in the field of the global energy approach for the benefit of decarbonization. This would involve courses on technical, economic and environmental aspects of the energy mix. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it can lead to an apparent decrease in battery safety and reliability. Therefore, predicting remaining useful life from the current to end state and preventing possible dangerous incidents are essential for battery health management. A novel method from the perspective of providing aging features reference and balance prognostics speed and precision is proposed. Firstly, the used dataset is preprocessed in many ways. Then, through feature engineering, 79 features are extracted in the dataset from three perspectives: direct, evolution, statistics, and most representative 16 features are filtered to form the final feature set based on the method of combining discretization and importance. Feature engineering is established to find crucial information within the dataset that correlates highly with remaining life and represents most battery recession paths. Finally, the sparse autoencoder and Transformer integrated approach is proposed to build the life prediction model, and it can learn the temporal relationship between the feature set and remaining cycles quickly and accurately. According to the forecasting results of 62 batteries with different aging conditions, the error can reach 7.43% by only using the feature sequence under 30 cycle lengths. For early life prediction, the error of using the first 100 cycles data is at most 2.6%. • A novel aging feature filtering approach is proposed based on feature engineering. • A prediction model is established by using a sparse autoencoder and Transformer. • More accurate RUL and early life of a battery can be predicted by using fewer cycle data. • After the feature engineering presented in this paper, the final aging feature set selected has strong applicability. [ABSTRACT FROM AUTHOR]

• K.S Bfr can store Cd2+ in protein cavities in vitro. • The engineering bacteria with K.S Bfr can enhance the removal of Cd2+ from water. • Bacterioferritin up-regulates the ribosomal metabolic pathway. Cadmium can enter water through various industrial pollutants, posing serious threats to the ecological environment and human health. This study unveiled an effective method for removing Cd2+ from water by introducing bacterioferritin, known as Candidatus Kuenenia stuttgartiensis (K.S Bfr), into engineering bacteria (EB) to facilitate metal ion removal via K.S Bfr storage. Additionally, the paper explores the mechanism behind Cd2+ removal by EB using K.S Bfr. The binding kinetics revealed that EB's Cd2+ binding capacity was significantly higher than that of wild bacteria, and Cd2+ was found to enter the protein cavity of K.S Bfr in vitro. When exposed to Cd2+ concentrations ranging from 0 to 2.5 mM, EB demonstrated a survival rate above 70 %, whereas wild bacteria's survival rate remained below 50 %. The superior Cd2+ removal capacity of EB can be attributed to the intracellular storage capacity of K.S Bfr. Tandem Mass Tags quantitative proteomics analysis showed that EB mitigated cytotoxicity mainly by up-regulating the expression of binding, homeostasis, efflux, and ribosome-related proteins. K.S Bfr was identified as a major pathway for bacteria to remove Cd2+ since it provided independent storage space for Cd2+ and ensured intracellular metal ion homeostasis. This study presents an effective strategy for treating metal wastewater, offering promising new applications for bioremediation. [ABSTRACT FROM AUTHOR]

Recently, chalcogenides have attracted much attention as electrocatalysts in hydrogen evolution reaction (HER). However, few studies have been conducted on the electrocatalytic properties of gallium oxides and chalcogenides. In this paper, a Ga 2 XY (X ≠ Y, X, Y=O, S, Se, Te) defect structure doped by non-metal B, C, N, P, Si, and As have been designed. According to the study, the doping of non-metal atoms can significantly enhance their HER properties, the Ga 2 OSe-As Xi -Xi structure and Ga 2 SeTe-Si Xi -NM structure possess intensely excellent HER properties in this study with the Gibbs free energy of 0.01 eV and 0.00 eV, respectively. It is found that the Ga 2 SeTe structure has a more concentrated electron transfer range compared to the Ga 2 OSe structure, leading to a superior HER performance. This work provides a new idea for the study of HER electrocatalytic performance of the Ga 2 XY system, and it is expected to be applied to HER catalysts affordably and efficiently. • Screening of non-metal doped Ga 2 XY defective structures for HER electrocatalytic performance. • The introduction of Ga vacancies significantly enhances the HER performance of Ga 2 XY structures. • The Ga 2 SeTe defective doped structure has a more concentrated electron transfer range compared to the Ga 2 OSe structure. • The As-doped Ga 2 OSe structure and the Si-doped Ga 2 SeTe structure containing Ga defect have a near-zero ΔG H. [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]

Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering's use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage). [ABSTRACT FROM AUTHOR]

In this paper, we report simulation results for capacitance–voltage characteristics and temperature distribution in the cross‐section of p‐type gate‐all‐around nanosheet channel using an in‐house developed numerical simulator. The effects of material, channel width, and crystallographic orientation on electrical and thermal properties of p‐type nanosheet transistor are comprehensively investigated. The effect of channel engineering is analyzed, by evaluating density‐of‐states, hole density, current densities as well as distributions of temperature in the channel cross‐section. Finally, the thermal reliability of the device is addressed in terms of thermal resistance. The density‐of‐states and the hole density distribution at the oxide/channel interface can well explain the effective intrinsic capacitance obtained from the simulation. The better uniformity of the hole density distribution across the cross‐section of (110)/[001] channel, shows good promise for less performance fluctuation in terms of the thermal reliability issue. [ABSTRACT FROM AUTHOR]

Space grid structures are widely used in the field of structural engineering due to their good mechanical properties and simple construction procedures. This paper reviews the development of space grid structures and mainly introduces the development of space grid structures in China in recent years. Some typical applications of space grid structures are introduced and the technical developments are summarized. It shows that China not only has a large number of engineering applications of space grid structures, but also a rapid development of related technologies. Finally, some prospects for space grid structures are discussed. [ABSTRACT FROM AUTHOR]

This paper introduces the development process of building cable structures in China over the past 70 years according to three stages: the initial, slow, and rapid development. It also summarizes the types of cable components and joints of building cable structures. The structural characteristics and representative projects of suspension, cable membrane, beam string (cable-supported), and cable dome structures are reviewed in this paper. Finally, the achievements and prospects of cable structures in China are discussed. [ABSTRACT FROM AUTHOR]

The model-based robust approach is successfully applied to different industrial processes for improving their quality performance characteristics. The Robust engineering approach is implemented in cases of heteroscedasticity of the performance characteristics in presence of errors in the process parameters or noise factors, which is typical for the production processes. Higher quality and reproducibility of the quality characteristics can be achieved both for existing production processes or for technological process design at the production planning and development stages. In this paper the model-based robust engineering approach is implemented for the quality improvement of polymethyl methacrylate (PMMA) resist profiles, obtained by electron beam lithography process. Specific technological requirements concerning the developed profile structures, their variances (reproducibility) in production conditions and robustness toward the errors in the process parameters, are considered. Different multicriteria optimization approaches were applying and compared, aiming to simultaneously fulfil the requirements for the geometric characteristics of the positive resist PMMA. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution, which is a socio-technical, ideological, and rhetorical construction rooted in the neoliberal discourse that reflects key tenets of global capitalism, is believed to have considerable implications for the development of employees in advanced manufacturing environments. This paper aims to explore the ways in which the learning needs of employees in the context of the Fourth Industrial Revolution could be appropriately identified and how employees could further develop their skills through the design of suitable development curricula. To this end , the paper seeks to interrogate the ways in which the employees' learning needs are likely to be identified in the middle-range future, and problemate the focus of highly specialised and exclusively focused on Science, Technology, Engineering, and Mathematics (STEM) curricula that are likely to be designed to help employees respond to the perceived demands of the Fourth Industrial Revolution. The paper further seeks to explore a preferred liberal future, drawing on the work of Martha Nussbaum, to create the possibility for an alternative future guided by a more holistic conception of employee development through the establishment of learner-centred, liberal – and liberating – interdisciplinary Science, Technology, Engineering, Arts, and Mathematics (STEAM) curricula. [ABSTRACT FROM AUTHOR]

My comments concern the significant errors in the crystallographic part of the commented paper. The diffraction patterns are erroneously analysed. The studied sample contain two phases what can influence on the final results of experiments. [ABSTRACT FROM AUTHOR]

This paper introduces a new design strategy for the development of highly sensitive Multimode Interference (MMI) sensors by employing an engineered slot-waveguide as the foundation for the device. Waveguides typically adopted in MMI sensors have the evanescent field of their modes interact with the sample in the sensing region and, by doing that, the device sensitivity is limited by how distinctly confined the propagating modes are. Even though one propagating mode has a greater interaction with the sample than the other, they both are sensitive to changes in the refractive index in the sensing area, when the ideal situation is to keep one of them virtually insensitive to such changes. A multimode slot-waveguide with a central defect was proposed to improve two aspects: first, obtain one mode that is guided through the high-index central defect of the core and that is, by construction, physically separated from the sensing region; second, utilize another high-order mode that propagates through the low-index regions of the core and not only in direct contact with the sample, but with maximum field intensity in the region of interaction. For aqueous samples (refractive index approximately 1.33), the achieved sensitivity per sensor length was 6.0 rad · RIU - 1 · μ m - 1 and an estimated Limit-Of-Detection of 3.5 · 10 - 8 RIU - 1 , which places the slot-MMI sensor among the most sensitive devices in the current literature. [ABSTRACT FROM AUTHOR]

In this paper, we present a detailed study of the effect of WO 3 and Ni films in the presence of BP and BlueP-MDC nanomaterial layers on the sensitivity of a surface plasmon resonance (SPR) following the change in refractive index of the detection medium. Our nanostructure is based on the Kretschmann multilayer configuration composed of a BK7 prism, an Ag metallic film deposited on ZnO and a hybrid multilayer composed of WO 3 /Ni/2D-nanomaterials. In the first part, the study aims to optimize the effect of the thicknesses of the layers of the nanomaterials of the structure on the surface plasmon resonance. We obtain an angular sensitivity of the order of 480 deg/RIU for a figure of merit of 177.11 RIU−1 at the wavelength of 633 nm. These values are respectively 300% and 342% higher than those of a conventional SPR sensor (BK7/Ag/SM). After modeling the SPR biosensor studied in this paper, we showed how the plasmon resonance and sensitivity of the SPR sensor are affected by the refractive index of liquid biochemical elements. The proposed SPR structure can be used in highly sensitive urinary glucose detection and medical analysis applications. [Display omitted] • Engineering and optimization of the SPR device highly sensitive for biomedical processing and detection. • The optimized nanostructure shows the highest sensitivity 480 deg/RIU. • Highest sensitivity enhancement 300% for SPR biosensor. • This study demonstrated high FoM of 177.11 RIU-1 with an enhancement of 342%. • Detection of glucose concentration in human urine samples to test the glucose levels of people with diabetes. [ABSTRACT FROM AUTHOR]

Grouting lifting is the effective approach for treating differential settlement of existing tunnels, but it easily deteriorates the tunnel structure again and seriously threatens the operational safety of urban rail transit. Aiming to solve the above problem, this paper considers the interaction between the tunnel and the foundation, and establishes a series of analytical solutions of the stress, strain, axial force, and bending moment of the tunnel under single point grouting. Based on the characteristics of shield tunnel correction engineering and the analytical solution under single point grouting, the equation of the internal force and deformation of tunnel structure under multi-point grouting considering different grouting angles and different strata is obtained. The grouting correction engineering case for Shenzhen Metro Line 1 shows that the analytical solution to the displacement of the tunnel structure is well matched with the measured displacement of the tunnel structure is well matched with the measured data of the case and the difference is less than 15 %. The parameter analysis results indicate that the main loaded area of the tunnel under single point grouting is related to the stratum, and the main loaded area of the soft soil stratum is - 42. 4 ° + 42. 4 ° around the grouting point. Convergence deformation of the tunnel under multi-point grouting reaches the maximum value at the diameter passing through the center of the grouting zone and the minimum value at the diameter which is perpendicular to the former. Under the condition of limiting horizontal convergence deformation, the reasonable grouting angle range of the multi-point grouting zone is about 120 ° 150 °. This paper provides a theoretical basis that can immediately evaluate the mechanical structure performance and to determine whether to impose greater pressure when the tunnel is continuously lifted. [ABSTRACT FROM AUTHOR]

Understanding the anomalies generated by various subsurface targets and their responses to different geophysical techniques in various subsoil types is critical to near-surface geophysical investigations. Geophysical test site (GTS) plays a vital role in near-surface geophysical investigations and related Earth sciences to adequately predict the geometries and anomalies generated by the subsurface targets. Therefore, developing a GTS on a site requires some technical efficiencies, mechanical procedures, engineering concepts and scientific approach, depending on the operating environment and the purpose of construction. This paper reviews the evolving roles of GTS in engineering, sciences, and technology via remarkable pedagogical and scientific research. The procedures for designing and installing GTS were also discussed. Every constructed GTS is unique and has its operating environment and sets of scientific requirements. As a result, the execution of GTS should be subjected to numerous factors that invariably affect the overall long time usage and performance. Comparative studies of GTS activities indicate that GTS is a vital geophysical research and academic platform to enrich the outcomes of the geophysical modelling for near-surface geophysical applications in engineering, science and technology. The evolving application of GTS has greatly impacted the field of science and engineering by enhancing the knowledge and understanding of the earth's interior, which invariably affects the engineers, geophysicists, archaeologists and geologists to be critical in the analysis, interpretation, and providing precise and accurate information of subsurface anomalies underlying the uppermost soil of the earth's crust. After a critical investigation, it was noted that the installation of GTSs is usually conceived to replicate situations often encountered in field investigation contexts. Examination shows that GTS can provide an ideal platform for young geoscientists, engineers and archaeologists to acquire the requisite skills, knowledge, technical know-how, and professional techniques for resolving near-surface challenges in real-life work situations. More also, a well-developed and equipped GTS could be a watershed in technological advancement, research development, and new scientific ideas. The GTS platform also indicates a promising pedagogical approach to geophysical educational usage, research mobilization, and development of new shallow geophysical techniques for various near-surface investigation and calibrating/testing geophysical equipment, which invariably catalyzed engineering designs, scientific concepts and technological advancement. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

Resilient performance is a crucial characteristic of complex socio-technical systems, enabling them to sustain essential functionality during changing or stressful conditions. Resilience Engineering (RE), a sub-field of safety research, focuses on this perspective of resilience. RE emphasises its "cornerstone model", presenting the RE system goals of "anticipating, monitoring, responding and learning". The cornerstone of learning remains fragmented and undertheorized in the existing literature. This paper aims to enrich RE research and its practical implications by developing a nuanced and comprehensive understanding of the role of learning from resilient performance. To achieve this aim, a scoping literature review was conducted to assess how learning is conceptualised in the RE literature and the theoretical foundations on which previous work rest. The main findings show that RE researchers view learning as the process of understanding the system, sharing knowledge, and re-designing system properties. The application of established learning theories is limited. This paper contributes to research by proposing an organisational process for the RE cornerstone of learning, paving the way for deeper discussions in future studies about learning from resilient performance within complex socio-technical systems. • Organisational learning is a core requirement for the safety and optimal functionality of complex socio-technical systems. • Conceptualizations of organisational learning from resilient performance are under-developed. • The literature review shows that learning from resilient performance is fragmented and undertheorized. • The research lays groundwork for future discussions on learning from resilient performance • A refined organisational process for learning within RE, grounded in established learning theory, is proposed. [ABSTRACT FROM AUTHOR]

Background: The number of artificial intelligence (AI) literacy studies in K‐12 education has recently increased, with most research focusing on primary and secondary education contexts. Little research focuses on AI literacy programs in early childhood education. Objectives: The aim of this mixed‐methods study is to examine the feasibility of an AI literacy program called "AI4KG" and explore how it might affect kindergarteners' perceptions of robots and attitudes towards engineering and science. Methods: A total of 26 child–parent dyads recruited from a Hong Kong kindergarten were involved in this study, consisting of 26 children (Mage = 4 years, SD = 0.28) and their parents. Quantitative and qualitative data were collected through surveys and interviews designed to explore children's perceptions of robots and attitudes towards engineering and science, and parents' perceptions of the AI4KG intervention. Results: It is found that children have increased their perceptions of robots after the AI literacy program, but the AI4KG curriculum had no significant effects on kindergarten children's engineering and science attitudes. Most parents (22 out of 26) agreed that their children's AI knowledge, AI skills, and AI attitudes have been enhanced after learning through the AI4KG curriculum. Conclusions: This study suggests that the AI4KG curriculum is potentially effective in promoting early AI literacy and favourable attitudes towards the technology, but further research is needed to develop age‐appropriate measures and assess its long‐term impact on children's education and career paths. Lay Description: What is currently known about this topic?: AI literacy is becoming important as AI transforms society, but few studies have explored AI literacy in early childhood education.Prior research found AI activities can positively influence older children's views of robots and engineering. What does this paper add?: It evaluates an 8‐week AI literacy curriculum called AI4KG for kindergarteners in Hong Kong.The curriculum used tools like Teachable Machine and taught concepts through play and storytelling.It improved children's perceptions of robots but did not significantly impact engineering/science attitudes.Parents perceived improvements in children's AI knowledge, skills, and attitudes after the curriculum. Implications for practice/policy: The curriculum provides an example for developing age‐appropriate AI literacy activities.More rigorous research on impacts over time is needed, but results suggest potential benefits of AI literacy in early childhood.Policymakers could consider incorporating AI literacy into kindergarten curricula to prepare children for the future. [ABSTRACT FROM AUTHOR]