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Two trajectories underway transform human systems. Processes of growth/complexification have accelerated as stakeholders seek advantage from advances in connectivity/autonomy/sensing. Surprising empirical patterns also arise—puzzling collapses of critical valued services occur against a background of growth. In parallel, new scientific foundations have arisen from diverse directions explaining the observed anomalies and breakdowns, highlighting basic weaknesses of automata regardless of technology. Conceptual growth provides laws, theorems, and comprehensive theories that encompass the interplay of autonomy/people and complexity/adaptation across scales. One danger for synchronizing the trajectories is conceptual lag as researchers remain stuck in stale frames unable to keep pace with transformative change. Any approach that does not either build on the new conceptual advances—or provide alternative foundations—is no longer credible to match the scale and stakes of modern distributed layered systems and overcome the limits of automata. The paper examines longstanding challenges by contrasting progress then as the trajectories gathered steam, to situation now as change has accelerated. [ABSTRACT FROM AUTHOR]

In this paper, the application of the HAM-Padé technique for solving a system of differential equations is investigated. This method is a combination of the homotopy analysis method (HAM) and Padé approximation. The equations under consideration are the governing equations of an important physical problem that is also widely used in engineering. This study is a comparative research so that after solving the equations, the solutions obtained from the HAM-Padé method will be compared with the outcomes of the three previous studies. The comparison showed that the HAM-Padé solutions have an interesting agreement with the previous works and have good accuracy. [ABSTRACT FROM AUTHOR]

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

The mechanical performance of joints for any engineering applications requires accurate characterization of joint interfaces. This paper presents a simplified experimental procedure to estimate the cohesive zone model (CZM) parameters for predicting the interface failure of adhesively bonded joints subjected to mode I (opening) and mode II (shear) loading. The novelty of this procedure lies in predicting all CZM parameters—cohesive stiffness, strength, and cohesive energy (fracture toughness), of the bonded interface using one common experimental setup. It is observed that the cohesive strength and cohesive energy in shear loading are approximately 3.8 and 11.6 times, respectively, compared to the opening loading condition. Employing a triangular cohesive law, the CZM parameters are combined with appropriate damage initiation (quadratic stress) and evolution (power law) criteria. The proposed methodology is verified against experimental load–displacement responses of the bonded interface under mixed‐mode I/II (opening/shear) loading across a wide range of mode mixity (α = 0°, 15°, 30°, 45°, 60°, 75°, and 90°). The maximum error between the experimental and the numerical peak load is 5.11%. The acceptable agreement between the numerical and experimental results confirms the effectiveness of this method in investigating interface fracture in adhesively bonded joints under different loading conditions. [ABSTRACT FROM AUTHOR]

In mineral, environmental, and engineering explorations, we frequently encounter geological bodies with varied sizes, depths, and conductivity contrasts with surround rocks and try to interpret them with single survey data. The conventional three-dimensional (3-D) inversions significantly rely on the size of the grids, which should be smaller than the smallest geological target to achieve a good recovery to anomalous electric conductivity. However, this will create a large amount of unknowns to be solved and cost significant time and memory. In this paper, we present a multi-scale (MS) stochastic inversion scheme based on shearlet transform for airborne electromagnetic (AEM) data. The shearlet possesses the features of multi-direction and multi-scale, allowing it to effectively characterize the underground conductivity distribution in the transformed domain. To address the practical implementation of the method, we use a compressed sensing method in the forward modeling and sensitivity calculation, and employ a preconditioner that accounts for both the sampling rate and gradient noise to achieve a fast stochastic 3-D inversion. By gradually updating the coefficients from the coarse to fine scales, we obtain the multi-scale information on the underground electric conductivity. The synthetic data inversion shows that the proposed MS method can better recover multiple geological bodies with different sizes and depths with less time consumption. Finally, we conduct 3-D inversions of a field dataset acquired from Byneset, Norway. The results show very good agreement with the geological information. [ABSTRACT FROM AUTHOR]

This paper studies the influence of the gate voltage of the power amplifier (PA) on the drain current and efficiency. This study proposes a theory of controlling input non‐linearity to improve the efficiency of PAs. The theoretical efficiency of the inverse Class F PA that controls the input nonlinearity is within the range of 77% to 97%. A new design method for the inverse Class F PA reconstructs the design of the load admittance space into a region instead of a point. To verify the validity of the proposed theory, an inverse Class F PA is designed and fabricated using a commercial 10 W GaN high electron mobility transistor (HEMT). Results of the measurement show a high drain efficiency (DE) of 78.5%, an output power of 41.6 dBm, and a large signal gain of 12.1 dB at 1.5 GHz. The overall PA's size is controlled at 80*50 mm2$$ \mathrm{m}{\mathrm{m}}^2 $$. [ABSTRACT FROM AUTHOR]

A Rectangular core–shell (RCS), employed to three fins vertically stacked with gate oxide stack junctionless nanosheet, along with doping and gate/dielectric engineering, is explored. This paper proposes an N-type three fins vertically stacked with gate oxide stack junctionless nanosheet with opposite core doping in the channel referred to as RCS. The simulation results show that three fins vertically stacked with gate oxide stack with RCS junctionless nano-sheet exhibit reduced Ioff is 6.913E-21, increased Ion is 2.408E-07, Ion/Ioff ratio is 0.418×106, DIBL is 6.9mV and SS is 60.65 mV/dec, respectively. Furthermore, the P-type of three fins vertically stacked with gate oxide stack junctionless nanosheet and combined -type and P-type three fins vertically stacked with gate oxide stack junctionless nano-sheet were stimulated to construct the inverter circuit. The Voltage Transfer Characteristics (VTC) of three fins vertically stacked with gate oxide stack junctionless nano-sheet with RCS have HfO2 as the gate-oxide based inverter has been notably improved compared to SiO2, al2O3 as a gate oxide and conventional three fins vertically stacked with gate oxide stack junctionless nano-sheet inverter. [ABSTRACT FROM AUTHOR]

Data‐driven mechanics offers great potential in engineering applications, where the efficient simulation of complex and, in particular, path‐dependent material behavior is often challenging. Within this approach, conventional material models are replaced by data sets containing snapshots of stress, strain and the history of both assumed to be sufficiently accurate representations of the underlying material behavior. Based on these snapshots and on physical admissibility, a distance function is built which is minimized to yield the boundary value problems' solution. The aim of this paper is to apply this framework to accurately simulate the complex behavior of shape memory alloy wires under cyclic loading, under which these materials exhibit a degradation of their features, denoted functional fatigue. The constructed synthetic data sets are enriched by real experimental data, showcasing that the data‐driven method is capable of combining both approaches of classic material modeling and modern methods which directly incorporate experimental data. [ABSTRACT FROM AUTHOR]

Adiabatic quantum algorithm always requires that the system is kept in its ground state during the time evolution, but the interaction between the system and its environment often destroys the adiabaticity. In this paper we show that the reservoir engineering, i.e. the squeezing of the environment, can be used to enhance the adiabaticity of the system in the presence of environment noise. We use the non-Markovian quantum state diffusion (QSD) method to solve the system dynamics. Taking the Max-Cut problem as an example, the effects of squeezing strength and squeezing direction on the adiabatic fidelity are investigated. The results show that appropriate squeezing can enhance the fidelity, and this enhancement remains effective as system sizes vary. Moreover, the optimal squeezing direction changes with different temperature and spectrum bandwidth. Our work demonstrate that reservoir engineering is an effective approach to control the dynamics of the system, which can be used to boost the adiabaticity of the quantum algorithm in open systems. [ABSTRACT FROM AUTHOR]

Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of "modeler experience" (MX). MX includes factors such as user experience, motivation, integration, collaboration and versioning, and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling. [ABSTRACT FROM AUTHOR]

Optimization techniques play a crucial role in improving the performance of machine learning applications. However, traditional techniques may not be suitable for addressing complex problems that involve numerous parameters requiring adaptation. This paper presents a novel variant of the gradient-based optimizer (GBO) and investigates the effectiveness of integrating the harmony search (HS) algorithm to enhance the GBO algorithm. The HS algorithm offers several advantages, including fast convergence and fewer adjustable parameters. The proposed method, called GBOHS, is evaluated through three experiments. The first experiment focuses on solving global optimization problems, while the second experiment assesses its ability to select the most relevant features using sixteen benchmark feature selection datasets. The final experiment applies the GBOHS method to solve five real engineering problems. The results are compared against those of well-known optimization algorithms using various performance measures, including fitness function value and classification accuracy. The findings demonstrate that the proposed GBOHS method achieves high accuracy and consistently outperforms the compared methods across all the experiments, demonstrating its promising effectiveness. [ABSTRACT FROM AUTHOR]

Conceptual engineering, motivated one natural way, involves the search for new concepts. But to what extent does conceptual engineering as practiced involve such conceptual innovation, and to what extent can it do so? In this paper, I first argue that conceptual engineering as practiced surprisingly does not appear to involve conceptual innovation, and then I discuss problems regarding the extent to which it can involve conceptual innovation. [ABSTRACT FROM AUTHOR]

The new era is characterized by knowledge production diversity, interdisciplinary and collaborative innovation. Engineering universities urgently need to update their scientific talent cultivation mechanisms to stay aligned with these evolving demands. Over the past twenty years, the Department of Materials Chemistry at Harbin Institute of Technology has responded to the major strategic needs of national aerospace. It has developed a unique “Big Chemistry” approach to science education, characterized by its aerospace and defense focus and a “Trinity and Multidimensional Integration” model that combines multiple disciplines. This paper provides a systematic exploration and implementation of this model, detailing the formation process, conceptual framework, construction of a molecular knowledge system, and the cultivation of cross-disciplinary practical skills through “Triple mentorship and Triple Practice”. [ABSTRACT FROM AUTHOR]

The aeroengine control system is a safety critical system for aeroengine, crucial for generating reliable thrust or power within the entire flight envelope of the engine. This paper presents the evolution of the aeroengine control system, summarizes and analyzes the development of three key system-level technologies. including architecture design, control law design, and fault diagnosis and condition monitoring design, as well as simulation enabling technologies. The discussion on the applicability of these technologies in engineering is then conducted. The prospects for future development directions and key research areas are finally explored, providing a valuable reference for the design of next generation aeroengine control systems. [ABSTRACT FROM AUTHOR]

This paper introduces a class of novel high-accuracy energy-preserving numerical schemes tailored specifically for solving the nonlocal wave equation with Gaussian kernel, which plays a fundamental role in various scientific and engineering applications where traditional local wave equations are inadequate. Comprehensive numerical experiments, including comparisons with analytical solutions and benchmark tests, demonstrate the superior accuracy and energy-preserving capabilities of the proposed schemes. These high-accuracy energy-preserving schemes represent a valuable tool for researchers and practitioners in fields reliant on nonlocal wave equation modeling, offering enhanced predictive capabilities and robustness in capturing complex wave dynamics while ensuring long-term numerical stability. [ABSTRACT FROM AUTHOR]

The efficient solution (fast and accurate) of parametric partial differential equations (pPDE) is of major interest in many domains of science and engineering, enabling evaluations of the quantities of interest, optimization, control, and uncertainty propagation—all them under stringent real-time constraints. Different methodologies have been proposed in the past within the model order reduction (MOR) community, based on the use of reduced bases (RB) or the separated representation at the heart of the so-called proper generalized decompositions (PGD). In PGD, an alternate-direction strategy is employed to circumvent the integration issues of operating in multi-dimensional domains. Recently, physics informed neural networks (PINNs), a particular collocation schema where the unknown field is approximated by a neural network (NN), have emerged in the domain of scientific machine learning. PNNs combine the versatility of NN-based approximation with the ease of collocating pPDE. The present paper proposes a combination of both procedures to find an efficient solution for pPDE, that can either be viewed as an efficient collocation procedure for PINN, or as a monolithic PGD that bypasses the use of the fixed-point alternated directions. [ABSTRACT FROM AUTHOR]

The study of thermoelasticity problems holds significant importance in the field of engineering. When analyzing non-Fourier thermoelastic problems, it was found that as the thermal relaxation time increases, the finite element solution will face convergence difficulties. Therefore, it is necessary to use alternative methods to solve. This paper proposes a physics-informed neural network (PINN) based on the DeepXDE deep learning library to analyze thermoelastic problems, including classical thermoelastic problems, thermoelastic coupling problems, and generalized thermoelastic problems. The loss function is constructed based on equations, initial conditions, and boundary conditions. Unlike traditional data-driven methods, this approach does not rely on known solutions. By comparing with analytical and finite element solutions, the applicability and accuracy of the deep learning method have been validated, providing new insights for the study of thermoelastic problems. [ABSTRACT FROM AUTHOR]

Seismic engineering, a critical field with significant societal implications, often presents communication challenges due to the complexity of its concepts. This paper explores the role of Artificial Intelligence (AI), specifically OpenAI’s ChatGPT, in bridging these communication gaps. The study delves into how AI can simplify intricate seismic engineering terminologies and concepts, fostering enhanced understanding among students, professionals, and policymakers. It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering. Further, the study contemplates the potential implications of AI, highlighting its potential to transform decision-making processes, augment education, and increase public engagement. While acknowledging the promising future of AI in seismic engineering, the study also considers the inherent challenges and limitations, including data privacy and potential oversimplification of content. It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field. This exploration provides an insightful perspective on the future of seismic engineering, which could be closely intertwined with the evolution of AI. [ABSTRACT FROM AUTHOR]

Background: Effective learning in computer programming courses has been a constant challenge for university teachers and has become a relevant competence for current professionals. The literature on gamification in learning presents mixed results, mainly due to problems in instructional design and inconsistency in gamification. Studies with positive empirical results on computer programming formative activities and showing under what conditions gamification is effective still need to be included. Objectives: This study aimed to quantify the effects of gamification techniques in software tools that support computer programming learning applied to data‐structures university students. The study focused on determining whether there are differences in learning when using gamification techniques in online exercising formative activities. The effects of gamification on learning objectives with diverse complexity are studied. Methods: The study was conducted on computer engineering university students following a pretest‐intervention‐posttest design. They were divided into control and experimental groups, where both used a bank of exercises in Moodle to support learning. The experimental group considered gamification techniques, including points, leaderboards, and badges. Results and Conclusions: Gamification techniques significantly outperformed in learning gains in exercising activities without gamification. Regarding learning complexity, increasing taxonomic complexity did not negatively affect gamification learning effects. Lay Description: What is currently known about the subject matter?: Gamification has been explored as a strategy to enhance learning motivation in various educational contexts, including computer programmingLiterature shows mixed quantitative results on learning improvements under gamification.Few studies cover formative exercises, deal with computer programming tasks, or use a LMS like Moodle.Lack of literature exploring the conditions for effective gamification use, and involving learning objective complexity. What does this paper add to the existing body of research?: Quantifies the impact of gamification techniques on learning outcomes, specifically focusing on computer‐programming in university.Provides empirical evidence on gamification significantly improving learning gains compared to non‐gamified data‐structures exercisesGamification is effective in tasks of diverse levels of taxonomic complexity, with consistent effectiveness regardless of learning difficulty. Implications of study findings for practitioners: Gamification techniques, can be used in higher education to enhance student engagement and learning outcomes in computer programmingThe gamification information (e.g. points and ranking) do not require an automatic update, can be processed manually and published daily.It is effective to use gamification for learning tasks of medium‐high difficulty in Moodle on computer programming topics.It is key a thoughtful instructional design when applying gamification, ensuring alignment with learning objectives and task complexity. [ABSTRACT FROM AUTHOR]

The presence of convex corners will affect the earth pressure calculation, but there is a paucity of studies on calculating the ground pressure of convex corners in excavation engineering. To address this issue, equilateral convex corners are divided into two types according to the relative relationship between the side length size of equilateral convex corners and the excavation depth, and their respective earth pressure calculation model is established. The microelement static equilibrium equation is set by the horizontal layer analysis method, and then the equations for calculating active earth pressure strength, active earth pressure, and active earth pressure acting points on the equilateral convex corners are derived. The example analysis and comparative Plaxis three-dimensional (3D) finite-element verification show that the friction angle in the soil is an important factor in distinguishing between two kinds of equilateral convex corners, and the active earth pressure strength, the active earth pressure, and the acting point will be affected by the side length and rotation angle, where the influence of the side length on the active earth pressure strength is relatively small. The active earth pressure may show a variety of different variation laws affected by soil parameters. The acting point will move in both directions of the plane with the change of side length and rotation angle. The theoretical calculations of active earth pressure agree with the results of three-dimensional simulations, which may explain the rationality and practicality of the theoretical method in this paper. [ABSTRACT FROM AUTHOR]

In the process of using composite materials, crack damage is unavoidable. These cracks may cause a decrease in the structural performance of the material and may even pose a threat to the safety of the entire structure. In this paper, the shape memory alloy (SMA) is embedded into the composite material, and the strain transfer effect of the interface layer is considered. The crack monitoring and repair model of SMA composite material based on strain transfer are established using SMA resistance-sensing characteristics and SMA constitutive model. Based on this monitoring and repair model, the crack monitoring behavior of SMA under different initial states and temperature conditions is discussed, and the crack repair behavior of SMA under specific initial states and temperature conditions is also discussed. The research results show that the change in SMA resistance and the strain of the composite material crack are linearly segmented, and as the temperature increases, the composite material crack is gradually repaired. This study can provide theoretical guidance for the further engineering application of SMA composite material crack monitoring and repair theory. [ABSTRACT FROM AUTHOR]

The present article explores the potential and usefulness of virtual reality (VR) and augmented reality (AR) technologies in space engineering. VR is a realistic, interactive 3D computer-created world that you can explore as if you really are there. In this work, we explored VR technology usage in space borne systems and engineering. Usually, designers use native mechanical design software like CATIA and CREO to design and analyse various systems and subsystems. System designers must also analyse system interfaces, intricacies, and alignments from all angles in an interactive and immersive fashion to foresee and avoid potential issues. Therefore, an in-house virtual reality solution is developed to understand a complex engineering sub-system/system; its assembly, dis-assembly; sectional views and perform 360-degree analysis of system/sub-systems by direct interaction in a virtual environment. This technical paper also explores the use of AR technology for various users to understand intricate engineering models and assist the crew members remotely, even in case of unavailability of proper communication. The AR application is equipped with interactive simulations and demonstrations, enabling engineers to manipulate and investigate 3D models in a mixed-reality environment. Application is designed in a generalized way to take any complex engineering model as an input. The contribution of this work to the field of space technology lies in its innovative approach and in-house developments to visualize and interact with complex models through VR and AR-based solutions. [ABSTRACT FROM AUTHOR]

Efficient demand planning holds critical significance for businesses. In this research, the authors investigate the applicability of the Temporal Fusion Transformer, a neural network-based model, to address demand planning challenges. Specifically, they explore the potential benefits of incorporating additional information related to product characteristics and sales channel types. The primary objective of this study is to assess the advantages gained by incorporating these supplementary variables. The dataset utilized in this analysis originates from a company predominantly engaged in the sale of building engineering products. The authors initially focus on static attributes such as product groupings and time-varying attributes such as sales channel variations. This paper’s contribution lies in its comprehensive case study, which applies the Temporal Fusion Transformer model to a real-world demand planning problem of the company, including all its specifications and customizations. [ABSTRACT FROM AUTHOR]

Research shows a positive association between skilled migration and innovation. Related literature however is largely limited to the use of proxies such as patents, and publications. There is also a lack of attention to how innovation is accomplished in practices. This paper addresses these gaps with an examination of the innovative contributions made by immigrant engineers in Canada. Conceptually, informed by practice-based theories, it conceives innovation as a sociocultural and sociomaterial process that leads to the transformation of the object/motives of activities, i.e. the problem space to which actions are directed. Empirically, drawing on a thematic and situational analysis of the career accounts of 32 immigrant engineers, it shows that immigrants expand engineering practices by introducing, inter alias, new technologies, products, processes, policies and standards. It further traces the rise of the problem spaces, and the ways in which engineering objects and other practitioners are knotted into practices of innovation. It argues that while immigrants manage to introduce epistemic objects through continuous learning and knowledge translation, it is through the enrolment of other practitioners, and technologies and tools that relations of differences and power are (re)negotiated, and new ways of doing become amplified as innovation at work. [ABSTRACT FROM AUTHOR]

The paper outlines a methodology for validating the accuracy of a control system's runtime implementation. The runtime takes the form of a virtual machine executing portable code compliant with IEC 61131-3 standards. A formal model, comprising denotational semantics equations, has been devised to specify machine instruction decoding and operations, including arithmetic functions across various data types, arrays, and subprogram calls. The model also encompasses exception-handling mechanisms for runtime errors, such as division by zero and invalid array index access. This denotational model is translated into executable form using the functional F   ♯ language. Verification involves comparing the actual implementation of the virtual machine against this executable model. Any disparities between the model and implementation indicate deviations from the specification. Implemented within the CPDev engineering environment, this approach ensures consistent and predictable control program execution across different target platforms. [ABSTRACT FROM AUTHOR]

The welfare of society and the relationship between people and the natural environment are all directly impacted by engineering work, and codes of ethics are central to the profession. Yet many engineers struggle to incorporate these principles into their daily work because such codes typically emphasize professional conduct without reflecting on the role of engineering within such complex social and environmental systems. In this paper, we propose a human rights–based approach to engineering anchored in five core principles of distributive justice, broad participation, explicit consideration of duty-bearers, accountability for all actors involved, and indivisibility of rights. This is a new paradigm that draws on universal principles to shape individual ethical obligations and the norms of the profession to prevent risk, enhance access to the benefits of technology, and redress social and environmental harms resulting from engineered products or processes. This paradigm could transform both university education and professional practice by harmonizing existing engineering ethics with core human rights obligations to respect, protect, and fulfill human rights. [ABSTRACT FROM AUTHOR]

This paper addresses the exploration of thermoelastic damping (TED) in a microbeam resonator, utilizing the Quintanilla generalized thermoelasticity theory. The study emphasized analyzing the influence of beam height on the microbeam resonators TED and also conducted a thorough assessment of the frequency shift and normalized attenuation in microbeam resonators. These outcomes are then compared with different materials, like silicon (Si) and lead (Pb). The current undertaking demonstrates its utility in tackling challenges associated with science and engineering. [ABSTRACT FROM AUTHOR]

With the development of the oil industry, the accuracy of the conventional well structure design method will be lower and lower. In order to ensure the smooth progress of drilling construction, this paper proposes a risk assessment method for the formation be drilled based on the equivalent drilling fluid density matrix, which reduces the use of empirical coefficients in the well structure design and realizes the fine design of the well structure. [ABSTRACT FROM AUTHOR]

This paper presents the first results of a teaching-learning proposal in relation to how to teach science using an instrument for measuring magnitudes and how female students of the teaching degree learn in a meaningful way. The aim is for future teachers to learn science, about science and how to explain it, by doing and reflecting on what an instrument is based on, its design, construction, and how we apply it to investigate. An inquiry-based scientific and engineering activity is promoted which, through instrumentation, gives importance to argumentation and modelling by applying inquiry processes. The first results are also presented based on the reports and metacognitive reflections made by the students. Some difficulties and strengths of the methodology have been identified. [ABSTRACT FROM AUTHOR]

The vehicular ad-hoc network is a technology that enables vehicles to interact with each other and the surrounding infrastructure, aiming to enhance road safety and driver comfort. However, it is susceptible to various security attacks. Among these attacks, the position falsification attack is regarded as one of the most serious, in which the malicious nodes tamper with their transmitted location. Thus, developing effective misbehavior detection schemes capable of detecting such attacks is crucial. Many of these schemes employ machine learning techniques to detect misbehavior based on the features of the exchanged messages. However, the studies that identify the impact of feature engineering on schemes' performance and highlight the most efficient features and algorithms are limited. This paper conducts a comprehensive literature survey to identify the key features and algorithms used in the literature that lead to the best-performing models. Then, a comparative study using the VeReMi dataset, which is publicly available, is performed to assess six models implemented using three different machine learning algorithms and two feature sets: one comprising selected and derived features and the other including all message features. The findings show that two of the suggested models that employ feature engineering perform almost equally to existing studies in identifying two types of position falsification attacks while exhibiting performance improvements in detecting other types. Furthermore, the results of evaluating the proposed models using another simulation exhibit a substantial improvement achieved by employing feature engineering techniques, where the average accuracy of the models is increased by 6.31–47%, depending on the algorithm used. [ABSTRACT FROM AUTHOR]

This paper explores a historical innovation created by Henry Muncaster: a stationary steam engine featuring a single-cylinder horizontal design with a crosshead trunk guide. Through the application of engineering graphics techniques, we have elucidated the functioning of this invention by developing a 3D CAD model based on the original drawings published in Model Engineer magazine in 1957. However, the geometric modeling process faced challenges due to missing and erroneous dimensions for several components. Consequently, dimensional, geometric, and movement constraints were applied to ensure the coherence and functionality of the 3D CAD model, alongside conducting an interference analysis. Ultimately, the proper alignment of the cylinder and crosshead was ascertained, which is crucial for maintaining uniform forces and motions within the steam engine. This alignment is pivotal for achieving balanced operation, minimizing vibrations, and enhancing the overall efficiency of the invention. [ABSTRACT FROM AUTHOR]

Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability and methodology to support, deepen, and extend those contributions. [ABSTRACT FROM AUTHOR]

In Industry 5.0, the scarcity of data on defective components in smart manufacturing leads to imbalanced data sets. This imbalance poses a significant challenge to the development of robust Machine Learning (ML) models, which typically require a rich variety of data for effective training. The imbalance not only restricts the models’ accuracy but also their applicability in diverse industrial scenarios. To tackle this issue, our research delves into the capabilities of Deep Generative Models, with a special focus on Generative Adversarial Networks, for the generation of synthetic data. This approach is aimed at rectifying dataset imbalances, thereby enhancing the training process of ML models. We demonstrate how synthetic data can substantially bolster the performance and reliability of ML models in industrial settings. Furthermore, the paper presents an innovative MLOps pipeline and architecture, meticulously designed to incorporate Deep Generative Models (DGMs) into the entire ML development cycle. This solution is automated and goes beyond mere automation; it is self-optimizing and capable of making necessary corrections, specifically engineered to address the dual challenges of data imbalance and scarcity, thus enabling more precise and dependable ML applications in smart manufacturing. [ABSTRACT FROM AUTHOR]

The literature on laboratory objectives in engineering education research is scattered and inconsistent. Systematic literature reviews identified the need for better understanding. This paper ranks the laboratory learning objectives across the cognitive, psychomotor and affective domains to improve scaffolding. It provides an opportunity for reflection, a pathway to confirm assessment alignment, and opens future research areas. To accomplish this, the Laboratory Learning Objectives Measurement (LLOM) instrument is used to survey 160 academics from around the world representing 18 engineering disciplines. The results suggest that the collective ranking order does represent a framework that can be used broadly. However, for greater alignment with consensus thinking, discipline rankings should be used. The cognitive domain was deemed the most important. These results provide the community's opinion and may not necessarily be best practice, providing an opportunity for reflection. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Piezotronic devices have attracted a great deal of attention due to their potential applications in self-powered tactile sensing, nano-device memory, human-electronic interface, etc. As the size of piezotronic devices shrinks, some interesting quantum effects begin to appear. In this paper, we establish a theory oriented to the engineering application of piezoelectric semiconductors, called quantum-corrected phenomenological (QCP) theory, by coupling the density-gradient theory and the linear piezoelectricity theory through Gauss's law. For numerical verification, we specifically studied the electromechanical behaviors in GaN/AlGaN heterostructure quantum wells (QWs) with both infinite and finite barrier height. The results of electron density, electric potential, and quantum potential are provided, and their dependence on the doping density, the applied stress, and the Al mole fraction is investigated. Some interesting quantum effects are revealed, and their influencing mechanisms are well investigated from a macroscopic perspective. Not only do the conclusions drawn in this paper enrich the fundamental understanding of the piezotronic effect in a QW structure, but also the proposed QCP theory can serve as a valuable tool for future device engineering. [ABSTRACT FROM AUTHOR]

Background Study: The research discusses the need for a paradigm shift in engineering education current practices to accommodate the digital native students. The paper emphasizes the importance of integrating disruptive technologies, namely Virtual Reality (VR) through Head Mounted Displays VR (HMD VR) and Desktop Based VR (DB VR) and comparing it with the traditional teaching methods. The research focuses on second‐year undergraduate students enrolled in a concrete structures course. Objectives: The objective of the study is to analyse the impact of DB VR and HMD VR on students' motivation, performance and workload in comparison with traditional 2D drawings. The study also aims to investigate how simulation sickness and system usability could impact students' motivation and performance when interpreting concrete structures. Methods: The study divided second‐year civil engineering students into three groups, 2D, DB VR and HMD VR. The performance and motivation levels of students were assessed based on the number of mistakes identified and an intrinsic motivation survey. Furthermore, data on system usability and simulation sickness were collected. Spearman rank correlation was conducted to analyse the relationships between the variables tested. Results: The results have shown that students' motivation and performance were much higher when using VR in general and HMD VR specifically, students' efficiency and accuracy were also observed. On the other hand, usability was found to be moderately correlated with motivation, with students rating HMD VR as the most usable approach. A moderate inverse correlation was observed between simulation sickness and motivation, indicating that higher simulation sickness led to lower motivation levels. Conclusion: The study concludes that integrating VR in general, DB VR, or HMD VR into a concrete structures course for undergraduate students can significantly improve students' performance and motivation. Furthermore, enhancing usability of any of the methods used can also improve the performance and motivation. On the other hand, simulation sickness inversely affects motivation and performance, therefore, VR developers and educators should not neglect it. Lay Description: What is already known about the topic: Traditional teaching methods in engineering education require improvement.Integrating digital technologies into engineering education enhances performance.Digital technologies can potentially enhance motivation. What this paper adds: Compares traditional teaching methods for concrete structures interpretation with virtual reality.Establishes correlations between motivation, performance and usabilityIntroduces how desktop‐based VR and head‐mounted display VR can produce varying results. Implications for practice and/or policy: Decision‐makers can use the information to integrate updated pedagogy into engineering curricula.Allows for further exploration of different digital technologies that can be used for concrete structure interpretation.Allows instructors to introduce virtual reality in their courses. [ABSTRACT FROM AUTHOR]

Descriptive geometry has indispensable applications in many engineering activities. A summary of these is provided in the first chapter of this paper, preceded by a brief introduction into the methods of representation and mathematical recognition related to our research area, such as projection perpendicular to a single plane, projection images created by perpendicular projection onto two mutually perpendicular image planes, but placed on one plane, including the research of curves and movements, visual representation and perception relying on a mathematical approach, and studies on toothed driving pairs and tool geometry in order to place the development presented here among them. As a result of the continuous variability of the technological environment according to various optimization aspects, the engineering activities must also be continuously adapted to the changes, for which an appropriate approach and formulation are required from the practitioners of descriptive geometry, and can even lead to improvement in the field of descriptive geometry. The imaging procedures are always based on the methods and theorems of descriptive geometry. Our aim was to examine the spatial variation in the wear of the tool edge and the machining of the components of toothed drive pairs using two cameras. Resolving contradictions in spatial geometry reconstruction research is a constant challenge, to which a possible answer in many cases is the searching for the right projection direction, and positioning cameras appropriately. A special method of enumerating the possible infinite viewpoints for the reconstruction of tool surface edge curves is presented in the second part of this paper. In the case of the monitoring the shape geometry, taking into account the interchangeability of the projection directions, i.e., the property of symmetry, all images made from two perpendicular directions were taken into account. The procedure for determining the correct directions in a mathematically exact way is also presented through examples. A new criterion was formulated for the tested tooth edge of the hob to take into account the shading of the tooth next to it. The analysis and some of the results of the Monge mapping, suitable for the solution of a mechanical engineering task to be solved in a specific technical environment, namely defining the conditions for camera placements that ensure reconstructibility are also presented. Taking physical shadowing into account, conclusions can be drawn about the degree of distortion of the machined surface from the spatial deformation of the edge curve of the tool reconstructed with correctly positioned cameras. [ABSTRACT FROM AUTHOR]

Probability and random processes is considered by students to be conceptually one of the most difficult subjects in the undergraduate electrical and computer engineering curriculum. There are numerous reasons for this difficulty encountered by the students. First off, humans are not innately good at probabilistic intuition. Traditionally, this subject has been introduced in a very abstract manner without emphasis on real-world applications from electrical and computer engineering discipline. In addition, extensive use of interactive simulation and visualization tools, offering an alternative way of developing probabilistic intuition, is usually missing from traditional course offerings. This paper presents a unique pedagogical approach to teaching an introductory probability course offered to electrical and computer engineering juniors. The salient features of the proposed pedagogical approach include more emphasis on real-world electrical and computer engineering problems that show the applications of abstract probabilistic concepts; extensive hands-on and interactive MATLAB® simulations of real-world electrical and computer engineering problems that are tightly integrated into the curriculum; highlighting the frequentist approach to build probabilistic intuition using simulations; concrete examples showing how naive probabilistic intuition can be erroneous and how to develop correct probabilistic intuition based on systematically modeling, simulating, and analyzing a problem; and application-based simulations driving the abstract theory rather than the other way around. This pedagogical approach was implemented in a course offered to electrical and computer engineering undergraduates at Purdue University Northwest. The paper presents a concrete example illustrating how the salient features of the proposed pedagogical approach were implemented as part of this course and student data from the courses to validate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

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

The first issue of Ozone: Science & Engineering (OS&E)was published in early 1979 with Dr L. J. Bollyky as theEditor-in-Chief. This was a milestone for the International Ozone Association, enabling professional recognition of the advances in ozone technology. Since this first issue, 45 volumes of Ozone: Science & Engineering have been published containing 238 individual issues, 1719 technical articles and nearly 22,000 pages. Dr Rip Rice became Editor-in-Chief in 1985 and continued this position until 1998. Barry Loeb was the editor until 2024. Under the leadership of these gentlemen, OS&E expanded from about 70 pages per issue four times per year to 100 pages, then to six issues per year, at about 100 pages. In 2001, the dimensions of the journal were increased to accommodate even more manuscripts. In 1989, the Harvey M. Rosen Memorial Award was established to recognize the "best paper" published in Ozone: Science & Engineering during the two-year period between World Congresses. The selection of this award is determined by the Editorial Board of Ozone: Science & Engineering. To date, 19 papers have received this prestigious award. OS&E has followed and reported the development of ozone technologies via peer reviewed articles from ozone researchers, manufacturers and consultants. This paper reviews the development of ozone technologies as reported by OS&E and summarizes how the technologies have evolved to meet environmental and other needs. [ABSTRACT FROM AUTHOR]

Carbon nanotube cement-based grouting materials currently mostly stay in the pure slurry research stage, and there is little research in the field of rock crack reinforcement combined with the engineering application background. Therefore, on the basis of existing research, it is important to expand the dispersion of carbon nanotubes in cement paste, and to carry out research on the reinforcement of fractured rocks in combination with the engineering background. This paper combines the actual needs of construction engineering to prepare carbon-based nanomaterials, and analyzes its impact on construction engineering in combination with experiments. Moreover, this paper analyzes the influence of carbon nanotubes on the macro-mechanical properties of cement-based materials, and studies the influence of modified carbon nanotubes on the macro-mechanical properties of cement-based materials. In addition, this paper explores the micromechanical properties of carbon nanotube cement materials at the nanometer scale. The research results verify the effectiveness of carbon-based nanomaterials. [ABSTRACT FROM AUTHOR]

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

Questions are used to obtain information, stimulate thinking, and redirect reasoning. Academics in higher education use questions on a daily basis to stimulate thinking and reasoning in students. Final examination papers are used by academics to assess the retention and application skills of students. The assumption, however, exists that questions relating to application skills at universities of technology should start to dominate the higher academic levels in education, with a subsequent drop in questions regarding retention skills. These questions may be categorized as either higher order or lower order questions. This article attempts to distinguish between these two types of questions in light of Bloom's taxonomy, with similar concepts such as deep and surface learning being examined. The literature review is applied to an electrical engineering module titled Electronics, which serves as the case study. The results of this study indicate that a high percentage of the final examination papers dealt with the objective "Application," where students had to make use of numerous mathematical equations to solve various unknowns. The results also indicated that academics in electronics are using more lower order than higher order questions in their final examination papers. A balance is suggested between these two types of questions for various academic levels at universities of technology. [ABSTRACT FROM AUTHOR]

Applications of electrochemical detection methods in microfluidic paper-based analytical devices (μPADs) has revolutionized the area of point-of-care (POC) testing towards highly sensitive and selective quantification of various (bio)chemical analytes in a miniaturized, low-coat, rapid, and user-friendly manner. Shortly after the initiation, these relatively new modulations of μPADs, named as electrochemical paper-based analytical devices (ePADs), gained widespread popularity within the POC research community thanks to the inherent advantages of both electrochemical sensing and usage of paper as a suitable substrate for POC testing platforms. Even though general aspects of ePADs such as applications and fabrication techniques, have already been reviewed multiple times in the literature, herein, we intend to provide a critical engineering insight into the area of ePADs by focusing particularly on the practical strategies utilized to enhance their analytical performance (i.e. sensitivity), while maintaining the desired simplicity and efficiency intact. Basically, the discussed strategies are driven by considering the parameters potentially affecting the generated electrochemical signal in the ePADs. Some of these parameters include the type of filter paper, electrode fabrication methods, electrode materials, fluid flow patterns, etc. Besides, the limitations and challenges associated with the development of ePADs are discussed, and further insights and directions for future research in this field are proposed. • Parameters contributing to the performance of electrochemical paper-based analytical devices (ePADs) are outlined. • Strategies on paper substrate, electrode fabrication, fluid flow manipulation to enhance ePADs performance are discussed. • Strategies on device design, hyphenation of analytical procedures, and signal amplification are reviewed. • Perspectives focusing on development of new methodologies to enhance ePADs performance are proposed. [ABSTRACT FROM AUTHOR]