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

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

In this framework, experimental tests on different battery technologies, BMS and cooling systems are extremely important to extend the battery life and BEV performance (Sehil et al. [[13]]). Electrical machines and magnetic components, high-voltage voltage power converters, and battery storage systems are some basic bricks to build an effective and efficient energy conversion environment. Electrical Machines and Power Converters The electrical power conversion from renewable energy sources plays a crucial role in the development of a sustainable society. Finally, the fil rouge of every contribution in this section is oriented to investigate the power converters, electrical machines, magnetic components, and battery storage systems in actual energy conversion applications for renewable energy sources integration by a modelling approach. [Extracted from the article]

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

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]

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope. The third paper, authored by Jeong et al. [[14]], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. With rapid advancements in artificial intelligence (AI), the Internet of Things (IOTs), and communication networks, the future of information and communication engineering (ICE) promises remarkable opportunities and challenges. 10.3390/app12199801 18 Zhang M., Wang C., Zhu W., Shen Y. An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning. [Extracted from the article]

X. Li, D. Zhang, and Y. Hou [[21]] monitored ground deformation during the excavation process of shield tunnels in water-rich strata and verified the results using the numerical software FLAC 3D. Thus, secondary grouting of the existing tunnel should be carried out promptly before the tunnel underpass excavation. The first paper, authored by X. Li, Y. Yang, X. Li, and H. Liu [[14]], introduced three shield tunnel construction cases to acquire the main driving parameters for the evaluation of cutting head clogging. The second paper, prepared by J. Du, Q. Fang, J. Wang, and G. Wang [[2]], divided the process of a high-speed train passing through a railway tunnel into three stages, according to the spatial relationship between the train and the tunnel. [Extracted from the article]

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

In order to improve the efficiency and accuracy of the modeling and design work of the sluice gate project, this paper proposes an automatic generation template of the sluice gate project with customized semantics and project layout scheme, aiming at realizing the rapid assembling of all kinds of components of the sluice gate project. In the construction process, this paper first starts from basic parametric modeling and proposes constraints as the basis of modeling. Subsequently, a template library framework is developed based on the constraints to ensure that the generated templates have a high degree of standardization and consistency. Finally, an efficient and flexible template library is successfully constructed by using the customized classes and functions of Revit API, which provides powerful technical support for the modeling and design work of sluice gate engineering. This achievement helps to promote the informationization and intelligent development of the water conservancy engineering industry, and its versatility and scalability also make it have a wide range of application prospects in other water conservancy engineering fields. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. The tool supports the emulation or the actual connectivity of a large number of IoT devices. The emulated devices can generate realistic correlated IoT data and stream it via the message queuing telemetry transport (MQTT) protocol to a big data platform. The tool allows students to design ML models with different algorithms for their chosen use cases and train them for decision-making based on the streamed data. Moreover, the paper proposes learning outcomes to be targeted when integrating the tool into an electrical engineering curriculum. The tool is evaluated using a comprehensive survey. The survey results show that the students gained significant knowledge about IoT concepts after using the tool, even though many of them already had prior knowledge of IoT. The results also indicate that the tool noticeably improved the students' practical skills in designing real-world use cases and helped them understand fundamental machine learning analytics with an intuitive user interface. [ABSTRACT FROM AUTHOR]

Currently, the fundamental tenets of systems engineering are supported by a model-based approach to minimize risks and avoid design changes in late development stages. The models are used to formalize, analyze, design, optimize, and verify system development and artifacts, helping developers integrate engineering development across domains. Although model-based development is well established in specific domains, such as software, mechanical systems, and electrical systems, its role in integrated development from a system perspective is still a challenge for industry. The model-based systems engineering (MBSE) tool-chain is an emerging technique in the area of systems engineering and is expected to become a next-generation approach for supporting model integration across domains. This article presents a literature review to highlight the usage and state of the art to generally specify the current understanding of MBSE tool-chain concepts. Moreover, the results are used for identifying the usage, advantages, barriers, concerns, and trends of tool-chain development from an MBSE perspective. [ABSTRACT FROM AUTHOR]

This Special Issue includes sixteen (16) high-quality papers presented during the 2020 IEEE International Conference on High-Voltage Engineering and Application (ICHVE 2020) spanning the above research areas. The Special Issue was welcomed with great interest, as ICHVE attracts recent advancements in all fields of high-voltage engineering and applications. Electrical energy is polymorphic, with voltage levels varying between a few volts to MVs and frequencies from a few Hz to MHz. Closing Remarks The papers published in this Special Issue report on the progress made in various high-voltage applications. [Extracted from the article]

With the rapid progress of big data and artificial intelligence, machine learning technologies such as learning and adaptive control have emerged as a research focus in petroleum engineering. They have various applications in oilfield development, such as parameter prediction, optimization scheme deployment, and performance evaluation. This paper provides a comprehensive review of these applications in three key scenarios of petroleum engineering, namely hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection. This article first introduces the steps and methods of machine learning processing in these scenarios, then discusses the advantages, disadvantages, existing challenges, and future prospects of these machine learning methods. Furthermore, this article compares and contrasts the strengths and weaknesses of these machine learning methods, aiming to help researchers select and improve their methods. Finally, this paper identifies some potential development trends and research directions of machine learning in petroleum engineering based on the current issues. [ABSTRACT FROM AUTHOR]

Engineering managers leverage the expertise of engineers in their teams to inform decisions. Engineers may convey their expertise in the form of opinions and/or judgements. Given a decision, it is common to elicit and aggregate the expertise from various engineers to capture a broader set of experiences and knowledge. Establishing an internally and externally consistent aggregation framework is therefore paramount to yield a meaningful aggregation, that is, to make sure that the expertise of each engineer is accounted for reasonably. However, we contend that most de facto aggregation techniques lack such consistency and lead to the inadequate use and aggregation of engineering expertise. In this paper, we investigate the consistency or lack thereof of various expertise aggregation techniques. We derive implications of such inconsistencies and provide recommendations about how they may be overcome. We illustrate our discussion using safety decisions in engineering as a notional case. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the state of engineering and design research for sustainable construction. It aims to report the current status and future trends within this dynamic field, combining econometric and content analysis using the Bibliometrix R encapsulation tool. This study reviewed academic journals using bibliometric analysis. We selected articles by searching the Scopus database. Primarily relevant articles were identified and screened. The dataset comprised a substantial compilation of 731 journal articles from 278 sources published between January 2000 and June 2023, which formed the basis of the in-depth analysis. The importance of sustainable construction is gradually gaining more attention, and engineering and design are the significant phases of construction. This research identifies that sustainable construction is nothing in isolation; instead, it warrants the holistic integration of multiple factors, as illustrated in the Sankey diagram. Recycling, durability, life cycle assessment, innovative materials, and energy efficiency have emerged as central themes, reflecting global concern to enhance sustainability, reduce environmental impacts, and optimize resource utilization. This study is a valuable resource for researchers, practitioners, and policymakers, offering guidelines for collaborative efforts towards sustainable development. This paper paves the way for interdisciplinary cooperation and strategic alignment among diverse stakeholders, promoting innovative approaches to sustainable construction. [ABSTRACT FROM AUTHOR]

The general purpose of this study was to determine the potential of using a gamified platform in the development of scientific writing skills among engineering students at a Peruvian university. To this end, a gamified web platform named Call for Papers for Engineers was designed. This platform contains mini-games focused on developing reading and writing skills for articles related to the engineering area. A quantitative methodological approach was employed, with a quasi-experimental design involving two groups: an experimental group and a control group, with pre-and post-test measurements. Additionally, the gamified platform was validated through expert judgment, and user satisfaction levels were assessed. The main results indicate that the content developed in the course and the use of the gamified web platform were effective teaching methods, as the students in the experimental group demonstrated higher performance after using the gamified platform compared to the control group. Furthermore, participants in the study expressed satisfaction with the use of this technological resource, finding it motivating and user-friendly. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and academics. The initial electronics-development tasks of a smart home project were prompted to the ChatGPT system to find out its capabilities and limitations. We wanted to obtain detailed information on the central processing controller units and the actual sensors usable for the specific project, their specifications and recommendations on the hardware and software design flow additionally. Furthermore, an extensive literature survey was requested to see if the bot could offer scientific papers covering the given topic. It was found that the ChatGPT responded with proper recommendations on controllers. However, the suggested sensor units, the hardware and software design were only partially acceptable, with occasional errors in specifications and generated code. The results of the literature survey showed that non-acceptable, fabricated citations (fake authors list, title, journal details and DOI—Digital Object identifier) were presented by the bot. The paper provides a detailed qualitative analysis, a performance analysis and critical discussion of the aforementioned aspects while providing the query set, the generated answers and codes as supplied data with the goal to give added value to electronics researchers and developers if trying to reach out for the tools in their profession. [ABSTRACT FROM AUTHOR]

The text describes the 27th International Database Engineering and Applications Symposium (IDEAS-2023) held in Heraklion, Crete, Greece. The conference serves as a platform for data engineering researchers, practitioners, developers, and application users to exchange ideas and experiences. The text also highlights six selected papers from the conference, covering topics such as real-time database operations on an FPGA, optimized path planning for robots, meshfree interpolation of scattered data, optimizing distributed SPARQL queries using machine learning, specification mining over temporal data, and feature selection for improved classification performance. The authors of these papers present their research findings and methodologies in their respective fields. [Extracted from the article]

The experimental results presented in the paper and achieved using real datasets from Shanghai Telecom indicate that DQN-ESPA outperforms state-of-the-art algorithms such as the simulated annealing placement algorithm, Top-K placement algorithm, K-Means placement algorithm, and random placement algorithm. High-performance supercomputers and emerging computing clusters created in research and development centres are rapidly increasing available computing power, which scientists are eager to use to implement increasingly advanced computing methods [[1]]. Thus, computationally demanding artificial intelligence algorithms and computational fluid dynamics methods are used more widely to consider complex engineering issues and verify and provide new information on entropy or information theory concepts [[2]]. As can be seen above, the original research articles, as well as review articles focused on optimization by artificial intelligence (AI) algorithms on computational and entropy issues, have been submitted to the Special Issue. [Extracted from the article]

The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric's low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties. [ABSTRACT FROM AUTHOR]

This article provides an overview of laser surface engineering in the field of tribology, which focuses on friction, wear, and lubrication between surfaces in motion. It introduces a special issue on laser surface engineering for tribology, which includes thirteen papers covering topics such as laser cladding, surface texturing, and additive manufacturing. Each paper explores different aspects of laser surface engineering and offers insights for future research and development in the field. The article discusses various research contributions, including the impact of preheating temperatures on laser-clad coatings, the use of laser-assisted surface texturing to reduce friction and wear, and the proposal of a nitride layer to resist tribocorrosion. The findings contribute to improving the wear and corrosion resistance of materials through laser surface engineering. The article concludes by suggesting future research directions and emphasizing the importance of tailoring laser surface engineering approaches to meet industry-specific requirements. [Extracted from the article]

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]

With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering. [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]

Delaying an electromagnetic (EM) wave pulse on a thin screen for a significant time before releasing it is highly desired in many applications, such as optical camouflage, information storage, and wave–matter interaction boosting. However, available approaches to achieve this goal either require thick and complex systems or suffer from low efficiencies and a short delay time. This paper proposes an ultra-thin meta-platform that can significantly delay an EM-wave pulse after reflection. Specifically, our meta-platform consists of three meta-surfaces integrated together, of which two are responsible for efficiently coupling incident EM-wave pulse into surface waves (SWs) and vice versa, and the third one supports SWs exhibiting significantly reduced group velocity. We employ theoretical model analyses, full-wave simulations, and microwave experiments to validate the proposed concept. Our experiments demonstrate a 13 ns delay of an EM pulse centered at 12.975 GHz, enabled by a λ/8-thick and 38-λ-long meta-device with an efficiency of 32% (or 70%) with (or without) material loss taken into account. A larger delay time can be enabled by devices with larger sizes considering that the SWs group velocity of our device can be further reduced via dispersion engineering. These findings establish a new road for delaying an EM-wave pulse with ultra-thin screens, which may lead to many promising applications in integration optics. [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]

With the continuous development of the geological engineering field, high-voltage electric pulse plasma rock-fracturing technology has become a research hotspot in recent years. It is now widely recognized that this fracturing technology has many application prospects and great economic benefits. Through the research process of this technology, it has proven to be an efficient and new type of rock-fracturing technology, which overcomes the problems of high cost, low efficiency, high safety risk, and serious pollution associated with traditional rock-fracturing technology. Also, it has unique advantages in terms of protecting the environment and reducing damage to the surrounding buildings. This paper reviews the research history of plasma shock wave influencing factors and pulsed discharge plasma rock-fracturing technology, summarizes the research on this technology from the perspectives of the mechanism of high-voltage electric pulse plasma rock fracturing and practical application, and discusses the feasibility of this technology when applied to the field of tunnel boring as well as the future development direction. This technology can be better used in the tunnel-boring field, which can greatly improve the tunnel-boring efficiency, but at present, the research on plasma rock-fracturing-assisted tunnel boring is still in the laboratory research stage, which lacks systematic research equipment and judging indexes, and the follow-up should focus on improving the systematic research capabilities of the plasma rock-fracturing-assisted tunnel-boring equipment, and systematically and comprehensively carry out research on rock-fracturing by plasma-assisted excavation equipment. [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 use of model-based systems engineering (MBSE) has been increasingly explored recently in industries that require multi-discipline engineering coordination. In the European space industry, applying MBSE for the engineering of space systems has been an ongoing undertaking on many missions. In the following paper, the MBSE activities in CAMEO conducted during the A/B1 phases of a typical Earth observation satellite engineered by Airbus are discussed in detail in the form of a case study. The analyses shown are based around the modeling of the spacecraft electrical interfaces in CAMEO. This model was used to automate electrical interface control documents (EICDs) and enable the control of electrical interface development. These methodologies were further put in the context of Airbus' satellite design processes to assess the benefits of an MBSE approach to the current electrical interface engineering procedure and the potential for the reuse of CAMEO models between satellite projects. The reduction in system engineering effort through the reuse of models to modularly create similar satellite systems for efficient concept evaluation and comparison is a clear benefit. [ABSTRACT FROM AUTHOR]

(1) Background: Romanian earthquakes caused severe damage over time to a significant number of constructions, and that is why efforts are being made to make structural systems safer. (2) Methods: For structural systems with protection against seismic actions or vibrational actions that have linear viscous dissipation devices, the requirement to assess the equivalent modal damping rate for the entire functional assembly related to the other dynamic parameters arises. (3) Results: This article presents the analytical development of formulas for the compound damping and circular frequency when anti-seismic devices have different dynamic characteristics and their application in order to solve some real engineering cases of bridges and viaducts in Romania with distinct viscoelastic supports. In support of this idea, some experimental tests on a beam system resting on two different anti-seismic elastic supports highlighted the fact that the compound damping of the system can be calculated with the relations established in this paper, provided that the displacements in the horizontal direction of excitation are in the linear domain. Also, we determined the seismic response considering the Vrancea 1977 accelerogram for critical damping ratios of 5% and 18.5%, and then we obtained the variation in the factor of transmissibility depending on the frequency, in order to highlight the optimized value of the equivalent amortization/damping. (4) Conclusions: In the specific context of Romanian seismicity, seismic isolation through the use of isolators with different characteristics represents an optimal technical solution, and it is also optimal from an economic point of view, with an appropriate level of dynamic isolation obtained. [ABSTRACT FROM AUTHOR]

Magnetorheological dampers (MRDs) are of great interest in engineering due to their continuously adjustable damping characteristics. Accurate models are essential for optimizing MRDs and analyzing system dynamics. However, conventional methods widely overlook the impact of excitation frequency and amplitude. To address this issue, this work proposes a modified Bouc–Wen model that can be adapted to various excitation conditions. The model's parameters depend on the current, excitation frequency, and amplitude. The mechanical characteristics of the MRD were analyzed by the tests. The parameters in the Bouc–Wen model were identified by combining the harmonic balance method and the genetic algorithm. The modified Bouc–Wen model was established by analyzing the variation of each parameter with current, excitation frequency, and amplitude. Finally, the agreement between the modified prediction model and the test results was verified under sinusoidal excitation of 80 mm and 1 Hz. The average relative errors were 3.87%, 2.82%, 2.45%, 2.19%, and 3.27% for current excitations of 0 A, 0.5 A, 1 A, 1.5 A, and 2.0 A, respectively. Since the MRD in this paper operates from 0.5 Hz to 2 Hz, the modified model was validated in the same range. Experiments demonstrate that the modified Bouc–Wen model efficiently and accurately describes the mechanical properties of the MRD under various excitation conditions. [ABSTRACT FROM AUTHOR]

Unbonded flexible risers have been widely used in the field of offshore engineering in recent years due to their excellent performance in extreme dynamic marine environments, structural compliance, low installation cost, and low quality. And, the internal pressure capacity of unbonded flexible risers is an important indicator of the mechanical performance of unbonded flexible risers. Based on a 2.5-inch, 8-layer typical unbonded flexible riser model, this paper examines the burst failure of the pressure armor layer. Firstly, the balance equation of each separate cylindrical layer and helical layer is derived by functional principle, and then the overall theoretical modeling of an unbonded flexible riser under axisymmetric loads is established by additionally considering the geometric relation between adjacent layers. Secondly, fully considering the complex cross-sectional geometric characteristics and the interlayer's contact with the unbonded flexible riser, a simplified numerical 7-layer model is established by Abaqus, and the material with elastic-plastic properties is conferred. Finally, the validity of the proposed theoretical and numerical methods is verified through the axisymmetric behavior of the test data. Then the burst failure of the pressure armor layer is analyzed based on the material. At an internal pressure of 42 MPa, the pressure armor layer reached its yield stress of 300 MPa, with the entire cross-section yielding between 42 MPa and 42.5 MPa. Additionally, the effect of the friction coefficient is examined. [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]

Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for pursuit ISVs. Firstly, a specific two-player zero-sum pursuit–evasion game (ZSPEG)-based target-pursuit model is formed. To ensure the vehicles reach a quick consensus, a target-guided relay-pursuit mechanism and the corresponding pursuit payoffs are designed. Meanwhile, under the fictitious play framework, the behavioral pattern and the strategies of the target could be fictitiously learned. Furthermore, mixed-strategy Nash equilibrium (MNE) is employed to determine the motions for the vehicles, the value of which is the best response in the proposed ZSPEG model. Finally, simulations verify the effectiveness of the above methods in multi-ISV surface pursuit. [ABSTRACT FROM AUTHOR]

Coastal zones experience increasing climatic and human pressures, which lead to growing risks and tipping points (TPs) under future scenarios and natural resource scarcity. To avoid crossing TPs with irreversible coastal losses, this paper proposes the development of adaptation pathways based on advanced coastal oceanography and engineering knowledge that enables a comprehensive assessment of evolving coastal risks. These pathways feature sequential interventions steered by simulations and observations as a function of available coastal resources (mainly space and sediment) and risks for infrastructure and socioeconomic assets. Such an adaptation has been developed for urban and peri-urban Mediterranean beaches, considering conventional coastal engineering together with nature-based solutions (NbS). Both types of interventions are assessed in terms of key physical variables, which serve to evaluate performance and estimate TPs. This analysis supports the new coastal protection and management plan promoted by the regional government of Catalonia and the coastal adaptation plan of the central government of Spain. The approach and results illustrate the potential of adaptation pathways for beach sustainability, enhancing the compatibility between short-/long-term coastal protection objectives under present/future climate and management scenarios. The development of adaptation pathways underpins increasing stakeholder cooperation to achieve shared decisions for coastal sustainability. [ABSTRACT FROM AUTHOR]

The loader is one of the most widely used pieces of engineering machinery in the world for soil transportation, loading and unloading materials, and low-intensity shovel digging operations in harsh and complex operating conditions; it requires very frequent shifting and has other challenging characteristics. In order to realize automatic frequent shifting, we need to better design the shifting rules in the shifting process, improve the shifting quality and working efficiency, and solve the key engineering problems of energy saving and high efficiency in the shifting process of loaders. In this paper, a 7-ton wheel loader is taken as the research object, the loader shoveling process of the four operating modes is analyzed, and a multi-mode variable parameter shift law is designed. Aiming at the complicated and nonlinear characteristics of the power transmission system of the loader, an intelligent shift control method based on an RBF neural network is proposed. Finally, the simulation test and the clutch shift oil pressure test are carried out. From the test results, the clutch test oil pressure curve obviously shows a four-stage upward trend during shifting, and the buffering effect is obvious. The designed multi-mode variable-parameter intelligent shift law of the loader is reasonable and feasible, and the shift recognition rate reaches 97.92%, which provides theoretical support for the realization of intelligent automatic speed change control of the loader, and it certainly has engineering value. [ABSTRACT FROM AUTHOR]