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When replacing equipment at its end of life, industries often have the option to select higher efficiency technologies. The choice to invest in an improved piece of equipment rather than a simple in-kind replacement is driven by many factors, including costs, performance, and familiarity with new technology options. Engineering models that account for equipment stock turnover, such as ENERGY 2020, typically assume that this decision is primarily driven by the difference in marginal costs: higher up front capital costs must be, at minimum, balanced by lower lifetime energy costs for an upgrade to be pursued. Stock turnover analysis requires detailed data inputs regarding the costs and performance of new equipment. For simplicity, a common approach is to develop assumed correlations that reflect the trade-off between marginal capital cost and efficiency and compare these to energy prices to select an efficiency level for new equipment. In this study, we present a novel method to develop such trade-off curves based on a technology-explicit approach rather than a qualitative correlation assumption. We generate trade-off curves for two common types of industrial devices: electric machine drive and natural gas steam generation for the case study of the Canadian pulp and paper sector. The curves demonstrate that the efficiency of new devices can be expected to vary significantly based on energy prices. At current energy prices, we find that newly purchased machine drive and steam generation devices would have an optimal efficiency level of 91% and 75%, respectively. [ABSTRACT FROM AUTHOR]

There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data's correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract. Efficient data extraction from research papers accelerates science and engineering. Here, the authors develop an automated approach which uses conversational large language models to achieve high precision and recall in extracting materials data. [ABSTRACT FROM AUTHOR]

The authors discuss common control software maintenance tasks and tool capabilities based on existing research, and demonstrate the implementation of these capabilities in the 4diac IDE. However, there is limited empirical research on the usability and utility of model-based development environments for visual programming languages. The MODELS conference series is the premier venue for model-based software and systems engineering covering all aspects of modeling, from languages and methods to tools and applications. [Extracted from the article]

The article presents a study that seeks to model the effects of the yellow-phase dilemma, which is one of the factors that lead to intersection-signal-related incidents. It utilizes the vehicle trajectory data to come up with a strategy that measures the values of the causal subjects in varied speeds. Video-capturing methods are used to structure the dynamic zone model of the said study that was conducted in Fairfield, Ohio. Information on dilemma zone (DZ) and option zone (OZ) are offered.

The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model can be applied to simulate hydrological processes. This model has been widely used in recent days because it is openly accessible to the public. The search result from this study identifies 180 cases discussing the model applications in Indonesian watersheds in peer-reviewed journal articles and conference papers. The model has been used in Indonesia since 2005, but the highest application is in 2020 with 42 articles. In the context of application distribution, Java Island ranks first place with 123 scientific publications. However, the HEC-HMS model is predominantly used to simulate the "Small" and "Very Small" watershed size categories. The availability of insufficient field data is huge obstacle for hydrological modeling in Indonesia. Furthermore, the model may be extended to address the issues in Indonesia's eastern watersheds. Moreover, the model also can be used to assess the impact of climate change on streamflow. [ABSTRACT FROM AUTHOR]

Software systems incorporating artificial intelligence (AI) technology are called software 2.0, and their development is spreading in various fields. The purpose of this study is to enable quality engineers to easily evaluate the quality of software 2.0. For this purpose, this paper proposes a quality model that can be used by quality engineers. In order to propose a new quality model for quality engineers, this paper selects and reorganizes quality elements from general-purpose quality models and guidelines, and reconstructs quality characteristics and their evaluation methods. The quality model is considered useful for quality engineers. The paper also identifies important quality characteristics in 24 different application areas and confirms the applicability of the proposed model. The result shows that the model is useful for quality engineers to evaluate the quality of software 2.0. [ABSTRACT FROM AUTHOR]

The paper by K. Zhang et al. [[6]] studies calcium leaching in hydraulics applications, formulated as an inverse problem, which is studied using genetic algorithms and the finite element method. The continuing achievements in Information Technology and Computer science in recent decades provide new tools for engineers in the design of devices and systems, with significant advances both in numerical and in analytical methods of analysis. 10.3390/math10101619 3 Feng F., Yuan M., Xia Y., Xu H., Feng P., Li X. Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal. [Extracted from the article]

There is currently a lot of interest in applying machine learning (ML) techniques to problems in geotechnical (soil and rock) engineering and adjacent fields such as engineering geology. Recent literature emphasizes the need to focus beyond methodological challenges, and the importance of data centricity, transparency, suitability for practice and geotechnical context – together, the so-called "data-centric geotechnics". This review paper offers additional perspective to be contemplated for successful applications of ML in geotechnics: one should explore and discuss (i) the problem to be solved, (ii) the type, quality and quantity of data, and (iii) the methodology/algorithm. The paper further discusses that more strict guidelines and protocols are required for evaluating data and trained ML models if they are to be accepted and successfully integrated into practice. In the transition to data-centric practices, geotechnical engineering, a traditionally data-poor field, has much to learn from fields where decision-making based on data has a long and rich history. [ABSTRACT FROM AUTHOR]

In the paper, I Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non i - I floating vehicle i , Chen et al. ([4]) present the method of estimating non-floating vehicles' driving information according to floating vehicles' information. The authors presented the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy (EWVDFS) based on Weighted Vehicle Density Feedback Strategy (WVDFS). In the paper, A I robust lane detection method based on hyperbolic model i , Li et al. ([9]) investigated a robust lane detection method under structured roads. The paper I Road network i - I based region of interest mining and social relationship recommendation i by Tan and Zhang ([17]) proposed road context-based active region extraction algorithm (RAREA) which explores the method to extract the specific regions within the road network. [Extracted from the article]

A. Montisci, S. Cargangiu, G. Sias, B. Cannas and A. Fanni, in [[16]], present a real time bolometer tomographic reconstruction algorithm in nuclear fusion reactors based on the combination of highly correlated grid elements to yield a square coefficient matrix with sufficiently low condition number. The procedure was derived from the polynomial reproduction properties satisfied by the Hermite splines without requiring the explicit construction/evaluation of the basic functions. The paper authored by Y.E. Shao and S.C. Lin [[1]] presents an innovative time-delay neural network-based classifier to diagnose the quality variables that cause out-of-control signals for a normal multivariate process with variance shifts. It is now clear that cooperation between academia and industries is crucial for social, cultural, technological and economic progress and innovation. [Extracted from the article]

Selected papers We classify the eleven papers included in this special section into two categories: (1) metamodeling and model transformations, and (2) domain and application specific MBE. The seven papers included in the category of domain and application specific MBE describe MBE solutions crossing various domains and applications: content management, automotive, avionics, civil engineering, critical software system, robotics. The IEEE/ACM International Conference on Model Driven Engineering Languages and Systems (MODELS) serves as a venue for researchers and practitioners to share recent advances and practices in the area of model-based software and system engineering (MBE including MBSE). [Extracted from the article]

The pressure on systems engineering is ever‐increasing to support the development and implementation of systems that meet a complex environment's demands. As a growing discipline, systems engineering requires insight into past research to identify opportunities for future growth. Analyzing the bibliometric data on published research provides valuable information on a scientific discipline's past progress and future prospects. Therefore, this paper extracts the research topics published in INCOSE's journal Systems Engineering and the annual international symposium proceedings to analyze their composition and allocation to papers. The implemented process applies natural language processing and topic modeling to extract the main topics from these papers' titles and abstracts. Analyzing these research topics' composition and mapping them to processed articles helps to understand their relative importance. The analysis's output confirms the importance of modeling in systems engineering, as it is the most popular topic. The additional focus of research papers on the systems engineering process, practice, and methodologies also indicates that the field is still growing and evolving. Some important topics to systems engineering, which were not found as prominent topics, are humans' roles in systems, verification and validation, and other specialty fields. This new knowledge about the structure of research into systems engineering can identify future research project opportunities to continue growing the field. [ABSTRACT FROM AUTHOR]

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions. [ABSTRACT FROM AUTHOR]

Rockburst is an extremely hazardous geological disaster. In order to accurately predict the hazardous degree of rockbursts, this paper proposes eight new classification models for predicting the intensity level of rockbursts based on intelligent optimisation algorithms and deep learning techniques and collects 287 sets of real rockburst data to form a sample database, in which six quantitative indicators are selected as feature parameters. In order to validate the effectiveness of the constructed eight machine learning prediction models, the study selected Accuracy, Precision, Recall and F1 Score to evaluate the prediction performance of each model. The results show that the NGO-CNN-BiGRU-Attention model has the best prediction performance, with an accuracy of 0.98. Subsequently, engineering validation of the model is carried out using eight sets of real rockburst data from Daxiangling Tunnel, and the results show that the model has a strong generalisation ability and can satisfy the relevant engineering applications. In addition, this paper also uses SHAP technology to quantify the impact of different factors on the rockburst intensity level and found that the elastic strain energy index and stress ratio have the greatest impact on the rockburst intensity level. [ABSTRACT FROM AUTHOR]

David A. Huffman (1925–1999) is best known in computer science for his work in information theory, particularly Huffman codes, and best known in origami as a pioneer of curved-crease folding. But during his early paper folding in the 1970s, he also designed and folded over a 100 different straight-crease origami tessellations. Unlike most origami tessellations designed in the past 20 years, Huffman's straight-crease tessellations are mostly three-dimensional, rigidly foldable, and have no locking mechanism. In collaboration with Huffman's family, our goal is to document all of his designs by reverse-engineering his models into the corresponding crease patterns, or in some cases, matching his models with his sketches of crease patterns. Here, we describe several of Huffman's origami tessellations that are most interesting historically, mathematically, and artistically. [ABSTRACT FROM AUTHOR]

Introduction: In the construction of emergency engineering, the effective participation of organizations such as government and construction enterprises can improve engineering emergency services to emergency adaptive, which faces the challenge of the enthusiasm of enterprises' emergency participation. This study proposed a new diversified social governance mode for public emergency facility construction. Methods: This paper empirically analyzes the performance of stock market returns before and after construction enterprises' participation in emergency engineering. Results: Against the backdrop of COVID-19, the analysis based on the sample data of 141 listed companies found that both construction enterprises directly involved in emergency hospital construction and non-state-owned enterprises involved in donations have higher excess return rates. In contrast, social concern has a positive mediating effect between emergency donations and stock market returns. The study suggests that stock market returns from donation behavior and reputation capital become incentives for firms to actively participate in emergency donations, providing a behavioral basis for professional donations by construction firms. Discussion: Based on the above assumptions, this paper proposes the organizational model of emergency engineering construction and the "Engineering Community" relationship based on the "Engineering Multi-governance" theory. This paper is the first to study the emergency engineering construction model from the perspective of professional donation behavior. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on automatic code generation of user-centered serious games. We decided to break the study in two parts: one study about serious games with model driven engineering, and another study about user-centered serious games. This paper presents an extension of a paper presented at CONISOFT 20 where a systematic review of 5 years old at the time of writing was presented exclusively. The systematic literature review conducted in this paper covers a decade of information from January 2012 to June 2022. The main objective is to know the literature that helps to mitigate the costs and time of software development in serious games. The overall conclusion is that there is still work to be done to combine serious user-centered games and automatic generation. This paper is a systematic review that identifies relevant publications and provides an overview of research areas and publication venues. In addition, Research perspectives were classified according to common objectives, techniques, and approaches. Finally, is presented point out challenges and opportunities for future research and development. [ABSTRACT FROM AUTHOR]

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development. [ABSTRACT FROM AUTHOR]

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain. Author summary: Supervised machine learning is increasingly used to predict the function and properties of proteins. The performance obtained with these methods relies on a multitude of factors including how data is represented, how observations are distributed, how training is conducted, and how performance is measured. In this paper, we systematically assess the importance of these different components in a protein regression pipeline. We discuss the benefits of using representations extracted from protein language models, the impact of the choice of regression algorithm, and the role of uncertainty. Finally, to avoid misleading performance claims, we stress the need for carefully aligning the train/test setup to reflect the setting in which the prediction algorithm will ultimately be applied. [ABSTRACT FROM AUTHOR]

In many applications, artificial neural networks are best trained for a task by following a curriculum, in which simpler concepts are learned before more complex ones. This curriculum can be hand-crafted by the engineer or optimised like other hyperparameters, by evaluating many curricula. However, this is computationally intensive and the hyperparameters are unlikely to generalise to new datasets. An attractive alternative, demonstrated in influential prior works, is that the network could choose its own curriculum by monitoring its learning. This would be particularly beneficial for continual learning, in which the network must learn from an environment that is changing over time, relevant both to practical applications and in the modelling of human development. In this paper we test the generality of this approach using a proof-of-principle model, training a network on two sequential tasks under static and continual conditions, and investigating both the benefits of a curriculum and the handicap induced by continuous learning. Additionally, we test a variety of prior task-switching metrics, and find that in some cases even in this simple scenario the a network is often unable to choose the optimal curriculum, as the benefits are sometimes only apparent with hindsight, at the end of training. We discuss the implications of the results for network engineering and models of human development. [ABSTRACT FROM AUTHOR]

Stochastic complex networks with multi-weights which were driven by Brownian motion were widely investigated by many researchers. However, Brownian motion is not suitable for the modeling of engineering issues by reason of its variance, which is infinite at any time. So, in this paper, a novel kind of stochastic complex network with multi-weights driven by second-order process is developed. To disclose how the weights and second-order process affect the dynamical properties of stochastic complex networks with multi-weights driven by the second-order process, we discuss exponential stability of the system. Two types of sufficient criteria are provided to ascertain exponential stability of the system on the basis of Kirchhoff's matrix tree theorem and the Lyapunov method. Finally, some numerical examples are given to verify the correctness and validity of our results. [ABSTRACT FROM AUTHOR]

In order to better evaluate and promote human health, this paper analyzes the influence of different inertial-measurement-unit signals, different sensor locations, different activity intensities and different signal fusion schemes on the accuracy of physical strength consumption estimation during walking and running activities. Different pattern recognition methods, such as the Counts-based linear regression model, the typical non-linear model based on decision tree and artificial neural network, and the end-to-end convolutional neural network model, are analyzed and compared. Our findings are as follows: 1) For the locations of sensors during walking and running activities, the physical strength consumption prediction accuracy at the ankle location is higher than that at the hip location. Therefore, wearing an inertial-measurement-unit at the ankle can improve the accuracy of the model. 2) Regarding the types of activity signals during walking and running activities, the impact of accelerometer signals on hip and ankle prediction accuracy is not significantly different, while the gyroscope model is more sensitive to the location, with higher prediction accuracy at the ankle than at the hip. In addition, the physical strength consumption prediction accuracy of accelerometer signals is higher than that of gyroscope signals, and fusion of accelerometer and gyroscope signals can improve the accuracy of physical strength consumption prediction. 3) For different data analysis models during walking and running activities, the artificial neural network model that integrates different sensor locations and inertial-measurement-unit signals with different activity intensities has the lowest mean squared error for the measurement of physical strength consumption. The non-linear models based on decision tree and artificial neural network have better physical strength consumption prediction capabilities than the Counts-based linear regression model, especially for high-intensity activity energy consumption prediction. In addition, feature engineering models are generally better than convolutional neural network model in terms of overall performance and prediction results under the three different activity intensities. Furthermore, as the activity intensity increases, the performance of all physical strength consumption calculation models decreases. We recommend using the artificial neural network model based on multi-signal fusion to estimate physical strength consumption during walking and running activities because this model exhibits strong generalization ability in cross-validation and test results, and its stability under different activity intensities is better than that of the other three models. To the best of our knowledge, this paper is the first to delve deeply and in detail into methods for estimating physical strength consumption. Undoubtedly, our paper will have an impact on research related to topics such as intelligent wearable devices and subsequent methods for estimating physical strength consumption, which are directly related to physical health. [ABSTRACT FROM AUTHOR]

Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications. [ABSTRACT FROM AUTHOR]

A Review and Scientometric Analysis of Global Building Information Modeling (BIM) Research in the Architecture, Engineering and Construction (AEC) Industry (10.3390/buildings9100210) [[1]] B By Ziwen Liu, Yujie Lu and Lu Chang Peh (Figure 1) b In the recent decade, Building Information Modeling (BIM) has widely been adopted in the Architectural, Engineering and Construction (AEC) industry and completely upended the way we build. In addition, 11 clusters of global BIM research were also identified including construction project, green BIM, construction safety planning, automated IFC-based workflow and so on. However, most of the current studies focus on reviewing BIM for management, BIM for green building and BIM for infrastructure. [Extracted from the article]

This paper introduces an aircraft wing simulation data set (AWSD) created by an automatic workflow based on creating models, meshing, simulating the wing flight flow field solution, and parameterizing solution results. AWSD is a flexible, independent wing collection of simulations with specific engineering requirements. The data set is applicable to handle computer geometry processing tasks. In contrast to the existing 3D model data set, there are some advantages the scale of this data set is not limited by the collection source, the data files have high quality, no defects, redundancy, and other problems, and the models and simulation are all designed for the specific actual engineering demand. Moreover, AWSD has the characteristics of rich information and a similar model structure, which contributes to the construction of the surrogate model. On the other hand, this data set is suitable for advancing research of data mining in computational geometry graphics. To solve the problem that the CFD flows field results are not intuitive, this paper used the resampling method of surface data to sample the result to the model surface, then segmented the re-sampled 3D mesh surface, and compared with the differences among K-means algorithm, Mini-Batch K-means algorithm, and Spectral Clustering algorithm. AWSD provides 300 sets of models, meshes, CFD simulation results, and parametric results based on ARAP (As-Rigid-As-Possible) and Harmonic mapping for advancing the construction of engineering surrogate models, 3D mesh segmentation, surface resampling, and related geometric processing tasks. [ABSTRACT FROM AUTHOR]

This paper describes effective design procedures and identify construction challenges encountered in mega revamp projects. Lessons learned through the execution of these projects are discussed. The paper fills existing gaps and pieces together engineering requirements for critical structures that are used in heavy industry. Numerical and simplified analytical models are provided to assist the engineers to model load transfers with reasonable accuracy. An overview of loading mechanisms of various systems is also provided for operational, testing and lifting conditions. Industrial examples are provided for steel modules, pipe racks, and equipment support systems. The procedures presented in the paper are computationally efficient and can be implemented in practice. The theoretical basis and merits of various methods used in the practice are also discussed. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims at discovering the traditional techniques of Persian architecture for covering large-span spaces with a kind of ribbed vault titled "Karbandi". This structure is generated by intersecting several arches with a harmonic stellar geometry. Preliminary studies show that span factor affects the structural form of karbandi and large-span cases, despite similar architectural forms, have different structural systems and specific construction methods. The main focus of this paper is how karbandi has been designed and built on large-spans. To answer this question, the configuration and construction of a large-span karbandi in Tabriz Bazaar were recognized. Design/methodology/approach: Data collection of the research was initially done in three parallel directions through the archival study of restoration documents, direct observation of the corpus of the vaultings and interviews with the master mason of the Haj-Mohammad-Qoli Timche restoration team. Then by cross-referencing the gathered data, the construction process of the karbandi was simulated in Rhino 6 and Grasshoppers software and its BIM-M models were created in three levels of development: LOD300, LOD350 and LOD400. In the next step, the preliminary BIM-M models of the karbandi were presented to the interviewed mason and revised and completed based on his comments. Findings: Analyzing the BIM models by reverse engineering, resulted in (1) Discovering a unique self-supporting masonry construction method applied for the erection of karbandi vaulting on large-spans. (2) Finding the effect of scale factor on the architectural and structural form of the karbandi vault. (3) Discovering the connection types of the karbandi vault based on the construction details. Originality/value: Despite the wide applications of karbandi vaults throughout history, very little information of their construction techniques is available. The techniques have mostly been experientially and orally passed down from masters to apprentices and rarely been documented. The quest to design and construct a karbandi vault is therefore like solving a puzzle whose most important guide is historical cases. Due to the geometric complexity of karbandi and its ability to cover large-span spaces, solving the puzzle can lead to achieving some technical ideas for masonry cross-ribbed vaulting. A great riddle of the karbandi vaults is how to design and build them on a large span. [ABSTRACT FROM AUTHOR]

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach. [ABSTRACT FROM AUTHOR]

Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same underlying entity, with applications ranging from healthcare to e-commerce. Traditional ER solutions required considerable manual expertise, including domain-specific feature engineering, as well as identification and curation of training data. Recently released large language models (LLMs) provide an opportunity to make ER more seamless and domain-independent. Because of LLMs' pre-trained knowledge, the matching step in ER can be made easier by just prompting. However, it is also well known that LLMs can pose risks, that the quality of their outputs can depend on how prompts are engineered, and that the cost of using LLMs can be significant. Unfortunately, a systematic experimental study on the effects of different prompting methods and their respective cost for solving domain-specific entity matching using LLMs, like ChatGPT, has been lacking thus far. This paper aims to address this gap by conducting such a study. We consider some relatively simple and cost-efficient ER prompt engineering methods and apply them to perform product matching on two real-world datasets widely used in the community. We select two well-known e-commerce datasets and provide extensive experimental results to show that an LLM like GPT3.5 is viable for high-performing product matching and, interestingly, that more complicated and detailed (and hence, expensive) prompting methods do not necessarily outperform simpler approaches. We provide brief discussions on qualitative and error analysis, including a study of the inter-consistency of different prompting methods to determine whether they yield stable outputs. Finally, we consider some limitations of LLMs when used as a product matcher in potential real-world e-commerce applications. [ABSTRACT FROM AUTHOR]

This paper presents an innovative mathematical model of solid fuel gasification, which is not described in the available literature. The calculation of the components of the heterogeneous phase (including both solid and gaseous phases) as well as the calculation of the homogeneous phase (only gaseous components) is based on the balance of the total amounts of carbon, oxygen, hydrogen, and nitrogen entering the reactor space. Additionally, this paper introduces a new method for calculating the composition of the gaseous phase, based on reducing the heterogeneous mixture (composed of solid and gaseous phases) to a homogeneous gaseous phase. This approach to calculating the gaseous phase composition in the solid fuel gasification process has not been found by the authors in the cited literature. This paper also presents a model for calculating the heterogeneous and gaseous phases using the number of moles that participate in the assumed chemical reactions of the solid fuel gasification process. This approach to calculating the composition of the heterogeneous and gaseous phases of the solid fuel gasification process is also not represented in the cited literature. For comparison with the literature data, municipal solid waste (MSW) and cashew nut shell (Cashew Shell Char (CNSC)) were used as fuels in the calculation of gasification composition. The results of the calculation of the gaseous phase composition using the model presented in the paper show good agreement with the data from the literature. The calculation of the composition of the heterogeneous mixture during the steam gasification of MSW (α = 0.4) shows the presence of a solid phase (carbon) up to approximately 735 °C. At that temperature, the synthetic gas contains only gaseous components: CO = 33.10%, H2 = 52.70%, CH4 = 2.54%, CO2 = 4.97, H2O = 5.93% and N2 = 0.76%. Increasing the temperature above 735 °C eliminates the solid phase from the equilibrium mixture. The literature data on solid fuel gasification generally do not consider the proportion of the solid phase (carbon) in the equilibrium mixture. To satisfy the material balance at the input and output of the gasification reactor, it is necessary to determine the proportion of the solid phase (carbon) in the equilibrium mixture. Since the proportion of the solid phase (carbon) in the heterogeneous equilibrium mixture can only be determined through measurement, the development and application of a mathematical model in engineering practice is of great importance, so this developed model can be considered a useful tool for simulating the influence of process parameters on gas characteristics. [ABSTRACT FROM AUTHOR]

In the near future, international space agencies have planned to achieve significant milestones in investigating the utilization of Global Navigation Satellite Systems (GNSS) within and beyond the current space service volume up to their application to lunar missions. These initiatives aim to demonstrate the feasibility of GNSS navigation at lunar altitudes. Based on the outcomes of such demonstrations, dozens of lunar missions will likely be equipped with a GNSS receiver to support autonomous navigation in the lunar proximity. Relying on non-invasive, consolidated differential techniques, GNSS will enable baseline estimation, thus supporting a number of potential applications to lunar orbiters such as collaborative navigation, formation flight, orbital manoeuvers, remote sensing, augmentation systems and beyond. Unfortunately, the large dynamics and the geometry of such differential GNSS scenarios set them apart from current terrestrial and low-earth orbit use cases. These characteristics result in an increased sensitivity to measurements time misalignment among orbiters. Hence, this paper offers a review of baseline estimation methods and characterizes the divergences and limitations w.r.t. to terrestrial applications. The study showcases the estimation of the baseline length between a lunar CubeSat mission, VMMO, and the communication relay Lunar Pathfinder mission. Notably, real GNSS measurements generated by an Engineering Model of the NaviMoon receiver in the European Space Agency (ESA/ESTEC) Radio Navigation Laboratory are utilized. A radio-frequency constellation simulator is used to generate the GNSS signals in these hardware-in-the-loop tests. The performed analyses showed the invalidity of common terrestrial differential GNSS ranging techniques for space scenarios due to the introduction of significant biases. Improved ranging algorithms were proposed and their potential to cancel ranging errors common to both receivers involved was confirmed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce WindFormer, an innovative transformer-based model engineered for short-term wind speed forecasting, leveraging multivariate time series data. Unlike traditional approaches, WindFormer excels in processing diverse meteorological features—temperature, humidity, and power—to intricately map their spatiotemporal interdependencies with wind speeds. Utilizing a novel unsupervised pre-training strategy, WindFormer initially learns from vast volumes of unlabeled data to capture generalized feature representations. This foundation enhances the subsequent fine-tuning phase on labeled wind speed data, in which our model demonstrates exceptional predictive accuracy. Empirical evaluations across various public datasets illustrate that WindFormer markedly surpasses both conventional statistical models and contemporary deep learning techniques. The model not only achieves superior accuracy in forecasting wind speeds but also reveals a significant enhancement in handling complex spatiotemporal data dynamics. These advancements facilitate more effective wind farm management and power grid scheduling, making a substantial impact on operational efficiencies and renewable energy utilization. Our findings confirm the robustness of WindFormer in a real-world setting, underscoring its potential as a pivotal tool in meteorological and energy sectors. The integration of unsupervised pre-training with multi-task fine-tuning establishes a new benchmark for short-term wind speed prediction. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging holds significant promise in remote sensing applications, particularly for land cover and land-use classification, thanks to its ability to capture rich spectral information. However, leveraging hyperspectral data for accurate segmentation poses critical challenges, including the curse of dimensionality and the scarcity of ground truth data, that hinder the accuracy and efficiency of machine learning approaches. This paper presents a holistic approach for adaptive optimized hyperspectral-based land cover and land-use segmentation using automated machine learning (AutoML). We address the challenges of high-dimensional hyperspectral data through a revamped machine learning pipeline, thus emphasizing feature engineering tailored to hyperspectral classification tasks. We propose a framework that dissects feature engineering into distinct steps, thus allowing for comprehensive model generation and optimization. This framework incorporates AutoML techniques to streamline model selection, hyperparameter tuning, and data versioning, thus ensuring robust and reliable segmentation results. Our empirical investigation demonstrates the efficacy of our approach in automating feature engineering and optimizing model performance, even without extensive ground truth data. By integrating automatic optimization strategies into the segmentation workflow, our approach offers a systematic, efficient, and scalable solution for hyperspectral-based land cover and land-use classification. [ABSTRACT FROM AUTHOR]

The Cyber Evaluation and Management Toolkit (CEMT) is an open-source university research-based plugin for commercial digital model-based systems engineering tools that streamlines conducting cybersecurity risk evaluations for complex cyber-physical systems. The authors developed this research tool to assist the Australian Defence Force (ADF) with the cybersecurity evaluation of complicated systems operating in an increasingly contested and complex cyber environment. This paper briefly outlines the functionality of the CEMT including the inputs, methodology, and outputs required to apply the toolkit using a sample model of the process applied to a generic insider threat attack. A face validity trial was conducted on the CEMT, surveying subject-matter experts in the field of complex cybersecurity analysis and risk assessment to present the generic case study and gather data on the expected benefits of a real-world implementation of the process. The results of the face validity broadly supports the effectiveness and usability of the CEMT, providing justification for industry research trials of the CEMT. [ABSTRACT FROM AUTHOR]

The secant shear modulus is an important parameter in several geotechnical engineering models and calculation approaches. The characteristics of this parameter are very complex due to the hysteretic behavior of cyclically loaded soils. Its characteristics primarily depend on the mean effective confining stress and the void ratio. In this paper, the characteristics of the secant shear modulus of sand under large shear strain amplitude are investigated in detail based on the results of several cyclic triaxial and simple shear tests under drained and undrained conditions. The evaluation of the secant shear modulus based on the shape of the open stress–strain hysteresis loop is then discussed. Under certain loading conditions, the secant shear modulus defined from the reloading and unloading branches of the loop obviously differ. The evolution of the stiffness index, which measures the change in the secant shear modulus due to cyclic loading, shows that the drainage conditions play an essential role in the behavior of the secant shear modulus. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses articles within the issue on topics including a soft computing technique for analyzing the risk assessment of COVID-19 daily death cases in various countries, influence of structure surface boundary conditions on pressure characteristics of underwater explosion, and probabilistic method in traffic and transportation engineering.

Within this paper, we discuss a specific approach to system‐level modeling using activities, with the goal of writing English‐language requirements for the System of Interest (SOI), and then using formulas which interact with the model to proceduralize the authorship process. This overall approach makes it possible for most technical participants in a program to do most of their development work in a collaborative modeling environment, spending most of their time dealing with technical issues rather than language issues, while implementing a rapid translation to English‐language functional requirements toward the end of the process which may eventually be automated. [ABSTRACT FROM AUTHOR]

Featured Application: ReMAHA–CatBoost is an advanced machine learning model designed for predicting traffic accident severity. It is constructed in two parts: ReMAHA (relief–F-based genetic algorithm with over-sampling algorithm for weighted Mahalanobis distance) and CatBoost, to offer an innovative solution in the field of imbalanced data classification. Key Features and Highlights: (1) ReMAHA Over-sampling: ReMAHA employs the Relief–F algorithm for feature selection and combines it with an innovative over-sampling technique to enhance prediction accuracy for minority classes; (2) Feature Engineering: The model leverages feature engineering to determine the significance of different attributes, enabling it to make precise predictions regarding accident severity; and (3) CatBoost Integration: ReMAHA incorporates CatBoost, a state-of-the-art gradient-boosting algorithm, to improve predictive performance by mitigating issues like overfitting and prediction bias. This paper elucidates the working principles of oversampling algorithms in machine learning tasks based on imbalanced datasets, specifically addressing how to resolve the issue of low accuracy stemming from imbalanced data at the data level. Based on the experimental results presented in this paper, it is evident that ReMAHA–CatBoost outperforms several other oversampling algorithms and models, especially on the US–Accidents traffic accident dataset characterized by an extreme class imbalance ratio of 91.40. This improved performance enhances the precision of traffic accident severity prediction. Using historical information from traffic accidents to predict accidents has always been an area of active exploration by researchers in the field of transportation. However, predicting only the occurrence of traffic accidents is insufficient for providing comprehensive information to relevant authorities. Therefore, further classification of predicted traffic accidents is necessary to better identify and prevent potential hazards and the escalation of accidents. Due to the significant disparity in the occurrence rates of different severity levels of traffic accidents, data imbalance becomes a critical issue. To address the challenge of predicting extremely imbalanced traffic accident events, this paper introduces a predictive framework named ReMAHA–CatBoost. To evaluate the effectiveness of ReMAHA–CatBoost, we conducted experiments on the US–Accidents traffic accident dataset, where the class label imbalance reaches up to 91.40 times. The experimental results demonstrate that the proposed model in this paper exhibits exceptional predictive performance in the domain of imbalanced traffic accident prediction. [ABSTRACT FROM AUTHOR]

Rotary inverted pendulum (RIP) is a classical model of control engineering. Paper deals with a PID-adaptive structure which is based on structure of neuron to train Kp, Ki, Kd through operation. In simulation, our adaptive controller is proven to work in larger range than classical PID controller. Through experimental model using STM32F4, we prove vibration of system under adaptive-PID is smaller than under classical PID structure. Then, combination of neuron network (NN) and PID control can be used as simple structure for single-input multi output (SIMO) systems which are similar to RIP. [ABSTRACT FROM AUTHOR]

A maximum power point (MPP) always exists in photovoltaic (PV) cells, but a mismatch between PV system circuit parameters, weather conditions and system structure leads to the possibility that the MPP may not be tracked successfully. In addition, the introduction of an isolation transformer into a basic PV system allows for moderate values of the converter duty cycle and electrical isolation. However, there is no comprehensive research on MPPT (maximum power point tracking) constraint conditions for different isolated PV systems, which seriously hinders the application of isolated PV systems and the development of a related linear control theory. Therefore, in this paper, the overall mathematical models of different isolated PV systems are first established based on the PV cell engineering model and the MPP linear model, and then, two sets of constraint conditions are found for the successful realization of MPPT. These MPPT constraint conditions (MCCs) describe in detail the direct mathematical relationships between PV cell parameters, weather conditions and circuit parameters. Finally, based on the MPP linear model and MCCs, two new MPPT methods are designed for isolated PV systems. Considering the MCCs proposed in this paper, a suitable range of load and transformer ratios can be estimated from the measured data of irradiance and temperature in a certain area, and the range of MPPs existing in PV systems with different structures can be estimated, which is a good guide for circuit design, theoretical derivation and product selection for PV systems. Meanwhile, comparative experiments confirm the rapidity and accuracy of the two proposed MPPT methods, with the MPPT time improving from 0.23 s to 0.03 s, and they have the advantages of a simple program, small computational volume and low hardware cost. [ABSTRACT FROM AUTHOR]

Matrix-variate Gaussian-type or Wishart-type distributions in the real domain are widely used in the literature. When the exponential trace has an arbitrary power and when the factors involving a determinant and a trace enter into the model or a matrix-variate gamma-type or Wishart-type model with an exponential trace having an arbitrary power, they are extremely difficult to handle. One such model with factors involving a trace and a determinant and the exponential trace having an arbitrary power, in the real domain, is known in the literature as the Kotz model. No explicit evaluation of the normalizing constant in the Kotz model seems to be available. The normalizing constant that is widely used in the literature, is interpreted as the normalizing constant in the general model, and that is referred to as a Kotz model does not seem to be correct. One of the main contributions in this paper is the introduction of matrix-variate distributions in the real and complex domains belonging to the Gaussian-type, gamma-type, and type 1 and type 2 beta-types, or Mathai's pathway family, when the exponential trace has an arbitrary power and explicit evaluations of the normalizing constants therein. All of these models are believed to be new. Another new contribution is the logistic-based extensions of the models in the real and complex domains, with the exponential trace having an arbitrary exponent and connecting to extended zeta functions introduced by this author recently. The techniques and steps used at various stages in this paper will be highly useful for people working in multivariate statistical analysis, as well as for people applying such models in engineering problems, communication theory, quantum physics, and related areas, apart from statistical applications. [ABSTRACT FROM AUTHOR]

Methane dexterity and degasification in heavily stratified subsurface water aquifers sandwiched with coal seams surreptitiously expedited the recent fire explosion in Enugu State, South Eastern Nigeria. The verisimilitude is to divulge the non-availability of coal bed methane (CBM) reservoir engineering modeling technology. The aim of this study is to demonstrate the didactic, luminous and effective volubility of CBM reservoir engineering modeling techniques for extrication of the wellbore fire explosion paradox in Enugu state, Nigeria. The Schlumberger Eclipse simulated reservoir has a dimension of 8 ×8×2 with dual porosity of 0.05 and 0.15 located at depths of 1,000ft, 1,200ft and 1,400ft. Results inconceivably showed improvement in coal quality by 72.83% in the y direction indicating increased methane palpitation upsurge in that direction. Results of initial 40% CO2 isotherm fracture gas gave a cumulative maximum field gas pressure (FPR) of 540psi for the 1,400ft model. This indicates higher gas volatility with shallow depths. It is admonished to critically evaluate aquifers in zones deeper than 700ft to forestall future petrifying devastating occurrences triggered by maladroitness. [ABSTRACT FROM AUTHOR]

An editorial is presented on the current research status of modeling of heterogeneous materials. In order to make up for the shortcomings of homogeneous materials and meet the needs of industry development, heterogeneous materials have been increasingly applied in aviation, automobile, bridge and other fields. Heterogeneous materials refer to advanced materials with non-uniform components.

In order to obtain a better numerical simulation method for fluid–structure interaction (FSI), the IB-LBM combining the lattice Boltzmann method (LBM) and immersed boundary method (IBM) has been studied more than a decade. For this purpose, an explicit correction force scheme of IB-LBM was proposed in this paper. Different from the current IB-LBMs, this paper introduced the particle distribution function to the interpolation process from the fluid grids to the immersed boundary at the mesoscopic level and directly applied the LBM force models to obtain the interface force with a simple form and explicit process. Then, in order to ensure the mass conservation in the local area of the interface, this paper corrected the obtained interface force with the correction matrix, forming the total explicit-correction-force (ECP) scheme of IB-LBM. The results of four numerical tests were used to verify the order of accuracy and effectiveness of the present method. The streamline penetration is limited and the numerical simulation with certain application significance is successful for complex boundary conditions such as the movable rigid bodies (free oscillation of the flapping foil) and flexible deformable bodies (free deformation of cylinders). In summary, we obtained a simple and alternative simulation method that can achieve good simulation results for engineering reference models with complex boundary problems. [ABSTRACT FROM AUTHOR]