Search

With the development of high-performance computing technology, the number of scientific publications regarding computational modelling of applications with the Discrete Element Method/Modelling (DEM) approaches in agricultural engineering has risen in the past decades. Many granular materials, e.g. grains, fruits and soils in agricultural engineering are processed, and thus a better understanding of these granular media with DEM is of great significance in design and optimization of tools and process in agricultural engineering. In this review, the theory and background of DEM have been introduced. Some improved contact models discussed in the literature for accurately predicting the contact force between two interacting particles have been compared. Accurate approximation of irregular particle shapes is of great importance in DEM simulations to model real particles in agricultural engineering. New algorithms to approximate irregular particle shapes, e.g. overlapping multi-sphere approach, ellipsoid, etc., have been summarized. Some remarkable engineering applications of the improved numerical models developed and implemented in DEM are discussed. Finally, potential applications of DEM and some suggested further works are addressed in the last section of this review. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) signals are often referred to as the language of the heart and have been widely utilized for diagnosing various heart ailments, particularly arrhythmias. Consequently, numerous machine learning models have been employed to automatically detect heart disorders using ECG signals. In this research, the primary objective is to detect arrhythmias using a center-symmetric self-organized textual pattern. A novel feature engineering model has been introduced, which includes the following components: (i) multilevel feature extraction using the proposed center-symmetric self-organized textual pattern (CSSOTP), (ii) iterative neighborhood component analysis (INCA), and (iii) classification using k-nearest neighbors (kNN) with 10-fold cross-validation. During the feature extraction phase, a multilevel feature extraction model incorporating maximum absolute pooling (MAP) and the proposed CSSOTP was applied. The CSSOTP was designed to select the most appropriate pattern for the given data block. A large language model (LLM) was leveraged to generate text for creating patterns, and ChatGPT was used to assist in text generation. To identify the most informative features, the INCA feature selector was employed, and the selected features were subsequently classified using the kNN classifier. An impressive 96.20 % classification accuracy was achieved by the CSSOTP-based feature engineering model when tested on an ECG dataset containing 17 classes. Furthermore, comparisons with state-of-the-art feature engineering models were conducted, demonstrating that the proposed model has superior classification capabilities. [ABSTRACT FROM AUTHOR]

This paper presents modelling of the long-term performance of engineered barrier systems (EBS) in crystalline host rock in terms of coupled thermo-hydromechanical (THM) processes in a specific case, considering also the impact of salinity linked with geochemistry. This study has been used as a supporting document for the safety case in the operating licence application for the Olkiluoto spent nuclear fuel repository in Finland. The disposal design chosen is the KBS-3V (Kärnbränslesäkerhet in Swedish, "nuclear fuel safety"; 3, version number; V, vertical) which consists of placing the canisters in vertical deposition holes surrounded by the EBS. The buffer components consist of compacted blocks of Wyoming-type bentonite surrounded by pillow pellets manufactured with the same material. Bulgarian and/or Italian granular filling (GraFi) materials are the backfilling material in the deposition tunnels. The Barcelona basic model (BBM) was considered for modelling the geomechanical behaviour of compacted buffer blocks and GraFi materials filling the deposition tunnels. The Barcelona expansive model (BExM), which consists of a double structure (macro-micro porosity), was considered for the pellets. A laboratory testing campaign (thermal conductivity, water retention curve, oedometer, and infiltration tests) was carried out in order to calibrate the THM model parameters of the corresponding materials. Model-data uncertainties, challenges in 3D THM modelling, and the methodology followed have been provided in terms of modelling capabilities. We implemented 3D THM simulation of an individual deposition hole (canister, notch/chamfer, and buffer materials) drilled into a deposition tunnel (backfill material) in CODE_BRIGHT as a finite element method (FEM) program. This study presents results related to THM performance of the EBS, such as peak temperature, time required to reach full saturation in buffer and backfill, the evolution of dry densities according to permeabilities, the development of swelling pressure in buffer and backfill, and, consequently, deformations in buffer and backfill domains. A sensitivity analysis plan was followed in order to deal with various factors affecting the long-term THM performance of the EBS. In the sensitivity analysis, buffer and backfill design options (different filling material alternatives), geological conditions (saline water, rock permeability, and heterogeneous rock) and numerical simulation options (different numerical model options, issues related to geometry and meshing) were investigated. The performance targets and design specifications set for the buffer and backfill are also discussed. The paper concludes with a summary how the THM design under a certain configuration (geometry, initial conditions, boundary conditions, and buffer and backfill materials) meets the performance targets set for the buffer, backfill, and host rock. [ABSTRACT FROM AUTHOR]

Parental behavior comprises a set of crucial actions essential for offspring survival. In this study, a double transgenic mouse model engineered to specifically express channelrhodopsin-2 (ChR2) in paraventricular hypothalamic nucleus (PVN)-oxytocin neurons and ablate lateral hypothalamic area (LHA)-melanin-concentrating hormone (MCH) neurons was used to determine the relationship between PVN-oxytocin neurons and LHA-MCH neurons associated with parental behavior. Optogenetic stimulation of ChR2-expressing PVN-oxytocin neurons induces typical parental behavior with intact LHA-MCH neurons. However, after the partial ablation of LHA-MCH neurons, even optogenetic stimulation of PVN-oxytocin neurons failed to induce parental behavior in virgin male mice, resulting in neglect rather than parental behavior. Furthermore, approximately half of the subjects exhibited burying behavior toward pups, suggesting that pups became aversive stimuli, and male mice actively performed burying behavior to avoid these aversive stimuli. This study emphasizes the novel aspect of oxytocin neurons that could result in neglect in the absence of LHA-MCH neurons regulation. [ABSTRACT FROM AUTHOR]

The presentation of a constitutive model could help researchers to predict the mechanical behavior of a material, which also contributes to the further generalization of the material. This paper is to explore the tensile constitutive model of engineered cementitious composites (ECCs) reinforced by high-strength steel wire mesh based on experiments and numerical simulations. DIANA was used to simulate the tensile process of the specimens, and experiments were carried out to validate the numerical model. The effect of the ECCs' tensile strength, reinforcement ratio and specimen size were considered during the specimen design process. The results showed that most of the errors of the simulated values compared to the experimental results were within 5%, which proved that the numerical model was quite accurate. The proposed constitutive model revealed the different roles played by ECCs and high-strength steel wires at different stress stages, and the calculation results were in high agreement with the simulation results, indicating the effectiveness of the constitutive model. The study in this paper could provide an important reference for the popularization and application of ECCs reinforced by high-strength steel wire mesh. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE), mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Various heterogeneous cross-domain models are essential for such systems, and model-driven engineering plays a pivotal role in the design, development, and maintenance of these models. We believe models and model-driven engineering play a similarly crucial role in the context of a VE of a DT. Due to the rapidly growing popularity of DTs and their use in diverse domains and use-cases, the methodologies, tools, and practices for designing, developing, and maintaining the corresponding VEs differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins. In this paper, we present our analysis and findings from this study, which is based on seven research questions. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for the development and maintenance of the corresponding VEs. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin's lifecycle. [ABSTRACT FROM AUTHOR]

The design of pile foundations hinges significantly on the pile-bearing capacity (PBC), a crucial factor affected by numerous soil features and parameters. Piles serve as a key component in relocating structural loads to the ground, underscoring the need for accurately determining PBC in geotechnical design. While prior research has delved into utilizing artificial neural networks (ANN) for predicting PBC, they encounter shortcomings such as grappling with finding global minima and slow convergence rates. This study introduces Support Vector Regression (SVR) as a machine-learning approach, augmented by two meta-heuristic optimization techniques: the Giant Trevally Optimizer (GTO) and Smell Agent Optimization (SAO), to achieve optimal results. These techniques empower engineers and data scientists to develop more precise and reliable predictive models in geotechnical engineering, ultimately enhancing the safety and efficiency of foundation design and construction. Drawing upon an extensive dataset compiled from previous studies, a predictive model was constructed to estimate PBC using soft computing techniques. The outcomes overwhelmingly favored the SVGT model, a fusion of the SVR model with GTO, demonstrating excellent anticipative capacities with an outstanding R2 value of 0.998 and an impressively low RMSE value of 91.21. [ABSTRACT FROM AUTHOR]

• Disciplines such as biomaterials, biofabrication, and tissue engineering are converging to create more advanced tissue-like constructs that accurately reproduce detailed physical and biological properties. • Researchers now have access to a range of tools that enable them to replicate microenvironmental features such as heterogeneity, anisotropy, hierarchical structures, compositional gradients and interfaces, and spatiotemporal cues. • Advanced models are strengthening our understanding of the mechanisms that govern tissue function and disease. • While not fully resembling the complexity of human tissue, engineered models enable researchers to investigate and adapt subsets of parameters, addressing targeted research questions. Advances in tissue engineering for both system modeling and organ regeneration depend on embracing and recapitulating the target tissue's functional and structural complexity. Microenvironmental features such as anisotropy, heterogeneity, and other biochemical and mechanical spatiotemporal cues are essential in regulating tissue development and function. Novel biofabrication strategies and innovative biomaterial design have emerged as promising tools to better reproduce such features. These facilitate a transition towards high-fidelity biomimetic structures, offering opportunities for a deeper understanding of tissue function and the development of superior therapies. In this review, we explore some of the key structural and compositional aspects of tissues, lay out how to achieve similar outcomes with current fabrication strategies, and identify the main challenges and promising avenues for future research. [ABSTRACT FROM AUTHOR]

Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC's engineering properties (i.e., Poisson's Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC's engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders. [ABSTRACT FROM AUTHOR]

The ring penetrometer is a shallow rotational penetrometer that has been developed to characterise the mechanical behaviour of surficial marine sediments. The strain-rate dependency of soils is crucial to the design of a wide range of offshore geotechnical infrastructure founded in the upper layers of the seabed (e.g. pipelines, cables and shallow foundations). This paper explores the potential application of a ring penetrometer test to measure the strain-rate dependency of the interface friction generated in soft soils at low stresses. Large-deformation numerical models of the test are developed using an elastoplastic constitutive model and a viscoplastic variant with strain softening. Using parameters representative of kaolin clay and a calcareous silt from an offshore location, the numerical analyses demonstrate a clear and measurable influence of both the viscous and strain-softening behaviours on the device–soil interface friction. These simulations were used to design suitable experimental protocols for multi-rate ring penetrometer tests, the results of which yielded a strain-rate dependency of 9–16% and 22–26% per log cycle in the kaolin clay and calcareous silt, respectively, which compare favourably with measurements derived from T-bar twitch experiments. Finally, models are presented that can be applied in the interpretation of varying-rate ring penetrometer test data for application in practice. [ABSTRACT FROM AUTHOR]

This paper proposes the use of Pierre Bourdieu's sociological concepts of social fields, capital, and habitus to analyze water management in Colombia. By mapping the social dynamics of water management, this study examines the interactions and power relationships among agents, including government agencies, private companies, academic institutions, non-profits, and local communities. The analysis reveals how various forms of capital, such as economic, cultural, social, and symbolic, influence water management practices, policies, and the distribution of power. Integrating agent-based modeling with hydrological simulations provides a more nuanced understanding of how social dynamics influence water management. This interdisciplinary approach helps develop more adaptive and equitable strategies by capturing the complex interactions between human behavior and environmental factors. This study highlights the need to localize the analysis of the social field to capture regional customs and specific social dynamics. This localized approach ensures that water management strategies are more relevant, context sensitive, and sustainable. This paper advocates for the wider adoption of agent-based modeling in water management, proposing a methodology that combines the engineering principles of practical problem solving and adaptive design with an understanding of the social complexities in water management. [ABSTRACT FROM AUTHOR]

Cross-Site Scripting (XSS) remains a significant threat to web application security, exploiting vulnerabilities to hijack user sessions and steal sensitive data. Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats. This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression (LR), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Networks (DNN). Utilizing the XSS-Attacks-2021 dataset, which comprises 460 instances across various real-world traffic-related scenarios, this framework significantly enhances XSS attack detection. Our approach, which includes rigorous feature engineering and model tuning, not only optimizes accuracy but also effectively minimizes false positives (FP) (0.13%) and false negatives (FN) (0.19%). This comprehensive methodology has been rigorously validated, achieving an unprecedented accuracy of 99.87%. The proposed system is scalable and efficient, capable of adapting to the increasing number of web applications and user demands without a decline in performance. It demonstrates exceptional real-time capabilities, with the ability to detect XSS attacks dynamically, maintaining high accuracy and low latency even under significant loads. Furthermore, despite the computational complexity introduced by the hybrid ensemble approach, strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications. Designed for easy integration with existing web security systems, our framework supports adaptable Application Programming Interfaces (APIs) and a modular design, facilitating seamless augmentation of current defenses. This innovation represents a significant advancement in cybersecurity, offering a scalable and effective solution for securing modern web applications against evolving threats. [ABSTRACT FROM AUTHOR]

Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Qo; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, Rt. This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Qo, Rt, and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling. [ABSTRACT FROM AUTHOR]

The planned expansion of wind energy in the German Bight is creating much more densely staggered wind farms and wind farm clusters. This results in a significantly greater influence of the generated wakes on energy production of neighboring wind farms. The Dornier‐128 research aircraft operated by the Technische Universität of Braunschweig was used to measure the wind field in the lee of single and multiple wind farm clusters in the German Bight on 4 days during July 2020 and July 2021. The data at 120 m aMSL (above mean sea level) were analyzed to identify wake areas and the wind speed decrease behind the wind farm clusters. The observations were then compared to a range of numerical data including the mesoscale model Weather Research and Forecasting (WRF) applying a wind farm parameterization (WRF with wind farm parameterization [WRF‐WF]) to model wake effects and an engineering model with different setups. A model calibrated on a single wind farm is established as the baseline. A modification with a lower wake recovery, the TurbOPark model, and a WRF‐coupled model make up the three additional declinations considered. Overall, the models compared well to the measurement data in the direct vicinity of the wind farms and up to 20–30 km downstream of the wind farm clusters. The accuracy in wind speed prediction of the model results decreased with distance to the wind farms, where the mesoscale model (WRF‐WF) exhibited a more consistent performance across varying distances. [ABSTRACT FROM AUTHOR]

Engineering projects must meet quality and schedule requirements during construction. This is a typical multi-objective problem and a difficult point in the management of engineering enterprises. To address these issues, a research study proposes an intelligent multi-objective optimisation technique. First, analyse the optimisation objectives of the enterprise in the context of digitalisation. Then, construct a multi-objective cost optimisation model for engineering enterprises. Second, the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm is introduced to solve multi- objective problems. To improve the multi-objective optimisation effect of the model, the inertia weight parameters and particle learning behaviour are optimised and adjusted, as the model is prone to getting stuck in local optima. In the performance test of the algorithm model, the optimised MOPSO model can accurately search for the minimum value of 0 at the position (0, 0) under the Rastrig in function, and at the same time, the number of iteration convergence is the least. The GA, ACOM, and traditional MOPSO models have more iterative convergence times, and the optimisation results are 0.10, 0.15, and 0.14, respectively. It can be seen that the performance of the optimised MOPSO model is better. In the specific example analysis, using the optimised MOPSO solution, the project cost was reduced from 31 million yuan in the contract to 30.52 million yuan, and the construction period was shortened from 588 days to 540 days, and met the environmental protection and quality requirements. The research content can provide important decision support for engineering project managers. [ABSTRACT FROM AUTHOR]

Increasing the operating speed of the trains on modern networks necessitates performing dynamic analyses to assess the performance of bridges under passage of trains. The detailed investigation of their responses requires constructing complex computational models capable to take the train–track–bridge interaction effects into account. Such models have successfully been developed; however, employing those elaborated models for practical engineering applications, or to perform studies that require a large number of analyses may become infeasible. Among such situations are conducting probabilistic investigations, screening of entire networks, or sensitivity analyses. These concerns have been addressed by employing simplified models mostly relying on moving load modeling strategy which disregards the train–track–bridge interaction effects. Those neglected contributions can be compensated by implementing additional correction factors. The distribution of loads within track is one of those disregarded effects where a reduction factor is recommended by design guidelines to take its contribution into account. It has been shown that the existing relationship for these reduction factors delivers an acceptable performance for vertical accelerations, while showing a less favorable performance for displacements. Then, a data-driven strategy is adopted in this study to propose easy-to-apply relationships for reduction factors of deflections, due to load distribution within the track. In this context, three different distributive lengths of triangular load footprints have been considered, namely 2.0, 2.5 and 3.0m. The procedure employed has trained and tested for more than 1200 train configurations, comprising conventional, articulated and regular vehicles, and including several tens of thousand data points for each distributive length. The performance observed in the new models revealed a considerable improvement with respect to the existing relationship. [ABSTRACT FROM AUTHOR]

We study the analysis and refinement of a predictive engineering model for enabling rapid prediction of Hall thruster system performance across a range of operating and environmental conditions and epistemic and aleatoric uncertainties. In particular, we describe an approach by which experimentally-observed facility effects are assimilated into the model, with a specific focus on facility background pressure. We propose a multifidelity, multidisciplinary approach for Bayesian calibration of an integrated system comprised of a set of component models. Furthermore, we perform uncertainty quantification over the calibrated model to assess the effects of epistemic and aleatoric uncertainty. This approach is realized on a coupled system of cathode, thruster, and plume models that predicts global quantities of interest (QoIs) such as thrust, efficiency, and discharge current as a function of operating conditions such as discharge voltage, mass flow rate, and background chamber pressure. As part of the calibration and prediction, we propose a number of metrics for assessing predictive model quality. Based on these metrics, we found that our proposed framework produces a calibrated model that is more accurate, sometimes by an order of magnitude, than engineering models using nominal parameters found in the literature. We also found for many QoIs that the remaining uncertainty was not sufficient to account for discrepancy with experimental data, and that existing models for facility effects do not sufficiently capture experimental trends. Finally, we confirmed through a global sensitivity analysis the prior intuition that anomalous transport dominates model uncertainty, and we conclude by suggesting several paths for future model improvement. We envision that the proposed metrics and procedures can guide the refinement of future model development activities. [ABSTRACT FROM AUTHOR]

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of language models in software engineering tasks, promoting their use in production environments and ensuring that deployed software is defect-free efficiently. [ABSTRACT FROM AUTHOR]

Besides experimental and numerical simulation approaches, empirical engineering models have emerged as significant tools for elucidating the intricate relationship between vent area and maximum internal overpressure. In the current study, a semi-empirical engineering model, Sinha & Wen model, is improved by modifying the flame critical radius and corresponding flame velocity. The comparison with the experimental data verifies that the modified model can predict the maximum internal overpressure of vented hydrogen explosions in the hydrogen concentrations range from 20 vol%-50 vol%, with an error of 40% in enclosures with an aspect ratio less than 4. By normalizing the laminar burning speed S L with hydrogen explosion index K G and modifying the correlation account for flame area in the pipeline, the modified Sinha & Wen model can be used for predicting the maximum internal overpressure of vented hydrogen explosions in pipelines with an aspect ratio of less than 17. The prediction errors are all less than 40%. • The performance of Sinha & Wen model is assessed for various hydrogen concentrations (22%–50%) and ignition locations. • The Sinha & Wen model is refined by modifying the flame critical radius and corresponding flame velocity. • The modified model Ⅰ can accurately predict vented hydrogen explosions in vessels with aspect ratio less than 4. • The modified model Ⅱ can accurately predict vented hydrogen explosions in square pipelines with aspect ratio of 4 ≤ L / D ≤ 17. [ABSTRACT FROM AUTHOR]

Cell membranes are crucial elements in living organisms, serving as protective barriers and providing structural support for cells. They regulate numerous exchange and communication processes between cells and their environment, including interactions with other cells, tissues, ions, xenobiotics, and drugs. However, the complexity and heterogeneity of cell membranes—comprising two asymmetric layers with varying compositions across different cell types and states (e.g., healthy vs. diseased)—along with the challenges of manipulating real cell membranes represent significant obstacles for in vivo studies. To address these challenges, researchers have developed various methodologies to create model cell membranes or membrane fragments, including mono- or bilayers organized in planar systems. These models facilitate fundamental studies on membrane component interactions as well as the interactions of membrane components with external agents, such as drugs, nanoparticles (NPs), or biomarkers. The applications of model cell membranes have extended beyond basic research, encompassing areas such as biosensing and nanoparticle camouflage to evade immune detection. In this review, we highlight advancements in the engineering of planar model cell membranes, focusing on the nanoarchitectonic tools used for their fabrication. We also discuss approaches for incorporating challenging materials, such as proteins and enzymes, into these models. Finally, we present our view on future perspectives in the field of planar model cell membranes. [ABSTRACT FROM AUTHOR]

Emerging deep learning-based fault diagnosis methods have advanced in the current industrial scenarios of various working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address the challenge of fault diagnosis under unseen working conditions, a domain generation framework for unseen conditions fault diagnosis is proposed, which consists of an Adaptive Feature Fusion Domain Generation Network (AFFN) and a Mix-up Augmentation Method (MAM) for both the data and domain spaces. AFFN is utilized to fuse domain-invariant and domain-specific representations to improve the model's generalization performance. MAM enhances the model's exploration ability for unseen domain boundaries. The diagnostic framework with AFFN and MAM can effectively learn more discriminative features from multiple source domains to perform different generalization tasks for unseen working loads and machines. The feasibility of the proposed unseen conditions diagnostic framework is validated on the SDUST and PU datasets and achieved peak diagnostic accuracies of 94.15% and 93.27%, respectively. [ABSTRACT FROM AUTHOR]

Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA‐TTA which means a hybrid of the whale optimizer algorithm (WOA) and tiki‐taka algorithm (TTA). The hybrid WOA‐TTA combines the exploitation phase of WOA with the exploration phase of TTA. Two stages in the hybridized model are suggested. First, the WOA exploitation phase incorporates the TTA mechanism. Second, a new approach is included in the research phase to enhance the result with each iteration to a set of unconstrained benchmark test functions and engineering design applications. To verify the performance of the improved algorithm, thirteen benchmark functions have been used to compare HWOA‐TTA with the classical intelligent population algorithms (PSO, TTA, and WOA). The hybrid algorithm is applied to two well‐known engineering mathematical models. The experiments show that the HWOA‐TTA outperforms other algorithms. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of the supply prediction of logistics transportation vehicles in road transportation. Aiming at the problem that the supply data of logistics transportation has the characteristics of long sequential data, numerous influencing factors, and a significant spatiotemporal evolution law, which leads to the lack of accuracy of supply predictions, this paper proposes a supply prediction method for logistics transportation based on an improved Informer model. Firstly, multidimensional feature engineering is applied to historical supply data to enhance the interpretability of labeled data. Secondly, a spatiotemporal convolutional network is designed to extract the spatiotemporal features of the supply volume. Lastly, a long short-term memory (LSTM) model is introduced to capture the supply volume's long- and short-term dependencies, and the predicted value is derived through a multilayer perceptron. The experimental results show that mean square error (MSE) is reduced by 73.8%, 79.36%, 82.24%, 78.58%, 77.02%, 53.96%, and 40.38%, and mean absolute error (MAE) is reduced by 52%, 59.5%, 60.36%, 57.52%, 53.9%, 31.21%, and 36.58%, respectively, when compared to the auto-regressive integrated moving average (ARIMA), support vector regression (SVR), LSTM, gated recurrent units (GRUs), a back propagation neural network (BPNN), and Informer and InformerStack single models; compared with the ARIMA + BPNN, ARIMA + GRU and ARIMA + LSTM integrated models, the MSE is reduced by 74.88%, 71.56%, and 74.07%, respectively, and the MAE is reduced by 51.31%, 50%, and 52.02%; it effectively reduces the supply prediction error and improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

This article is devoted to enhancing the mathematical models for traffic engineering routing and load balancing to ensure differentiated quality of service for packet flows with varying priorities in communication networks. The research focuses on formulating updated load-balancing conditions considering packet-flow priorities and conducting a comparative analysis of different models to identify the most effective solution. The study introduces a multipath routing model based on the type of service and presents numerical research to evaluate the proposed solutions in varying network conditions. The findings allowed us to select the model that offers the most precise regulation of service differentiation for different priority flows by optimizing the load-balancing strategy. This model directs higher-priority packet flows through less heavily loaded links and routes, while lower-priority flows are sent through more heavily loaded paths. Moreover, the routes for higher-priority flows are designed to have fewer hops than those for lower-priority flows. This approach improves network resource utilization and simplifies the architecture for delivering differentiated quality of service, potentially reducing the need for the manual configuration of congestion management mechanisms. The study concludes that the proposed enhanced model is the most effective for practical implementation in modern networks, offering a robust solution for managing diverse priority traffic. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of the track and bridge system change continuously with the mileage and operating environment, exhibiting highly significant nonlinear and nonuniform spatial distribution features. Based on the Generalized Rayleigh–Ritz Method (GRRM) and Component Mode Synthesis (CMS), this work presents a novel method to establish a three-dimensional model of train–track–bridge interaction that considers the nonuniform distribution of track–bridge system parameters and the nonlinear contact between track and bridge layers. Unlike traditional Finite Element Method (FEM) models, the GRRM-based structure employs generalized displacements without nodal degrees of freedom, thus avoiding the complex processes of element discretization and assembly when constructing tracks/bridges with nonuniform physical and geometric parameters. Additionally, when dealing with the nonlinear contact relationship between track and bridge layers, this model does not require incremental or iterative methods. By introducing CMS to truncate the modes of each substructure in the track–bridge system and establishing the reduced-order model of the entire system based on boundary conditions at the coupling interface, the matrix dimensions can be effectively reduced, thereby improving computational efficiency. The accuracy and efficiency of this newly developed train–track–bridge dynamic model have been validated through comparisons with FEM-based models and measured results. Finally, several numerical examples demonstrate the engineering practicality of this model. [ABSTRACT FROM AUTHOR]

Background: The type II based CRISPR-Cas system remains restrictedly utilized in archaea, a featured domain of life that ranks parallelly with Bacteria and Eukaryotes. Methanococcus maripaludis, known for rapid growth and genetic tractability, serves as an exemplary model for studying archaeal biology and exploring CO2−based biotechnological applications. However, tools for controlled gene regulation remain deficient and CRISPR-Cas tools still need improved in this archaeon, limiting its application as an archaeal model cellular factory. Results: This study not only improved the CRISPR-Cas9 system for optimizing multiplex genome editing and CRISPR plasmid construction efficiencies but also pioneered an effective CRISPR interference (CRISPRi) system for controlled gene regulation in M. maripaludis. We developed two novel strategies for balanced expression of multiple sgRNAs, facilitating efficient multiplex genome editing. We also engineered a strain expressing Cas9 genomically, which simplified the CRISPR plasmid construction and facilitated more efficient genome modifications, including markerless and scarless gene knock-in. Importantly, we established a CRISPRi system using catalytic inactive dCas9, achieving up to 100-fold repression on target gene. Here, sgRNAs targeting near and downstream regions of the transcription start site and the 5′end ORF achieved the highest repression efficacy. Furthermore, we developed an inducible CRISPRi-dCas9 system based on TetR/tetO platform. This facilitated the inducible gene repression, especially for essential genes. Conclusions: Therefore, these advancements not only expand the toolkit for genetic manipulation but also bridge methodological gaps for controlled gene regulation, especially for essential genes, in M. maripaludis. The robust toolkit developed here paves the way for applying M. maripaludis as a vital model archaeal cell factory, facilitating fundamental biological studies and applied biotechnology development of archaea. [ABSTRACT FROM AUTHOR]

Knowledge creation in education is a critical practice for advancing collective knowledge and fostering innovation within a student community. Students play vital roles in identifying gaps and collaborative work to improve community ideas from discourse, but idea quality can be suboptimal, affected by a lack of resources or diversity of ideas. The use of generative Artificial Intelligence and large language models (LLMs) in education has allowed work on idea-centric discussions to advance in ways that were previously unfeasible. However, the use of LLMs requires specific skill sets in prompt engineering, relevant to the in-context technique known as Chain-of-Thought (CoT) for generating and supporting improvable ideas in student discourse. A total of 721 discourse turns consisting of 272 relevant question–answer pairs and 149 threads of student discourse data were collected from 31 students during a two-day student Knowledge Building Design Studio (sKBDS). Student responses were augmented using the CoT approach and the LLM-generated responses were compared with students' original responses. Findings are illustrated using two threads to show that CoT-augmented inputs for the LLMs can generate responses that support improvable ideas in the context of knowledge creation. This study presents work from authentic student discourse and has implications for research and classroom practice. [ABSTRACT FROM AUTHOR]

Simulating the free decay motion and wave radiation from a heaving semi-submerged sphere poses significant computational challenges due to its three-dimensional complexity. By leveraging axisymmetry, we reduce the problem to a two-dimensional simulation, significantly decreasing computational demands while maintaining accuracy. In this paper, we exploit axisymmetry to perform a large ensemble of Computational Fluid Dynamics (CFDs) simulations, aiming to evaluate and maximize both accuracy and efficiency, using the Reynolds Averaged Navier–Stokes (RANS) solver interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering. [ABSTRACT FROM AUTHOR]

This article examines the practices associated with technical solutions to energy transitions. In particular, it considers the role of 'problem solving' as a partial view on the world with extraordinary influence. As the core epistemological practice of engineers, problem solving starts with selective framings of problems in which social or political conditions are taken as given. Engineering, as a form of applied science, therefore offers ethical choices that may be eclipsed by technical framings. This article highlights the reflexive practices of engineers engaged in modelling practices for energy transitions, who are addressing socio-technical problems in formally constrained ways that both enable and limit the thinkability of different future horizons. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals contain information about the brain's state as they reflect the brain's functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection. [ABSTRACT FROM AUTHOR]

This paper looks at the bacteria chemotaxis control process utilizing the System Modeling Language (SysML) to leverage well‐defined and proven engineering tools for architecting, analyzing, and refining complex systems. It proposes a new methodology called reverse‐engineering object‐oriented systems engineering method (RE‐OOSEM) that converts descriptive biology research information into descriptive systems engineering information. It utilizes SysML and model‐based systems engineering (MBSE) to capture system architecture from biological system knowledge and inputs them into systems engineering tools. From an engineering point of view, this allows greater insight into how biological systems operate and suggests how much model detail is required to uncover a top‐down system understanding. RE‐OOSEM methodology guides the SysML chemotaxis control capture process. SysML syntax is used instead of biological syntax to facilitate biological chemotaxis control system analysis from an engineered system point of view. The model can act as a scaffolding to help uncover system function, the relationships of system components and processes, and bioinformatic phenotype and genotype correlation. An executable MathWorks Stateflow chemotaxis control process model based on the SysML architectural model is included. The results show the following engineering perspective observations. (1) Several control components are not dedicated but are available and utilized when needed. (2) Individual chemoreceptors act together as a sensor array. (3) Phosphate groups act as a signaling mechanism. (4) Methylation via CH3 groups of the chemoreceptor results in sensitivity adaptation. (5) Closed‐loop control collaboratively utilizes ligand bonding, phosphorylation, and methylation. (6) Timing relationships of the control subprocesses give insight into the system's architecture. [ABSTRACT FROM AUTHOR]

Effective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience‐oriented. This study presents a machine learning–informed soil conditioning strategy, aiming at enabling automatic decision‐making for soil conditioning during tunneling. The proposed procedure includes feature engineering to process raw data, the selection of an optimal model, and a committee‐based uncertainty quantification strategy to evaluate the reliability of models. High‐dimensional data visualization techniques are leveraged to examine the influence of data distribution on the model's performance. Results demonstrate that feature engineering and the selection of models lay a foundation for machine learning–enabled automatic soil conditioning, while historical soil conditioning parameters should be incorporated in models to account for time‐dependent features during tunneling. The committee‐based uncertainty quantification strategy can effectively identify the reliability of both interpolated and extrapolated predictions. The proposed data‐driven soil conditioning framework can promote the application of machine learning toward automating earth pressure balance (EPB) shield tunneling. [ABSTRACT FROM AUTHOR]

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency. [ABSTRACT FROM AUTHOR]

Engineering Planning is the primary task of underground engineering. The planning dimension of underground engineering still remains at the two-dimensional level, lacking a technical platform for processing and analyzing multi-dimensional and multi-source information. Especially due to insufficient information under hidden geological conditions, the quality of subsequent design and construction cannot be guaranteed. BIM + GIS can achieve the unification and technical application of data at the macro scale and three-dimensional models at the micro scale. Therefore, based on BIM + GIS, the technical architecture of an underground engineering auxiliary planning and design platform was established, Taking the underground pipe gallery project as an example, simulation analysis was conducted A comprehensive model integrating engineering and three-dimensional geology has been established. Based on the three-dimensional spatial conflict analysis results of geological and underground pipe gallery engineering, optimization design was carried out. The research results can clearly demonstrate the correlation between underground pipe gallery engineering and geographical environment, enabling planners to carry out planning and design work from both macro and micro perspectives, improving the technical level of planning and design, and providing new avenues for innovation in underground engineering planning and design technology. [ABSTRACT FROM AUTHOR]

Introduction: Computer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature engineering model in acute of ischemic stroke. Methods: We use our dataset. which contains 324 patient's CT images with two classes; these classes are large and small brain vessel occlusion. We divided the collected image into horizontal and vertical patches. Then, pretrained AlexNet was utilized to extract deep features. Here, fc6 and fc7 (sixth and seventh fully connected layers) layers have been used to extract deep features from the created patches. The generated features from patches have been concatenated/merged to generate the final feature vector. In order to select the best combination from the generated final feature vector, an iterative selector (iterative neighborhood component analysis—INCA) has been used, and this selector has chosen 43 features. These 43 features have been used for classification. In the last phase, we used a kNN classifier with tenfold cross-validation. Results: By using 43 features and a kNN classifier, our AlexNet-based deep feature engineering model surprisingly attained 100% classification accuracy. Conclusion: The obtained perfect classification performance clearly demonstrated that our proposal could separate large and small brain vessel occlusion detection in non-contrast CT images. In this aspect, this model can assist neurology experts with the early recanalization chance. [ABSTRACT FROM AUTHOR]

Evolving microbes for desirable phenotypes, including stress resistance or the production of specific compounds, is a powerful approach in biotechnology and precision fermentation. Genetic diversity is essential for evolving microorganisms as it provides the raw material for screening/selection of improved variants. Efficient evolutionary engineering often requires tools that increase genetic diversity through tuning of mutation rates and the genomic location of mutations. Novel synthetic biology tools enable a controlled increase of the genetic diversity of the host. These tools can be implemented either in vitro or in vivo and target different types of mutations and target windows. Evolutionary engineering helps to improve cellular processes and metabolic pathways by, for example, optimizing gene stoichiometry or improving enzyme catalysis. The natural process of evolutionary adaptation is often exploited as a powerful tool to obtain microbes with desirable traits. For industrial microbes, evolutionary engineering is often used to generate variants that show increased yields or resistance to stressful industrial environments, thus obtaining superior microbial cell factories. However, even in large populations, the natural supply of beneficial mutations is typically low, which implies that obtaining improved microbes is often time-consuming and inefficient. To overcome this limitation, different techniques have been developed that boost mutation rates. While some of these methods simply increase the overall mutation rate across a genome, others use recent developments in DNA synthesis, synthetic biology, and CRISPR-Cas techniques to control the type and location of mutations. This review summarizes the most important recent developments and methods in the field of evolutionary engineering in model microorganisms. It discusses how both in vitro and in vivo approaches can increase the genetic diversity of the host, with a special emphasis on in vivo techniques for the optimization of metabolic pathways for precision fermentation. [ABSTRACT FROM AUTHOR]

The use of Lamb waves within a guided wave (GW)–based scheme holds promise toward monitoring and nondestructive evaluation (NDE) of plate structures. Their short wavelength enables interaction with small defects and they can travel long distances, thus offering extensive spatial coverage. In boosting the performance of these schemes for more advanced damage identification tasks, such as precise damage localization and quantification, the fusion of measurement data with models is advantageous. Such a hybrid scheme, which relies on the inclusion of engineering models, is hampered by the short wavelengths of GW-based schemes. Short wavelengths require a fine discretization of numerical models in space and in time, which results in high computational costs. In alleviating this issue, we propose a reduced-order model (ROM) relying on exploitation of the frequency response function (FRF) principle, which is parameterized with respect to the positioning of local defects. Through appropriate coordinate transformations, the surrogate, constructed based on the matching pursuit (MP) algorithm, can exploit the mechanical properties of the wave so that only a small amount of training simulations are needed. The efficacy of the proposed surrogate is demonstrated in a synthetic inverse setting, using a particle swarm optimization (PSO) strategy. [ABSTRACT FROM AUTHOR]

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC's stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes. [ABSTRACT FROM AUTHOR]

Groundwater is a vital factor affecting the stability of water-rich slope of open-pit mine, and the distribution of groundwater inside water-rich slope is always uncertain. To investigate the uncertainty of groundwater distribution inside the water-rich slope, based on the engineering background of DEZIWA open-pit mine, this paper adopts the research method of field investigation and test (single and multiple borehole pumping tests), numerical simulation (Visual MODFOW), and theoretical analysis (nonlinear fitting theory, distribution fitting theory, and one-sample Kolmogorov–Smirnov test) to study the uncertainty of the distribution of groundwater table line within the water-rich slope of open-pit mine. Results obtained from the above research indicate that, Visual MODFLOW is an effective tool for obtaining groundwater distribution information inside the water-rich slope of open-pit mine. When the open-pit mine excavated to the final boundary, it is found that within multiple cross-sections of the southern and western regions of water-rich slope of the open-pit mine, groundwater table line can all be delineated by a series of 3-term Fourier equations, which can be characterized by identical equation forms but varying fitting coefficients. Furthermore, based on the findings of distribution fitting and one-sample Kolmogorov–Smirnov test, it becomes evident that the probability distribution of the fitting coefficients of the aforementioned 3-term Fourier equations can all be described by the normal distribution models. Use the established normal distribution models in this paper, uncertainty of the groundwater distribution within the water-rich slope of DEZIWA open-pit mine can be described indirectly and quantitatively, and the research methods of this paper can provide a meaningful reference to the slope engineering with similar conditions. [ABSTRACT FROM AUTHOR]

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods. [ABSTRACT FROM AUTHOR]

As mining engineering progresses into the deep excavation phase, the intensification of high pressure, high temperature, strong disturbances, and complex geological conditions becomes increasingly prominent. Researchers perform stability analysis on the excavation area to reduce potential safety hazards during the extraction process. Developing a detailed numerical calculation model that accurately reflects the true geological structure is essential for numerical simulation analysis in mining engineering. Based on the excellent 3D geological modeling capabilities of 3D Mine software, this paper introduces a new 3D geological and numerical unified modeling method (3DMine-Rhino-HyperMesh) involving multi-software coupling and details the specific steps and concepts of this modeling approach. Subsequently, using a certain open-pit mine in Panzhihua as a backdrop, a detailed geological and numerical unified model is established, reflecting the true geological structure of the mining area, and the potential failure mechanisms of the mine slope are analyzed. The results indicate that the modeling method aligns well with the actual geological conditions, enhancing the grid quality of the numerical model and offering a new modeling approach for simulating and analyzing large complex geological entities in mining operations. [ABSTRACT FROM AUTHOR]

In view of the fact that dynamic stall models in the wind energy industry such as ONERA model, Beddoes–Leishman model, and Snel model are mostly semi‐empirical models, and the determination of empirical time constants has a great influence on the model accuracy. To optimize the time constant in dynamic stall model and improve the prediction accuracy of unsteady aerodynamics, the influence of boundary layer and pressure lag on the unsteady performance of the S809 airfoil under 2D flow conditions is explored using a simple semi‐empirical dynamic stall engineering model. The proposed model consists of four first‐order differential equations accounting for attached flow and dynamic separation flow of trailing edge based on the Theodorsen theory. A validation is carried out by the wind tunnel experiment in the Key Laboratory of Wind and Solar Energy Utilization Technology of the Ministry of Education at Inner Mongolia University of Technology. The main conclusions are as follows. The time constants for lag in pressure and boundary layer both have a great influence on the unsteady lift coefficient. When the mean angle of attack is relatively small and the airflow is between the attached flow and the separated flow, appropriately reducing the time constant can make the prediction results closer to the experimental values. When the mean angle of attack is relatively large and the airflow is in condition of fully separated flow, the time constant value can be appropriately increased. The influence of pressure lag and boundary layer lag on the unsteady drag coefficient is not significant. [ABSTRACT FROM AUTHOR]

In this research, a sawing mechanism of a mobile buckling machine was innovatively designed for the forest resources and environmental characteristics of the northeast region of China. The northeast region of China is rich in forest resources, but the climate is cold, which puts high demands on the performance and adaptability of mechanical equipment. In this study, the three-dimensional modeling of the saw-cutting mechanism was completed by Pro/Engineer 5.0 modeling software, and an in-depth dynamics simulation analysis was carried out using dynamics simulation software. The study aims to assess the applicability of this saw-cutting mechanism in the northeast region of China, analyze its design characteristics, and explore possible directions for performance optimization. Through this study, we expect to provide valuable references for future technological innovations, as well as to promote the practical application and development of mobile buckling machines in cold regions. This research not only has regional characteristics, but also reflects the innovative idea of combining mechanical equipment design with environmental adaptability. [ABSTRACT FROM AUTHOR]

This paper studies the role of trade for the joint uptake of AI‐enabled automation in manufacturing and engineering. It develops an agent‐based model (ABM) where the agents are heterogeneous manufacturers and engineering firms. The ABM features two technology‐related business models: engineering as a face‐to‐face consultancy service and engineering as automated software. The software adoption rate follows an S‐shaped curve for manufacturers and a boom and bust cycle for engineers. In the early phase, shortage of engineers constrains AI uptake, while engineers become abundant when AI is fully adopted. Trade affects the cut‐off productivity level at which manufacturers switch technology, the shape of the adoption rate curve, and the incentives for engineers to develop software. Bulky transactions and different productivity distributions across countries are drivers of trade in their own right. [ABSTRACT FROM AUTHOR]

Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity analysis and inference, framing it as a classification task. We generate textual narratives from original traffic crash tabular data using a pre-built template infused with domain knowledge. Additionally, we incorporated Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing the crash causes and then inferring the severity. This study also examine the impact of prompt engineering specifically designed for crash severity inference. The LLMs were tasked with crash severity inference to: (1) evaluate the models' capabilities in crash severity analysis, (2) assess the effectiveness of CoT and domain-informed prompt engineering, and (3) examine the reasoning abilities with the CoT framework. Our results showed that LLaMA3-70B consistently outperformed the other models, particularly in zero-shot settings. The CoT and Prompt Engineering techniques significantly enhanced performance, improving logical reasoning and addressing alignment issues. Notably, the CoT offers valuable insights into LLMs' reasoning process, unleashing their capacity to consider diverse factors such as environmental conditions, driver behavior, and vehicle characteristics in severity analysis and inference. [ABSTRACT FROM AUTHOR]

This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon. [ABSTRACT FROM AUTHOR]

We propose a framework for predicting sensor event sequences (SES) in smart homes, which can proactively support residents' activities and alert them if activities are not completed as intended. We leverage ongoing activity recognition to enhance the prediction performance, employing a GPT2-based model typically used for sentence generation. We hypothesize that the relationship between ongoing activities and SES patterns is akin to the relationship between topics and word sequence patterns in natural language processing (NLP), enabling us to apply the GPT2-based model to SES prediction. We empirically evaluated our method using two real-world datasets in which residents performed their usual daily activities. Our experimental results demonstrates that the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that leveraging knowledge on ongoing activity can further improve performance to 0.837. Achieving these SES predictions using the ongoing activity recognition model required simple feature engineering and modeling, yielding a performance rate of approximately 80%. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the Crayfish Optimization Algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA–XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA–XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells. [ABSTRACT FROM AUTHOR]