The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education....The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.展开更多
This paper focuses on concrete technical management in construction engineering.It explains the core elements,including production mix ratio,analyzes problems of traditional models,presents innovative management model...This paper focuses on concrete technical management in construction engineering.It explains the core elements,including production mix ratio,analyzes problems of traditional models,presents innovative management models like BIM+GIS and their applications,and covers aspects such as job competence standards and prefabricated modular construction regulations,emphasizing the significance and development direction of innovative models.展开更多
To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precis...To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.展开更多
Based on the study of the Mechanical Design and Automation major and its relevance to teaching reform in higher education engineering programs,a project-based teaching model was introduced.This approach integrates tea...Based on the study of the Mechanical Design and Automation major and its relevance to teaching reform in higher education engineering programs,a project-based teaching model was introduced.This approach integrates teaching design,scheme argumentation,and the implementation of teaching activities with the project serving as the central framework.Course knowledge points are derived from the project topics,forming the foundation for a structured knowledge framework.The course content is modularized in alignment with the project design,enabling students to engage with professional courses on a module-by-module basis,guided by the project.Each course utilizes the project topic as a practical case,facilitating project-led teaching.A teaching system tailored to the research project is proposed,establishing a professional course structure closely linked to the project objectives.展开更多
The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by sub...The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.展开更多
This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrat...This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrating the CIPP model with AI technology,a novel intelligent educational evaluation system was designed.Through experimental validation and case studies,the system demonstrated significant effectiveness in improving teaching quality,facilitating personalized student development,and optimizing educational resource allocation.Additionally,the study predicts potential changes this system could bring to the education industry and proposes relevant policy recommendations.Although the current research has limitations,with technological advancements in the future,this system is expected to provide stronger support for innovations in engineering education models.展开更多
Existing creep constitutive models rarely incorporate studies on the coupling mechanism between creep damage and rock strain softening/hardening.This study analyzed the strain softening and hardening behaviors of argi...Existing creep constitutive models rarely incorporate studies on the coupling mechanism between creep damage and rock strain softening/hardening.This study analyzed the strain softening and hardening behaviors of argillaceous sandstone and sandy mudstone during load-induced failure based on the plastic increment theory.These behaviors were then coupled with an improved Burgers creep model to establish a coupled creep-damage and plastic softening/hardening model.Finally,the validity and engineering applicability of the proposed model were verified through FLAC~(3D)numerical simulations.The numerical simulation results of standard cylindrical specimens show that the established coupling model can effectively reflect the unloading creep deformation law and failure characteristics of argillaceous sandstone and sandy mudstone.Taking the diversion tunnel of a hydropower station in Northwest China as an example for engineering application,the coupled creep-damage and plastic softening/hardening model is introduced into FLAC~(3D)to carry out numerical simulation calculation of the tunnel under excavation and unsupported creep conditions.The results show that the uncoordinated deformation of the upper and lower walls of the surrounding rock of the tunnel is more prominent.When the buried depth of the tunnel increases to 80 m,the monitoring point C in the sandy mudstone area of the upper wall shows nonlinear accelerated deformation under unsupported creep conditions,and the maximum displacement in the horizontal direction reaches 44.5 mm,and the maximum displacement in the vertical direction reaches 53.5 mm.The coupled creep-damage and plastic softening/hardening model established in the research results can well describe the whole process of uncoordinated deformation and failure in the unloading creep process of soft-hard interbedded rock mass.展开更多
Improving the specific,technical,economic,and environmental characteristics of piston engines(ICE)operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries.The aim ...Improving the specific,technical,economic,and environmental characteristics of piston engines(ICE)operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries.The aim of the study was to optimize the parameters of the ICE working cycle after replacing the base fuel(propane-butane blend)with syngas from wood sawdust to improve its technical and economic performance based on mathematical modeling.The modeling results were verified through experimental studies(differences for key parameters did not exceed 4.0%).The object of the study was an electric generator based on a single-cylinder spark ignition engine with a power of 1 kW.The article describes the main approaches to creating a mathematical model of the engine working cycle,a test bench for modeling verification,physicochemical properties of the base fuel(propane-butane blend),and laboratory syngas.It was shown that replacing the fuel from a propane-butane blend to laboratory syngas caused a decrease in engine efficiency to 33%(the efficiency of the base ICE was 0.179 vs.the efficiency of 0.119 for the converted ICE for the 0.59 kW power mode).Engine efficiency was chosen as the key criterion for optimizing the working cycle.As a result of optimization,the efficiency of the converted syngas engine was 6.1%higher than that of the base engine running on the propane-butane blend,and the power drop did not exceed 8.0%.Thus,careful fine-tuning of the working cycle parameters allows increasing the technical and economic characteristics of the syngas engine to the level of ICEs running on traditional types of fuel.展开更多
This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data character...This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making.展开更多
Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that e...Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that enhance the platform's chances of surviving different scenarios.Such scenarios can involve various types of threats that can affect the platform's survivability.Among such,blast waves impacting the platform's structure represent critical conditions that have not yet been studied in detail.That is,frameworks for vulnerability assessment that can deal with blast loading have not been presented yet.In this context,this work presents a fast-running engineering tool that can quantify the risk that a structure fails when it is subjected to blast loading from the detonation of high explosive-driven threats detonating at various distances from the structure itself.The tool has been implemented in an in-house software that calculates vulnerability to various impacting objects,and its capabilities have been shown through a simplified,yet realistic,case study.The novelty of this research lies in the development of an integrated computational environment capable of calculating the platform's vulnerability to blast waves,without the need for running expensive finite element simulations.In fact,the proposed tool is fully based on analytical models integrated with a probabilistic approach for vulnerability calculation.展开更多
Artificial intelligence is reshaping radiology by enabling automated report generation,yet evaluating the clinical accuracy and relevance of these reports is a challenging task,as traditional natural language generati...Artificial intelligence is reshaping radiology by enabling automated report generation,yet evaluating the clinical accuracy and relevance of these reports is a challenging task,as traditional natural language generation metrics like BLEU and ROUGE prioritize lexical overlap over clinical relevance.To address this gap,we propose a novel semantic assessment framework for evaluating the accuracy of artificial intelligence-generated radiology reports against ground truth references.We trained 5229 image–report pairs from the Indiana University chest X-ray dataset on the R2GenRL model and generated a benchmark dataset on test data from the Indiana University chest X-ray and MIMIC-CXR datasets.These datasets were selected for their public availability,large scale,and comprehensive coverage of diverse clinical cases in chest radiography,enabling robust evaluation and comparison with prior work.Results demonstrate that the Mistral model,particularly with task-oriented prompting,achieves superior performance(up to 91.9%accuracy),surpassing other models and closely aligning with established metrics like BERTScore-F1(88.1%)and CLIP-Score(88.7%).Statistical analyses,including paired t-tests(p<0.01)and analysis of variance(p<0.05),confirm significant improvements driven by structured prompting.Failure case analysis reveals limitations,such as over-reliance on lexical similarity,underscoring the need for domain-specific fine-tuning.This framework advances the evaluation of artificial intelligence-driven(AI-driven)radiology report generation,offering a robust,clinically relevant metric for assessing semantic accuracy and paving the way for more reliable automated systems in medical imaging.展开更多
The article by Granjeiro et al provided a thorough review of the role of stem cell models in the development of advanced therapy medicinal products.It emphasized the potential of stem cell models to refine preclinical...The article by Granjeiro et al provided a thorough review of the role of stem cell models in the development of advanced therapy medicinal products.It emphasized the potential of stem cell models to refine preclinical studies and align with regulatory requirements for clinical applications.This article introduced a new perspective on enhancing the transition of stem cell research into clinical practice,focusing on the importance of international regulatory harmonization and the need for standardization in stem cell-based therapies.展开更多
Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textur...Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.展开更多
The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling ...The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.展开更多
To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fus...To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.展开更多
The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficultie...The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.展开更多
Internationally earthquake insurance,like all other insurance (fire,auto),adopted actuarial approach in the past, which is,based on historical loss experience to determine insurance rate.Due to the fact that earthquak...Internationally earthquake insurance,like all other insurance (fire,auto),adopted actuarial approach in the past, which is,based on historical loss experience to determine insurance rate.Due to the fact that earthquake is a rare event with severe consequence,irrational determination of premium rate and lack of understanding scale of potential loss led to many insurance companies insolvent after Northridge earthquake in 1994. Along with recent advances in earth science,computer science and engineering,computerized loss estimation methodologies based on first principles have been developed to the point that losses from destructive earthquakes can be quantified with reasonable accuracy using scientific modeling techniques. This paper intends to introduce how engineering models can assist to quantify earthquake risk and how insurance industry can use this information to manage their risk in the United States and abroad.展开更多
In this study, combustion of methane was simulated using four kinetic models of methane in CHEMKIN 4.1.1 for 0-D closed internal combustion （IC） engine reactor. Two detailed （GRIMECH3.0 ＆ UBC MECH2.0） and two red...In this study, combustion of methane was simulated using four kinetic models of methane in CHEMKIN 4.1.1 for 0-D closed internal combustion （IC） engine reactor. Two detailed （GRIMECH3.0 ＆ UBC MECH2.0） and two reduced （One step ＆ Four steps） models were examined for various IC engine designs. The detailed models （GRIMECH3.0, ＆ UBC MECH2.0） and 4-step models successfully predicted the combustion while global model was unable to predict any combustion reaction. This study illustrated that the detailed model showed good concordances in the prediction of chamber pressure, temperature and major combustion species profiles. The detailed models also exhibited the capabilities to predict the pollutants formation in an IC engine while the reduced schemes showed failure in the prediction of pollutants emissions. Although, there are discrepancies among the profiles of four considered model, the detailed models （GRIMECH3.0 ＆ UBC MECH2.0） produced the acceptable agreement in the species prediction and formation of pollutants.展开更多
The core education function of higher vocational colleges is to train technical talents with high quality,so as to meet the needs of talents in the development stage of our society.Under the guidance of talent trainin...The core education function of higher vocational colleges is to train technical talents with high quality,so as to meet the needs of talents in the development stage of our society.Under the guidance of talent training,higher vocational colleges need to pay attention to establishing an all-round and three-dimensional education model,and promote innovation of higher vocational education on the basis of this.It is also a way to promote the innovation of higher vocational education to vigorously promote the construction of“post,course,competition,certificate”mode in the construction of education mode.Through the construction of“post,course,competition,certificate”mode,the education mode of higher vocational colleges is gradually improved,so as to strengthen the effectiveness of talent training in higher vocational colleges.Therefore,in this paper,the author puts forward some suggestions to promote the construction of the integrated education mode of the electronic information engineering technology major in higher vocational colleges,so as to help improve the talent training level of higher vocational colleges.展开更多
An automatic generation method of geological cross-sections in dredging engineering based on 3D geological solid models is presented.The 3D geological models are built applying the non-uniform rational B-splines(NURBS...An automatic generation method of geological cross-sections in dredging engineering based on 3D geological solid models is presented.The 3D geological models are built applying the non-uniform rational B-splines(NURBS) technique,and a 2D profile can be calculated and generated automatically through Boolean operation to meet the demands of dredging projects.Moreover,an automatic marking method for geological attributes is put forward based on database technology,and the geological attributes include the profile name,scale,horizontal and vertical relative coordinates,geological lithology,and 2D standard lithology legend.At the same time,the automatic marking method can also provide an interactive mode for geological engineers to edit and modify the profile in the modeling system.Practical engineering applications show that the automatic generation method is a simple,flexible,fast and precise visual graphics rendering process that can create 2D standard profiles automatically and efficiently.This method also provides a convenient support tool for geological engineering digital analysis.展开更多
基金supported in part by the Teaching Reform Project of Chongqing University of Posts and Telecommunications,China under Grant No.XJG23234Chongqing Municipal Higher Education Teaching Reform Research Project under Grant No.203399the Doctoral Direct Train Project of Chongqing Science and Technology Bureau under Grant No.CSTB2022BSXM-JSX0007。
文摘The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.
文摘This paper focuses on concrete technical management in construction engineering.It explains the core elements,including production mix ratio,analyzes problems of traditional models,presents innovative management models like BIM+GIS and their applications,and covers aspects such as job competence standards and prefabricated modular construction regulations,emphasizing the significance and development direction of innovative models.
基金Yongxian Huang supported by Projects of Guangzhou Science and Technology Plan(2023A04J0409)。
文摘To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.
基金The 2023 Qingdao Institute of Technology Campus-Level Teaching and Research Project“Research on Project-Based Teaching Model for Engineering Majors in Colleges and Universities”(2023JY005)。
文摘Based on the study of the Mechanical Design and Automation major and its relevance to teaching reform in higher education engineering programs,a project-based teaching model was introduced.This approach integrates teaching design,scheme argumentation,and the implementation of teaching activities with the project serving as the central framework.Course knowledge points are derived from the project topics,forming the foundation for a structured knowledge framework.The course content is modularized in alignment with the project design,enabling students to engage with professional courses on a module-by-module basis,guided by the project.Each course utilizes the project topic as a practical case,facilitating project-led teaching.A teaching system tailored to the research project is proposed,establishing a professional course structure closely linked to the project objectives.
文摘The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.
基金Liaoning Provincial Social Science Planning Fund“Research on the Educational Intelligent Evaluation System Based on the CIPP Model and Artificial Intelligence under the Background of New Engineering”(L22BTJ005)。
文摘This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrating the CIPP model with AI technology,a novel intelligent educational evaluation system was designed.Through experimental validation and case studies,the system demonstrated significant effectiveness in improving teaching quality,facilitating personalized student development,and optimizing educational resource allocation.Additionally,the study predicts potential changes this system could bring to the education industry and proposes relevant policy recommendations.Although the current research has limitations,with technological advancements in the future,this system is expected to provide stronger support for innovations in engineering education models.
基金funded by the Natural Science Foundation of China(Grant No.U22A20600)。
文摘Existing creep constitutive models rarely incorporate studies on the coupling mechanism between creep damage and rock strain softening/hardening.This study analyzed the strain softening and hardening behaviors of argillaceous sandstone and sandy mudstone during load-induced failure based on the plastic increment theory.These behaviors were then coupled with an improved Burgers creep model to establish a coupled creep-damage and plastic softening/hardening model.Finally,the validity and engineering applicability of the proposed model were verified through FLAC~(3D)numerical simulations.The numerical simulation results of standard cylindrical specimens show that the established coupling model can effectively reflect the unloading creep deformation law and failure characteristics of argillaceous sandstone and sandy mudstone.Taking the diversion tunnel of a hydropower station in Northwest China as an example for engineering application,the coupled creep-damage and plastic softening/hardening model is introduced into FLAC~(3D)to carry out numerical simulation calculation of the tunnel under excavation and unsupported creep conditions.The results show that the uncoordinated deformation of the upper and lower walls of the surrounding rock of the tunnel is more prominent.When the buried depth of the tunnel increases to 80 m,the monitoring point C in the sandy mudstone area of the upper wall shows nonlinear accelerated deformation under unsupported creep conditions,and the maximum displacement in the horizontal direction reaches 44.5 mm,and the maximum displacement in the vertical direction reaches 53.5 mm.The coupled creep-damage and plastic softening/hardening model established in the research results can well describe the whole process of uncoordinated deformation and failure in the unloading creep process of soft-hard interbedded rock mass.
基金the Ministry of Science and Higher Education of the Russian Federation(Ural Federal University Program of Development within the Priority-2030 Program)is gratefully acknowledged.
文摘Improving the specific,technical,economic,and environmental characteristics of piston engines(ICE)operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries.The aim of the study was to optimize the parameters of the ICE working cycle after replacing the base fuel(propane-butane blend)with syngas from wood sawdust to improve its technical and economic performance based on mathematical modeling.The modeling results were verified through experimental studies(differences for key parameters did not exceed 4.0%).The object of the study was an electric generator based on a single-cylinder spark ignition engine with a power of 1 kW.The article describes the main approaches to creating a mathematical model of the engine working cycle,a test bench for modeling verification,physicochemical properties of the base fuel(propane-butane blend),and laboratory syngas.It was shown that replacing the fuel from a propane-butane blend to laboratory syngas caused a decrease in engine efficiency to 33%(the efficiency of the base ICE was 0.179 vs.the efficiency of 0.119 for the converted ICE for the 0.59 kW power mode).Engine efficiency was chosen as the key criterion for optimizing the working cycle.As a result of optimization,the efficiency of the converted syngas engine was 6.1%higher than that of the base engine running on the propane-butane blend,and the power drop did not exceed 8.0%.Thus,careful fine-tuning of the working cycle parameters allows increasing the technical and economic characteristics of the syngas engine to the level of ICEs running on traditional types of fuel.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-DDRSP2501).
文摘This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making.
文摘Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that enhance the platform's chances of surviving different scenarios.Such scenarios can involve various types of threats that can affect the platform's survivability.Among such,blast waves impacting the platform's structure represent critical conditions that have not yet been studied in detail.That is,frameworks for vulnerability assessment that can deal with blast loading have not been presented yet.In this context,this work presents a fast-running engineering tool that can quantify the risk that a structure fails when it is subjected to blast loading from the detonation of high explosive-driven threats detonating at various distances from the structure itself.The tool has been implemented in an in-house software that calculates vulnerability to various impacting objects,and its capabilities have been shown through a simplified,yet realistic,case study.The novelty of this research lies in the development of an integrated computational environment capable of calculating the platform's vulnerability to blast waves,without the need for running expensive finite element simulations.In fact,the proposed tool is fully based on analytical models integrated with a probabilistic approach for vulnerability calculation.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2024-RS-2024-00436773).
文摘Artificial intelligence is reshaping radiology by enabling automated report generation,yet evaluating the clinical accuracy and relevance of these reports is a challenging task,as traditional natural language generation metrics like BLEU and ROUGE prioritize lexical overlap over clinical relevance.To address this gap,we propose a novel semantic assessment framework for evaluating the accuracy of artificial intelligence-generated radiology reports against ground truth references.We trained 5229 image–report pairs from the Indiana University chest X-ray dataset on the R2GenRL model and generated a benchmark dataset on test data from the Indiana University chest X-ray and MIMIC-CXR datasets.These datasets were selected for their public availability,large scale,and comprehensive coverage of diverse clinical cases in chest radiography,enabling robust evaluation and comparison with prior work.Results demonstrate that the Mistral model,particularly with task-oriented prompting,achieves superior performance(up to 91.9%accuracy),surpassing other models and closely aligning with established metrics like BERTScore-F1(88.1%)and CLIP-Score(88.7%).Statistical analyses,including paired t-tests(p<0.01)and analysis of variance(p<0.05),confirm significant improvements driven by structured prompting.Failure case analysis reveals limitations,such as over-reliance on lexical similarity,underscoring the need for domain-specific fine-tuning.This framework advances the evaluation of artificial intelligence-driven(AI-driven)radiology report generation,offering a robust,clinically relevant metric for assessing semantic accuracy and paving the way for more reliable automated systems in medical imaging.
文摘The article by Granjeiro et al provided a thorough review of the role of stem cell models in the development of advanced therapy medicinal products.It emphasized the potential of stem cell models to refine preclinical studies and align with regulatory requirements for clinical applications.This article introduced a new perspective on enhancing the transition of stem cell research into clinical practice,focusing on the importance of international regulatory harmonization and the need for standardization in stem cell-based therapies.
文摘Panoramic images, offering a 360-degree view, are essential in virtual reality(VR) and augmented reality(AR), enhancing realism with high-quality textures. However, acquiring complete and high-quality panoramic textures is challenging. This paper introduces a method using generative adversarial networks(GANs) and the contrastive language-image pretraining(CLIP) model to restore and control texture in panoramic images. The GAN model captures complex structures and maintains consistency, while CLIP enables fine-grained texture control via semantic text-image associations. GAN inversion optimizes latent codes for precise texture details. The resulting low dynamic range(LDR) images are converted to high dynamic range(HDR) using the Blender engine for seamless texture blending. Experimental results demonstrate the effectiveness and flexibility of this method in panoramic texture restoration and generation.
基金The Construction S&T Project of the Department of Transportation of Sichuan Province(Grant No.2023A02)the National Natural Science Foundation of China(No.52109135).
文摘The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.
基金supported by the National Natural Science Foundation of China(No.51875465)the Shaanxi Province Postdoctoral Research Project Funding,Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(No.CX2023002)+1 种基金the Civil Aircraft Scientific Research Projectthe Fund of Shanghai Engineering Research Center of Civil Aircraft Health Monitoring(No.GCZX-2022-01).
文摘To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.
文摘The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.
文摘Internationally earthquake insurance,like all other insurance (fire,auto),adopted actuarial approach in the past, which is,based on historical loss experience to determine insurance rate.Due to the fact that earthquake is a rare event with severe consequence,irrational determination of premium rate and lack of understanding scale of potential loss led to many insurance companies insolvent after Northridge earthquake in 1994. Along with recent advances in earth science,computer science and engineering,computerized loss estimation methodologies based on first principles have been developed to the point that losses from destructive earthquakes can be quantified with reasonable accuracy using scientific modeling techniques. This paper intends to introduce how engineering models can assist to quantify earthquake risk and how insurance industry can use this information to manage their risk in the United States and abroad.
文摘In this study, combustion of methane was simulated using four kinetic models of methane in CHEMKIN 4.1.1 for 0-D closed internal combustion （IC） engine reactor. Two detailed （GRIMECH3.0 ＆ UBC MECH2.0） and two reduced （One step ＆ Four steps） models were examined for various IC engine designs. The detailed models （GRIMECH3.0, ＆ UBC MECH2.0） and 4-step models successfully predicted the combustion while global model was unable to predict any combustion reaction. This study illustrated that the detailed model showed good concordances in the prediction of chamber pressure, temperature and major combustion species profiles. The detailed models also exhibited the capabilities to predict the pollutants formation in an IC engine while the reduced schemes showed failure in the prediction of pollutants emissions. Although, there are discrepancies among the profiles of four considered model, the detailed models （GRIMECH3.0 ＆ UBC MECH2.0） produced the acceptable agreement in the species prediction and formation of pollutants.
文摘The core education function of higher vocational colleges is to train technical talents with high quality,so as to meet the needs of talents in the development stage of our society.Under the guidance of talent training,higher vocational colleges need to pay attention to establishing an all-round and three-dimensional education model,and promote innovation of higher vocational education on the basis of this.It is also a way to promote the innovation of higher vocational education to vigorously promote the construction of“post,course,competition,certificate”mode in the construction of education mode.Through the construction of“post,course,competition,certificate”mode,the education mode of higher vocational colleges is gradually improved,so as to strengthen the effectiveness of talent training in higher vocational colleges.Therefore,in this paper,the author puts forward some suggestions to promote the construction of the integrated education mode of the electronic information engineering technology major in higher vocational colleges,so as to help improve the talent training level of higher vocational colleges.
基金Supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of China(No.51021004)Tianjin Research Program of Application Foundation and Advanced Technology(No.12JCZDJC29200)National Key Technology R and D Program in the 12th Five-Year Plan of China(No.2011BAB10B06)
文摘An automatic generation method of geological cross-sections in dredging engineering based on 3D geological solid models is presented.The 3D geological models are built applying the non-uniform rational B-splines(NURBS) technique,and a 2D profile can be calculated and generated automatically through Boolean operation to meet the demands of dredging projects.Moreover,an automatic marking method for geological attributes is put forward based on database technology,and the geological attributes include the profile name,scale,horizontal and vertical relative coordinates,geological lithology,and 2D standard lithology legend.At the same time,the automatic marking method can also provide an interactive mode for geological engineers to edit and modify the profile in the modeling system.Practical engineering applications show that the automatic generation method is a simple,flexible,fast and precise visual graphics rendering process that can create 2D standard profiles automatically and efficiently.This method also provides a convenient support tool for geological engineering digital analysis.
