Eton Systems-a member of TMAS,the Swedish Textile Machinery Association-is taking part in the current Microfactories System Innovation project which is working on the development of a fully automated workflow for seco...Eton Systems-a member of TMAS,the Swedish Textile Machinery Association-is taking part in the current Microfactories System Innovation project which is working on the development of a fully automated workflow for second hand garments.Eton is contributing its well-proven transport system for material handling to the project,which also involves spe-cialists at the Swedish School of Textiles in Boras,the Au-tomation Region innovation cluster at Mälardalen University and the national collaboration platform iHubs Sweden.展开更多
Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automat...Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.展开更多
In recent years,large multifaceted spatial,temporal,and spatio-temporal databases have attained significant popularity and importance in the database community.In order to perform preliminary investigation,exploratory...In recent years,large multifaceted spatial,temporal,and spatio-temporal databases have attained significant popularity and importance in the database community.In order to perform preliminary investigation,exploratory visual analysis of such data-sets is highly desirable.To facilitate the convenient and efficient visualization,scientists and practitioners often need to convert the spatial component of the data-set into a more usable format.Though among the various formats available today in spatial data science community,Geographical Markup Language(GML)adheres to its central position and is of our interest in this work.The development of a tool to satisfy the spatial format conversion needs tailored to every user’s needs from scratch is difficult,time-consuming,and requires skills not easy to possess.We developed Auto-ConViz,to solve the issue stated above.It accepts the spatial component in GML format,converts that into shapefile format,and facilitates informative and automated interaction with the data-sets.It supports basic query and geospatial analysis and visualization tasks and offers functionalities such as zooming,panning,and feature selection.Furthermore,our software leverages navigation to classical ArcGIS software interface for users interested in more intensive analysis.AutoConViz serves both the database and geographical information system communities to explore insights of spatiotemporal databases and will help to further geospatial research and development.展开更多
We investigate highly sophisticated mechanisms that merge and automate interoperability of heterogeneous traditional information systems together with the World Wide Web as one world. In particular, we introduce the A...We investigate highly sophisticated mechanisms that merge and automate interoperability of heterogeneous traditional information systems together with the World Wide Web as one world. In particular, we introduce the ABB system that employs most of the Benevolent Builders (BB) which are assertions, integration rules, ABB network graph and agents to activate the components’ versatility to reconcile the semantics involved in data sharing in order to withstand the terrific dynamic computer technology in the present and future information age. The ABB is a global application system with its operation covering local databases to the Internet. The first three BB are passive objects, whereas, the agent has a strong versatility to perceive events, perform actions, communicate, make commitments, and satisfy claims. Due to the BB’s power of intelligence, ABB also has the capability to filter out and process only the relevant operational sources like preferences (i.e. customer’s interest) from the sites. The ABB’s richness in knowledge and flexibility to accommodate various data models, manages to link: system to system or firm to firm regardless of the field such as: engineering, insurance, medical, space science, and education, to mention a few.展开更多
Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function....Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.展开更多
Threat modeling is of increasing importance to IT security,and it is a complex and resource demanding task.The aim of automating threat modeling is to simplify model creation by using data that are already available.H...Threat modeling is of increasing importance to IT security,and it is a complex and resource demanding task.The aim of automating threat modeling is to simplify model creation by using data that are already available.However,the collected data often lack context;this can make the automated models less precise in terms of domain knowledge than those created by an expert human modeler.The lack of domain knowledge in modeling automation can be addressed with ontologies.In this paper,we introduce an ontology framework to improve automatic threat modeling.The framework is developed with conceptual modeling and validated using three different datasets:a small scale utility lab,water utility control network,and university IT environment.The framework produced successful results such as standardizing input sources,removing duplicate name entries,and grouping application software more logically.展开更多
Threat modeling is of increasing importance to IT security,and it is a complex and resource demanding task.The aim of automating threat modeling is to simplify model creation by using data that are already available.H...Threat modeling is of increasing importance to IT security,and it is a complex and resource demanding task.The aim of automating threat modeling is to simplify model creation by using data that are already available.However,the collected data often lack context;this can make the automated models less precise in terms of domain knowledge than those created by an expert human modeler.The lack of domain knowledge in modeling automation can be addressed with ontologies.In this paper,we introduce an ontology framework to improve automatic threat modeling.The framework is developed with conceptual modeling and validated using three different datasets:a small scale utility lab,water utility control network,and university IT environment.The framework produced successful results such as standardizing input sources,removing duplicate name entries,and grouping application software more logically.展开更多
To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints ...To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.展开更多
With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape th...Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape the future through intelligent automation.This is a key milestone on the way to achieving Rieter’s vision 2027-the fully automated spinning mill.展开更多
We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)with...We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)within 10 min.Unlike conventional methods,sTED minimizes cell loss and enhances reproducibility,achieving>90%cell viability in mouse tissues and>60%in human tumors,with 1.5×10^(4)-2.5×10^(4)cells/mg yield from mouse liver.Tailored for biopsies and ultrasmall samples,sTED addresses critical standardization challenges in organoid-based research.展开更多
AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource managem...AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.展开更多
The spraying robot for building exterior walls is an innovative technology in the field of modern construction.This paper discusses its design structure,application cases,technical benefits,and industrial impacts.Rese...The spraying robot for building exterior walls is an innovative technology in the field of modern construction.This paper discusses its design structure,application cases,technical benefits,and industrial impacts.Research shows that this type of robot improves the efficiency and quality of exterior wall construction.Its intelligent design enhances operation accuracy and safety,reduces costs and risks,and strengthens application ability in complex environments,showing broad application prospects and symbolizing the development trend of intelligence and automation in the industry.In the future,it is necessary to strengthen its intelligence and adaptive ability further,explore multi-function design,promote automation technology,and ensure construction safety and economic benefits.展开更多
Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhyt...Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhythm of 100-120 times/min for CC.[1,2]Interruptions of the CC must be as short as possible and are related to ventilation,defibrillation and turnover of the rescuers.Most of the automated external defibrillators(AEDs)require interruptions of the CC to perform rhythm analysis.Among the numerous marketed models of AEDs,some provide real-time feedback about the quality of the CC.展开更多
The textile industry,with its centuries-old heritage,is undergoing an unprecedented transformation-one where robots are stealing the spotlight.In factory floors that once hummed with the bustling activity of skilled w...The textile industry,with its centuries-old heritage,is undergoing an unprecedented transformation-one where robots are stealing the spotlight.In factory floors that once hummed with the bustling activity of skilled workers,automated systems are now the rising stars,quietly revolutionizing every aspect of production.展开更多
Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual...Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual Reality technologies will revolutionise training,patient education,and simulation-based care planning.Clinical pharmacists need to be ready and upskill to prepare for emerging technologies.The ethical,regulatory,and educational frameworks surrounding artificial intelligence and precision medicine will require constant attention,but the potential benefits for patient outcomes are unprecedented.Clinical pharmacists are in a prime position to design a new era in precision medicine,where technology works hand in hand with humans to transform healthcare.展开更多
The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and cen...The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and centrifugal honey separation,expose beekeepers to potential bee stings and frequently compromise honeycomb integrity.To address these limitations,we designed an automated honey-harvesting robot capable of autonomous frame extraction and beeswax removal.The robot mainly consists of a mobile mechanism equipped with image recognition for beehive localization,a magnetic adsorption-based beehive frame handling device(60.8 N maximum suction)coupled with a cross-slide mechanism for precise frame manipulation,and a thermal beeswax layer-melting apparatus,with optimal melting parameters(15 m/s airflow at 90℃ for 30 seconds)determined through rigorous thermal flow simulations utilizing FLUENT/Mechanical software.Field experiments demonstrated beehive frames handling success rate exceeding 85%,beeswax layer removal efficacy over 80% and damage of honeycombs below 30%.The experiment results validate the robot's operational reliability and its capacity to automate critical harvesting procedures.This study significantly reduces the labor intensity for beekeepers,effectively eliminates the risk of direct human-bee contact and improves the removal of beeswax layer,thereby catalyzing the modernization of the beekeeping industry.展开更多
The rapid advancement of Artificial Intelligence(AI)and automation has significantly transformed the accounting profession,shifting the role of accountants from routine data processors to strategic decision makers and...The rapid advancement of Artificial Intelligence(AI)and automation has significantly transformed the accounting profession,shifting the role of accountants from routine data processors to strategic decision makers and ethical stewards of technology.This conceptual study explores how AI and automation are reshaping accounting tasks,transforming required competencies,and redefining professional responsibilities.By analyzing relevant literature and theoretical frameworks,this paper identifies the evolving skill sets,both technical such as data analytics and AI literacy,and nontechnical such as critical thinking and ethical judgment,that are essential for modern accountants.The study also emphasizes the importance of continuous education,ethical integrity,and adaptive learning in navigating the digital transformation of accounting.Ultimately,this paper contributes to a deeper understanding of how accountants can maintain relevance and add value in an increasingly automated and data driven environment.展开更多
CT:As one of the exhibition owners,what were the key factors that led CEMATEX to decide to host ITMA ASIA 2025 in Singapore?Alex Zucchi:In response to requests from our members for an exhibition in Asia outside of Chi...CT:As one of the exhibition owners,what were the key factors that led CEMATEX to decide to host ITMA ASIA 2025 in Singapore?Alex Zucchi:In response to requests from our members for an exhibition in Asia outside of China,we decided to hold a combined exhibition in a second Asian location to support our members.It will also provide a reputable sourcing platform to help textile and garment manufacturers in the region modernize their operations.CT:Exhibition booths sold out very quickly.What motivates companies to participate in the exhibition?Alex Zucchi:The Singapore edition targets the South and Southeast Asia markets,as well as the Middle East.These are key textile and garment producing hubs.Hence,machinery makers are keen to reach out to buyers in the region.展开更多
The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.展开更多
文摘Eton Systems-a member of TMAS,the Swedish Textile Machinery Association-is taking part in the current Microfactories System Innovation project which is working on the development of a fully automated workflow for second hand garments.Eton is contributing its well-proven transport system for material handling to the project,which also involves spe-cialists at the Swedish School of Textiles in Boras,the Au-tomation Region innovation cluster at Mälardalen University and the national collaboration platform iHubs Sweden.
基金supported by the National Natural Science Foundation of China(Grant Nos.52306126,22350710788,12432010,11988102,92270203)the Xplore Prize.
文摘Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.
文摘In recent years,large multifaceted spatial,temporal,and spatio-temporal databases have attained significant popularity and importance in the database community.In order to perform preliminary investigation,exploratory visual analysis of such data-sets is highly desirable.To facilitate the convenient and efficient visualization,scientists and practitioners often need to convert the spatial component of the data-set into a more usable format.Though among the various formats available today in spatial data science community,Geographical Markup Language(GML)adheres to its central position and is of our interest in this work.The development of a tool to satisfy the spatial format conversion needs tailored to every user’s needs from scratch is difficult,time-consuming,and requires skills not easy to possess.We developed Auto-ConViz,to solve the issue stated above.It accepts the spatial component in GML format,converts that into shapefile format,and facilitates informative and automated interaction with the data-sets.It supports basic query and geospatial analysis and visualization tasks and offers functionalities such as zooming,panning,and feature selection.Furthermore,our software leverages navigation to classical ArcGIS software interface for users interested in more intensive analysis.AutoConViz serves both the database and geographical information system communities to explore insights of spatiotemporal databases and will help to further geospatial research and development.
文摘We investigate highly sophisticated mechanisms that merge and automate interoperability of heterogeneous traditional information systems together with the World Wide Web as one world. In particular, we introduce the ABB system that employs most of the Benevolent Builders (BB) which are assertions, integration rules, ABB network graph and agents to activate the components’ versatility to reconcile the semantics involved in data sharing in order to withstand the terrific dynamic computer technology in the present and future information age. The ABB is a global application system with its operation covering local databases to the Internet. The first three BB are passive objects, whereas, the agent has a strong versatility to perceive events, perform actions, communicate, make commitments, and satisfy claims. Due to the BB’s power of intelligence, ABB also has the capability to filter out and process only the relevant operational sources like preferences (i.e. customer’s interest) from the sites. The ABB’s richness in knowledge and flexibility to accommodate various data models, manages to link: system to system or firm to firm regardless of the field such as: engineering, insurance, medical, space science, and education, to mention a few.
基金the National Natural Science Foundation of China[62525301,62127811,62433019]the New Cornerstone Science Foundation through the XPLORER PRIZEthe financial support by the China Postdoctoral Science Foundation[GZB20240797].
文摘Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.
基金This work has received funding from the European Unions H2020 research and innovation programme under the Grant Agreement No.832907Swedish Governmental Agency for Innovation Systems(Vinnova),the Swedish Energy Agency,SweGRIDS,and STandUP for Energy.
文摘Threat modeling is of increasing importance to IT security,and it is a complex and resource demanding task.The aim of automating threat modeling is to simplify model creation by using data that are already available.However,the collected data often lack context;this can make the automated models less precise in terms of domain knowledge than those created by an expert human modeler.The lack of domain knowledge in modeling automation can be addressed with ontologies.In this paper,we introduce an ontology framework to improve automatic threat modeling.The framework is developed with conceptual modeling and validated using three different datasets:a small scale utility lab,water utility control network,and university IT environment.The framework produced successful results such as standardizing input sources,removing duplicate name entries,and grouping application software more logically.
基金received funding from the European Unions H2020 research and innovation programme under the Grant Agreement No.832907Swedish Governmental Agency for Innovation Systems(Vinnova)the Swedish Energy Agency,SweGRIDS,and STandUP for Energy.
文摘Threat modeling is of increasing importance to IT security,and it is a complex and resource demanding task.The aim of automating threat modeling is to simplify model creation by using data that are already available.However,the collected data often lack context;this can make the automated models less precise in terms of domain knowledge than those created by an expert human modeler.The lack of domain knowledge in modeling automation can be addressed with ontologies.In this paper,we introduce an ontology framework to improve automatic threat modeling.The framework is developed with conceptual modeling and validated using three different datasets:a small scale utility lab,water utility control network,and university IT environment.The framework produced successful results such as standardizing input sources,removing duplicate name entries,and grouping application software more logically.
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)。
文摘To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
文摘Precision,speed and cost efficiency are all indispensable,especially in challenging times.Rieter has put together a powerful portfolio for ITMA ASIA+CITME 2025 that gives spinning mills the chance to actively shape the future through intelligent automation.This is a key milestone on the way to achieving Rieter’s vision 2027-the fully automated spinning mill.
基金supported by the National Natural Science Foundation of China(Nos.32371470 and 82341019)the Department of Science and Technology of Guangdong Province(No.2023B0909020003).
文摘We developed a small-tissue extraction device(sTED),an automated system that integrates 1-min mechanical dissociation and enzymatic digestion to extract viable primary cells from ultrasmall tissue samples(5-20 mg)within 10 min.Unlike conventional methods,sTED minimizes cell loss and enhances reproducibility,achieving>90%cell viability in mouse tissues and>60%in human tumors,with 1.5×10^(4)-2.5×10^(4)cells/mg yield from mouse liver.Tailored for biopsies and ultrasmall samples,sTED addresses critical standardization challenges in organoid-based research.
文摘AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.
基金Design and Research of Intelligent Construction Device for the“Water-in-Sand”Process of High-Rise Building Exterior Wall(Project No.2022KQNCX189)。
文摘The spraying robot for building exterior walls is an innovative technology in the field of modern construction.This paper discusses its design structure,application cases,technical benefits,and industrial impacts.Research shows that this type of robot improves the efficiency and quality of exterior wall construction.Its intelligent design enhances operation accuracy and safety,reduces costs and risks,and strengthens application ability in complex environments,showing broad application prospects and symbolizing the development trend of intelligence and automation in the industry.In the future,it is necessary to strengthen its intelligence and adaptive ability further,explore multi-function design,promote automation technology,and ensure construction safety and economic benefits.
文摘Basic life support for cardiac arrest associates cardiopulmonary resuscitation(CPR)and defibrillation.CPR relies on chest compressions(CC)and ventilation.Current guidelines on CPR recommend a depth of 5-6 cm at a rhythm of 100-120 times/min for CC.[1,2]Interruptions of the CC must be as short as possible and are related to ventilation,defibrillation and turnover of the rescuers.Most of the automated external defibrillators(AEDs)require interruptions of the CC to perform rhythm analysis.Among the numerous marketed models of AEDs,some provide real-time feedback about the quality of the CC.
文摘The textile industry,with its centuries-old heritage,is undergoing an unprecedented transformation-one where robots are stealing the spotlight.In factory floors that once hummed with the bustling activity of skilled workers,automated systems are now the rising stars,quietly revolutionizing every aspect of production.
文摘Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual Reality technologies will revolutionise training,patient education,and simulation-based care planning.Clinical pharmacists need to be ready and upskill to prepare for emerging technologies.The ethical,regulatory,and educational frameworks surrounding artificial intelligence and precision medicine will require constant attention,but the potential benefits for patient outcomes are unprecedented.Clinical pharmacists are in a prime position to design a new era in precision medicine,where technology works hand in hand with humans to transform healthcare.
基金National Natural Science Foundation of China(31700644)Shandong Province Postdoctoral Innovation Project(SDCX-ZG-202400195)。
文摘The conventional honey production is dominated by fragmented,small-scale individual farming models.The traditional approach of honey-harvesting involving manual beehive frames extraction,beeswax layer excision and centrifugal honey separation,expose beekeepers to potential bee stings and frequently compromise honeycomb integrity.To address these limitations,we designed an automated honey-harvesting robot capable of autonomous frame extraction and beeswax removal.The robot mainly consists of a mobile mechanism equipped with image recognition for beehive localization,a magnetic adsorption-based beehive frame handling device(60.8 N maximum suction)coupled with a cross-slide mechanism for precise frame manipulation,and a thermal beeswax layer-melting apparatus,with optimal melting parameters(15 m/s airflow at 90℃ for 30 seconds)determined through rigorous thermal flow simulations utilizing FLUENT/Mechanical software.Field experiments demonstrated beehive frames handling success rate exceeding 85%,beeswax layer removal efficacy over 80% and damage of honeycombs below 30%.The experiment results validate the robot's operational reliability and its capacity to automate critical harvesting procedures.This study significantly reduces the labor intensity for beekeepers,effectively eliminates the risk of direct human-bee contact and improves the removal of beeswax layer,thereby catalyzing the modernization of the beekeeping industry.
文摘The rapid advancement of Artificial Intelligence(AI)and automation has significantly transformed the accounting profession,shifting the role of accountants from routine data processors to strategic decision makers and ethical stewards of technology.This conceptual study explores how AI and automation are reshaping accounting tasks,transforming required competencies,and redefining professional responsibilities.By analyzing relevant literature and theoretical frameworks,this paper identifies the evolving skill sets,both technical such as data analytics and AI literacy,and nontechnical such as critical thinking and ethical judgment,that are essential for modern accountants.The study also emphasizes the importance of continuous education,ethical integrity,and adaptive learning in navigating the digital transformation of accounting.Ultimately,this paper contributes to a deeper understanding of how accountants can maintain relevance and add value in an increasingly automated and data driven environment.
文摘CT:As one of the exhibition owners,what were the key factors that led CEMATEX to decide to host ITMA ASIA 2025 in Singapore?Alex Zucchi:In response to requests from our members for an exhibition in Asia outside of China,we decided to hold a combined exhibition in a second Asian location to support our members.It will also provide a reputable sourcing platform to help textile and garment manufacturers in the region modernize their operations.CT:Exhibition booths sold out very quickly.What motivates companies to participate in the exhibition?Alex Zucchi:The Singapore edition targets the South and Southeast Asia markets,as well as the Middle East.These are key textile and garment producing hubs.Hence,machinery makers are keen to reach out to buyers in the region.
文摘The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
