In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data be...In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.展开更多
Research suggests that transient institutions, i.e., institutions with short-term investment horizon,make management focus on short-term earnings goals. This study examines incentive in terms of CEO cash compensation ...Research suggests that transient institutions, i.e., institutions with short-term investment horizon,make management focus on short-term earnings goals. This study examines incentive in terms of CEO cash compensation that explains why management concentrates on short-term earnings results when transient institutions hold high levels of ownership. Using quarterly consensus analysts＇ expectations as a proxy for short-term earnings benchmarks, the author finds that CEO cash compensation and the frequency with which management misses quarterly earnings benchmarks in a year （MISSNUMt） are more strongly negatively associated in firms with high transient institutional ownership than in firms with low transient institutional ownership, suggesting that transient institutions strengthen the inverse relation between CEO cash pay and missing short-term earnings benchmarks and hence increase pressure on management in terms of cash pay for short-term results. Moreover, the author shows that change in CEO cash compensation is positively associated with change in transient institutional ownership, consistent with the idea that selling shares by transient institutions influences the boards of portfolio firms in CEO cash compensation decision. This study contributes to the governance literature and is relevant to business managers by providing additional evidence that transient institutions provide less patient capital and may not benefit long-run firm value creation.展开更多
With the adoption of the Luanda Declaration at the end of the conference,it was fairly evident that African governments could no longer deny the causal links or intersection-between the environment and health care for...With the adoption of the Luanda Declaration at the end of the conference,it was fairly evident that African governments could no longer deny the causal links or intersection-between the environment and health care for people across the continent.展开更多
Soil classification is the foundation for exchange and extension of research findings in soil science and for modern management of soil resources. This study explained database and research methodology to create a cro...Soil classification is the foundation for exchange and extension of research findings in soil science and for modern management of soil resources. This study explained database and research methodology to create a cross-reference system for translating the Genetic Soil Classification of China (GSCC) into the Chinese Soil Taxonomy (CST). With the help of the CST keys, each of the 2 540 soil species in GSCC has been interpreted to its corresponding soil order, suborder, great group, and sub-group in CST. According to the methodology adopted, the assigned soil species have been linked one another to their corresponding polygons in the 1:1000000 digital soil map of China. Referencibility of each soil species between the GSCC and CST systems was determined statistically on the basis of distribution area of each soil species at a high taxon level of the two systems. The soils were then sorted according to their maximum referencibility and classified into three categories for discussion. There were 19 soil great groups in GSCC with maximum referencibility > 90% and 22 great groups between 60%-90%. These soil great groups could serve as cross-reference benchmarks. There were 19 great groups in GSCC with maximum referencibility < 60%, which could be used as cross-reference benchmarks until new and better results were available. For these soils, if the translation was made at a lower soil taxon level or on a regional basis, it would improve their referencibility enabling them to serve as new cross-reference benchmarks.展开更多
There exists a gap between control theory and control practice,i.e.,all control methods suggested by researchers are not implemented in real systems and,on the other hand,many important in dustrial problems are not st...There exists a gap between control theory and control practice,i.e.,all control methods suggested by researchers are not implemented in real systems and,on the other hand,many important in dustrial problems are not studied in the academic research.Benchmark problems can help close this gap and provide many opportunities for members in both the controls theory and application communities.The goal is to survey and give pointers to different general controls and modeling related benchmark problems that can serve as inspiration for future benchmarks and then specifically focus the benchmark coverage on automotive control engineering application.In the paper reflections are given on how different categories of benchmark designers,benchmark solvers and third part users can benefit from providing,solving,and studying benchmark problems.The paper also collects information about several benchmark problems and gives pointers to papers than give more detailed information about different problems that have been presented.展开更多
Helicopter EMS (HEMS) allows for patients to be quickly transported into regional cardiac centers, often to receive primary percutaneous coronary intervention (PCI). Since PCI is a time-critical therapy, it is importa...Helicopter EMS (HEMS) allows for patients to be quickly transported into regional cardiac centers, often to receive primary percutaneous coronary intervention (PCI). Since PCI is a time-critical therapy, it is important that patients get to primary PCI as quickly as possible. HEMS crews’ “on-scene” times for trauma patients have been extensively studied, and recent years have seen many efforts to minimize the time required to prepare patients for transport. There has been less attention to interfacility transport “scene times” for HEMS crews at referring hospitals;this includes stabilization times for preparing cardiac patients for loading onto aircraft for HEMS transport to primary PCI. In the absence of guiding evidence, system benchmarking and quality improvement are difficult. Therefore the current study was undertaken, to assess and describe the HEMS crew “on-scene” times or “patient stabilization times” (PSTs) at referring hospitals, for interfacility transported cardiac patients flown for primary PCI. Descriptive analysis identified a PST median of 19 minutes (interquartile range 15 - 24), and univariate analyses using Kruskal-Wallis testing found no association between prolonged PST and sending unit type (Emergency Department versus other), off-hours transports, or relatively frequent (at least monthly) use of HEMS (p for all comparisons > 0.64). Outlier PSTs, defined a priori as those exceeding the median by at least a half-hour, were found in 12% of all cases. These data could be useful as a starting point for system planning and benchmarking efforts in regionalized systems of acute cardiac care.展开更多
Piráis a reading comprehension dataset focused on the ocean,the Brazilian coast,and climate change,built from a collection of scientific abstracts and reports on these topics.This dataset represents a versatile l...Piráis a reading comprehension dataset focused on the ocean,the Brazilian coast,and climate change,built from a collection of scientific abstracts and reports on these topics.This dataset represents a versatile language resource,particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge.Despite its potential,a detailed set of baselines has not yet been developed for Pirá.By creating these baselines,researchers can more easily utilize Piráas a resource for testing machine learning models across a wide range of question answering tasks.In this paper,we define six benchmarks over the Pirádataset,covering closed generative question answering,machine reading comprehension,information retrieval,open question answering,answer triggering,and multiple choice question answering.As part of this effort,we have also produced a curated version of the original dataset,where we fixed a number of grammar issues,repetitions,and other shortcomings.Furthermore,the dataset has been extended in several new directions,so as to face the aforementioned benchmarks:translation of supporting texts from English into Portuguese,classification labels for answerability,automatic paraphrases of questions and answers,and multiple choice candidates.The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pirádataset.展开更多
Automatic modal identification via automatically interpreting the stabilization diagram provides key technique in bridge structural health monitoring.This paper reviews the progress in the area of automatic modal iden...Automatic modal identification via automatically interpreting the stabilization diagram provides key technique in bridge structural health monitoring.This paper reviews the progress in the area of automatic modal identification based on interpreting the stabilization diagram.The whole identification process is divided into four steps from establishing the stabilization diagram to removing the outliers in the identification results.The criteria and algorithms used in each step in the existing studies are carefully summarized and classified.Comparisons between typical methods in cleaning and interpreting the stabilization diagram are also conducted.Real structure benchmarks used in the existing studies to validate the proposed automatic modal identification methods are also summarized.Based on the review and comparison,the specific ratio method for cleaning the stabilization diagram,the hierarchical clustering method for interpreting the stabilization diagram and the adjusted boxplot for removing the outliers in the identification results are the most suitable methods for each step.The key point of automatic modal identification based on interpreting the stabilization diagram has also discussed,and it is recommended to pay more attention to cleaning the stabilization diagram.Future study about automatic modal identification under situation with very few sensors deployed should be more concerned.This review aims to help researchers and practitioners in implementing existing automatic modal identification algorithms effectively and developing more suitable and practical methods for civil engineering structures in the future.展开更多
This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narw...This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.展开更多
Purpose-Prominent at the intersections of national educational agencies,higher education,and international educational performance assessments are two reform standards:“benchmarks”determining optimal student perform...Purpose-Prominent at the intersections of national educational agencies,higher education,and international educational performance assessments are two reform standards:“benchmarks”determining optimal student performance,and“empirical evidence”for determining the quality of reform practices.These two notions are often taken as connecting policy and research to effective changes in many countries.The article examines the historical and cultural principles about educational change and its sciences embedded in these standards through examining OECD’s PISA and the McKinsey&Company reports that draw on PISA’s data.Findings/Originality/Value-First,the reports express salvation themes associated with modernity;that is,the promise of a better future through governing the present.The promise is to provide nations with data and models to achieve social equality,economic prosperity,and a participatory democracy.Second,the promise of the future is not descriptive of some present reality but to fabricate the universal characteristics about society and individuals.The numbers embody social and psychological categories about a desired unity of all students.Third,the“empirical evidence”of the international assessment entails a particular notion of science and“evidence”;one that paradoxically uses the universals in comparing and creating divisions.展开更多
The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggl...The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggle with this aspect.A potential reason is that the benchmarks used for training and evaluation may not adequately offer a diverse set of transferable tasks.Although recent studies have developed bench-marking environments to address this shortcoming,they typically fall short in providing tasks that both ensure a solid foundation for generalization and exhibit significant variability.To overcome these limitations,this work introduces the concept that‘objects are composed of more fundamental components’in environment design,as implemented in the proposed environment called summon the magic(StM).This environment generates tasks where objects are derived from extensible and shareable basic components,facilitating strategy reuse and enhancing generalization.Furthermore,two new metrics,adaptation sensitivity range(ASR)and parameter correlation coefficient(PCC),are proposed to better capture and evaluate the generalization process of RL agents.Experimental results show that increasing the number of basic components of the object reduces the proximal policy optimization(PPO)agent’s training-testing gap by 60.9%(in episode reward),significantly alleviating overfitting.Additionally,linear variations in other environmental factors,such as the training monster set proportion and the total number of basic components,uniformly decrease the gap by at least 32.1%.These results highlight StM’s effectiveness in benchmarking and probing the generalization capabilities of RL algorithms.展开更多
Given the rapid growth of sustainable construction strategies globally and the importance of resiliency in civil infrastructure,it is crucial to adopt best practices.Modular construction is one such practice and is co...Given the rapid growth of sustainable construction strategies globally and the importance of resiliency in civil infrastructure,it is crucial to adopt best practices.Modular construction is one such practice and is considered a better alternative to conventional construction in terms of resilience,construction times,resource efficiency,and sustainability.However,the continued expansion of modular construction relies on quantifying and evaluating its sustainability and the purported benefits.This paper develops and checks feasibility through an integrated multi-level decision support framework to empirically evaluate the sustainability performances of single-family residential modular homes.Criteria and indicator development and calculation,benchmark scale establishment,quantitative and qualitative data collection from literature and surveys,and multi-criteria decision analysis are unique aspects of this framework.The results of the two case studies located in the Okanagan region,Canada showed that modular homes perform at a higher level of sustainability than their conventional counterparts across multiple metrics and levels related to environmental and economic factors.The modular homes scored eco-efficiency values of 62.5 and 56.0,respectively and fell into higher performance range.The proposed frame-work offers flexibility in examining different dimensions of sustainability,providing valuable insights into the key parameters that need to be addressed to enhance overall sustainability.This research,which integrates life cycle thinking and decision-making,helps the construction industry and,municipalities,governments,and pol-icymakers in making informed decisions on the selection of suitable construction methods in city developments and move towards a more resilient and sustainable sector.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
Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-spe...Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.展开更多
The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and f...The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and frequency-step disturbance scenario are considered.Thus,the advantages of flexible regulation of renewable generations are wasted,resulting in excessive curtailment of wind and solar resources.In this study,a method for optimizing the frequency regulation reserve of wind PV storage power stations was developed.Moreover,a station frequency regulation model was constructed,considering the field dynamic response and the coupling between the station and system frequency dynamics.Furthermore,a method for the online evaluation of the station frequency regulation was proposed based on the benchmark governor fitting.This method helps in overcoming the capacity-based reserve static setting.Finally,an optimization model was developed,along with the proposal of the linearized solving algorithm.The field data from the JH4#station in China’s MX power grid was considered for validation.The proposed method achieves a 24.77%increase in the station income while ensuring the system frequency stability when compared with the grid code-based method.展开更多
Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits.However,when benchmarking these methods,researchers...Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits.However,when benchmarking these methods,researchers use known solutions to only a single form of benchmark problem.This paper proposes a comparison platform for systematic benchmarking of topology optimization methods using both binary and relaxed forms.A greyness measure is implemented to evaluate how far a solution is from the desired binary form.The well-known ZhouRozvany(ZR)problem is selected as the benchmarking problem here,making use of available global solutions for both its relaxed and binary forms.The recently developed non-penalization Smooth-edged Material Distribution for Optimizing Topology(SEMDOT),well-established Solid Isotropic Material with Penalization(SIMP),and continuation methods are studied on this platform.Interestingly,in most cases,the grayscale solutions obtained by SEMDOT demonstrate better performance in dealing with the ZR problem than SIMP.The reasons are investigated and attributed to the usage of two different regularization techniques,namely,the Heaviside smooth function in SEMDOT and the power-law penalty in SIMP.More importantly,a simple-to-use benchmarking graph is proposed for evaluating newly developed topology optimization methods.展开更多
Prompt fission neutron spectra(PFNS)have a significant role in nuclear science and technology.In this study,the PFNS for^(239)Pu are evaluated using both differential and integral experimental data.A method that lever...Prompt fission neutron spectra(PFNS)have a significant role in nuclear science and technology.In this study,the PFNS for^(239)Pu are evaluated using both differential and integral experimental data.A method that leverages integral criticality benchmark experiments to constrain the PFNS data is introduced.The measured central values of the PFNS are perturbed by constructing a covariance matrix.The PFNS are sampled using two types of covariance matrices,either generated with an assumed correlation matrix and incorporating experimental uncertainties or derived directly from experimental reports.The joint Monte Carlo transport code is employed to perform transport simulations on five criticality benchmark assemblies by utilizing perturbed PFNS data.Extensive simulations result in an optimized PFNS that shows improved agreement with the integral criticality benchmark experiments.This study introduces a novel approach for optimizing differential experimental data through integral experiments,particularly when a covariance matrix is not provided.展开更多
基金supported in part by the National Natural Science Foundation of China(62125306)Zhejiang Key Research and Development Project(2024C01163)the State Key Laboratory of Industrial Control Technology,China(ICT2024A06)
文摘In recent decades,control performance monitoring(CPM)has experienced remarkable progress in research and industrial applications.While CPM research has been investigated using various benchmarks,the historical data benchmark(HIS)has garnered the most attention due to its practicality and effectiveness.However,existing CPM reviews usually focus on the theoretical benchmark,and there is a lack of an in-depth review that thoroughly explores HIS-based methods.In this article,a comprehensive overview of HIS-based CPM is provided.First,we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo:static and dynamic properties.The static property portrays time-independent variability in system output,and the dynamic property describes temporal behavior driven by closed-loop feedback.Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two perspectives.Specifically,two mainstream solutions for CPM methods are summarized,including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior.Furthermore,this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.
文摘Research suggests that transient institutions, i.e., institutions with short-term investment horizon,make management focus on short-term earnings goals. This study examines incentive in terms of CEO cash compensation that explains why management concentrates on short-term earnings results when transient institutions hold high levels of ownership. Using quarterly consensus analysts＇ expectations as a proxy for short-term earnings benchmarks, the author finds that CEO cash compensation and the frequency with which management misses quarterly earnings benchmarks in a year （MISSNUMt） are more strongly negatively associated in firms with high transient institutional ownership than in firms with low transient institutional ownership, suggesting that transient institutions strengthen the inverse relation between CEO cash pay and missing short-term earnings benchmarks and hence increase pressure on management in terms of cash pay for short-term results. Moreover, the author shows that change in CEO cash compensation is positively associated with change in transient institutional ownership, consistent with the idea that selling shares by transient institutions influences the boards of portfolio firms in CEO cash compensation decision. This study contributes to the governance literature and is relevant to business managers by providing additional evidence that transient institutions provide less patient capital and may not benefit long-run firm value creation.
文摘With the adoption of the Luanda Declaration at the end of the conference,it was fairly evident that African governments could no longer deny the causal links or intersection-between the environment and health care for people across the continent.
基金Project supported by the National Natural Science Foundation of China (No. 40471081)the Frontal Field Project of the Chinese Academy of Sciences (No. ISSASIP0201) the Key Innovation Project of Chinese Academy of Sciences (No.KZCX3-SW-427).
文摘Soil classification is the foundation for exchange and extension of research findings in soil science and for modern management of soil resources. This study explained database and research methodology to create a cross-reference system for translating the Genetic Soil Classification of China (GSCC) into the Chinese Soil Taxonomy (CST). With the help of the CST keys, each of the 2 540 soil species in GSCC has been interpreted to its corresponding soil order, suborder, great group, and sub-group in CST. According to the methodology adopted, the assigned soil species have been linked one another to their corresponding polygons in the 1:1000000 digital soil map of China. Referencibility of each soil species between the GSCC and CST systems was determined statistically on the basis of distribution area of each soil species at a high taxon level of the two systems. The soils were then sorted according to their maximum referencibility and classified into three categories for discussion. There were 19 soil great groups in GSCC with maximum referencibility > 90% and 22 great groups between 60%-90%. These soil great groups could serve as cross-reference benchmarks. There were 19 great groups in GSCC with maximum referencibility < 60%, which could be used as cross-reference benchmarks until new and better results were available. For these soils, if the translation was made at a lower soil taxon level or on a regional basis, it would improve their referencibility enabling them to serve as new cross-reference benchmarks.
文摘There exists a gap between control theory and control practice,i.e.,all control methods suggested by researchers are not implemented in real systems and,on the other hand,many important in dustrial problems are not studied in the academic research.Benchmark problems can help close this gap and provide many opportunities for members in both the controls theory and application communities.The goal is to survey and give pointers to different general controls and modeling related benchmark problems that can serve as inspiration for future benchmarks and then specifically focus the benchmark coverage on automotive control engineering application.In the paper reflections are given on how different categories of benchmark designers,benchmark solvers and third part users can benefit from providing,solving,and studying benchmark problems.The paper also collects information about several benchmark problems and gives pointers to papers than give more detailed information about different problems that have been presented.
文摘Helicopter EMS (HEMS) allows for patients to be quickly transported into regional cardiac centers, often to receive primary percutaneous coronary intervention (PCI). Since PCI is a time-critical therapy, it is important that patients get to primary PCI as quickly as possible. HEMS crews’ “on-scene” times for trauma patients have been extensively studied, and recent years have seen many efforts to minimize the time required to prepare patients for transport. There has been less attention to interfacility transport “scene times” for HEMS crews at referring hospitals;this includes stabilization times for preparing cardiac patients for loading onto aircraft for HEMS transport to primary PCI. In the absence of guiding evidence, system benchmarking and quality improvement are difficult. Therefore the current study was undertaken, to assess and describe the HEMS crew “on-scene” times or “patient stabilization times” (PSTs) at referring hospitals, for interfacility transported cardiac patients flown for primary PCI. Descriptive analysis identified a PST median of 19 minutes (interquartile range 15 - 24), and univariate analyses using Kruskal-Wallis testing found no association between prolonged PST and sending unit type (Emergency Department versus other), off-hours transports, or relatively frequent (at least monthly) use of HEMS (p for all comparisons > 0.64). Outlier PSTs, defined a priori as those exceeding the median by at least a half-hour, were found in 12% of all cases. These data could be useful as a starting point for system planning and benchmarking efforts in regionalized systems of acute cardiac care.
基金The work was carried out at the Center for Artificial Intelligence(C4AI-USP)with support from the São Paulo Research Foundation(FAPESP grant#2019/07665-4)from the IBM Corporation.This research was also partially supported by ItaúUnibanco S.A.+1 种基金M.M.Joséand F.Nakasato have been supported by the ItaúScholarship Program(PBI)of the Data Science Center(C2D)of the Escola Politécnica da Universidade de São PauloWe acknowledge support by CAPES-Finance Code 001.A.H.R.Costa and F.G.Cozman were partially supported by CNPq grants 310085/2020-9 and 305753/2022-3 respectively.Paulo Pirozelli was supported by the FAPESP grant 2019/26762-0.
文摘Piráis a reading comprehension dataset focused on the ocean,the Brazilian coast,and climate change,built from a collection of scientific abstracts and reports on these topics.This dataset represents a versatile language resource,particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge.Despite its potential,a detailed set of baselines has not yet been developed for Pirá.By creating these baselines,researchers can more easily utilize Piráas a resource for testing machine learning models across a wide range of question answering tasks.In this paper,we define six benchmarks over the Pirádataset,covering closed generative question answering,machine reading comprehension,information retrieval,open question answering,answer triggering,and multiple choice question answering.As part of this effort,we have also produced a curated version of the original dataset,where we fixed a number of grammar issues,repetitions,and other shortcomings.Furthermore,the dataset has been extended in several new directions,so as to face the aforementioned benchmarks:translation of supporting texts from English into Portuguese,classification labels for answerability,automatic paraphrases of questions and answers,and multiple choice candidates.The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pirádataset.
基金supported by National Key R&D Program of China(No.2019YFB1600702)the National Natural Science Foundation of China(No.51878059).
文摘Automatic modal identification via automatically interpreting the stabilization diagram provides key technique in bridge structural health monitoring.This paper reviews the progress in the area of automatic modal identification based on interpreting the stabilization diagram.The whole identification process is divided into four steps from establishing the stabilization diagram to removing the outliers in the identification results.The criteria and algorithms used in each step in the existing studies are carefully summarized and classified.Comparisons between typical methods in cleaning and interpreting the stabilization diagram are also conducted.Real structure benchmarks used in the existing studies to validate the proposed automatic modal identification methods are also summarized.Based on the review and comparison,the specific ratio method for cleaning the stabilization diagram,the hierarchical clustering method for interpreting the stabilization diagram and the adjusted boxplot for removing the outliers in the identification results are the most suitable methods for each step.The key point of automatic modal identification based on interpreting the stabilization diagram has also discussed,and it is recommended to pay more attention to cleaning the stabilization diagram.Future study about automatic modal identification under situation with very few sensors deployed should be more concerned.This review aims to help researchers and practitioners in implementing existing automatic modal identification algorithms effectively and developing more suitable and practical methods for civil engineering structures in the future.
文摘This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.
文摘Purpose-Prominent at the intersections of national educational agencies,higher education,and international educational performance assessments are two reform standards:“benchmarks”determining optimal student performance,and“empirical evidence”for determining the quality of reform practices.These two notions are often taken as connecting policy and research to effective changes in many countries.The article examines the historical and cultural principles about educational change and its sciences embedded in these standards through examining OECD’s PISA and the McKinsey&Company reports that draw on PISA’s data.Findings/Originality/Value-First,the reports express salvation themes associated with modernity;that is,the promise of a better future through governing the present.The promise is to provide nations with data and models to achieve social equality,economic prosperity,and a participatory democracy.Second,the promise of the future is not descriptive of some present reality but to fabricate the universal characteristics about society and individuals.The numbers embody social and psychological categories about a desired unity of all students.Third,the“empirical evidence”of the international assessment entails a particular notion of science and“evidence”;one that paradoxically uses the universals in comparing and creating divisions.
基金Supported by the National Key R&D Program of China(No.2023YFB4502200)the National Natural Science Foundation of China(No.U22A2028,61925208,62222214,62341411,62102398,62102399,U20A20227,62302478,62302482,62302483,62302480,62302481)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0660300,XDB0660301,XDB0660302)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(No.YSBR-029)the Youth Innovation Promotion Association of Chinese Academy of Sciences and Xplore Prize.
文摘The challenge of enhancing the generalization capacity of reinforcement learning(RL)agents remains a formidable obstacle.Existing RL methods,despite achieving superhuman performance on certain benchmarks,often struggle with this aspect.A potential reason is that the benchmarks used for training and evaluation may not adequately offer a diverse set of transferable tasks.Although recent studies have developed bench-marking environments to address this shortcoming,they typically fall short in providing tasks that both ensure a solid foundation for generalization and exhibit significant variability.To overcome these limitations,this work introduces the concept that‘objects are composed of more fundamental components’in environment design,as implemented in the proposed environment called summon the magic(StM).This environment generates tasks where objects are derived from extensible and shareable basic components,facilitating strategy reuse and enhancing generalization.Furthermore,two new metrics,adaptation sensitivity range(ASR)and parameter correlation coefficient(PCC),are proposed to better capture and evaluate the generalization process of RL agents.Experimental results show that increasing the number of basic components of the object reduces the proximal policy optimization(PPO)agent’s training-testing gap by 60.9%(in episode reward),significantly alleviating overfitting.Additionally,linear variations in other environmental factors,such as the training monster set proportion and the total number of basic components,uniformly decrease the gap by at least 32.1%.These results highlight StM’s effectiveness in benchmarking and probing the generalization capabilities of RL algorithms.
文摘Given the rapid growth of sustainable construction strategies globally and the importance of resiliency in civil infrastructure,it is crucial to adopt best practices.Modular construction is one such practice and is considered a better alternative to conventional construction in terms of resilience,construction times,resource efficiency,and sustainability.However,the continued expansion of modular construction relies on quantifying and evaluating its sustainability and the purported benefits.This paper develops and checks feasibility through an integrated multi-level decision support framework to empirically evaluate the sustainability performances of single-family residential modular homes.Criteria and indicator development and calculation,benchmark scale establishment,quantitative and qualitative data collection from literature and surveys,and multi-criteria decision analysis are unique aspects of this framework.The results of the two case studies located in the Okanagan region,Canada showed that modular homes perform at a higher level of sustainability than their conventional counterparts across multiple metrics and levels related to environmental and economic factors.The modular homes scored eco-efficiency values of 62.5 and 56.0,respectively and fell into higher performance range.The proposed frame-work offers flexibility in examining different dimensions of sustainability,providing valuable insights into the key parameters that need to be addressed to enhance overall sustainability.This research,which integrates life cycle thinking and decision-making,helps the construction industry and,municipalities,governments,and pol-icymakers in making informed decisions on the selection of suitable construction methods in city developments and move towards a more resilient and sustainable sector.
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Funded by the Spanish Government and FEDER funds(AEI/FEDER,UE)under grant PID2021-124502OB-C42(PRESECREL)the predoctoral program“Concepción Arenal del Programa de Personal Investigador en formación Predoctoral”funded by Universidad de Cantabria and Cantabria’s Government(BOC 18-10-2021).
文摘Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.
基金supported by the Scientific Research Project of China Three Gorges Group Co.LTD(Contract Number:202103368).
文摘The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and frequency-step disturbance scenario are considered.Thus,the advantages of flexible regulation of renewable generations are wasted,resulting in excessive curtailment of wind and solar resources.In this study,a method for optimizing the frequency regulation reserve of wind PV storage power stations was developed.Moreover,a station frequency regulation model was constructed,considering the field dynamic response and the coupling between the station and system frequency dynamics.Furthermore,a method for the online evaluation of the station frequency regulation was proposed based on the benchmark governor fitting.This method helps in overcoming the capacity-based reserve static setting.Finally,an optimization model was developed,along with the proposal of the linearized solving algorithm.The field data from the JH4#station in China’s MX power grid was considered for validation.The proposed method achieves a 24.77%increase in the station income while ensuring the system frequency stability when compared with the grid code-based method.
文摘Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits.However,when benchmarking these methods,researchers use known solutions to only a single form of benchmark problem.This paper proposes a comparison platform for systematic benchmarking of topology optimization methods using both binary and relaxed forms.A greyness measure is implemented to evaluate how far a solution is from the desired binary form.The well-known ZhouRozvany(ZR)problem is selected as the benchmarking problem here,making use of available global solutions for both its relaxed and binary forms.The recently developed non-penalization Smooth-edged Material Distribution for Optimizing Topology(SEMDOT),well-established Solid Isotropic Material with Penalization(SIMP),and continuation methods are studied on this platform.Interestingly,in most cases,the grayscale solutions obtained by SEMDOT demonstrate better performance in dealing with the ZR problem than SIMP.The reasons are investigated and attributed to the usage of two different regularization techniques,namely,the Heaviside smooth function in SEMDOT and the power-law penalty in SIMP.More importantly,a simple-to-use benchmarking graph is proposed for evaluating newly developed topology optimization methods.
基金supported by the National Natural Science Foundation of China(No.12347126)。
文摘Prompt fission neutron spectra(PFNS)have a significant role in nuclear science and technology.In this study,the PFNS for^(239)Pu are evaluated using both differential and integral experimental data.A method that leverages integral criticality benchmark experiments to constrain the PFNS data is introduced.The measured central values of the PFNS are perturbed by constructing a covariance matrix.The PFNS are sampled using two types of covariance matrices,either generated with an assumed correlation matrix and incorporating experimental uncertainties or derived directly from experimental reports.The joint Monte Carlo transport code is employed to perform transport simulations on five criticality benchmark assemblies by utilizing perturbed PFNS data.Extensive simulations result in an optimized PFNS that shows improved agreement with the integral criticality benchmark experiments.This study introduces a novel approach for optimizing differential experimental data through integral experiments,particularly when a covariance matrix is not provided.
