一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家...一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.展开更多
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of t...Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of the catalyst. Although several effective models have been proposed in previous research to address anomaly detection in chemical processes, most fail to adequately capture the spatial-temporal dependencies of multi-source, mixed-frequency information. In this study, an innovative multi-source mixed-frequency information fusion framework based on a spatial-temporal graph attention network (MIF-STGAT) is proposed to investigate the causes of FCC regenerator catalyst loss anomalies for guide onsite operational management, enhancing the long-term stability of FCC unit operations. First, a reconstruction-based dual-encoder-decoder framework is developed to facilitate the acquisition of mixed-frequency features and information fusion during the FCC regenerator catalyst loss process. Subsequently, a graph attention network and a multilayer long short-term memory network with a differential structure are integrated into the reconstruction-based dual-encoder-shared-decoder framework to capture the dynamic fluctuations and critical features associated with anomalies. Experimental results from the Chinese FCC industrial process demonstrate that MIF-STGAT achieves excellent accuracy and interpretability for anomaly detection.展开更多
The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF...The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF)argon plasma,and the temperatures were measured using a DPV-2000 monitor.A model combining the electromagnetism,thermal flow,and heat transfer characteristics of powder during in-flight heating in argon plasma was proposed.The melting processes of CeO_(2)powders of different diameters,with and without thermal resistance effect,were investigated.Results show that the heating process of CeO_(2)powder particles consists of three main stages,one of which is relevant to a dimensionless parameter known as the Biot number.When the Biot value≥0.1,thermal resistance increases significantly,especially for the larger powders.The predicted temperature of the particles at the outlet(1800–2880 K)is in good agreement with the experimental result.展开更多
The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from...The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).展开更多
Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durabili...Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operato...Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.展开更多
Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing addit...Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)sum...This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)summary writing,or(3)reading comprehension.Eye-tracking data were collected during reading,measuring early(first fixation duration,first pass duration)and late(go-past time,total fixation duration)eye movements.During writing,source-text rereading was tracked via fixation counts and durations.Results showed that task type did not affect initial lexical access,as first fixation duration showed no group differences.However,both production groups exhibited significantly longer first pass durations than the reading comprehension group.Late measures revealed a gradient pattern:the continuation writing group spent significantly longer gopast time and total fixation duration than the summary writing group,which exceeded the reading comprehension group.This indicates that continuation tasks promoted deeper cognitive engagement during reading.During writing,the continuation writing group spent more time rereading the source text with higher fixation counts than the summary writing group.These findings suggest that continuation writing triggers more intensive reader-text interaction during pre-writing and enhances comprehension-production coupling through sustained attention to input during writing.This study sheds light on the cognitive mechanisms underlying the theoretical and pedagogical value of xu-argument.展开更多
Suancai has a lengthy history and a wide range of categories,which has some influence on the pickled diet culture around the world.Suancai production is transitioning to a large-scale,standardized production due to th...Suancai has a lengthy history and a wide range of categories,which has some influence on the pickled diet culture around the world.Suancai production is transitioning to a large-scale,standardized production due to the growth of the market.It has a unique flavor and is rich in nutrients,and its abundance of free amino acids,vitamins and phenolics has many positive effects on the human body.This review gives the types and history of suancai,as well as its impact on the world’s pickled culture.The changes in nutritional composition and flavor of suancai during fermentation are summarized.It presented the production technology and influencing factors of the northeast suancai,examined the quality and safety issues in suancai,and put forth some ideas and opinions on the standardization development of the suancai industry.It also summarized the geographic distribution and flora diversity of pickles around the world.In order to provide some knowledge and guidance for the promotion of modern industrial production in the suancai industry.展开更多
The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurode...The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.展开更多
Dynamic melt modification of polyethylene via the direct grafting of peroxide fragments shows promise for the development of processable functionalized materials.In this study,four linear low-density polyethylenes(LLD...Dynamic melt modification of polyethylene via the direct grafting of peroxide fragments shows promise for the development of processable functionalized materials.In this study,four linear low-density polyethylenes(LLDPEs)with comparable molecular weights but different short-chain branch(SCB)contents(ranging of 5-66 per 1000 carbon atoms)were modified via dynamic melt mixing using 2 wt% benzoyl peroxide at 145℃ and 50 r/min for 30 min.The influence of SCB content on the processability and structure of the resulting products was systematically investigated.All modified products exhibited good melt processability with melt flow rates(MFR)ranging from 0.46 g/10min to 1.07 g/10min.Products derived from low-SCB LLDPEs showed a lower MFR,higher cross-linking content,a larger number of long-chain branches,and a higher degree of benzoyl grafting.In contrast,those produced from high-SCB LLDPEs exhibited improved processability,reduced cross-linking,fewer long-chain branches,and lower benzoyl grafting levels.A detailed structural investigation of the soluble and insoluble fractions,which were separated using trichlorobenzene fractionation,was conducted to analyze the structural features of various modified products and demonstrate that the SCB content(i.e.,tertiary carbon density)significantly influences radical coupling during dynamic modification.Elevated tertiary carbon density,by introducing greater steric hindrance,suppresses radical coupling during dynamic modification,thereby reducing the efficiency of both crosslinking and peroxide fragment grafting.These findings provide new insights into the structure-reactivity relationships in peroxide-induced polyethylene modification and lay the foundation for tailoring material properties via dynamic processing.展开更多
Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and requir...Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.展开更多
Iron and steel industry is one of the main sources of air pollution emissions in China.The sintering process is an important link in the blast furnace ironmaking process,but it is also accompanied by a large number of...Iron and steel industry is one of the main sources of air pollution emissions in China.The sintering process is an important link in the blast furnace ironmaking process,but it is also accompanied by a large number of pollutants.Under the background of ultra-low emissions,iron and steel enterprises urgently need to upgrade their existing processes to address the existing process in practical application problems.In this study,a steel group in Gansu Province was taken as an example.By comparing and analyzing the pollutant emission characteristics before and after the ultra-low emission retrofit,the collaborative control effect of the combined process on SO_(2),NO_(x),particulate matter,and dioxins after the new retrofit was systematically evaluated.The results show that after the retrofit,the concentrations of particulate matter,SO_(2) and NO_(x) have dropped to near-zero levels,and the dioxin removal efficiency has reached 98.87%,with all indicators being better than the national ultra-low emission standards.The study confirms that the optimal combination of multi-pollutant collaborative treatment technologies is the key to achieving efficient emission reduction,among which selective catalytic reduction technology has a particularly significant synergistic removal effect on NO_(x) and dioxins.This study provides an important technical reference and practical basis for the ultra-low emission retrofit of the steel industry,and has important guiding significance for promoting the green retrofit of the industry.Its ultra-low emission retrofit is of great significance for achieving green and low-carbon development.展开更多
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
基金supported by the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
基金supported by the Innovative Research Group Project of the National Natural Science Foundation of China(22021004)Sinopec Major Science and Technology Projects(321123-1).
文摘Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of the catalyst. Although several effective models have been proposed in previous research to address anomaly detection in chemical processes, most fail to adequately capture the spatial-temporal dependencies of multi-source, mixed-frequency information. In this study, an innovative multi-source mixed-frequency information fusion framework based on a spatial-temporal graph attention network (MIF-STGAT) is proposed to investigate the causes of FCC regenerator catalyst loss anomalies for guide onsite operational management, enhancing the long-term stability of FCC unit operations. First, a reconstruction-based dual-encoder-decoder framework is developed to facilitate the acquisition of mixed-frequency features and information fusion during the FCC regenerator catalyst loss process. Subsequently, a graph attention network and a multilayer long short-term memory network with a differential structure are integrated into the reconstruction-based dual-encoder-shared-decoder framework to capture the dynamic fluctuations and critical features associated with anomalies. Experimental results from the Chinese FCC industrial process demonstrate that MIF-STGAT achieves excellent accuracy and interpretability for anomaly detection.
基金National Natural Science Foundation of China(11875039)Shanxi Scholarship Council of China(2023-033)+2 种基金Fundamental Research Program of Shanxi Province(202303021221071)China Baowu Low Carbon Metallurgical Innovation Foundation(2022)2023 Anhui Major Industrial Innovation Plan Project。
文摘The in-flight heating process of cerium dioxide(CeO_(2))powders was investigated through experiments and numerical simulations.In the experiment,CeO_(2)powder(average size of 30μm)was injected into radio-frequency(RF)argon plasma,and the temperatures were measured using a DPV-2000 monitor.A model combining the electromagnetism,thermal flow,and heat transfer characteristics of powder during in-flight heating in argon plasma was proposed.The melting processes of CeO_(2)powders of different diameters,with and without thermal resistance effect,were investigated.Results show that the heating process of CeO_(2)powder particles consists of three main stages,one of which is relevant to a dimensionless parameter known as the Biot number.When the Biot value≥0.1,thermal resistance increases significantly,especially for the larger powders.The predicted temperature of the particles at the outlet(1800–2880 K)is in good agreement with the experimental result.
基金Sichuan Science and Technology Program(2025ZNSFSC1341)Fundamental Research Funds for the Central Universities(J2022-090,25CAFUC04087)。
文摘The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).
基金National MCF Energy R&D Program(2024YFE03260300)。
文摘Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
基金support of the“Pioneer”and“Leading Goose”Research&Development Program of Zhejiang(2024C01028)the State Key Laboratory of Industrial Control Technology,China(ICT2024C04)are gratefully acknowledged.
文摘Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.
基金National Key Research and Development Program of China(2022YFB4600902)Shandong Provincial Science Foundation for Outstanding Young Scholars(ZR2024YQ020)。
文摘Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)summary writing,or(3)reading comprehension.Eye-tracking data were collected during reading,measuring early(first fixation duration,first pass duration)and late(go-past time,total fixation duration)eye movements.During writing,source-text rereading was tracked via fixation counts and durations.Results showed that task type did not affect initial lexical access,as first fixation duration showed no group differences.However,both production groups exhibited significantly longer first pass durations than the reading comprehension group.Late measures revealed a gradient pattern:the continuation writing group spent significantly longer gopast time and total fixation duration than the summary writing group,which exceeded the reading comprehension group.This indicates that continuation tasks promoted deeper cognitive engagement during reading.During writing,the continuation writing group spent more time rereading the source text with higher fixation counts than the summary writing group.These findings suggest that continuation writing triggers more intensive reader-text interaction during pre-writing and enhances comprehension-production coupling through sustained attention to input during writing.This study sheds light on the cognitive mechanisms underlying the theoretical and pedagogical value of xu-argument.
基金supported by the Foundation of National Dairy Technology Innovation Center(2022-Open Funding Project-12)Foundation of National Dairy Technology Innovation Center(2022-Scientific Research-9)+2 种基金Key Project of National Dairy Innovation Research Center of Inner Mongolia(2021-National Dairy Innovation Research Center-8)Key Projects of Research Operating Expenses of Provincial Research Institutes in Heilongjiang Province(CZKYF2021-2-B017)Key Project of Natural Science Foundation of Heilongjiang Province(ZD2022C007).
文摘Suancai has a lengthy history and a wide range of categories,which has some influence on the pickled diet culture around the world.Suancai production is transitioning to a large-scale,standardized production due to the growth of the market.It has a unique flavor and is rich in nutrients,and its abundance of free amino acids,vitamins and phenolics has many positive effects on the human body.This review gives the types and history of suancai,as well as its impact on the world’s pickled culture.The changes in nutritional composition and flavor of suancai during fermentation are summarized.It presented the production technology and influencing factors of the northeast suancai,examined the quality and safety issues in suancai,and put forth some ideas and opinions on the standardization development of the suancai industry.It also summarized the geographic distribution and flora diversity of pickles around the world.In order to provide some knowledge and guidance for the promotion of modern industrial production in the suancai industry.
文摘The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.
基金financially supported by the Science and Technology Project of PetroChina Company Limited,China(No.2022DJ6314)the National Natural Science Foundation of China(No.52173056)。
文摘Dynamic melt modification of polyethylene via the direct grafting of peroxide fragments shows promise for the development of processable functionalized materials.In this study,four linear low-density polyethylenes(LLDPEs)with comparable molecular weights but different short-chain branch(SCB)contents(ranging of 5-66 per 1000 carbon atoms)were modified via dynamic melt mixing using 2 wt% benzoyl peroxide at 145℃ and 50 r/min for 30 min.The influence of SCB content on the processability and structure of the resulting products was systematically investigated.All modified products exhibited good melt processability with melt flow rates(MFR)ranging from 0.46 g/10min to 1.07 g/10min.Products derived from low-SCB LLDPEs showed a lower MFR,higher cross-linking content,a larger number of long-chain branches,and a higher degree of benzoyl grafting.In contrast,those produced from high-SCB LLDPEs exhibited improved processability,reduced cross-linking,fewer long-chain branches,and lower benzoyl grafting levels.A detailed structural investigation of the soluble and insoluble fractions,which were separated using trichlorobenzene fractionation,was conducted to analyze the structural features of various modified products and demonstrate that the SCB content(i.e.,tertiary carbon density)significantly influences radical coupling during dynamic modification.Elevated tertiary carbon density,by introducing greater steric hindrance,suppresses radical coupling during dynamic modification,thereby reducing the efficiency of both crosslinking and peroxide fragment grafting.These findings provide new insights into the structure-reactivity relationships in peroxide-induced polyethylene modification and lay the foundation for tailoring material properties via dynamic processing.
基金funded by Fundación CajaCanarias and Fundación Bancaria“la Caixa”,grant number 2023DIG11.
文摘Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.
基金supported by the Key Research and Development Program of Gansu Province(22YF7FA070)the National Natural Science Foundation of China(22406076,22466026)the Basic Research Project of Yunnan Province(202301BE070001-017,202401CF070139,202401AS070085)。
文摘Iron and steel industry is one of the main sources of air pollution emissions in China.The sintering process is an important link in the blast furnace ironmaking process,but it is also accompanied by a large number of pollutants.Under the background of ultra-low emissions,iron and steel enterprises urgently need to upgrade their existing processes to address the existing process in practical application problems.In this study,a steel group in Gansu Province was taken as an example.By comparing and analyzing the pollutant emission characteristics before and after the ultra-low emission retrofit,the collaborative control effect of the combined process on SO_(2),NO_(x),particulate matter,and dioxins after the new retrofit was systematically evaluated.The results show that after the retrofit,the concentrations of particulate matter,SO_(2) and NO_(x) have dropped to near-zero levels,and the dioxin removal efficiency has reached 98.87%,with all indicators being better than the national ultra-low emission standards.The study confirms that the optimal combination of multi-pollutant collaborative treatment technologies is the key to achieving efficient emission reduction,among which selective catalytic reduction technology has a particularly significant synergistic removal effect on NO_(x) and dioxins.This study provides an important technical reference and practical basis for the ultra-low emission retrofit of the steel industry,and has important guiding significance for promoting the green retrofit of the industry.Its ultra-low emission retrofit is of great significance for achieving green and low-carbon development.
