In today’s complex and rapidly changing business environment,the traditional single-organization service model can no longer meet the needs of multi-organization collaborative processing.Based on existing business pr...In today’s complex and rapidly changing business environment,the traditional single-organization service model can no longer meet the needs of multi-organization collaborative processing.Based on existing business process engine technologies,this paper proposes a distributed heterogeneous process engine collaboration method for crossorganizational scenarios.The core of this method lies in achieving unified access and management of heterogeneous engines through a business process model adapter and a common operation interface.The key technologies include:Meta-Process Control Architecture,where the central engine(meta-process scheduler)decomposes the original process into fine-grained sub-processes and schedules their execution in a unified order,ensuring consistency with the original process logic;Process Model Adapter,which addresses the BPMN2.0 model differences among heterogeneous engines such as Flowable and Activiti through a matching-and-replacement mechanism,providing a unified process model standard for different engines;Common Operation Interface,which encapsulates the REST APIs of heterogeneous engines and offers a single,standardized interface for process deployment,instance management,and status synchronization.This method integrates multiple techniques to address API differences,process model incompatibilities,and execution order consistency issues among heterogeneous engines,delivering a unified,flexible,and scalable solution for cross-organizational process collaboration.展开更多
A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of m...A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of meteo-rological drought months using the 1-month Standardized Precipitation Index(SPI)in South Sulawesi.Based on SPI,meteorological drought characteristics are inversely proportional to drought event intensity,which can be modeled using a Non-Homogeneous Poisson Process,specifically the Power Law Process.The estimation method employs Maximum Likelihood Estimation(MLE),where drought event intensities are treated as random variables over a set time interval.Future drought months are estimated using the cumulative Power Law Process function,with theβandγparameters more significant than 0.The probability of drought months is determined using the Non-Homogeneous Poisson Process,which models event occurrence over time,considering varying intensities.The results indicate that,of the 24 districts/cities in South Sulawesi,14 experienced meteorological drought based on the SPI and Power Law Process model.The estimated number of months of drought occurrence in the next 12 months is one month of drought with an occurrence probability value of 0.37 occurring in November in the Selayar,Bulukumba,Bantaeng,Jeneponto,Takalar and Gowa areas,in October in the Sinjai,Barru,Bone,Soppeng,Pinrang and Pare-pare areas,as well as in December in the Maros and Makassar areas.展开更多
In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment techni...In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.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
Objective:To investigate the application effects of intelligent guidance systems in optimizing health check-up process management.Methods:A total of 400 examinees who underwent physical examinations at the hospital’s...Objective:To investigate the application effects of intelligent guidance systems in optimizing health check-up process management.Methods:A total of 400 examinees who underwent physical examinations at the hospital’s Health Management Center from January to December 2024 were randomly divided into a control group(200 cases)and an observation group(200 cases).The control group used traditional manual guidance methods,while the observation group employed the intelligent guidance system.The study compared two groups in terms of completion time,waiting time for each procedure,check-up efficiency scores,examinee satisfaction,and report issuance time.Results:The overall examination time in the observation group(85.3±12.7 minutes)was significantly shorter than that in the control group(142.6±18.5 minutes)(P<0.01);average waiting time per procedure decreased by 62.4%;check-up efficiency scores(8.9±0.8 points)were significantly higher than those in the control group(5.2±1.1 points)(P<0.01);satisfaction reached 96.5%,significantly higher than the control group’s 78.0%(P<0.01);and report issuance time was advanced by 1.5 days.Conclusion:Intelligent guidance systems can significantly optimize check-up processes,improve work efficiency,and examinee satisfaction,demonstrating significant clinical application value.展开更多
Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as ...Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as heuristics, mathematical programming, and pinch analysis. Nonetheless, progress continues from different formulations of design problems using bottom-up approaches, to the utilization of new tools such as artificial intelligence (AI). It was not until recently that AI methods were involved again in assisting the decision-making steps for chemical engineers. This has led to a gap in understanding AI's capabilities and limitations within the field of CPD research. Thus, this article aims to provide an overview of conventional methods for process synthesis, integration, and intensification approaches and survey emerging AI-assisted process design applications to bridge the gap. A review of all AI-assisted methods is highlighted, where AI is used as a key component within a design framework, to explain the utility of AI with comparative examples. The studies were categorized into supervised and reinforcement learning based on the machine learning training principles they used to enhance the understanding of requirements, benefits, and challenges that come with it. Furthermore, we provide challenges and prospects that can facilitate or hinder the progress of AI-assisted approaches in the future.展开更多
In November 1984,China launched its first expedition to the Southern Ocean and the Antarctic continent,culminating in the establishment of its first year-round research station—Great Wall Station—on the Antarctic Pe...In November 1984,China launched its first expedition to the Southern Ocean and the Antarctic continent,culminating in the establishment of its first year-round research station—Great Wall Station—on the Antarctic Peninsula in February 1985.Forty years later,in February 2024,China’s fifth research station,Qinling Station,commenced operations on Inexpress-ible Island near Terra Nova Bay.展开更多
1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex...1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex and interactive process designs,modern plantwide systems are becoming increasingly sophisticated.The operation of these processes is typically characterized by the complexity of individual units(subsystems)and the intricate interactions between geographically distributed units through networks of material and energy flows,as well as control loops[1].展开更多
Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of ...Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of different soil microbial taxa under environmental changes are still disputed,which limits the understanding of the sustainability of desert restoration.Hence,we investigated the soil microbial community characteristics and functional attributes of grassland desert(GD),desert steppe(DS),typical steppe(TS),and artificial forest(AF)in the Mu Us Desert,China.Our findings confirmed the geographical conservation of soil microbial composition but highlighted decreased microbial diversity in TS.Meanwhile,the abundance of rare taxa and microbial community stability in TS improved.Heterogeneous and homogeneous selection determined the assembly of rare and abundant bacterial taxa,respectively,with both being significantly influenced by soil moisture.In contrast,fungal communities displayed stochastic processes and exhibited sensitivity to soil nutrient conditions.Furthermore,our investigation revealed a noteworthy augmentation in bacterial metabolic functionality in TS,aligning with improved vegetation restoration and the assemblage of abundant bacterial taxa.However,within nutrient-limited soils(GD,DS,and AF),the assembly dynamics of rare fungal taxa assumed a prominent role in augmenting their metabolic capacity and adaptability to desert ecosystems.These results highlighted the variations in the assembly processes and metabolic functions of soil microorganisms during vegetation reestablishment and provided corresponding theoretical support for anthropogenic revegetation of desert ecosystems.展开更多
Transpiration cooling is crucial for the performance of aerospace engine components,relying heavily on the processing quality and accuracy of microchannels.Laser powder bed fusion(LPBF)offers the potential for integra...Transpiration cooling is crucial for the performance of aerospace engine components,relying heavily on the processing quality and accuracy of microchannels.Laser powder bed fusion(LPBF)offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication,essential for engine cooling applications.However,optimizing LPBF’s extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge,especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery.This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures.250 parameter combinations,including laser power,scanning speed,channel diameter,and spot compensation,were designed across ten high-throughput specimens.This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy.Employing Bayesian optimization,the Gaussian process model accurately predicted processing outcomes over a broad parameter range.The correlation between various processing parameters,processing quality and accuracy was revealed,and various optimized process combinations were summarized.Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach.The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF,which has broad application value.展开更多
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ...This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.展开更多
Low-valent sulfur oxy-acid salts(LVSOs)represent a category of oxygen-containing salts characterized by their potent reducing capabilities.Notably,sulfite,dithionite,and thiosulfate are prevalent reducing agents that ...Low-valent sulfur oxy-acid salts(LVSOs)represent a category of oxygen-containing salts characterized by their potent reducing capabilities.Notably,sulfite,dithionite,and thiosulfate are prevalent reducing agents that are readily available,cost-effective,and exhibit minimal ecological toxicity.These LVSOs have the ability to generate or promote the generation of strong oxidants or reductants,which makes them widely used in advanced oxidation processes(AOPs)and advanced reduction processes(ARPs).This article provides a comprehensive review of the recent advancements in AOPs and ARPs involving LVSOs,alongside an examination of the fundamental principles governing the generation of active species within these processes.LVSOs fulfill three primary functions in AOPs:Serving as sources of reactive oxygen species(ROS),auxiliary agents,and activators.Particular attention is devoted to elucidating the reaction mechanisms through which LVSOs,in conjunction with metal ions,metal oxides,ultraviolet light(UV),and ozone,produce potent oxidizing agents in both homogeneous and heterogeneous systems.Regarding ARPs,this review delineates the mechanisms by which LVSOs generate strong reducing agents,including hydrated electrons,hydrogen radicals,and sulfite radicals,under UV irradiation,while also exploring the interactions between these reductants and pollutants.The review identifies existing gaps within the current framework and proposes future research avenues to address these challenges.展开更多
Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing ...Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.展开更多
Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–...Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–uniquely tests man-made fiber properties in addition to cotton.It provides critical data to optimize fiber process control for cotton,man-made fibers,and blended yarns.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.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
[Objectives] To optimize the crystallization process of ceftriaxone sodium using response surface methodology (RSM) for enhancing both the crystallization rate and the quality of the final product. [Methods] Four key ...[Objectives] To optimize the crystallization process of ceftriaxone sodium using response surface methodology (RSM) for enhancing both the crystallization rate and the quality of the final product. [Methods] Four key factors, including crystallization temperature, stirring speed, solvent drop rate, and seed crystal content, were employed as independent variables, while the crystallization rate served as the response variable. The Box-Behnken response surface method was utilized for the optimization design. [Results] The optimal parameters for the crystallization process, determined through optimization, were as follows: a temperature of 10.6 ℃, a stirring rate of 150 rpm, a solvent drop rate of 1.50 mL/min, and a seed crystal content of 0.12 g. Validation tests conducted under these conditions yielded an average crystallization rate of 94.38% for the refined product. [Conclusions] The crystallization efficiency of ceftriaxone sodium is markedly enhanced, thereby offering substantial support for its industrial production and clinical application.展开更多
this paper,we study the exponential non-uniform Berry-Esseen bound for the maximum likelihood estimator of some time inhomogeneous diffusion process.As applications,the optimal uniform Berry-Esseen bound and optimal C...this paper,we study the exponential non-uniform Berry-Esseen bound for the maximum likelihood estimator of some time inhomogeneous diffusion process.As applications,the optimal uniform Berry-Esseen bound and optimal Cramer-type moderate deviations of the Ornstein-Uhlenbeck process andα-Brownian bridge can be obtained.The main methods are the change of measure method and asymptotic analysis technique.展开更多
Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in ...Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.展开更多
Background Pain sensitivity is critical for preventing non-suicidal self-injury(NSSI)behaviours;however,individuals engaging in such behaviours often exhibit decreased pain sensitivity,which may undermine this natural...Background Pain sensitivity is critical for preventing non-suicidal self-injury(NSSI)behaviours;however,individuals engaging in such behaviours often exhibit decreased pain sensitivity,which may undermine this natural safeguard.The dorsolateral prefrontal cortex(DLPFC)is a key region involved in pain regulation,and recent approaches using transcranial direct current stimulation(tDCS)to target the DLPFC have shown potential for modulating pain processing and restoring normal pain perception for individuals engaging in NSSI behaviours.Aims This study aimed to explore the immediate and short-term effects of a single session of tDCS on pain sensitivity in individuals with NSSI,as well as its secondary effects on mood and NSSI-related factors.Methods In this randomised,double-blind,parallel,sham-controlled clinical trial,participants with a history of NSSI were randomly assigned to receive either active or sham tDCS.The intervention consisted of a single 20 min tDCS session targeting the left DLPFC.The primary outcome was pain sensitivity,measured by the pressure pain threshold(PPT)and heat pain score(HPS).Secondary and additional outcomes included NSSI urges,NSSI resistance,self-efficacy in resisting NSSI,mood-related variables and exploratory cognitive-affective processes such as rumination,self-criticism and self-perceived pain sensitivity,assessed at baseline,immediately post-intervention,and at 24 hours,1 week and 2 weeks follow-ups.Results For the primary outcomes,no significant differences between groups were observed for pain sensitivity(PPT,padj=0.812;HPS,padj=0.608).However,an exploratory sensitivity analysis treating each trial as an individual observation revealed a significant effect on HPS(padj=0.036).For the secondary and additional outcomes,although there were initial improvements in joyful feelings and reductions in negative affect at 2 weeks post-intervention,these effects did not remain significant after multiple comparison corrections.Notably,reductions in rumination were statistically significant at both 1-week and 2-week follow-ups(1 week,p_(adj)=0.040;2 weeks,p_(adj)=0.042).There were no significant effects on NSSI urges,NSSI resistance,self-efficacy in resisting NSSI or self-criticism.Conclusions A single session of tDCS over the left DLPFC did not produce significant changes in pain sensitivity in individuals with NSSI.A sensitivity analysis indicated an effect on heat pain sensitivity,possibly reflecting changes in brain activity,warranting confirmation through neuroimaging.These findings suggest that tDCS warrants further investigation for its potential to influence pain-related cognitive-affective processes in individuals with NSSI.展开更多
文摘In today’s complex and rapidly changing business environment,the traditional single-organization service model can no longer meet the needs of multi-organization collaborative processing.Based on existing business process engine technologies,this paper proposes a distributed heterogeneous process engine collaboration method for crossorganizational scenarios.The core of this method lies in achieving unified access and management of heterogeneous engines through a business process model adapter and a common operation interface.The key technologies include:Meta-Process Control Architecture,where the central engine(meta-process scheduler)decomposes the original process into fine-grained sub-processes and schedules their execution in a unified order,ensuring consistency with the original process logic;Process Model Adapter,which addresses the BPMN2.0 model differences among heterogeneous engines such as Flowable and Activiti through a matching-and-replacement mechanism,providing a unified process model standard for different engines;Common Operation Interface,which encapsulates the REST APIs of heterogeneous engines and offers a single,standardized interface for process deployment,instance management,and status synchronization.This method integrates multiple techniques to address API differences,process model incompatibilities,and execution order consistency issues among heterogeneous engines,delivering a unified,flexible,and scalable solution for cross-organizational process collaboration.
基金funded by Hasanuddin University,grant number 00309/UN4.22/PT.01.03/2024.
文摘A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of meteo-rological drought months using the 1-month Standardized Precipitation Index(SPI)in South Sulawesi.Based on SPI,meteorological drought characteristics are inversely proportional to drought event intensity,which can be modeled using a Non-Homogeneous Poisson Process,specifically the Power Law Process.The estimation method employs Maximum Likelihood Estimation(MLE),where drought event intensities are treated as random variables over a set time interval.Future drought months are estimated using the cumulative Power Law Process function,with theβandγparameters more significant than 0.The probability of drought months is determined using the Non-Homogeneous Poisson Process,which models event occurrence over time,considering varying intensities.The results indicate that,of the 24 districts/cities in South Sulawesi,14 experienced meteorological drought based on the SPI and Power Law Process model.The estimated number of months of drought occurrence in the next 12 months is one month of drought with an occurrence probability value of 0.37 occurring in November in the Selayar,Bulukumba,Bantaeng,Jeneponto,Takalar and Gowa areas,in October in the Sinjai,Barru,Bone,Soppeng,Pinrang and Pare-pare areas,as well as in December in the Maros and Makassar areas.
基金supported by the Major Science and Technology Project of Zhongshan City(No.2022AJ004)the Key Basic and Applied Research Program of Guangdong Province(Nos.2019B030302010 and 2022B1515120082)Guangdong Science and Technology Innovation Project(No.2021TX06C111).
文摘In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.
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Objective:To investigate the application effects of intelligent guidance systems in optimizing health check-up process management.Methods:A total of 400 examinees who underwent physical examinations at the hospital’s Health Management Center from January to December 2024 were randomly divided into a control group(200 cases)and an observation group(200 cases).The control group used traditional manual guidance methods,while the observation group employed the intelligent guidance system.The study compared two groups in terms of completion time,waiting time for each procedure,check-up efficiency scores,examinee satisfaction,and report issuance time.Results:The overall examination time in the observation group(85.3±12.7 minutes)was significantly shorter than that in the control group(142.6±18.5 minutes)(P<0.01);average waiting time per procedure decreased by 62.4%;check-up efficiency scores(8.9±0.8 points)were significantly higher than those in the control group(5.2±1.1 points)(P<0.01);satisfaction reached 96.5%,significantly higher than the control group’s 78.0%(P<0.01);and report issuance time was advanced by 1.5 days.Conclusion:Intelligent guidance systems can significantly optimize check-up processes,improve work efficiency,and examinee satisfaction,demonstrating significant clinical application value.
基金financial support from The University of Manchester
文摘Conceptual process design (CPD) research focuses on finding design alternatives that address various design problems. It has a long history of well-established methodologies to answer these complex questions, such as heuristics, mathematical programming, and pinch analysis. Nonetheless, progress continues from different formulations of design problems using bottom-up approaches, to the utilization of new tools such as artificial intelligence (AI). It was not until recently that AI methods were involved again in assisting the decision-making steps for chemical engineers. This has led to a gap in understanding AI's capabilities and limitations within the field of CPD research. Thus, this article aims to provide an overview of conventional methods for process synthesis, integration, and intensification approaches and survey emerging AI-assisted process design applications to bridge the gap. A review of all AI-assisted methods is highlighted, where AI is used as a key component within a design framework, to explain the utility of AI with comparative examples. The studies were categorized into supervised and reinforcement learning based on the machine learning training principles they used to enhance the understanding of requirements, benefits, and challenges that come with it. Furthermore, we provide challenges and prospects that can facilitate or hinder the progress of AI-assisted approaches in the future.
文摘In November 1984,China launched its first expedition to the Southern Ocean and the Antarctic continent,culminating in the establishment of its first year-round research station—Great Wall Station—on the Antarctic Peninsula in February 1985.Forty years later,in February 2024,China’s fifth research station,Qinling Station,commenced operations on Inexpress-ible Island near Terra Nova Bay.
基金the National Natural Science Foundation of China(NSFC)(62103283)the Australia Research Council’s Discovery Pro-jects Scheme(DP220100355).
文摘1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex and interactive process designs,modern plantwide systems are becoming increasingly sophisticated.The operation of these processes is typically characterized by the complexity of individual units(subsystems)and the intricate interactions between geographically distributed units through networks of material and energy flows,as well as control loops[1].
基金supported by the National Natural Science Foundation of China(No.42007428)the National Forage Industry Technology System Program of China(No.CARS34)+1 种基金the Key Research and Development Program of Shaanxi,China(No.2022SF-285)Shaanxi Province Forestry Science and Technology Innovation Program,China(No.SXLK2022-02-14)。
文摘Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of different soil microbial taxa under environmental changes are still disputed,which limits the understanding of the sustainability of desert restoration.Hence,we investigated the soil microbial community characteristics and functional attributes of grassland desert(GD),desert steppe(DS),typical steppe(TS),and artificial forest(AF)in the Mu Us Desert,China.Our findings confirmed the geographical conservation of soil microbial composition but highlighted decreased microbial diversity in TS.Meanwhile,the abundance of rare taxa and microbial community stability in TS improved.Heterogeneous and homogeneous selection determined the assembly of rare and abundant bacterial taxa,respectively,with both being significantly influenced by soil moisture.In contrast,fungal communities displayed stochastic processes and exhibited sensitivity to soil nutrient conditions.Furthermore,our investigation revealed a noteworthy augmentation in bacterial metabolic functionality in TS,aligning with improved vegetation restoration and the assemblage of abundant bacterial taxa.However,within nutrient-limited soils(GD,DS,and AF),the assembly dynamics of rare fungal taxa assumed a prominent role in augmenting their metabolic capacity and adaptability to desert ecosystems.These results highlighted the variations in the assembly processes and metabolic functions of soil microorganisms during vegetation reestablishment and provided corresponding theoretical support for anthropogenic revegetation of desert ecosystems.
基金project supported by the National Natural Science Foundation of China(Grant Nos.52225503 and 52405380)National Key Research and Development Program(Grant Nos.2023YFB4603303 and 2023YFB4603304)+4 种基金Key Research and Development Program of Jiangsu Province(Grant Nos.BE2022069 and BE2022069-3)National Natural Science Foundation of China for Creative Research Groups(Grant No.51921003)The 15th Batch of“Six Talents Peaks”Innovative Talents Team Program of Jiangsu province(Grant Nos.TD-GDZB-001)Shanghai Aerospace Science and Technology Innovation Fund Project(Grant No.SAST2023-066)The Fundamental Research Funds for the Central Universities(Grant Nos.NS2023035 and NP2024128)。
文摘Transpiration cooling is crucial for the performance of aerospace engine components,relying heavily on the processing quality and accuracy of microchannels.Laser powder bed fusion(LPBF)offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication,essential for engine cooling applications.However,optimizing LPBF’s extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge,especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery.This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures.250 parameter combinations,including laser power,scanning speed,channel diameter,and spot compensation,were designed across ten high-throughput specimens.This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy.Employing Bayesian optimization,the Gaussian process model accurately predicted processing outcomes over a broad parameter range.The correlation between various processing parameters,processing quality and accuracy was revealed,and various optimized process combinations were summarized.Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach.The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF,which has broad application value.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.
基金supported by Natural Science Foundation of China(Nos.52070133,42107073,42477075)Natural Science Foundation of Sichuan Province(No.2024NSFSC0130)+2 种基金the Sichuan Science and Technology Program(No.2024NSFTD0014)Key Laboratory of Jiangxi Province for Persistent Pollutants Prevention Control and Resource Reuse(No.2023SSY02061)Key R&D Program of Heilongjiang Province(No.2023ZX02C01)。
文摘Low-valent sulfur oxy-acid salts(LVSOs)represent a category of oxygen-containing salts characterized by their potent reducing capabilities.Notably,sulfite,dithionite,and thiosulfate are prevalent reducing agents that are readily available,cost-effective,and exhibit minimal ecological toxicity.These LVSOs have the ability to generate or promote the generation of strong oxidants or reductants,which makes them widely used in advanced oxidation processes(AOPs)and advanced reduction processes(ARPs).This article provides a comprehensive review of the recent advancements in AOPs and ARPs involving LVSOs,alongside an examination of the fundamental principles governing the generation of active species within these processes.LVSOs fulfill three primary functions in AOPs:Serving as sources of reactive oxygen species(ROS),auxiliary agents,and activators.Particular attention is devoted to elucidating the reaction mechanisms through which LVSOs,in conjunction with metal ions,metal oxides,ultraviolet light(UV),and ozone,produce potent oxidizing agents in both homogeneous and heterogeneous systems.Regarding ARPs,this review delineates the mechanisms by which LVSOs generate strong reducing agents,including hydrated electrons,hydrogen radicals,and sulfite radicals,under UV irradiation,while also exploring the interactions between these reductants and pollutants.The review identifies existing gaps within the current framework and proposes future research avenues to address these challenges.
文摘Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.
文摘Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–uniquely tests man-made fiber properties in addition to cotton.It provides critical data to optimize fiber process control for cotton,man-made fibers,and blended yarns.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
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Supported by Central Guided Local Science and Technology Development Funds(ZY20230102)Guilin Scientific Research and Technology Development Programme Project(2023010301-1,20220104-4)+1 种基金Guangxi Science and Technology Programme Project(GK AB24010263)Guangxi Innovation Driving Development Special Funds Project(GK AA22096020).
文摘[Objectives] To optimize the crystallization process of ceftriaxone sodium using response surface methodology (RSM) for enhancing both the crystallization rate and the quality of the final product. [Methods] Four key factors, including crystallization temperature, stirring speed, solvent drop rate, and seed crystal content, were employed as independent variables, while the crystallization rate served as the response variable. The Box-Behnken response surface method was utilized for the optimization design. [Results] The optimal parameters for the crystallization process, determined through optimization, were as follows: a temperature of 10.6 ℃, a stirring rate of 150 rpm, a solvent drop rate of 1.50 mL/min, and a seed crystal content of 0.12 g. Validation tests conducted under these conditions yielded an average crystallization rate of 94.38% for the refined product. [Conclusions] The crystallization efficiency of ceftriaxone sodium is markedly enhanced, thereby offering substantial support for its industrial production and clinical application.
基金supported by the NSFC(12101358,12471095)the Natural Science Foundation of Hubei Province in China(2024AFC020)the Fundamental Research Funds for the Central Universities,South-Central MinZu University(CZY23010)。
文摘this paper,we study the exponential non-uniform Berry-Esseen bound for the maximum likelihood estimator of some time inhomogeneous diffusion process.As applications,the optimal uniform Berry-Esseen bound and optimal Cramer-type moderate deviations of the Ornstein-Uhlenbeck process andα-Brownian bridge can be obtained.The main methods are the change of measure method and asymptotic analysis technique.
基金support from the following foundations:the National Natural Science Foundation of China(62322309,62433004)Shanghai Science and Technology Innovation Action Plan(23S41900500)Shanghai Pilot Program for Basic Research(22TQ1400100-16).
文摘Fault diagnosis in industrial process is essential for ensuring production safety and efficiency.However,existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains,resulting in suboptimal performance and robustness.Therefore,this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive(SmdaNet).First,the method uses deep belief networks(DBN)to build a diagnostic model.Hard samples are mined based on the loss values,dividing the data set into hard and easy samples.Second,elastic weight consolidation(EWC)is used to train the model on hard samples,effectively preventing information forgetting.Finally,the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions.Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy,robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.
基金supported by National Natural Science Foundation of China(82471564)YT is supported by National Natural Science Foundation of China(32322035,32171078).
文摘Background Pain sensitivity is critical for preventing non-suicidal self-injury(NSSI)behaviours;however,individuals engaging in such behaviours often exhibit decreased pain sensitivity,which may undermine this natural safeguard.The dorsolateral prefrontal cortex(DLPFC)is a key region involved in pain regulation,and recent approaches using transcranial direct current stimulation(tDCS)to target the DLPFC have shown potential for modulating pain processing and restoring normal pain perception for individuals engaging in NSSI behaviours.Aims This study aimed to explore the immediate and short-term effects of a single session of tDCS on pain sensitivity in individuals with NSSI,as well as its secondary effects on mood and NSSI-related factors.Methods In this randomised,double-blind,parallel,sham-controlled clinical trial,participants with a history of NSSI were randomly assigned to receive either active or sham tDCS.The intervention consisted of a single 20 min tDCS session targeting the left DLPFC.The primary outcome was pain sensitivity,measured by the pressure pain threshold(PPT)and heat pain score(HPS).Secondary and additional outcomes included NSSI urges,NSSI resistance,self-efficacy in resisting NSSI,mood-related variables and exploratory cognitive-affective processes such as rumination,self-criticism and self-perceived pain sensitivity,assessed at baseline,immediately post-intervention,and at 24 hours,1 week and 2 weeks follow-ups.Results For the primary outcomes,no significant differences between groups were observed for pain sensitivity(PPT,padj=0.812;HPS,padj=0.608).However,an exploratory sensitivity analysis treating each trial as an individual observation revealed a significant effect on HPS(padj=0.036).For the secondary and additional outcomes,although there were initial improvements in joyful feelings and reductions in negative affect at 2 weeks post-intervention,these effects did not remain significant after multiple comparison corrections.Notably,reductions in rumination were statistically significant at both 1-week and 2-week follow-ups(1 week,p_(adj)=0.040;2 weeks,p_(adj)=0.042).There were no significant effects on NSSI urges,NSSI resistance,self-efficacy in resisting NSSI or self-criticism.Conclusions A single session of tDCS over the left DLPFC did not produce significant changes in pain sensitivity in individuals with NSSI.A sensitivity analysis indicated an effect on heat pain sensitivity,possibly reflecting changes in brain activity,warranting confirmation through neuroimaging.These findings suggest that tDCS warrants further investigation for its potential to influence pain-related cognitive-affective processes in individuals with NSSI.
