This paper conducted a more comprehensive review and comparative analysis of the two heavy to blizzard processes that occurred in the Beijing area during December 13-15,2023,and February 20-21,2024,in terms of compreh...This paper conducted a more comprehensive review and comparative analysis of the two heavy to blizzard processes that occurred in the Beijing area during December 13-15,2023,and February 20-21,2024,in terms of comprehensive weather situation diagnosis,forecasting,and decision-making services,and summarized the meteorological service support experience of such heavy snow weather processes.It was found that both blizzard processes were jointly influenced by the 700 hPa southwesterly warm and humid jet stream and the near-surface easterly backflow;the numerical forecast was relatively accurate in the overall description of the snowfall process,and the forecast bias of the position of the 700 hPa southwesterly warm and humid jet stream determined the bias of the snowfall magnitude forecast at a certain point;when a deviation was found between the actual snowfall and the forecast,the cause should be analyzed in a timely manner,and the warning and forecast conclusions should be updated.With the full cooperation of relevant departments,it can greatly make up for the deviation of the early forecast snowfall amount,and ensure the safety and efficiency of people's travel.展开更多
Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industria...Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process.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
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
1 Introduction Dissecting the dynamics of cell statesiscrucial for understanding various biological processes,such as tissue development and tumor drug responses.Recent advancements in single-cell lineage tracing(scLT...1 Introduction Dissecting the dynamics of cell statesiscrucial for understanding various biological processes,such as tissue development and tumor drug responses.Recent advancements in single-cell lineage tracing(scLT)technologies provide effective ways to track single-cell lineages through heritable cellular barcodes,while simultaneously detecting the molecular states of cells by sequencing[1].展开更多
Design a precision electroplating mechanical structure for automobiles based on finite element analysis method and analyze its mechanical properties.Taking the automobile steering knuckle as the research object,ABAQUS...Design a precision electroplating mechanical structure for automobiles based on finite element analysis method and analyze its mechanical properties.Taking the automobile steering knuckle as the research object,ABAQUS parametric modeling technology is used to construct its three-dimensional geometric model,and geometric simplification is carried out.Two surface treatment processes,HK-35 zinc nickel alloy electroplating and pure zinc electroplating,were designed,and the influence of different coatings on the mechanical properties of steering knuckles was compared and analyzed through numerical simulation.At the same time,standard specimens were prepared for salt spray corrosion testing and scratch method combined strength testing to verify the numerical simulation results.The results showed that under emergency braking and composite working conditions,the peak Von Mises stress of the zinc nickel alloy coating was 119.85 MPa,which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Its equivalent strain value was 652×10^(-6),which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Experimental data confirms that zinc nickel alloy coatings exhibit significant advantages in stress distribution uniformity,strain performance,and load-bearing capacity in high stress zones.The salt spray corrosion test further indicates that the coating has superior corrosion resistance and coating substrate interface bonding strength,which can significantly improve the mechanical stability and long-term reliability of automotive precision electroplating mechanical structures.展开更多
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
We analyzed accident factors in a 2020 ship collision case that occurred off Kii Oshima Island using the SHELL model analysis and examined corresponding collision prevention measures.The SHELL model analysis is a fram...We analyzed accident factors in a 2020 ship collision case that occurred off Kii Oshima Island using the SHELL model analysis and examined corresponding collision prevention measures.The SHELL model analysis is a framework for identifying accident factors related to human abilities and characteristics,hardware,software,and the environment.Beyond assessing the accident factors in each element,we also examined the interrelationship between humans and each element.This study highlights the importance of(1)training to enhance situational awareness,(2)improving decision-making skills,and(3)establishing structured decision-making procedures to prevent maritime collision accidents.Additionally,we considered safety measures through(4)hardware enhancements and(5)environmental measures.Furthermore,to prevent accidents,implementing measures grounded in(6)predictions is deemed effective.This study identified accident factors through prediction alongside the SHELL model analysis and proposed countermeasures based on the findings.By applying these predictions,more countermeasures can be derived,which,when combined strategically,can significantly aid in preventing maritime collision accidents.展开更多
The solid electrolyte interphase(SEI)layer,formed on the electrode through electrolyte decomposition,has garnered significant attention over the past several decades.Numerous characterization studies have shown that t...The solid electrolyte interphase(SEI)layer,formed on the electrode through electrolyte decomposition,has garnered significant attention over the past several decades.Numerous characterization studies have shown that the SEI enhances the stability of both the electrolyte and electrode,particularly by mitigating the well-known cation-solvent co-intercalation in graphite electrodes in lithium-ion batteries.However,recent electrolyte exchange experiments have revealed that variations in electrolyte solvation structure and the resulting desolvation behaviors play a more dominant role than the SEI in influencing electrolyte and electrode stability,which in turn critically impacts battery performance.In addition to contributing to the ongoing debate,electrolyte exchange experiments have proven to be a valuable tool for analyzing failures in electrolytes,electrodes,and batteries.This review highlights the application of electrolyte exchange experiments across various metal-ion batteries,incorporating diverse combinations of electrolytes and electrodes.It examines the influence of electrolyte solvation structures and desolvation behaviors on the stability of both electrolytes and electrodes.The aim is to enhance the methodology of electrolyte exchange experiments to deepen the understanding of the molecular interactions among metal ions,anions,and solvents within the electrolyte.This approach complements existing insights into SEI effects,providing a more thorough and accurate framework for battery failure analysis.展开更多
In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Exis...In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.展开更多
With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This...With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This study aims to explore the development strategies of real-time data analysis and decision-support systems,and analyze their application status and future development trends in various industries.The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems,and then discusses in detail the key technical aspects such as system architecture,data collection and processing,analysis methods,and visualization techniques.展开更多
Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its ...Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations.Te availability of annotated datasets is limited,and preprocessing of the noisy content is even more challenging,sometimes resulting in the removal of important cues of sentiment from the input.To overcome such problems,this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy.Specifcally,it uses the CAMeLBERT model fnetuned for the Multi-Domain Arabic Resources for Sentiment Analysis(MARSA)dataset containing more than 56,000 manually annotated tweets annotated across political,social,sports,and technology domains.Te proposed method avoids extensive use of preprocessing and shows that raw data provides better results because they tend to retain more linguistic features.Te fne-tuned CAMeLBERT model produces state-of-the-art accuracy of 92%,precision of 91.7%,recall of 92.3%,and F1-score of 91.5%,outperforming standard machine learning models and ensemble-based/deep learning techniques.Our performance comparisons against other pre-trained models,namely AraBERTv02-twitter and MARBERT,show that transformer-based architectures are consistently the best suited when dealing with noisy Arabic texts.Tis work leads to a strong remedy for the problems in Arabic sentiment analysis and provides recommendations on easy tuning of the pre-trained models to adapt to challenging linguistic features and domain-specifc tasks.展开更多
The analysis of ancient genomics provides opportunities to explore human population history across both temporal and geographic dimensions(Haak et al.,2015;Wang et al.,2021,2024)to enhance the accessibility and utilit...The analysis of ancient genomics provides opportunities to explore human population history across both temporal and geographic dimensions(Haak et al.,2015;Wang et al.,2021,2024)to enhance the accessibility and utility of these ancient genomic datasets,a range of databases and advanced statistical models have been developed,including the Allen Ancient DNA Resource(AADR)(Mallick et al.,2024)and AdmixTools(Patterson et al.,2012).While upstream processes such as sequencing and raw data processing have been streamlined by resources like the AADR,the downstream analysis of these datasets-encompassing population genetics inference and spatiotemporal interpretation-remains a significant challenge.The AADR provides a unified collection of published ancient DNA(aDNA)data,yet its file-based format and reliance on command-line tools,such as those in Admix-Tools(Patterson et al.,2012),require advanced computational expertise for effective exploration and analysis.These requirements can present significant challenges forresearchers lackingadvanced computational expertise,limiting the accessibility and broader application of these valuable genomic resources.展开更多
Metal foils have emerged as one of the promising materials for anode-free batteries due to their high energy density and scalability in production.The unclear lithium plating/stripping kinetics of metal foil current c...Metal foils have emerged as one of the promising materials for anode-free batteries due to their high energy density and scalability in production.The unclear lithium plating/stripping kinetics of metal foil current collectors in anode-free batteries was addressed by using the non-destructive distribution of relaxation times(DRT)analysis to systematically investigate the lithium transport behavior of 14 metal foils and its correlation with electrochemical performance.By integrating energy-dispersive spectro scopy(EDS),cyclic voltammetry(CV),and galvanostatic testing,the exceptional properties of indium(In),tin(Sn),and silver(Ag)were revealed:the Li-In alloying reaction exhibits high reversibility,Li-Sn alloys demonstrate outstanding cycling stability,and the Li-Ag solid-solution mechanism provides an ideal lithium deposition interface on the silver substrate.The DRT separates the polarization internal resistance of lithium ions passing through the SEI layer(R_(sei),τ2)and the polarization internal resistance of lithium ions undergoing charge transfer reaction at the electrolyte/electrode interface(R_(ct),τ3)by decoupling the electrochemical impedance spectroscopy(EIS).For the first time,the correlation betweenτ2,τ3,and the cycle life/Coulombic efficiency of alloy/solid-solution metals was established,while non-alloy metals are not suitable for this method due to differences in lithium deposition mechanisms.This study not only illuminates the structure-property relationship governing the lithium kinetics of metal foil electrodes but also provides a novel non-destructive analytical strategy and theoretical guidance for the rational design of stable anodes in high-energy-density batteries,facilitating the efficient screening and optimization of anode-free battery.展开更多
In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of...In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of infor-mation and insights,particularly when examining the temporal aspect,allowing the extraction of relevant and non-obvious knowledge.The triadic concept analysis theory has been proposed to describe the ternary re-lationships between objects,attributes,and conditions.In this study,we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules,which are similar to association rules but incorporate temporal relations.Through four case studies,we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns,enhance decision-making processes,and deepen our understanding of temporal dynamics.These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.展开更多
Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evalua...Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evaluated through a multidimensional framework encompassing market dynamics,macroeconomic factors,financial stability,governance,supply chains,and production safety.By integrating the Analytic Hierarchy Process(AHP)with entropy weighting,a hybrid weighting model that mitigates the limitations of singular methods is established.The analysis of this study identifies financial risk(weight:0.347)and production safety(weight:0.298)as dominant risk drivers.These quantitative insights offer a basis for resource prioritization and targeted risk mitigation strategies in chemical enterprises.展开更多
The connection efficiency of composite pre-tightened multi-tooth joint is low because of uneven load distribution and single load transmission path.In this paper,based on the principle of bio-tooth(suture)structure,co...The connection efficiency of composite pre-tightened multi-tooth joint is low because of uneven load distribution and single load transmission path.In this paper,based on the principle of bio-tooth(suture)structure,combining soft material with fractal,a composite pre-tightened multi-hierarchy tooth joint is proposed,and the bearing performance and failure process of the joint through experiments and finite element method under tensile load.First,the ultimate bearing capacity,load distribution ratio,and failure process of different hierarchies of teeth joints are studied through experiments.Then,the progressive damage models of different hierarchies of tooth joints are established,and experiments verify the validity of the finite element model.Finally,the effects of soft material and increasing tooth hierarchy on the failure process and bearing capacity of composite pre-tightened tooth joints are analyzed by the finite element method.The following conclusions can be drawn:(1)The embedding of soft materials changed the failure process of the joint.Increasing the tooth hierarchy can give the joint more load transfer paths,but the failure process of the joint is complicated.(2)Embedding soft materials and increasing the tooth hierarchy simultaneously can effectively improve the bearing capacity of composite pre-tightened tooth joints,which is 87.8%higher than that of traditional three-tooth joints.展开更多
Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flo...Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flood frequency analysis(FFA)and flood susceptibility mapping cannot be overstated.This study focuses on the Haora River basin in Tripura,a region prone to frequent flooding due to a combination of natural and anthropogenic factors.This study evaluates the suitability of the Log-Pearson Type Ⅲ(LP-Ⅲ)and Gumbel Extreme Value-1(EV-1)distributions for estimating peak discharges and delineates floodsusceptible zones in the Haora River basin,Tripura.Using 40 years of peak discharge data(1984-2023),the LP-Ⅲ distribution was identified as the most appropriate model based on goodness-of-fit tests.Flood susceptibility mapping,integrating 16 thematic layers through the Analytical Hierarchy Process,identified 8%,64%,and 26%of the area as high,moderate,and low susceptibility zones,respectively,with a model success rate of 0.81.The findings highlight the need for improved flood management strategies,such as enhancing river capacity and constructing flood spill channels.These insights are critical for designing targeted flood mitigation measures in the Haora basin and other flood-prone regions.展开更多
Sentiment Analysis,a significant domain within Natural Language Processing(NLP),focuses on extracting and interpreting subjective information-such as emotions,opinions,and attitudes-from textual data.With the increasi...Sentiment Analysis,a significant domain within Natural Language Processing(NLP),focuses on extracting and interpreting subjective information-such as emotions,opinions,and attitudes-from textual data.With the increasing volume of user-generated content on social media and digital platforms,sentiment analysis has become essential for deriving actionable insights across various sectors.This study presents a systematic literature review of sentiment analysis methodologies,encompassing traditional machine learning algorithms,lexicon-based approaches,and recent advancements in deep learning techniques.The review follows a structured protocol comprising three phases:planning,execution,and analysis/reporting.During the execution phase,67 peer-reviewed articles were initially retrieved,with 25 meeting predefined inclusion and exclusion criteria.The analysis phase involved a detailed examination of each study’s methodology,experimental setup,and key contributions.Among the deep learning models evaluated,Long Short-Term Memory(LSTM)networks were identified as the most frequently adopted architecture for sentiment classification tasks.This review highlights current trends,technical challenges,and emerging opportunities in the field,providing valuable guidance for future research and development in applications such as market analysis,public health monitoring,financial forecasting,and crisis management.展开更多
This paper analyzes the advantages of legal digital currencies and explores their impact on bank big data practices.By combining bank big data collection and processing,it clarifies that legal digital currencies can e...This paper analyzes the advantages of legal digital currencies and explores their impact on bank big data practices.By combining bank big data collection and processing,it clarifies that legal digital currencies can enhance the efficiency of bank data processing,enrich data types,and strengthen data analysis and application capabilities.In response to future development needs,it is necessary to strengthen data collection management,enhance data processing capabilities,innovate big data application models,and provide references for bank big data practices,promoting the transformation and upgrading of the banking industry in the context of legal digital currencies.展开更多
文摘This paper conducted a more comprehensive review and comparative analysis of the two heavy to blizzard processes that occurred in the Beijing area during December 13-15,2023,and February 20-21,2024,in terms of comprehensive weather situation diagnosis,forecasting,and decision-making services,and summarized the meteorological service support experience of such heavy snow weather processes.It was found that both blizzard processes were jointly influenced by the 700 hPa southwesterly warm and humid jet stream and the near-surface easterly backflow;the numerical forecast was relatively accurate in the overall description of the snowfall process,and the forecast bias of the position of the 700 hPa southwesterly warm and humid jet stream determined the bias of the snowfall magnitude forecast at a certain point;when a deviation was found between the actual snowfall and the forecast,the cause should be analyzed in a timely manner,and the warning and forecast conclusions should be updated.With the full cooperation of relevant departments,it can greatly make up for the deviation of the early forecast snowfall amount,and ensure the safety and efficiency of people's travel.
基金supported by the Research Program of the Liaoning Liaohe Laboratory(LLL23ZZ-05-012)China Academy of Engineering Institute of Land Cooperation Consulting Project(2023-DFZD-60-02)+3 种基金the Key Research and Development Program of Liaoning Province(2023JH26/10200011)the National Natural Science Foundation of China(61991404)the National Key Research and Development Program of China(2024YFB3309700)the Science and Technology Major Project 2024 of Liaoning Province(2024JH1/11700048).
文摘Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process.
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.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金supported by the National Key Research and Development Program of China(Nos.2020YFA0712403 and 2021YFF1200901)the National Natural Science Foundation of China(NSFC)(Grant Nos.62133006 and 92268104)+1 种基金the Tsinghua University Initiative Scientific Research Program(No.20221080076)the China Postdoctoral Science Foundation(No.2022M721839).
文摘1 Introduction Dissecting the dynamics of cell statesiscrucial for understanding various biological processes,such as tissue development and tumor drug responses.Recent advancements in single-cell lineage tracing(scLT)technologies provide effective ways to track single-cell lineages through heritable cellular barcodes,while simultaneously detecting the molecular states of cells by sequencing[1].
文摘Design a precision electroplating mechanical structure for automobiles based on finite element analysis method and analyze its mechanical properties.Taking the automobile steering knuckle as the research object,ABAQUS parametric modeling technology is used to construct its three-dimensional geometric model,and geometric simplification is carried out.Two surface treatment processes,HK-35 zinc nickel alloy electroplating and pure zinc electroplating,were designed,and the influence of different coatings on the mechanical properties of steering knuckles was compared and analyzed through numerical simulation.At the same time,standard specimens were prepared for salt spray corrosion testing and scratch method combined strength testing to verify the numerical simulation results.The results showed that under emergency braking and composite working conditions,the peak Von Mises stress of the zinc nickel alloy coating was 119.85 MPa,which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Its equivalent strain value was 652×10^(-6),which was lower than that of the pure zinc coating and the alkaline electroplated zinc layer.Experimental data confirms that zinc nickel alloy coatings exhibit significant advantages in stress distribution uniformity,strain performance,and load-bearing capacity in high stress zones.The salt spray corrosion test further indicates that the coating has superior corrosion resistance and coating substrate interface bonding strength,which can significantly improve the mechanical stability and long-term reliability of automotive precision electroplating mechanical structures.
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
文摘We analyzed accident factors in a 2020 ship collision case that occurred off Kii Oshima Island using the SHELL model analysis and examined corresponding collision prevention measures.The SHELL model analysis is a framework for identifying accident factors related to human abilities and characteristics,hardware,software,and the environment.Beyond assessing the accident factors in each element,we also examined the interrelationship between humans and each element.This study highlights the importance of(1)training to enhance situational awareness,(2)improving decision-making skills,and(3)establishing structured decision-making procedures to prevent maritime collision accidents.Additionally,we considered safety measures through(4)hardware enhancements and(5)environmental measures.Furthermore,to prevent accidents,implementing measures grounded in(6)predictions is deemed effective.This study identified accident factors through prediction alongside the SHELL model analysis and proposed countermeasures based on the findings.By applying these predictions,more countermeasures can be derived,which,when combined strategically,can significantly aid in preventing maritime collision accidents.
基金supported by the Jilin Provincial Scientific and Technological Development Program(YDZJ202401572ZYTS)the Overseas Expertise Introduction Project for Discipline Innovation of China(D18012)+1 种基金Education Department of Jilin Province(JJKH20240678KJ)the National Natural Science Foundation of China(22122904,22109155,22379136)。
文摘The solid electrolyte interphase(SEI)layer,formed on the electrode through electrolyte decomposition,has garnered significant attention over the past several decades.Numerous characterization studies have shown that the SEI enhances the stability of both the electrolyte and electrode,particularly by mitigating the well-known cation-solvent co-intercalation in graphite electrodes in lithium-ion batteries.However,recent electrolyte exchange experiments have revealed that variations in electrolyte solvation structure and the resulting desolvation behaviors play a more dominant role than the SEI in influencing electrolyte and electrode stability,which in turn critically impacts battery performance.In addition to contributing to the ongoing debate,electrolyte exchange experiments have proven to be a valuable tool for analyzing failures in electrolytes,electrodes,and batteries.This review highlights the application of electrolyte exchange experiments across various metal-ion batteries,incorporating diverse combinations of electrolytes and electrodes.It examines the influence of electrolyte solvation structures and desolvation behaviors on the stability of both electrolytes and electrodes.The aim is to enhance the methodology of electrolyte exchange experiments to deepen the understanding of the molecular interactions among metal ions,anions,and solvents within the electrolyte.This approach complements existing insights into SEI effects,providing a more thorough and accurate framework for battery failure analysis.
基金supported by the Global Research and Innovation Platform Fund for Scientific Big Data Transmission(Grant No.241711KYSB20180002)National Key Research and Development Project of China(Grant No.2019YFB1405801).
文摘In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.
文摘With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This study aims to explore the development strategies of real-time data analysis and decision-support systems,and analyze their application status and future development trends in various industries.The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems,and then discusses in detail the key technical aspects such as system architecture,data collection and processing,analysis methods,and visualization techniques.
基金funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations.Te availability of annotated datasets is limited,and preprocessing of the noisy content is even more challenging,sometimes resulting in the removal of important cues of sentiment from the input.To overcome such problems,this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy.Specifcally,it uses the CAMeLBERT model fnetuned for the Multi-Domain Arabic Resources for Sentiment Analysis(MARSA)dataset containing more than 56,000 manually annotated tweets annotated across political,social,sports,and technology domains.Te proposed method avoids extensive use of preprocessing and shows that raw data provides better results because they tend to retain more linguistic features.Te fne-tuned CAMeLBERT model produces state-of-the-art accuracy of 92%,precision of 91.7%,recall of 92.3%,and F1-score of 91.5%,outperforming standard machine learning models and ensemble-based/deep learning techniques.Our performance comparisons against other pre-trained models,namely AraBERTv02-twitter and MARBERT,show that transformer-based architectures are consistently the best suited when dealing with noisy Arabic texts.Tis work leads to a strong remedy for the problems in Arabic sentiment analysis and provides recommendations on easy tuning of the pre-trained models to adapt to challenging linguistic features and domain-specifc tasks.
基金by the National Key Research and Development Program of China(2023YFC3303701-02 and 2024YFC3306701)the National Natural Science Foundation of China(T2425014 and 32270667)+3 种基金the Natural Science Foundation of Fujian Province of China(2023J06013)the Major Project of the National Social Science Foundation of China granted to Chuan-Chao Wang(21&ZD285)Open Research Fund of State Key Laboratory of Genetic Engineering at Fudan University(SKLGE-2310)Open Research Fund of Forensic Genetics Key Laboratory of the Ministry of Public Security(2023FGKFKT07).
文摘The analysis of ancient genomics provides opportunities to explore human population history across both temporal and geographic dimensions(Haak et al.,2015;Wang et al.,2021,2024)to enhance the accessibility and utility of these ancient genomic datasets,a range of databases and advanced statistical models have been developed,including the Allen Ancient DNA Resource(AADR)(Mallick et al.,2024)and AdmixTools(Patterson et al.,2012).While upstream processes such as sequencing and raw data processing have been streamlined by resources like the AADR,the downstream analysis of these datasets-encompassing population genetics inference and spatiotemporal interpretation-remains a significant challenge.The AADR provides a unified collection of published ancient DNA(aDNA)data,yet its file-based format and reliance on command-line tools,such as those in Admix-Tools(Patterson et al.,2012),require advanced computational expertise for effective exploration and analysis.These requirements can present significant challenges forresearchers lackingadvanced computational expertise,limiting the accessibility and broader application of these valuable genomic resources.
基金supported by the Quzhou Science and Technology Bureau Project(2023D023,2023D030,2023D002,and2024D028)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(LZY23B030002)+3 种基金the Shijiazhuang Shangtai Technology Co.,Ltd.Hebei Provincial Department of Science and Technology(24291101Z)the International Cooperation Projects of Sichuan Provincial Department of Science and Technology(2021YFH0126)the Sichuan Provincial Science and Technology Department's key research project(2023YFG0203)。
文摘Metal foils have emerged as one of the promising materials for anode-free batteries due to their high energy density and scalability in production.The unclear lithium plating/stripping kinetics of metal foil current collectors in anode-free batteries was addressed by using the non-destructive distribution of relaxation times(DRT)analysis to systematically investigate the lithium transport behavior of 14 metal foils and its correlation with electrochemical performance.By integrating energy-dispersive spectro scopy(EDS),cyclic voltammetry(CV),and galvanostatic testing,the exceptional properties of indium(In),tin(Sn),and silver(Ag)were revealed:the Li-In alloying reaction exhibits high reversibility,Li-Sn alloys demonstrate outstanding cycling stability,and the Li-Ag solid-solution mechanism provides an ideal lithium deposition interface on the silver substrate.The DRT separates the polarization internal resistance of lithium ions passing through the SEI layer(R_(sei),τ2)and the polarization internal resistance of lithium ions undergoing charge transfer reaction at the electrolyte/electrode interface(R_(ct),τ3)by decoupling the electrochemical impedance spectroscopy(EIS).For the first time,the correlation betweenτ2,τ3,and the cycle life/Coulombic efficiency of alloy/solid-solution metals was established,while non-alloy metals are not suitable for this method due to differences in lithium deposition mechanisms.This study not only illuminates the structure-property relationship governing the lithium kinetics of metal foil electrodes but also provides a novel non-destructive analytical strategy and theoretical guidance for the rational design of stable anodes in high-energy-density batteries,facilitating the efficient screening and optimization of anode-free battery.
文摘In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of infor-mation and insights,particularly when examining the temporal aspect,allowing the extraction of relevant and non-obvious knowledge.The triadic concept analysis theory has been proposed to describe the ternary re-lationships between objects,attributes,and conditions.In this study,we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules,which are similar to association rules but incorporate temporal relations.Through four case studies,we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns,enhance decision-making processes,and deepen our understanding of temporal dynamics.These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.
文摘Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evaluated through a multidimensional framework encompassing market dynamics,macroeconomic factors,financial stability,governance,supply chains,and production safety.By integrating the Analytic Hierarchy Process(AHP)with entropy weighting,a hybrid weighting model that mitigates the limitations of singular methods is established.The analysis of this study identifies financial risk(weight:0.347)and production safety(weight:0.298)as dominant risk drivers.These quantitative insights offer a basis for resource prioritization and targeted risk mitigation strategies in chemical enterprises.
基金funded by the National Natural Science Foundation of China(52478138).
文摘The connection efficiency of composite pre-tightened multi-tooth joint is low because of uneven load distribution and single load transmission path.In this paper,based on the principle of bio-tooth(suture)structure,combining soft material with fractal,a composite pre-tightened multi-hierarchy tooth joint is proposed,and the bearing performance and failure process of the joint through experiments and finite element method under tensile load.First,the ultimate bearing capacity,load distribution ratio,and failure process of different hierarchies of teeth joints are studied through experiments.Then,the progressive damage models of different hierarchies of tooth joints are established,and experiments verify the validity of the finite element model.Finally,the effects of soft material and increasing tooth hierarchy on the failure process and bearing capacity of composite pre-tightened tooth joints are analyzed by the finite element method.The following conclusions can be drawn:(1)The embedding of soft materials changed the failure process of the joint.Increasing the tooth hierarchy can give the joint more load transfer paths,but the failure process of the joint is complicated.(2)Embedding soft materials and increasing the tooth hierarchy simultaneously can effectively improve the bearing capacity of composite pre-tightened tooth joints,which is 87.8%higher than that of traditional three-tooth joints.
文摘Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flood frequency analysis(FFA)and flood susceptibility mapping cannot be overstated.This study focuses on the Haora River basin in Tripura,a region prone to frequent flooding due to a combination of natural and anthropogenic factors.This study evaluates the suitability of the Log-Pearson Type Ⅲ(LP-Ⅲ)and Gumbel Extreme Value-1(EV-1)distributions for estimating peak discharges and delineates floodsusceptible zones in the Haora River basin,Tripura.Using 40 years of peak discharge data(1984-2023),the LP-Ⅲ distribution was identified as the most appropriate model based on goodness-of-fit tests.Flood susceptibility mapping,integrating 16 thematic layers through the Analytical Hierarchy Process,identified 8%,64%,and 26%of the area as high,moderate,and low susceptibility zones,respectively,with a model success rate of 0.81.The findings highlight the need for improved flood management strategies,such as enhancing river capacity and constructing flood spill channels.These insights are critical for designing targeted flood mitigation measures in the Haora basin and other flood-prone regions.
基金supported by the“Technology Commercialization Collaboration Platform Construction”project of the Innopolis Foundation(Project Number:2710033536)the Competitive Research Fund of The University of Aizu,Japan.
文摘Sentiment Analysis,a significant domain within Natural Language Processing(NLP),focuses on extracting and interpreting subjective information-such as emotions,opinions,and attitudes-from textual data.With the increasing volume of user-generated content on social media and digital platforms,sentiment analysis has become essential for deriving actionable insights across various sectors.This study presents a systematic literature review of sentiment analysis methodologies,encompassing traditional machine learning algorithms,lexicon-based approaches,and recent advancements in deep learning techniques.The review follows a structured protocol comprising three phases:planning,execution,and analysis/reporting.During the execution phase,67 peer-reviewed articles were initially retrieved,with 25 meeting predefined inclusion and exclusion criteria.The analysis phase involved a detailed examination of each study’s methodology,experimental setup,and key contributions.Among the deep learning models evaluated,Long Short-Term Memory(LSTM)networks were identified as the most frequently adopted architecture for sentiment classification tasks.This review highlights current trends,technical challenges,and emerging opportunities in the field,providing valuable guidance for future research and development in applications such as market analysis,public health monitoring,financial forecasting,and crisis management.
文摘This paper analyzes the advantages of legal digital currencies and explores their impact on bank big data practices.By combining bank big data collection and processing,it clarifies that legal digital currencies can enhance the efficiency of bank data processing,enrich data types,and strengthen data analysis and application capabilities.In response to future development needs,it is necessary to strengthen data collection management,enhance data processing capabilities,innovate big data application models,and provide references for bank big data practices,promoting the transformation and upgrading of the banking industry in the context of legal digital currencies.
