一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家...一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing addit...Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
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
The complexity of the seismicity pattern for the subduction zone along the oceanic plate triggered the outer rise events and revealed cyclic tectonic deformation conditions along the plate subduction zones.The outer r...The complexity of the seismicity pattern for the subduction zone along the oceanic plate triggered the outer rise events and revealed cyclic tectonic deformation conditions along the plate subduction zones.The outer rise earthquakes have been observed along the Sunda arc,following the estimated rupture area of the 2005 M_(W)8.6 Nias earthquakes.Here,we used kinematic waveform inversion(KIWI)to obtain the source parameters of the 14 May 2021 M_(W)6.6 event off the west coast of northern Sumatra and to define the fault plane that triggered this outer rise event.The KIWI algorithm allows two types of seismic source to be configured:the moment tensor model to describe the type of shear with six moment tensor components and the Eikonal model for the rupture of pure double-couple sources.This method was chosen for its flexibility to be applied for different sources of seismicity and also for the automated full-moment tensor solution with real-time monitoring.We used full waveform traces from 8 broadband seismic stations within 1000 km epicentral distances sourced from the Incorporated Research Institutions for Seismology(IRIS-IDA)and Geofon GFZ seismic record databases.The initial origin time and hypocenter values are obtained from the IRIS-IDA.The synthetic seismograms used in the inversion process are based on the existing regional green function database model and were accessed from the KIWI Tools Green's Function Database.The obtained scalar seismic moment value is 1.18×10^(19)N·m,equivalent to a moment magnitude M_(W)6.6.The source parameters are 140°,44°,and−99°for the strike,dip,and rake values at a centroid depth of 10.2 km,indicating that this event is a normal fault earthquake that occurred in the outer rise area.The outer rise events with normal faults typically occur at the shallow part of the plate,with nodal-plane dips predominantly in the range of 30°-60°on the weak oceanic lithosphere due to hydrothermal alteration.The stress regime around the plate subduction zone varies both temporally and spatially due to the cyclic influences of megathrust earthquakes.Tensional outer rise earthquakes tend to occur after the megathrust events.The relative timing of these events is not known due to the viscous relaxation of the down going slab and poroelastic response in the trench slope region.The occurrence of the 14 May 2021 earthquake shows the seismicity in the outer rise region in the strongly coupled Sunda arc subduction zone due to elastic bending stress within the duration of the seismic cycle.展开更多
To explore the best preparation process for terminal blend(TB)composite-modified asphalt and to filter its formulation with excellent performance,this study evaluates the performance of TB composite modified asphalt b...To explore the best preparation process for terminal blend(TB)composite-modified asphalt and to filter its formulation with excellent performance,this study evaluates the performance of TB composite modified asphalt by physical property index,microscopic morphology,rheological testing,and infrared spectroscopy on multiple scales.The results show that the best preparation process for TB-modified asphalt is stirring at 260℃ for 4 h at 400 rpm,which significantly reduces the modification time of the asphalt.From a physical property viewpoint,the TB composite-modified asphalt sample with 5% styrene-butadiene-styrene(SBS)+1% aromatics+0.1% sulfur exhibits high-comprehensive,high-and low-temperature properties.More-over,its crosslinked mesh structure comprises black rubber particles uniformly interwoven in the middle,which further enhances the performance of the asphalt and results in an excellent performance formulation.In addition,the sample with 5%SBS content has a higher G*value and smaller δ value than that with 3%SBS content,indicating that its high-temperature resistance is improved.The effect of adding 3%SBS content on the viscoelastic ratio is,to some extent,less than that caused by 20% rubber powder.展开更多
High entropy alloys(HEAs)have recently attracted significant attention due to their exceptional mechanical properties and potential applications across various fields.Friction stir welding and processing(FSW/P),as not...High entropy alloys(HEAs)have recently attracted significant attention due to their exceptional mechanical properties and potential applications across various fields.Friction stir welding and processing(FSW/P),as notable solid-state welding and processing techniques,have been proved effectiveness in enhancing microstructures and mechanical properties of HEAs.This review article summarizes the current status of FSW/P of HEAs.The welding materials and conditions used for FSW/P in HEAs are reviewed and discussed.The effects of FSW/P on the evolutions of grain structure,texture,dislocation,and secondary phase for different HEAs are highlighted.Furthermore,the influences of FSW/P on the mechanical properties of various HEAs are analyzed.Finally,potential applications,challenges,and future directions of FSW/P in HEAs are forecasted.Overall,FSW/P enable to refine grains of HEAs through dynamic recrystallization and to activate diverse deformation mechanisms of HEAs through tailoring phase structures,thereby significantly improving the strength,hardness,and ductility of both single-and dual-phase HEAs.Future progress in this field will rely on comprehensive optimization of processing parameters and alloy composition,integration of multi-scale modeling with advanced characterization for in-depth exploration of microstructural mechanisms,systematic evaluation of functional properties,and effective bridging of the gap between laboratory research and industrial application.The review aims to provide an overview of recent advancements in the FSW/P of HEAs and encourage further research in this area.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
Overweight and obesity has been a major public health problem globally.It was estimated that more than 2.1 billion adults were affected by overweight or obese in 2021 worldwide,about one fifth of whom lived in China^(...Overweight and obesity has been a major public health problem globally.It was estimated that more than 2.1 billion adults were affected by overweight or obese in 2021 worldwide,about one fifth of whom lived in China^([1]).By 2050,the country is forecast to remain the one with the largest population of overweight and obese globally^([1]),if no effective strategies were applied on overweight/obesity control.展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.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
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
基金National Key Research and Development Program of China(2022YFB4600902)Shandong Provincial Science Foundation for Outstanding Young Scholars(ZR2024YQ020)。
文摘Wire arc additive manufacturing(WAAM)has emerged as a promising approach for fabricating large-scale components.However,conventional WAAM still faces challenges in optimizing microstructural evolution,minimizing additive-induced defects,and alleviating residual stress and deformation,all of which are critical for enhancing the mechanical performance of the manufactured parts.Integrating interlayer friction stir processing(FSP)into WAAM significantly enhances the quality of deposited materials.However,numerical simulation research focusing on elucidating the associated thermomechanical coupling mechanisms remains insufficient.A comprehensive numerical model was developed to simulate the thermomechanical coupling behavior in friction stir-assisted WAAM.The influence of post-deposition FSP on the coupled thermomechanical response of the WAAM process was analyzed quantitatively.Moreover,the residual stress distribution and deformation behavior under both single-layer and multilayer deposition conditions were investigated.Thermal analysis of different deposition layers in WAAM and friction stir-assisted WAAM was conducted.Results show that subsequent layer deposition induces partial remelting of the previously solidified layer,whereas FSP does not cause such remelting.Furthermore,thermal stress and deformation analysis confirm that interlayer FSP effectively mitigates residual stresses and distortion in WAAM components,thereby improving their structural integrity and mechanical properties.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘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 the National Natural Science Foundation of China(Grant No.42130312)。
文摘The complexity of the seismicity pattern for the subduction zone along the oceanic plate triggered the outer rise events and revealed cyclic tectonic deformation conditions along the plate subduction zones.The outer rise earthquakes have been observed along the Sunda arc,following the estimated rupture area of the 2005 M_(W)8.6 Nias earthquakes.Here,we used kinematic waveform inversion(KIWI)to obtain the source parameters of the 14 May 2021 M_(W)6.6 event off the west coast of northern Sumatra and to define the fault plane that triggered this outer rise event.The KIWI algorithm allows two types of seismic source to be configured:the moment tensor model to describe the type of shear with six moment tensor components and the Eikonal model for the rupture of pure double-couple sources.This method was chosen for its flexibility to be applied for different sources of seismicity and also for the automated full-moment tensor solution with real-time monitoring.We used full waveform traces from 8 broadband seismic stations within 1000 km epicentral distances sourced from the Incorporated Research Institutions for Seismology(IRIS-IDA)and Geofon GFZ seismic record databases.The initial origin time and hypocenter values are obtained from the IRIS-IDA.The synthetic seismograms used in the inversion process are based on the existing regional green function database model and were accessed from the KIWI Tools Green's Function Database.The obtained scalar seismic moment value is 1.18×10^(19)N·m,equivalent to a moment magnitude M_(W)6.6.The source parameters are 140°,44°,and−99°for the strike,dip,and rake values at a centroid depth of 10.2 km,indicating that this event is a normal fault earthquake that occurred in the outer rise area.The outer rise events with normal faults typically occur at the shallow part of the plate,with nodal-plane dips predominantly in the range of 30°-60°on the weak oceanic lithosphere due to hydrothermal alteration.The stress regime around the plate subduction zone varies both temporally and spatially due to the cyclic influences of megathrust earthquakes.Tensional outer rise earthquakes tend to occur after the megathrust events.The relative timing of these events is not known due to the viscous relaxation of the down going slab and poroelastic response in the trench slope region.The occurrence of the 14 May 2021 earthquake shows the seismicity in the outer rise region in the strongly coupled Sunda arc subduction zone due to elastic bending stress within the duration of the seismic cycle.
基金Funded by the National Natural Science Foundation of China(No.52278446)。
文摘To explore the best preparation process for terminal blend(TB)composite-modified asphalt and to filter its formulation with excellent performance,this study evaluates the performance of TB composite modified asphalt by physical property index,microscopic morphology,rheological testing,and infrared spectroscopy on multiple scales.The results show that the best preparation process for TB-modified asphalt is stirring at 260℃ for 4 h at 400 rpm,which significantly reduces the modification time of the asphalt.From a physical property viewpoint,the TB composite-modified asphalt sample with 5% styrene-butadiene-styrene(SBS)+1% aromatics+0.1% sulfur exhibits high-comprehensive,high-and low-temperature properties.More-over,its crosslinked mesh structure comprises black rubber particles uniformly interwoven in the middle,which further enhances the performance of the asphalt and results in an excellent performance formulation.In addition,the sample with 5%SBS content has a higher G*value and smaller δ value than that with 3%SBS content,indicating that its high-temperature resistance is improved.The effect of adding 3%SBS content on the viscoelastic ratio is,to some extent,less than that caused by 20% rubber powder.
基金supported by National Natural Science Foundation of China(Grant No.52171032)Hebei Natural Science Foundation(Grant No.E2023501002)Fundamental Research Funds for the Central Universities(Grant No.2024GFYD003)。
文摘High entropy alloys(HEAs)have recently attracted significant attention due to their exceptional mechanical properties and potential applications across various fields.Friction stir welding and processing(FSW/P),as notable solid-state welding and processing techniques,have been proved effectiveness in enhancing microstructures and mechanical properties of HEAs.This review article summarizes the current status of FSW/P of HEAs.The welding materials and conditions used for FSW/P in HEAs are reviewed and discussed.The effects of FSW/P on the evolutions of grain structure,texture,dislocation,and secondary phase for different HEAs are highlighted.Furthermore,the influences of FSW/P on the mechanical properties of various HEAs are analyzed.Finally,potential applications,challenges,and future directions of FSW/P in HEAs are forecasted.Overall,FSW/P enable to refine grains of HEAs through dynamic recrystallization and to activate diverse deformation mechanisms of HEAs through tailoring phase structures,thereby significantly improving the strength,hardness,and ductility of both single-and dual-phase HEAs.Future progress in this field will rely on comprehensive optimization of processing parameters and alloy composition,integration of multi-scale modeling with advanced characterization for in-depth exploration of microstructural mechanisms,systematic evaluation of functional properties,and effective bridging of the gap between laboratory research and industrial application.The review aims to provide an overview of recent advancements in the FSW/P of HEAs and encourage further research in this area.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
文摘Overweight and obesity has been a major public health problem globally.It was estimated that more than 2.1 billion adults were affected by overweight or obese in 2021 worldwide,about one fifth of whom lived in China^([1]).By 2050,the country is forecast to remain the one with the largest population of overweight and obese globally^([1]),if no effective strategies were applied on overweight/obesity control.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
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.
