In order to investigate the self-priming process of the self-priming pump,an unsteady simulation was conducted where the Navier-Stokes equations were used with the Lagrangian-Eularian mo-del.In course of this investig...In order to investigate the self-priming process of the self-priming pump,an unsteady simulation was conducted where the Navier-Stokes equations were used with the Lagrangian-Eularian mo-del.In course of this investigation,the volume fractions,pressure distribution and self-priming time were carried out.By analyzing the volume,velocity and pressure distribution of the gas-liquid two-phase flow at different time,the two-phase content via the variation law of the two-phase flow in the pump was carried out.By monitoring and analyzing the gas-liquid flow at the outlet of the pump,the self-priming time and crucial periods were given.Two phenomena were mainly characterized by the self-priming process such as the gas-liquid mixing and separation,which occur in the early stage of self-priming process.During that period the gas-liquid mixing clouds appear on the outer edge of the impeller,and the instantaneous void fraction at the inlet and outlet of the impeller decreases obviously.It was also established from the transient study that the gas has a major influence on the hydraulic performance of the pump at the early stage of operation.To increase the usage of self-priming pump and to also understand the energy conversion of the pump,it is very essential to investigate and establish the basic working principle of the self-priming pump.展开更多
The gas-liquid two-phase flow patterns of a centrifugal pump during the self-priming process were investigated numerically and experimentally.The Euler-Euler multiphase model and SST k-ω turbulence model were applied...The gas-liquid two-phase flow patterns of a centrifugal pump during the self-priming process were investigated numerically and experimentally.The Euler-Euler multiphase model and SST k-ω turbulence model were applied for simulating the self-priming process.Meanwhile,the changes of motor speed and self-priming height were considered in the simulation.The overall transient two-phase flow features and water level distributions were mapped.Results showed that the self-priming process was divided into three stages.The liquid level in inlet-pipe rose in oscillation during self-priming process.The variations of water level during self-priming process of numerical simulation and test result agreed well.The inlet-pipe(Ver)was filled at 22 s and 24 s respectively numerically and experimentally.The bubble cloud circulated in the volute during middle stage of self-priming process,and breakup into smaller bubbles by shear force and tongue,and then discharged into chamber.The bubbles in the outlet-pipe mainly included bubbly flow and slug flow at the last stage of self-priming process,which is morphologically consistent with the test results.Also,during the self-priming process,the reflux liquid was pressed by blades and fully mixed with gas;that is the way to realizing the function of self-priming.展开更多
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
Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure throug...Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure through self-priming. However, their pressure frequency and cavitation characteristics remain unclear, resulting in an inability to fully utilize resonance and cavitation erosion to break coal and rock. In this study, high-frequency pressure testing, high-speed photography, and large eddy simulation(LES) are used to investigate the distribution of the pressure frequency band, evolution law of the cavitation cloud, and its regulation mechanism of a continuous waterjet, SOPW, and AFESOPW. The results indicated that the excitation of the plunger pump, shearing layer vortex, and bubble collapse corresponded to the three high-amplitude frequency bands of the waterjet pressure. AFESOPWs have an additional self-priming frequency that can produce a larger amplitude under a synergistic effect with the second high-amplitude frequency band. A better cavitation effect was produced after self-priming the annulus fluid, and the shedding frequency of the cavitation clouds of the three types of waterjets was linearly related to the cavitation number. The peak pressure of the waterjet and cavitation erosion effect can be improved by modulating the waterjet pressure oscillation frequency and cavitation shedding frequency.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
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
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
The self-priming process of a pump involves a complex gas-liquid two-phase flow.Studying the distribution of gas and water and the evolution of their flow in the pump is of great importance to optimize this process an...The self-priming process of a pump involves a complex gas-liquid two-phase flow.Studying the distribution of gas and water and the evolution of their flow in the pump is of great importance to optimize this process and shorten the pump self-priming time.In the present study,a standard k-εturbulence model and a multiphase flow model have been used to simulate the self-priming pump process considering four different reflux hole areas.A comparison of the distribution of air and water distribution on the axial surface and inside the volume have been carried out for the different considered cases.The pattern formed by the streamlines at different times during the whole self-priming process has also been investigated.The results show that the velocity at the trailing edge of the impeller outlet is the largest.The flow in the pump cavity is.complicated by the formation of vortices.The number,shape and location of the vortices change depending on the considered configuration.展开更多
A self-priming pump is a centrifugal pump that has the ability to prime itself. Typically, its performance dependson the configuration of its reflux hole. In this study, the ANSYS FLUENT software is used to investigat...A self-priming pump is a centrifugal pump that has the ability to prime itself. Typically, its performance dependson the configuration of its reflux hole. In this study, the ANSYS FLUENT software is used to investigate the effectsof three different radial positions of the reflux hole on gas-liquid two-phase distribution, pressure pulsation, andimp during self-priming. The research results indicate that: (1) The effective channel size for the reflux liquid toenter the volute varies depending on the location of the reflux hole. The effect of the impeller rotation on thereflux liquid becomes more obvious as the setting distance of the reflux aperture decreases. (2) The position ofthe reflux hole significantly affects the gas phase mass fraction inside the impeller, resulting in a significant reductionin the time it takes for the mass fraction to exceed 80%. (3) The position of the reflux hole significantly affectsthe average pressure on each monitoring surface. (4) Placing the reflux hole at a excessively distant radial distancecan result in an excessive vertical component. (5) The self-priming performance of the pump can be improved tosome extent by placing the return hole at a small radial distance.展开更多
Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a w...Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the p...Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.展开更多
Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network act...Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network active layer morphology,featuring a bulk p-in structure and proper vertical segregation achieved through additive-assisted layer-by-layer deposition.This optimized hierarchical gradient fibrillar morphology and optical management synergistically facilitates exciton diffusion,reduces recombination losses,and enhances light capture capability.This approach not only offers a solution to achieving high-efficiency devices but also demonstrates the potential for commercial applications of OSCs.展开更多
Fenton and Fenton-like processes,which could produce highly reactive species to degrade organic contaminants,have been widely used in the field of wastewater treatment.Therein,the chemistry of Fenton process including...Fenton and Fenton-like processes,which could produce highly reactive species to degrade organic contaminants,have been widely used in the field of wastewater treatment.Therein,the chemistry of Fenton process including the nature of active oxidants,the complicated reactions involved,and the behind reason for its strongly pH-dependent performance,is the basis for the application of Fenton and Fenton-like processes in wastewater treatment.Nevertheless,the conflicting views still exist about the mechanism of the Fenton process.For instance,reaching a unanimous consensus on the nature of active oxidants(hydroxyl radical or tetravalent iron)in this process remains challenging.This review comprehensively examined the mechanism of the Fenton process including the debate on the nature of active oxidants,reactions involved in the Fenton process,and the behind reason for the pH-dependent degradation of contaminants in the Fenton process.Then,we summarized several strategies that promote the Fe(Ⅱ)/Fe(Ⅲ)cycle,reduce the competitive consumption of active oxidants by side reactions,and replace the Fenton reagent,thus improving the performance of the Fenton process.Furthermore,advances for the future were proposed including the demand for the high-accuracy identification of active oxidants and taking advantages of the characteristic of target contaminants during the degradation of contaminants by the Fenton process.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.51879120)。
文摘In order to investigate the self-priming process of the self-priming pump,an unsteady simulation was conducted where the Navier-Stokes equations were used with the Lagrangian-Eularian mo-del.In course of this investigation,the volume fractions,pressure distribution and self-priming time were carried out.By analyzing the volume,velocity and pressure distribution of the gas-liquid two-phase flow at different time,the two-phase content via the variation law of the two-phase flow in the pump was carried out.By monitoring and analyzing the gas-liquid flow at the outlet of the pump,the self-priming time and crucial periods were given.Two phenomena were mainly characterized by the self-priming process such as the gas-liquid mixing and separation,which occur in the early stage of self-priming process.During that period the gas-liquid mixing clouds appear on the outer edge of the impeller,and the instantaneous void fraction at the inlet and outlet of the impeller decreases obviously.It was also established from the transient study that the gas has a major influence on the hydraulic performance of the pump at the early stage of operation.To increase the usage of self-priming pump and to also understand the energy conversion of the pump,it is very essential to investigate and establish the basic working principle of the self-priming pump.
基金supported by the National Natural Science Foundation of China(51609212,51606167,51779226 and 51976193)。
文摘The gas-liquid two-phase flow patterns of a centrifugal pump during the self-priming process were investigated numerically and experimentally.The Euler-Euler multiphase model and SST k-ω turbulence model were applied for simulating the self-priming process.Meanwhile,the changes of motor speed and self-priming height were considered in the simulation.The overall transient two-phase flow features and water level distributions were mapped.Results showed that the self-priming process was divided into three stages.The liquid level in inlet-pipe rose in oscillation during self-priming process.The variations of water level during self-priming process of numerical simulation and test result agreed well.The inlet-pipe(Ver)was filled at 22 s and 24 s respectively numerically and experimentally.The bubble cloud circulated in the volute during middle stage of self-priming process,and breakup into smaller bubbles by shear force and tongue,and then discharged into chamber.The bubbles in the outlet-pipe mainly included bubbly flow and slug flow at the last stage of self-priming process,which is morphologically consistent with the test results.Also,during the self-priming process,the reflux liquid was pressed by blades and fully mixed with gas;that is the way to realizing the function of self-priming.
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
基金supported by the program for National Natural Science Foundation of China (Nos. 52174173, 52274188, and 52104190)the Joint Funds of the National Natural Science Foundation of China (No. U24A2091)+1 种基金The Natural Science Foundation of Henan Polytechnic University (No. B2021-2)Double FirstClass Initiative of Safety and Energy Engineering (Henan Polytechnic University) (Nos. AQ20240703 and AQ20230304)。
文摘Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure through self-priming. However, their pressure frequency and cavitation characteristics remain unclear, resulting in an inability to fully utilize resonance and cavitation erosion to break coal and rock. In this study, high-frequency pressure testing, high-speed photography, and large eddy simulation(LES) are used to investigate the distribution of the pressure frequency band, evolution law of the cavitation cloud, and its regulation mechanism of a continuous waterjet, SOPW, and AFESOPW. The results indicated that the excitation of the plunger pump, shearing layer vortex, and bubble collapse corresponded to the three high-amplitude frequency bands of the waterjet pressure. AFESOPWs have an additional self-priming frequency that can produce a larger amplitude under a synergistic effect with the second high-amplitude frequency band. A better cavitation effect was produced after self-priming the annulus fluid, and the shedding frequency of the cavitation clouds of the three types of waterjets was linearly related to the cavitation number. The peak pressure of the waterjet and cavitation erosion effect can be improved by modulating the waterjet pressure oscillation frequency and cavitation shedding frequency.
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%.
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.
文摘The self-priming process of a pump involves a complex gas-liquid two-phase flow.Studying the distribution of gas and water and the evolution of their flow in the pump is of great importance to optimize this process and shorten the pump self-priming time.In the present study,a standard k-εturbulence model and a multiphase flow model have been used to simulate the self-priming pump process considering four different reflux hole areas.A comparison of the distribution of air and water distribution on the axial surface and inside the volume have been carried out for the different considered cases.The pattern formed by the streamlines at different times during the whole self-priming process has also been investigated.The results show that the velocity at the trailing edge of the impeller outlet is the largest.The flow in the pump cavity is.complicated by the formation of vortices.The number,shape and location of the vortices change depending on the considered configuration.
基金the National Natural Science Foundation of China(Research Project No.52169018).
文摘A self-priming pump is a centrifugal pump that has the ability to prime itself. Typically, its performance dependson the configuration of its reflux hole. In this study, the ANSYS FLUENT software is used to investigate the effectsof three different radial positions of the reflux hole on gas-liquid two-phase distribution, pressure pulsation, andimp during self-priming. The research results indicate that: (1) The effective channel size for the reflux liquid toenter the volute varies depending on the location of the reflux hole. The effect of the impeller rotation on thereflux liquid becomes more obvious as the setting distance of the reflux aperture decreases. (2) The position ofthe reflux hole significantly affects the gas phase mass fraction inside the impeller, resulting in a significant reductionin the time it takes for the mass fraction to exceed 80%. (3) The position of the reflux hole significantly affectsthe average pressure on each monitoring surface. (4) Placing the reflux hole at a excessively distant radial distancecan result in an excessive vertical component. (5) The self-priming performance of the pump can be improved tosome extent by placing the return hole at a small radial distance.
基金funded by CONAHCYT grant(252808)to GFCONAHCYT’s“Estancias Posdoctorales por México”program(662350)to HTB。
文摘Recent reports suggest that aging is not solely a physiological process in living beings;instead, it should be considered a pathological process or disease(Amorim et al., 2022). Consequently, this process involves a wide range of factors, spanning from genetic to environmental factors, and even includes the gut microbiome(GM)(Mayer et al., 2022). All these processes coincide at some point in the inflammatory process, oxidative stress, and apoptosis, at different degrees in various organs and systems that constitute a living organism(Mayer et al., 2022;AguilarHernández et al., 2023).
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unitpower generation costs.The service life of these modules directly affects these costs. Over time, the performanceof PV modules gradually declines due to internal degradation and external environmental factors.This cumulativedegradation impacts the overall reliability of photovoltaic power generation. This study addresses the complexdegradation process of PV modules by developing a two-stage Wiener process model. This approach accountsfor the distinct phases of degradation resulting from module aging and environmental influences. A powerdegradation model based on the two-stage Wiener process is constructed to describe individual differences inmodule degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization(EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) isutilized to identify critical change points in PV module degradation trajectories. To validate the universality andeffectiveness of the proposed method, a comparative analysis is conducted against other established life predictiontechniques for PV modules.
基金Technology Development Program of Jilin Province(YDZJ202201ZYTS640)the National Key Research and Development Program of China(2022YFB4200400)funded by MOST+4 种基金the National Natural Science Foundation of China(52172048 and 52103221)Shandong Provincial Natural Science Foundation(ZR2021QB024 and ZR2021ZD06)Guangdong Basic and Applied Basic Research Foundation(2023A1515012323,2023A1515010943,and 2024A1515010023)the Qingdao New Energy Shandong Laboratory open Project(QNESL OP 202309)the Fundamental Research Funds of Shandong University.
文摘Recently published in Joule,Feng Liu and colleagues from Shanghai Jiaotong University reported a record-breaking 20.8%power conversion efficiency in organic solar cells(OSCs)with an interpenetrating fibril network active layer morphology,featuring a bulk p-in structure and proper vertical segregation achieved through additive-assisted layer-by-layer deposition.This optimized hierarchical gradient fibrillar morphology and optical management synergistically facilitates exciton diffusion,reduces recombination losses,and enhances light capture capability.This approach not only offers a solution to achieving high-efficiency devices but also demonstrates the potential for commercial applications of OSCs.
基金supported by the National Natural Science Foundation of China(Nos.22206050 and 52270047).
文摘Fenton and Fenton-like processes,which could produce highly reactive species to degrade organic contaminants,have been widely used in the field of wastewater treatment.Therein,the chemistry of Fenton process including the nature of active oxidants,the complicated reactions involved,and the behind reason for its strongly pH-dependent performance,is the basis for the application of Fenton and Fenton-like processes in wastewater treatment.Nevertheless,the conflicting views still exist about the mechanism of the Fenton process.For instance,reaching a unanimous consensus on the nature of active oxidants(hydroxyl radical or tetravalent iron)in this process remains challenging.This review comprehensively examined the mechanism of the Fenton process including the debate on the nature of active oxidants,reactions involved in the Fenton process,and the behind reason for the pH-dependent degradation of contaminants in the Fenton process.Then,we summarized several strategies that promote the Fe(Ⅱ)/Fe(Ⅲ)cycle,reduce the competitive consumption of active oxidants by side reactions,and replace the Fenton reagent,thus improving the performance of the Fenton process.Furthermore,advances for the future were proposed including the demand for the high-accuracy identification of active oxidants and taking advantages of the characteristic of target contaminants during the degradation of contaminants by the Fenton process.
