In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the tw...In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the two light beams. Since both light beams are diffracted when passing through the optical systems, the spatial resolution of ghost imaging is in general lower than that of a corresponding conventional imaging system. When Gaussian-shaped light spots are used to illuminate an object, randomly scanning across the object plane, in the ghost imaging scheme, we show th√at by localizing central positions of the spots of the reference light beam, the resolution can be increased by a factor of 2^(1/2) same as that of the corresponding conventional imaging system. We also find that the resolution can be further enhanced by setting an appropriate threshold to the bucket measurement of ghost imaging.展开更多
Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 ma...Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 males and 14 females(median age, 48.5y; range, 28–62y) with pathologically proven insulinomas were included in this study. Selective angiographies were performed with the superior mesenteric artery(SMA), gastroduodenal artery(GDA), proximal splenic artery, and midsplenic artery in all individuals. Then ASVS procedures were followed after angiographies for these arteries. Clinical characteristics of the patient and the tumor number, location, and size were recorded. The accuracy of preoperative localization of insulinomas was tested. Results: A total of 22 tumors were identified by histopathological diagnosis. The mean size of the tumor was 1.40±0.60 cm. Five tumors were in the head/neck region and 17 in the body/tail region. ASVS accurately localized 17/20(85%) cases with only biochemical data and 19/20(95%) cases with biochemical data and angiography images. Variant pancreatic arterial anatomy was revealed in 2 false cases with inferior pancreatic artery replaced by the superior mesenteric artery. Conclusion: ASVS was highly accurate in localizing insulinomas and should be performed in most of the patients with suspected insulinomas before the operation.展开更多
In order to measure the position and orientation of in-vivo medical micro-devices without the line-of- sight constraints, a wireless magnetic sensor is developed for an electromagnetic localization method. In the elec...In order to measure the position and orientation of in-vivo medical micro-devices without the line-of- sight constraints, a wireless magnetic sensor is developed for an electromagnetic localization method. In the electromagnetic localization system, the wireless magnetic sensor is embedded in the micro-devices to measure alternating magnetic signals. The wireless magnetic sensor is composed of an induction coil, a signal processor, a radio frequency （R.F） transmitter, a power manager and batteries. Based on the principle of electromagnetic induction, the induction coil converts the alternating magnetic signals into electrical signals. Via the RF transmitter, the useful data am wirelessly sent outside the body. According to the relation between the magnetic signals and the location, the position and orientation of the micro-devices can be calculated. The experiments demonstrate the feasibility of localizing in-vivo medical micro-devices with the wireless magnetic sensor. The novel localization system is accurate and robust.展开更多
In the networking of loitering munitions during a battle,clustering and localizing algorithms become a major problem because of their highly dynamic topological structure,incomplete connectivity,and limited energy.Thi...In the networking of loitering munitions during a battle,clustering and localizing algorithms become a major problem because of their highly dynamic topological structure,incomplete connectivity,and limited energy.This paper proposed swarm intelligence based collaborative localizing,clustering,and routing scheme for an ad hoc network of loitering munitions in a satellite denied environment.A hybrid algorithm was first devised by integrating an improved coyote optimization algorithm with a simplified grey wolf optimizer under the sinusoidal crossover strategy.The performance of this algorithm was considerably improved thanks to integration.On this basis,a swarm intelligence based localizing algorithm was presented.Bounding cubes were created to reduce the initial search space,which effectively lowered the localizing error.Second,an energysaving clustering algorithm based on the hybrid algorithm was put forward to enhance the clustering efficiency by virtue of grey wolf hierarchy.Meanwhile,an analysis model was developed to determine the optimal number of clusters using the lowest possible number of transmissions.Ultimately,a compressed sensing routing scheme based on the hybrid algorithm was proposed to transmit data from a cluster head to a base station.This algorithm constructed an efficient routing tree from the cluster head to the base station,so as to reduce the routing delay and transmission count.As revealed in the results of simulation experiments,the proposed collaborative localizing,clustering and routing algorithms achieved better performance than other popular algorithms employed in various scenarios.展开更多
In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization s...In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization system comes from the usage of beacon systems each of which is composed of an RF single receiver and an ultrasonic transmitter.The RF single receiver gets the synchronization signal from the mobile robot,and the ultrasonic transmitter sends ultrasonic signal,thus the distance from the beacon to the ultrasonic receiver can be measured.The position of a beacon in coordinate system of robot can be calculated according to distance information from the beacons to two ultrasonic receivers which are mounted on the robot.Based on the coordinate transformation,the position of a mobile robot can be calculated from the beacon＇s absolute position information in the global coordinate system.Experiments demonstrate the effectiveness of the proposed method in real world applications.展开更多
Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequ...Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.展开更多
Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance.Video-based behavior recognition has emerged as a primary technique for obtaining such inf...Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance.Video-based behavior recognition has emerged as a primary technique for obtaining such information due to its accuracy and robustness.Video-based models generally predict a single behavior from a single video segment of a fixed duration.However,during periods of high activity in poultry,behavior transition may occur within a video segment,and existing models often fail to capture such transitions effectively.This limitation highlights the insufficient temporal resolution of video-based behavior recognition models.This study presents a chicken behavior recognition and localization model,CBLFormer,which is based on spatiotemporal feature learning.The model was designed to recognize behaviors that occur before and after transitions in video segments and to localize the corresponding time interval for each behavior.An improved transformer block,the cascade encoder-decoder network(CEDNet),a transformer-based head,and weighted distance intersection over union(WDIoU)loss were integrated into CBLFormer to enhance the model's ability to distinguish between different behavior categories and locate behavior boundaries.For the training and testing of CBLFormer,a dataset was created by collecting videos from 320 chickens across different ages and rearing densities.The results showed that CBLFormer achieved a mAP@0.5:0.95 of 98.34%on the test set.The integration of CEDNet contributed the most to the performance improvement of CBLFormer.The visualization results confirmed that the model effectively captured the behavioral boundaries of chickens and correctly recognized behavior categories.The transfer learning results demonstrated that the model is applicable to chicken behavior recognition and localization tasks in real-world poultry farms.The proposed method handles cases where poultry behavior transitions occur within the video segment and improves the temporal resolution of video-based behavior recognition models.展开更多
In this research, an arbitrarily oriented electric dipole at subsurface is used to simulate Seismogenic Electromagnetic(SEM) radiation emanating from a seismic zone during its gestation phase. Analytical synthesis of ...In this research, an arbitrarily oriented electric dipole at subsurface is used to simulate Seismogenic Electromagnetic(SEM) radiation emanating from a seismic zone during its gestation phase. Analytical synthesis of responses at the Lijiang magnetotelluric(MT) station has revealed that SEM radiation could induce identifiable anomalies in the electromagnetic(EM)spectrum, apparent resistivity and phase within specific frequency bands. Background variations were extracted from long-term observation data of Dali and Lijiang MT stations, enabling the identification of SEM anomalies related to the Yunlong and Yangbi earthquakes. Multiple parameters of dipole sources at subsurface were obtained by applying the Differential Ant Colony Optimization(DACO) algorithm to anomalous data of two stations with multi-frequencies and various response functions. The spatial distribution of these predicted dipoles is predominantly clustered in or around the seismogenic area, with their azimuthal orientation aligning towards the seismogenic fault in general. This study has demonstrated the potential of using subsurface electric dipole simulations for SEM radiation analysis, offering a feasible approach for the prediction and understanding of seismogenic zones.展开更多
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni...Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.展开更多
BACKGROUND Data comparing the outcomes of hepatocellular carcinoma(HCC)ablation by multibipolar radiofrequency ablation(mbp-RFA)and microwave ablation(MWA)are lacking.This study compares safety and efficacy of the two...BACKGROUND Data comparing the outcomes of hepatocellular carcinoma(HCC)ablation by multibipolar radiofrequency ablation(mbp-RFA)and microwave ablation(MWA)are lacking.This study compares safety and efficacy of the two techniques in treatment-naive HCC.AIM To compare the risk of local tumor progression(LTP)according to the technique;secondary endpoints included technique efficacy rate at one-month,overall survival and major complication rate.METHODS A bi-institutional retrospective analysis of patients undergoing treatment-naive HCC ablation by either technique was performed.Inverse probability of treatment weighting was used to compare the two groups.Mixed effects multivariate Cox regression was applied to identify risk factors for LTP.RESULTS A total of 362 patients(mean age,66.1±6.2 years,308 men)were included,of which 242(323 tumors)treated by mbp-RFA and 120(168 tumors)by MWA.After a median follow-up of 27 months,cumulative LTP was 11.4%after mbp-RFA and 25.2%after MWA.Independent risk factors for LTP at multivariate analysis were MWA(hazard ratio=2.85,P<0.001)and tumor size(hazard ratio=1.08,P<0.001).Two-year LTP-free survival was higher after mbp-RFA than MWA regardless of size(<3 cm:96%vs 87.1%,P<0.01;≥3 cm:87.5%vs 74%,P=0.04).Technique efficacy rate was higher after mbp-RFA(94.1%vs 87.5%,P=0.01).No difference was observed in major complication rate(9.5%vs 7.5%,P=0.59),nor 5-year overall survival(63.6%vs 58.3%,P=0.33).CONCLUSION Mbp-RFA leads to better local tumor control of treatment-naïve HCC than MWA regardless of tumor size and has better primary efficacy,while maintaining a comparable safety profile.展开更多
Controllable synthesis of ultrathin metallene nanosheets and rational design of their spatial arrangement in favor of electrochemical catalysis are critical for their renewable energy applications.Here,a biomimetic de...Controllable synthesis of ultrathin metallene nanosheets and rational design of their spatial arrangement in favor of electrochemical catalysis are critical for their renewable energy applications.Here,a biomimetic design of“Trunk-Branch-Leaf”strategy is proposed to prepare the ultrathin edge-riched Zn-ene“leaves”with a thickness of~2.5 nm,adjacent Zn-ene cross-linked with each other,which are supported by copper nanoneedle“branches”on copper mesh“trunks,”named as Zn-ene/Cu-CM.The resulting superstructure enables the formation of an interconnected network and multiple channels,which can be used as an electrocatalytic CO_(2) reduction reaction(CO_(2)RR)electrode to allow a fast charge and mass transfer as well as a large electrolyte reservoir.By virtue of the distinctive structure,the obtained Zn-ene/Cu-CM electrode exhibits excellent selectivity and activity toward CO production with a maximum Faradaic efficiency of 91.3%and incredible partial current density up to 40 mA cm^(−2),outperforming most of the state-of-the-art Zn-based electrodes for CO_(2) reduction.The phenolphthalein color probe combined with in situ attenuated total reflection-infrared spectroscopy uncovered the formation of the localized pseudo-alkaline microenvironment at the interface of the Zn-ene/Cu-CM electrode.Theoretical calculations confirmed that the localized pH as the origin is responsible for the adsorption of CO_(2) at the interface and the generation of *COOH and *CO intermediates.This study offers valuable insights into developing efficient electrodes through synergistic regulation of reaction microenvironments and active sites,thereby facilitating the electrolysis of practical CO_(2) conversion.展开更多
BACKGROUND The liver represents a common site of distant metastasis in patients with esophageal cancer(EC).Conventional chemotherapy(CMT)presents limited efficacy for EC,and EC patients with liver metastases typically...BACKGROUND The liver represents a common site of distant metastasis in patients with esophageal cancer(EC).Conventional chemotherapy(CMT)presents limited efficacy for EC,and EC patients with liver metastases typically experience a poor prognosis,highlighting an urgent need to explore novel treatment approaches.This study evaluated the overall efficacy and safety of CMT vs CMT combined with immune checkpoint inhibitors(ICIs)in the treatment of EC patients with liver metastases.Furthermore,prognostic factors influencing outcomes in this patient population were identified.AIM To evaluate the efficacy and safety of first-line chemoimmunotherapy for EC patients with liver metastases and to analyze prognostic factors.METHODS This retrospective study included 126 EC patients with liver metastases at Zhejiang Cancer Hospital between 2014 and 2024.Patients receiving CMT were compared with those receiving CMT+ICI.Analyzed variables included clinicopathological features,treatment history,characteristics of metastasis,systemic and local treatments,overall survival(OS),and treatment-related adverse events(TRAEs).Prognostic factors were evaluated using univariate and multivariate Cox proportional-hazards regression models.Finally,efficacy outcomes and TRAE profiles were compared between the two groups.RESULTS A significant difference in median OS was identified between the two groups(10.8 months in the CMT group vs 20.8 months in the CMT+ICI group,P=0.004).The CMT+ICI group also demonstrated a significantly longer median progression-free survival of 11.7 months(P<0.001).Patients receiving combination therapy exhibited significantly improved systemic objective response rate and disease control rate.Multivariate analysis identified key factors significantly influencing OS in EC patients with liver metastases:Karnofsky Performance Status score≥70,receipt of local therapy for liver metastases,and the number of cycles of CMT and immunotherapy received.Furthermore,the incidence of TRAEs did not significantly differ between the CMT+ICI and CMT groups.CONCLUSION For EC patients with liver metastases,the combination of CMT and ICIs demonstrates significantly superior efficacy compared with CMT alone,while maintaining manageable TRAEs.展开更多
Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community asse...Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community assembly in virgin and aging MP plastispheres across different habitats is poorly understood.This study aims to assess the variations in bacterial community assembly across different niches and habitats with an in situ ex-periment,in which constructed forest wetland(FW),natural lake wetland(LW),and lotus pond wetland(LP)were habitats,and plastispheres of virgin and aging low-density polyethylene(LDPE)MPs,as well as surround-ing wetland soils were niches.Significant niche-related differences in bacterial communities were observed,with lower diversity and enrichment of potential plastic-degrading bacteria in the plastisphere than in the soil bacterial communities.Furthermore,habitat-related differences exerted a more pronounced influence on the beta-diversity patterns of the bacterial communities.The linear regression analyses indicated that the local species pool con-tributed more to bacterial community assembly in the LW wetland,whereas the relative abundance of species was the primary factor in the LP wetland.The null model analysis indicated that plastisphere bacterial communi-ties were predominantly driven by the stochastic process,with a more deterministic assembly observed in the LP wetland and soil bacterial communities.Additionally,the primary ecological process shaping plastisphere com-munities shifted from drift in the virgin LDPE to homogenising dispersal in the aging LDPE.This study provides new insights into the fate and ecological impacts of MPs in wetlands,thereby facilitating the effective regulations of plastic pollution.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Cell function has a tight relationship with cell architecture.Distribution of proteins to the correct compartment is one of the functions of the traffic pathway through the Golgi apparatus.The others are to ensure pro...Cell function has a tight relationship with cell architecture.Distribution of proteins to the correct compartment is one of the functions of the traffic pathway through the Golgi apparatus.The others are to ensure proper protein folding,the addition of post-translational modifications,and delivering to intracellular and extracellular destinations.Astrocytes are fundamental homeostatic cells,controlling multiple aspects of the central nervous system physiology,such as ion balance,nutrients,blood flow,neurotransmitters,and responses to insults.Astrocytes are polarized cells,and,such as neurons,extensively use the secretory pathway for secreting factors and exposing functional receptors,channels,and transporters on the plasma membrane.In this review,we will underline the importance of studying the Golgi apparatus and the secretory pathway in astrocytes,based on the possible tight connection between the Golgi apparatus and astrocytes’homeostatic function.Given the topic of this review,we will provide examples mostly about the Golgi apparatus structure,function,localization,and its involvement in astrocytes’homeostatic response,with an insight into congenital glycosylation disorders,as an example of a potential future field in the study of astrocyte homeostatic failure and Golgi apparatus alteration.展开更多
Compared to the well-studied two-dimensional(2D)ferroelectricity,the appearance of 2D antiferroelectricity is much rarer,where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely orien...Compared to the well-studied two-dimensional(2D)ferroelectricity,the appearance of 2D antiferroelectricity is much rarer,where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely oriented.Using NbOCl_(2) monolayer with competing ferroelectric(FE)and antiferroelectric(AFE)phases as a 2D material platform,we demonstrate the emergence of intrinsic antiferroelectricity in NbOCl_(2) monolayer under experimentally accessible shear strain,along with new functionality associated with electric field-induced AFE-to-FE phase transition.Specifically,the complex configuration space accommodating FE and AFE phases,polarization switching kinetics,and finite temperature thermodynamic properties of 2D NbOCl_(2) are all accurately predicted by large-scale molecular dynamics simulations based on deep learning interatomic potential model.Moreover,room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional collinear polarization arrangement is predicted in shear-deformed NbOCl_(2) monolayer.The transition from AFE to FE phase in 2D NbOCl_(2) can be triggered by a low critical electric field,leading to a double polarization–electric(P–E)loop with small hysteresis.A new type of optoelectronic device composed of AFE-NbOCl_(2) is proposed,enabling electric“writing”and nonlinear optical“reading”logical operation with fast operation speed and low power consumption.展开更多
A directional random laser mediated by transverse Anderson localization in a disordered glass optical fiber is reported.Previous demonstrations of random lasers have found limited applications because of their multi-d...A directional random laser mediated by transverse Anderson localization in a disordered glass optical fiber is reported.Previous demonstrations of random lasers have found limited applications because of their multi-directionality and chaotic fluctuations in the laser emission.The random laser presented in this paper operates in the Anderson localization regime.The disorder induced localized states form isolated local channels that make the output laser beam highly directional and stabilize its spectrum.The strong transverse disorder and longitudinal invariance result in isolated lasing modes with negligible interaction with their surroundings,traveling back and forth in a Fabry–Perot cavity formed by the air–fiber interfaces.It is shown that if a localized input pump is scanned across the disordered fiber input facet,the output laser signal follows the transverse position of the pump.Moreover,a uniformly distributed pump across the input facet of the disordered fiber generates a laser signal with very low spatial coherence that can be of practical importance in many optical platforms including image transport with fiber bundles.展开更多
We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber.The cell samples are illuminated by an incoherent ...We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber.The cell samples are illuminated by an incoherent LED light source.A deep convolutional neural network is applied to the image reconstruction process.The network training uses data generated by a setup with straight fiber at room temperature(∼20°C)but can be utilized directly for high-fidelity reconstruction of cell images that are transported through fiber with a few degrees bend or fiber with segments heated up to 50°C.In addition,cell images located several millimeters away from the bare fiber end can be transported and recovered successfully without the assistance of distal optics.We provide evidence that the trained neural network is able to transfer its learning to recover images of cells featuring very different morphologies and classes that are never“seen”during the training process.展开更多
Freight has become one of the major contributors to air pollution.This research proposes a method to systematically estimate truck vehicle emissions at the road segment level through localizing MOVES,a widely-used veh...Freight has become one of the major contributors to air pollution.This research proposes a method to systematically estimate truck vehicle emissions at the road segment level through localizing MOVES,a widely-used vehicle emission estimation model.We first design a protocol of converting percentage values of rotating speed and torque of engine to second-by-second vehicle speed to accommodate the differences between driving cycles adopted in local emission standards and those used in MOVES.In order to identify the best model year for estimating emissions under different local emission standards,we propose an approach of comparing emission outcomes rather than emission factors,considering the differences in unit used between MOVES and emission standards.To calculate road seg mentlevel emission factors,we weight original factors by integrating vehicle fleet informa tion which contains the shares of vehicles under different emission standards and at different ages.We apply the approach to a major freight corridor area in Shanghai and cal culate emission factors by air pollutant,average speed of road sections,and road type.Dynamic emissions of each road section per hour are calculated to reflect the spatial dis tribution of truck emissions.The research outcomes may help local departments,especially in developing countries,better estimate freight vehicle emissions and make policies corre spondingly to control their impacts on public health.展开更多
Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines.Given the advantage of obtaining accurate diagnosis results,multi-sensor fusion has l...Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines.Given the advantage of obtaining accurate diagnosis results,multi-sensor fusion has long been studied in the fault diagnosis field.However,existing studies suffer from two weaknesses.First,the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types.Second,the localization for multi-source faults is seldom investigated,although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable.This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations(MSRs).First,an MSR model is developed to learn MSRs automatically and further obtain fault recognition results.Second,centrality measures are employed to analyze the MSR graphs learned by the MSR model,and fault sources are therefore determined.The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump.Results show the proposed method’s validity in diagnosing fault types and sources.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11534008,11605126,and 11804271)the Fund from the Ministry of Science and Technology of China(Grant No.2016YFA0301404)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province,China(Grant No.2017JQ1025)the Doctoral Fund of the Ministry of Education of China(Grant Nos.2016M592772 and 2018M631137)the Fundamental Research Funds for the Central Universities
文摘In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the two light beams. Since both light beams are diffracted when passing through the optical systems, the spatial resolution of ghost imaging is in general lower than that of a corresponding conventional imaging system. When Gaussian-shaped light spots are used to illuminate an object, randomly scanning across the object plane, in the ghost imaging scheme, we show th√at by localizing central positions of the spots of the reference light beam, the resolution can be increased by a factor of 2^(1/2) same as that of the corresponding conventional imaging system. We also find that the resolution can be further enhanced by setting an appropriate threshold to the bucket measurement of ghost imaging.
基金This work was supported by the Shanghai Pujiang Program(16PJ1406200)the Scientific Research Innovation Projects of Shanghai Municipal Education Commission(15ZZ060)
文摘Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 males and 14 females(median age, 48.5y; range, 28–62y) with pathologically proven insulinomas were included in this study. Selective angiographies were performed with the superior mesenteric artery(SMA), gastroduodenal artery(GDA), proximal splenic artery, and midsplenic artery in all individuals. Then ASVS procedures were followed after angiographies for these arteries. Clinical characteristics of the patient and the tumor number, location, and size were recorded. The accuracy of preoperative localization of insulinomas was tested. Results: A total of 22 tumors were identified by histopathological diagnosis. The mean size of the tumor was 1.40±0.60 cm. Five tumors were in the head/neck region and 17 in the body/tail region. ASVS accurately localized 17/20(85%) cases with only biochemical data and 19/20(95%) cases with biochemical data and angiography images. Variant pancreatic arterial anatomy was revealed in 2 false cases with inferior pancreatic artery replaced by the superior mesenteric artery. Conclusion: ASVS was highly accurate in localizing insulinomas and should be performed in most of the patients with suspected insulinomas before the operation.
基金Sup.ported by the High TechnologyResearch and Development Programme of China （No.2006AA04Z368）, the National Natural Science Foundation of China （No. 30900320, 30570485） and Innovation Program of Shanghai Municipal Education Commission （No. 10YZ93）.
文摘In order to measure the position and orientation of in-vivo medical micro-devices without the line-of- sight constraints, a wireless magnetic sensor is developed for an electromagnetic localization method. In the electromagnetic localization system, the wireless magnetic sensor is embedded in the micro-devices to measure alternating magnetic signals. The wireless magnetic sensor is composed of an induction coil, a signal processor, a radio frequency （R.F） transmitter, a power manager and batteries. Based on the principle of electromagnetic induction, the induction coil converts the alternating magnetic signals into electrical signals. Via the RF transmitter, the useful data am wirelessly sent outside the body. According to the relation between the magnetic signals and the location, the position and orientation of the micro-devices can be calculated. The experiments demonstrate the feasibility of localizing in-vivo medical micro-devices with the wireless magnetic sensor. The novel localization system is accurate and robust.
文摘In the networking of loitering munitions during a battle,clustering and localizing algorithms become a major problem because of their highly dynamic topological structure,incomplete connectivity,and limited energy.This paper proposed swarm intelligence based collaborative localizing,clustering,and routing scheme for an ad hoc network of loitering munitions in a satellite denied environment.A hybrid algorithm was first devised by integrating an improved coyote optimization algorithm with a simplified grey wolf optimizer under the sinusoidal crossover strategy.The performance of this algorithm was considerably improved thanks to integration.On this basis,a swarm intelligence based localizing algorithm was presented.Bounding cubes were created to reduce the initial search space,which effectively lowered the localizing error.Second,an energysaving clustering algorithm based on the hybrid algorithm was put forward to enhance the clustering efficiency by virtue of grey wolf hierarchy.Meanwhile,an analysis model was developed to determine the optimal number of clusters using the lowest possible number of transmissions.Ultimately,a compressed sensing routing scheme based on the hybrid algorithm was proposed to transmit data from a cluster head to a base station.This algorithm constructed an efficient routing tree from the cluster head to the base station,so as to reduce the routing delay and transmission count.As revealed in the results of simulation experiments,the proposed collaborative localizing,clustering and routing algorithms achieved better performance than other popular algorithms employed in various scenarios.
基金supported by the MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency)(NIPA-2010-C1090-1021-0010)
文摘In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization system comes from the usage of beacon systems each of which is composed of an RF single receiver and an ultrasonic transmitter.The RF single receiver gets the synchronization signal from the mobile robot,and the ultrasonic transmitter sends ultrasonic signal,thus the distance from the beacon to the ultrasonic receiver can be measured.The position of a beacon in coordinate system of robot can be calculated according to distance information from the beacons to two ultrasonic receivers which are mounted on the robot.Based on the coordinate transformation,the position of a mobile robot can be calculated from the beacon＇s absolute position information in the global coordinate system.Experiments demonstrate the effectiveness of the proposed method in real world applications.
文摘Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.
基金Supported by Scientific Research Fund of Zhejiang Provincial Education Department(Y202457020).
文摘Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance.Video-based behavior recognition has emerged as a primary technique for obtaining such information due to its accuracy and robustness.Video-based models generally predict a single behavior from a single video segment of a fixed duration.However,during periods of high activity in poultry,behavior transition may occur within a video segment,and existing models often fail to capture such transitions effectively.This limitation highlights the insufficient temporal resolution of video-based behavior recognition models.This study presents a chicken behavior recognition and localization model,CBLFormer,which is based on spatiotemporal feature learning.The model was designed to recognize behaviors that occur before and after transitions in video segments and to localize the corresponding time interval for each behavior.An improved transformer block,the cascade encoder-decoder network(CEDNet),a transformer-based head,and weighted distance intersection over union(WDIoU)loss were integrated into CBLFormer to enhance the model's ability to distinguish between different behavior categories and locate behavior boundaries.For the training and testing of CBLFormer,a dataset was created by collecting videos from 320 chickens across different ages and rearing densities.The results showed that CBLFormer achieved a mAP@0.5:0.95 of 98.34%on the test set.The integration of CEDNet contributed the most to the performance improvement of CBLFormer.The visualization results confirmed that the model effectively captured the behavioral boundaries of chickens and correctly recognized behavior categories.The transfer learning results demonstrated that the model is applicable to chicken behavior recognition and localization tasks in real-world poultry farms.The proposed method handles cases where poultry behavior transitions occur within the video segment and improves the temporal resolution of video-based behavior recognition models.
基金financially supported by the National Natural Science Foundation of China (Grant No. 41574064)the Independent Research Projects of State Key Laboratory of Earthquake Dynamics (Grant No. LED2023A07)the National Major Science and Technology Facilities Project (Grant No. 1512Z0000001)。
文摘In this research, an arbitrarily oriented electric dipole at subsurface is used to simulate Seismogenic Electromagnetic(SEM) radiation emanating from a seismic zone during its gestation phase. Analytical synthesis of responses at the Lijiang magnetotelluric(MT) station has revealed that SEM radiation could induce identifiable anomalies in the electromagnetic(EM)spectrum, apparent resistivity and phase within specific frequency bands. Background variations were extracted from long-term observation data of Dali and Lijiang MT stations, enabling the identification of SEM anomalies related to the Yunlong and Yangbi earthquakes. Multiple parameters of dipole sources at subsurface were obtained by applying the Differential Ant Colony Optimization(DACO) algorithm to anomalous data of two stations with multi-frequencies and various response functions. The spatial distribution of these predicted dipoles is predominantly clustered in or around the seismogenic area, with their azimuthal orientation aligning towards the seismogenic fault in general. This study has demonstrated the potential of using subsurface electric dipole simulations for SEM radiation analysis, offering a feasible approach for the prediction and understanding of seismogenic zones.
基金funded by Ongoing Research Funding Program for Project number(ORF-2025-648),King Saud University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.
文摘BACKGROUND Data comparing the outcomes of hepatocellular carcinoma(HCC)ablation by multibipolar radiofrequency ablation(mbp-RFA)and microwave ablation(MWA)are lacking.This study compares safety and efficacy of the two techniques in treatment-naive HCC.AIM To compare the risk of local tumor progression(LTP)according to the technique;secondary endpoints included technique efficacy rate at one-month,overall survival and major complication rate.METHODS A bi-institutional retrospective analysis of patients undergoing treatment-naive HCC ablation by either technique was performed.Inverse probability of treatment weighting was used to compare the two groups.Mixed effects multivariate Cox regression was applied to identify risk factors for LTP.RESULTS A total of 362 patients(mean age,66.1±6.2 years,308 men)were included,of which 242(323 tumors)treated by mbp-RFA and 120(168 tumors)by MWA.After a median follow-up of 27 months,cumulative LTP was 11.4%after mbp-RFA and 25.2%after MWA.Independent risk factors for LTP at multivariate analysis were MWA(hazard ratio=2.85,P<0.001)and tumor size(hazard ratio=1.08,P<0.001).Two-year LTP-free survival was higher after mbp-RFA than MWA regardless of size(<3 cm:96%vs 87.1%,P<0.01;≥3 cm:87.5%vs 74%,P=0.04).Technique efficacy rate was higher after mbp-RFA(94.1%vs 87.5%,P=0.01).No difference was observed in major complication rate(9.5%vs 7.5%,P=0.59),nor 5-year overall survival(63.6%vs 58.3%,P=0.33).CONCLUSION Mbp-RFA leads to better local tumor control of treatment-naïve HCC than MWA regardless of tumor size and has better primary efficacy,while maintaining a comparable safety profile.
基金supports of the National Natural Science Foundation of China(NSFC)(52021004,52394202)key project of the Joint Fund for Innovation and Development of Chongqing Natural Science Foundation(CSTB2022NSCQ-LZX0013)+1 种基金the National Natural Science Foundation of China(NSFC)(52301232,and 52476056)the Natural Science Foundation of Chongqing Province(2024NSCQ-MSX1109).
文摘Controllable synthesis of ultrathin metallene nanosheets and rational design of their spatial arrangement in favor of electrochemical catalysis are critical for their renewable energy applications.Here,a biomimetic design of“Trunk-Branch-Leaf”strategy is proposed to prepare the ultrathin edge-riched Zn-ene“leaves”with a thickness of~2.5 nm,adjacent Zn-ene cross-linked with each other,which are supported by copper nanoneedle“branches”on copper mesh“trunks,”named as Zn-ene/Cu-CM.The resulting superstructure enables the formation of an interconnected network and multiple channels,which can be used as an electrocatalytic CO_(2) reduction reaction(CO_(2)RR)electrode to allow a fast charge and mass transfer as well as a large electrolyte reservoir.By virtue of the distinctive structure,the obtained Zn-ene/Cu-CM electrode exhibits excellent selectivity and activity toward CO production with a maximum Faradaic efficiency of 91.3%and incredible partial current density up to 40 mA cm^(−2),outperforming most of the state-of-the-art Zn-based electrodes for CO_(2) reduction.The phenolphthalein color probe combined with in situ attenuated total reflection-infrared spectroscopy uncovered the formation of the localized pseudo-alkaline microenvironment at the interface of the Zn-ene/Cu-CM electrode.Theoretical calculations confirmed that the localized pH as the origin is responsible for the adsorption of CO_(2) at the interface and the generation of *COOH and *CO intermediates.This study offers valuable insights into developing efficient electrodes through synergistic regulation of reaction microenvironments and active sites,thereby facilitating the electrolysis of practical CO_(2) conversion.
基金Supported by National Natural Science Foundation of China,No.82303672Zhejiang Provincial Health Commission and Zhejiang Provincial Administration of Traditional Chinese Medicine through the Targeted Project for Medical and Health Research,No.2025ZL017and China Primary Health Care Foundation,No.ZLMY20240311001ZJ.
文摘BACKGROUND The liver represents a common site of distant metastasis in patients with esophageal cancer(EC).Conventional chemotherapy(CMT)presents limited efficacy for EC,and EC patients with liver metastases typically experience a poor prognosis,highlighting an urgent need to explore novel treatment approaches.This study evaluated the overall efficacy and safety of CMT vs CMT combined with immune checkpoint inhibitors(ICIs)in the treatment of EC patients with liver metastases.Furthermore,prognostic factors influencing outcomes in this patient population were identified.AIM To evaluate the efficacy and safety of first-line chemoimmunotherapy for EC patients with liver metastases and to analyze prognostic factors.METHODS This retrospective study included 126 EC patients with liver metastases at Zhejiang Cancer Hospital between 2014 and 2024.Patients receiving CMT were compared with those receiving CMT+ICI.Analyzed variables included clinicopathological features,treatment history,characteristics of metastasis,systemic and local treatments,overall survival(OS),and treatment-related adverse events(TRAEs).Prognostic factors were evaluated using univariate and multivariate Cox proportional-hazards regression models.Finally,efficacy outcomes and TRAE profiles were compared between the two groups.RESULTS A significant difference in median OS was identified between the two groups(10.8 months in the CMT group vs 20.8 months in the CMT+ICI group,P=0.004).The CMT+ICI group also demonstrated a significantly longer median progression-free survival of 11.7 months(P<0.001).Patients receiving combination therapy exhibited significantly improved systemic objective response rate and disease control rate.Multivariate analysis identified key factors significantly influencing OS in EC patients with liver metastases:Karnofsky Performance Status score≥70,receipt of local therapy for liver metastases,and the number of cycles of CMT and immunotherapy received.Furthermore,the incidence of TRAEs did not significantly differ between the CMT+ICI and CMT groups.CONCLUSION For EC patients with liver metastases,the combination of CMT and ICIs demonstrates significantly superior efficacy compared with CMT alone,while maintaining manageable TRAEs.
基金supported by Shanghai Municipal Natural Science Foundation,China(No.21ZR1446800)the National Natural Science Foundation of China(No.41877425)the Fundamental Research Funds for the Central Universities(No.226-2024-00052)。
文摘Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community assembly in virgin and aging MP plastispheres across different habitats is poorly understood.This study aims to assess the variations in bacterial community assembly across different niches and habitats with an in situ ex-periment,in which constructed forest wetland(FW),natural lake wetland(LW),and lotus pond wetland(LP)were habitats,and plastispheres of virgin and aging low-density polyethylene(LDPE)MPs,as well as surround-ing wetland soils were niches.Significant niche-related differences in bacterial communities were observed,with lower diversity and enrichment of potential plastic-degrading bacteria in the plastisphere than in the soil bacterial communities.Furthermore,habitat-related differences exerted a more pronounced influence on the beta-diversity patterns of the bacterial communities.The linear regression analyses indicated that the local species pool con-tributed more to bacterial community assembly in the LW wetland,whereas the relative abundance of species was the primary factor in the LP wetland.The null model analysis indicated that plastisphere bacterial communi-ties were predominantly driven by the stochastic process,with a more deterministic assembly observed in the LP wetland and soil bacterial communities.Additionally,the primary ecological process shaping plastisphere com-munities shifted from drift in the virgin LDPE to homogenising dispersal in the aging LDPE.This study provides new insights into the fate and ecological impacts of MPs in wetlands,thereby facilitating the effective regulations of plastic pollution.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
文摘Cell function has a tight relationship with cell architecture.Distribution of proteins to the correct compartment is one of the functions of the traffic pathway through the Golgi apparatus.The others are to ensure proper protein folding,the addition of post-translational modifications,and delivering to intracellular and extracellular destinations.Astrocytes are fundamental homeostatic cells,controlling multiple aspects of the central nervous system physiology,such as ion balance,nutrients,blood flow,neurotransmitters,and responses to insults.Astrocytes are polarized cells,and,such as neurons,extensively use the secretory pathway for secreting factors and exposing functional receptors,channels,and transporters on the plasma membrane.In this review,we will underline the importance of studying the Golgi apparatus and the secretory pathway in astrocytes,based on the possible tight connection between the Golgi apparatus and astrocytes’homeostatic function.Given the topic of this review,we will provide examples mostly about the Golgi apparatus structure,function,localization,and its involvement in astrocytes’homeostatic response,with an insight into congenital glycosylation disorders,as an example of a potential future field in the study of astrocyte homeostatic failure and Golgi apparatus alteration.
基金supported by the National Natural Science Foundation of China (Grant No.11574244 for G.Y.G.)the XJTU Research Fund for AI Science (Grant No.2025YXYC011 for G.Y.G.)the Hong Kong Global STEM Professorship Scheme (for X.C.Z.)。
文摘Compared to the well-studied two-dimensional(2D)ferroelectricity,the appearance of 2D antiferroelectricity is much rarer,where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely oriented.Using NbOCl_(2) monolayer with competing ferroelectric(FE)and antiferroelectric(AFE)phases as a 2D material platform,we demonstrate the emergence of intrinsic antiferroelectricity in NbOCl_(2) monolayer under experimentally accessible shear strain,along with new functionality associated with electric field-induced AFE-to-FE phase transition.Specifically,the complex configuration space accommodating FE and AFE phases,polarization switching kinetics,and finite temperature thermodynamic properties of 2D NbOCl_(2) are all accurately predicted by large-scale molecular dynamics simulations based on deep learning interatomic potential model.Moreover,room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional collinear polarization arrangement is predicted in shear-deformed NbOCl_(2) monolayer.The transition from AFE to FE phase in 2D NbOCl_(2) can be triggered by a low critical electric field,leading to a double polarization–electric(P–E)loop with small hysteresis.A new type of optoelectronic device composed of AFE-NbOCl_(2) is proposed,enabling electric“writing”and nonlinear optical“reading”logical operation with fast operation speed and low power consumption.
文摘A directional random laser mediated by transverse Anderson localization in a disordered glass optical fiber is reported.Previous demonstrations of random lasers have found limited applications because of their multi-directionality and chaotic fluctuations in the laser emission.The random laser presented in this paper operates in the Anderson localization regime.The disorder induced localized states form isolated local channels that make the output laser beam highly directional and stabilize its spectrum.The strong transverse disorder and longitudinal invariance result in isolated lasing modes with negligible interaction with their surroundings,traveling back and forth in a Fabry–Perot cavity formed by the air–fiber interfaces.It is shown that if a localized input pump is scanned across the disordered fiber input facet,the output laser signal follows the transverse position of the pump.Moreover,a uniformly distributed pump across the input facet of the disordered fiber generates a laser signal with very low spatial coherence that can be of practical importance in many optical platforms including image transport with fiber bundles.
文摘We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber.The cell samples are illuminated by an incoherent LED light source.A deep convolutional neural network is applied to the image reconstruction process.The network training uses data generated by a setup with straight fiber at room temperature(∼20°C)but can be utilized directly for high-fidelity reconstruction of cell images that are transported through fiber with a few degrees bend or fiber with segments heated up to 50°C.In addition,cell images located several millimeters away from the bare fiber end can be transported and recovered successfully without the assistance of distal optics.We provide evidence that the trained neural network is able to transfer its learning to recover images of cells featuring very different morphologies and classes that are never“seen”during the training process.
基金The research is also supported by the Shanghai sailing project(Grant ID 20YF1451700)Shanghai Municipal Bureau of Ecology and Environment(Grant ID Huhuanke 2022-25).
文摘Freight has become one of the major contributors to air pollution.This research proposes a method to systematically estimate truck vehicle emissions at the road segment level through localizing MOVES,a widely-used vehicle emission estimation model.We first design a protocol of converting percentage values of rotating speed and torque of engine to second-by-second vehicle speed to accommodate the differences between driving cycles adopted in local emission standards and those used in MOVES.In order to identify the best model year for estimating emissions under different local emission standards,we propose an approach of comparing emission outcomes rather than emission factors,considering the differences in unit used between MOVES and emission standards.To calculate road seg mentlevel emission factors,we weight original factors by integrating vehicle fleet informa tion which contains the shares of vehicles under different emission standards and at different ages.We apply the approach to a major freight corridor area in Shanghai and cal culate emission factors by air pollutant,average speed of road sections,and road type.Dynamic emissions of each road section per hour are calculated to reflect the spatial dis tribution of truck emissions.The research outcomes may help local departments,especially in developing countries,better estimate freight vehicle emissions and make policies corre spondingly to control their impacts on public health.
基金supported by the National Natural Science Foundation of China(Grant No.52025056)the Fundamental Research Funds for the Central Universities.
文摘Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines.Given the advantage of obtaining accurate diagnosis results,multi-sensor fusion has long been studied in the fault diagnosis field.However,existing studies suffer from two weaknesses.First,the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types.Second,the localization for multi-source faults is seldom investigated,although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable.This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations(MSRs).First,an MSR model is developed to learn MSRs automatically and further obtain fault recognition results.Second,centrality measures are employed to analyze the MSR graphs learned by the MSR model,and fault sources are therefore determined.The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump.Results show the proposed method’s validity in diagnosing fault types and sources.
