The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional...The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional maturity varies,with China leading due to strong government support,followed by Europe and the United States.A significant challenge is the lack of industry standards,causing inconsistencies in charger types and payment systems.Efforts are underway,to ensure interoperability and reliability.Interoperability is crucial for the success of EV HPC infrastructure,ensuring seamless integration among charge points,management systems,and service providers.Despite the use of protocols like the OCPP(Open Charge Point Protocol),variations in implementation create complexities.Ensuring uniform standards across the ecosystem is essential for reliability and efficiency.Vendor-specific error codes,which are more detailed than standardized codes,are vital for diagnosing issues but lack standardization,adding complexity.Addressing these challenges is key to supporting widespread EV adoption and enhancing user experience.To provide a compelling driver value proposition,EV charging services must be reliable and seamless.The operations and maintenance of the HPC network must be cost-effective and leverage the intelligence of the integrated ecosystem.The technical complexity of managing high-power DC charging,combined with diverse authentication and payment systems,results in numerous potential issues.Moving from reactive to predictive maintenance is essential for undisrupted operations and a smooth driver experience.Shell’s Intelligent Operations Technology Strategy incorporates GenAI elements in its advanced analytics and operational performance management tools.By ingesting big data from multiple sources across the EV ecosystem,Shell engineers can perform detailed pattern recognition and targeted troubleshooting.Monitoring,configurable alerting,and remote fixing based on auto-healing and targeted auto-allocation enhance charger availability and reduce downtime.This automation has evolved Shell’s maintenance and operations strategy from reactive to predictive,improving overall charger performance and user satisfaction.Key achievements include transitioning to prescriptive and preventive asset management approaches,significantly improving uptime and charging experience,and increasing commercial value through cost reduction and enhanced revenue.Future challenges include evolving OCPP,integrating data from non-OCPP systems,and ensuring interoperability across diverse systems.Standardization and cross-collaboration within the industry are essential for smooth interoperability,higher uptime,and increased CSR(charging success rate).Technological innovations will further shape the industry,promoting stabilization and efficiency as it matures.展开更多
Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-...Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.展开更多
Our concern is to investigate controlled remote implementation of partially unknown operations with multiple layers.We first propose a scheme to realize the remote implementation of singlequbit operations belonging to...Our concern is to investigate controlled remote implementation of partially unknown operations with multiple layers.We first propose a scheme to realize the remote implementation of singlequbit operations belonging to the restricted sets.Then,the proposed scheme is extended to the case of single-qudit operations.As long as the controller and the higher-layer senders consent,the receiver can restore the desired state remotely operated by the sender.It is worth mentioning that the recovery operation is deduced by general formulas which clearly reveal the relationship with the measurement outcomes.For the sake of clarity,two specific examples with two levels are given respectively.In addition,we discuss the influence of amplitude-damping noise and utilize weak measurement and measurement reversal to effectively resist noise.展开更多
Digital twin shows broad application prospects in the aerospace field.This paper introduces a generalized satellite digital twin system in detail.With the innovative design concepts of modularization,generalization an...Digital twin shows broad application prospects in the aerospace field.This paper introduces a generalized satellite digital twin system in detail.With the innovative design concepts of modularization,generalization and modeling,on the one hand,the system has successfully achieved the reuse of software modules among different satellite models;on the other hand,it has achieved the reuse of software modules between the digital twin and the testing system,significantly improving the development efficiency of the digital twin system.The paper elaborates on the technical architecture and application fields of this digital twin system,and further prospects its future development.At the same time,through a real inorbit case,the engineering value of the digital twin system is strongly demonstrated.展开更多
In this article,we conduct a study on mixed quasi-martingale Hardy spaces that are defined by means of the mixed L_(p)-norm.By utilizing Doob’s inequalities,we explore the atomic decomposition and quasi-martingale in...In this article,we conduct a study on mixed quasi-martingale Hardy spaces that are defined by means of the mixed L_(p)-norm.By utilizing Doob’s inequalities,we explore the atomic decomposition and quasi-martingale inequalities of mixed quasi-martingale Hardy spaces.Moreover,we furnish sufficient conditions for the boundedness ofσ-sublinear operators in these spaces.These findings extend the existing conclusions regarding mixed quasi-martingale Hardy spaces defined with the help of the mixed L_(p)-norm.展开更多
Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challeng...Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.展开更多
BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initi...BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.展开更多
The paper presents modern perception of crisis management and its importance for security in the context of non-military threats. It shows the role of the police as a part of a governmental system, performing their ta...The paper presents modern perception of crisis management and its importance for security in the context of non-military threats. It shows the role of the police as a part of a governmental system, performing their tasks in all stages of crisis management. And it is about simulator crisis situation.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
文摘The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional maturity varies,with China leading due to strong government support,followed by Europe and the United States.A significant challenge is the lack of industry standards,causing inconsistencies in charger types and payment systems.Efforts are underway,to ensure interoperability and reliability.Interoperability is crucial for the success of EV HPC infrastructure,ensuring seamless integration among charge points,management systems,and service providers.Despite the use of protocols like the OCPP(Open Charge Point Protocol),variations in implementation create complexities.Ensuring uniform standards across the ecosystem is essential for reliability and efficiency.Vendor-specific error codes,which are more detailed than standardized codes,are vital for diagnosing issues but lack standardization,adding complexity.Addressing these challenges is key to supporting widespread EV adoption and enhancing user experience.To provide a compelling driver value proposition,EV charging services must be reliable and seamless.The operations and maintenance of the HPC network must be cost-effective and leverage the intelligence of the integrated ecosystem.The technical complexity of managing high-power DC charging,combined with diverse authentication and payment systems,results in numerous potential issues.Moving from reactive to predictive maintenance is essential for undisrupted operations and a smooth driver experience.Shell’s Intelligent Operations Technology Strategy incorporates GenAI elements in its advanced analytics and operational performance management tools.By ingesting big data from multiple sources across the EV ecosystem,Shell engineers can perform detailed pattern recognition and targeted troubleshooting.Monitoring,configurable alerting,and remote fixing based on auto-healing and targeted auto-allocation enhance charger availability and reduce downtime.This automation has evolved Shell’s maintenance and operations strategy from reactive to predictive,improving overall charger performance and user satisfaction.Key achievements include transitioning to prescriptive and preventive asset management approaches,significantly improving uptime and charging experience,and increasing commercial value through cost reduction and enhanced revenue.Future challenges include evolving OCPP,integrating data from non-OCPP systems,and ensuring interoperability across diverse systems.Standardization and cross-collaboration within the industry are essential for smooth interoperability,higher uptime,and increased CSR(charging success rate).Technological innovations will further shape the industry,promoting stabilization and efficiency as it matures.
基金co-supported by the National Key Research and Development Program of China(No. 2021YFB3301504)the National Natural Science Foundation of China (Nos. 62072415, 62036010, 42301526, 62372416 and 62472389)the National Natural Science Foundation of Henan Province, China (No. 242300421215)
文摘Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant Nos.62172341,12071132)the Natural Science Foundation of Henan Province of China(Grant No.242300420276)the Joint Fund of Henan Province Science and Technology R&D Program(Grant No.225200810032)。
文摘Our concern is to investigate controlled remote implementation of partially unknown operations with multiple layers.We first propose a scheme to realize the remote implementation of singlequbit operations belonging to the restricted sets.Then,the proposed scheme is extended to the case of single-qudit operations.As long as the controller and the higher-layer senders consent,the receiver can restore the desired state remotely operated by the sender.It is worth mentioning that the recovery operation is deduced by general formulas which clearly reveal the relationship with the measurement outcomes.For the sake of clarity,two specific examples with two levels are given respectively.In addition,we discuss the influence of amplitude-damping noise and utilize weak measurement and measurement reversal to effectively resist noise.
文摘Digital twin shows broad application prospects in the aerospace field.This paper introduces a generalized satellite digital twin system in detail.With the innovative design concepts of modularization,generalization and modeling,on the one hand,the system has successfully achieved the reuse of software modules among different satellite models;on the other hand,it has achieved the reuse of software modules between the digital twin and the testing system,significantly improving the development efficiency of the digital twin system.The paper elaborates on the technical architecture and application fields of this digital twin system,and further prospects its future development.At the same time,through a real inorbit case,the engineering value of the digital twin system is strongly demonstrated.
基金Supported by the National Natural Science Foundation of China(11871195)。
文摘In this article,we conduct a study on mixed quasi-martingale Hardy spaces that are defined by means of the mixed L_(p)-norm.By utilizing Doob’s inequalities,we explore the atomic decomposition and quasi-martingale inequalities of mixed quasi-martingale Hardy spaces.Moreover,we furnish sufficient conditions for the boundedness ofσ-sublinear operators in these spaces.These findings extend the existing conclusions regarding mixed quasi-martingale Hardy spaces defined with the help of the mixed L_(p)-norm.
文摘Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.
基金the Natural Science Foundation of Yongchuan District,No.2023yc-jckx20021.
文摘BACKGROUND Breast cancer is one of the most prevalent malignancies affecting women worldwide,with approximately 2.3 million new cases diagnosed annually.Breast cancer stem cells(BCSCs)play pivotal roles in tumor initiation,progression,metastasis,therapeutic resistance,and disease recurrence.Cancer stem cells possess selfrenewal capacity,multipotent differentiation potential,and enhanced tumorigenic activity,but their molecular characteristics and regulatory mechanisms require further investigation.AIM To comprehensively characterize the molecular features of BCSCs through multiomics approaches,construct a prognostic prediction model based on stem cellrelated genes,reveal cell-cell communication networks within the tumor microenvironment,and provide theoretical foundation for personalized treatment strategies.METHODS Flow cytometry was employed to detect the expression of BCSC surface markers(CD34,CD45,CD29,CD90,CD105).Transcriptomic analysis was performed to identify differentially expressed genes.Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model.Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns.Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.RESULTS Flow cytometric analysis revealed the highest expression of CD105(96.30%),followed by CD90(68.43%)and CD34(62.64%),while CD29 showed lower expression(7.16%)and CD45 exhibited the lowest expression(1.19%).Transcriptomic analysis identified 3837 significantly differentially expressed genes(1478 upregulated and 2359 downregulated).Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes,and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups(P<0.001).Single-cell analysis revealed tumor cellular heterogeneity,and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes.MED18 gene showed significantly higher expression in malignant tissues(P<0.001)and occupied a central position in cell-cell communication networks,exhibiting significant correlations with tumor cells,macrophages,fibroblasts,and endothelial cells.CONCLUSION This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches,identified reliable surface markers and key regulatory genes,and constructed a prognostic prediction model with clinical application value.
文摘The paper presents modern perception of crisis management and its importance for security in the context of non-military threats. It shows the role of the police as a part of a governmental system, performing their tasks in all stages of crisis management. And it is about simulator crisis situation.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
