Objective:To evaluate the implementation effectiveness of standardized protective care procedures during hydrofluoric acid procedures in the dental department.Methods:This study was conducted from June 2023 to June 20...Objective:To evaluate the implementation effectiveness of standardized protective care procedures during hydrofluoric acid procedures in the dental department.Methods:This study was conducted from June 2023 to June 2025,selecting 90 patients who underwent hydrofluoric acid-related treatments in the outpatient dental department of this hospital during this period as subjects.Forty-five patients treated between June 2023 and June 2024 received conventional protective care(pre-intervention group),while 45 patients treated between June 2024 and June 2025 underwent the standardized protective care protocol(post-intervention group).Thirteen healthcare personnel participated in both pre-and post-intervention treatment phases.Based on the different nursing models,indicators such as the incidence of adverse events in patients and the exposure rate of healthcare personnel before and after implementation were compared to evaluate the effectiveness of the nursing intervention.Results:The incidence of oral mucosal irritation reactions in patients was lower after implementation,p<0.05.Compared with the pre-implementation period,the incidence of procedure-related adverse events decreased after implementation,p<0.05.There was a significant difference in the occupational exposure rate of healthcare personnel before and after implementation,with a higher rate observed before implementation(p<0.05).Post-implementation,healthcare personnel achieved higher compliance scores for pre-procedure preparation,intra-procedure protection,and post-procedure handling(p<0.05).Patient satisfaction with treatment was lower pre-implementation than post-implementation(p<0.05).Conclusion:Adherence to standardized protective care procedures during hydrofluoric acid operations by dental department staff in outpatient settings standardizes practitioner techniques,effectively prevents oral mucosal irritation in patients,reduces occupational exposure risks for staff,minimizes adverse procedural events,and consequently enhances patient treatment satisfaction.This demonstrates significant practical value.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.展开更多
Objective:Arrhythmia-induced cardiomyopathy(AIC)is a reversible dilated cardiomyopathy induced by rapid or irregular heartbeat.Acupuncture has a long history of use in the treatment of cardiac diseases,and Xinshu(BL15...Objective:Arrhythmia-induced cardiomyopathy(AIC)is a reversible dilated cardiomyopathy induced by rapid or irregular heartbeat.Acupuncture has a long history of use in the treatment of cardiac diseases,and Xinshu(BL15)is a key acupoint.However,the underlying mechanism of acupuncture at BL15 in the treatment of AIC has not yet been elucidated.Methods:AIC was induced in adult male Sprague-Dawley(SD)rats by continuous administration of acetylcholine(ACh)-CaCl2 and treatment with electroacupuncture(EA)at bilateral BL15.Echocardiography was used to evaluate cardiac function;the rotarod test for motor coordination and performance;hematoxylin and eosin(HE)staining for the morphology of ventricles;electrocardiogram for susceptibility,inducibility,and duration of atrial fibrillation(AF);and electrical and optical mapping in isolated rat hearts maintained by the Langendorff perfusion system for electrical conduction and intracellular handling,respectively.Reverse transcription quantitative polymerase chain reaction(RT-qPCR)and Western blotting were used to determine the levels of cardiac conduction and intracellular calcium-handling proteins in the ventricle.Results:The results showed that EA improved the ejection factor and morphological indices on echocardiography,restored motor coordination and performance,and alleviated ventricular dilation and AF onset.EA alleviates atrial conduction disorders,shortens APD80,and decreases calcium handling in rats with AIC.Cx43 was downregulated and CaMKII was upregulated,and both effects were reversed by EA treatment.Conclusion:Our study provides a novel AIC model with abnormal electrical propagation and calcium handling that can be protected by EA at BL15.This potential mechanism may be associated with the modulation of Cx43 and CaMKII expression.展开更多
On a sunny afternoon,Nelly Wahome mounted a smartphone on a tripod to film one of Mombasa’s tourist hotspots-Nyali Resort,a popular northern beach resort in Kenya.Wahome,25,shared the latest video clip via her TikTok...On a sunny afternoon,Nelly Wahome mounted a smartphone on a tripod to film one of Mombasa’s tourist hotspots-Nyali Resort,a popular northern beach resort in Kenya.Wahome,25,shared the latest video clip via her TikTok handle in a quest to enchant her followers with the day’s tourism scene along Nyali’s coastline.Within a few hours of uploading,her content attracted hundreds of views.展开更多
Visa-Free Trips Double Border inspection agencies across China handled 64.88 million cross-border trips by foreigners in 2024,up 82.9 percent from a year earlier.Among them,more than 20 million inbound trips by foreig...Visa-Free Trips Double Border inspection agencies across China handled 64.88 million cross-border trips by foreigners in 2024,up 82.9 percent from a year earlier.Among them,more than 20 million inbound trips by foreigners were made visa-free,a year-on-year increase of 112.3 percent,according to statistics released by the National Immigration Administration on 14 January.展开更多
The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and materia...The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard.This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-Ⅱ(NSGA-Ⅱ)to find the optimal configuration of workstations in a shipyard layout.The proposed method focuses on simultaneously minimizing two material handling costs,namely work-based material handling and duration-based material handling.NSGA-Ⅱ determines the order of workstations in the shipyard layout.The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-Ⅱ into a complete design.Considering that this study is a case of multiobjective optimization,the performance for both objectives at each iteration is presented in a 3D graph.Results indicate that after 500 iterations,the optimal configuration yields a work-based MHC of 163670.0 WBM-units and a duration-based MHC of 34750 DBM-units.Starting from a random solution,the efficiency of NSGA-Ⅱ demonstrates significant improvements,achieving a 50.19%reduction in work-based MHC and a 48.58%reduction in duration-based MHC.展开更多
Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)a...Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.展开更多
Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper...Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper maintenance of survey meters are important in order to ascertain their accuracy and reliability. This study provides a comprehensive retrospective assessment of the calibration behaviour, durability, and fault trends of 160 survey meters, spanning ten different models. They were calibrated at the Secondary Standard Dosimetry Laboratory (SSDL) in Nigeria over a decade (2012-2023) using an X-Ray Beam Irradiator Model X80-225K and Cs-137 irradiator (OB6) with a PTW reference spherical chamber traceable to the IAEA SSDL in Seibersdorf, Austria. The calibration stability of each model was evaluated, revealing that models like Instrument A and Instrument B demonstrated high reliability with calibration factors close to the ideal value of 1, while models like Instrument C exhibited higher variability, suggesting less consistent performance for dose rate monitoring. Fault analysis showed that the most common issues were related to the battery compartment, indicating a need for improved handling practices. Correlation analysis reveals no statistically significant correlation between calibration factor and age of survey meter across the analysed models. The study concludes that regular calibration, proper handling, and user training are crucial for maintaining the accuracy and longevity of radiation detectors.展开更多
Multi-Object Tracking(MOT)represents a fundamental but computationally demanding task in computer vision,with particular challenges arising in occluded and densely populated environments.While contemporary tracking sy...Multi-Object Tracking(MOT)represents a fundamental but computationally demanding task in computer vision,with particular challenges arising in occluded and densely populated environments.While contemporary tracking systems have demonstrated considerable progress,persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment.This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features.Proposed framework employs:(1)a Height Modulated and Scale Adaptive Spatial Intersection-over-Union(HMSIoU)metric for improved spatial correspondence estimation across variable object scales and partial occlusions;(2)a feature extraction module generating discriminative appearance descriptors for identity maintenance;and(3)a recovery association mechanism for refining matches between unassociated tracks and detections.Comprehensive evaluation on standard MOT17 and MOT20 benchmarks demonstrates significant improvements in tracking consistency,with state-of-the-art performance across key metrics including HOTA(64),MOTA(80.7),IDF1(79.8),and IDs(1379).These results substantiate the efficacy of our Cue-Tracker framework in complex real-world scenarios characterized by occlusions and crowd interactions.展开更多
The cooperative control of ride comfort and handling stability in automobile suspension systems presents a significant challenge in intelligent chassis system design.This complexity arises from the high degrees of fre...The cooperative control of ride comfort and handling stability in automobile suspension systems presents a significant challenge in intelligent chassis system design.This complexity arises from the high degrees of freedom,diverse operating conditions,and inherent trade-offs between performance metrics in full-car suspension systems.In this paper,a novel switching control strategy is proposed to better balance ride comfort and handling stability for a full-car suspension system.The system integrates a ride comfort controller and an anti-rollover controller,guided by a new rollover risk assessment indicator that requires fewer state variables.First,a vehicle suspension simplification model approach is introduced,reducing the fourteen-degree-of-freedom full-car suspension model to three two-degree-of-freedom models:vertical,pitch and roll.Based on these simplified models,vertical,roll,and pitch controllers are designed,simplifying the controller design process for full-car suspension systems.The ride comfort controller is constructed using the modal energy method in conjunction with the simplified model controllers,while the roll controller functions as the anti-rollover controller.The proposed rollover risk assessment indicator serves as the switching criterion between handling stability and ride comfort control.Experimental results demonstrate that the proposed switching control strategy effectively adapts to various road conditions,enabling the semi-active variable damping suspension system to perform multi-modal switching.Compared to a well-tuned passive suspension,vertical,roll,and pitch accelerations are reduced by 14.13%,13.02%and 13.08%,respectively,significantly improving ride comfort.Additionally,the system effectively mitigates rollover risk,achieving reductions in roll angle,roll speed,and roll acceleration by 19.69%,16.40%,and 29.96%,respectively,thereby greatly enhancing vehicle safety.Overall,the proposed switching control strategy achieves a successful balance between ride comfort and handling stability,enhancing overall driving performance.展开更多
This paper introduces an intelligent garbage-handling trolley model based on an STM32 single chip microcomputer as the control core.The device is driven by four independent motors to achieve automatic tracking,automat...This paper introduces an intelligent garbage-handling trolley model based on an STM32 single chip microcomputer as the control core.The device is driven by four independent motors to achieve automatic tracking,automatic obstacle avoidance,and fixed-point docking.Using external execution structure to realize the car without the use of a mechanical arm,complete garbage collection,storage,and uninstall function.On this basis,the type of garbage is marked by color,and the color recognition sensor is applied to realize the type recognition after garbage collection and put into the corresponding unloading point,to realize its intelligent classification function.It can automatically complete the established task autonomously.展开更多
Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production.Conventional methods relying on empirical correlations and assumptions often result in high costs,time consumption,ina...Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production.Conventional methods relying on empirical correlations and assumptions often result in high costs,time consumption,inaccuracies,and uncertainties.This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield,Northwestern Uganda.The group method of data handling with differential evolution(GMDH-DE)algorithm was used to predict permeability due to its capability to manage complex,nonlinear relationships between variables,reduced computation time,and parameter optimization through evolutionary algorithms.Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing,the GMDH-DE outperformed the group method of data handling(GMDH)and random forest(RF)in predicting permeability with higher accuracy and lower computation time.The GMDH-DE achieved an R^(2)of 0.9985,RMSE of 3.157,MAE of 2.366,and ME of 0.001 during training,and for testing,the ME,MAE,RMSE,and R^(2)were 1.3508,12.503,21.3898,and 0.9534,respectively.Additionally,the GMDH-DE demonstrated a 41%reduction in processing time compared to GMDH and RF.The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin,Tanzania,which lacks core data.Shapley additive explanations(SHAP)analysis identified thermal neutron porosity(TNPH),effective porosity(PHIE),and spectral gamma-ray(SGR)as the most critical parameters in permeability prediction.Therefore,the GMDH-DE model offers a novel,efficient,and accurate approach for fast permeability prediction,enhancing hydrocarbon exploration and production.展开更多
Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation r...Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.展开更多
基金Construction of Standardized Protective Nursing Plan for Hydrofluoric Acid Operation in Stomatology and Re search on Injury Prevention Effect(Project No.:FZ2025101)。
文摘Objective:To evaluate the implementation effectiveness of standardized protective care procedures during hydrofluoric acid procedures in the dental department.Methods:This study was conducted from June 2023 to June 2025,selecting 90 patients who underwent hydrofluoric acid-related treatments in the outpatient dental department of this hospital during this period as subjects.Forty-five patients treated between June 2023 and June 2024 received conventional protective care(pre-intervention group),while 45 patients treated between June 2024 and June 2025 underwent the standardized protective care protocol(post-intervention group).Thirteen healthcare personnel participated in both pre-and post-intervention treatment phases.Based on the different nursing models,indicators such as the incidence of adverse events in patients and the exposure rate of healthcare personnel before and after implementation were compared to evaluate the effectiveness of the nursing intervention.Results:The incidence of oral mucosal irritation reactions in patients was lower after implementation,p<0.05.Compared with the pre-implementation period,the incidence of procedure-related adverse events decreased after implementation,p<0.05.There was a significant difference in the occupational exposure rate of healthcare personnel before and after implementation,with a higher rate observed before implementation(p<0.05).Post-implementation,healthcare personnel achieved higher compliance scores for pre-procedure preparation,intra-procedure protection,and post-procedure handling(p<0.05).Patient satisfaction with treatment was lower pre-implementation than post-implementation(p<0.05).Conclusion:Adherence to standardized protective care procedures during hydrofluoric acid operations by dental department staff in outpatient settings standardizes practitioner techniques,effectively prevents oral mucosal irritation in patients,reduces occupational exposure risks for staff,minimizes adverse procedural events,and consequently enhances patient treatment satisfaction.This demonstrates significant practical value.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
基金supported by the National Key R&D Program of China(2022YFC3500405,2019YFC1712105)The National Science Foundation of China(82374075)+1 种基金The National Comprehensive Traditional Chinese Medicine Reform Demonstration Zone Science and Technology Collaborative Development Project(GZY-KJS-SD-2024-046)Taishan Scholar Youth Project of Shandong Province(tsqn202306188).
文摘Objective:Arrhythmia-induced cardiomyopathy(AIC)is a reversible dilated cardiomyopathy induced by rapid or irregular heartbeat.Acupuncture has a long history of use in the treatment of cardiac diseases,and Xinshu(BL15)is a key acupoint.However,the underlying mechanism of acupuncture at BL15 in the treatment of AIC has not yet been elucidated.Methods:AIC was induced in adult male Sprague-Dawley(SD)rats by continuous administration of acetylcholine(ACh)-CaCl2 and treatment with electroacupuncture(EA)at bilateral BL15.Echocardiography was used to evaluate cardiac function;the rotarod test for motor coordination and performance;hematoxylin and eosin(HE)staining for the morphology of ventricles;electrocardiogram for susceptibility,inducibility,and duration of atrial fibrillation(AF);and electrical and optical mapping in isolated rat hearts maintained by the Langendorff perfusion system for electrical conduction and intracellular handling,respectively.Reverse transcription quantitative polymerase chain reaction(RT-qPCR)and Western blotting were used to determine the levels of cardiac conduction and intracellular calcium-handling proteins in the ventricle.Results:The results showed that EA improved the ejection factor and morphological indices on echocardiography,restored motor coordination and performance,and alleviated ventricular dilation and AF onset.EA alleviates atrial conduction disorders,shortens APD80,and decreases calcium handling in rats with AIC.Cx43 was downregulated and CaMKII was upregulated,and both effects were reversed by EA treatment.Conclusion:Our study provides a novel AIC model with abnormal electrical propagation and calcium handling that can be protected by EA at BL15.This potential mechanism may be associated with the modulation of Cx43 and CaMKII expression.
文摘On a sunny afternoon,Nelly Wahome mounted a smartphone on a tripod to film one of Mombasa’s tourist hotspots-Nyali Resort,a popular northern beach resort in Kenya.Wahome,25,shared the latest video clip via her TikTok handle in a quest to enchant her followers with the day’s tourism scene along Nyali’s coastline.Within a few hours of uploading,her content attracted hundreds of views.
文摘Visa-Free Trips Double Border inspection agencies across China handled 64.88 million cross-border trips by foreigners in 2024,up 82.9 percent from a year earlier.Among them,more than 20 million inbound trips by foreigners were made visa-free,a year-on-year increase of 112.3 percent,according to statistics released by the National Immigration Administration on 14 January.
基金Supported by Direktorat Riset dan Pengembangan(Directorate of Research and Development)Universitas Indonesia(NKB-690/UN2.RST/HKP.05.00/2022).
文摘The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard.This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-Ⅱ(NSGA-Ⅱ)to find the optimal configuration of workstations in a shipyard layout.The proposed method focuses on simultaneously minimizing two material handling costs,namely work-based material handling and duration-based material handling.NSGA-Ⅱ determines the order of workstations in the shipyard layout.The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-Ⅱ into a complete design.Considering that this study is a case of multiobjective optimization,the performance for both objectives at each iteration is presented in a 3D graph.Results indicate that after 500 iterations,the optimal configuration yields a work-based MHC of 163670.0 WBM-units and a duration-based MHC of 34750 DBM-units.Starting from a random solution,the efficiency of NSGA-Ⅱ demonstrates significant improvements,achieving a 50.19%reduction in work-based MHC and a 48.58%reduction in duration-based MHC.
文摘Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.
文摘Radiation detectors, such as survey meters, are essential for ensuring radiation safety in various sectors, including healthcare, industrial processing, emergency response, etc. However, regular calibration and proper maintenance of survey meters are important in order to ascertain their accuracy and reliability. This study provides a comprehensive retrospective assessment of the calibration behaviour, durability, and fault trends of 160 survey meters, spanning ten different models. They were calibrated at the Secondary Standard Dosimetry Laboratory (SSDL) in Nigeria over a decade (2012-2023) using an X-Ray Beam Irradiator Model X80-225K and Cs-137 irradiator (OB6) with a PTW reference spherical chamber traceable to the IAEA SSDL in Seibersdorf, Austria. The calibration stability of each model was evaluated, revealing that models like Instrument A and Instrument B demonstrated high reliability with calibration factors close to the ideal value of 1, while models like Instrument C exhibited higher variability, suggesting less consistent performance for dose rate monitoring. Fault analysis showed that the most common issues were related to the battery compartment, indicating a need for improved handling practices. Correlation analysis reveals no statistically significant correlation between calibration factor and age of survey meter across the analysed models. The study concludes that regular calibration, proper handling, and user training are crucial for maintaining the accuracy and longevity of radiation detectors.
文摘Multi-Object Tracking(MOT)represents a fundamental but computationally demanding task in computer vision,with particular challenges arising in occluded and densely populated environments.While contemporary tracking systems have demonstrated considerable progress,persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment.This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features.Proposed framework employs:(1)a Height Modulated and Scale Adaptive Spatial Intersection-over-Union(HMSIoU)metric for improved spatial correspondence estimation across variable object scales and partial occlusions;(2)a feature extraction module generating discriminative appearance descriptors for identity maintenance;and(3)a recovery association mechanism for refining matches between unassociated tracks and detections.Comprehensive evaluation on standard MOT17 and MOT20 benchmarks demonstrates significant improvements in tracking consistency,with state-of-the-art performance across key metrics including HOTA(64),MOTA(80.7),IDF1(79.8),and IDs(1379).These results substantiate the efficacy of our Cue-Tracker framework in complex real-world scenarios characterized by occlusions and crowd interactions.
基金Supported by the Australian Research Council’s Discovery Project(Grant No.DP200100149)Taishan Scholars Program of Shandong Province(Grant No.tsqn202211062)Chinses Scholarship Council(Grant No.202006690005).
文摘The cooperative control of ride comfort and handling stability in automobile suspension systems presents a significant challenge in intelligent chassis system design.This complexity arises from the high degrees of freedom,diverse operating conditions,and inherent trade-offs between performance metrics in full-car suspension systems.In this paper,a novel switching control strategy is proposed to better balance ride comfort and handling stability for a full-car suspension system.The system integrates a ride comfort controller and an anti-rollover controller,guided by a new rollover risk assessment indicator that requires fewer state variables.First,a vehicle suspension simplification model approach is introduced,reducing the fourteen-degree-of-freedom full-car suspension model to three two-degree-of-freedom models:vertical,pitch and roll.Based on these simplified models,vertical,roll,and pitch controllers are designed,simplifying the controller design process for full-car suspension systems.The ride comfort controller is constructed using the modal energy method in conjunction with the simplified model controllers,while the roll controller functions as the anti-rollover controller.The proposed rollover risk assessment indicator serves as the switching criterion between handling stability and ride comfort control.Experimental results demonstrate that the proposed switching control strategy effectively adapts to various road conditions,enabling the semi-active variable damping suspension system to perform multi-modal switching.Compared to a well-tuned passive suspension,vertical,roll,and pitch accelerations are reduced by 14.13%,13.02%and 13.08%,respectively,significantly improving ride comfort.Additionally,the system effectively mitigates rollover risk,achieving reductions in roll angle,roll speed,and roll acceleration by 19.69%,16.40%,and 29.96%,respectively,thereby greatly enhancing vehicle safety.Overall,the proposed switching control strategy achieves a successful balance between ride comfort and handling stability,enhancing overall driving performance.
文摘This paper introduces an intelligent garbage-handling trolley model based on an STM32 single chip microcomputer as the control core.The device is driven by four independent motors to achieve automatic tracking,automatic obstacle avoidance,and fixed-point docking.Using external execution structure to realize the car without the use of a mechanical arm,complete garbage collection,storage,and uninstall function.On this basis,the type of garbage is marked by color,and the color recognition sensor is applied to realize the type recognition after garbage collection and put into the corresponding unloading point,to realize its intelligent classification function.It can automatically complete the established task autonomously.
基金supported by the Major National Science and Technology Programs in the“Thirteenth Five-Year”Plan period(Grant No.2017ZX05032-002-004)the Innovation Team Funding of Natural Science Foundation of Hubei Province,China(Grant No.2021CFA031)the Chinese Scholarship Council(CSC)and Silk Road Institute for their support in terms of stipend.
文摘Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production.Conventional methods relying on empirical correlations and assumptions often result in high costs,time consumption,inaccuracies,and uncertainties.This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield,Northwestern Uganda.The group method of data handling with differential evolution(GMDH-DE)algorithm was used to predict permeability due to its capability to manage complex,nonlinear relationships between variables,reduced computation time,and parameter optimization through evolutionary algorithms.Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing,the GMDH-DE outperformed the group method of data handling(GMDH)and random forest(RF)in predicting permeability with higher accuracy and lower computation time.The GMDH-DE achieved an R^(2)of 0.9985,RMSE of 3.157,MAE of 2.366,and ME of 0.001 during training,and for testing,the ME,MAE,RMSE,and R^(2)were 1.3508,12.503,21.3898,and 0.9534,respectively.Additionally,the GMDH-DE demonstrated a 41%reduction in processing time compared to GMDH and RF.The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin,Tanzania,which lacks core data.Shapley additive explanations(SHAP)analysis identified thermal neutron porosity(TNPH),effective porosity(PHIE),and spectral gamma-ray(SGR)as the most critical parameters in permeability prediction.Therefore,the GMDH-DE model offers a novel,efficient,and accurate approach for fast permeability prediction,enhancing hydrocarbon exploration and production.
文摘Aiming at various faults in an air conditioning system,the fault characteristics are analyzed.The influence of the faults on the energy consumption and thermal comfort of the system are also discussed.The simulation results show that the measurement faults of the supply air temperature can lead to the increase in energy consumption.According to the fault characteristics,a data-driven method based on a neural network is presented to detect and diagnose the faults of air handling units.First,the historical data are selected to train the neural network so that it can recognize and predict the operation of the system.Then,the faults can be diagnosed by calculating the relative errors denoting the difference between the measuring values and the prediction outputs.Finally,the fault diagnosis strategy using the neural network is validated by using a simulator based on the TRNSYS platform.The results show that the neural network can diagnose different faults of the temperature,the flow rate and the pressure sensors in the air conditioning system.
