Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow...Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow (MP) corn (Zea mays L.) and soybean (Glycine max L.) production systems. The first trial was conducted on a Maryhill silt loam (Typic Hapludalf) at Elora, Ontario, Canada, the second on a Brookston clay loam (Typic Argiaquoll) at Woodslee, Ontario, Canada, and the third on a Thorp silt loam (Argiaquic Argialboll) at Urbana, Illinois, USA. No-tillage led to significantly higher SOC concentrations in the top 5 cm compared to MP at all 3 sites. However, NT resulted in significantly lower SOC in sub-surface soils as compared to MP at Woodslee (10-20 cm, P = 0.01) and Urbana (20-30 cm, P < 0.10). No-tillage had significantly more SOC storage than MP at the Elora site (3.3 Mg C ha-1) and at the Woodslee site (6.2 Mg C ha-1) on an equivalent mass basis (1350 Mg ha-1 soil equivalent mass). Similarly, NT had greater SOC storage than MP at the Urbana site (2.7 Mg C ha-1) on an equivalent mass basis of 675 Mg ha-1 soil. However, these differences disappeared when the entire plow layer was evaluated for both the Woodslee and Urbana sites as a result of the higher SOC concentrations in MP than in NT at depth. Using the minimum detectable difference technique, we observed that up to 1500 soil sample per tillage treatment comparison will have to be collected and analyzed for the Elora and Woodslee sites and over 40 soil samples per tillage treatment comparison for the Urbana to statistically separate significant differences in the SOC contents of sub-plow depth soils. Therefore, it is impracticable, and at the least prohibitively expensive, to detect tillage-induced differences in soil C beyond the plow layer in various soils.展开更多
To utilizing the characteristic of radar cross section （RCS） of the low detectable aircraft, a special path planning algorithm to eluding radars by the variable RCS is presented. The algorithm first gives the RCS ch...To utilizing the characteristic of radar cross section （RCS） of the low detectable aircraft, a special path planning algorithm to eluding radars by the variable RCS is presented. The algorithm first gives the RCS changing model of low detectable aircraft, then establishes a threat model of a ground-based air defense system according to the relations between RCS and the radar range coverage. By the new cost functions of the flight path, which consider both factors of the survival probability and the distance of total route, this path planning method is simulated based on the Dijkstra algorithm, and the planned route meets the flight capacity constraints. Simulation results show that using the effective path planning algorithm, the low detectable aircraft can give full play to its own advantage of stealth to achieve the purpose of silent penetration.展开更多
In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation cou...In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation counter,volume,and energy res-olution of scintillation crystals,and flight altitude of an aircraft was investigated.To verify this theory,experi-mental devices based on NaI and CeBr 3 scintillation counters were prepared,and the potassium,uranium,and thorium contents in calibration pads obtained via the stripping ratio method and theory were compared.The MDACs of AGS under different conditions were calculated and analyzed using the proposed theory and the Monte Carlo method.The relative errors found via a comparison of the experimental and theoretical results were less than 4%.The theory of MDAC can guide the work of AGS in probing areas with low radioactivity.展开更多
We present a new quantum protocol for solving detectable Byzantine agreement problem between threeparties by employing one quantum key distribution protocol.The protocol is suggested by a special four-qubit entangleds...We present a new quantum protocol for solving detectable Byzantine agreement problem between threeparties by employing one quantum key distribution protocol.The protocol is suggested by a special four-qubit entangledstate instead of singlet states,which shows that singlet states are not necessary to achieve detectable Byzantine agreement.展开更多
Background:Myxomas are the most common primary cardiac tumors.Angiographically detectable neovascularity(ADN)of myxoma is increasingly being reported as a result of the use of coronary angiography(CAG)to detect corona...Background:Myxomas are the most common primary cardiac tumors.Angiographically detectable neovascularity(ADN)of myxoma is increasingly being reported as a result of the use of coronary angiography(CAG)to detect coronary artery disease.However,the clinical signifi cance of these fi ndings is not fully understood.Methods:We enrolled 59 patients with cardiac myxoma who also underwent CAG between January 2013 and October 2018.Patients were followed up for a mean of 28.9 months(range 1-69 months).The clinical features,echocardiography measurements,pathological examination fi ndings,CAG results,and outcomes during follow-up were compared between patients with ADN and patients without ADN.Results:ADN was found in 25 patients(42.4%).The arteries feeding the ADN included the right coronary artery(n=15),the left circumfl ex coronary artery(n=7),and both arteries(n=3).The patients with ADN had a higher proportion of eosinophils(3.2%vs.2.2%,P=0.03)and higher low-density lipoprotein cholesterol level(2.7 mmol/L vs.2.2 mmol/L,P=0.02).Myxoma pedicles were more likely to be located in the interatrial septum in patients with ADN(96%vs.73.5%,P=0.02).No signifi cant correlation was observed between the groups in clinical manifestations,atrial arrhythmia,myxoma size,cardiac chamber size,left ventricular ejection fraction,and the prevalence of complication with coronary artery disease[16%in the ADN group(n=4)vs.20.6%in the non-ADN group(n=7),P=0.66].However,patients with ADN tended to have a lower incidence of major adverse cardiac and cerebrovascular events on long-term follow-up(0%vs.14.7%,P=0.07).Conclusion:CAG-detected ADN in patients with cardiac myxoma is associated with a borderline lower rate of major adverse cardiac and cerebrovascular events.展开更多
In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence ...In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
The determination of the effective minimum detectable activity （MDA） of radionuclides by a detection system plays an im- portant role in environmental radiation monitoring. In this study, the responses of an NaI（TI...The determination of the effective minimum detectable activity （MDA） of radionuclides by a detection system plays an im- portant role in environmental radiation monitoring. In this study, the responses of an NaI（TI） airborne γ ray spectrometry （AGRS） system to different radionuclides （137Cs and 131I） were investigated using the Monte Carlo technique. The MDA values were determined under different conditions according to the counting spectra obtained from the Monte Carlo simulation. The equivalent mass thickness method was applied to the Monte Carlo modeling for monitoring ground radiation to reduce sta- tistical uncertainty. The secondary source method was used to monitor both air and ground radiation. A quadratic relationship was found between the MDA and activity concentration. An exponential relationship was found between the MDA and altitude The MDA of a specific radionuclide from external detectors was found to be superior to that obtained from internal detectors under the same conditions. The MDA values in an NaI（Tl） AGRS system under different conditions can be estimated based on the results of this study.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulati...OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.展开更多
Single orbit bistatic space-based radar （SBR） is composed of two radars in the same orbit. The characteristics of the clutter Doppler-angle spectrum of a single orbit bistatic SBR show that the slope of the mainbeam...Single orbit bistatic space-based radar （SBR） is composed of two radars in the same orbit. The characteristics of the clutter Doppler-angle spectrum of a single orbit bistatic SBR show that the slope of the mainbeam clutter spectrum is highly sensitive to the cone angles. Therefore, the minimum detectable velocity of the bistatic system is dependent on the cone angle. Then a new combined working mode of singleorbit bistatic SBR system was developed in which one radar will act as the transmitter and another as the receiver to improve detection performance for all angles. Simulation results by space-time adaptive processing verify the improved detection performance. The new design also reduces the average power of each radar system and the size and weight of the on-board solar array-battery system.展开更多
Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer charact...Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improvi...Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes.This review provides a comprehensive overview of current and emerging biomarkers for HCC,including alpha-fetoprotein,des-gamma-carboxy prothrombin,glypican-3,Golgi protein 73,osteopontin,and microRNAs.Despite advancements,the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages.The review emphasizes the importance of integrating multi-omics approaches,combining genomics,proteomics,and metabolomics,to develop more robust biomarker panels.Such integrative methods have the potential to capture the complex molecular landscape of HCC,offering insights into disease mechanisms and identifying targets for personalized therapies.The significance of large-scale validation studies,collaboration between research institutions and clinical settings,and consideration of regulatory pathways for clinical implementation is also discussed.In conclusion,while substantial progress has been made in biomarker discovery,continued research and innovation are essential to address the remaining challenges.The successful translation of these discoveries into clinical practice will require rigorous validation,standardization of protocols,and crossdisciplinary collaboration.By advancing the development and application of novel biomarkers,we can improve the early detection and management of HCC,ultimately enhancing patient survival and quality of life.展开更多
Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for glo...Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.展开更多
文摘Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow (MP) corn (Zea mays L.) and soybean (Glycine max L.) production systems. The first trial was conducted on a Maryhill silt loam (Typic Hapludalf) at Elora, Ontario, Canada, the second on a Brookston clay loam (Typic Argiaquoll) at Woodslee, Ontario, Canada, and the third on a Thorp silt loam (Argiaquic Argialboll) at Urbana, Illinois, USA. No-tillage led to significantly higher SOC concentrations in the top 5 cm compared to MP at all 3 sites. However, NT resulted in significantly lower SOC in sub-surface soils as compared to MP at Woodslee (10-20 cm, P = 0.01) and Urbana (20-30 cm, P < 0.10). No-tillage had significantly more SOC storage than MP at the Elora site (3.3 Mg C ha-1) and at the Woodslee site (6.2 Mg C ha-1) on an equivalent mass basis (1350 Mg ha-1 soil equivalent mass). Similarly, NT had greater SOC storage than MP at the Urbana site (2.7 Mg C ha-1) on an equivalent mass basis of 675 Mg ha-1 soil. However, these differences disappeared when the entire plow layer was evaluated for both the Woodslee and Urbana sites as a result of the higher SOC concentrations in MP than in NT at depth. Using the minimum detectable difference technique, we observed that up to 1500 soil sample per tillage treatment comparison will have to be collected and analyzed for the Elora and Woodslee sites and over 40 soil samples per tillage treatment comparison for the Urbana to statistically separate significant differences in the SOC contents of sub-plow depth soils. Therefore, it is impracticable, and at the least prohibitively expensive, to detect tillage-induced differences in soil C beyond the plow layer in various soils.
文摘To utilizing the characteristic of radar cross section （RCS） of the low detectable aircraft, a special path planning algorithm to eluding radars by the variable RCS is presented. The algorithm first gives the RCS changing model of low detectable aircraft, then establishes a threat model of a ground-based air defense system according to the relations between RCS and the radar range coverage. By the new cost functions of the flight path, which consider both factors of the survival probability and the distance of total route, this path planning method is simulated based on the Dijkstra algorithm, and the planned route meets the flight capacity constraints. Simulation results show that using the effective path planning algorithm, the low detectable aircraft can give full play to its own advantage of stealth to achieve the purpose of silent penetration.
基金This work was supported by the Sichuan Science and Technology Program(No.2020JDRC0108)the National Science Foundation of China(Nos.41774147 and 41774190).
文摘In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation counter,volume,and energy res-olution of scintillation crystals,and flight altitude of an aircraft was investigated.To verify this theory,experi-mental devices based on NaI and CeBr 3 scintillation counters were prepared,and the potassium,uranium,and thorium contents in calibration pads obtained via the stripping ratio method and theory were compared.The MDACs of AGS under different conditions were calculated and analyzed using the proposed theory and the Monte Carlo method.The relative errors found via a comparison of the experimental and theoretical results were less than 4%.The theory of MDAC can guide the work of AGS in probing areas with low radioactivity.
基金Supported by National Natural Science Foundation of China under Grant Nos.60873191,60903152,and 60821001SRFDP under Grant No.200800131016+5 种基金Beijing Nova Program under Grant No.2008B51Key Project of Chinese Ministry of Education under Grant No.109014Beijing Natural Science Foundation under Grant No.4072020China Postdoctoral Science Foundation under Grant No.20090450018National Laboratory for Modern Communications Science Foundation of China under Grant No.9140C1101010601ISN Open Foundation
文摘We present a new quantum protocol for solving detectable Byzantine agreement problem between threeparties by employing one quantum key distribution protocol.The protocol is suggested by a special four-qubit entangledstate instead of singlet states,which shows that singlet states are not necessary to achieve detectable Byzantine agreement.
文摘Background:Myxomas are the most common primary cardiac tumors.Angiographically detectable neovascularity(ADN)of myxoma is increasingly being reported as a result of the use of coronary angiography(CAG)to detect coronary artery disease.However,the clinical signifi cance of these fi ndings is not fully understood.Methods:We enrolled 59 patients with cardiac myxoma who also underwent CAG between January 2013 and October 2018.Patients were followed up for a mean of 28.9 months(range 1-69 months).The clinical features,echocardiography measurements,pathological examination fi ndings,CAG results,and outcomes during follow-up were compared between patients with ADN and patients without ADN.Results:ADN was found in 25 patients(42.4%).The arteries feeding the ADN included the right coronary artery(n=15),the left circumfl ex coronary artery(n=7),and both arteries(n=3).The patients with ADN had a higher proportion of eosinophils(3.2%vs.2.2%,P=0.03)and higher low-density lipoprotein cholesterol level(2.7 mmol/L vs.2.2 mmol/L,P=0.02).Myxoma pedicles were more likely to be located in the interatrial septum in patients with ADN(96%vs.73.5%,P=0.02).No signifi cant correlation was observed between the groups in clinical manifestations,atrial arrhythmia,myxoma size,cardiac chamber size,left ventricular ejection fraction,and the prevalence of complication with coronary artery disease[16%in the ADN group(n=4)vs.20.6%in the non-ADN group(n=7),P=0.66].However,patients with ADN tended to have a lower incidence of major adverse cardiac and cerebrovascular events on long-term follow-up(0%vs.14.7%,P=0.07).Conclusion:CAG-detected ADN in patients with cardiac myxoma is associated with a borderline lower rate of major adverse cardiac and cerebrovascular events.
文摘In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
基金supported by the National Defense Basic Scientific Research Project(Grant No.B2520133077)National High-tech R&D Program of China("863"Program)(Grant No.2012AA061803)
文摘The determination of the effective minimum detectable activity （MDA） of radionuclides by a detection system plays an im- portant role in environmental radiation monitoring. In this study, the responses of an NaI（TI） airborne γ ray spectrometry （AGRS） system to different radionuclides （137Cs and 131I） were investigated using the Monte Carlo technique. The MDA values were determined under different conditions according to the counting spectra obtained from the Monte Carlo simulation. The equivalent mass thickness method was applied to the Monte Carlo modeling for monitoring ground radiation to reduce sta- tistical uncertainty. The secondary source method was used to monitor both air and ground radiation. A quadratic relationship was found between the MDA and activity concentration. An exponential relationship was found between the MDA and altitude The MDA of a specific radionuclide from external detectors was found to be superior to that obtained from internal detectors under the same conditions. The MDA values in an NaI（Tl） AGRS system under different conditions can be estimated based on the results of this study.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
基金Modernization of Traditional Chinese Medicine Project of National Key R&D Program of China:The construction of the theoretical system of Traditional Chinese Medicine nonpharmacological therapy based on body surface stimulation(2023YFC3502704)Sichuan Provincial Science and Technology Program Project:Research and Development of Chinese Medicine Intelligent Tongue Diagnosis Equipment for Digestive System Chinese Medicine Advantageous Diseases(2023YFS0327)+2 种基金Research and Development of Chinese Medicine Intelligent Detection System for Intestinal Functions(2024YFFK0044)Research and Application of Chinese Medicine Diagnosis and Treatment Program for Herpes Zoster Treated by Shu Pai Fire Acupuncture(2024YFFK0089)Major Research and Development Project of The China Academy of Chinese Medical Sciences Innovation:Construction and application of the theoretical research mode of Traditional Chinese Medicine diagnosis and treatment of modern diseases(CI2021A00104)。
文摘OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.
文摘Single orbit bistatic space-based radar （SBR） is composed of two radars in the same orbit. The characteristics of the clutter Doppler-angle spectrum of a single orbit bistatic SBR show that the slope of the mainbeam clutter spectrum is highly sensitive to the cone angles. Therefore, the minimum detectable velocity of the bistatic system is dependent on the cone angle. Then a new combined working mode of singleorbit bistatic SBR system was developed in which one radar will act as the transmitter and another as the receiver to improve detection performance for all angles. Simulation results by space-time adaptive processing verify the improved detection performance. The new design also reduces the average power of each radar system and the size and weight of the on-board solar array-battery system.
文摘Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
文摘Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes.This review provides a comprehensive overview of current and emerging biomarkers for HCC,including alpha-fetoprotein,des-gamma-carboxy prothrombin,glypican-3,Golgi protein 73,osteopontin,and microRNAs.Despite advancements,the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages.The review emphasizes the importance of integrating multi-omics approaches,combining genomics,proteomics,and metabolomics,to develop more robust biomarker panels.Such integrative methods have the potential to capture the complex molecular landscape of HCC,offering insights into disease mechanisms and identifying targets for personalized therapies.The significance of large-scale validation studies,collaboration between research institutions and clinical settings,and consideration of regulatory pathways for clinical implementation is also discussed.In conclusion,while substantial progress has been made in biomarker discovery,continued research and innovation are essential to address the remaining challenges.The successful translation of these discoveries into clinical practice will require rigorous validation,standardization of protocols,and crossdisciplinary collaboration.By advancing the development and application of novel biomarkers,we can improve the early detection and management of HCC,ultimately enhancing patient survival and quality of life.
基金funded by the National Natural Science Foundation of China(Nos.22374055,22022404,22074050,82172055)the National Natural Science Foundation of Hubei Province(No.22022CFA033)the Fundamental Research Funds for the Central Universities(Nos.CCNU24JCPT001,CCNU24JCPT020)。
文摘Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.
