Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may...Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.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
Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two ...Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.展开更多
Asymmetric allylic C—H functionalization is a valuable and challenging research area. Different from the conventional direct allylic C—H cleavage strategy, transition metal-catalyzed migratory allylic substitution o...Asymmetric allylic C—H functionalization is a valuable and challenging research area. Different from the conventional direct allylic C—H cleavage strategy, transition metal-catalyzed migratory allylic substitution of remote dienes has emerged as a new route to achieve allylic C—H functionalization enantioselectively. This review provides a detailed summary of the development and advance of this strategy, introduces the related mechanistic processes, and discusses the area based on the types of catalysts and products.展开更多
“Go!Faster!”“Pass the ball!”Echoes of encouragement ring across the football field at Yisa Primary School,nestled high in the mountains of Butuo County in Liangshan Yi Autonomous Prefecture,southwest China’s Sich...“Go!Faster!”“Pass the ball!”Echoes of encouragement ring across the football field at Yisa Primary School,nestled high in the mountains of Butuo County in Liangshan Yi Autonomous Prefecture,southwest China’s Sichuan Province.Against a backdrop of cloudwrapped peaks,girls in jerseys dart across the turf with infectious energy.展开更多
Background Evidence on the effects of different exercise interventions on cognitive function is insufficient.Aims To evaluate the feasibility and effects of remotely supervised aerobic exercise(AE)and resistance exerc...Background Evidence on the effects of different exercise interventions on cognitive function is insufficient.Aims To evaluate the feasibility and effects of remotely supervised aerobic exercise(AE)and resistance exercise(RE)interventions in older adults with mild cognitive impairment(MCI).Methods This study is a 6-month pilot three-arm randomised controlled trial.Eligible participants(n=108)were recruited and randomised to the AE group,RE group or control(CON)group with a 1:1:1 ratio.Interventions were delivered at home with remote supervision.We evaluated participants’global cognition,memory,executive function,attention,physical activity levels,physical performance and muscle strength of limbs at baseline,3 months(T1)and 6 months(T2)after randomisation.A linear mixed-effects model was adopted for data analyses after controlling for covariates.Tukey’s method was used for adjusting for multiple comparisons.Sensitivity analyses were performed after excluding individuals with low compliance rates.Results 15(13.89%)participants dropped out.The median compliance rates in the AE group and RE group were 67.31%and 93.27%,respectively.After adjusting for covariates,the scores of the Alzheimer’s Disease Assessment Scale-Cognitive subscale in the AE group decreased by 2.04(95%confidence interval(CI)−3.41 to−0.67,t=−2.94,p=0.004)and 1.53(95%CI−2.88 to−0.17,t=−2.22,p=0.028)points more than those in the CON group at T1 and T2,respectively.The effects of AE were still significant at T1(estimate=−1.70,95%CI−3.20 to−0.21,t=−2.69,p=0.021),but lost statistical significance at T2 after adjusting for multiple comparisons.As for executive function,the Stroop time interference in the RE group decreased by 11.76 s(95%CI−21.62 to−1.90,t=−2.81,p=0.015)more than that in the AE group at T2 after Tukey’s adjustment.No other significant effects on cognitive functions were found.Conclusions Both remotely supervised AE and RE programmes are feasible in older adults with MCI.AE has positive effects on global cognition,and RE improves executive function.展开更多
Blue carbon ecosystems,including mangroves,seagrasses,and salt marshes,play a crucial role in mitigating climate change by capturing and storing atmospheric CO_(2)at rates exceeding those of terrestrial forests.This s...Blue carbon ecosystems,including mangroves,seagrasses,and salt marshes,play a crucial role in mitigating climate change by capturing and storing atmospheric CO_(2)at rates exceeding those of terrestrial forests.This study explores the potential of HCWs(Human-Controlled Wetlands)in the Italian Venice Lagoon as an underappreciated component of the global blue carbon pool.Using GEE(Google Earth Engine),we conducted a large-scale assessment of carbon sequestration in these wetlands,demonstrating its advantages over traditional in situ methods in addressing spatial variability.Our findings highlight the significance of below-water mud sediments as primary carbon reservoirs,with a TC(Total Carbon)content of 3.81%±0.94%and a stable storage function akin to peat,reinforced by high CEC(Cation Exchange Capacity).GEE analysis identified a redoximorphic zone at a depth of 20-30 cm,where microbial respiration shifts to anaerobic pathways,preventing carbon release and maintaining long-term sequestration.The study also evaluates key factors affecting remote sensing accuracy,including tidal variations,water depth,and sky cover.The strong correlation between field-measured and satellite-derived carbon parameters(R^(2)>0.85)confirms the reliability of our approach.Furthermore,we developed a GEE-based script for monitoring sediment bioturbation,leveraging Sentinel-1 SAR(Synthetic Aperture Radar)and Sentinel-2 optical data to quantify biological disturbances affecting carbon fluxes.Our results underscore the value of HCWs for carbon sequestration,reinforcing the need for targeted conservation strategies.The scalability and efficiency of remote sensing methodologies,particularly GEE,make them essential for the long-term monitoring of blue carbon ecosystems and the development of effective climate mitigation policies.展开更多
In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-sp...In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.展开更多
INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This colla...INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m^(2)(Chinanews,2024).展开更多
Memory enables organisms to encode,store,and retrieve information essential for interacting with and adapting to a dynamic environment.As an internal representation of the external world,memory serves as a crucial bri...Memory enables organisms to encode,store,and retrieve information essential for interacting with and adapting to a dynamic environment.As an internal representation of the external world,memory serves as a crucial bridge between past experiences and future behaviors.However,the brain continuously forms new memories,raising the question of how new memories are integrated without disrupting previously formed ones.展开更多
Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water.How-ever,water quality has been adversely affected by plastic pollution,a global environmental disaster ...One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water.How-ever,water quality has been adversely affected by plastic pollution,a global environmental disaster that has an effect on aquatic life,wildlife,and human health.To prevent these effects,better monitoring,detection,characterisation,quanti-fication,and tracking of aquatic plastic pollution at regional and global scales is urgently needed.Remote sensing tech-nology is regarded as a useful technique,as it offers a promising new and less labour-intensive tool for the detection,quantification,and characterisation of aquatic plastic pollution.The study seeks to supplement to the body of scientific literature by compiling original data on the monitoring of plastic pollution in aquatic environments using remote sensing technology,which can function as a cost saving method for water pollution and risk management in developing nations.This article provides a profound analysis of plastic pollution,including its categories,sources,distribution,chemical properties,and potential risks.It also provides an in-depth review of remote sensing technologies,satellite-derived in-dices,and research trends related to their applicability.Additionally,the study clarifies the difficulties in using remote sensing technologies for aquatic plastic monitoring and practical ways to reduce aquatic plastic pollution.The study will improve the understanding of aquatic plastic pollution,health hazards,and the suitability of remote sensing technology for aquatic plastic contamination monitoring studies among researchers and interested parties.展开更多
Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic...Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.展开更多
The convolutional neural network(CNN)method based on DeepLabv3+has some problems in the semantic segmentation task of high-resolution remote sensing images,such as fixed receiving field size of feature extraction,lack...The convolutional neural network(CNN)method based on DeepLabv3+has some problems in the semantic segmentation task of high-resolution remote sensing images,such as fixed receiving field size of feature extraction,lack of semantic information,high decoder magnification,and insufficient detail retention ability.A hierarchical feature fusion network(HFFNet)was proposed.Firstly,a combination of transformer and CNN architectures was employed for feature extraction from images of varying resolutions.The extracted features were processed independently.Subsequently,the features from the transformer and CNN were fused under the guidance of features from different sources.This fusion process assisted in restoring information more comprehensively during the decoding stage.Furthermore,a spatial channel attention module was designed in the final stage of decoding to refine features and reduce the semantic gap between shallow CNN features and deep decoder features.The experimental results showed that HFFNet had superior performance on UAVid,LoveDA,Potsdam,and Vaihingen datasets,and its cross-linking index was better than DeepLabv3+and other competing methods,showing strong generalization ability.展开更多
Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-...Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.展开更多
This paper presents a standardised workflow for conducting hazard assessments of mass wasting processes in remote mountain areas with limited data.The methodology integrates geomorphological mapping and remote sensing...This paper presents a standardised workflow for conducting hazard assessments of mass wasting processes in remote mountain areas with limited data.The methodology integrates geomorphological mapping and remote sensing techniques and is adaptable to different national standards,thus ensuring its applicability in a variety of contexts.The principal objective is to guarantee the safety of mountainous regions,particularly in the vicinity of essential infrastructure,where the scope for implementing structural measures is restricted.The framework commences with comprehensive geomorphological mapping,which facilitates the identification of past hazardous processes and potential future hazards.New technologies,such as uncrewed aerial vehicles(UAVs),are employed to create high-resolution DEMs,which are particularly beneficial in regions with limited data availability.These models facilitate the assessment of potential hazards and inform decisions regarding protective measures.The utilisation of UAVs enhances the accuracy and efficiency of data collection,particularly in remote mountainous regions where alternative remotely sensed information may be unavailable.The integration of modern approaches into traditional hazard assessment methods allows for a comprehensive analysis of the spatial distribution of factors driving mass wasting processes.This workflow provides valuable insights that assist in the prioritisation of interventions and the optimisation of risk reduction in high mountainous areas.展开更多
This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and desi...This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format.Subsequently,a remote data transmission link using 4G signals and data processing methods is introduced.The advantages of remote diagnosis are analyzed,and common methods such as correlation analysis,fault diagnosis,and fault prediction are explained in detail.Then,challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed,along with development prospects in technological innovation,algorithm optimization,and application promotion.This research provides ideas for remote analysis and diagnosis based on TCMS data,contributing to the safe and efficient operation of rail vehicles.展开更多
Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relat...Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.展开更多
Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central server.This requirement outpaces the capacity of t...Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central server.This requirement outpaces the capacity of traditional communication methods.To tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data.To solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases.This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management.Additionally,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain efficiency.Experimental results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.展开更多
This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental ...This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental design and qualitative analysis (N = 120), we examine how AI intermediaries influence communication dynamics, relationship building, and decision-making processes. Results indicate that while AMC initially creates barriers to trust formation, it ultimately leads to enhanced communication outcomes and stronger professional relationships when implemented with appropriate transparency and support. The study revealed a 31% improvement in cross-cultural understanding and a 24% increase in negotiation satisfaction rates when using AI-mediated channels with proper transparency measures. These findings contribute to the theoretical understanding of technology-mediated communication and practical applications for organizations implementing AI communication tools.展开更多
基金supported partly by the National Natural Science Foundation of China,No.82071332the Chongqing Natural Science Foundation Joint Fund for Innovation and Development,No.CSTB2023NSCQ-LZX0041 (both to ZG)。
文摘Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.
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.
基金supported by National Key R&D Program of China(2022YFD2000100)National Natural Science Foundation of China(42401400)Zhejiang Provincial Key Research and Development Program(2023C02018).
文摘Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.
文摘Asymmetric allylic C—H functionalization is a valuable and challenging research area. Different from the conventional direct allylic C—H cleavage strategy, transition metal-catalyzed migratory allylic substitution of remote dienes has emerged as a new route to achieve allylic C—H functionalization enantioselectively. This review provides a detailed summary of the development and advance of this strategy, introduces the related mechanistic processes, and discusses the area based on the types of catalysts and products.
文摘“Go!Faster!”“Pass the ball!”Echoes of encouragement ring across the football field at Yisa Primary School,nestled high in the mountains of Butuo County in Liangshan Yi Autonomous Prefecture,southwest China’s Sichuan Province.Against a backdrop of cloudwrapped peaks,girls in jerseys dart across the turf with infectious energy.
基金funded by the National Natural Science Foundation of China(81871854,72374014)the National Key R&D Program of China(2020YFC2008804)+1 种基金the Shanghai Jiao Tong University Young Talent Cultivation Program in Liberal Arts(2024QN041)the Shanghai Jiao Tong University School of Medicine:Nursing Development Program(SJTUHLXK2024).
文摘Background Evidence on the effects of different exercise interventions on cognitive function is insufficient.Aims To evaluate the feasibility and effects of remotely supervised aerobic exercise(AE)and resistance exercise(RE)interventions in older adults with mild cognitive impairment(MCI).Methods This study is a 6-month pilot three-arm randomised controlled trial.Eligible participants(n=108)were recruited and randomised to the AE group,RE group or control(CON)group with a 1:1:1 ratio.Interventions were delivered at home with remote supervision.We evaluated participants’global cognition,memory,executive function,attention,physical activity levels,physical performance and muscle strength of limbs at baseline,3 months(T1)and 6 months(T2)after randomisation.A linear mixed-effects model was adopted for data analyses after controlling for covariates.Tukey’s method was used for adjusting for multiple comparisons.Sensitivity analyses were performed after excluding individuals with low compliance rates.Results 15(13.89%)participants dropped out.The median compliance rates in the AE group and RE group were 67.31%and 93.27%,respectively.After adjusting for covariates,the scores of the Alzheimer’s Disease Assessment Scale-Cognitive subscale in the AE group decreased by 2.04(95%confidence interval(CI)−3.41 to−0.67,t=−2.94,p=0.004)and 1.53(95%CI−2.88 to−0.17,t=−2.22,p=0.028)points more than those in the CON group at T1 and T2,respectively.The effects of AE were still significant at T1(estimate=−1.70,95%CI−3.20 to−0.21,t=−2.69,p=0.021),but lost statistical significance at T2 after adjusting for multiple comparisons.As for executive function,the Stroop time interference in the RE group decreased by 11.76 s(95%CI−21.62 to−1.90,t=−2.81,p=0.015)more than that in the AE group at T2 after Tukey’s adjustment.No other significant effects on cognitive functions were found.Conclusions Both remotely supervised AE and RE programmes are feasible in older adults with MCI.AE has positive effects on global cognition,and RE improves executive function.
文摘Blue carbon ecosystems,including mangroves,seagrasses,and salt marshes,play a crucial role in mitigating climate change by capturing and storing atmospheric CO_(2)at rates exceeding those of terrestrial forests.This study explores the potential of HCWs(Human-Controlled Wetlands)in the Italian Venice Lagoon as an underappreciated component of the global blue carbon pool.Using GEE(Google Earth Engine),we conducted a large-scale assessment of carbon sequestration in these wetlands,demonstrating its advantages over traditional in situ methods in addressing spatial variability.Our findings highlight the significance of below-water mud sediments as primary carbon reservoirs,with a TC(Total Carbon)content of 3.81%±0.94%and a stable storage function akin to peat,reinforced by high CEC(Cation Exchange Capacity).GEE analysis identified a redoximorphic zone at a depth of 20-30 cm,where microbial respiration shifts to anaerobic pathways,preventing carbon release and maintaining long-term sequestration.The study also evaluates key factors affecting remote sensing accuracy,including tidal variations,water depth,and sky cover.The strong correlation between field-measured and satellite-derived carbon parameters(R^(2)>0.85)confirms the reliability of our approach.Furthermore,we developed a GEE-based script for monitoring sediment bioturbation,leveraging Sentinel-1 SAR(Synthetic Aperture Radar)and Sentinel-2 optical data to quantify biological disturbances affecting carbon fluxes.Our results underscore the value of HCWs for carbon sequestration,reinforcing the need for targeted conservation strategies.The scalability and efficiency of remote sensing methodologies,particularly GEE,make them essential for the long-term monitoring of blue carbon ecosystems and the development of effective climate mitigation policies.
基金the National Science Foundation of China (Grant No. NSFC: 92162213)the Geology Department Faculty of Science of Al-Azhar University (Assiut Branch)+2 种基金the China Scholarship CouncilChang'an UniversityIstanbul Technical University's Scientific Research Project (BAP Project ID: 45396, code: FHD-2024-45396)
文摘In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.
基金supported by the National Natural Science Foundation of China(Nos.42371094,41907253)partially supported by the Interdisciplinary Cultivation Program of Xidian University(No.21103240005)the Postdoctoral Fellowship Program of CPSF(No.GZB20240589)。
文摘INTRODUCTION.On May 1st,2024,around 2:10 a.m.,a catastrophic collapse occurred along the Meilong Expressway near Meizhou City,Guangdong Province,China,at coordinates 24°29′24″N and 116°40′25″E.This collapse resulted in a pavement failure of approximately 17.9 m in length and covering an area of about 184.3 m^(2)(Chinanews,2024).
文摘Memory enables organisms to encode,store,and retrieve information essential for interacting with and adapting to a dynamic environment.As an internal representation of the external world,memory serves as a crucial bridge between past experiences and future behaviors.However,the brain continuously forms new memories,raising the question of how new memories are integrated without disrupting previously formed ones.
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
文摘One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water.How-ever,water quality has been adversely affected by plastic pollution,a global environmental disaster that has an effect on aquatic life,wildlife,and human health.To prevent these effects,better monitoring,detection,characterisation,quanti-fication,and tracking of aquatic plastic pollution at regional and global scales is urgently needed.Remote sensing tech-nology is regarded as a useful technique,as it offers a promising new and less labour-intensive tool for the detection,quantification,and characterisation of aquatic plastic pollution.The study seeks to supplement to the body of scientific literature by compiling original data on the monitoring of plastic pollution in aquatic environments using remote sensing technology,which can function as a cost saving method for water pollution and risk management in developing nations.This article provides a profound analysis of plastic pollution,including its categories,sources,distribution,chemical properties,and potential risks.It also provides an in-depth review of remote sensing technologies,satellite-derived in-dices,and research trends related to their applicability.Additionally,the study clarifies the difficulties in using remote sensing technologies for aquatic plastic monitoring and practical ways to reduce aquatic plastic pollution.The study will improve the understanding of aquatic plastic pollution,health hazards,and the suitability of remote sensing technology for aquatic plastic contamination monitoring studies among researchers and interested parties.
基金supported by the Office of Naval Research(Grant Nos.N00014-16-1-2094 and N00014-24-1-2547)the Lawrence Livermore National Laboratory(Grant No.B622827)the National Science Foundation.Y.-S.J.acknowledges support from KRISS(Grant Nos.25011026 and 25011211).
文摘Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.
基金supported by National Natural Science Foundation of China(No.52374155)Anhui Provincial Natural Science Foundation(No.2308085 MF218).
文摘The convolutional neural network(CNN)method based on DeepLabv3+has some problems in the semantic segmentation task of high-resolution remote sensing images,such as fixed receiving field size of feature extraction,lack of semantic information,high decoder magnification,and insufficient detail retention ability.A hierarchical feature fusion network(HFFNet)was proposed.Firstly,a combination of transformer and CNN architectures was employed for feature extraction from images of varying resolutions.The extracted features were processed independently.Subsequently,the features from the transformer and CNN were fused under the guidance of features from different sources.This fusion process assisted in restoring information more comprehensively during the decoding stage.Furthermore,a spatial channel attention module was designed in the final stage of decoding to refine features and reduce the semantic gap between shallow CNN features and deep decoder features.The experimental results showed that HFFNet had superior performance on UAVid,LoveDA,Potsdam,and Vaihingen datasets,and its cross-linking index was better than DeepLabv3+and other competing methods,showing strong generalization ability.
基金funded by the National Key Research and Development Program of China(Grant No.2020YFA0608103)the National Science Foundation of China(Grant Nos.42265012 and 31770765).
文摘Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.
基金Open access funding provided by University of Natural Resources and Life Sciences Vienna(BOKU).
文摘This paper presents a standardised workflow for conducting hazard assessments of mass wasting processes in remote mountain areas with limited data.The methodology integrates geomorphological mapping and remote sensing techniques and is adaptable to different national standards,thus ensuring its applicability in a variety of contexts.The principal objective is to guarantee the safety of mountainous regions,particularly in the vicinity of essential infrastructure,where the scope for implementing structural measures is restricted.The framework commences with comprehensive geomorphological mapping,which facilitates the identification of past hazardous processes and potential future hazards.New technologies,such as uncrewed aerial vehicles(UAVs),are employed to create high-resolution DEMs,which are particularly beneficial in regions with limited data availability.These models facilitate the assessment of potential hazards and inform decisions regarding protective measures.The utilisation of UAVs enhances the accuracy and efficiency of data collection,particularly in remote mountainous regions where alternative remotely sensed information may be unavailable.The integration of modern approaches into traditional hazard assessment methods allows for a comprehensive analysis of the spatial distribution of factors driving mass wasting processes.This workflow provides valuable insights that assist in the prioritisation of interventions and the optimisation of risk reduction in high mountainous areas.
文摘This article focuses on the remote diagnosis and analysis of rail vehicle status based on the data of the Train Control Management System(TCMS).It first expounds on the importance of train diagnostic analysis and designs a unified TCMS data frame transmission format.Subsequently,a remote data transmission link using 4G signals and data processing methods is introduced.The advantages of remote diagnosis are analyzed,and common methods such as correlation analysis,fault diagnosis,and fault prediction are explained in detail.Then,challenges such as data security and the balance between diagnostic accuracy and real-time performance are discussed,along with development prospects in technological innovation,algorithm optimization,and application promotion.This research provides ideas for remote analysis and diagnosis based on TCMS data,contributing to the safe and efficient operation of rail vehicles.
基金funded by National Natural Science Foundation of China(61603245).
文摘Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.
基金supported in part by the National Natural Science Foundation of China under Grant No.62062031in part by the MIC/SCOPE#JP235006102+2 种基金in part by JST ASPIRE Grant Number JPMJAP2325in part by ROIS NII Open Collaborative Research under Grant 24S0601in part by collaborative research with Toyota Motor Corporation,Japan。
文摘Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central server.This requirement outpaces the capacity of traditional communication methods.To tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data.To solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases.This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management.Additionally,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain efficiency.Experimental results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.
文摘This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental design and qualitative analysis (N = 120), we examine how AI intermediaries influence communication dynamics, relationship building, and decision-making processes. Results indicate that while AMC initially creates barriers to trust formation, it ultimately leads to enhanced communication outcomes and stronger professional relationships when implemented with appropriate transparency and support. The study revealed a 31% improvement in cross-cultural understanding and a 24% increase in negotiation satisfaction rates when using AI-mediated channels with proper transparency measures. These findings contribute to the theoretical understanding of technology-mediated communication and practical applications for organizations implementing AI communication tools.
