Organic semiconductor materials have shown unique advantages in the development of optoelectronic devices due to their ease of preparation,low cost,lightweight,and flexibility.In this work,we explored the application ...Organic semiconductor materials have shown unique advantages in the development of optoelectronic devices due to their ease of preparation,low cost,lightweight,and flexibility.In this work,we explored the application of the organic semiconductor Y6-1O single crystal in photodetection devices.Firstly,Y6-1O single crystal material was prepared on a silicon substrate using solution droplet casting method.The optical properties of Y6-1O material were characterized by polarized optical microscopy,fluorescence spectroscopy,etc.,confirming its highly single crystalline performance and emission properties in the near-infrared region.Phototransistors based on Y6-1O materials with different thicknesses were then fabricated and tested.It was found that the devices exhibited good visible to near-infrared photoresponse,with the maximum photoresponse in the near-infrared region at 785 nm.The photocurrent on/off ratio reaches 10^(2),and photoresponsivity reaches 16 mA/W.It was also found that the spectral response of the device could be regulated by gate voltage as well as the material thickness,providing important conditions for optimizing the performance of near-infrared photodetectors.This study not only demonstrates the excellent performance of organic phototransistors based on Y6-1O single crystal material in near-infrared detection but also provides new ideas and directions for the future development of infrared detectors.展开更多
The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this l...The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.展开更多
Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a...Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.展开更多
Background Cotton is an important crop providing the most natural fibers all over the world. The cotton genomics community has utilized whole genome sequencing data to construct an elite gene pool in which functional ...Background Cotton is an important crop providing the most natural fibers all over the world. The cotton genomics community has utilized whole genome sequencing data to construct an elite gene pool in which functional genes are related to agronomic traits. However, the functional validation of these genes is hindered by time-consuming and inefficient genetic transformation methods. Thus, establishing a transient transformation system of high efficiency is necessary for cotton genomics.Results To improve the efficiency of transient transformation, we used the protoplasts isolated from the etiolated cotyledon as recipient. The enzymatic digestion buffer comprised 1.5%(w/v) cellulase, 0.75%(w/v) macerozyme, and 1% hemicellulase, osmotically buffered with 0.4 mol·L^(-1) mannitol. After 5 h of dark incubation at 25℃, uniform cotton protoplasts were successfully isolated with a yield of 4.6 × 10^(6) protoplasts per gram(fresh weight) and 95% viability. We incubated 100 μL protoplasts(2.5 × 10^(5)·m L^(-1)) with 15 μg plasmid in the solution of 0.4 mol·L^(-1) mannitol and 40% PEG 4000 for 15 min, ultimately achieving an optimal transient transfection efficiency of 71.47%.Conclusions This transient system demonstrated effective utility in cellular biology research through successful applications in subcellular localization analyses, bimolecular fluorescence complementation(Bi FC) verification, and prime editing vector validation. Through systematic optimization, we established an efficient and expedited protoplast-based transient transformation system and successfully applied this platform to cotton functional genomics studies.展开更多
To reveal the effects of environmental and loading conditions, as well as asphalt properties on the nonlinear rheological behavior of asphalt, the large amplitude oscillation shear(LAOS) test was introduced, and the F...To reveal the effects of environmental and loading conditions, as well as asphalt properties on the nonlinear rheological behavior of asphalt, the large amplitude oscillation shear(LAOS) test was introduced, and the Fourier transform rheology, Lissajous curve method, and the LAOS fatigue test have been applied to investigate the nonlinear rheological behavior of asphalt binders. The research results indicate that a decrease in temperature, an increase in shear frequency and strain level, the introduction of polymer modifiers, and the aging effect of asphalt can significantly increase the nonlinearity of asphalt, manifested by the higher relative magnitude of the third harmonic and zero-strain nonlinear coefficient. For the two polymer modifiers selected in this study, the 4%polyurethane modifier exhibits a higher nonlinear lifting effect than the 4% styrene-butadiene-styrene(SBS). The impact of long-term aging on nonlinear viscoelasticity is observably greater than that of short-term aging. The zero-strain nonlinear coefficient estimated based on the average value method can accurately characterize the nonlinear viscoelasticity of asphalt, which can serve as an effective supplement to the relative magnitude of the third harmonic. All asphalts exhibit shear thinning behavior under the test temperature of 24℃, and the decrease in test temperature, the increase in shear rate and strain level, the introduction of modifiers, and the aging effect of asphalt all exacerbate the shear thinning behavior of asphalt. In addition, the fatigue failure process of asphalt materials is accompanied by an increasing degree of nonlinearity.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
Water conservation initiatives satisfy the demand for water supply,electricity generation,irrigation,and flood control.While helping humanity,they have also altered the ecosystem of natural rivers,impacted river ecolo...Water conservation initiatives satisfy the demand for water supply,electricity generation,irrigation,and flood control.While helping humanity,they have also altered the ecosystem of natural rivers,impacted river ecology,disrupted river continuity,and jeopardized the existence of aquatic creatures in rivers.Studying the impacts of dam construction on rivers can enhance our knowledge of river ecological and environmental concerns and help sustain the health of river ecosystems,thereby realizing the harmony between humans and water in both theoretical and practical aspects.This study used bibliometrics and constructed an author-keyword 2-mode matrix network using Co-Occurrence software to identify the hotspots and research trend in eco-hydrology of dammed rivers.We identified‘FLOW’‘SEDIMENT’‘QUALITY’and‘MODEL’as the research hotspots in the ecological impact of dammed rivers,and combined the related literatures,we highlight the research progress in the four directions.Then the research shortcomings and prospect were discussed,including strengthening the monitoring and analysis of critical ecological variables,enhancing the hydrological monitoring density for small rivers,strengthening the research of relationship between eutrophication and zooplankton,establishing multiscale approaches,and combining multi-sources information technologies to improve parameter accuracy in the model research.展开更多
Pseudo Human Settlements(PHS)are a fundamental element in human settlements geography,serving as an innovative frontier in the exploration of human–land relationships.Since entering the information age,PHS have emerg...Pseudo Human Settlements(PHS)are a fundamental element in human settlements geography,serving as an innovative frontier in the exploration of human–land relationships.Since entering the information age,PHS have emerged as a new catalyst for people's lives and urban development.Based on the Baidu Index,cold hot spot analysis and the Pearson correlation coefficient method were used to evaluate the spatiotemporal variation characteristics of the development of the quality of PHS at different levels in the three provinces of Northeast China(TPNC)during 2011–2022 and to characterize the influence of the system and factors.The results indicated that:1)temporally,PHS exhibits significant fluctuations,with an overall pattern of rapid increase followed by a gradual decline;2)spatially,PHS is marked by regional differentiation,with“three-core”dominance and a“cluster-like”distribution;3)systematically,the five major PHS systems generally exhibit an ascending and then a descending trend;4)in terms of influence,the socialization system serves as the core influence of PHS,with WeChat,JD.COM,and others are identified as the core influencing factors of subsystems.The findings of this study can provide scientific guidance for diversifying approaches to human settlements,promoting sustainable urban development,and revitalizing Northeast China.展开更多
Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linka...Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linkages,the importance of marine economic net-work research is beginning to emerge.The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development.Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China,we use social network analysis(SNA)to discuss the characteristics of its evolution as of 2010,2015,and 2020 and its governance.The following results were obtained.1)In terms of topological characteristics,the scale of the marine economic network in China’s coastal areas has accelerated and expan-ded,and the connections have become increasingly close;thus,this development has complex network characteristics.2)In terms of spatial structure,the intensity of the connection fluctuates and does not form stable development support;the group structure gradually becomes clear,but the overall pattern is fragmented;there are spatial differences in marine economic agglomeration radiation;the radi-ation effect of the eastern marine economic circle is obvious;and the polarization effect of northern and southern marine economic circles is significant.On this basis,we construct a framework for the governance of a marine economic network with the market,the government,and industry as the three governing bodies.By clarifying the driving factors and building objectives of marine economic network construction,this study aims to foster the high-quality development of China’s marine economy.展开更多
With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk manageme...With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.展开更多
The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on ...The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.展开更多
在快速城镇化与活态遗产旅游开发背景下,少数民族传统村落作为传承民族地域文化的独特地理单元,村落地域空间系统空心化态势日渐显著,呈现出共性与个性兼具的多维空间异化。基于此,论文以湖南省湘西侗族坪坦村为例,运用指标构建、质性...在快速城镇化与活态遗产旅游开发背景下,少数民族传统村落作为传承民族地域文化的独特地理单元,村落地域空间系统空心化态势日渐显著,呈现出共性与个性兼具的多维空间异化。基于此,论文以湖南省湘西侗族坪坦村为例,运用指标构建、质性研究及制度分析与发展(institutional analysis and development,IAD)框架,构建了“空心化现状量化评估—空心化空间异变剖析—空心化形成机制探究”的研究框架,探讨了坪坦村空心化的现状表征、空间异变特征及形成机制。研究结果显示:①从空心化量化结果来看,坪坦村空心化呈现出传统建筑外观保护尚好、但人口空心化和建筑空心化问题依旧显著的特异性;同时,新村、商业街与传统村落的空心化程度存在微观地域分异特征。②从空心化具体特征来看,坪坦村人口空间呈现出就业非农化和流动保守化的趋势,建筑空间表现为宏观层面的空间肌理破碎化和微观层面的住宅建筑低能化,文化空间则面临表征脱域化与传承断层化的挑战。③从空心化形成机制来看,坪坦村空心化是在宏观社会环境与微观局限空间共同催化以及外源性主体与内生性主体联合驱动下,呈现出村落地域空间系统异化的复杂演变过程。研究旨在探索少数民族传统村落空心化特征与形成机制的一般规律,以期为城乡融合与乡村振兴在同类型微观地域空间的有效落地提供科学依据,推动少数民族传统村落的活态存续。展开更多
基金Supported by the National Key Research and Development Program of China(2021YFB2012601)National Natural Science Foundation of China(12204109)+1 种基金Science and Technology Innovation Plan of Shanghai Science and Technology Commission(21JC1400200)Higher Education Indus⁃try Support Program of Gansu Province(2022CYZC-06)。
文摘Organic semiconductor materials have shown unique advantages in the development of optoelectronic devices due to their ease of preparation,low cost,lightweight,and flexibility.In this work,we explored the application of the organic semiconductor Y6-1O single crystal in photodetection devices.Firstly,Y6-1O single crystal material was prepared on a silicon substrate using solution droplet casting method.The optical properties of Y6-1O material were characterized by polarized optical microscopy,fluorescence spectroscopy,etc.,confirming its highly single crystalline performance and emission properties in the near-infrared region.Phototransistors based on Y6-1O materials with different thicknesses were then fabricated and tested.It was found that the devices exhibited good visible to near-infrared photoresponse,with the maximum photoresponse in the near-infrared region at 785 nm.The photocurrent on/off ratio reaches 10^(2),and photoresponsivity reaches 16 mA/W.It was also found that the spectral response of the device could be regulated by gate voltage as well as the material thickness,providing important conditions for optimizing the performance of near-infrared photodetectors.This study not only demonstrates the excellent performance of organic phototransistors based on Y6-1O single crystal material in near-infrared detection but also provides new ideas and directions for the future development of infrared detectors.
基金Our research was funded by the Sichuan Key Provincial Research Base of Intelligent Tourism(No.ZHZJ23-02)supported by the Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A006)+1 种基金Additional support was provided by the National Cultural and Tourism Science and Technology Innovation Research andDevelopment Project(No.202417)the Lantern Culture and Crafts Innovation Key Laboratory Project of the Sichuan ProvincialDepartment of Culture and Tourism(No.SCWLCD-A02).
文摘The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘Addressing the current challenges in transforming pixel displacement into physical displacement in visual monitoring technologies,as well as the inability to achieve precise full-field monitoring,this paper proposes a method for identifying the structural dynamic characteristics of wind turbines based on visual monitoring data fusion.Firstly,the Lucas-Kanade Tomasi(LKT)optical flow method and a multi-region of interest(ROI)monitoring structure are employed to track pixel displacements,which are subsequently subjected to band pass filtering and resampling operations.Secondly,the actual displacement time history is derived through double integration of the acquired acceleration data and subsequent band pass filtering.The scale factor is obtained by applying the least squares method to compare the visual displacement with the displacement derived from double integration of the acceleration data.Based on this,the multi-point displacement time histories under physical coordinates are obtained using the vision data and the scale factor.Subsequently,when visual monitoring of displacements becomes impossible due to issues such as image blurring or lens occlusion,the structural vibration equation and boundary condition constraints,among other key parameters,are employed to predict the displacements at unknown monitoring points,thereby enabling full-field displacement monitoring and dynamic characteristic testing of the structure.Finally,a small-scale shaking table test was conducted on a simulated wind turbine structure undergoing shutdown to validate the dynamic characteristics of the proposed method through test verification.The research results indicate that the proposed method achieves a time-domain error within the submillimeter range and a frequency-domain accuracy of over 99%,effectively monitoring the full-field structural dynamic characteristics of wind turbines and providing a basis for the condition assessment of wind turbine structures.
基金supported by Biological Breeding of Early Maturing and Disease Resistant Cotton Varieties (NO.2023ZD04041)the Project of China Agriculture Research System (Grant No. CARS-15-06)+2 种基金Natural Science Foundation of Henan Province (Grant No. 232300421041 and 222300420382)National Natural Science Foundation of China (Grant No. U21 A20213)the Central Public-interest Scientific Institution Basal Research Fund (Grant No. 1610162023017 and 1610162023028)。
文摘Background Cotton is an important crop providing the most natural fibers all over the world. The cotton genomics community has utilized whole genome sequencing data to construct an elite gene pool in which functional genes are related to agronomic traits. However, the functional validation of these genes is hindered by time-consuming and inefficient genetic transformation methods. Thus, establishing a transient transformation system of high efficiency is necessary for cotton genomics.Results To improve the efficiency of transient transformation, we used the protoplasts isolated from the etiolated cotyledon as recipient. The enzymatic digestion buffer comprised 1.5%(w/v) cellulase, 0.75%(w/v) macerozyme, and 1% hemicellulase, osmotically buffered with 0.4 mol·L^(-1) mannitol. After 5 h of dark incubation at 25℃, uniform cotton protoplasts were successfully isolated with a yield of 4.6 × 10^(6) protoplasts per gram(fresh weight) and 95% viability. We incubated 100 μL protoplasts(2.5 × 10^(5)·m L^(-1)) with 15 μg plasmid in the solution of 0.4 mol·L^(-1) mannitol and 40% PEG 4000 for 15 min, ultimately achieving an optimal transient transfection efficiency of 71.47%.Conclusions This transient system demonstrated effective utility in cellular biology research through successful applications in subcellular localization analyses, bimolecular fluorescence complementation(Bi FC) verification, and prime editing vector validation. Through systematic optimization, we established an efficient and expedited protoplast-based transient transformation system and successfully applied this platform to cotton functional genomics studies.
基金supported by the National Key Research and Development Program of China(2023YFB2603500).
文摘To reveal the effects of environmental and loading conditions, as well as asphalt properties on the nonlinear rheological behavior of asphalt, the large amplitude oscillation shear(LAOS) test was introduced, and the Fourier transform rheology, Lissajous curve method, and the LAOS fatigue test have been applied to investigate the nonlinear rheological behavior of asphalt binders. The research results indicate that a decrease in temperature, an increase in shear frequency and strain level, the introduction of polymer modifiers, and the aging effect of asphalt can significantly increase the nonlinearity of asphalt, manifested by the higher relative magnitude of the third harmonic and zero-strain nonlinear coefficient. For the two polymer modifiers selected in this study, the 4%polyurethane modifier exhibits a higher nonlinear lifting effect than the 4% styrene-butadiene-styrene(SBS). The impact of long-term aging on nonlinear viscoelasticity is observably greater than that of short-term aging. The zero-strain nonlinear coefficient estimated based on the average value method can accurately characterize the nonlinear viscoelasticity of asphalt, which can serve as an effective supplement to the relative magnitude of the third harmonic. All asphalts exhibit shear thinning behavior under the test temperature of 24℃, and the decrease in test temperature, the increase in shear rate and strain level, the introduction of modifiers, and the aging effect of asphalt all exacerbate the shear thinning behavior of asphalt. In addition, the fatigue failure process of asphalt materials is accompanied by an increasing degree of nonlinearity.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
基金Under the auspices of National Key R&D Program of China(No.2019YFC0409104)。
文摘Water conservation initiatives satisfy the demand for water supply,electricity generation,irrigation,and flood control.While helping humanity,they have also altered the ecosystem of natural rivers,impacted river ecology,disrupted river continuity,and jeopardized the existence of aquatic creatures in rivers.Studying the impacts of dam construction on rivers can enhance our knowledge of river ecological and environmental concerns and help sustain the health of river ecosystems,thereby realizing the harmony between humans and water in both theoretical and practical aspects.This study used bibliometrics and constructed an author-keyword 2-mode matrix network using Co-Occurrence software to identify the hotspots and research trend in eco-hydrology of dammed rivers.We identified‘FLOW’‘SEDIMENT’‘QUALITY’and‘MODEL’as the research hotspots in the ecological impact of dammed rivers,and combined the related literatures,we highlight the research progress in the four directions.Then the research shortcomings and prospect were discussed,including strengthening the monitoring and analysis of critical ecological variables,enhancing the hydrological monitoring density for small rivers,strengthening the research of relationship between eutrophication and zooplankton,establishing multiscale approaches,and combining multi-sources information technologies to improve parameter accuracy in the model research.
基金National Natural Science Foundation of China,No.42471246,No.42201221Liaoning Province Natural Science,No.2023-MS-254+1 种基金Liaoning Province Social Science Planning Fund Project,No.L22CJY016Dalian Federation of Social Sciences,No.2022dlskzd037。
文摘Pseudo Human Settlements(PHS)are a fundamental element in human settlements geography,serving as an innovative frontier in the exploration of human–land relationships.Since entering the information age,PHS have emerged as a new catalyst for people's lives and urban development.Based on the Baidu Index,cold hot spot analysis and the Pearson correlation coefficient method were used to evaluate the spatiotemporal variation characteristics of the development of the quality of PHS at different levels in the three provinces of Northeast China(TPNC)during 2011–2022 and to characterize the influence of the system and factors.The results indicated that:1)temporally,PHS exhibits significant fluctuations,with an overall pattern of rapid increase followed by a gradual decline;2)spatially,PHS is marked by regional differentiation,with“three-core”dominance and a“cluster-like”distribution;3)systematically,the five major PHS systems generally exhibit an ascending and then a descending trend;4)in terms of influence,the socialization system serves as the core influence of PHS,with WeChat,JD.COM,and others are identified as the core influencing factors of subsystems.The findings of this study can provide scientific guidance for diversifying approaches to human settlements,promoting sustainable urban development,and revitalizing Northeast China.
基金Under the auspices of the Key Research Base of Humanities and Social Sciences of the Ministry of Education of China(No.22JJD790029)。
文摘Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linkages,the importance of marine economic net-work research is beginning to emerge.The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development.Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China,we use social network analysis(SNA)to discuss the characteristics of its evolution as of 2010,2015,and 2020 and its governance.The following results were obtained.1)In terms of topological characteristics,the scale of the marine economic network in China’s coastal areas has accelerated and expan-ded,and the connections have become increasingly close;thus,this development has complex network characteristics.2)In terms of spatial structure,the intensity of the connection fluctuates and does not form stable development support;the group structure gradually becomes clear,but the overall pattern is fragmented;there are spatial differences in marine economic agglomeration radiation;the radi-ation effect of the eastern marine economic circle is obvious;and the polarization effect of northern and southern marine economic circles is significant.On this basis,we construct a framework for the governance of a marine economic network with the market,the government,and industry as the three governing bodies.By clarifying the driving factors and building objectives of marine economic network construction,this study aims to foster the high-quality development of China’s marine economy.
基金the National Natural Science Foundation of China(U2033213)the Fundamental Research Funds for the Central Universities(FZ2021ZZ01,FZ2022ZX50).
文摘With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.
文摘在快速城镇化与活态遗产旅游开发背景下,少数民族传统村落作为传承民族地域文化的独特地理单元,村落地域空间系统空心化态势日渐显著,呈现出共性与个性兼具的多维空间异化。基于此,论文以湖南省湘西侗族坪坦村为例,运用指标构建、质性研究及制度分析与发展(institutional analysis and development,IAD)框架,构建了“空心化现状量化评估—空心化空间异变剖析—空心化形成机制探究”的研究框架,探讨了坪坦村空心化的现状表征、空间异变特征及形成机制。研究结果显示:①从空心化量化结果来看,坪坦村空心化呈现出传统建筑外观保护尚好、但人口空心化和建筑空心化问题依旧显著的特异性;同时,新村、商业街与传统村落的空心化程度存在微观地域分异特征。②从空心化具体特征来看,坪坦村人口空间呈现出就业非农化和流动保守化的趋势,建筑空间表现为宏观层面的空间肌理破碎化和微观层面的住宅建筑低能化,文化空间则面临表征脱域化与传承断层化的挑战。③从空心化形成机制来看,坪坦村空心化是在宏观社会环境与微观局限空间共同催化以及外源性主体与内生性主体联合驱动下,呈现出村落地域空间系统异化的复杂演变过程。研究旨在探索少数民族传统村落空心化特征与形成机制的一般规律,以期为城乡融合与乡村振兴在同类型微观地域空间的有效落地提供科学依据,推动少数民族传统村落的活态存续。
