The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of applic...The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of application.In addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and jitter.In this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder pressure.The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS states.Apart from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode.In order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual pressure.Through mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these scenes.This research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.展开更多
Flexible zinc-air batteries(FZABs)are featured with safety and high theoretical capacity and become one of the ideal energy supply devices for flexible electronics.However,the lack of cost-effective electrocatalysts r...Flexible zinc-air batteries(FZABs)are featured with safety and high theoretical capacity and become one of the ideal energy supply devices for flexible electronics.However,the lack of cost-effective electrocatalysts remains a major obstacle to their commercialization.Herein,we synthesized a porous dodecahedral nitrogen-doped carbon(NC)material with Co and Mn bimetallic co-embedding(CoxMni-x@NC)as a highly efficient oxygen reduction reaction(ORR)catalyst for ZABs.The incorporation of Mn effectively modulates the electronic structure of Co sites,which may lead to optimized energetics with oxygen-containing intermediates thereby significantly enhancing catalytic performance.Notably,the optimized Co4Mn1@NC catalyst exhibits superior E1/2(0.86 V)and jL(limiting current density,5.96 mA cm-2)compared to Pt/C and other recent reports.Moreover,aqueous ZAB using Co4Mn1@NC as a cathodic catalyst demonstrates a high peak power density of 163.9 mW cm-2 and maintains stable charging and discharging for over 650 h.Furthermore,FZAB based on Co4Mn1@NC can steadily operate within the temperature range of-10 to 40 ℃,demonstrating the potential for practical applications in complex climatic conditions.展开更多
Postural control is based upon the fusion of sensory cues coming from multiple sources requiring continuously adaptation that may be altered due to aging, leading to the poor postural equilibrium in older adults. Ther...Postural control is based upon the fusion of sensory cues coming from multiple sources requiring continuously adaptation that may be altered due to aging, leading to the poor postural equilibrium in older adults. Therefore, the purpose of this study was to examine the adaptation in the relationship between the visual information and the body sway in older adults. Fifteen older (70 ± 7.6 years) and 15 younger adults (19 ± 1.1 years) stood upright inside of a moving room. Each participant performed 7 trials, each lasting 60 s, in which in the first 3 trials the room oscillated at 0.2 Hz, amplitude of 0.6 cm, and peak-to-peak velocity of 0.6 cm/s. In the fourth trial, the room oscillated at 0.2 Hz but with amplitude of 3.5 cm and peak-to-peak velocity of 3.5 cm/s. In the following 3 trials, the room oscillated with the same parameters of the first three trials. Body sway magnitude was examined through mean sway amplitude, and the relationship between visual information and induced body sway was examined through coherence and gain. Visual manipulation induced corresponding body sway in both older and younger adults, with no difference being observed between groups in the first three trials. In the fourth trial, mean sway amplitude, coherence and gain values were higher for the older compared to younger adults. Moreover, in the last three trials, older adults still showed higher gain values than observed for the younger adults. Taken together, these results suggest that older adults adapt to abrupt changes in visual cues, but not with the same magnitude as younger adults. Yet, older adults do not take advantage of experienced sensory changes in order to adapt the use of the vision information in the following experiences, indicating the less capability of adaptation to the sensory changes.展开更多
Purpose:This study aims to develop a transdisciplinary informal curriculum for climate change education(CCE)to increase the adaptive capacity of the small-farm milk-producing sector in Encarnacion de Diaz,Jalisco,M...Purpose:This study aims to develop a transdisciplinary informal curriculum for climate change education(CCE)to increase the adaptive capacity of the small-farm milk-producing sector in Encarnacion de Diaz,Jalisco,México.Design/Approach/Methods:A sustainable rural livelihood framework assessing six types of capital(animal,financial,human,natural,physical,and sociocultural)in a sequential exploratory method design was used to determine the adaptive capacity of 6l milk producers to climate change.Several interrelated aspects of capital are associated with milk producers'vulnerability to climate change.Findings:Dairy farmers'knowledge is based on traditional,historical,and cultural ways of interacting with their environment.Respecting this knowledge allowed us to use their experiential knowledge to co-jointly develop a CCE model to decrease the vulnerability of each of the six identified types of capital,with financial,human,and sociocultural capital being the most vulnerable.Originality/Value:Using local knowledge to cultivate adaptive actions for climate change and reducing the vulnerability of affected communities is essential when developing an informal CCE curriculum.展开更多
The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers.Specific lightweight cryptosystems are needed to protec...The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers.Specific lightweight cryptosystems are needed to protect those links,as the hardware node tends to be resource-constrained.Then Pseudo Random Number Generators are employed to produce random keys,whose final behavior depends on the initial seed.To guarantee good mathematical behavior,most key generators need an unpredictable voltage signal as input.However,physical signals evolve slowly and have a significant autocorrelation,so they do not have enough entropy to support highrandomness seeds.Then,electronic mechanisms to generate those high-entropy signals artificially are required.This paper proposes a robust hyperchaotic circuit to obtain such unpredictable electric signals.The circuit is based on a hyperchaotic dynamic system,showing a large catalog of structures,four different secret parameters,and producing four high entropy voltage signals.Synchronization schemes for the correct secret key calculation and distribution among all remote communicating modules are also analyzed and discussed.Security risks and intruder and attacker models for the proposed solution are explored,too.An experimental validation based on circuit simulations and a real hardware implementation is provided.The results show that the random properties of PRNG improved by up to 11%when seeds were calculated through the proposed circuit.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Ecological conservation is at a crossroad as environmental stresses around the world intensify and traditional models of conservation exhibit intrinsic weaknesses in their response to present and future problems.In th...Ecological conservation is at a crossroad as environmental stresses around the world intensify and traditional models of conservation exhibit intrinsic weaknesses in their response to present and future problems.In the project,we evaluated novel approaches integrating adaptive management,technological innovations,and community-based action towards more efficient sustainable conservation results and ecosystem resilience.The multi-site experimental design was based on comparison between conventional reserve management and novel integrative models implemented in diverse ecological zones.Data were collected over a period of three years employing remote sensing technologies,in situ biodiversity assessments,and large socioeconomic surveys.These instruments enabled a robust and multi-dimensional measurement of variables such as species diversity,ecological resilience,community engagement,and stakeholder engagement.The results indicate that adaptive strategies significantly enhance real-time decision-making abilities and enhance long-term ecosystem resilience.Further,technology-driven monitoring greatly enhances data accuracy,responsiveness,and early warning capabilities.Besides that,community-based conservation initiatives were found to be pivotal in facilitating local stewardship,enhancing participatory governance,and enabling more adaptive and adaptive policy systems.This research rejects mainstream conservation paradigms by placing importance on flexibility,interdisciplinarity,and inclusivity of governance systems in effectively mitigating the impacts of climate change and loss of biodiversity.Our findings offer strong evidence that emerging paradigms of conservation can provide greater ecological and social sustainability than traditional methods.These results support the need for a paradigm shift towards conservation strategies that are dynamic,collaborative,and technologically integrated,with significant implications for policy formulation as well as operational environmental management.展开更多
目的探讨在急性缺血性脑卒中患者中应用直接抽吸一次性取栓(A direct aspiration First-Pass thrombectomy,ADAPT)进行血管再通的安全性、可行性及技术优势。方法回顾性分析本院神经内科2021年3月至2023年10月接受血管再通术治疗的54例...目的探讨在急性缺血性脑卒中患者中应用直接抽吸一次性取栓(A direct aspiration First-Pass thrombectomy,ADAPT)进行血管再通的安全性、可行性及技术优势。方法回顾性分析本院神经内科2021年3月至2023年10月接受血管再通术治疗的54例急性脑卒中患者。根据取栓技术的不同,患者被分为研究组(应用ADAPT技术直接抽吸取栓,34例)和对照组[应用Solitaire FR支架机械取栓术(Solitaire FR with intracranial support catheter for mechanical thrombectomy,SWIM),20例]。比较两组的取栓次数、手术操作时间、血管完全再通率、术前与术后2周美国国立卫生研究院卒中量表(National institutes of health stroke scale,NIHSS)评分、并发症发生率及术后3个月良好预后率。结果两组采用不同取栓技术后,研究组的取栓次数和手术操作时间均低于对照组(P<0.05)。术前两组的NIHSS评分差异无统计学意义(P>0.05)。术后2周,研究组的NIHSS评分显著低于对照组(P<0.05)。两组的血管完全再通率分别为70.59%和75.00%,术后3个月良好预后率分别为64.71%和60.00%,两组间差异无统计学意义(P>0.05)。研究组的并发症发生率(8.82%)显著低于对照组(20.00%)(P<0.05)。结论与SWIM取栓技术相比,ADAPT技术在血管再通率上无显著差异,但能显著减少急性脑卒中患者的取栓次数和手术操作时间,提升术后3个月的良好预后率,改善术后2周的NIHSS评分,并降低并发症发生率。ADAPT技术在改善患者功能恢复和降低并发症方面显示了更大的潜力,为急性缺血性脑卒中的临床治疗提供了有力的替代方案。展开更多
A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the s...A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models...Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.展开更多
Agricultural pests cause enormous losses in annual agricultural production.Understanding the evolutionary responses and adaptive capacity of agricultural pests under climate change is crucial for establishing sustaina...Agricultural pests cause enormous losses in annual agricultural production.Understanding the evolutionary responses and adaptive capacity of agricultural pests under climate change is crucial for establishing sustainable and environmentally friendly agricultural pest management.In this study,we integrate climate modeling and landscape genomics to investigate the distributional dynamics of the cotton bollworm(Helicoverpa armigera)in the adaptation to local environments and resilience to future climate change.Notably,the predicted inhabitable areas with higher suitability for the cotton bollworm could be eight times larger in the coming decades.Climate change is one of the factors driving the dynamics of distribution and population differentiation of the cotton bollworm.Approximately 19,000 years ago,the cotton bollworm expanded from its ancestral African population,followed by gradual occupations of the European,Asian,Oceanian,and American continents.Furthermore,we identify seven subpopulations with high dispersal and adaptability which may have an increased risk of invasion potential.Additionally,a large number of candidate genes and SNPs linked to climatic adaptation were mapped.These findings could inform sustainable pest management strategies in the face of climate change,aiding future pest forecasting and management planning.展开更多
Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wil...Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances a...Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52202494,52202495)Chongqing Special Project for Technological Innovation and Application Development(Grant No.CSTB2022TIAD-DEX0014).
文摘The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of application.In addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and jitter.In this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder pressure.The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS states.Apart from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode.In order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual pressure.Through mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these scenes.This research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.
基金supported by the National Natural Science Foundation of China(22275166,51972286and 22005268)the Zhejiang Provincial Natural Science Foundation of China(LZ21E020003and LQ20B010011)+2 种基金the Fundamental Research Funds for the Provincial Universities of Zhe-jiang(RF-B2023002and RF-C-2023025)the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang(2020R01002)China National University Student Innovation and Entrepreneurship Development Program(202310337065).
文摘Flexible zinc-air batteries(FZABs)are featured with safety and high theoretical capacity and become one of the ideal energy supply devices for flexible electronics.However,the lack of cost-effective electrocatalysts remains a major obstacle to their commercialization.Herein,we synthesized a porous dodecahedral nitrogen-doped carbon(NC)material with Co and Mn bimetallic co-embedding(CoxMni-x@NC)as a highly efficient oxygen reduction reaction(ORR)catalyst for ZABs.The incorporation of Mn effectively modulates the electronic structure of Co sites,which may lead to optimized energetics with oxygen-containing intermediates thereby significantly enhancing catalytic performance.Notably,the optimized Co4Mn1@NC catalyst exhibits superior E1/2(0.86 V)and jL(limiting current density,5.96 mA cm-2)compared to Pt/C and other recent reports.Moreover,aqueous ZAB using Co4Mn1@NC as a cathodic catalyst demonstrates a high peak power density of 163.9 mW cm-2 and maintains stable charging and discharging for over 650 h.Furthermore,FZAB based on Co4Mn1@NC can steadily operate within the temperature range of-10 to 40 ℃,demonstrating the potential for practical applications in complex climatic conditions.
文摘Postural control is based upon the fusion of sensory cues coming from multiple sources requiring continuously adaptation that may be altered due to aging, leading to the poor postural equilibrium in older adults. Therefore, the purpose of this study was to examine the adaptation in the relationship between the visual information and the body sway in older adults. Fifteen older (70 ± 7.6 years) and 15 younger adults (19 ± 1.1 years) stood upright inside of a moving room. Each participant performed 7 trials, each lasting 60 s, in which in the first 3 trials the room oscillated at 0.2 Hz, amplitude of 0.6 cm, and peak-to-peak velocity of 0.6 cm/s. In the fourth trial, the room oscillated at 0.2 Hz but with amplitude of 3.5 cm and peak-to-peak velocity of 3.5 cm/s. In the following 3 trials, the room oscillated with the same parameters of the first three trials. Body sway magnitude was examined through mean sway amplitude, and the relationship between visual information and induced body sway was examined through coherence and gain. Visual manipulation induced corresponding body sway in both older and younger adults, with no difference being observed between groups in the first three trials. In the fourth trial, mean sway amplitude, coherence and gain values were higher for the older compared to younger adults. Moreover, in the last three trials, older adults still showed higher gain values than observed for the younger adults. Taken together, these results suggest that older adults adapt to abrupt changes in visual cues, but not with the same magnitude as younger adults. Yet, older adults do not take advantage of experienced sensory changes in order to adapt the use of the vision information in the following experiences, indicating the less capability of adaptation to the sensory changes.
文摘Purpose:This study aims to develop a transdisciplinary informal curriculum for climate change education(CCE)to increase the adaptive capacity of the small-farm milk-producing sector in Encarnacion de Diaz,Jalisco,México.Design/Approach/Methods:A sustainable rural livelihood framework assessing six types of capital(animal,financial,human,natural,physical,and sociocultural)in a sequential exploratory method design was used to determine the adaptive capacity of 6l milk producers to climate change.Several interrelated aspects of capital are associated with milk producers'vulnerability to climate change.Findings:Dairy farmers'knowledge is based on traditional,historical,and cultural ways of interacting with their environment.Respecting this knowledge allowed us to use their experiential knowledge to co-jointly develop a CCE model to decrease the vulnerability of each of the six identified types of capital,with financial,human,and sociocultural capital being the most vulnerable.Originality/Value:Using local knowledge to cultivate adaptive actions for climate change and reducing the vulnerability of affected communities is essential when developing an informal CCE curriculum.
基金supported by Comunidad de Madrid within the framework of the Multiannual Agreement with Universidad Politecnica de Madrid to encourage research by young doctors(PRINCE).
文摘The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers.Specific lightweight cryptosystems are needed to protect those links,as the hardware node tends to be resource-constrained.Then Pseudo Random Number Generators are employed to produce random keys,whose final behavior depends on the initial seed.To guarantee good mathematical behavior,most key generators need an unpredictable voltage signal as input.However,physical signals evolve slowly and have a significant autocorrelation,so they do not have enough entropy to support highrandomness seeds.Then,electronic mechanisms to generate those high-entropy signals artificially are required.This paper proposes a robust hyperchaotic circuit to obtain such unpredictable electric signals.The circuit is based on a hyperchaotic dynamic system,showing a large catalog of structures,four different secret parameters,and producing four high entropy voltage signals.Synchronization schemes for the correct secret key calculation and distribution among all remote communicating modules are also analyzed and discussed.Security risks and intruder and attacker models for the proposed solution are explored,too.An experimental validation based on circuit simulations and a real hardware implementation is provided.The results show that the random properties of PRNG improved by up to 11%when seeds were calculated through the proposed circuit.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the Lebanese International University(LIU)with a funding amount of$500.
文摘Ecological conservation is at a crossroad as environmental stresses around the world intensify and traditional models of conservation exhibit intrinsic weaknesses in their response to present and future problems.In the project,we evaluated novel approaches integrating adaptive management,technological innovations,and community-based action towards more efficient sustainable conservation results and ecosystem resilience.The multi-site experimental design was based on comparison between conventional reserve management and novel integrative models implemented in diverse ecological zones.Data were collected over a period of three years employing remote sensing technologies,in situ biodiversity assessments,and large socioeconomic surveys.These instruments enabled a robust and multi-dimensional measurement of variables such as species diversity,ecological resilience,community engagement,and stakeholder engagement.The results indicate that adaptive strategies significantly enhance real-time decision-making abilities and enhance long-term ecosystem resilience.Further,technology-driven monitoring greatly enhances data accuracy,responsiveness,and early warning capabilities.Besides that,community-based conservation initiatives were found to be pivotal in facilitating local stewardship,enhancing participatory governance,and enabling more adaptive and adaptive policy systems.This research rejects mainstream conservation paradigms by placing importance on flexibility,interdisciplinarity,and inclusivity of governance systems in effectively mitigating the impacts of climate change and loss of biodiversity.Our findings offer strong evidence that emerging paradigms of conservation can provide greater ecological and social sustainability than traditional methods.These results support the need for a paradigm shift towards conservation strategies that are dynamic,collaborative,and technologically integrated,with significant implications for policy formulation as well as operational environmental management.
文摘目的探讨在急性缺血性脑卒中患者中应用直接抽吸一次性取栓(A direct aspiration First-Pass thrombectomy,ADAPT)进行血管再通的安全性、可行性及技术优势。方法回顾性分析本院神经内科2021年3月至2023年10月接受血管再通术治疗的54例急性脑卒中患者。根据取栓技术的不同,患者被分为研究组(应用ADAPT技术直接抽吸取栓,34例)和对照组[应用Solitaire FR支架机械取栓术(Solitaire FR with intracranial support catheter for mechanical thrombectomy,SWIM),20例]。比较两组的取栓次数、手术操作时间、血管完全再通率、术前与术后2周美国国立卫生研究院卒中量表(National institutes of health stroke scale,NIHSS)评分、并发症发生率及术后3个月良好预后率。结果两组采用不同取栓技术后,研究组的取栓次数和手术操作时间均低于对照组(P<0.05)。术前两组的NIHSS评分差异无统计学意义(P>0.05)。术后2周,研究组的NIHSS评分显著低于对照组(P<0.05)。两组的血管完全再通率分别为70.59%和75.00%,术后3个月良好预后率分别为64.71%和60.00%,两组间差异无统计学意义(P>0.05)。研究组的并发症发生率(8.82%)显著低于对照组(20.00%)(P<0.05)。结论与SWIM取栓技术相比,ADAPT技术在血管再通率上无显著差异,但能显著减少急性脑卒中患者的取栓次数和手术操作时间,提升术后3个月的良好预后率,改善术后2周的NIHSS评分,并降低并发症发生率。ADAPT技术在改善患者功能恢复和降低并发症方面显示了更大的潜力,为急性缺血性脑卒中的临床治疗提供了有力的替代方案。
基金The National Natural Science Foundation of China(No.U19B2031).
文摘A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
文摘Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.
基金funded by the National Natural Science Foundation of China(32372546)Shenzhen Science and Technology Program(KQTD20180411143628272)+1 种基金the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences and STI 2030-Major Projects(2022ZD04021)the National Key Research and Development Program of China(2023YFD2200700)。
文摘Agricultural pests cause enormous losses in annual agricultural production.Understanding the evolutionary responses and adaptive capacity of agricultural pests under climate change is crucial for establishing sustainable and environmentally friendly agricultural pest management.In this study,we integrate climate modeling and landscape genomics to investigate the distributional dynamics of the cotton bollworm(Helicoverpa armigera)in the adaptation to local environments and resilience to future climate change.Notably,the predicted inhabitable areas with higher suitability for the cotton bollworm could be eight times larger in the coming decades.Climate change is one of the factors driving the dynamics of distribution and population differentiation of the cotton bollworm.Approximately 19,000 years ago,the cotton bollworm expanded from its ancestral African population,followed by gradual occupations of the European,Asian,Oceanian,and American continents.Furthermore,we identify seven subpopulations with high dispersal and adaptability which may have an increased risk of invasion potential.Additionally,a large number of candidate genes and SNPs linked to climatic adaptation were mapped.These findings could inform sustainable pest management strategies in the face of climate change,aiding future pest forecasting and management planning.
基金funded by the National Natural Science Foundation of China(grant no.32270238 and 31870311).
文摘Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金the National Key Research and Development Program of China(2021YFA0717900)National Natural Science Foundation of China(62471251,62405144,62288102,22275098,and 62174089)+1 种基金Basic Research Program of Jiangsu(BK20240033,BK20243057)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB402).
文摘Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.
