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
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
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
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
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
This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Ham...This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Hamiltonian principle.An analysis of internal flow’s influence on the vibration characteristics of flexible marine risers is conducted.Then,for the uncertain environmental disturbance,the adaptive fuzzy logic system is introduced to dynamically approximate the boundary disturbance,and a robust adaptive fuzzy boundary control is proposed.The uniform boundedness of the closed-loop system is proved based on Lyapunov theory.The well-posedness of the closed-loop system is proved by operator semigroup theory.The proposed control’s effectiveness is validated through comparison with existing control methods.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
Objectives:Teachers are facing unprecedented new challenges leading them to face an increasing number of tasks that are not part of their job,as well as having to cope with the additional skills acquisition that comes...Objectives:Teachers are facing unprecedented new challenges leading them to face an increasing number of tasks that are not part of their job,as well as having to cope with the additional skills acquisition that comes with non-traditional forms of teaching and learning,and increased work pressure leading to an increase in the rate of teachers leaving the profession.Therefore,this study aims to explore the mechanism of the career calling on job burnout through career adaptability and work engagement.Methods:This study conducted a cross-sectional survey of 465 primary and secondary school teachers(PSST)in China's Mainland from the perspective of work adjustment and used structural equation modeling(SEM)to examine the mediating roles of career adaptability and work engagement in the relationship between teachers’career calling and job burnout.Results:The results show that PSSTs are above average in career calling,career adaptability,and work engagement,while job burnout is below average.A significant positive or negative correlation exists between career calling,career adaptability,work engagement,and job burnout.The result of path analysis indicates that career adaptability and work engagement exert an indirect influence on the job burnout of PSST through three paths:namely,the independent intermediary role of career adaptability(EV=−0.144),the independent intermediary role of work engagement(EV=0.172)and the chain intermediary role of the two(EV=0.176).Conclusion:This study emphasizes the importance of career adaptability and work engagement in teacher development in regulating career calling and job burnout.Therefore,on the one hand,we think that if managers want to reduce teachers’job burnout,they need to pay more attention to teachers’career adaptability and work engagement,rather than relying solely on teachers’career calling.On the other hand,it is to remind teachers not to rely on their adjustment to adapt to the work,but also to need outside help as much as possible.展开更多
Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inh...Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a tra...The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.展开更多
This study explores the cultural,social,and academic adaptation experiences of international students in Wenzhou,China.Based on surveys and interviews with 52 students from 20 countries—predominantly Morocco—the res...This study explores the cultural,social,and academic adaptation experiences of international students in Wenzhou,China.Based on surveys and interviews with 52 students from 20 countries—predominantly Morocco—the research investigates key challenges and coping strategies related to local integration.The findings indicate that while Wenzhou offers a generally supportive academic environment—enhanced by AI integration and practical teaching methods—language barriers continue to hinder students’daily life,academic engagement,and social interactions.Limited Mandarin proficiency made it difficult for many students to build friendships with locals and navigate everyday tasks.Cultural adaptation also presented obstacles,particularly in adjusting to local food and social norms.Despite these challenges,students employed various strategies to facilitate integration,such as attending HSK language courses,watching Chinese media,and initiating conversations with local peers.While most participants described the local community as welcoming,perceptions varied based on individual experiences and language ability.The study highlights the importance of enhanced language support and structured cross-cultural exchange initiatives in improving international students’experiences.It contributes to the broader discourse on international student mobility by offering insights from a second-tier Chinese city,emphasizing the role of institutional practices in shaping adaptation outcomes.展开更多
To address the significant degradation of Space-Time Adaptive Processing(STAP)performance when the array elements have mutual coupling and gain/phase errors,a STAP algorithm with adaptive calibration for the above two...To address the significant degradation of Space-Time Adaptive Processing(STAP)performance when the array elements have mutual coupling and gain/phase errors,a STAP algorithm with adaptive calibration for the above two array errors is proposed in this article.First,based on a defined error matrix that simultaneously considers both array mutual coupling and gain/phase errors,a STAP signal model including these errors is given.Then,utilizing the defined signal model,it is demonstrated that the estimation of the defined error matrix can be formulized as a standard convex optimization problem with the low-rank structure of the clutter covariance matrix and the subspace projection theory.Once the defined error matrix is estimated by solving the convex optimization problem,it is illustrated that a STAP method with adaptive calibration of the mutual coupling and gain/phase errors is coined.Analyses also show that the proposed adaptive calibration algorithm only needs one training sample to construct the adaptive weight vector.Therefore,it can achieve a good detection performance even with severe non-homogeneous clutter environments.Finally,the simulation experiments verify the effectiveness of the proposed algorithm and the correctness of the analytical results.展开更多
As climate change triggers unprecedented ecological shifts,it becomes imperative to understand the genetic underpinnings of species’adaptability.Adaptive introgression significantly contributes to organismal adaptati...As climate change triggers unprecedented ecological shifts,it becomes imperative to understand the genetic underpinnings of species’adaptability.Adaptive introgression significantly contributes to organismal adaptation to new environments by introducing genetic variation across species boundaries.However,despite growing recognition of its importance,the extent to which adaptive introgression has shaped the evolutionary history of closely related species remains poorly understood.Here we employed population genetic analyses of high-throughput sequencing data to investigate the interplay between genetic introgression and local adaptation in three species of spruce trees in the genus Picea(P.asperata,P.crassifolia,and P.meyeri).We find distinct genetic differentiation among these species,despite a substantial gene flow.Crucially,we find bidirectional adaptive introgression between allopatrically distributed species pairs and unearthed dozens of genes linked to stress resilience and flowering time.These candidate genes most likely have promoted adaptability of these spruces to historical environmental changes and may enhance their survival and resilience to future climate changes.Our findings highlight that adaptive introgression could be prevalent and bidirectional in a topographically complex area,and this could have contributed to rich genetic variation and diverse habitat usage by tree species.展开更多
The environment has an important impact on maize(Zea mays L.)production,making it necessary to identify plant adaptation regions that are suitable for different maize varieties.Traditional methods using field trials a...The environment has an important impact on maize(Zea mays L.)production,making it necessary to identify plant adaptation regions that are suitable for different maize varieties.Traditional methods using field trials are costly and restricted to a limited number of areas.Identifying adaptation regions based on climate data has great potential,but a basic understanding and a prediction approach for diverse maize varieties are lacking.Here,we collected a representative dataset comprising 32,840 data points from the National Maize Variety Trial Data Management Platform.We employed three traits to characterize the adaptability of different maize varieties:PH(plant height),DTS(days to silking),and yield.First,we quantified the contributions of variety(V),environment(E),and V×E to variance in the three adaptationrelated traits.The mean contributions of E to variance in PH,DTS,and yield were 54.50%,82.87%,and 75.92%,respectively,suggesting that environmental effects are crucial for phenotype construction.Second,we analyzed correlations between the three traits and three environmental indices:GDD(growing degree days),PRE(precipitation),and SSD(sunshine duration).The highest absolute correlation coefficients between phenotypes and environmental indices were 0.15–0.69 at the whole-data level.To predict variety adaptation on a national scale,we modeled the three traits using environmental indices and best linear unbiased predictors(BLUPs)via the random forest algorithm.The predictive abilities of our models for PH,DTS,and yield were 0.90(MAE=9.95 cm),0.99(MAE=1.09 d),and 0.95(MAE=0.55 t ha^(−1)),respectively,indicating that our proposed framework can predict adaptationrelated traits for diverse maize varieties in China.展开更多
Understanding the ecological adaptation of tree species can not only reveal the evolutionary potential but also benefit biodiversity conservation under global climate change.Quercus is a keystone genus in Northern Hem...Understanding the ecological adaptation of tree species can not only reveal the evolutionary potential but also benefit biodiversity conservation under global climate change.Quercus is a keystone genus in Northern Hemisphere forests,and its wide distribution in diverse ecosystems and long evolutionary history make it an ideal model for studying the genomic basis of ecological adaptations.Here we used a newly sequenced genome of Quercus gilva,an evergreen oak species from East Asia,with 18 published Fagales genomes to determine how Fagaceae genomes have evolved,identify genomic footprints of ecological adaptability in oaks in general,as well as between evergreen and deciduous oaks.We found that oak species exhibited a higher degree of genomic conservation and stability,as indicated by the absence of large-scale chromosomal structural variations or additional whole-genome duplication events.In addition,we identified expansion and tandem repetitions within gene families that contribute to plant physical and chemical defense(e.g.,cuticle biosynthesis and oxidosqualene cyclase genes),which may represent the foundation for the ecological adaptation of oak species.Circadian rhythm and hormone-related genes may regulate the habits of evergreen and deciduous oaks.This study provides a comprehensive perspective on the ecological adaptations of tree species based on phylogenetic,genome evolutionary,and functional genomic analyses.展开更多
Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response ...Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.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 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.
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financially supported by Sichuan Science and Technology Program(Grant No.2023NSFSC1980).
文摘This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Hamiltonian principle.An analysis of internal flow’s influence on the vibration characteristics of flexible marine risers is conducted.Then,for the uncertain environmental disturbance,the adaptive fuzzy logic system is introduced to dynamically approximate the boundary disturbance,and a robust adaptive fuzzy boundary control is proposed.The uniform boundedness of the closed-loop system is proved based on Lyapunov theory.The well-posedness of the closed-loop system is proved by operator semigroup theory.The proposed control’s effectiveness is validated through comparison with existing control methods.
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funded by Humanities and Social Sciences Foundation and Natural Science Foundation of Nanjing University of Posts and Telecommunications(NYY222055,NY224176)General Subject of Educational Science Planning in Jiangsu Province(C/2024/01/76)National Natural Science Foundation of China(62307025).
文摘Objectives:Teachers are facing unprecedented new challenges leading them to face an increasing number of tasks that are not part of their job,as well as having to cope with the additional skills acquisition that comes with non-traditional forms of teaching and learning,and increased work pressure leading to an increase in the rate of teachers leaving the profession.Therefore,this study aims to explore the mechanism of the career calling on job burnout through career adaptability and work engagement.Methods:This study conducted a cross-sectional survey of 465 primary and secondary school teachers(PSST)in China's Mainland from the perspective of work adjustment and used structural equation modeling(SEM)to examine the mediating roles of career adaptability and work engagement in the relationship between teachers’career calling and job burnout.Results:The results show that PSSTs are above average in career calling,career adaptability,and work engagement,while job burnout is below average.A significant positive or negative correlation exists between career calling,career adaptability,work engagement,and job burnout.The result of path analysis indicates that career adaptability and work engagement exert an indirect influence on the job burnout of PSST through three paths:namely,the independent intermediary role of career adaptability(EV=−0.144),the independent intermediary role of work engagement(EV=0.172)and the chain intermediary role of the two(EV=0.176).Conclusion:This study emphasizes the importance of career adaptability and work engagement in teacher development in regulating career calling and job burnout.Therefore,on the one hand,we think that if managers want to reduce teachers’job burnout,they need to pay more attention to teachers’career adaptability and work engagement,rather than relying solely on teachers’career calling.On the other hand,it is to remind teachers not to rely on their adjustment to adapt to the work,but also to need outside help as much as possible.
文摘Adult neurogenesis persists after birth in the subventricular zone, with new neurons migrating to the granule cell layer and glomerular layers of the olfactory bulb, where they integrate into existing circuitry as inhibitory interneurons. The generation of these new neurons in the olfactory bulb supports both structural and functional plasticity, aiding in circuit remodeling triggered by memory and learning processes. However, the presence of these neurons, coupled with the cellular diversity within the olfactory bulb, presents an ongoing challenge in understanding its network organization and function. Moreover,the continuous integration of new neurons in the olfactory bulb plays a pivotal role in regulating olfactory information processing. This adaptive process responds to changes in epithelial composition and contributes to the formation of olfactory memories by modulating cellular connectivity within the olfactory bulb and interacting intricately with higher-order brain regions. The role of adult neurogenesis in olfactory bulb functions remains a topic of debate. Nevertheless, the functionality of the olfactory bulb is intricately linked to the organization of granule cells around mitral and tufted cells. This organizational pattern significantly impacts output, network behavior, and synaptic plasticity, which are crucial for olfactory perception and memory. Additionally, this organization is further shaped by axon terminals originating from cortical and subcortical regions. Despite the crucial role of olfactory bulb in brain functions and behaviors related to olfaction, these complex and highly interconnected processes have not been comprehensively studied as a whole. Therefore, this manuscript aims to discuss our current understanding and explore how neural plasticity and olfactory neurogenesis contribute to enhancing the adaptability of the olfactory system. These mechanisms are thought to support olfactory learning and memory, potentially through increased complexity and restructuring of neural network structures, as well as the addition of new granule granule cells that aid in olfactory adaptation. Additionally, the manuscript underscores the importance of employing precise methodologies to elucidate the specific roles of adult neurogenesis amidst conflicting data and varying experimental paradigms. Understanding these processes is essential for gaining insights into the complexities of olfactory function and behavior.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
文摘The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.
基金supported by Cultural and Ideological Progress Director Center of Ouhai District of Wenzhou(2024-135F).
文摘This study explores the cultural,social,and academic adaptation experiences of international students in Wenzhou,China.Based on surveys and interviews with 52 students from 20 countries—predominantly Morocco—the research investigates key challenges and coping strategies related to local integration.The findings indicate that while Wenzhou offers a generally supportive academic environment—enhanced by AI integration and practical teaching methods—language barriers continue to hinder students’daily life,academic engagement,and social interactions.Limited Mandarin proficiency made it difficult for many students to build friendships with locals and navigate everyday tasks.Cultural adaptation also presented obstacles,particularly in adjusting to local food and social norms.Despite these challenges,students employed various strategies to facilitate integration,such as attending HSK language courses,watching Chinese media,and initiating conversations with local peers.While most participants described the local community as welcoming,perceptions varied based on individual experiences and language ability.The study highlights the importance of enhanced language support and structured cross-cultural exchange initiatives in improving international students’experiences.It contributes to the broader discourse on international student mobility by offering insights from a second-tier Chinese city,emphasizing the role of institutional practices in shaping adaptation outcomes.
基金co-supported by the National Natural Science Foundation of China(No.12374431)。
文摘To address the significant degradation of Space-Time Adaptive Processing(STAP)performance when the array elements have mutual coupling and gain/phase errors,a STAP algorithm with adaptive calibration for the above two array errors is proposed in this article.First,based on a defined error matrix that simultaneously considers both array mutual coupling and gain/phase errors,a STAP signal model including these errors is given.Then,utilizing the defined signal model,it is demonstrated that the estimation of the defined error matrix can be formulized as a standard convex optimization problem with the low-rank structure of the clutter covariance matrix and the subspace projection theory.Once the defined error matrix is estimated by solving the convex optimization problem,it is illustrated that a STAP method with adaptive calibration of the mutual coupling and gain/phase errors is coined.Analyses also show that the proposed adaptive calibration algorithm only needs one training sample to construct the adaptive weight vector.Therefore,it can achieve a good detection performance even with severe non-homogeneous clutter environments.Finally,the simulation experiments verify the effectiveness of the proposed algorithm and the correctness of the analytical results.
基金the Project of Qinghai provincial central government guides local funds for science and technology development(2024ZY005).
文摘As climate change triggers unprecedented ecological shifts,it becomes imperative to understand the genetic underpinnings of species’adaptability.Adaptive introgression significantly contributes to organismal adaptation to new environments by introducing genetic variation across species boundaries.However,despite growing recognition of its importance,the extent to which adaptive introgression has shaped the evolutionary history of closely related species remains poorly understood.Here we employed population genetic analyses of high-throughput sequencing data to investigate the interplay between genetic introgression and local adaptation in three species of spruce trees in the genus Picea(P.asperata,P.crassifolia,and P.meyeri).We find distinct genetic differentiation among these species,despite a substantial gene flow.Crucially,we find bidirectional adaptive introgression between allopatrically distributed species pairs and unearthed dozens of genes linked to stress resilience and flowering time.These candidate genes most likely have promoted adaptability of these spruces to historical environmental changes and may enhance their survival and resilience to future climate changes.Our findings highlight that adaptive introgression could be prevalent and bidirectional in a topographically complex area,and this could have contributed to rich genetic variation and diverse habitat usage by tree species.
基金funded by the National Science and Technology Major Project(2022ZD0115703)the Beijing Postdoctoral Research Foundation(2023-ZZ-116).
文摘The environment has an important impact on maize(Zea mays L.)production,making it necessary to identify plant adaptation regions that are suitable for different maize varieties.Traditional methods using field trials are costly and restricted to a limited number of areas.Identifying adaptation regions based on climate data has great potential,but a basic understanding and a prediction approach for diverse maize varieties are lacking.Here,we collected a representative dataset comprising 32,840 data points from the National Maize Variety Trial Data Management Platform.We employed three traits to characterize the adaptability of different maize varieties:PH(plant height),DTS(days to silking),and yield.First,we quantified the contributions of variety(V),environment(E),and V×E to variance in the three adaptationrelated traits.The mean contributions of E to variance in PH,DTS,and yield were 54.50%,82.87%,and 75.92%,respectively,suggesting that environmental effects are crucial for phenotype construction.Second,we analyzed correlations between the three traits and three environmental indices:GDD(growing degree days),PRE(precipitation),and SSD(sunshine duration).The highest absolute correlation coefficients between phenotypes and environmental indices were 0.15–0.69 at the whole-data level.To predict variety adaptation on a national scale,we modeled the three traits using environmental indices and best linear unbiased predictors(BLUPs)via the random forest algorithm.The predictive abilities of our models for PH,DTS,and yield were 0.90(MAE=9.95 cm),0.99(MAE=1.09 d),and 0.95(MAE=0.55 t ha^(−1)),respectively,indicating that our proposed framework can predict adaptationrelated traits for diverse maize varieties in China.
基金supported by the National Natural Science Foundation of China(No.31901217)the Special Fund for Scientific Research of Shanghai Landscaping and City Appearance Administrative Bureau(grant numbers G192422,G242414,and G242416).
文摘Understanding the ecological adaptation of tree species can not only reveal the evolutionary potential but also benefit biodiversity conservation under global climate change.Quercus is a keystone genus in Northern Hemisphere forests,and its wide distribution in diverse ecosystems and long evolutionary history make it an ideal model for studying the genomic basis of ecological adaptations.Here we used a newly sequenced genome of Quercus gilva,an evergreen oak species from East Asia,with 18 published Fagales genomes to determine how Fagaceae genomes have evolved,identify genomic footprints of ecological adaptability in oaks in general,as well as between evergreen and deciduous oaks.We found that oak species exhibited a higher degree of genomic conservation and stability,as indicated by the absence of large-scale chromosomal structural variations or additional whole-genome duplication events.In addition,we identified expansion and tandem repetitions within gene families that contribute to plant physical and chemical defense(e.g.,cuticle biosynthesis and oxidosqualene cyclase genes),which may represent the foundation for the ecological adaptation of oak species.Circadian rhythm and hormone-related genes may regulate the habits of evergreen and deciduous oaks.This study provides a comprehensive perspective on the ecological adaptations of tree species based on phylogenetic,genome evolutionary,and functional genomic analyses.
基金supported by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.
