In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial...In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial panel data from China(2012–2022)and adopting a business environment perspective,this study applies a Panel Extended Regression Model(PERM),a Panel Simultaneous Equation Model(PSEM),and a Tobit-IV model to analyze how the development of the digital economy influences regional innovation.The results reveal a pronounced U-shaped relationship between the digital economy and the regional innovation performance at the provincial level in China,with the business environment serving as a significant mediator in this relationship.Moreover,regional innovation performance in China exhibits a“ratchet effect,”with the impact of the digital economy varying markedly across regions.While the eastern and western regions have entered an upward phase,whereby the digital economy boosts innovation,the central region displays a weaker effect.Further analysis indicates that the synergy between the business environment and the digital economy in driving innovation remains suboptimal.These findings were supported by robust checks.This study offers theoretical insights and empirical evidence that support the coordinated development of digital government and the digital factor market,as well as business environment reforms that are in alignment with the innovation demands of the digital era.展开更多
The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a mo...The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.展开更多
As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies r...As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.展开更多
The Hai Phong-Ha Long coastal area,with its World Natural Heritage site of Ha Long Bay-Cat Ba islands,has been under intense pressure from rapid development to meet the socio-economic goals set by Hai Phong City and Q...The Hai Phong-Ha Long coastal area,with its World Natural Heritage site of Ha Long Bay-Cat Ba islands,has been under intense pressure from rapid development to meet the socio-economic goals set by Hai Phong City and Quang Ninh Province.As such,urgent land needs for infrastructure construction of economic sectors and urbanization have led to intensive coastal reclamation and seafill leveling,and their environmental consequences.The objective of this study is to assess the adverse environmental effects of coastal reclamation in the Hai Phong—Ha Long area,focusing on ecosystems,environmental quality,and seabed morphology at a regional scale.To achieve this objective,the study employed the regular techniques of environmental assessment methods,such as checklists,matrices,network diagrams,and overlay maps,to appraise these environmental consequences.The results show three main impacted natural components,including coastal ecosystems,environmental qualities,and morphological seabeds,besides coastline changes and socio-economic issues.The most impacted component was coastal ecosystems,followed by the coastal environmental qualities of seawater and sediments,and then the morphological seabed.Based on the study results,it is recommended that during the development of an integrated coastal management plan for the coastal area of Hai Phong–Ha Long,environmental issues of coastal reclamation and seafill leveling must be given much attention.展开更多
In recent years,intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins.The implementation of forestry proj...In recent years,intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins.The implementation of forestry projects,coupled with continuous population growth,has increased the need for systematic assessments of ecological effects to ensure sustainable development in arid inland river basins.This study generated a 22-a(2000-2021)remote sensing ecological index(RSEI)data series for the Manas River Basin,a typical arid inland river basin in China,utilizing Moderate Resolution Imaging Spectroradiometer(MODIS)data and the Google Earth Engine(GEE)platform.We examined the spatiotemporal patterns of ecological environmental quality in the Manas River Basin through the Theil-Sen estimator,Mann-Kendall trend test,coefficient of variation(CV),and Hurst index.Furthermore,we employed the Optimal Parameter-based Geographical Detector(OPGD)method to quantify the influence of seven key drivers:elevation,slope,temperature,precipitation,gross domestic product(GDP),population density,and land use change.The key findings revealed that the basin's ecological environmental quality showed significant improvement(mean RSEI of 0.38,with a range of 0.34-0.41),with areas exhibiting good and excellent grades increasing by 16.71%,particularly in the midstream oasis region and upstream mountainous region,while areas exhibiting poor and relatively poor grades decreased by 11.52%in the downstream desert region.Spatial heterogeneity of ecological environmental quality was pronounced,with 32.23%of the areas showing localized degradation,the midstream oasis region exhibiting sustainable recovery potential(Hurst index>0.50),and only 36.67%of the areas maintaining stable and highly stable conditions(primarily in the upstream mountainous region).The OPGD analysis revealed that temperature(q-value=0.496-0.780),land use change(q-value=0.705-0.782),and elevation(q-value=0.245-0.637)were dominant factors,with the influence of land use change increasing during 2000-2020.Strong interaction effects emerged between land use change and temperature(q-value>0.705)and between land use change and elevation(q-value=0.751 in 2020),highlighting intensified human-nature coupling.These findings provide vital perspectives for ecosystem management in arid inland river basins under both climate and anthropogenic pressures.展开更多
Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general contr...Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general control zones—are established based on the ecological red line,the lower-limit line for environmental quality,and the resource use line.Human activities are regulated through a permit system.Integrated and multifactorial protection of soil,plant,hydrological,and atmospheric elements is promoted at the regional level.A follow-up assessment contributes to the improvement of policy implementation and effectiveness.This study demonstrates the achievements of the TLOP policy in Sichuan Province.Results show that(1)276 protection zones have been established under the ecological red line,covering key ecosystems and protected areas to ensure environmental security.Under the lower-limit line,1,626 functional(priority,key,and general control)zones have been designated to regulate air,water,and soil quality,enhancing environmental capacity and pollution control.(2)Through the integration and merging of the three lines,1,128 integrated management zones have been established,including 375,625,and 128 priority protection,critical control,and general control zones,respectively.Each zone has its own list of environmental permits to regulate human activities according to different environmental protection and natural resource development regimes.(3)The design of the follow-up assessment index system was informed by regional primary functions and industrial structure.The index system for provinces and cities is structured around three primary indicators—implementation updating,application,and guarantees—and 15 secondary indicators.The system for critical control zones is structured around environmental access,management,and effectiveness and 14 secondary indicators.A stringent environmental zoning system has been established through the TLOP policy,thereby safeguarding environmental security,promoting harmonious existence between humans and nature,and supporting the vision of Beautiful China.展开更多
The accelerating impacts of climate change,rising temperatures,extreme weather events,and biodiversity loss underscore the urgent need for widespread public awareness.This research explores why climate change awarenes...The accelerating impacts of climate change,rising temperatures,extreme weather events,and biodiversity loss underscore the urgent need for widespread public awareness.This research explores why climate change awareness is not just beneficial but essential for effective environmental stewardship and the long-term health of our planet.The research proffers informed communities,encouraging sustainable practices,and driving policy advocacy,awareness serves as a model for collective action.This call to consciousness challenges individuals,institutions,and nations to recognize their role in shaping a resilient,sustainable future for the Earth.Methodology adopted in this research is a mixed-method design,involving both qualitative and quasi-experimental designs,which engages the use of focus group discussions and oral interviews to explore deeper insights into perceptions,biodiversity loss consciousness,and environmental depletion challenges.Also,applicable under the qualitative method is the secondary data collection mode,namely,reports from IPCC,government policy documents,and existing literature related to the context of the research.The empirical and scientific data analysis was presented from the data collected and was coded and subjected to analysis using a paired samples t-test.The study is grounded on the theory of“Value-Belief-Norm”(VBN)developed by Stern et al.The VBN theory posits that individuals are more likely to engage in pro-environmental behaviour when their values(especially biosphere and altruistic),beliefs(about environmental consequences),and norms(personal responsibility to act)align.The findings of this study underscore the critical role of climate change awareness in fostering environmental and earth stewardship.The paper recommends that Governments of the country(State and federal)should take urgent steps in sensitising the general public on the causes and impact of climate change.展开更多
Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with env...Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.展开更多
The coastal regions of southern China experience the country's most frequent convective weather.Accurately representing the low-level upstream atmospheric state over the data-sparse South China Sea(SCS)is crucial ...The coastal regions of southern China experience the country's most frequent convective weather.Accurately representing the low-level upstream atmospheric state over the data-sparse South China Sea(SCS)is crucial for reliable convection predictions in numerical models.Utilizing 10 years of radiosonde observations launched over the SCS,this study presents the upstream offshore convective environments and evaluates the global model data performance including NCEP FNL,ERA5,CRA-40,JRA-3Q,and MERRA-2.Results show that thermodynamic state variables such as temperature and humidity exhibit greater biases than kinetic variables,particularly at low levels.Deeper-layer parameters exhibit smaller uncertainties,especially wind-related variables,while moisture-related parameters have the largest uncertainties,compared to shallower-layer parameters.All model data tend to underestimate the conditional instability and equilibrium level,while overestimating the condensation level,storm relative helicity(SRH),with minimal bias in lapse rate,convective inhibition,vertical wind shear(VWS),and mean winds.These biases primarily arise from the model data's underestimation of temperature and moisture below 700 hPa and lower wind speeds below 950 hPa.Among the global models,CRA-40 performs best in dynamic parameters,with highest correlation and lowest mean absolute error in low-level winds,SRH,VWS,and mean winds.ERA5 excels in thermodynamic parameters.Additional convective-permitting numerical experiments indicate that minor initial condition errors over the upstream ocean significantly affect coastal rainfall production.The rainfall production on windward coasts is most sensitive to the low-level air temperature errors during nocturnal hours,while the rainfall over the PRD is most sensitive to the low-level wind errors.展开更多
The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in therm...The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in thermal photonic metamaterials(TPMs).This work introduces a novel multi-obj ective design framework and demonstrates the design,fabrication,and validation of a TPM operating under extreme temperatures up to 1873 K.We have established a holistic design framework integrating temperaturedependent neural network and Pareto multi-obj ective optimization to co-optimize spectral response,component light-weighting,and structural efficiency.The framework achieves 100 times faster computation than genetic algorithms.The performance of the designed TPM was evaluated under various atmospheric models and detection distances.The TPM achieved a peak radiance suppression efficiency of 82%and a maximum attenuation of-7.4 dB at 1200-1500 K.Experimentally,we fabricated an all-dielectric TPM using a refractory TiO_(2)/BeO multilayer stack with only 5 layers and 2um total thickness.The optimized structure shows high reflectivity(0.62 at 3-5 um;0.48 at 8-14μm)for radiative suppression and high emissivity(0.87 at 5-8μm)for radiative cooling.The TPM withstands 1873 K for 12 h in air with less than 3%spectral drift,retaining excellent mechanical properties.On high-temperature components,it achieves 40-50%radiative suppression and 40-60 K(~10.1 kW m^(-2))radiative cooling at 1100 K,endures over 20 times thermal shock cycles(>150 K s^(-1),700-1500 K),and maintains stable performance over 5 cycles,with 78%visible and 98%microwave transmittance.This work establishes a new paradigm in the design and application of photonic materials for extreme environments.展开更多
Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posi...Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.展开更多
In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although ...In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.展开更多
Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant...Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant’s eight-step concept analysis approach was utilized to identify and define the attributes,antecedents,and consequences of reflective supervision in the ICU.An extensive literature search was conducted across various databases,including Google Scholar,CINAHL,PubMed.Articles published from 2005 to 2025 were identified.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)2020 statement to indicate the included articles and extract related data based on relevance.Results Forty articles were included in the analysis.The identified attributes included the supervisor-supervisee relationship,effective communication,teamwork,collaborations,reflection,competencies,feedback,continuous support,and autonomous choice.The identified antecedents included participation,supportive supervision,flexibility,open-door policy,training,and motivation.Consequences impacting the success of reflective supervision were identified as promotion of resiliency,autonomy,work-life balance,self-awareness,increased self-esteem,professional development,critical thinking,increased job satisfaction,and enhanced commitment.Conclusions Reflective supervision is a complex professional self-care strategy that enhances ICU practice,by promoting nurses’well-being,self-awareness,therapeutic skills,and professional development.展开更多
Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoti...Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoting high-quality development of new energy in China.This paper constructs an evaluation index system for the development of NEVs and the ecological environment.It uses game theory combining weighting model,particle swarm optimized projection tracking evaluation model,coupling coordination degree model,and machine learning algorithms to calculate and analyze the level of coupling coordination development of NEVs and the ecological environment in China from 2010 to 2021,and identifies the driving factors.The research results show that:(i)From 2010 to 2021,the development index of NEVs in China has steadily increased from 0.085 to 0.634,while the ecological environment level index significantly rose from 0.170 to 0.884,reflecting the continuous development of China in both NEVs and the ecological environment.(ii)From 2010 to 2012,the two systems—new energy vehicle(NEV)development and the ecological environment—were in a period of imbalance and decline.From 2013 to 2016,they underwent a transition period,and from 2017 to 2021,they entered a period of coordinated development showing a trend of benign and continuous improvement.By 2021,they reached a good level of coordination.(iii)Indicators such as the number of patents granted for NEVs,water consumption per unit of GDP,and energy consumption per unit of GDP are the main driving factors affecting the coupling coordination development of NEVs and the ecological environment in China.展开更多
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
Understanding the impacts of human activities on the plateau’s living environment is essential for advancing modernization pathways that promote harmony between humanity and nature.However,studies on the dynamic inte...Understanding the impacts of human activities on the plateau’s living environment is essential for advancing modernization pathways that promote harmony between humanity and nature.However,studies on the dynamic interactions between human activities and the living environment on the Qinghai-Xizang Plateau(QXP)remain limited,with a paucity of quantitative relationship analyses.This study established an assessment framework to evaluate human influences on the living environment in QXP,using data on typical human activities,ecological conditions,and human settlements.Within this framework,the spatial analysis methods and the coupling coordination model were used to examine the spatio-temporal characteristics and relationship of human activities and living environment on the QXP from 2000 to 2020.The geographical detector model was then applied to identify the key factors influencing the plateau’s human living environment.Subsequently,the four-quadrant analysis model was adopted to assess human influences on the living environment.The results indicate that the human activity intensity(HAI)on the QXP remained relatively low yet increased by 15.41%from 2000 to 2020.Spatially,the human living environment quality(LEQ)improved from northwest to southeast,with 61.14%of the areas remaining stable and 18.47%experiencing slight improvement.The analysis of coupling coordination revealed a continuous improvement between the HAI and LEQ,with the areas of high and relatively high coordinated types increasing by more than 9%.Precipitation and urban-rural construction were identified as the primary factors influencing changes in the LEQ.The interaction between the HAI and LEQ was strengthening,with 40.44%classified as coordinated development type and 38.35%as development-environment conflict type.These findings provide valuable insights for enhancing the resilience of human settlements and promoting green development across the plateau.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Prussian blue analogs(PBAs)are considered one of the excellent cathode materials for sodium-ion batteries due to their low cost and high theoretical specific capacity,especially sodium-rich iron-based PBAs(FeHCF)can p...Prussian blue analogs(PBAs)are considered one of the excellent cathode materials for sodium-ion batteries due to their low cost and high theoretical specific capacity,especially sodium-rich iron-based PBAs(FeHCF)can provide higher energy density.FeHCF has a poor charge/discharge platform stability at high voltages(FeC_(6)moiety),which is mainly affected by its coordination environment.In this research,Cu^(+)(six-coordinated),which is close to the ionic radius of Fe^(2+),was used for substitution,the FeC_(6)vacancies of FeHCF were reduced,and the coordination environment was optimized.The low Cu^(+)-substituted FeHCF(Cu^(+)0.625)has an optimal electrochemical performance at 8.5 mA/g with a reversible specific capacity of 142 mA h/g and FeC_(6)moiety contribution of more than 68 mA h/g,which is superior to that of unmodified and other Cu^(2+)-substituted FeHCFs.In situ tests demonstrate the reversible structural stability of the Cu^(+)0.625,supporting the stability of their high-voltage platform capacity.This Cu^(+)substitution strategy further enriches the approach to optimize the coordination environment of sodium-rich FeHCF.展开更多
Antibiotic resistance genes(ARGs) are recognized as a primary threat to the sustainability of environment and human health in the 21^(st) century.Nanomaterials(NMs) have attracted substantial attention due to their un...Antibiotic resistance genes(ARGs) are recognized as a primary threat to the sustainability of environment and human health in the 21^(st) century.Nanomaterials(NMs) have attracted substantial attention due to their unique dimensions and structures.Unfortunately,emerging evidence suggests that NMs may facilitate the transmission of ARGs.It is crucial to elucidate how NMs affect the evolution and dissemination of ARGs.The current review comprehensively examines the role of NMs in the widespread transmission of ARGs in aquatic environments and the underlying mechanisms involved in the process.It aims to clarify the effects and mechanisms of NMs on the horizontal gene transfer processes that are associated with ARGs,including the enhancement of cell membrane permeability,the formation of nanopores on membranes,promotion of mutagenesis,and the generation of reactive oxygen species(ROSs).Furthermore,the trade-off between the removal of ARGs and horizontal transfer has been elucidated.The review aspires to guide future research directions,advance knowledge on the implications of NMs in the field of ARGs' transmission,and provide a theoretical foundation for the development of safer and more effective applications of NMs.展开更多
With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,exist...With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.展开更多
基金National Social Science Fund of China(18KXS009)the Sichuan Provincial Soft Science Program(22JDR0261)the Sichuan University“From 0 to 1”Innovation Research Program(2021CXC10)。
文摘In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial panel data from China(2012–2022)and adopting a business environment perspective,this study applies a Panel Extended Regression Model(PERM),a Panel Simultaneous Equation Model(PSEM),and a Tobit-IV model to analyze how the development of the digital economy influences regional innovation.The results reveal a pronounced U-shaped relationship between the digital economy and the regional innovation performance at the provincial level in China,with the business environment serving as a significant mediator in this relationship.Moreover,regional innovation performance in China exhibits a“ratchet effect,”with the impact of the digital economy varying markedly across regions.While the eastern and western regions have entered an upward phase,whereby the digital economy boosts innovation,the central region displays a weaker effect.Further analysis indicates that the synergy between the business environment and the digital economy in driving innovation remains suboptimal.These findings were supported by robust checks.This study offers theoretical insights and empirical evidence that support the coordinated development of digital government and the digital factor market,as well as business environment reforms that are in alignment with the innovation demands of the digital era.
基金National Key Research&Development Program of China,No.2021YFC3201201Ningxia Key Research and Development Program(Special Talents),No.2023BSB03021+1 种基金Natural Science Foundation of Ningxia,No.2023AAC05014University First-Class Discipline Construction Project of Ningxia,No.NXYLXK2021A03。
文摘The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.
基金supported by Kyonggi University Research Grant 2025.
文摘As large,room-scale environments become increasingly common,their spatial complexity increases due to variable,unstructured elements.Consequently,demand for room-scale service robots is surging,yet most technologies remain corridor-centric,and autonomous navigation in expansive rooms becomes unstable even around static obstacles.Existing approaches face several structural limitations.These include the labor-intensive requirement for large-scale object annotation and continual retraining,as well as the vulnerability of vanishing point or linebased methods when geometric cues are insufficient.In addition,the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness.To address these challenges,we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments.The algorithm operates in three layers:Layer 1 performs dimension-wise boundary detection;Layer 2 estimates vanishing points,refines the precise perspective space,and extracts a floor mask;and Layer 3 conducts 3D spatial mapping and obstacle recognition.The proposed method was experimentally validated across various geometric-deficient room-scale environments,including lobbies,seminar rooms,conference rooms,cafeterias,and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles.Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction.For obstacle recognition and classification,it obtained F1 scores of 0.980 in obstacle absent areas,0.913 in solid obstacle environments,and 0.939 in skeleton-type sparse obstacle environments,confirming its high precision and reliability in geometric-deficient room-scale environments.
基金supported by the project“Development of Comprehensive Solutions for Environmental Management in the Northeast Coastal Waters of Viet Nam in an Age of Global Changes”(Code:NDT/ITA/2024/07)under the framework of the bilateral scientific and technological cooperation program between Vietnam and Italy(2024-2027).
文摘The Hai Phong-Ha Long coastal area,with its World Natural Heritage site of Ha Long Bay-Cat Ba islands,has been under intense pressure from rapid development to meet the socio-economic goals set by Hai Phong City and Quang Ninh Province.As such,urgent land needs for infrastructure construction of economic sectors and urbanization have led to intensive coastal reclamation and seafill leveling,and their environmental consequences.The objective of this study is to assess the adverse environmental effects of coastal reclamation in the Hai Phong—Ha Long area,focusing on ecosystems,environmental quality,and seabed morphology at a regional scale.To achieve this objective,the study employed the regular techniques of environmental assessment methods,such as checklists,matrices,network diagrams,and overlay maps,to appraise these environmental consequences.The results show three main impacted natural components,including coastal ecosystems,environmental qualities,and morphological seabeds,besides coastline changes and socio-economic issues.The most impacted component was coastal ecosystems,followed by the coastal environmental qualities of seawater and sediments,and then the morphological seabed.Based on the study results,it is recommended that during the development of an integrated coastal management plan for the coastal area of Hai Phong–Ha Long,environmental issues of coastal reclamation and seafill leveling must be given much attention.
基金supported by the National Natural Science Foundation of China(32360084).
文摘In recent years,intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins.The implementation of forestry projects,coupled with continuous population growth,has increased the need for systematic assessments of ecological effects to ensure sustainable development in arid inland river basins.This study generated a 22-a(2000-2021)remote sensing ecological index(RSEI)data series for the Manas River Basin,a typical arid inland river basin in China,utilizing Moderate Resolution Imaging Spectroradiometer(MODIS)data and the Google Earth Engine(GEE)platform.We examined the spatiotemporal patterns of ecological environmental quality in the Manas River Basin through the Theil-Sen estimator,Mann-Kendall trend test,coefficient of variation(CV),and Hurst index.Furthermore,we employed the Optimal Parameter-based Geographical Detector(OPGD)method to quantify the influence of seven key drivers:elevation,slope,temperature,precipitation,gross domestic product(GDP),population density,and land use change.The key findings revealed that the basin's ecological environmental quality showed significant improvement(mean RSEI of 0.38,with a range of 0.34-0.41),with areas exhibiting good and excellent grades increasing by 16.71%,particularly in the midstream oasis region and upstream mountainous region,while areas exhibiting poor and relatively poor grades decreased by 11.52%in the downstream desert region.Spatial heterogeneity of ecological environmental quality was pronounced,with 32.23%of the areas showing localized degradation,the midstream oasis region exhibiting sustainable recovery potential(Hurst index>0.50),and only 36.67%of the areas maintaining stable and highly stable conditions(primarily in the upstream mountainous region).The OPGD analysis revealed that temperature(q-value=0.496-0.780),land use change(q-value=0.705-0.782),and elevation(q-value=0.245-0.637)were dominant factors,with the influence of land use change increasing during 2000-2020.Strong interaction effects emerged between land use change and temperature(q-value>0.705)and between land use change and elevation(q-value=0.751 in 2020),highlighting intensified human-nature coupling.These findings provide vital perspectives for ecosystem management in arid inland river basins under both climate and anthropogenic pressures.
基金supported by the National Natural Science Foundation of China(grant numbers 42171085)and the National Key R&D Program of China(Grant No.2024YFF1307801,2024YFF1307804).
文摘Integrated environmental management is important for sustainable development.Under China’s“Three Lines One Permit”(TLOP)policy,three types of management zones—priority protection,critical control,and general control zones—are established based on the ecological red line,the lower-limit line for environmental quality,and the resource use line.Human activities are regulated through a permit system.Integrated and multifactorial protection of soil,plant,hydrological,and atmospheric elements is promoted at the regional level.A follow-up assessment contributes to the improvement of policy implementation and effectiveness.This study demonstrates the achievements of the TLOP policy in Sichuan Province.Results show that(1)276 protection zones have been established under the ecological red line,covering key ecosystems and protected areas to ensure environmental security.Under the lower-limit line,1,626 functional(priority,key,and general control)zones have been designated to regulate air,water,and soil quality,enhancing environmental capacity and pollution control.(2)Through the integration and merging of the three lines,1,128 integrated management zones have been established,including 375,625,and 128 priority protection,critical control,and general control zones,respectively.Each zone has its own list of environmental permits to regulate human activities according to different environmental protection and natural resource development regimes.(3)The design of the follow-up assessment index system was informed by regional primary functions and industrial structure.The index system for provinces and cities is structured around three primary indicators—implementation updating,application,and guarantees—and 15 secondary indicators.The system for critical control zones is structured around environmental access,management,and effectiveness and 14 secondary indicators.A stringent environmental zoning system has been established through the TLOP policy,thereby safeguarding environmental security,promoting harmonious existence between humans and nature,and supporting the vision of Beautiful China.
文摘The accelerating impacts of climate change,rising temperatures,extreme weather events,and biodiversity loss underscore the urgent need for widespread public awareness.This research explores why climate change awareness is not just beneficial but essential for effective environmental stewardship and the long-term health of our planet.The research proffers informed communities,encouraging sustainable practices,and driving policy advocacy,awareness serves as a model for collective action.This call to consciousness challenges individuals,institutions,and nations to recognize their role in shaping a resilient,sustainable future for the Earth.Methodology adopted in this research is a mixed-method design,involving both qualitative and quasi-experimental designs,which engages the use of focus group discussions and oral interviews to explore deeper insights into perceptions,biodiversity loss consciousness,and environmental depletion challenges.Also,applicable under the qualitative method is the secondary data collection mode,namely,reports from IPCC,government policy documents,and existing literature related to the context of the research.The empirical and scientific data analysis was presented from the data collected and was coded and subjected to analysis using a paired samples t-test.The study is grounded on the theory of“Value-Belief-Norm”(VBN)developed by Stern et al.The VBN theory posits that individuals are more likely to engage in pro-environmental behaviour when their values(especially biosphere and altruistic),beliefs(about environmental consequences),and norms(personal responsibility to act)align.The findings of this study underscore the critical role of climate change awareness in fostering environmental and earth stewardship.The paper recommends that Governments of the country(State and federal)should take urgent steps in sensitising the general public on the causes and impact of climate change.
基金Supported by Changsha Tobacco Company Science and Technology Project(2020-2024A04).
文摘Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030610,42275006,41805035,and 42305001)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515030210)+2 种基金the Guangdong Provincial Marine Meteorology Science Data Center(Grant No.2024B1212070014)the Open Project of the Xiamen Key Laboratory of Straits Meteorology(Grant Nos.HXQX202304 and 2024KF02)the Key Innovation Team of the China Meteorological Administration(Grant No.CMA2023ZD08)。
文摘The coastal regions of southern China experience the country's most frequent convective weather.Accurately representing the low-level upstream atmospheric state over the data-sparse South China Sea(SCS)is crucial for reliable convection predictions in numerical models.Utilizing 10 years of radiosonde observations launched over the SCS,this study presents the upstream offshore convective environments and evaluates the global model data performance including NCEP FNL,ERA5,CRA-40,JRA-3Q,and MERRA-2.Results show that thermodynamic state variables such as temperature and humidity exhibit greater biases than kinetic variables,particularly at low levels.Deeper-layer parameters exhibit smaller uncertainties,especially wind-related variables,while moisture-related parameters have the largest uncertainties,compared to shallower-layer parameters.All model data tend to underestimate the conditional instability and equilibrium level,while overestimating the condensation level,storm relative helicity(SRH),with minimal bias in lapse rate,convective inhibition,vertical wind shear(VWS),and mean winds.These biases primarily arise from the model data's underestimation of temperature and moisture below 700 hPa and lower wind speeds below 950 hPa.Among the global models,CRA-40 performs best in dynamic parameters,with highest correlation and lowest mean absolute error in low-level winds,SRH,VWS,and mean winds.ERA5 excels in thermodynamic parameters.Additional convective-permitting numerical experiments indicate that minor initial condition errors over the upstream ocean significantly affect coastal rainfall production.The rainfall production on windward coasts is most sensitive to the low-level air temperature errors during nocturnal hours,while the rainfall over the PRD is most sensitive to the low-level wind errors.
基金supported by National Key Research and Development Program of China(2024YFA1210500,2023YFB4606105)Fundamental Research Center Projects(52488301)of National Natural Science Foundation of China(NSFC)+1 种基金Key Research Program of Frontier Sciences(ZDBS-LYJSC030)of Chinese Academy of SciencesWestern Light Program(xbzg-zdsys-202402)of Chinese Academy of Sciences。
文摘The development of infrared engineering technologies for extreme environments remains a formidable challenge due to the inherent trade-offs among optical performance,thermal stability,and mechanical integrity in thermal photonic metamaterials(TPMs).This work introduces a novel multi-obj ective design framework and demonstrates the design,fabrication,and validation of a TPM operating under extreme temperatures up to 1873 K.We have established a holistic design framework integrating temperaturedependent neural network and Pareto multi-obj ective optimization to co-optimize spectral response,component light-weighting,and structural efficiency.The framework achieves 100 times faster computation than genetic algorithms.The performance of the designed TPM was evaluated under various atmospheric models and detection distances.The TPM achieved a peak radiance suppression efficiency of 82%and a maximum attenuation of-7.4 dB at 1200-1500 K.Experimentally,we fabricated an all-dielectric TPM using a refractory TiO_(2)/BeO multilayer stack with only 5 layers and 2um total thickness.The optimized structure shows high reflectivity(0.62 at 3-5 um;0.48 at 8-14μm)for radiative suppression and high emissivity(0.87 at 5-8μm)for radiative cooling.The TPM withstands 1873 K for 12 h in air with less than 3%spectral drift,retaining excellent mechanical properties.On high-temperature components,it achieves 40-50%radiative suppression and 40-60 K(~10.1 kW m^(-2))radiative cooling at 1100 K,endures over 20 times thermal shock cycles(>150 K s^(-1),700-1500 K),and maintains stable performance over 5 cycles,with 78%visible and 98%microwave transmittance.This work establishes a new paradigm in the design and application of photonic materials for extreme environments.
文摘Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.
基金funded by the National Natural Science Foundation of China under Grant 62306128the Leading Innovation Project of Changzhou Science and Technology Bureau underGrant CQ20230072+2 种基金the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Science and Technology Development Plan Project of Jilin Provinceunder Grant 20240101382JCthe National KeyR esearch and Development Program of China under Grant 2023YFF1105102.
文摘In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.
文摘Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant’s eight-step concept analysis approach was utilized to identify and define the attributes,antecedents,and consequences of reflective supervision in the ICU.An extensive literature search was conducted across various databases,including Google Scholar,CINAHL,PubMed.Articles published from 2005 to 2025 were identified.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)2020 statement to indicate the included articles and extract related data based on relevance.Results Forty articles were included in the analysis.The identified attributes included the supervisor-supervisee relationship,effective communication,teamwork,collaborations,reflection,competencies,feedback,continuous support,and autonomous choice.The identified antecedents included participation,supportive supervision,flexibility,open-door policy,training,and motivation.Consequences impacting the success of reflective supervision were identified as promotion of resiliency,autonomy,work-life balance,self-awareness,increased self-esteem,professional development,critical thinking,increased job satisfaction,and enhanced commitment.Conclusions Reflective supervision is a complex professional self-care strategy that enhances ICU practice,by promoting nurses’well-being,self-awareness,therapeutic skills,and professional development.
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0102)the China Scholarship Council Program(202406190114)。
文摘Studying the coupling coordination development of new energy vehicles(NEVs)and the ecological environment in China is helpful in promoting the development of NEVs in the country and is of great significance in promoting high-quality development of new energy in China.This paper constructs an evaluation index system for the development of NEVs and the ecological environment.It uses game theory combining weighting model,particle swarm optimized projection tracking evaluation model,coupling coordination degree model,and machine learning algorithms to calculate and analyze the level of coupling coordination development of NEVs and the ecological environment in China from 2010 to 2021,and identifies the driving factors.The research results show that:(i)From 2010 to 2021,the development index of NEVs in China has steadily increased from 0.085 to 0.634,while the ecological environment level index significantly rose from 0.170 to 0.884,reflecting the continuous development of China in both NEVs and the ecological environment.(ii)From 2010 to 2012,the two systems—new energy vehicle(NEV)development and the ecological environment—were in a period of imbalance and decline.From 2013 to 2016,they underwent a transition period,and from 2017 to 2021,they entered a period of coordinated development showing a trend of benign and continuous improvement.By 2021,they reached a good level of coordination.(iii)Indicators such as the number of patents granted for NEVs,water consumption per unit of GDP,and energy consumption per unit of GDP are the main driving factors affecting the coupling coordination development of NEVs and the ecological environment in China.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
基金The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0608。
文摘Understanding the impacts of human activities on the plateau’s living environment is essential for advancing modernization pathways that promote harmony between humanity and nature.However,studies on the dynamic interactions between human activities and the living environment on the Qinghai-Xizang Plateau(QXP)remain limited,with a paucity of quantitative relationship analyses.This study established an assessment framework to evaluate human influences on the living environment in QXP,using data on typical human activities,ecological conditions,and human settlements.Within this framework,the spatial analysis methods and the coupling coordination model were used to examine the spatio-temporal characteristics and relationship of human activities and living environment on the QXP from 2000 to 2020.The geographical detector model was then applied to identify the key factors influencing the plateau’s human living environment.Subsequently,the four-quadrant analysis model was adopted to assess human influences on the living environment.The results indicate that the human activity intensity(HAI)on the QXP remained relatively low yet increased by 15.41%from 2000 to 2020.Spatially,the human living environment quality(LEQ)improved from northwest to southeast,with 61.14%of the areas remaining stable and 18.47%experiencing slight improvement.The analysis of coupling coordination revealed a continuous improvement between the HAI and LEQ,with the areas of high and relatively high coordinated types increasing by more than 9%.Precipitation and urban-rural construction were identified as the primary factors influencing changes in the LEQ.The interaction between the HAI and LEQ was strengthening,with 40.44%classified as coordinated development type and 38.35%as development-environment conflict type.These findings provide valuable insights for enhancing the resilience of human settlements and promoting green development across the plateau.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金supported by the Key Talent Project of Gansu Province(2025RCXM017)the National Natural Science Foundation of China(52261040)+2 种基金the Postgraduate Innovation Star Program of Gansu Province(2025CXZX-476)the Major Science and Technology Project of Gansu Province(22ZD6GA008)the Innovation Driven Assistance Engineering Project of Gansu Association for Science and Technology(GXH20250325-5).
文摘Prussian blue analogs(PBAs)are considered one of the excellent cathode materials for sodium-ion batteries due to their low cost and high theoretical specific capacity,especially sodium-rich iron-based PBAs(FeHCF)can provide higher energy density.FeHCF has a poor charge/discharge platform stability at high voltages(FeC_(6)moiety),which is mainly affected by its coordination environment.In this research,Cu^(+)(six-coordinated),which is close to the ionic radius of Fe^(2+),was used for substitution,the FeC_(6)vacancies of FeHCF were reduced,and the coordination environment was optimized.The low Cu^(+)-substituted FeHCF(Cu^(+)0.625)has an optimal electrochemical performance at 8.5 mA/g with a reversible specific capacity of 142 mA h/g and FeC_(6)moiety contribution of more than 68 mA h/g,which is superior to that of unmodified and other Cu^(2+)-substituted FeHCFs.In situ tests demonstrate the reversible structural stability of the Cu^(+)0.625,supporting the stability of their high-voltage platform capacity.This Cu^(+)substitution strategy further enriches the approach to optimize the coordination environment of sodium-rich FeHCF.
基金supported by the State Key Laboratory of Urban Water Resource and Environment (Harbin Institute of Technology) (No.2022TS13)the key projects of National Natural Science Foundation of China (No.2019YFC0408503)the Key Research Program of Wuhan (No.2022022202015015)。
文摘Antibiotic resistance genes(ARGs) are recognized as a primary threat to the sustainability of environment and human health in the 21^(st) century.Nanomaterials(NMs) have attracted substantial attention due to their unique dimensions and structures.Unfortunately,emerging evidence suggests that NMs may facilitate the transmission of ARGs.It is crucial to elucidate how NMs affect the evolution and dissemination of ARGs.The current review comprehensively examines the role of NMs in the widespread transmission of ARGs in aquatic environments and the underlying mechanisms involved in the process.It aims to clarify the effects and mechanisms of NMs on the horizontal gene transfer processes that are associated with ARGs,including the enhancement of cell membrane permeability,the formation of nanopores on membranes,promotion of mutagenesis,and the generation of reactive oxygen species(ROSs).Furthermore,the trade-off between the removal of ARGs and horizontal transfer has been elucidated.The review aspires to guide future research directions,advance knowledge on the implications of NMs in the field of ARGs' transmission,and provide a theoretical foundation for the development of safer and more effective applications of NMs.
基金support of the National Key Research and Development Plan(No.2021YFB3302501)the financial support of the National Science Foundation of China(No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(No.DUT25GF207).
文摘With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.
