Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimen...Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.展开更多
Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in a...Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.展开更多
Plants constitute nearly 80%of the planet’s total biomass(Bar-On et al.,2018);however,this vital group is experiencing severe threats,and recent evaluations indicate that approximately 45%of the world's described...Plants constitute nearly 80%of the planet’s total biomass(Bar-On et al.,2018);however,this vital group is experiencing severe threats,and recent evaluations indicate that approximately 45%of the world's described plant species are at risk of extinction(Bachman et al.,2024).The number of plant extinctions has increased by 60%in the last 100 years(Di Marco et al.,2017).Over the past 250 years,571 plant species have gone extinct—more than twice the combined total of extinct birds,mammals,and amphibians(217 species)(Briggs,2019).展开更多
This study compares the environmental sustainability of five alternatives for the remediation of marine sediments of one of the most polluted coastal sites in Europe(Bagnoli-Coroglio bay,Mediterranean Sea),using the L...This study compares the environmental sustainability of five alternatives for the remediation of marine sediments of one of the most polluted coastal sites in Europe(Bagnoli-Coroglio bay,Mediterranean Sea),using the Life Cycle Assessment(LCA)methodology.The treatments are either in-situ or exsitu,the latter requiring an initial dredging to transport the contaminated sediments to the management site.More in detail,four ex-situ remediation technologies based on landfilling,bioremediation,electrokinetic technique and soil washing were identified.These technologies are compared to an in-situ strategy currently under validation for enhancing bioremediation of the polluted sediments of the Bagnoli-Coroglio site.Our results indicate that the disposal in landfilling site is the worst option in most categories(e.g.,650 kg CO_(2) eq./t of treated sediment,considering the nearest landfilling site),followed by the bioremediation,mainly due to the high energy demand.Electrokinetic remediation,soil washing,and innovative in-situ technology represent the most sustainable options.In particular,the new in-situ technology appears to be the least impacting in all categories(e.g.,54 kg CO_(2) eq./t of treated sediment),although it is expected to require longer treatment time(estimated up to 12 months based on its potential efficiency).It can reduce the impact on climate change more than 12 times compared to the disposal and 7 times compared to bioremediation in addition to the possibility to avoid/reduce the dredging operations and the consequent dispersion of pollutants.The results open relevant perspectives towards more eco-sustainable and costly effective actions for the reclamation of contaminated marine sediments.展开更多
Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogen...Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.展开更多
Correction to:Journal of Forestry Research(2025)36:124 https://doi.org/10.1007/s11676-025-01918-8 In this article the author’s name Yasutomo Hoshika was incorrectly written as Yasutoma Hoshika.The original article ha...Correction to:Journal of Forestry Research(2025)36:124 https://doi.org/10.1007/s11676-025-01918-8 In this article the author’s name Yasutomo Hoshika was incorrectly written as Yasutoma Hoshika.The original article has been corrected.展开更多
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment mo...This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.展开更多
When evaluating the seismic safety and reliability of complex engineering structures,it is a critical problem to reasonably consider the randomness and multi-dimensional nature of ground motions.To this end,a proposed...When evaluating the seismic safety and reliability of complex engineering structures,it is a critical problem to reasonably consider the randomness and multi-dimensional nature of ground motions.To this end,a proposed modeling strategy of multi-dimensional stochastic earthquakes is addressed in this study.This improved seismic model has several merits that enable it to better provide seismic analyses of structures.Specifically,at first,the ground motion model is compatible with the design response spectrum.Secondly,the evolutionary power spectrum involved in the model and the design response spectrum are constructed accordingly with sufficient consideration of the correlation between different seismic components.Thirdly,the random function-based dimension-reduction representation is applied,by which seismic modeling is established,with three elementary random variables.Numerical simulations of multi-dimensional stochastic ground motions in a specific design scenario indicate the effectiveness of the proposed modeling strategy.Moreover,the multi-dimensional seismic response and the global reliability of a high-rise frame-core tube structure is discussed in detail to further illustrate the engineering applicability of the proposed method.The analytical investigations demonstrate that the suggested stochastic model of multi-dimensional ground motion is available for accurate seismic response analysis and dynamic reliability assessment of complex engineering structures for performance-based seismic resistance design.展开更多
Controlling heavy metal pollution in agricultural soil has been a significant challenge.These heavy metals seriously threaten the surrounding ecological environment and human health.The effective assessment and remedi...Controlling heavy metal pollution in agricultural soil has been a significant challenge.These heavy metals seriously threaten the surrounding ecological environment and human health.The effective assessment and remediation of heavy metals in agricultural soils are crucial.These two aspects support each other,forming a close and complete decisionmaking chain.Therefore,this review systematically summarizes the distribution characteristics of soil heavy metal pollution,the correlation between soil and crop heavy metal contents,the presence pattern and migration and transformation mode of heavy metals in the soil-crop system.The advantages and disadvantages of the risk evaluation tools and models of heavy metal pollution in farmland are further outlined,which provides important guidance for an in-depth understanding of the characteristics of heavymetal pollution in farmland soils and the assessment of the environmental risk.Soil remediation strategies involve multiple physical,chemical,biological and even combined technologies,and this paper compares the potential and effect of the above current remediation technologies in heavy metal polluted farmland soils.Finally,the main problems and possible research directions of future heavy metal risk assessment and remediation technologies in agricultural soils are prospected.This review provides new ideas for effective assessment and selection of remediation technologies based on the characterization of soil heavy metals.展开更多
Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy densit...Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries,without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries.To address this issue,our work comprehensively compares commercial prismatic lithium iron phosphate(LFP) battery,lithium nickel cobalt manganese oxide(NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter.The thermal hazard of the three cells is then qualitatively assessed from thermal stability,early warning and thermal runaway severity perspectives by integrating eight characteristic parameters.The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity.However,the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway,allowing the generated gas to be released as early as possible and detected in a timely manner,providing sufficient time for early warning.Finally,a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment.Overall,LFP cells exhibit the lowest thermal hazard,followed by the Na-ion cells and NCM523 cells.This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells,providing crucial guidance for development and application of Na-ion cell.展开更多
Phthalate esters(PAEs),recognized as endocrine disruptors,are released into the environment during usage,thereby exerting adverse ecological effects.This study investigates the occurrence,sources,and risk assessment o...Phthalate esters(PAEs),recognized as endocrine disruptors,are released into the environment during usage,thereby exerting adverse ecological effects.This study investigates the occurrence,sources,and risk assessment of PAEs in surface water obtained from 36 sampling points within the Yellow River and Yangtze River basins.The total concentration of PAEs in the Yellow River spans from124.5 to 836.5 ng/L,with Dimethyl phthalate(DMP)(75.4±102.7 ng/L)and Diisobutyl phthalate(DiBP)(263.4±103.1 ng/L)emerging as the predominant types.Concentrations exhibit a pattern of upstream(512.9±202.1 ng/L)>midstream(344.5±135.3 ng/L)>downstream(177.8±46.7 ng/L).In the Yangtze River,the total concentration ranges from 81.9 to 441.6 ng/L,with DMP(46.1±23.4 ng/L),Diethyl phthalate(DEP)(93.3±45.2 ng/L),and DiBP(174.2±67.6 ng/L)as the primary components.Concentration levels follow a midstream(324.8±107.3 ng/L)>upstream(200.8±51.8 ng/L)>downstream(165.8±71.6 ng/L)pattern.Attention should be directed towards the moderate ecological risks of DiBP in the upstream of HH,and both the upstream and midstream of CJ need consideration for the moderate ecological risks associated with Di-n-octyl phthalate(DNOP).Conversely,in other regions,the associated risk with PAEs is either low or negligible.The main source of PAEs in Yellow River is attributed to the release of construction land,while in the Yangtze River Basin,it stems from the accumulation of pollutants in lakes and forests discharged into the river.These findings are instrumental for pinpointing sources of PAEs pollution and formulating control strategies in the Yellow and Yangtze Rivers,providing valuable insights for global PAEs research in other major rivers.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Understanding the levels,causes,and sources of fluoride in groundwater is critical for public health,effective water resource management,and sustainable utilization.This study employs multivariate statistical methods,...Understanding the levels,causes,and sources of fluoride in groundwater is critical for public health,effective water resource management,and sustainable utilization.This study employs multivariate statistical methods,hazard quotient assessment,and geochemical analyses,such as mineral saturation index,ionic activities,and Gibbs diagrams,to investigate the hydrochemical characteristics,causes,and noncarcinogenic risks of fluoride in Red bed groundwater and geothermal water in the Guang'an area and neighboring regions.Approximately 9%of the Red bed groundwater samples contain fluoride concentrations exceeding 1 mg·L^(-1).The predominant water types identified are Cl-Na and HCO_(3)-Na,primarily influenced by evapotranspiration.Low-fluoride groundwater and high-fluoride geothermal water exhibit distinct hydrochemical types HCO_(3)-Ca and SO_(4)-Ca,respectively,which are mainly related to the weathering of carbonate,sulfate,and fluorite-containing rocks.Correlation analysis reveals that fluoride content in Red bed groundwater is positively associated with Na^(+),Cl^(-),SO_(4)^(2-),and TDS(r^(2)=0.45-0.64,p<0.01),while in geothermal water,it correlates strongly with pH,K^(+),Ca^(2+),and Mg^(2+)(r^(2)=0.52-0.80,p<0.05).Mineral saturation indices and ionic activities indicate that ion exchange processes and the dissolution of minerals such as carbonatite and fluorite are important sources of fluoride in groundwater.The enrichment of fluorine in the Red bed groundwater is linked to evaporation,cation exchange and dissolution of fluorite,caused by the lithologic characteristics of the red bed in this area.However,it exhibits minimal correlation with the geothermal water in the adjacent area.The noncarcinogenic health risk assessment indicates that 7%(n=5)of Red bed groundwater points exceed the fluoride safety limit for adults,while 12%(n=8)exceed the limit for children.These findings underscore the importance of avoiding highly fluoridated red bed groundwater as a direct drinking source and enhancing groundwater monitoring to mitigate health risks associated with elevated fluoride levels.展开更多
Earthquakes pose significant perils to the built environment in urban areas.To avert the calamitous aftermath of earthquakes,it is imperative to construct seismic resilient cities.Due to the intricacy of the concept o...Earthquakes pose significant perils to the built environment in urban areas.To avert the calamitous aftermath of earthquakes,it is imperative to construct seismic resilient cities.Due to the intricacy of the concept of urban seismic resilience(USR),its assessment is a large-scale system engineering issue.The assessment of USR should be based on the notion of urban seismic capacity(USC)assessment,which includes casualties,economic loss,and recovery time as criteria.Functionality loss is also included in the assessment of USR in addition to these criteria.The assessment indicator system comprising five dimensions(building and lifeline infrastructure,environment,society,economy,and institution)and 20 indicators has been devised to quantify USR.The analytical hierarchy process(AHP)is utilized to compute the weights of the criteria,dimensions,and indicators in the urban seismic resilience assessment(USRA)indicator system.When the necessary data for a city are obtainable,the seismic resilience of that city can be assessed using this framework.To illustrate the proposed methodology,a moderate-sized city in China was selected as a case study.The assessment results indicate a high level of USR,suggesting that the city possesses strong capabilities to withstand and recover from potential future earthquakes.展开更多
Background There is scarce data about comparisons between geriatric assessment tools in patients with aortic stenosis(AS).We aimed to describe the geriatric profile of patients with AS undergoing transcatheter aortic ...Background There is scarce data about comparisons between geriatric assessment tools in patients with aortic stenosis(AS).We aimed to describe the geriatric profile of patients with AS undergoing transcatheter aortic valve implantation(TAVI)and to analyze the ability of different tools for predicting clinical outcomes in this context.Methods This was a single center retrospective registry including patients with AS undergoing TAVI and surviving to hospital discharge.The primary endpoint was all-cause mortality or need for urgent readmission one year after TAVI.Results A total of 377 patients were included(mean age of 80.4 years).Most patients were independent or mildly dependent,with an optimal cognitive status.The proportion of frailty ranged from 17.6%to 49.8%.A total of 20 patients(5.3%)died and 110/377 patients(29.2%)died or were readmitted during follow up.Overall,most components of the geriatric assessment showed an association with clinical outcomes.Disability for instrumental activities showed a significant association with mortality and a strong association with the rate of mortality or readmission.The association between frailty and clinical outcomes was higher for short physical performance battery(SPPB),essential frailty toolset(EFT)and the frailty index based on comprehensive geriatric assessment(IF-VIG)and lower for Fried criteria and FRAIL scale.Conclusions AS patients from this series presented a good physical performance,optimal cognitive status and a reasonably low prevalence of frailty.The best predictive ability was observed for disability for instrumental activities and frailty as measured by the EFT,SPPB and the IF-VIG.展开更多
To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
The traditional transient stability assessment(TSA)model for power systems has three disadvantages:capturing critical information during faults is difficult,aperiodic and oscillatory unstable conditions are not distin...The traditional transient stability assessment(TSA)model for power systems has three disadvantages:capturing critical information during faults is difficult,aperiodic and oscillatory unstable conditions are not distinguished,and poor generalizability is exhibited by systems with high renewable energy penetration.To address these issues,a novel ResGRU architecture for TSA is proposed in this study.First,a residual neural network(ResNet)is used for deep feature extraction of transient information.Second,a bidirectional gated recurrent unit combined with a multi-attention mechanism(BiGRU-Attention)is used to establish temporal feature dependencies.Their combination constitutes a TSA framework based on the ResGRU architecture.This method predicts three transient conditions:oscillatory instability,aperiodic instability,and stability.The model was trained offline using stochastic gradient descent with a thermal restart(SGDR)optimization algorithm in the offline training phase.This significantly improves the generalizability of the model.Finally,simulation tests on IEEE 145-bus and 39-bus systems confirmed that the proposed method has higher adaptability,accuracy,scalability,and rapidity than the conventional TSA approach.The proposed model also has superior robustness for PMU incomplete configurations,PMU noisy data,and packet loss.展开更多
Due to their high mechanical compliance and excellent biocompatibility,conductive hydrogels exhibit significant potential for applications in flexible electronics.However,as the demand for high sensitivity,superior me...Due to their high mechanical compliance and excellent biocompatibility,conductive hydrogels exhibit significant potential for applications in flexible electronics.However,as the demand for high sensitivity,superior mechanical properties,and strong adhesion performance continues to grow,many conventional fabrication methods remain complex and costly.Herein,we propose a simple and efficient strategy to construct an entangled network hydrogel through a liquid-metal-induced cross-linking reaction,hydrogel demonstrates outstanding properties,including exceptional stretchability(1643%),high tensile strength(366.54 kPa),toughness(350.2 kJ m^(−3)),and relatively low mechanical hysteresis.The hydrogel exhibits long-term stable reusable adhesion(104 kPa),enabling conformal and stable adhesion to human skin.This capability allows it to effectively capture high-quality epidermal electrophysiological signals with high signal-to-noise ratio(25.2 dB)and low impedance(310 ohms).Furthermore,by integrating advanced machine learning algorithms,achieving an attention classification accuracy of 91.38%,which will significantly impact fields like education,healthcare,and artificial intelligence.展开更多
Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure.Assessment of hurricane risk furnishes a spatial depiction of the interplay among ...Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure.Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard,vulnerability,exposure,and mitigation capacity,crucial for understanding and managing the risks hurricanes pose to communities.These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios.A systematic review was conducted,encompassing 94 articles,to scrutinize the structure,data inputs,assumptions,methodologies,perils modelled,and key predictors of hurricane risk.This review identified key research gaps essential for enhancing future risk assessments.The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril,commonly storm surge and flood,resulting in inadequacies in disaster resilience planning.Most risk assessments were based on hurricane frequency rather than hurricane damage,which is more insightful for policymakers.Furthermore,considering secondary indirect impacts stemming from hurricanes,including real estate market and business interruption,could enrich economic impact assessments.Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5%of studies.The top six predictive factors for hurricane risk were land use,slope,precipitation,elevation,population density,and soil texture/drainage.Another notable research gap identified was the potential of machine learning techniques in risk assessments,offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions.Existing machine learning based risk assessments leverage random forest models(42%of studies)followed by neural network models(19%of studies),with further research required to investigate diverse machine learning algorithms such as ensemble models.A further research gap is model validation,in particular assessing transferability to a new study region.Additionally,harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments.By addressing these research gaps,hurricane risk assessments can furnish invaluable insights for national policymakers,facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities.To the authors’knowledge,this represents the first literature review specifically dedicated to quantitative hurricane risk assessments,encompassing a comparison of Multi-criteria Decision Making(MCDM),numerical models,and machine learning models.Ultimately,advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future.展开更多
To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on...To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on postearthquake loss and recovery time is improved.A threelevel function tree model is established,which can consider the dynamic changes in weight coefficients of different category of components relative to their functional losses.Bayesian networks are utilized to quantify the impact of weather conditions,construction technology levels,and worker skill levels on component repair time.A method for determining the real-time functional recovery curve of buildings based on the component repair process is proposed.Taking a three-story teaching building as an example,the seismic resilience indices under basic earthquakes and rare earthquakes are calculated.The results show that the seismic resilience grade of the teaching building is comprehensively judged as GradeⅢ,and its resilience grade is more significantly affected by postearthquake loss.The proposed method can be used to predict the seismic resilience of buildings prior to earthquakes,identify weak components within buildings,and provide guidance for taking measures to enhance the seismic resilience of buildings.展开更多
基金supported by the National Natural Science Foundation of China(22072048)the Guangdong Provincial Department of Science and Technology(2021A1515010128 and 2022A0505050013).
文摘Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0208)the National Natural Science Foundation of China(Nos.42171148 and 42330512)the Key R&D Project from the Science and Technology Department of Tibet(No.XZ202501ZY0030).
文摘Nitrogen(N)and phosphorus(P)are essential nutrients and can significantly impact primary productivity of the ecosystem causing water environmental problems.However,their cycling mechanisms are not well understood in alpine mountains with climate change.Hence,94 samples of river water were collected from 2018 to 2020 in the headwaters of the Shule River Basin to assess the nutrients spatiotemporal distribution and combined ap-proach of water quality index to assess water quality and potential sources.The findings depict that high nutrient concentrations were found to coincide with snowmelt and glacial meltwater and rainfall recharge periods,while total flux peaked from June to September due to increased runoff.Notably,total nitrogen(TN)concentrations were significantly higher near the town,primarily attributed to the replenishment of nitrate(NO_(3)^(‒)-N)from live-stock manure.The high total P(TP)was near the glacier,which was attributed to the transportation of glacial sediments into the river,and pH was another critical factor.N was the primary nutrient limiting factor for the growth of phytoplankton in river water.Although the migration and transport of nutrients have altered with climate change,river water quality is good in alpine mountains based on an overall evaluation.These findings contribute to enriching nutrient datasets and highlight the importance of water resource management and water quality assessment in sensitive and fragile alpine mountains.
基金support of the ORG.one project of Oxford Nanopore Technologies(ONT),the Rufford Grants(45249-1)the Idea Wild Grants(Project ID-KJOSINDI0125-00)the Mohamed Bin Zyed Species Conservation(MBZ)(GEF Grant no-240535253)Funds in our efforts to conserve threatened trees in the Western Ghats Biodiversity Hotspot Forest regions.
文摘Plants constitute nearly 80%of the planet’s total biomass(Bar-On et al.,2018);however,this vital group is experiencing severe threats,and recent evaluations indicate that approximately 45%of the world's described plant species are at risk of extinction(Bachman et al.,2024).The number of plant extinctions has increased by 60%in the last 100 years(Di Marco et al.,2017).Over the past 250 years,571 plant species have gone extinct—more than twice the combined total of extinct birds,mammals,and amphibians(217 species)(Briggs,2019).
基金support in the literature analysis.This study has been carried out in the framework of the project funded by EU entitled“Bioremediation of contaminated sediments in coastal areas of exindustrial sites-LIFE SEDREMED”(No.LIFE20 ENV/IT/000572).
文摘This study compares the environmental sustainability of five alternatives for the remediation of marine sediments of one of the most polluted coastal sites in Europe(Bagnoli-Coroglio bay,Mediterranean Sea),using the Life Cycle Assessment(LCA)methodology.The treatments are either in-situ or exsitu,the latter requiring an initial dredging to transport the contaminated sediments to the management site.More in detail,four ex-situ remediation technologies based on landfilling,bioremediation,electrokinetic technique and soil washing were identified.These technologies are compared to an in-situ strategy currently under validation for enhancing bioremediation of the polluted sediments of the Bagnoli-Coroglio site.Our results indicate that the disposal in landfilling site is the worst option in most categories(e.g.,650 kg CO_(2) eq./t of treated sediment,considering the nearest landfilling site),followed by the bioremediation,mainly due to the high energy demand.Electrokinetic remediation,soil washing,and innovative in-situ technology represent the most sustainable options.In particular,the new in-situ technology appears to be the least impacting in all categories(e.g.,54 kg CO_(2) eq./t of treated sediment),although it is expected to require longer treatment time(estimated up to 12 months based on its potential efficiency).It can reduce the impact on climate change more than 12 times compared to the disposal and 7 times compared to bioremediation in addition to the possibility to avoid/reduce the dredging operations and the consequent dispersion of pollutants.The results open relevant perspectives towards more eco-sustainable and costly effective actions for the reclamation of contaminated marine sediments.
基金supported by the National Natural Science Foundation of China (Nos.42422705,42207175,42177117 and 42577170)the Ningbo Youth Leading Talent Project (No.2024QL051)+1 种基金the Chinese Academy of Engineering Science and Technology Strategy Consulting Project (No.2025-XZ-57)the Central Government Funding Program for Guiding Local Science and Technology Development (No.2025ZY01028)。
文摘Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.
文摘Correction to:Journal of Forestry Research(2025)36:124 https://doi.org/10.1007/s11676-025-01918-8 In this article the author’s name Yasutomo Hoshika was incorrectly written as Yasutoma Hoshika.The original article has been corrected.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413)。
文摘This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
基金National Natural Science Foundation of China under Grant Nos.51978543,52108444,and 51778343Plan of Outstanding Young and Middle-aged Scientific and Technological Innovation Team in the Universities of Hubei Province with Project No.T2020010Natural Science Foundation of Hebei Province under Grant No.E2021512001。
文摘When evaluating the seismic safety and reliability of complex engineering structures,it is a critical problem to reasonably consider the randomness and multi-dimensional nature of ground motions.To this end,a proposed modeling strategy of multi-dimensional stochastic earthquakes is addressed in this study.This improved seismic model has several merits that enable it to better provide seismic analyses of structures.Specifically,at first,the ground motion model is compatible with the design response spectrum.Secondly,the evolutionary power spectrum involved in the model and the design response spectrum are constructed accordingly with sufficient consideration of the correlation between different seismic components.Thirdly,the random function-based dimension-reduction representation is applied,by which seismic modeling is established,with three elementary random variables.Numerical simulations of multi-dimensional stochastic ground motions in a specific design scenario indicate the effectiveness of the proposed modeling strategy.Moreover,the multi-dimensional seismic response and the global reliability of a high-rise frame-core tube structure is discussed in detail to further illustrate the engineering applicability of the proposed method.The analytical investigations demonstrate that the suggested stochastic model of multi-dimensional ground motion is available for accurate seismic response analysis and dynamic reliability assessment of complex engineering structures for performance-based seismic resistance design.
基金supported by the National Natural Science Foundation of China(Nos.52100184,and U22A20617).
文摘Controlling heavy metal pollution in agricultural soil has been a significant challenge.These heavy metals seriously threaten the surrounding ecological environment and human health.The effective assessment and remediation of heavy metals in agricultural soils are crucial.These two aspects support each other,forming a close and complete decisionmaking chain.Therefore,this review systematically summarizes the distribution characteristics of soil heavy metal pollution,the correlation between soil and crop heavy metal contents,the presence pattern and migration and transformation mode of heavy metals in the soil-crop system.The advantages and disadvantages of the risk evaluation tools and models of heavy metal pollution in farmland are further outlined,which provides important guidance for an in-depth understanding of the characteristics of heavymetal pollution in farmland soils and the assessment of the environmental risk.Soil remediation strategies involve multiple physical,chemical,biological and even combined technologies,and this paper compares the potential and effect of the above current remediation technologies in heavy metal polluted farmland soils.Finally,the main problems and possible research directions of future heavy metal risk assessment and remediation technologies in agricultural soils are prospected.This review provides new ideas for effective assessment and selection of remediation technologies based on the characterization of soil heavy metals.
基金supported by the National Key R&D Program of China(No.2022YFE0207400)supported by the Xiaomi Young Talents Programsupported by the Youth Innovation Promotion Association CAS(No.Y201768)。
文摘Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries,without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries.To address this issue,our work comprehensively compares commercial prismatic lithium iron phosphate(LFP) battery,lithium nickel cobalt manganese oxide(NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter.The thermal hazard of the three cells is then qualitatively assessed from thermal stability,early warning and thermal runaway severity perspectives by integrating eight characteristic parameters.The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity.However,the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway,allowing the generated gas to be released as early as possible and detected in a timely manner,providing sufficient time for early warning.Finally,a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment.Overall,LFP cells exhibit the lowest thermal hazard,followed by the Na-ion cells and NCM523 cells.This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells,providing crucial guidance for development and application of Na-ion cell.
基金supported by the Ministry of Science and Technology of China(Nos.2021YFC3200904 and 2022YFC3203705)the National Natural Science Foundation of China(Nos.52270012 and 52070184).
文摘Phthalate esters(PAEs),recognized as endocrine disruptors,are released into the environment during usage,thereby exerting adverse ecological effects.This study investigates the occurrence,sources,and risk assessment of PAEs in surface water obtained from 36 sampling points within the Yellow River and Yangtze River basins.The total concentration of PAEs in the Yellow River spans from124.5 to 836.5 ng/L,with Dimethyl phthalate(DMP)(75.4±102.7 ng/L)and Diisobutyl phthalate(DiBP)(263.4±103.1 ng/L)emerging as the predominant types.Concentrations exhibit a pattern of upstream(512.9±202.1 ng/L)>midstream(344.5±135.3 ng/L)>downstream(177.8±46.7 ng/L).In the Yangtze River,the total concentration ranges from 81.9 to 441.6 ng/L,with DMP(46.1±23.4 ng/L),Diethyl phthalate(DEP)(93.3±45.2 ng/L),and DiBP(174.2±67.6 ng/L)as the primary components.Concentration levels follow a midstream(324.8±107.3 ng/L)>upstream(200.8±51.8 ng/L)>downstream(165.8±71.6 ng/L)pattern.Attention should be directed towards the moderate ecological risks of DiBP in the upstream of HH,and both the upstream and midstream of CJ need consideration for the moderate ecological risks associated with Di-n-octyl phthalate(DNOP).Conversely,in other regions,the associated risk with PAEs is either low or negligible.The main source of PAEs in Yellow River is attributed to the release of construction land,while in the Yangtze River Basin,it stems from the accumulation of pollutants in lakes and forests discharged into the river.These findings are instrumental for pinpointing sources of PAEs pollution and formulating control strategies in the Yellow and Yangtze Rivers,providing valuable insights for global PAEs research in other major rivers.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金supported by the China Geological Survey Project(Nos.DD20220864 and DD20243077).
文摘Understanding the levels,causes,and sources of fluoride in groundwater is critical for public health,effective water resource management,and sustainable utilization.This study employs multivariate statistical methods,hazard quotient assessment,and geochemical analyses,such as mineral saturation index,ionic activities,and Gibbs diagrams,to investigate the hydrochemical characteristics,causes,and noncarcinogenic risks of fluoride in Red bed groundwater and geothermal water in the Guang'an area and neighboring regions.Approximately 9%of the Red bed groundwater samples contain fluoride concentrations exceeding 1 mg·L^(-1).The predominant water types identified are Cl-Na and HCO_(3)-Na,primarily influenced by evapotranspiration.Low-fluoride groundwater and high-fluoride geothermal water exhibit distinct hydrochemical types HCO_(3)-Ca and SO_(4)-Ca,respectively,which are mainly related to the weathering of carbonate,sulfate,and fluorite-containing rocks.Correlation analysis reveals that fluoride content in Red bed groundwater is positively associated with Na^(+),Cl^(-),SO_(4)^(2-),and TDS(r^(2)=0.45-0.64,p<0.01),while in geothermal water,it correlates strongly with pH,K^(+),Ca^(2+),and Mg^(2+)(r^(2)=0.52-0.80,p<0.05).Mineral saturation indices and ionic activities indicate that ion exchange processes and the dissolution of minerals such as carbonatite and fluorite are important sources of fluoride in groundwater.The enrichment of fluorine in the Red bed groundwater is linked to evaporation,cation exchange and dissolution of fluorite,caused by the lithologic characteristics of the red bed in this area.However,it exhibits minimal correlation with the geothermal water in the adjacent area.The noncarcinogenic health risk assessment indicates that 7%(n=5)of Red bed groundwater points exceed the fluoride safety limit for adults,while 12%(n=8)exceed the limit for children.These findings underscore the importance of avoiding highly fluoridated red bed groundwater as a direct drinking source and enhancing groundwater monitoring to mitigate health risks associated with elevated fluoride levels.
基金supported by the National Key R&D Program of China(No.2023YFC3805100)the National Natural Science Foundation of China(Nos.52222811 and 52494963)。
文摘Earthquakes pose significant perils to the built environment in urban areas.To avert the calamitous aftermath of earthquakes,it is imperative to construct seismic resilient cities.Due to the intricacy of the concept of urban seismic resilience(USR),its assessment is a large-scale system engineering issue.The assessment of USR should be based on the notion of urban seismic capacity(USC)assessment,which includes casualties,economic loss,and recovery time as criteria.Functionality loss is also included in the assessment of USR in addition to these criteria.The assessment indicator system comprising five dimensions(building and lifeline infrastructure,environment,society,economy,and institution)and 20 indicators has been devised to quantify USR.The analytical hierarchy process(AHP)is utilized to compute the weights of the criteria,dimensions,and indicators in the urban seismic resilience assessment(USRA)indicator system.When the necessary data for a city are obtainable,the seismic resilience of that city can be assessed using this framework.To illustrate the proposed methodology,a moderate-sized city in China was selected as a case study.The assessment results indicate a high level of USR,suggesting that the city possesses strong capabilities to withstand and recover from potential future earthquakes.
文摘Background There is scarce data about comparisons between geriatric assessment tools in patients with aortic stenosis(AS).We aimed to describe the geriatric profile of patients with AS undergoing transcatheter aortic valve implantation(TAVI)and to analyze the ability of different tools for predicting clinical outcomes in this context.Methods This was a single center retrospective registry including patients with AS undergoing TAVI and surviving to hospital discharge.The primary endpoint was all-cause mortality or need for urgent readmission one year after TAVI.Results A total of 377 patients were included(mean age of 80.4 years).Most patients were independent or mildly dependent,with an optimal cognitive status.The proportion of frailty ranged from 17.6%to 49.8%.A total of 20 patients(5.3%)died and 110/377 patients(29.2%)died or were readmitted during follow up.Overall,most components of the geriatric assessment showed an association with clinical outcomes.Disability for instrumental activities showed a significant association with mortality and a strong association with the rate of mortality or readmission.The association between frailty and clinical outcomes was higher for short physical performance battery(SPPB),essential frailty toolset(EFT)and the frailty index based on comprehensive geriatric assessment(IF-VIG)and lower for Fried criteria and FRAIL scale.Conclusions AS patients from this series presented a good physical performance,optimal cognitive status and a reasonably low prevalence of frailty.The best predictive ability was observed for disability for instrumental activities and frailty as measured by the EFT,SPPB and the IF-VIG.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
基金financially supported by State Key Laboratory of HVDC No.SKLHVDC-2023-KF-03.
文摘The traditional transient stability assessment(TSA)model for power systems has three disadvantages:capturing critical information during faults is difficult,aperiodic and oscillatory unstable conditions are not distinguished,and poor generalizability is exhibited by systems with high renewable energy penetration.To address these issues,a novel ResGRU architecture for TSA is proposed in this study.First,a residual neural network(ResNet)is used for deep feature extraction of transient information.Second,a bidirectional gated recurrent unit combined with a multi-attention mechanism(BiGRU-Attention)is used to establish temporal feature dependencies.Their combination constitutes a TSA framework based on the ResGRU architecture.This method predicts three transient conditions:oscillatory instability,aperiodic instability,and stability.The model was trained offline using stochastic gradient descent with a thermal restart(SGDR)optimization algorithm in the offline training phase.This significantly improves the generalizability of the model.Finally,simulation tests on IEEE 145-bus and 39-bus systems confirmed that the proposed method has higher adaptability,accuracy,scalability,and rapidity than the conventional TSA approach.The proposed model also has superior robustness for PMU incomplete configurations,PMU noisy data,and packet loss.
基金supported by the National Key Research&Development Program of China(grant no.2022YFC3500503)the National Natural Science Foundation of China(grant nos.62227807,12374171,12004034,62402041)+2 种基金the Beijing Institute of Technology Research Fund Program for Young Scholars,Chinathe Fundamental Research Funds for the Central Universities(grant nos.2024CX06060)Beijing Youth Talent Lifting Project.
文摘Due to their high mechanical compliance and excellent biocompatibility,conductive hydrogels exhibit significant potential for applications in flexible electronics.However,as the demand for high sensitivity,superior mechanical properties,and strong adhesion performance continues to grow,many conventional fabrication methods remain complex and costly.Herein,we propose a simple and efficient strategy to construct an entangled network hydrogel through a liquid-metal-induced cross-linking reaction,hydrogel demonstrates outstanding properties,including exceptional stretchability(1643%),high tensile strength(366.54 kPa),toughness(350.2 kJ m^(−3)),and relatively low mechanical hysteresis.The hydrogel exhibits long-term stable reusable adhesion(104 kPa),enabling conformal and stable adhesion to human skin.This capability allows it to effectively capture high-quality epidermal electrophysiological signals with high signal-to-noise ratio(25.2 dB)and low impedance(310 ohms).Furthermore,by integrating advanced machine learning algorithms,achieving an attention classification accuracy of 91.38%,which will significantly impact fields like education,healthcare,and artificial intelligence.
基金supported by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),University of Technology Sydney(UTS),Australia and was supported by the Research Training Program(RTP)of the Australian Government.
文摘Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure.Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard,vulnerability,exposure,and mitigation capacity,crucial for understanding and managing the risks hurricanes pose to communities.These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios.A systematic review was conducted,encompassing 94 articles,to scrutinize the structure,data inputs,assumptions,methodologies,perils modelled,and key predictors of hurricane risk.This review identified key research gaps essential for enhancing future risk assessments.The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril,commonly storm surge and flood,resulting in inadequacies in disaster resilience planning.Most risk assessments were based on hurricane frequency rather than hurricane damage,which is more insightful for policymakers.Furthermore,considering secondary indirect impacts stemming from hurricanes,including real estate market and business interruption,could enrich economic impact assessments.Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5%of studies.The top six predictive factors for hurricane risk were land use,slope,precipitation,elevation,population density,and soil texture/drainage.Another notable research gap identified was the potential of machine learning techniques in risk assessments,offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions.Existing machine learning based risk assessments leverage random forest models(42%of studies)followed by neural network models(19%of studies),with further research required to investigate diverse machine learning algorithms such as ensemble models.A further research gap is model validation,in particular assessing transferability to a new study region.Additionally,harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments.By addressing these research gaps,hurricane risk assessments can furnish invaluable insights for national policymakers,facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities.To the authors’knowledge,this represents the first literature review specifically dedicated to quantitative hurricane risk assessments,encompassing a comparison of Multi-criteria Decision Making(MCDM),numerical models,and machine learning models.Ultimately,advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future.
基金The National Key Research and Development Program of China(No.2023YFC3805003)。
文摘To quantify the seismic resilience of buildings,a method for evaluating functional loss from the component level to the overall building is proposed,and the dual-parameter seismic resilience assessment method based on postearthquake loss and recovery time is improved.A threelevel function tree model is established,which can consider the dynamic changes in weight coefficients of different category of components relative to their functional losses.Bayesian networks are utilized to quantify the impact of weather conditions,construction technology levels,and worker skill levels on component repair time.A method for determining the real-time functional recovery curve of buildings based on the component repair process is proposed.Taking a three-story teaching building as an example,the seismic resilience indices under basic earthquakes and rare earthquakes are calculated.The results show that the seismic resilience grade of the teaching building is comprehensively judged as GradeⅢ,and its resilience grade is more significantly affected by postearthquake loss.The proposed method can be used to predict the seismic resilience of buildings prior to earthquakes,identify weak components within buildings,and provide guidance for taking measures to enhance the seismic resilience of buildings.
