With improvements in care of at-risk neonates, more and more children survive. This makes it increasingly important to assess, soon after birth, the prognosis of children with hypoxic-ischemic encephalopathy. Computed...With improvements in care of at-risk neonates, more and more children survive. This makes it increasingly important to assess, soon after birth, the prognosis of children with hypoxic-ischemic encephalopathy. Computed tomography, ultrasound, and conventional magnetic resonance imaging are helpful to diagnose brain injury, but cannot quantify white matter damage. In this study, ten full-term infants without brain injury and twenty-two full-term neonates with hypoxic-ischemic encephalopathy （14 moderate cases and 8 severe cases） underwent diffusion tensor imaging to assess its feasibility in evaluating white matter damage in this condition. Results demonstrated that fractional anisotropy, voxel volume, and number of fiber bundles were different in some brain areas between infants with brain injury and those without brain injury. The correlation between fractional anisotropy values and neonatal behavioral neurological assessment scores was closest in the posterior limbs of the internal capsule. We conclude that diffusion tensor imaging can quantify white matter injury in neonates with hypoxic-ischemic encephalopathy.展开更多
39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.Ar...39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
Analysis Method of ^(131)I Activity in Carbon Cartridge and Internal Dose Assessment for Nuclear Medicine Workers.Shuo Wang1,Fei Tuo1,Jian-feng Zhang1,Xiao-liang Li1,Bao-lu Yang1,Qiang Zhou1,Ze-shu Li1,Shu-ying Kong1,...Analysis Method of ^(131)I Activity in Carbon Cartridge and Internal Dose Assessment for Nuclear Medicine Workers.Shuo Wang1,Fei Tuo1,Jian-feng Zhang1,Xiao-liang Li1,Bao-lu Yang1,Qiang Zhou1,Ze-shu Li1,Shu-ying Kong1,and Wei-hao Qin1(1.National Institute for Radiological Protection,Chinese Center for Disease Control and Prevention,Beijing,100088,China.)展开更多
Analysis of the environmental and economic performance of fishing vessels has received limited attention compared with other ship types despite their notable contribution to global greenhouse gas(GHG)emissions.This st...Analysis of the environmental and economic performance of fishing vessels has received limited attention compared with other ship types despite their notable contribution to global greenhouse gas(GHG)emissions.This study evaluates the carbon footprint(CF)and economic viability of a liquefied natural gas(LNG)-fueled fishing vessel,using real engine operation simulations to provide precise and dynamic evaluation of fuel consumption and GHG emissions.Operational profiles are obtained through the utilization of onboard monitoring systems,whereas engine performance is simulated using the 1D/0D AVL Boost^(TM)model.Life cycle assessment(LCA)is conducted to quantify the environmental impact,whereas life cycle cost assessment(LCCA)is performed to analyze the profitability of LNG as an alternative fuel.The potential impact of the future fuel price uncertainties is addressed using Monte Carlo simulations.The LCA findings indicate that LNG has the potential to reduce the CF of the vessel by 14%to 16%,in comparison to a diesel power system configuration that serves as the baseline scenario.The LCCA results further indicate that the total cost of an LNG-powered ship is lower by 9.5%-13.8%,depending on the share of LNG and pilot fuels.This finding highlights the potential of LNG to produce considerable environmental benefits while addressing economic challenges under diverse operational and fuel price conditions.展开更多
Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analys...Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analysis was performed using data and urine samples from 70 pregnant women in their third trimester recruited at Qujing Central Hospital.Urinary BPA was measured by HPLC-MS/MS.Participants were stratified into high and low BPA exposure groups based on the median concentration.Results:BPA was detected in all samples(100%)with a median concentration of 2.41μg/L(IQR:0.68-4.96).The high BPA exposure group(≥2.41μg/L)had a significantly higher proportion of gestational diabetes mellitus(GDM)(42.9%vs.17.1%,p=0.021)and a lower median fetal birth weight(3250 g vs.3450 g,p=0.048)compared to the low exposure group.Conclusion:This pilot study reveals ubiquitous BPA exposure in pregnant women from Northeastern Yunnan.The observed preliminary associations with GDM and reduced fetal birth weight warrant further investigation in larger,longitudinal studies.展开更多
Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in mariti...Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in maritime shipping using the levelized cost of energy methodology.It includes a detailed comparative analysis based on essential criteria and sensitivity assessments to highlight the economic impacts of technological advancements.Key factors influencing total costs include fuel costs,carbon pricing,and energy demands for carbon capture.The findings reveal that methanol is more cost-effective than heavy fuel oil(HFO)when priced below 3000 CNY/t,assuming HFO costs 4400 CNY/t.Additionally,methanol with post-combustion carbon capture is less expensive than pre-combustion carbon capture.When carbon prices rise above 480 CNY/t,carbon capture technologies prove more economical than purchasing carbon emission allowances for HFO and liquefied natural gas.Enhanc-ing the use of exhaust gas waste heat is recommended for cost savings.Post-combustion carbon capture also shows greater efficiency,requiring about 1.1 GJ/t less energy than pre-combustion methods,leading to lower overall costs.Future research should focus on market mechanisms to stabilize fuel prices and develop less energy-intensive carbon capture technologies.This study offers critical insights into effective decarbonization strategies for advancing global maritime trade in the present and future.展开更多
Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and ev...Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and evaluation systems for arbitrators in China have long faced challenges such as the vague criteria for competency and an unclear professionalization path for arbitrators. To address these issues, this study is grounded in the context of actual Chinese arbitration practice and based on the competency iceberg model. Through a methodological approach encompassing literature reviews, behavioral event interviews, expert revisions, and questionnaire surveys, a Chinese Arbitrator Competency Scale was developed and validated in this study. Examination of the findings indicated that the scale needed to consist of five dimensions—communication and coordination, cognitive skills, ethical conduct, work motivation, and personality traits—and possess a total of 28 specific indicators. Confirmatory analysis of the factors demonstrates a good fit for the five-dimensional model, with each of the dimensions exhibiting high reliability and validity. This scale is innovative in integrating the competency elements with Chinese characteristics, such as commercial acumen, crosscultural mediation skills, and adaptability to the local rule of law. This research not only enriches the competency theory in regard to the field of human resource management but also provides a scientific framework of standards and measurement tools for the selection, training, and evaluation of arbitrators. It thus has significant practical value for enhancing the professionalism and international competitiveness of China's arbitration system.展开更多
Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to ev...Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.展开更多
This report presents a forensic evaluation of a case involving blindness(visual acuity grade 5)following a bee/wasp sting to the left eye.Through systematic analysis of the patient’s multiple hospital admissions,post...This report presents a forensic evaluation of a case involving blindness(visual acuity grade 5)following a bee/wasp sting to the left eye.Through systematic analysis of the patient’s multiple hospital admissions,postoperative follow-up data,and a review of the pathological mechanisms of ocular injury caused by bee venom,this study comprehensively assesses the injury characteristics,treatment course,and visual outcomes.Bee venom induces severe complications such as corneal damage,uveitis,cataract,and secondary glaucoma through multiple mechanisms including direct cytotoxicity,immune-inflammatory responses,and enzymatic hydrolysis.Despite interventions including anterior chamber irrigation,phacoemulsification with intraocular lens implantation,and antiglaucoma surgery,the affected eye ultimately lost light perception.Forensic examination confirmed the absence of light perception in the left eye and abnormal visual pathway function,consistent with clinical observations.According to the relevant Chinese disability assessment standard(JR/T 0083-2013,Article 4.2.2),the injury was classified as grade 7 disability.This study provides an in-depth discussion of the mechanisms and key forensic identification points in bee-sting-induced blindness,offering a scientific reference for similar forensic clinical cases.展开更多
Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai C...Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.展开更多
Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ...Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.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
With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Tradition...With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.展开更多
Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevatio...Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevations along the mid-sagittal plane further contribute to a three-dimensional auditory experience.This study aimed to characterize the variability in vertical sound localization abilities among normal-hearing(NH)individuals using spatialized audio.Materials and Methods:Fifty-one NH participants(aged 18 to 35 years)completed three vertical localization tasks under headphones as part of a single-group,within-subject experimental study.These tasks included two-plane identification:(1)top-down localization,(2)front-back localization,and one discrimination task in the front plane.Hierarchical Cluster Analysis(HCA)was employed to identify distinct patterns in spatial localization profiles specific to the vertical-median plane.Fisher's Discriminant Function Analysis(FDA)was used to validate the accuracy of HCA and estimate classification error.Results:HCA revealed three distinct listener clusters:(1)cluster 1 with good performance across all three tasks,(2)cluster 2 with selective impairment in top-bottom identification,and(3)cluster 3 with selective deficits in front-back identification.FDA validated group membership of the clusters identified by the HCA,with a prediction accuracy of 98%.Conclusions:Individuals with clinically NH exhibited three distinct vertical localization profiles:uniform performers,those impaired in top-bottom identification,and those impaired in front-back identification.These profiles may be linked to the interplay between acoustic and non-acoustic perceptual factors.展开更多
Nonlinear static procedures are widely adopted in structural engineering practice for seismic performance assessment due to their simplicity and computational efficiency.However,their reliability depends heavily on ho...Nonlinear static procedures are widely adopted in structural engineering practice for seismic performance assessment due to their simplicity and computational efficiency.However,their reliability depends heavily on how the nonlinear behaviour of structural components is represented.The recent earthquakes in Albania(2019)and Türkiye(2023)have underscored the need for accurate assessment techniques,particularly for older reinforced concrete buildings with poor detailing.This study quantifies the discrepancies between default and user-defined component modelling in pushover analysis of pre-modern reinforced concrete structures,analysing two representative low-and mid-rise reinforced concrete frame buildings.The lumped plasticity approach incorporates moment-rotation relationships derived from actual member properties and reinforcement configurations,while the distributed plasticity approach uses software-generated default properties based on modern codes.Results show that the distributed plasticity models systematically overestimate both the strength and the deformation capacity by up to 35%compared to lumped plasticity models,especially in buildings with poor detailing and low concrete strength.These findings demonstrate that default software procedures,widely used in practice but not validated for pre-modern structures,produce dangerously unconservative seismic performance estimates.The study provides quantitative evidence of the critical need for tailored modelling strategies that reflect the actual conditions of the existing building stock.展开更多
To ensure the compatibility between rolling stock and infrastructure when dynamically assessing railway bridges under high-speed traffic,the damping properties considered in the calculation model significantly influen...To ensure the compatibility between rolling stock and infrastructure when dynamically assessing railway bridges under high-speed traffic,the damping properties considered in the calculation model significantly influence the predicted acceleration amplitude at resonance.However,due to the normative specifications of EN 1991-2,which are considered to be overly conservative,damping factors that are far below the actual damping have to be used when predicting vibrations of railway bridges,which means that accelerations at resonance tend to be overestimated to an uneconomical extent.Comparisons between damping factors prescribed by the standard and those identified based on in situ structure measurements always reveal a large discrepancy between reality and regulation.Given this background,this contribution presents a novel approach for defining the damping factor of railway bridges with ballasted tracks,where the damping factor for bridges is mathematically determined based on three different two-dimensional mechanical models.The basic principle of the approach for mathematically determining the damping factor is to separately define and superimpose the dissipative contributions of the supporting structure(including the substructure)and the superstructure.Using the results of a measurement campaign on 15 existing steel railway bridges in the Austrian rail network,the presented mechanical models are calibrated,and by analysing the energy dissipation in the ballasted track,guiding principles for practical application are defined.This guideline is intended to establish an alternative to the currently valid specifications of EN 1991-2,enabling the damping factor of railway bridges to be assessed in a realistic range by mathematical calculation and thus without the need for extensive in situ measurements on the individual structure.In this way,the existing potential of the infrastructure with regard to the damping properties of bridges can be utilised.This contribution focuses on steel bridges,but the mathematical approach for determining the damping factor applies equally to other bridge types(concrete,composite,or filler beam).展开更多
Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly deve...Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly develops and deployment increases,these impacts are becoming increasingly evident.A comprehensive understanding of these impacts is crucial for sustainable development.Based on the harmonization of previous detailed life cycle assessment(LCA)studies,this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power.Using this simplified LCA model,we assess the global warming potential(GWP),acidification potential(AP),and cumulative energy demand(CED)of wind power at the regional scale for 2022 and under three future scenarios(high-power wind turbine promotion,reduced wind curtailment,and a comprehensive development scenario).The results indicate that in 2022,the life cycle GWP,AP,and CED of wind power in western China were 10.76 g CO_(2) eq/kWh,0.177 g SO_(2) eq/kWh,and 17.6 kJ/kWh,respectively.Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia,Gansu,Qinghai,Ningxia,and Xinjiang,producing average decreases of 8.64%in GWP,8.39%in AP,and 9.26%in CED.In contrast,for Guangxi,Chongqing,Sichuan,Guizhou,Yunnan,Xizang,and Shaanxi,the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment,producing average decreases of 3.45%,3.09%,and 4.29%in GWP,AP,and CED,respectively.These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.展开更多
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me...Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.展开更多
基金supported by a grant from the Clinical Medicine Science and Technology Projects in Jiangsu Province of China,No.BL2014037a grant from the Changzhou City Science and Technology Support Plan in China,No.CE20165027+1 种基金a grant from the Changzhou Health Development Planning Commission Major Projects in China,No.ZD201515the Changzhou High-Level Health Personnel Training Project Funding
文摘With improvements in care of at-risk neonates, more and more children survive. This makes it increasingly important to assess, soon after birth, the prognosis of children with hypoxic-ischemic encephalopathy. Computed tomography, ultrasound, and conventional magnetic resonance imaging are helpful to diagnose brain injury, but cannot quantify white matter damage. In this study, ten full-term infants without brain injury and twenty-two full-term neonates with hypoxic-ischemic encephalopathy （14 moderate cases and 8 severe cases） underwent diffusion tensor imaging to assess its feasibility in evaluating white matter damage in this condition. Results demonstrated that fractional anisotropy, voxel volume, and number of fiber bundles were different in some brain areas between infants with brain injury and those without brain injury. The correlation between fractional anisotropy values and neonatal behavioral neurological assessment scores was closest in the posterior limbs of the internal capsule. We conclude that diffusion tensor imaging can quantify white matter injury in neonates with hypoxic-ischemic encephalopathy.
文摘39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
文摘Analysis Method of ^(131)I Activity in Carbon Cartridge and Internal Dose Assessment for Nuclear Medicine Workers.Shuo Wang1,Fei Tuo1,Jian-feng Zhang1,Xiao-liang Li1,Bao-lu Yang1,Qiang Zhou1,Ze-shu Li1,Shu-ying Kong1,and Wei-hao Qin1(1.National Institute for Radiological Protection,Chinese Center for Disease Control and Prevention,Beijing,100088,China.)
文摘Analysis of the environmental and economic performance of fishing vessels has received limited attention compared with other ship types despite their notable contribution to global greenhouse gas(GHG)emissions.This study evaluates the carbon footprint(CF)and economic viability of a liquefied natural gas(LNG)-fueled fishing vessel,using real engine operation simulations to provide precise and dynamic evaluation of fuel consumption and GHG emissions.Operational profiles are obtained through the utilization of onboard monitoring systems,whereas engine performance is simulated using the 1D/0D AVL Boost^(TM)model.Life cycle assessment(LCA)is conducted to quantify the environmental impact,whereas life cycle cost assessment(LCCA)is performed to analyze the profitability of LNG as an alternative fuel.The potential impact of the future fuel price uncertainties is addressed using Monte Carlo simulations.The LCA findings indicate that LNG has the potential to reduce the CF of the vessel by 14%to 16%,in comparison to a diesel power system configuration that serves as the baseline scenario.The LCCA results further indicate that the total cost of an LNG-powered ship is lower by 9.5%-13.8%,depending on the share of LNG and pilot fuels.This finding highlights the potential of LNG to produce considerable environmental benefits while addressing economic challenges under diverse operational and fuel price conditions.
文摘Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analysis was performed using data and urine samples from 70 pregnant women in their third trimester recruited at Qujing Central Hospital.Urinary BPA was measured by HPLC-MS/MS.Participants were stratified into high and low BPA exposure groups based on the median concentration.Results:BPA was detected in all samples(100%)with a median concentration of 2.41μg/L(IQR:0.68-4.96).The high BPA exposure group(≥2.41μg/L)had a significantly higher proportion of gestational diabetes mellitus(GDM)(42.9%vs.17.1%,p=0.021)and a lower median fetal birth weight(3250 g vs.3450 g,p=0.048)compared to the low exposure group.Conclusion:This pilot study reveals ubiquitous BPA exposure in pregnant women from Northeastern Yunnan.The observed preliminary associations with GDM and reduced fetal birth weight warrant further investigation in larger,longitudinal studies.
基金supported by the National Key R&D Program of China(No.2022YFC3701500)the Key R&D Plan Projects of Zhejiang Province(No.2024SSYS0072)Zhejiang Provincial Natural Science Foundation(No.LDT23E0601).
文摘Ship operations are crucial to global trade,and their decarbonization is essential to mitigate climate change.This study evaluates the economic viability of existing and emerging decarbonization technologies in maritime shipping using the levelized cost of energy methodology.It includes a detailed comparative analysis based on essential criteria and sensitivity assessments to highlight the economic impacts of technological advancements.Key factors influencing total costs include fuel costs,carbon pricing,and energy demands for carbon capture.The findings reveal that methanol is more cost-effective than heavy fuel oil(HFO)when priced below 3000 CNY/t,assuming HFO costs 4400 CNY/t.Additionally,methanol with post-combustion carbon capture is less expensive than pre-combustion carbon capture.When carbon prices rise above 480 CNY/t,carbon capture technologies prove more economical than purchasing carbon emission allowances for HFO and liquefied natural gas.Enhanc-ing the use of exhaust gas waste heat is recommended for cost savings.Post-combustion carbon capture also shows greater efficiency,requiring about 1.1 GJ/t less energy than pre-combustion methods,leading to lower overall costs.Future research should focus on market mechanisms to stabilize fuel prices and develop less energy-intensive carbon capture technologies.This study offers critical insights into effective decarbonization strategies for advancing global maritime trade in the present and future.
基金Research on Problems and Countermeasures in Building the Capacity of the Grassroots International Chambers of Commerce in the Context of High-Quality Development (W2024H03841)a key research project of the China Council for the Promotion of International Trade in 2025。
文摘Arbitration is a key non-litigation commercial mechanism for the resolution of disputes, and the quality and credibility of its awards depend largely on the competency of the arbitrators. However, the selection and evaluation systems for arbitrators in China have long faced challenges such as the vague criteria for competency and an unclear professionalization path for arbitrators. To address these issues, this study is grounded in the context of actual Chinese arbitration practice and based on the competency iceberg model. Through a methodological approach encompassing literature reviews, behavioral event interviews, expert revisions, and questionnaire surveys, a Chinese Arbitrator Competency Scale was developed and validated in this study. Examination of the findings indicated that the scale needed to consist of five dimensions—communication and coordination, cognitive skills, ethical conduct, work motivation, and personality traits—and possess a total of 28 specific indicators. Confirmatory analysis of the factors demonstrates a good fit for the five-dimensional model, with each of the dimensions exhibiting high reliability and validity. This scale is innovative in integrating the competency elements with Chinese characteristics, such as commercial acumen, crosscultural mediation skills, and adaptability to the local rule of law. This research not only enriches the competency theory in regard to the field of human resource management but also provides a scientific framework of standards and measurement tools for the selection, training, and evaluation of arbitrators. It thus has significant practical value for enhancing the professionalism and international competitiveness of China's arbitration system.
基金supported by the National Natural Science Foundation of China(42361144880)the Science and Technology Program of Xizang Autonomous Region,China(XZ202402ZD0001)the Qinghai Province Basic Research Program Project,China(2024-ZJ-904).
文摘Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.
文摘This report presents a forensic evaluation of a case involving blindness(visual acuity grade 5)following a bee/wasp sting to the left eye.Through systematic analysis of the patient’s multiple hospital admissions,postoperative follow-up data,and a review of the pathological mechanisms of ocular injury caused by bee venom,this study comprehensively assesses the injury characteristics,treatment course,and visual outcomes.Bee venom induces severe complications such as corneal damage,uveitis,cataract,and secondary glaucoma through multiple mechanisms including direct cytotoxicity,immune-inflammatory responses,and enzymatic hydrolysis.Despite interventions including anterior chamber irrigation,phacoemulsification with intraocular lens implantation,and antiglaucoma surgery,the affected eye ultimately lost light perception.Forensic examination confirmed the absence of light perception in the left eye and abnormal visual pathway function,consistent with clinical observations.According to the relevant Chinese disability assessment standard(JR/T 0083-2013,Article 4.2.2),the injury was classified as grade 7 disability.This study provides an in-depth discussion of the mechanisms and key forensic identification points in bee-sting-induced blindness,offering a scientific reference for similar forensic clinical cases.
基金supported by the Northeast Geological Science and Technology Innovation Center of China Geological Survey(Grant NO.QCJJ2022-43)the Natural Resources Comprehensive Survey Project(Grant Nos.DD20230470,DD20230508)the National Groundwater Monitoring Network Operation and Maintenance Program(Grant No.DD20251300109).
文摘Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.
基金supported by the National Key R&D Program of China(No.2023YFF1304002-05)the National Social Science Fund of China(No.22BTJ005)the National Natural Science Foundation of China(No.32572049)。
文摘Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.
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funded in part by the Fundamental Research Funds for the Central Universities under Grant NS2023052in part by the Natural Science Foundation of Jiangsu Province of China under Grants No.BK20231439 and No.BK20222012.
文摘With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.
文摘Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevations along the mid-sagittal plane further contribute to a three-dimensional auditory experience.This study aimed to characterize the variability in vertical sound localization abilities among normal-hearing(NH)individuals using spatialized audio.Materials and Methods:Fifty-one NH participants(aged 18 to 35 years)completed three vertical localization tasks under headphones as part of a single-group,within-subject experimental study.These tasks included two-plane identification:(1)top-down localization,(2)front-back localization,and one discrimination task in the front plane.Hierarchical Cluster Analysis(HCA)was employed to identify distinct patterns in spatial localization profiles specific to the vertical-median plane.Fisher's Discriminant Function Analysis(FDA)was used to validate the accuracy of HCA and estimate classification error.Results:HCA revealed three distinct listener clusters:(1)cluster 1 with good performance across all three tasks,(2)cluster 2 with selective impairment in top-bottom identification,and(3)cluster 3 with selective deficits in front-back identification.FDA validated group membership of the clusters identified by the HCA,with a prediction accuracy of 98%.Conclusions:Individuals with clinically NH exhibited three distinct vertical localization profiles:uniform performers,those impaired in top-bottom identification,and those impaired in front-back identification.These profiles may be linked to the interplay between acoustic and non-acoustic perceptual factors.
文摘Nonlinear static procedures are widely adopted in structural engineering practice for seismic performance assessment due to their simplicity and computational efficiency.However,their reliability depends heavily on how the nonlinear behaviour of structural components is represented.The recent earthquakes in Albania(2019)and Türkiye(2023)have underscored the need for accurate assessment techniques,particularly for older reinforced concrete buildings with poor detailing.This study quantifies the discrepancies between default and user-defined component modelling in pushover analysis of pre-modern reinforced concrete structures,analysing two representative low-and mid-rise reinforced concrete frame buildings.The lumped plasticity approach incorporates moment-rotation relationships derived from actual member properties and reinforcement configurations,while the distributed plasticity approach uses software-generated default properties based on modern codes.Results show that the distributed plasticity models systematically overestimate both the strength and the deformation capacity by up to 35%compared to lumped plasticity models,especially in buildings with poor detailing and low concrete strength.These findings demonstrate that default software procedures,widely used in practice but not validated for pre-modern structures,produce dangerously unconservative seismic performance estimates.The study provides quantitative evidence of the critical need for tailored modelling strategies that reflect the actual conditions of the existing building stock.
基金funded by the Austrian Federal Railways(ÖBB Infrastruktur AG)in the context of the research project‘VeMoDiss’(acronym)。
文摘To ensure the compatibility between rolling stock and infrastructure when dynamically assessing railway bridges under high-speed traffic,the damping properties considered in the calculation model significantly influence the predicted acceleration amplitude at resonance.However,due to the normative specifications of EN 1991-2,which are considered to be overly conservative,damping factors that are far below the actual damping have to be used when predicting vibrations of railway bridges,which means that accelerations at resonance tend to be overestimated to an uneconomical extent.Comparisons between damping factors prescribed by the standard and those identified based on in situ structure measurements always reveal a large discrepancy between reality and regulation.Given this background,this contribution presents a novel approach for defining the damping factor of railway bridges with ballasted tracks,where the damping factor for bridges is mathematically determined based on three different two-dimensional mechanical models.The basic principle of the approach for mathematically determining the damping factor is to separately define and superimpose the dissipative contributions of the supporting structure(including the substructure)and the superstructure.Using the results of a measurement campaign on 15 existing steel railway bridges in the Austrian rail network,the presented mechanical models are calibrated,and by analysing the energy dissipation in the ballasted track,guiding principles for practical application are defined.This guideline is intended to establish an alternative to the currently valid specifications of EN 1991-2,enabling the damping factor of railway bridges to be assessed in a realistic range by mathematical calculation and thus without the need for extensive in situ measurements on the individual structure.In this way,the existing potential of the infrastructure with regard to the damping properties of bridges can be utilised.This contribution focuses on steel bridges,but the mathematical approach for determining the damping factor applies equally to other bridge types(concrete,composite,or filler beam).
基金supported by the National Key Research and Development Program of China(Grant No.2022YFF1303405).
文摘Compared with traditional energy sources,wind power has a lower environmental impact.However,emissions are still generated across the life cycle of wind turbines,from production to recycling.As wind power rapidly develops and deployment increases,these impacts are becoming increasingly evident.A comprehensive understanding of these impacts is crucial for sustainable development.Based on the harmonization of previous detailed life cycle assessment(LCA)studies,this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power.Using this simplified LCA model,we assess the global warming potential(GWP),acidification potential(AP),and cumulative energy demand(CED)of wind power at the regional scale for 2022 and under three future scenarios(high-power wind turbine promotion,reduced wind curtailment,and a comprehensive development scenario).The results indicate that in 2022,the life cycle GWP,AP,and CED of wind power in western China were 10.76 g CO_(2) eq/kWh,0.177 g SO_(2) eq/kWh,and 17.6 kJ/kWh,respectively.Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia,Gansu,Qinghai,Ningxia,and Xinjiang,producing average decreases of 8.64%in GWP,8.39%in AP,and 9.26%in CED.In contrast,for Guangxi,Chongqing,Sichuan,Guizhou,Yunnan,Xizang,and Shaanxi,the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment,producing average decreases of 3.45%,3.09%,and 4.29%in GWP,AP,and CED,respectively.These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.
基金Science and Technology Innovation 2030 Major Project,Grant/Award Number:2023ZD0508506。
文摘Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.
