Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict...Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms,which involves an analy...The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms,which involves an analysis of the stiffness performance of numerous discrete points in the workspace.This necessitates time-consuming and inefficient calculation,which is particularly pronounced in the optimization design stage of the mechanism,where the variations in the global stiffness performance indices versus various dimensional and structural parameters need to be analyzed.This paper presents a semi-analytical approach for stiffness modeling of the novel(R(RPS&RP))&2-UPS parallel mechanism(referred to as the Trifree mechanism)and proposes“local”stiffness performance indices as alternatives to global indices.Drawing on the screw theory,the Cartesian stiffness matrix of the Trifree mechanism is formulated explicitly by considering the compliances of all elastic elements and the over-constraint characteristics inherent in the mechanism.Based on the spherical motion pattern of the Trifree mechanism,four special reference configurations are extracted within the workspace.This yields“local”stiffness performance indices capable of accurately evaluating the overall stiffness performance of the mechanism and effectively improving the computational efficiency.The variations in global and“local”stiffness performance indices versus key design parameters are investigated.Furthermore,the proposed indices are applied to the Tricept and Trimule mechanisms.The results demonstrate that the proposed indices exhibit excellent computational accuracy and efficiency in evaluating the overall stiffness performance of these spherical parallel mechanisms.Moreover,the stiffness performance of the novel parallel mechanism investigated in this study closely resembles that of the well-known Tricept and Trimule mechanisms.This research proposes a semi-analytic stiffness model of the Trifree mechanism and“local”stiffness performance indices to evaluate the overall stiffness performance,thereby substantially improving the computational efficiency without sacrificing accuracy.展开更多
Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method ca...Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.展开更多
Accurately forecasting the operational performance of a tunnel boring machine(TBM)in advance is useful for making timely adjustments to boring parameters,thereby enhancing overall boring efficiency.In this study,we us...Accurately forecasting the operational performance of a tunnel boring machine(TBM)in advance is useful for making timely adjustments to boring parameters,thereby enhancing overall boring efficiency.In this study,we used the Informer model to predict a critical performance parameter of the TBM,namely thrust.Leveraging data from the Guangzhou Metro Line 22 project on the big data platform in China,the model’s performance was validated,while data from Line 18 were used to assess its generalization capability.Results revealed that the Informer model surpasses random forest(RF),extreme gradient boosting(XGB),support vector regression(SVR),k-nearest neighbors(KNN),back propagation(BP),and long short-term memory(LSTM)models in both prediction accuracy and generalization performance.In addition,the optimal input lengths for maximizing accuracy in the single-time-step output model are within the range of 8–24,while for the multiple-time-step output model,the optimal input length is 8.Furthermore,the last predicted value in the case of multiple-time-step outputs showed the highest accuracy.It was also found that relaxation of the Pearson analysis method metrics to 0.95 improved the performance of the model.Finally,the prediction results were most affected by earth pressure,rotation speed,torque,boring speed,and the surrounding rock grade.The model can provide useful guidance for constructors when adjusting TBM operation parameters.展开更多
In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries,it is important to develop science-based strategies for optimally designing training pr...In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries,it is important to develop science-based strategies for optimally designing training programs.The purpose of the present study is to develop a novel method by the combined use of optimal control theory and a training-performance model for designing optimal training programs,with the hope of helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The training-performance model used in the proposed optimal control framework is a conceptual extension of the Banister impulse-response model that describes the dynamics of performance,training load(served as the control variable),fitness(the overall positive effects on performance),and fatigue(the overall negative effects on performance).The objective functional of the proposed optimal control framework is to maximize the fitness and minimize the fatigue on the competition day with the goal of maximizing the performance on the competition day while minimizing the cumulative training load during the training course.The Forward-Backward Sweep Method is used to solve the proposed optimal control framework to obtain the optimal solutions of performance,training load,fitness,and fatigue.The simulation results show that the performance on the competition day is higher while the cumulative training load during the training course is lower with using optimal control theory than those without,successfully showing the feasibility and benefits of using the proposed optimal control framework to design optimal training programs for helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The present feasibility study lays the foundation of the combined use of optimal control theory and training-performance models to design personalized optimal training programs in real applications in athletic training and sports science for helping athletes achieve the best performances in competitions while prevent overtraining and the risk of overuse injuries.展开更多
Obtaining unsteady hydrodynamic performance is of great significance for seaplane design.Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics(CFD)numer...Obtaining unsteady hydrodynamic performance is of great significance for seaplane design.Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics(CFD)numerical simulation,which are costly and time-consuming.Therefore,it is necessary to obtain unsteady hydrodynamic performance in a low-cost and high-precision manner.Due to the strong nonlinearity,complex data distribution,and temporal characteristics of unsteady hydrodynamic performance,the prediction of it is challenging.This paper proposes a Temporal Convolutional Diffusion Model(TCDM)for predicting the unsteady hydrodynamic performance of seaplanes given design parameters.Under the framework of a classifier-free guided diffusion model,TCDM learns the distribution patterns of unsteady hydrodynamic performance data with the designed denoising module based on temporal convolutional network and captures the temporal features of unsteady hydrodynamic performance data.Using CFD simulation data,the proposed method is compared with the alternative methods to demonstrate its accuracy and generalization.This paper provides a method that enables the rapid and accurate prediction of unsteady hydrodynamic performance data,expecting to shorten the design cycle of seaplanes.展开更多
Objectives:The aim of this study was to compare the measured physiological factors that limit running performance with real marathon results from world-class distance runners,evaluating the compatibility between measu...Objectives:The aim of this study was to compare the measured physiological factors that limit running performance with real marathon results from world-class distance runners,evaluating the compatibility between measured data and predicted results based on the previously suggested model.Methods:Four world-class East African marathon runners(three male,one female)underwent physiological running assessments to predict marathon performance times using a model based on˙V O_(2)peak,percentage of˙V O_(2)peak at the second ventilatory threshold,and running economy.Predictions were then compared to participants’best marathon times.Results:The measured˙V O_(2)peak of the world-class runners was 75.1±2.7 mL/kg/min.The second ventilatory threshold occurred at 85±3%of the peak,with a running economy of 63.7±2.4 mL/kg/min at 19.6±0.9 km/h.The predicted marathon performance time was 2:06:51±0:03:17 h:min:s for the males and 2:17:36 h:min:s for the female.Comparing these predictions to their personal best times,the average difference was 00:55±00:51 min:s(range:00:20-02:08).Conclusions:This research provides laboratory data on world-class road running athletes,reinforcing the link between marathon performance and˙V O_(2)peak,the percentage of˙VO_(2)peak at the second ventilatory threshold,and running economy.The examined athletes had lower˙V O_(2)peak compared to predicted values,highlighting the importance of running economy and fractional utilization of˙V O_(2)peak in achieving such performances.Future studies should continue to advance the field by including additional bioenergetic parameters measured during race conditions and expanding the participant cohort of elite marathoners,encompassing both sexes.展开更多
The department of Tillabéri is primarily affected by climatic phenomena, impacting crop yields, growing cycles, and consequently, the economic outcomes of agricultural operations. The objective of this study is t...The department of Tillabéri is primarily affected by climatic phenomena, impacting crop yields, growing cycles, and consequently, the economic outcomes of agricultural operations. The objective of this study is to analyze these impacts of climate disruption on the economic performance of farms. The methodology adopted for this study combined documentary research with field surveys conducted on a sample of 250 randomly selected farmers. The analytical methods used mainly consisted of linear regression, profitability calculations, and linear programming. The findings indicate that all productions across different crops have experienced a decrease over the past 30 years. For instance, the production of millet, sorghum, and cowpea, which were respectively 812 kg/ha, 260 kg/ha, and 100 kg/ha between the last 30 and 20 years, has now dropped to 412 kg/ha, 106 kg/ha, and 46 kg/ha respectively. A negative and significant effect on agricultural net margin was observed due to variables such as flooding, drought, pest invasion in rice fields, and temperature changes. Smallholder farms show a relatively low margin (46%) to cover their fixed costs, which may indicate a risk if fixed expenses are high. Furthermore, the analysis results from linear programming reveal that farmers could achieve an additional net profit per hectare of 116,861 FCFA, 217201.5 FCFA, and 291988.2 FCFA respectively for small, medium, and large producers by managing variable costs and health-related expenses for households.展开更多
Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouri...Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.展开更多
Scientific research and technological innovation are driving modern economies;however,a new form of property rights is required to compensate knowledge workers for their contributions.In 1994,the Science and Technolog...Scientific research and technological innovation are driving modern economies;however,a new form of property rights is required to compensate knowledge workers for their contributions.In 1994,the Science and Technology Bureau of Shenzhen,China implemented a policy to encourage scientists and engineers to develop innovative technologies that would provide them a share of the profits earned from their innovations.This created a new“shared property rights”system.China’s shared property model is so new that the conditions under which it can improve enterprise profits remain unclear.To answer this question,we obtained data from the China Stock Market and Accounting Research database for 904 Chinese enterprises that had implemented shared property rights for the first time between 2009 and 2021 and used a propensity score matching method and econometric models to evaluate their performance.The results indicated that shared property incentives improved corporate financial performance and that benefits increased gradually over time.The new approach showed a stronger positive effect than restricted stock options during the study period.The strength of the incentive was greater for core technical staff than for senior executives,suggesting that scientists,engineers,and computer programmers should be the targets of a shared property rights incentive program.To take full advantage of the new shared property rights institution,enterprise managers should set the implementation period at a reasonable length(5 to 10 years,based on our study results).Enterprises can also test two or more simultaneous approaches that account for the specific needs of each category of workers,based on a careful examination of their current situation and expected or desired future situations.展开更多
Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across vari...Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.展开更多
This study investigates the spatial courting between digital economic signs and local monetary overall performance throughout ten provinces in Sumatra,Indonesia,from 2019 to 2022.As digitalization hastens economic and...This study investigates the spatial courting between digital economic signs and local monetary overall performance throughout ten provinces in Sumatra,Indonesia,from 2019 to 2022.As digitalization hastens economic and business sports,devices together with fintech lending,e-cash,debit card usage,and e-commerce are increasingly more diagnosed as capability drivers of regional increase.But,the unequal distribution of digital infrastructure and monetary literacy across regions raises issues approximately the inclusivity of these benefits.constructing upon current findings by using Miranti et al.(2024),this research employs spatial econometric fashions-particularly the Spatial Lag model(SLM)and Spatial mistakes model(SEM)-to evaluate how digital variables influence provincial financial overall performance while accounting for spatial spillover consequences.The results reveal that fintech lending and debit card usage exert a positive and significant impact on economic growth,whereas the effect of e-money is negative,suggesting potential substitution effects or access constraints.Spatial dependency is also evident,as demonstrated by the significant lambda coefficient in the SEM model.These findings highlight the importance of spatially coordinated digital policies,particularly in addressing disparities and enhancing digital financial inclusion.The study concludes with policy recommendations aimed at fostering inclusive and spatially balanced digital economic development in Sumatra.展开更多
Background:Social media addiction,one of the behavioural addictions,is a significant predictor of job performance.It has also been posited that individuals whose fundamental requirements(e.g.,sleep)are not sufficientl...Background:Social media addiction,one of the behavioural addictions,is a significant predictor of job performance.It has also been posited that individuals whose fundamental requirements(e.g.,sleep)are not sufficiently met andwho lack adequate support(e.g.,perceived social support)are incapable of effectivelyharnessing theirpotential.The primary objective of this study is to examine themediating effects of sleep disorder and perceived social support on the relationship between social media addiction and job performance.Furthermore,it seeks to explore the moderating effects of perceived social support on sleep disorders and job performance.Methods:The data were collected through the questionnairemethod,and data analysis was performed using SPSS 26.0.Moreover,statistical analysis encompasses correlation analysis,mediation,and moderation analysis.The data were gathered from 488 healthcare professionals(57.2%female),whose ages ranged from 24 to 56 years(Meanage±SD=37.86±6.71),using a convenience sample approach.Results:The results revealed significant relationships between social media addiction,job performance,perceived social support,and sleep disorder.The findings indicate that social media addiction negatively predicts job performance(β=−0.11,p<0.05).Sleep disorder(effect size=−0.02,95%CI=[−0.04,−0.00])and perceived social support(effect size=−0.01,95%CI=[−0.02,−0.00])mediate this relationship.Furthermore,perceived social supportmoderates the pathway between sleep disorder and job performance(index ofmoderatedmediation:−0.0040,95%CI=[−0.0070,−0.0010]).Conclusions:This study suggests that social media addiction negatively affects job performance through sleep disorders and perceived social support among healthcare professionals.The study’s findings are significant,as they suggest that treatments aimed at alleviating sleep disorders and enhancing perceived social support among medical workers may improve their job performance.展开更多
During the COVID-19 pandemic,most businesses adopted hybrid work arrangements,adapting to changing workplace demands.This study explores how Technologies 4.0(advanced technologies)and employee motivation affect the li...During the COVID-19 pandemic,most businesses adopted hybrid work arrangements,adapting to changing workplace demands.This study explores how Technologies 4.0(advanced technologies)and employee motivation affect the link between hybrid work models and the performance of younger workers(millennials and Gen-Z)in Kerala’s IT sector.Based on a survey of 155 employees,the study shows that the hybrid work model has a significant impact on performance.While basic technologies do not have a major role,the study emphasizes that employee engagement is a key factor.As hybrid work options become more common,they will continue to shape the future of work.展开更多
Background:Large Language Models(LLMs)have gained much attention and,in part,have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses.Osteoarthrit...Background:Large Language Models(LLMs)have gained much attention and,in part,have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses.Osteoarthritis(OA)is a common topic in skeletal muscle disor-ders,and patients often seek information about it online.Our study evaluated the ability of 3 LLMs(ChatGPT-3.5,ChatGPT-4.0,and Perplexity)to accurately answer common OA-related queries.Methods:We defined 6 themes(pathogenesis,risk factors,clinical presentation,diagnosis,treatment and prevention,and prognosis)based on a generalization of 25 frequently asked questions about OA.Three consultant-level orthopedic specialists independently rated the LLMs’replies on a 4-point accuracy scale.Thefinal ratings for each response were determined using a majority consensus approach.Responses classified as“satisfactory”were evaluated for comprehensiveness on a 5-point scale.Results:ChatGPT-4.0 demonstrated superior accuracy,with 64%of responses rated as“excellent”,compared to 40%for ChatGPT-3.5 and 28%for Perplexity(Pearson’s x2 test with Fisher’s exact test,all p<0.001).All 3 LLM-chatbots had high mean comprehensiveness ratings(Perplexity=3.88;ChatGPT-4.0=4.56;ChatGPT-3.5=3.96,out of a maximum score of 5).The LLM-chatbots performed reliably across domains,except for“treatment and prevention”However,ChatGPT-4.0 still outperformed ChatGPT-3.5 and Perplexity,garnering 53.8%“excellent”ratings(Pearson’s x2 test with Fisher’s exact test,all p<0.001).Conclusion:Ourfindings underscore the potential of LLMs,specifically ChatGPT-4.0 and Perplexity,to deliver accurate and thorough responses to OA-related queries.Targeted correction of specific misconceptions to improve the accuracy of LLMs remains crucial.展开更多
Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning...Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning faces significant challenges in real-world scenarios due to environmental complexities and signal interferences.Considering this issue,this paper presents an approach for modeling and performance analysis of GNSS-based train positioning systems using Colored Petri Nets(CPNs).By systematically modeling the GNSS signal reception and processing process,the performance of the positioning system under various environment scenarios is evaluated.The system model integrates three types of interference signals(i.e.,Amplitude Modulation(AM)signals,Frequency Modulation(FM)signals,and pulse signals)while incorporating environmental factors such as terrain obstructions and tunnel shielding.Additionally,the Extended Kalman Filter(EKF)algorithm is employed to process GNSS observation data,providing accurate train position estimations.The simulation results demonstrate that signal interferences and complex environmental conditions significantly affect the GNSS-based positioning accuracy.This study offers a comprehensive framework for evaluating the performance of GNSS-based train positioning systems in different scenarios,highlighting critical factors that influence positioning accuracy and stability.展开更多
BACKGROUND:Large language models(LLMs)are being explored for disease prediction and diagnosis;however,their effi cacy for early sepsis identifi cation in emergency departments(EDs)remains unexplored.This study aims to...BACKGROUND:Large language models(LLMs)are being explored for disease prediction and diagnosis;however,their effi cacy for early sepsis identifi cation in emergency departments(EDs)remains unexplored.This study aims to evaluate MedGo,a novel medical LLM,as a decision-support tool for clinicians managing patients with suspected sepsis.METHODS:This retrospective study included anonymized medical records of 203 patients(mean age 79.9±10.2 years)with confi rmed sepsis from a tertiary hospital ED between January 2023 and January 2024.MedGo performance across nine sepsis-related assessment tasks was compared with that of two junior(<3 years of experience)and two senior(>10 years of experience)ED physicians.Assessments were scored on a 5-point Likert scale for accuracy,comprehensiveness,readability,and case-analysis skills.RESULTS:MedGo demonstrated diagnostic performance comparable to that of senior physicians across most metrics,achieving a median Likert score of 4 in accuracy,comprehensiveness,and readability.MedGo signifi cantly outperformed junior physicians(P<0.001 for accuracy and case-analysis skills).MedGo assistance significantly enhanced both junior(P<0.001)and senior(P<0.05)physicians'diagnostic accuracy.Notably,MedGo-assisted junior physicians achieved accuracy levels comparable to those of unassisted senior physicians.MedGo maintained consistent performance across varying sepsis severities.CONCLUSION:MedGo shows significant diagnostic efficacy for sepsis and effectively supports clinicians in the ED,particularly enhancing junior physicians’performance.Our study highlights the potential of MedGo as a valuable decision-support tool for sepsis management,paving the way for specialized sepsis AI models.展开更多
To elucidate the effect of calcite-regulated activated carbon(AC)structure on low-temperature denitrification performance of SCR catalysts,this work prepared a series of Mn-Ce/De-AC-xCaCO_(3)(x is the calcite content ...To elucidate the effect of calcite-regulated activated carbon(AC)structure on low-temperature denitrification performance of SCR catalysts,this work prepared a series of Mn-Ce/De-AC-xCaCO_(3)(x is the calcite content in coal)catalysts were prepared by the incipient wetness impregnation method,followed by acid washing to remove calcium-containing minerals.Comprehensive characterization and low-temperature denitrification tests revealed that calcite-induced structural modulation of coal-derived AC significantly enhances catalytic activity.Specifically,NO conversion increased from 88.3%of Mn-Ce/De-AC to 91.7%of Mn-Ce/De-AC-1CaCO_(3)(210℃).The improved SCR denitrification activity results from the enhancement of physicochemical properties including higher Mn^(4+)content and Ce^(4+)/Ce^(3+)ratio,an abundance of chemisorbed oxygen and acidic sites,which could strengthen the SCR reaction pathways(richer NH_(3)activated species and bidentate nitrate active species).Therefore,NO removal is enhanced.展开更多
Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be so...Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.展开更多
基金supported by the National Key Research and Development Program of China(No.2021YFB2600300).
文摘Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金Supported by National High-quality Development Project of China(Grant No.2340STCZB193).
文摘The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms,which involves an analysis of the stiffness performance of numerous discrete points in the workspace.This necessitates time-consuming and inefficient calculation,which is particularly pronounced in the optimization design stage of the mechanism,where the variations in the global stiffness performance indices versus various dimensional and structural parameters need to be analyzed.This paper presents a semi-analytical approach for stiffness modeling of the novel(R(RPS&RP))&2-UPS parallel mechanism(referred to as the Trifree mechanism)and proposes“local”stiffness performance indices as alternatives to global indices.Drawing on the screw theory,the Cartesian stiffness matrix of the Trifree mechanism is formulated explicitly by considering the compliances of all elastic elements and the over-constraint characteristics inherent in the mechanism.Based on the spherical motion pattern of the Trifree mechanism,four special reference configurations are extracted within the workspace.This yields“local”stiffness performance indices capable of accurately evaluating the overall stiffness performance of the mechanism and effectively improving the computational efficiency.The variations in global and“local”stiffness performance indices versus key design parameters are investigated.Furthermore,the proposed indices are applied to the Tricept and Trimule mechanisms.The results demonstrate that the proposed indices exhibit excellent computational accuracy and efficiency in evaluating the overall stiffness performance of these spherical parallel mechanisms.Moreover,the stiffness performance of the novel parallel mechanism investigated in this study closely resembles that of the well-known Tricept and Trimule mechanisms.This research proposes a semi-analytic stiffness model of the Trifree mechanism and“local”stiffness performance indices to evaluate the overall stiffness performance,thereby substantially improving the computational efficiency without sacrificing accuracy.
基金Projects(52302447,52388102,52372369)supported by the National Natural Science Foundation of China。
文摘Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.
基金supported by the National Natural Science Foundation of China(No.41827807)the Foundation of the Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology(No.2021B1212040003),China.
文摘Accurately forecasting the operational performance of a tunnel boring machine(TBM)in advance is useful for making timely adjustments to boring parameters,thereby enhancing overall boring efficiency.In this study,we used the Informer model to predict a critical performance parameter of the TBM,namely thrust.Leveraging data from the Guangzhou Metro Line 22 project on the big data platform in China,the model’s performance was validated,while data from Line 18 were used to assess its generalization capability.Results revealed that the Informer model surpasses random forest(RF),extreme gradient boosting(XGB),support vector regression(SVR),k-nearest neighbors(KNN),back propagation(BP),and long short-term memory(LSTM)models in both prediction accuracy and generalization performance.In addition,the optimal input lengths for maximizing accuracy in the single-time-step output model are within the range of 8–24,while for the multiple-time-step output model,the optimal input length is 8.Furthermore,the last predicted value in the case of multiple-time-step outputs showed the highest accuracy.It was also found that relaxation of the Pearson analysis method metrics to 0.95 improved the performance of the model.Finally,the prediction results were most affected by earth pressure,rotation speed,torque,boring speed,and the surrounding rock grade.The model can provide useful guidance for constructors when adjusting TBM operation parameters.
基金funded by the National Science and Technology Council,grant number NSTC 113-2221-E-002-136-.
文摘In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries,it is important to develop science-based strategies for optimally designing training programs.The purpose of the present study is to develop a novel method by the combined use of optimal control theory and a training-performance model for designing optimal training programs,with the hope of helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The training-performance model used in the proposed optimal control framework is a conceptual extension of the Banister impulse-response model that describes the dynamics of performance,training load(served as the control variable),fitness(the overall positive effects on performance),and fatigue(the overall negative effects on performance).The objective functional of the proposed optimal control framework is to maximize the fitness and minimize the fatigue on the competition day with the goal of maximizing the performance on the competition day while minimizing the cumulative training load during the training course.The Forward-Backward Sweep Method is used to solve the proposed optimal control framework to obtain the optimal solutions of performance,training load,fitness,and fatigue.The simulation results show that the performance on the competition day is higher while the cumulative training load during the training course is lower with using optimal control theory than those without,successfully showing the feasibility and benefits of using the proposed optimal control framework to design optimal training programs for helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The present feasibility study lays the foundation of the combined use of optimal control theory and training-performance models to design personalized optimal training programs in real applications in athletic training and sports science for helping athletes achieve the best performances in competitions while prevent overtraining and the risk of overuse injuries.
基金supported by the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011)the National Natural Science Foundation of China(No.12472236).
文摘Obtaining unsteady hydrodynamic performance is of great significance for seaplane design.Common methods for obtaining unsteady hydrodynamic performance data include tank test and Computational Fluid Dynamics(CFD)numerical simulation,which are costly and time-consuming.Therefore,it is necessary to obtain unsteady hydrodynamic performance in a low-cost and high-precision manner.Due to the strong nonlinearity,complex data distribution,and temporal characteristics of unsteady hydrodynamic performance,the prediction of it is challenging.This paper proposes a Temporal Convolutional Diffusion Model(TCDM)for predicting the unsteady hydrodynamic performance of seaplanes given design parameters.Under the framework of a classifier-free guided diffusion model,TCDM learns the distribution patterns of unsteady hydrodynamic performance data with the designed denoising module based on temporal convolutional network and captures the temporal features of unsteady hydrodynamic performance data.Using CFD simulation data,the proposed method is compared with the alternative methods to demonstrate its accuracy and generalization.This paper provides a method that enables the rapid and accurate prediction of unsteady hydrodynamic performance data,expecting to shorten the design cycle of seaplanes.
基金supported by adidas AG.MJ was not compensated for his contribution to this work.
文摘Objectives:The aim of this study was to compare the measured physiological factors that limit running performance with real marathon results from world-class distance runners,evaluating the compatibility between measured data and predicted results based on the previously suggested model.Methods:Four world-class East African marathon runners(three male,one female)underwent physiological running assessments to predict marathon performance times using a model based on˙V O_(2)peak,percentage of˙V O_(2)peak at the second ventilatory threshold,and running economy.Predictions were then compared to participants’best marathon times.Results:The measured˙V O_(2)peak of the world-class runners was 75.1±2.7 mL/kg/min.The second ventilatory threshold occurred at 85±3%of the peak,with a running economy of 63.7±2.4 mL/kg/min at 19.6±0.9 km/h.The predicted marathon performance time was 2:06:51±0:03:17 h:min:s for the males and 2:17:36 h:min:s for the female.Comparing these predictions to their personal best times,the average difference was 00:55±00:51 min:s(range:00:20-02:08).Conclusions:This research provides laboratory data on world-class road running athletes,reinforcing the link between marathon performance and˙V O_(2)peak,the percentage of˙VO_(2)peak at the second ventilatory threshold,and running economy.The examined athletes had lower˙V O_(2)peak compared to predicted values,highlighting the importance of running economy and fractional utilization of˙V O_(2)peak in achieving such performances.Future studies should continue to advance the field by including additional bioenergetic parameters measured during race conditions and expanding the participant cohort of elite marathoners,encompassing both sexes.
文摘The department of Tillabéri is primarily affected by climatic phenomena, impacting crop yields, growing cycles, and consequently, the economic outcomes of agricultural operations. The objective of this study is to analyze these impacts of climate disruption on the economic performance of farms. The methodology adopted for this study combined documentary research with field surveys conducted on a sample of 250 randomly selected farmers. The analytical methods used mainly consisted of linear regression, profitability calculations, and linear programming. The findings indicate that all productions across different crops have experienced a decrease over the past 30 years. For instance, the production of millet, sorghum, and cowpea, which were respectively 812 kg/ha, 260 kg/ha, and 100 kg/ha between the last 30 and 20 years, has now dropped to 412 kg/ha, 106 kg/ha, and 46 kg/ha respectively. A negative and significant effect on agricultural net margin was observed due to variables such as flooding, drought, pest invasion in rice fields, and temperature changes. Smallholder farms show a relatively low margin (46%) to cover their fixed costs, which may indicate a risk if fixed expenses are high. Furthermore, the analysis results from linear programming reveal that farmers could achieve an additional net profit per hectare of 116,861 FCFA, 217201.5 FCFA, and 291988.2 FCFA respectively for small, medium, and large producers by managing variable costs and health-related expenses for households.
文摘Accurate Global Horizontal Irradiance(GHI)forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources.Particularly considering the implications of the aggressive GHG emission targets,accurate GHI forecasting has become vital for developing,designing,and operational managing solar energy systems.This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA(Autoregressive Integrated Moving Average),Elaman NN(Elman Neural Network),RBFN(Radial Basis Function Neural Network),SVM(Support Vector Machine),LSTM(Long Short-Term Memory),Persistent,BPN(Back Propagation Neural Network),MLP(Multilayer Perceptron Neural Network),RF(Random Forest),and XGBoost(eXtreme Gradient Boosting)for assessing multi-seasonal forecasting of GHI.Used the India region data to evaluate the models’performance and forecasting ability.Research using forecasting models for seasonal Global Horizontal Irradiance(GHI)forecasting in winter,spring,summer,monsoon,and autumn.Substantiated performance effectiveness through evaluation metrics,such as Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),and R-squared(R^(2)),coded using Python programming.The performance experimentation analysis inferred that the most accurate forecasts in all the seasons compared to the other forecasting models the Random Forest and eXtreme Gradient Boosting,are the superior and competing models that yield Winter season-based forecasting XGBoost is the best forecasting model with MAE:1.6325,RMSE:4.8338,and R^(2):0.9998.Spring season-based forecasting XGBoost is the best forecasting model with MAE:2.599599,RMSE:5.58539,and R^(2):0.999784.Summer season-based forecasting RF is the best forecasting model with MAE:1.03843,RMSE:2.116325,and R^(2):0.999967.Monsoon season-based forecasting RF is the best forecasting model with MAE:0.892385,RMSE:2.417587,and R^(2):0.999942.Autumn season-based forecasting RF is the best forecasting model with MAE:0.810462,RMSE:1.928215,and R^(2):0.999958.Based on seasonal variations and computing constraints,the findings enable energy system operators to make helpful recommendations for choosing the most effective forecasting models.
基金supported by the National R&D Program China(No.2021xjkk0405).
文摘Scientific research and technological innovation are driving modern economies;however,a new form of property rights is required to compensate knowledge workers for their contributions.In 1994,the Science and Technology Bureau of Shenzhen,China implemented a policy to encourage scientists and engineers to develop innovative technologies that would provide them a share of the profits earned from their innovations.This created a new“shared property rights”system.China’s shared property model is so new that the conditions under which it can improve enterprise profits remain unclear.To answer this question,we obtained data from the China Stock Market and Accounting Research database for 904 Chinese enterprises that had implemented shared property rights for the first time between 2009 and 2021 and used a propensity score matching method and econometric models to evaluate their performance.The results indicated that shared property incentives improved corporate financial performance and that benefits increased gradually over time.The new approach showed a stronger positive effect than restricted stock options during the study period.The strength of the incentive was greater for core technical staff than for senior executives,suggesting that scientists,engineers,and computer programmers should be the targets of a shared property rights incentive program.To take full advantage of the new shared property rights institution,enterprise managers should set the implementation period at a reasonable length(5 to 10 years,based on our study results).Enterprises can also test two or more simultaneous approaches that account for the specific needs of each category of workers,based on a careful examination of their current situation and expected or desired future situations.
文摘Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.
文摘This study investigates the spatial courting between digital economic signs and local monetary overall performance throughout ten provinces in Sumatra,Indonesia,from 2019 to 2022.As digitalization hastens economic and business sports,devices together with fintech lending,e-cash,debit card usage,and e-commerce are increasingly more diagnosed as capability drivers of regional increase.But,the unequal distribution of digital infrastructure and monetary literacy across regions raises issues approximately the inclusivity of these benefits.constructing upon current findings by using Miranti et al.(2024),this research employs spatial econometric fashions-particularly the Spatial Lag model(SLM)and Spatial mistakes model(SEM)-to evaluate how digital variables influence provincial financial overall performance while accounting for spatial spillover consequences.The results reveal that fintech lending and debit card usage exert a positive and significant impact on economic growth,whereas the effect of e-money is negative,suggesting potential substitution effects or access constraints.Spatial dependency is also evident,as demonstrated by the significant lambda coefficient in the SEM model.These findings highlight the importance of spatially coordinated digital policies,particularly in addressing disparities and enhancing digital financial inclusion.The study concludes with policy recommendations aimed at fostering inclusive and spatially balanced digital economic development in Sumatra.
文摘Background:Social media addiction,one of the behavioural addictions,is a significant predictor of job performance.It has also been posited that individuals whose fundamental requirements(e.g.,sleep)are not sufficiently met andwho lack adequate support(e.g.,perceived social support)are incapable of effectivelyharnessing theirpotential.The primary objective of this study is to examine themediating effects of sleep disorder and perceived social support on the relationship between social media addiction and job performance.Furthermore,it seeks to explore the moderating effects of perceived social support on sleep disorders and job performance.Methods:The data were collected through the questionnairemethod,and data analysis was performed using SPSS 26.0.Moreover,statistical analysis encompasses correlation analysis,mediation,and moderation analysis.The data were gathered from 488 healthcare professionals(57.2%female),whose ages ranged from 24 to 56 years(Meanage±SD=37.86±6.71),using a convenience sample approach.Results:The results revealed significant relationships between social media addiction,job performance,perceived social support,and sleep disorder.The findings indicate that social media addiction negatively predicts job performance(β=−0.11,p<0.05).Sleep disorder(effect size=−0.02,95%CI=[−0.04,−0.00])and perceived social support(effect size=−0.01,95%CI=[−0.02,−0.00])mediate this relationship.Furthermore,perceived social supportmoderates the pathway between sleep disorder and job performance(index ofmoderatedmediation:−0.0040,95%CI=[−0.0070,−0.0010]).Conclusions:This study suggests that social media addiction negatively affects job performance through sleep disorders and perceived social support among healthcare professionals.The study’s findings are significant,as they suggest that treatments aimed at alleviating sleep disorders and enhancing perceived social support among medical workers may improve their job performance.
文摘During the COVID-19 pandemic,most businesses adopted hybrid work arrangements,adapting to changing workplace demands.This study explores how Technologies 4.0(advanced technologies)and employee motivation affect the link between hybrid work models and the performance of younger workers(millennials and Gen-Z)in Kerala’s IT sector.Based on a survey of 155 employees,the study shows that the hybrid work model has a significant impact on performance.While basic technologies do not have a major role,the study emphasizes that employee engagement is a key factor.As hybrid work options become more common,they will continue to shape the future of work.
基金supported by the Health and Medical Research Fund of the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region(HMRF/19202461)by a direct grant(2022/044)from the Chinese University of Hong Kong.
文摘Background:Large Language Models(LLMs)have gained much attention and,in part,have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses.Osteoarthritis(OA)is a common topic in skeletal muscle disor-ders,and patients often seek information about it online.Our study evaluated the ability of 3 LLMs(ChatGPT-3.5,ChatGPT-4.0,and Perplexity)to accurately answer common OA-related queries.Methods:We defined 6 themes(pathogenesis,risk factors,clinical presentation,diagnosis,treatment and prevention,and prognosis)based on a generalization of 25 frequently asked questions about OA.Three consultant-level orthopedic specialists independently rated the LLMs’replies on a 4-point accuracy scale.Thefinal ratings for each response were determined using a majority consensus approach.Responses classified as“satisfactory”were evaluated for comprehensiveness on a 5-point scale.Results:ChatGPT-4.0 demonstrated superior accuracy,with 64%of responses rated as“excellent”,compared to 40%for ChatGPT-3.5 and 28%for Perplexity(Pearson’s x2 test with Fisher’s exact test,all p<0.001).All 3 LLM-chatbots had high mean comprehensiveness ratings(Perplexity=3.88;ChatGPT-4.0=4.56;ChatGPT-3.5=3.96,out of a maximum score of 5).The LLM-chatbots performed reliably across domains,except for“treatment and prevention”However,ChatGPT-4.0 still outperformed ChatGPT-3.5 and Perplexity,garnering 53.8%“excellent”ratings(Pearson’s x2 test with Fisher’s exact test,all p<0.001).Conclusion:Ourfindings underscore the potential of LLMs,specifically ChatGPT-4.0 and Perplexity,to deliver accurate and thorough responses to OA-related queries.Targeted correction of specific misconceptions to improve the accuracy of LLMs remains crucial.
基金supported by the National Key Research and Development Program of China(2023YFB3907300)the Fundamental Research Funds for the Central Universities(2024JBMC002)the National Natural Science Foundation of China(T2222015,U2268206).
文摘Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning faces significant challenges in real-world scenarios due to environmental complexities and signal interferences.Considering this issue,this paper presents an approach for modeling and performance analysis of GNSS-based train positioning systems using Colored Petri Nets(CPNs).By systematically modeling the GNSS signal reception and processing process,the performance of the positioning system under various environment scenarios is evaluated.The system model integrates three types of interference signals(i.e.,Amplitude Modulation(AM)signals,Frequency Modulation(FM)signals,and pulse signals)while incorporating environmental factors such as terrain obstructions and tunnel shielding.Additionally,the Extended Kalman Filter(EKF)algorithm is employed to process GNSS observation data,providing accurate train position estimations.The simulation results demonstrate that signal interferences and complex environmental conditions significantly affect the GNSS-based positioning accuracy.This study offers a comprehensive framework for evaluating the performance of GNSS-based train positioning systems in different scenarios,highlighting critical factors that influence positioning accuracy and stability.
基金supported by grants from the National Natural Science Foundation of China(82470074 to LT)the municipal Natural Science Foundation of Shanghai Scientific Committee of China(22ZR1451000 to LT)+6 种基金the Peak Supporting Clinical Discipline of Shanghai Health Bureau(2023ZDFC0104 to LT)the Medical Discipline Construction Program of Shanghai Pudong New Area Health Commission(the Key Disciplines Program,PWZxk2022-17 to LT)the Scientifi c Research Program of Shanghai Pudong New Area Health Commission(the Joint Research and Development Program,PW2023D-07 to LT)the Healthcare Talents Elite Program of Shanghai Pudong New Area(2025PDWSYCBJ-03 to LT)the Youth Science and Technology Program of Shanghai Pudong New Area(PW2024B-11 to CX)the People’s Livelihood Research Project of Shanghai Pudong Science and Economy Commission(PKJ2023-Y39 to DZ)the Scientifi c Research Program of Shanghai Pudong New Area Health Commission(the Science Popularization Program,PWKP2024B-19 to XL).
文摘BACKGROUND:Large language models(LLMs)are being explored for disease prediction and diagnosis;however,their effi cacy for early sepsis identifi cation in emergency departments(EDs)remains unexplored.This study aims to evaluate MedGo,a novel medical LLM,as a decision-support tool for clinicians managing patients with suspected sepsis.METHODS:This retrospective study included anonymized medical records of 203 patients(mean age 79.9±10.2 years)with confi rmed sepsis from a tertiary hospital ED between January 2023 and January 2024.MedGo performance across nine sepsis-related assessment tasks was compared with that of two junior(<3 years of experience)and two senior(>10 years of experience)ED physicians.Assessments were scored on a 5-point Likert scale for accuracy,comprehensiveness,readability,and case-analysis skills.RESULTS:MedGo demonstrated diagnostic performance comparable to that of senior physicians across most metrics,achieving a median Likert score of 4 in accuracy,comprehensiveness,and readability.MedGo signifi cantly outperformed junior physicians(P<0.001 for accuracy and case-analysis skills).MedGo assistance significantly enhanced both junior(P<0.001)and senior(P<0.05)physicians'diagnostic accuracy.Notably,MedGo-assisted junior physicians achieved accuracy levels comparable to those of unassisted senior physicians.MedGo maintained consistent performance across varying sepsis severities.CONCLUSION:MedGo shows significant diagnostic efficacy for sepsis and effectively supports clinicians in the ED,particularly enhancing junior physicians’performance.Our study highlights the potential of MedGo as a valuable decision-support tool for sepsis management,paving the way for specialized sepsis AI models.
基金Supported by the Science and Technology Cooperation and Exchange special project of Cooperation of Shanxi Province(202404041101014)the Fundamental Research Program of Shanxi Province(202403021212333)+3 种基金the Joint Funds of the National Natural Science Foundation of China(U24A20555)the Lvliang Key R&D of University-Local Cooperation(2023XDHZ10)the Initiation Fund for Doctoral Research of Taiyuan University of Science and Technology(20242026)the Outstanding Doctor Funding Award of Shanxi Province(20242080).
文摘To elucidate the effect of calcite-regulated activated carbon(AC)structure on low-temperature denitrification performance of SCR catalysts,this work prepared a series of Mn-Ce/De-AC-xCaCO_(3)(x is the calcite content in coal)catalysts were prepared by the incipient wetness impregnation method,followed by acid washing to remove calcium-containing minerals.Comprehensive characterization and low-temperature denitrification tests revealed that calcite-induced structural modulation of coal-derived AC significantly enhances catalytic activity.Specifically,NO conversion increased from 88.3%of Mn-Ce/De-AC to 91.7%of Mn-Ce/De-AC-1CaCO_(3)(210℃).The improved SCR denitrification activity results from the enhancement of physicochemical properties including higher Mn^(4+)content and Ce^(4+)/Ce^(3+)ratio,an abundance of chemisorbed oxygen and acidic sites,which could strengthen the SCR reaction pathways(richer NH_(3)activated species and bidentate nitrate active species).Therefore,NO removal is enhanced.
基金Heilongjiang Provincial Natural Science Foundation of China (LH2021F009)。
文摘Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.
