Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigat...Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.展开更多
Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a fr...Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.展开更多
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc...This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.展开更多
Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this...Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this study,a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential,so that it becomes an early detection warning system that has an impact on increasing agricultural productivity.The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules,namely the C2S module.The C2S module consists of three sub-modules such as the convolutional block attention module(CBAM),the coordinate attention(CA)module,and the squeeze-and-excitation(SE)module.The dataset is constructed by eight classes,including seven classes of disease conditions and one class of health conditions.The experimental result shows that the proposed lightweight model has the optimal results,which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny.While the mAP50:95 also achieved the highest results compared to other models,including YOLOv3-Tiny,YOLOv4-Tiny,YOLOv5,and YOLOv7-Tiny.The advantage of the proposed lightweightmodel is the adaptability that supports it in constrained environments,such as edge computing systems.This proposedmodel can support a robust,precise,and convenient precision agriculture system for the user.展开更多
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
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
Objective:To address the prediction of hemorrhagic transformation(HT)after rt-PA thrombolysis in acute cerebral infarction(ACI)patients,a predictive model integrating data engineering,algorithm optimization,and modula...Objective:To address the prediction of hemorrhagic transformation(HT)after rt-PA thrombolysis in acute cerebral infarction(ACI)patients,a predictive model integrating data engineering,algorithm optimization,and modularity was constructed.This model resolves the technical limitation of traditional clinical statistical models that overemphasize analysis while neglecting deployment readiness,thereby providing a reusable technical solution for the implementation of medical risk prediction systems.Methods:A standardized dataset was built using data from 300 ACI patients who underwent rt-PA thrombolysis.Automated data cleaning,outlier correction,and feature engineering optimization(recursive feature elimination+variance inflation factor test)were applied to select core features.An L2-regularized logistic regression algorithm was employed to construct the prediction model,which was visualized via a nomogram.Multi-dimensional performance validation was conducted through cross-validation and probability calibration techniques.Results:The incidence of HT was20.0%.Multivariate analysis identified NIHSS score(OR=1.626),blood glucose(OR=1.662),platelet count(OR=0.975),age(OR=1.091),and history of diabetes(OR=0.343)as core predictors(P<0.05).Engineering validation demonstrated excellent discrimination(AUC=0.800,95%CI:0.752-0.856).After optimization with Platt scaling,the calibration curve was highly consistent with the actual probability,with a Brier score of0.092.The prediction time per sample was<50 ms,and the convergence efficiency of model training was improved by 30%.Conclusion:This model integrates core technologies including data preprocessing,feature optimization,and algorithm improvement,exhibiting low-latency,high stability,and deployability.It provides a standardized technical framework for transforming medical prediction models from statistical conclusions into engineered products.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
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
Blending the agility of aerial drones with the covert capabilities of underwater submersibles,the aerial-aquatic rotorcraft has garnered substantial interest due to their unparalleled capacity to traverse both air and...Blending the agility of aerial drones with the covert capabilities of underwater submersibles,the aerial-aquatic rotorcraft has garnered substantial interest due to their unparalleled capacity to traverse both air and water.Nevertheless,a critical hurdle for these vehicles lies in mitigating the adverse effects of repeatedly transitioning between these environments,particularly during water-surface takeoffs.Currently,research on the interference caused by rotors approaching water surfaces remains limited.This paper introduces a novel adaptive rotor aerodynamic model based on continuous finite vortex theory to predict rotor thrust within gas–liquid flow field.Initially,the model's sensitivity to system parameters was analyzed to optimize its predictive capabilities.Subsequently,a comprehensive ground/water experimental setup was designed to investigate the intricate aerodynamic interactions between the rotor flow field and water.By varying rotor sizes,the characteristics of the rotor flow field and water surface were examined at different rotor-water surface distances.The performance of different modeling methods was analyzed based on the rotor experimental data of a diameter of 0.38 m,and the prediction results were quantified using the percentage of the mean-square error.The results show that the average error of the finite vortex rotor model is the smallest.Finally,a novel transition boundary is proposed to divide the rotor flow field of the gas–liquid mixture into two stages.The thrust loss zone is defined to delineate the safe operating range of the aircraft,providing a basis for the design of aerial-aquatic rotorcraft.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
文摘Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.
文摘Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.
文摘This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.
基金supported by National Science and Technology Council(NSTC)Taiwan,Grant No.NSTC 113-2221-E-167-023.
文摘Mango is a plant with high economic value in the agricultural industry;thus,it is necessary to maximize the productivity performance of the mango plant,which can be done by implementing artificial intelligence.In this study,a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential,so that it becomes an early detection warning system that has an impact on increasing agricultural productivity.The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules,namely the C2S module.The C2S module consists of three sub-modules such as the convolutional block attention module(CBAM),the coordinate attention(CA)module,and the squeeze-and-excitation(SE)module.The dataset is constructed by eight classes,including seven classes of disease conditions and one class of health conditions.The experimental result shows that the proposed lightweight model has the optimal results,which increase by 13.15% of mAP50 compared to the original model YOLOv7-Tiny.While the mAP50:95 also achieved the highest results compared to other models,including YOLOv3-Tiny,YOLOv4-Tiny,YOLOv5,and YOLOv7-Tiny.The advantage of the proposed lightweightmodel is the adaptability that supports it in constrained environments,such as edge computing systems.This proposedmodel can support a robust,precise,and convenient precision agriculture system for the user.
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.
文摘Objective:To address the prediction of hemorrhagic transformation(HT)after rt-PA thrombolysis in acute cerebral infarction(ACI)patients,a predictive model integrating data engineering,algorithm optimization,and modularity was constructed.This model resolves the technical limitation of traditional clinical statistical models that overemphasize analysis while neglecting deployment readiness,thereby providing a reusable technical solution for the implementation of medical risk prediction systems.Methods:A standardized dataset was built using data from 300 ACI patients who underwent rt-PA thrombolysis.Automated data cleaning,outlier correction,and feature engineering optimization(recursive feature elimination+variance inflation factor test)were applied to select core features.An L2-regularized logistic regression algorithm was employed to construct the prediction model,which was visualized via a nomogram.Multi-dimensional performance validation was conducted through cross-validation and probability calibration techniques.Results:The incidence of HT was20.0%.Multivariate analysis identified NIHSS score(OR=1.626),blood glucose(OR=1.662),platelet count(OR=0.975),age(OR=1.091),and history of diabetes(OR=0.343)as core predictors(P<0.05).Engineering validation demonstrated excellent discrimination(AUC=0.800,95%CI:0.752-0.856).After optimization with Platt scaling,the calibration curve was highly consistent with the actual probability,with a Brier score of0.092.The prediction time per sample was<50 ms,and the convergence efficiency of model training was improved by 30%.Conclusion:This model integrates core technologies including data preprocessing,feature optimization,and algorithm improvement,exhibiting low-latency,high stability,and deployability.It provides a standardized technical framework for transforming medical prediction models from statistical conclusions into engineered products.
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.
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.
基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX24_0532)the Key Laboratory of Cross-Domain Flight Interdisciplinary Technology,China(Nos.2024-KF03001,2024-KF03003)the National Natural Science Foundation of China(No.12272169)for the financial support。
文摘Blending the agility of aerial drones with the covert capabilities of underwater submersibles,the aerial-aquatic rotorcraft has garnered substantial interest due to their unparalleled capacity to traverse both air and water.Nevertheless,a critical hurdle for these vehicles lies in mitigating the adverse effects of repeatedly transitioning between these environments,particularly during water-surface takeoffs.Currently,research on the interference caused by rotors approaching water surfaces remains limited.This paper introduces a novel adaptive rotor aerodynamic model based on continuous finite vortex theory to predict rotor thrust within gas–liquid flow field.Initially,the model's sensitivity to system parameters was analyzed to optimize its predictive capabilities.Subsequently,a comprehensive ground/water experimental setup was designed to investigate the intricate aerodynamic interactions between the rotor flow field and water.By varying rotor sizes,the characteristics of the rotor flow field and water surface were examined at different rotor-water surface distances.The performance of different modeling methods was analyzed based on the rotor experimental data of a diameter of 0.38 m,and the prediction results were quantified using the percentage of the mean-square error.The results show that the average error of the finite vortex rotor model is the smallest.Finally,a novel transition boundary is proposed to divide the rotor flow field of the gas–liquid mixture into two stages.The thrust loss zone is defined to delineate the safe operating range of the aircraft,providing a basis for the design of aerial-aquatic rotorcraft.
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.
