Public construction project data were analyzed to develop models for the actual construction duration and actual cost. Based on 50 civil engineering projects in Libya from the database of the Great Man-Made River Syst...Public construction project data were analyzed to develop models for the actual construction duration and actual cost. Based on 50 civil engineering projects in Libya from the database of the Great Man-Made River System 1992-2009, the models identified extension as a main factor that affecting both the actual duration and cost of a project, while considering the contract cost and contract duration as fixed for the project. The suitability of each model was confirmed by examining RMSE (root mean squared error), NSE (Nash-Sutcliffe efficiency), and MEF (modeling efficiency factor). The models were validated with data from 17 different projects. The validation of both models demonstrated that models are very practical and accurate. The models are very effective in capturing the uniqueness of Libyan construction industry practice.展开更多
The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a s...The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a significant breakthrough,overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts.This study explores the evolution of these advanced artificial intelligence forecast models,and based on the identified commonalities,proposes the“Three Large Rules”for large weather forecast models:a large number of parameters,a large number of predictands,and large potential applications.We discuss the capacity of artificial intelligence to revolutionize numerical weather prediction,briefly outlining the underlying reasons for the significant improvement in weather forecasting.While acknowledging the high accuracy,computational efficiency,and ease of deployment of large artificial intelligence forecast models,we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models.We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models.Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts.Finally,we illustrate how forecasters can leverage the large weather forecast models through an example by building an artificial intelligence model for global ocean wave forecast.展开更多
Significant advancements in the development of machine learning(ML)models for weather forecasting have produced remarkable results.State-of-the-art ML-based weather forecast models,such as FuXi,have demonstrated super...Significant advancements in the development of machine learning(ML)models for weather forecasting have produced remarkable results.State-of-the-art ML-based weather forecast models,such as FuXi,have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts(HRES)of the European Centre for MediumRange Weather Forecasts(ECMWF).However,a common limitation of these ML models is their tendency to generate increasingly smooth predictions as forecast lead times increase,which often results in the underestimation of intensities of extreme weather events.To address this challenge,we developed the FuXi-Extreme model,which employs a denoising diffusion probabilistic model(DDPM)to enhance finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts.An evaluation of extreme total precipitation(TP),10-meter wind speed(WS10),and 2-meter temperature(T2M)illustrates the superior performance of FuXi-Extreme over both FuXi and HRES.Moreover,when evaluating tropical cyclone(TC)forecasts based on International Best Track Archive for Climate Stewardship(IBTrACS)dataset,both FuXi and FuXiExtreme shows superior performance in TC track forecasts compared to HRES,but they show inferior performance in TC intensity forecasts in comparison to HRES.展开更多
Photocatalytic reduction of CO_(2) holds tremendous promise for alleviating the energy crisis.Despite the progress that has been,made,there are still some challenges to overcome,such as the realization under real sunl...Photocatalytic reduction of CO_(2) holds tremendous promise for alleviating the energy crisis.Despite the progress that has been,made,there are still some challenges to overcome,such as the realization under real sunlight rather than the simulation condition.In this work,ultrathin Ni_(2)(OH)(PO_(4))nanotubes(NTs)prepared through hydrothermal route are applied as a novel catalyst for photocatalytic reduction of CO_(2) under real sunlight.The prepared Ni_(2)(OH)(PO_(4))NTs exhibit a 11.3 μmol·h^(-1) CO production rate with,96.1% CO selectivity.Interestingly,Ni_(2)(OH)(PO_(4))NTs have a positive impact on the facilitation of photoreduction in diluted CO_(2).Notably,when the system is performed under real sunlight,Ni_(2)(OH)(PO_(4))NTs afford an accumulated CO of ca.26.8 nmol with,96.9% CO selectivity,exceeding most previous inorganic catalysts under simulated irradiation in the laboratory.Our experimental results demonstrate that the multisynergetic effects induced by surface-OH and the lattice strain serve as highly active sites for CO_(2) molecular adsorption and activation as well as electron transfer,hence enhancing photoreduction activity.Therefore,this work provides experimental basis that CO_(2) photocatalysis can be put into practical use.展开更多
文摘Public construction project data were analyzed to develop models for the actual construction duration and actual cost. Based on 50 civil engineering projects in Libya from the database of the Great Man-Made River System 1992-2009, the models identified extension as a main factor that affecting both the actual duration and cost of a project, while considering the contract cost and contract duration as fixed for the project. The suitability of each model was confirmed by examining RMSE (root mean squared error), NSE (Nash-Sutcliffe efficiency), and MEF (modeling efficiency factor). The models were validated with data from 17 different projects. The validation of both models demonstrated that models are very practical and accurate. The models are very effective in capturing the uniqueness of Libyan construction industry practice.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)。
文摘The rapid advancement of artificial intelligence technologies,particularly in recent years,has led to the emergence of several large parameter artificial intelligence weather forecast models.These models represent a significant breakthrough,overcoming the limitations of traditional numerical weather prediction models and indicating the emergence of profound potential tools for atmosphere-ocean forecasts.This study explores the evolution of these advanced artificial intelligence forecast models,and based on the identified commonalities,proposes the“Three Large Rules”for large weather forecast models:a large number of parameters,a large number of predictands,and large potential applications.We discuss the capacity of artificial intelligence to revolutionize numerical weather prediction,briefly outlining the underlying reasons for the significant improvement in weather forecasting.While acknowledging the high accuracy,computational efficiency,and ease of deployment of large artificial intelligence forecast models,we also emphasize the irreplaceable values of traditional numerical forecasts and explore the challenges in the future development of large-scale artificial intelligence atmosphere-ocean forecast models.We believe that the optimal future of atmosphere-ocean weather forecast lies in achieving a seamless integration of artificial intelligence and traditional numerical models.Such a synthesis is anticipated to offer a more advanced and reliable approach for improved atmosphere-ocean forecasts.Finally,we illustrate how forecasters can leverage the large weather forecast models through an example by building an artificial intelligence model for global ocean wave forecast.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240154)。
文摘Significant advancements in the development of machine learning(ML)models for weather forecasting have produced remarkable results.State-of-the-art ML-based weather forecast models,such as FuXi,have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts(HRES)of the European Centre for MediumRange Weather Forecasts(ECMWF).However,a common limitation of these ML models is their tendency to generate increasingly smooth predictions as forecast lead times increase,which often results in the underestimation of intensities of extreme weather events.To address this challenge,we developed the FuXi-Extreme model,which employs a denoising diffusion probabilistic model(DDPM)to enhance finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts.An evaluation of extreme total precipitation(TP),10-meter wind speed(WS10),and 2-meter temperature(T2M)illustrates the superior performance of FuXi-Extreme over both FuXi and HRES.Moreover,when evaluating tropical cyclone(TC)forecasts based on International Best Track Archive for Climate Stewardship(IBTrACS)dataset,both FuXi and FuXiExtreme shows superior performance in TC track forecasts compared to HRES,but they show inferior performance in TC intensity forecasts in comparison to HRES.
基金supported by the National Natural Science Foundation of China(Nos.21777046 and 21836002)the National Key Research and Development Program of China(No.2019YFA0210400)+2 种基金the Guangdong Innovative and Entrepreneurial Research Team Program(No.2016ZT06N569)the Guangdong Science and Technology Program(No.2020B121201003)the Science Technology Project of Guangzhou(No.201803030002).
文摘Photocatalytic reduction of CO_(2) holds tremendous promise for alleviating the energy crisis.Despite the progress that has been,made,there are still some challenges to overcome,such as the realization under real sunlight rather than the simulation condition.In this work,ultrathin Ni_(2)(OH)(PO_(4))nanotubes(NTs)prepared through hydrothermal route are applied as a novel catalyst for photocatalytic reduction of CO_(2) under real sunlight.The prepared Ni_(2)(OH)(PO_(4))NTs exhibit a 11.3 μmol·h^(-1) CO production rate with,96.1% CO selectivity.Interestingly,Ni_(2)(OH)(PO_(4))NTs have a positive impact on the facilitation of photoreduction in diluted CO_(2).Notably,when the system is performed under real sunlight,Ni_(2)(OH)(PO_(4))NTs afford an accumulated CO of ca.26.8 nmol with,96.9% CO selectivity,exceeding most previous inorganic catalysts under simulated irradiation in the laboratory.Our experimental results demonstrate that the multisynergetic effects induced by surface-OH and the lattice strain serve as highly active sites for CO_(2) molecular adsorption and activation as well as electron transfer,hence enhancing photoreduction activity.Therefore,this work provides experimental basis that CO_(2) photocatalysis can be put into practical use.
