As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,...As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.展开更多
The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities.Therefore,effective prediction of the distribution of Ulva is of great significance for disaster prevention and redu...The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities.Therefore,effective prediction of the distribution of Ulva is of great significance for disaster prevention and reduction.However,the prediction method of Ulva is mainly based on numerical simulation.There are two problems with these methods.First is that the initial distribution of Ulva is simulated using independent pixel-level particles.Besides,the influence of Ulva growth and diffusion on the drift is not considered.Therefore,this paper proposes a multi-module with a two-way feedback method(MTF)to solve the above problems.The main contributions of our approach are summarized as follows.First,the initialization module,the generation and elimination module,and the drive module are composed in our way.Second,we proposed an initialization method using rectangle objects to simulate the Ulva distribution extracted from remote sensing images.Thirdly,the drift and diffusion mechanism of the Ulva is considered to realize the two-way feedback between the generation and elimination module and the drive module.The results of our experiments show that the MTF performs better than the traditional method in predicting the drift and diffusion of Ulva.The code is already publicly available at https://github.com/UPCGIT/A-multi-module-with-a-two-way-feedback-method-for-Ulva-drift-diffusion.展开更多
Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically wei...Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically weighted regression(GWR)model to explore the spatial non-stationarity of near-miss collision risk,as detected by a vessel conflict ranking operator(VCRO)model from automatic identification system(AIS)data under the influence of sea fog in the Bohai Sea.Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data.The spatial distributions of near-miss collision risk,sea fog,and the parameters of GWR were mapped.The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea,in which near-miss collision risk in the fog season is significantly higher than that outside the fog season,especially in the northeast(the sea area near Yingkou Port and Bayuquan Port)and the southeast(the sea area near Yantai Port).GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season,with higher R-squared(0.890 in fog season,2018),than outside the fog season(0.723 in non-fog season,2018).GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally.Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.展开更多
Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identifica...Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identification.However,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil spills.To enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was proposed.Specifically,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity.Two sets of RadarSat-2 SAR data were used to test the proposed method.The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution.Compared with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational time.Therefore,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.展开更多
The emergence of highly virulent porcine epidemic diarrhea virus(PEDV) variants in China caused huge economic losses in 2010. Since then, large-scale sporadic outbreaks of PED caused by PEDV variants have occasionally...The emergence of highly virulent porcine epidemic diarrhea virus(PEDV) variants in China caused huge economic losses in 2010. Since then, large-scale sporadic outbreaks of PED caused by PEDV variants have occasionally occurred in China. However, the molecular diversity and epidemiology of PEDV in different provinces has not been completely understood. To determine the molecular diversity of PEDV in the Hubei Province of China, we collected 172 PED samples from 34 farms across the province in 2016 and performed reverse transcription polymerase chain reaction(RTPCR)by targeting the nucleocapsid(N) gene. Seventy-four samples were found to be PEDVpositive.We further characterized the complete spike(S) glycoprotein genes from the positive samples and found 21 different S genes with amino acid mutations. The PEDV isolates here presented most of the genotypes which were found previously in field isolates in East and SouthEast Asia, North America, and Europe. Besides the typical Genotypes Ⅰ and Ⅱ, the INDEX groups were also found. Importantly, 58 new amino acids mutant sites in the S genes, including 44 sites in S1 and 14 sites in S2, were first described. Our results revealed that the S genes of PEDV showed variation and that diverse genotypes of PEDV coexisted and were responsible for the PED outbreaks in Hubei in 2016. This work highlighted the complexity of the epidemiology of PEDV and emphasized the need for reassessing the efficacy of classic PEDV vaccines against emerging variant strains and developing new vaccines to facilitate the prevention and control of PEDV in fields.展开更多
基金support from the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No.101024139,the RILEM technical committee TC 279 WMR(valorisation of waste and secondary materials for roads),RILEM technical committee TC-264 RAP(asphalt pavement recycling)the Swiss National Science Foundation(SNF)grant 205121_178991/1 for the project titled“Urban Mining for Low Noise Urban Roads and Optimized Design of Street Canyons”,National Natural Science Foundation of China(No.51808462,51978547,52005048,52108394,52178414,52208420,52278448,52308447,52378429)+9 种基金China Postdoctoral Science Foundation(No.2023M730356)National Key R&D Program of China(No.2021YFB2601302)Natural Science Basic Research Program of Shaanxi(Program No.2023-JC-QN-0472)Postdoctoral Science Foundation of Anhui Province(2022B627)Shaanxi Provincial Science and Technology Department(No.2022 PT30)Key Technological Special Project of Xinxiang City(No.22ZD013)Key Laboratory of Intelligent Manufacturing of Construction Machinery(No.IMCM2021KF02)the Applied Basic Research Project of Sichuan Science and Technology Department(Free Exploration Type)(Grant No.2020YJ0039)Key R&D Support Plan of Chengdu Science and Technology Project-Technology Innovation R&D Project(Grant No.2019-YF05-00002-SN)the China Postdoctoral Science Foundation(Grant No.2018M643520).
文摘As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.
基金The Shandong Provincial Natural Science Foundation of China under contract No.ZR2019MD023the National Natural Science Foundation of China under contract No.41776182.
文摘The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities.Therefore,effective prediction of the distribution of Ulva is of great significance for disaster prevention and reduction.However,the prediction method of Ulva is mainly based on numerical simulation.There are two problems with these methods.First is that the initial distribution of Ulva is simulated using independent pixel-level particles.Besides,the influence of Ulva growth and diffusion on the drift is not considered.Therefore,this paper proposes a multi-module with a two-way feedback method(MTF)to solve the above problems.The main contributions of our approach are summarized as follows.First,the initialization module,the generation and elimination module,and the drive module are composed in our way.Second,we proposed an initialization method using rectangle objects to simulate the Ulva distribution extracted from remote sensing images.Thirdly,the drift and diffusion mechanism of the Ulva is considered to realize the two-way feedback between the generation and elimination module and the drive module.The results of our experiments show that the MTF performs better than the traditional method in predicting the drift and diffusion of Ulva.The code is already publicly available at https://github.com/UPCGIT/A-multi-module-with-a-two-way-feedback-method-for-Ulva-drift-diffusion.
文摘Sea fog is a disastrous weather phenomenon,posing a risk to the safety of maritime transportation.Dense sea fogs reduce visibility at sea and have frequently caused ship collisions.This study used a geographically weighted regression(GWR)model to explore the spatial non-stationarity of near-miss collision risk,as detected by a vessel conflict ranking operator(VCRO)model from automatic identification system(AIS)data under the influence of sea fog in the Bohai Sea.Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data.The spatial distributions of near-miss collision risk,sea fog,and the parameters of GWR were mapped.The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea,in which near-miss collision risk in the fog season is significantly higher than that outside the fog season,especially in the northeast(the sea area near Yingkou Port and Bayuquan Port)and the southeast(the sea area near Yantai Port).GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season,with higher R-squared(0.890 in fog season,2018),than outside the fog season(0.723 in non-fog season,2018).GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally.Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.
基金Supported by the National Key R&D Program of China(No.2017YFC1405600)the National Natural Science Foundation of China(Nos.41776182,42076182)the Natural Science Foundation of Shandong Province(No.ZR2016DM16)。
文摘Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identification.However,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil spills.To enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was proposed.Specifically,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity.Two sets of RadarSat-2 SAR data were used to test the proposed method.The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution.Compared with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational time.Therefore,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.
基金funded by the National Natural Science Foundation of China(31470260 and 81401672)the"Fundamental Research Funds for the Central Universities"(531107040975)
文摘The emergence of highly virulent porcine epidemic diarrhea virus(PEDV) variants in China caused huge economic losses in 2010. Since then, large-scale sporadic outbreaks of PED caused by PEDV variants have occasionally occurred in China. However, the molecular diversity and epidemiology of PEDV in different provinces has not been completely understood. To determine the molecular diversity of PEDV in the Hubei Province of China, we collected 172 PED samples from 34 farms across the province in 2016 and performed reverse transcription polymerase chain reaction(RTPCR)by targeting the nucleocapsid(N) gene. Seventy-four samples were found to be PEDVpositive.We further characterized the complete spike(S) glycoprotein genes from the positive samples and found 21 different S genes with amino acid mutations. The PEDV isolates here presented most of the genotypes which were found previously in field isolates in East and SouthEast Asia, North America, and Europe. Besides the typical Genotypes Ⅰ and Ⅱ, the INDEX groups were also found. Importantly, 58 new amino acids mutant sites in the S genes, including 44 sites in S1 and 14 sites in S2, were first described. Our results revealed that the S genes of PEDV showed variation and that diverse genotypes of PEDV coexisted and were responsible for the PED outbreaks in Hubei in 2016. This work highlighted the complexity of the epidemiology of PEDV and emphasized the need for reassessing the efficacy of classic PEDV vaccines against emerging variant strains and developing new vaccines to facilitate the prevention and control of PEDV in fields.
