The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of I...The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.展开更多
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural f...Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.展开更多
目的利用CRISPR/Cas9基因编辑技术构建斑马鱼map3k15敲除纯合品系,为进一步研究map3k15在肾脏疾病方面的作用提供动物模型。方法1)分析map3k15在斑马鱼中的表达模式;2)利用CRISPR/Cas9基因编辑技术构建斑马鱼map3k15敲除纯合品系;3)观察...目的利用CRISPR/Cas9基因编辑技术构建斑马鱼map3k15敲除纯合品系,为进一步研究map3k15在肾脏疾病方面的作用提供动物模型。方法1)分析map3k15在斑马鱼中的表达模式;2)利用CRISPR/Cas9基因编辑技术构建斑马鱼map3k15敲除纯合品系;3)观察map3k15缺失对斑马鱼表型的影响。结果1)map3k15在斑马鱼前肾中表达,且MAP3K15/Map3k15蛋白在多物种间具有高度保守性;2)成功构建了斑马鱼map3k15-/-突变体,并保留+2 bp及+1 bp 2种突变品系;3)map3k15突变斑马鱼在胚胎发育过程中出现卵黄囊、心包及头部的水肿,且随发育时间延长,症状逐渐加重。结论成功构建了map3k15敲除的纯合斑马鱼品系,为未来研究map3k15在肾脏发育及疾病中的作用提供了重要的模型。展开更多
基金supported by the Kempe post-doc fellowship via Project No.SMK21-0061,Sweden.Additional support was provided by the Wallenberg AI,Autonomous Systems and Software Program(WASP)funded by Knut and Alice Wallenberg Foundation.
文摘The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
基金supported by the Malaysia-Japan International Institute of Technology(MJIIT),Universiti Teknologi Malaysia.
文摘Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.
文摘目的利用CRISPR/Cas9基因编辑技术构建斑马鱼map3k15敲除纯合品系,为进一步研究map3k15在肾脏疾病方面的作用提供动物模型。方法1)分析map3k15在斑马鱼中的表达模式;2)利用CRISPR/Cas9基因编辑技术构建斑马鱼map3k15敲除纯合品系;3)观察map3k15缺失对斑马鱼表型的影响。结果1)map3k15在斑马鱼前肾中表达,且MAP3K15/Map3k15蛋白在多物种间具有高度保守性;2)成功构建了斑马鱼map3k15-/-突变体,并保留+2 bp及+1 bp 2种突变品系;3)map3k15突变斑马鱼在胚胎发育过程中出现卵黄囊、心包及头部的水肿,且随发育时间延长,症状逐渐加重。结论成功构建了map3k15敲除的纯合斑马鱼品系,为未来研究map3k15在肾脏发育及疾病中的作用提供了重要的模型。
