Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amo...Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.展开更多
After Morocco gained independence in 1956,the country’s historic cities,including Fez,Marrakesh,and Meknes,experienced rapid urban growth,decay,and the destruction of their rich cultural and architectural heritage.Th...After Morocco gained independence in 1956,the country’s historic cities,including Fez,Marrakesh,and Meknes,experienced rapid urban growth,decay,and the destruction of their rich cultural and architectural heritage.The rise in urbanisation,redevelopment projects,and tourism has raised concerns related to the urban gentrification and social sustainability of local communities.In addition,the influx of large-scale foreign investments and the conversion of traditional Moroccan houses into riad hotels have sparked tensions over land use,economic shifts,and the ongoing exploitation of historic cities.This research presents a case study of the world heritage city of Fez in Morocco,where these dynamics are particularly significant,as efforts are made to balance conservation and modern needs.The main question to be addressed is how can the surviving historic centres be regenerated while ensuring social sustainability for their inhabitants?The primary objective of this study is to explore the multifaceted urban regeneration strategies in Fez,focusing on urban planning,conservation efforts,economic revitalisation,and social development.Employing a mixed-method approach,this study draws on desk research,content analysis,fieldwork,observations,and qualitative interviews with key stakeholders.The findings suggest that the previous strategies focused on physical development and riad hotels to boost cultural tourism and tourist accommodation,exacerbating the gentrification and socioeconomic stratification of the local community.This study emphasises the“Ziyarates Fez”project,which provides an innovative approach to rehabilitating and reusing traditional houses for tourism accommodation without displacing local occupants.Furthermore,this project represents a holistic strategy for balancing economic and social sustainability in urban regeneration.This paper contributes to the expanding body of research on sustainable urban regeneration in historic cities.These results are anticipated to benefit academic research and the implementation of regeneration strategies in historic cities in Morocco and worldwide.展开更多
With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially w...With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially with the development of Online Travel Agency(OTA).However,the list of possibilities offered to tourists by these Web search engines(or even specialized tourist sites)can be overwhelming and relevant results are usually drowned in informational"noise",which prevents,or at least slows down the selection process.To assist tourists in trip planning and help them to find the information they are looking for,many recommender systems have been developed.In this article,we present an overview of the various recommendation approaches used in the field of tourism.From this study,an architecture and a conceptual framework for tourism recommender system are proposed,based on a hybrid recommendation approach.The proposed system goes beyond the recommendation of a list of tourist attractions,tailored to tourist preferences.It can be seen as a trip planner that designs a detailed program,including heterogeneous tourism resources,for a specific visit duration.The ultimate goal is to develop a recommender system based on big data technologies,artificial intelligence,and operational research to promote tourism in Morocco,specifically in the Daraa-Tafilalet region.展开更多
Many efforts have been exerted toward screening potential drugs for targets,and conducting wet experiments remains a laborious and time-consuming approach.Artificial intelligence methods,such as Convolutional Neural N...Many efforts have been exerted toward screening potential drugs for targets,and conducting wet experiments remains a laborious and time-consuming approach.Artificial intelligence methods,such as Convolutional Neural Network(CNN),are widely used to facilitate new drug discovery.Owing to the structural limitations of CNN,features extracted from this method are local patterns that lack global information.However,global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drugtarget affinity.A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes.This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction(FingerDTA),which uses CNN to extract local patterns and utilize fingerprints to characterize global information.These fingerprints are generated on the basis of the whole sequence of drugs or targets.Furthermore,FingerDTA achieves comparable performance on Davis and KIBA data sets.In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019(COVID-19),7 of the top 10 drugs have been confirmed potential by literature.Ultimately,the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets.All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git.展开更多
Monaco-A Fabulous Travel Destination Monaco,with its typical Mediterranean climate,bathes in sunlight for more than 300 days in a year.Its coastline,although only a few hundred
文摘Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.
文摘After Morocco gained independence in 1956,the country’s historic cities,including Fez,Marrakesh,and Meknes,experienced rapid urban growth,decay,and the destruction of their rich cultural and architectural heritage.The rise in urbanisation,redevelopment projects,and tourism has raised concerns related to the urban gentrification and social sustainability of local communities.In addition,the influx of large-scale foreign investments and the conversion of traditional Moroccan houses into riad hotels have sparked tensions over land use,economic shifts,and the ongoing exploitation of historic cities.This research presents a case study of the world heritage city of Fez in Morocco,where these dynamics are particularly significant,as efforts are made to balance conservation and modern needs.The main question to be addressed is how can the surviving historic centres be regenerated while ensuring social sustainability for their inhabitants?The primary objective of this study is to explore the multifaceted urban regeneration strategies in Fez,focusing on urban planning,conservation efforts,economic revitalisation,and social development.Employing a mixed-method approach,this study draws on desk research,content analysis,fieldwork,observations,and qualitative interviews with key stakeholders.The findings suggest that the previous strategies focused on physical development and riad hotels to boost cultural tourism and tourist accommodation,exacerbating the gentrification and socioeconomic stratification of the local community.This study emphasises the“Ziyarates Fez”project,which provides an innovative approach to rehabilitating and reusing traditional houses for tourism accommodation without displacing local occupants.Furthermore,this project represents a holistic strategy for balancing economic and social sustainability in urban regeneration.This paper contributes to the expanding body of research on sustainable urban regeneration in historic cities.These results are anticipated to benefit academic research and the implementation of regeneration strategies in historic cities in Morocco and worldwide.
文摘With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially with the development of Online Travel Agency(OTA).However,the list of possibilities offered to tourists by these Web search engines(or even specialized tourist sites)can be overwhelming and relevant results are usually drowned in informational"noise",which prevents,or at least slows down the selection process.To assist tourists in trip planning and help them to find the information they are looking for,many recommender systems have been developed.In this article,we present an overview of the various recommendation approaches used in the field of tourism.From this study,an architecture and a conceptual framework for tourism recommender system are proposed,based on a hybrid recommendation approach.The proposed system goes beyond the recommendation of a list of tourist attractions,tailored to tourist preferences.It can be seen as a trip planner that designs a detailed program,including heterogeneous tourism resources,for a specific visit duration.The ultimate goal is to develop a recommender system based on big data technologies,artificial intelligence,and operational research to promote tourism in Morocco,specifically in the Daraa-Tafilalet region.
基金funded by the China National Key Research and Development Program(No.2019YFA0904300).
文摘Many efforts have been exerted toward screening potential drugs for targets,and conducting wet experiments remains a laborious and time-consuming approach.Artificial intelligence methods,such as Convolutional Neural Network(CNN),are widely used to facilitate new drug discovery.Owing to the structural limitations of CNN,features extracted from this method are local patterns that lack global information.However,global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drugtarget affinity.A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes.This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction(FingerDTA),which uses CNN to extract local patterns and utilize fingerprints to characterize global information.These fingerprints are generated on the basis of the whole sequence of drugs or targets.Furthermore,FingerDTA achieves comparable performance on Davis and KIBA data sets.In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019(COVID-19),7 of the top 10 drugs have been confirmed potential by literature.Ultimately,the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets.All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git.
文摘Monaco-A Fabulous Travel Destination Monaco,with its typical Mediterranean climate,bathes in sunlight for more than 300 days in a year.Its coastline,although only a few hundred
