Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory m...Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index(NDVI), and topographic position index(TPI). Using Landsat-8 imagery and a Digital Elevation Model(DEM) within a Geographic Information System(GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index(DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.展开更多
Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection.Despite,new automated...Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection.Despite,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray imaging.Thoracic imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines.Through this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected patients.This model is trained on a dataset containing thousands of X-ray images collected from different sources.The model was tested and evaluated on an independent dataset.In order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning technique.This experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with pneumonia.This proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging goal.The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.展开更多
Sleep is not a luxury,but it is a necessity.If people sleep well,they will be more productive and start the morning in an excellent mood.On the other hand,people who do not sleep well,they start their morning very dro...Sleep is not a luxury,but it is a necessity.If people sleep well,they will be more productive and start the morning in an excellent mood.On the other hand,people who do not sleep well,they start their morning very drowsy irrespective of the other effects on their health,such as the disturbance of the circadian rhythm.In this paper,an automatic hybrid algorithm is developed to analyze sleep quality using basically the electroencephalogram(EEG)signal and polysomnographic report.The idea behind this is to perform the EEG signal processing in such a way as to be classified according to the sleep stages.Finally,we check if the subject passed through all the sleep cycles or not.To carry out this work,Python version 3 was used.展开更多
The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems.Battery energy storage can provide flex...The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems.Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios.This paper explores how the battery energy storage capacity requirement for compressed-air energy storage(CAES)will grow as the load demand increases.Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization.Results show that providing bulk CAES to the zero-emission power system offers substantial benefits,but it cannot fully compensate for the 100%variability of highly renewable power systems.The capacity requirement of CAES increases by≤33.3%with a 1.5 times increase in the load demand and by≤50%with a two-times increase in the load demand.In this analysis,a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation.The least competitive nuclear option plays this role and reduces system costs by 16.4%,curtails the annual main node by 36.8%,and decreases the CAES capacity requirements by≤80.7%in the case of a double-load demand.While CAES has potential in addressing renewable variability,its widespread deployment is constrained by geographical,societal,and economic factors.Therefore,if California is aiming for an energy system that is reliant on wind and solar power,then an additional dispatchable power source other than CAES or similar load flexibility is necessary.To fully harness the benefits of bulk CAES,the development and implementation of cost-effective approaches are crucial in significantly reducing system costs.展开更多
文摘Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index(NDVI), and topographic position index(TPI). Using Landsat-8 imagery and a Digital Elevation Model(DEM) within a Geographic Information System(GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index(DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.
基金This research is funded by the Deanship of Scientific Research at King Khalid University through Large Groups.(Project under grant number(RGP.2/111/43)).
文摘Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection.Despite,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray imaging.Thoracic imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines.Through this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected patients.This model is trained on a dataset containing thousands of X-ray images collected from different sources.The model was tested and evaluated on an independent dataset.In order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning technique.This experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with pneumonia.This proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging goal.The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.
文摘Sleep is not a luxury,but it is a necessity.If people sleep well,they will be more productive and start the morning in an excellent mood.On the other hand,people who do not sleep well,they start their morning very drowsy irrespective of the other effects on their health,such as the disturbance of the circadian rhythm.In this paper,an automatic hybrid algorithm is developed to analyze sleep quality using basically the electroencephalogram(EEG)signal and polysomnographic report.The idea behind this is to perform the EEG signal processing in such a way as to be classified according to the sleep stages.Finally,we check if the subject passed through all the sleep cycles or not.To carry out this work,Python version 3 was used.
文摘The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems.Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios.This paper explores how the battery energy storage capacity requirement for compressed-air energy storage(CAES)will grow as the load demand increases.Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization.Results show that providing bulk CAES to the zero-emission power system offers substantial benefits,but it cannot fully compensate for the 100%variability of highly renewable power systems.The capacity requirement of CAES increases by≤33.3%with a 1.5 times increase in the load demand and by≤50%with a two-times increase in the load demand.In this analysis,a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation.The least competitive nuclear option plays this role and reduces system costs by 16.4%,curtails the annual main node by 36.8%,and decreases the CAES capacity requirements by≤80.7%in the case of a double-load demand.While CAES has potential in addressing renewable variability,its widespread deployment is constrained by geographical,societal,and economic factors.Therefore,if California is aiming for an energy system that is reliant on wind and solar power,then an additional dispatchable power source other than CAES or similar load flexibility is necessary.To fully harness the benefits of bulk CAES,the development and implementation of cost-effective approaches are crucial in significantly reducing system costs.
