Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equ...Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equipment on ships.Compared to single-stack system,MFCS may be difficult to apply traditional energy management strategies(EMS)due to their complex structure.In this paper,a two-layer power allocation strategy for MFCS of a hydrogen fuel cell ship is proposed to reduce the complexity of the allocation task by splitting it into each layer of the EMS.The first layer of the EMSis centered on the Nonlinear Model Predictive Control(NMPC).The Northern Goshawk Optimization(NGO)algorithm is used to solve the nonlinear optimization problem in NMPC,and the local fine search is performed using sequential quadratic programming(SQP).Based on the power allocation results of the first layer,the second layer is centered on a fuzzy rule-based adaptive power allocation strategy(AP-Fuzzy).The membership function bounds of the fuzzy controller are related to the aging level of the MFCS.The Particle Swarm Optimization(PSO)algorithm is used to optimize the parameters of the residual membership function to improve the performance of the proposed strategy.The effectiveness of the proposed EMS is verified by comparing it with the traditional EMS.The experimental results show that the EMS proposed in this paper can ensure reasonable hydrogen consumption,slow down the FC aging and equalize its performance,effectively extend the system life,and ensure that the ship has good endurance after completing the mission.展开更多
This paper addresses the evolution problem governed by the fractional sweeping process with prox-regular nonconvex constraints.The values of the moving set are time and state-dependent.The aim is to illustrate how a f...This paper addresses the evolution problem governed by the fractional sweeping process with prox-regular nonconvex constraints.The values of the moving set are time and state-dependent.The aim is to illustrate how a fixed point method can establish an existence theorem for this fractional nonlinear evolution problem.By combining Schauder’s fixed point theorem with a well-posedness theorem when the set C is independent of the state u(i.e.C:=C(t),as presented in[22,23]),we prove the existence of a solution to our quasi-variational fractional sweeping process in infinite-dimensional Hilbert spaces.Similar to the conventional state-dependent sweeping process,achieving this result requires a condition on the size of the Lipschitz constant of the moving set relative to the state.展开更多
Biochar application to soil is commonly recognized to improve soil fertility and consequently biomass and food production sustainably.We re-examined the robustness of the underlying data and found that,of the 12000+ p...Biochar application to soil is commonly recognized to improve soil fertility and consequently biomass and food production sustainably.We re-examined the robustness of the underlying data and found that,of the 12000+ publications on“biochar and agriculture”used in meta-studies,only 109 Institute for Scientific Information(ISI)papers(or 0.9%)provide experimental data on the impacts on crop yield and/or biomass production.展开更多
The data of SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), installed on SeaStar, has been used to generate SSC (suspended sediment concentration) of complex and turbid coastal waters in China. In view of the problem...The data of SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), installed on SeaStar, has been used to generate SSC (suspended sediment concentration) of complex and turbid coastal waters in China. In view of the problems of the SeaDAS (SeaWiFS Data Analysis System) algorithm applied to China coastal waters, a new atmospheric correction algorithm is discussed, developed, and used for the SSC of East China coastal waters. The advantages of the new algorithm are described through the comparison of the results from different algorithms.展开更多
Using in-situ measurements from the Cassini spacecraft in 2013, we report an Earth substorm-like loading-unloading process at Saturn's distant magnetotail. We found that the loading process is featured with two di...Using in-situ measurements from the Cassini spacecraft in 2013, we report an Earth substorm-like loading-unloading process at Saturn's distant magnetotail. We found that the loading process is featured with two distinct processes: a rapid loading process that was likely driven by an internal source and a slow loading process that was likely driven by solar wind. Each of the two loading processes could also individually lead to an unloading process. The rapid internal loading process lasts for ~ 1-2 hours; the solar wind driven loading process lasts for ~ 3-18 hours and the following unloading process lasts for ~1-3 hours. In this letter, we suggest three possible loadingunloading circulations, which are fundamental in understanding the role of solar wind in driving giant planetary magnetospheric dynamics.展开更多
Soil organic matter(SOM),which associates carbon(C)to key plant nutrients,has been stored in soil for thousands of years.Scientists have long recognised its positive impact on key environmental functions such as food ...Soil organic matter(SOM),which associates carbon(C)to key plant nutrients,has been stored in soil for thousands of years.Scientists have long recognised its positive impact on key environmental functions such as food production and climate regulation.As soon as a virgin land(forest or grassland)is cultivated,there is a tendency for the soil to lose its SOM,and we still largely misunderstand the underlying mechanisms,leading to inappropriate decisions being taken to fight soil,climate,and overall ecosystem degradation.展开更多
Practical resolution of consolidation problems that we often face requires an extensive and solid knowledge of the different parameters highlighted by the Terzaghi one-dimensional consolidation theory. This theory, wi...Practical resolution of consolidation problems that we often face requires an extensive and solid knowledge of the different parameters highlighted by the Terzaghi one-dimensional consolidation theory. This theory, with its assumptions, leads to a partial differential equation of second order in space and first order in time of pore water pressure. Analytical and numerical resolutions of this equation allow determining the water pressure variation before and after the application of a charge. Numerical modeling has enabled the simulation of the whole results obtained by the two methods of resolution (pressure, degree of consolidation, time factor, among others) to have a physical analysis and a lawful observation that lead to a suitable understanding of the phenomenon of Terzaghi one-dimensional consolidation.展开更多
The carbon dioxide flux through the air-water interface of coastal freshwater ecosystems must be quantified to understand the regional balances of carbon and its transport through coastal and estuarine regions. The va...The carbon dioxide flux through the air-water interface of coastal freshwater ecosystems must be quantified to understand the regional balances of carbon and its transport through coastal and estuarine regions. The variations in air-sea CO2 fluxes in nearshore ecosystems can be caused by the variable influence of rivers. In the present study, the amount of carbon emitted from a tropical coastal river was estimated using climatological and biogeochemical measurements (2002-2010) obtained from the basin of the Capibaribe River, which is located in the most populous and industrialized area of the northeast region of Brazil. The results showed a mean CO2 flux of +225 mmol·m-2·d-1, mainly from organic material from the untreated domestic and industrial wastewaters that are released into the river. This organic material increased the dissolved CO2 concentration in the river waters, leading to a partial pressure of CO2 inthe aquatic environment that reached 31,000 μatm. The months of April, February and December (the dry period) showed the largest monthly means for the variables associated with the carbonate system (, DIC, CO2(aq), CO32-, TA, temperature and pH). This status reflects the state of permanent pollution in the basin of the Capibaribe River, due, in particular, to the discharge of untreated domestic wastewater, which results in the continuous mineralization of organic material. This mineralization significantly increases the dissolved CO2 content in the estuarine and coastal waters, which is later released to the atmosphere.展开更多
In this paper we show a boundary result of controllability by a new approach using a linear, continuous and surjective operator built from the solution of the heat system. And, subsequently, the border exact controlla...In this paper we show a boundary result of controllability by a new approach using a linear, continuous and surjective operator built from the solution of the heat system. And, subsequently, the border exact controllability of the 1D heat equation through a compactness criterion and the use of strategic zone actuators were established.展开更多
Kinetic-scale magnetic holes(KSMHs)are structures characterized by a significant magnetic depression with a length scale on the order of the proton gyroradius.These structures have been investigated in recent studies ...Kinetic-scale magnetic holes(KSMHs)are structures characterized by a significant magnetic depression with a length scale on the order of the proton gyroradius.These structures have been investigated in recent studies in near-Earth space,and found to be closely related to energy conversion and particle acceleration,wave-particle interactions,magnetic reconnection,and turbulence at the kineticscale.However,there are still several major issues of the KSMHs that need further study—including(a)the source of these structures(locally generated in near-Earth space,or carried by the solar wind),(b)the environmental conditions leading to their generation,and(c)their spatio-temporal characteristics.In this study,KSMHs in near-Earth space are investigated statistically using data from the Magnetospheric Multiscale mission.Approximately 200,000 events were observed from September 2015 to March 2020.Occurrence rates of such structures in the solar wind,magnetosheath,and magnetotail were obtained.We find that KSMHs occur in the magnetosheath at rates far above their occurrence in the solar wind.This indicates that most of the structures are generated locally in the magnetosheath,rather than advected with the solar wind.Moreover,KSMHs occur in the downstream region of the quasi-parallel shock at rates significantly higher than in the downstream region of the quasi-perpendicular shock,indicating a relationship with the turbulent plasma environment.Close to the magnetopause,we find that the depths of KSMHs decrease as their temporal-scale increases.We also find that the spatial-scales of the KSMHs near the subsolar magnetosheath are smaller than those in the flanks.Furthermore,their global distribution shows a significant dawn-dusk asymmetry(duskside dominating)in the magnetotail.展开更多
Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid ...Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).展开更多
The adsorption of CH3O and H on the (100) facet of gold was studied using self-consistent periodic density functional theory (DFT-GGA) calculations. The best binding site, energy, and structural parameter, as well as ...The adsorption of CH3O and H on the (100) facet of gold was studied using self-consistent periodic density functional theory (DFT-GGA) calculations. The best binding site, energy, and structural parameter, as well as the local density of states, of each species were determined. CH3O is predicted to strongly adsorb on the bridge and hollow sites, with the bridge site as preferred one, with one of the hydrogen atoms pointing toward a fourfold vacancy (bridge-H hollow). The top site was found to be unstable, the CH3O radical moving to the bridge –H top site during geometry optimization. Adsorption of H is unstable on the hollow site, the atom moving to the bridge site during geometry optimization. The 4-layer slab is predicted to be endothermic with respect to gaseous H2 and a clean Au surface.展开更多
The spectroscopy technique has many advantages over conventional analytical methods since it is fast and easy to implement and with no use of chemical extractants. The objective of this study is to quantify soil total...The spectroscopy technique has many advantages over conventional analytical methods since it is fast and easy to implement and with no use of chemical extractants. The objective of this study is to quantify soil total Carbon (C), available Phosphorus (P) and exchangeable potassium (K) using VIS-NIR reflectance spectroscopy. A total of 877 soils samples were collected in various agricultural fields in Mali. Multivariate analysis was applied to the recorded soils spectra to estimate the soil chemical properties. Results reveal the over performance of the Principal Component Regression (PCR) compared to the Partial Least Square Regression (PLSR). For coefficient of determination (R2), PLSR accounts for 0.29, 0.42 and 0.57;while the PCR gave 0.17, 0.34 and 0.50, respectively for C, P and K. Nevertheless, this study demonstrates the potential of the VIS-NIR reflectance spectroscopy in analyzing the soils chemical properties.展开更多
Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increas...Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.展开更多
The process of generating descriptive captions for images has witnessed significant advancements in last years,owing to the progress in deep learning techniques.Despite significant advancements,the task of thoroughly ...The process of generating descriptive captions for images has witnessed significant advancements in last years,owing to the progress in deep learning techniques.Despite significant advancements,the task of thoroughly grasping image content and producing coherent,contextually relevant captions continues to pose a substantial challenge.In this paper,we introduce a novel multimodal method for image captioning by integrating three powerful deep learning architectures:YOLOv8(You Only Look Once)for robust object detection,EfficientNetB7 for efficient feature extraction,and Transformers for effective sequence modeling.Our proposed model combines the strengths of YOLOv8 in detecting objects,the superior feature representation capabilities of EfficientNetB7,and the contextual understanding and sequential generation abilities of Transformers.We conduct extensive experiments on standard benchmark datasets to evaluate the effectiveness of our approach,demonstrating its ability to generate informative and semantically rich captions for diverse images.The experimental results showcase the synergistic benefits of integrating YOLOv8,EfficientNetB7,and Transformers in advancing the state-of-the-art in image captioning tasks.The proposed multimodal approach has yielded impressive outcomes,generating informative and semantically rich captions for a diverse range of images.By combining the strengths of YOLOv8,EfficientNetB7,and Transformers,the model has achieved state-of-the-art results in image captioning tasks.The significance of this approach lies in its ability to address the challenging task of generating coherent and contextually relevant captions while achieving a comprehensive understanding of image content.The integration of three powerful deep learning architectures demonstrates the synergistic benefits of multimodal fusion in advancing the state-of-the-art in image captioning.Furthermore,this approach has a profound impact on the field,opening up new avenues for research in multimodal deep learning and paving the way for more sophisticated and context-aware image captioning systems.These systems have the potential to make significant contributions to various fields,encompassing human-computer interaction,computer vision and natural language processing.展开更多
Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions ...Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions entered.This growth in size is the main obstacle to the blockchain,which makes it unsuitable for resource-constrained IoT environments.Moreover,conventional consensus algorithms such as PoW,PoS are very computationally heavy.This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm.The Multi-Zone Direct Acyclic Graph(DAG)Blockchain(Multizone-DAG-Blockchain)framework is proposed for the fog-based IoT environment.In this context,fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes,thus preserving the energy consumption of the IoT devices.Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validationmechanism(DPUF-VM)inwhichmultiple authentication certificates are verified in the blockchain.Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm.In the cloud,sensitive data is stored as ciphertext.Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes.The fog node first performs a redundancy analysis using the Jaccard Similarity(JS)measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network(N2IN)algorithm.A lightweight proof-of-authentication(PoAh)algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization(BoSo)algorithm for transaction validation.The proposed work is modeled in Network Simulator 3.26(ns-3.26),and the performance is evaluated in terms of energy consumption,storage cost,response time,and throughput.展开更多
Drug treatment, snail control, cercariae control, improved sanitation and health education are the effective strategies which are used to control the schistosomiasis. In this paper, we consider a deterministic model f...Drug treatment, snail control, cercariae control, improved sanitation and health education are the effective strategies which are used to control the schistosomiasis. In this paper, we consider a deterministic model for schistosomiasis transmission dynamics in order to explore the role of the several control strategies. The global stability of a schistosomiasis infection model that involves mating structure including male schistosomes, female schistosomes, paired schistosomes and snails is studied by constructing appropriate Lyapunov functions. We derive the basic reproduction number R0 for the deterministic model, and establish that the global dynamics are completely determined by the values of R0. We show that the disease can be eradicated when R0?≤1;otherwise, the system is persistent. In the case where ?R0?>1, we prove the existence, uniqueness and global asymptotic stability of an endemic steady state. Sensitivity analysis and simulations are carried out in order to determine the relative importance of different control strategies for disease transmission and prevalence. Next, optimal control theory is applied to investigate the control strategies for eliminating schistosomiasis using time dependent controls. The characterization of the optimal control is carried out via the Pontryagins Maximum Principle. The simulation results demonstrate that the insecticide is important in the control of schistosomiasis.展开更多
基金supported by the National Key R&D Program of China(2022YFB4301403).
文摘Hydrogen fuel cell ships are one of the key solutions to achieving zero carbon emissions in shipping.Multi-fuel cell stacks(MFCS)systems are frequently employed to fulfill the power requirements of high-load power equipment on ships.Compared to single-stack system,MFCS may be difficult to apply traditional energy management strategies(EMS)due to their complex structure.In this paper,a two-layer power allocation strategy for MFCS of a hydrogen fuel cell ship is proposed to reduce the complexity of the allocation task by splitting it into each layer of the EMS.The first layer of the EMSis centered on the Nonlinear Model Predictive Control(NMPC).The Northern Goshawk Optimization(NGO)algorithm is used to solve the nonlinear optimization problem in NMPC,and the local fine search is performed using sequential quadratic programming(SQP).Based on the power allocation results of the first layer,the second layer is centered on a fuzzy rule-based adaptive power allocation strategy(AP-Fuzzy).The membership function bounds of the fuzzy controller are related to the aging level of the MFCS.The Particle Swarm Optimization(PSO)algorithm is used to optimize the parameters of the residual membership function to improve the performance of the proposed strategy.The effectiveness of the proposed EMS is verified by comparing it with the traditional EMS.The experimental results show that the EMS proposed in this paper can ensure reasonable hydrogen consumption,slow down the FC aging and equalize its performance,effectively extend the system life,and ensure that the ship has good endurance after completing the mission.
基金supported by the Natural Science Foundation of Guangxi(2021GXNSFFA196004,2024GXNSFBA010337)the NNSF of China(12371312)+1 种基金the Natural Science Foundation of Chongqing(CSTB2024NSCQ-JQX0033)supported by the project cooperation between Guangxi Normal University and Yulin Normal University.
文摘This paper addresses the evolution problem governed by the fractional sweeping process with prox-regular nonconvex constraints.The values of the moving set are time and state-dependent.The aim is to illustrate how a fixed point method can establish an existence theorem for this fractional nonlinear evolution problem.By combining Schauder’s fixed point theorem with a well-posedness theorem when the set C is independent of the state u(i.e.C:=C(t),as presented in[22,23]),we prove the existence of a solution to our quasi-variational fractional sweeping process in infinite-dimensional Hilbert spaces.Similar to the conventional state-dependent sweeping process,achieving this result requires a condition on the size of the Lipschitz constant of the moving set relative to the state.
文摘Biochar application to soil is commonly recognized to improve soil fertility and consequently biomass and food production sustainably.We re-examined the robustness of the underlying data and found that,of the 12000+ publications on“biochar and agriculture”used in meta-studies,only 109 Institute for Scientific Information(ISI)papers(or 0.9%)provide experimental data on the impacts on crop yield and/or biomass production.
文摘The data of SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), installed on SeaStar, has been used to generate SSC (suspended sediment concentration) of complex and turbid coastal waters in China. In view of the problems of the SeaDAS (SeaWiFS Data Analysis System) algorithm applied to China coastal waters, a new atmospheric correction algorithm is discussed, developed, and used for the SSC of East China coastal waters. The advantages of the new algorithm are described through the comparison of the results from different algorithms.
基金supported by the National Science Foundation of China (41525016,41404117)
文摘Using in-situ measurements from the Cassini spacecraft in 2013, we report an Earth substorm-like loading-unloading process at Saturn's distant magnetotail. We found that the loading process is featured with two distinct processes: a rapid loading process that was likely driven by an internal source and a slow loading process that was likely driven by solar wind. Each of the two loading processes could also individually lead to an unloading process. The rapid internal loading process lasts for ~ 1-2 hours; the solar wind driven loading process lasts for ~ 3-18 hours and the following unloading process lasts for ~1-3 hours. In this letter, we suggest three possible loadingunloading circulations, which are fundamental in understanding the role of solar wind in driving giant planetary magnetospheric dynamics.
文摘Soil organic matter(SOM),which associates carbon(C)to key plant nutrients,has been stored in soil for thousands of years.Scientists have long recognised its positive impact on key environmental functions such as food production and climate regulation.As soon as a virgin land(forest or grassland)is cultivated,there is a tendency for the soil to lose its SOM,and we still largely misunderstand the underlying mechanisms,leading to inappropriate decisions being taken to fight soil,climate,and overall ecosystem degradation.
文摘Practical resolution of consolidation problems that we often face requires an extensive and solid knowledge of the different parameters highlighted by the Terzaghi one-dimensional consolidation theory. This theory, with its assumptions, leads to a partial differential equation of second order in space and first order in time of pore water pressure. Analytical and numerical resolutions of this equation allow determining the water pressure variation before and after the application of a charge. Numerical modeling has enabled the simulation of the whole results obtained by the two methods of resolution (pressure, degree of consolidation, time factor, among others) to have a physical analysis and a lawful observation that lead to a suitable understanding of the phenomenon of Terzaghi one-dimensional consolidation.
文摘The carbon dioxide flux through the air-water interface of coastal freshwater ecosystems must be quantified to understand the regional balances of carbon and its transport through coastal and estuarine regions. The variations in air-sea CO2 fluxes in nearshore ecosystems can be caused by the variable influence of rivers. In the present study, the amount of carbon emitted from a tropical coastal river was estimated using climatological and biogeochemical measurements (2002-2010) obtained from the basin of the Capibaribe River, which is located in the most populous and industrialized area of the northeast region of Brazil. The results showed a mean CO2 flux of +225 mmol·m-2·d-1, mainly from organic material from the untreated domestic and industrial wastewaters that are released into the river. This organic material increased the dissolved CO2 concentration in the river waters, leading to a partial pressure of CO2 inthe aquatic environment that reached 31,000 μatm. The months of April, February and December (the dry period) showed the largest monthly means for the variables associated with the carbonate system (, DIC, CO2(aq), CO32-, TA, temperature and pH). This status reflects the state of permanent pollution in the basin of the Capibaribe River, due, in particular, to the discharge of untreated domestic wastewater, which results in the continuous mineralization of organic material. This mineralization significantly increases the dissolved CO2 content in the estuarine and coastal waters, which is later released to the atmosphere.
文摘In this paper we show a boundary result of controllability by a new approach using a linear, continuous and surjective operator built from the solution of the heat system. And, subsequently, the border exact controllability of the 1D heat equation through a compactness criterion and the use of strategic zone actuators were established.
基金the National Natural Science Foundation of China(grants 41731068,41774153,41941001,41961130382,41431072,and 41704169)Royal Society NAF\R1\191047the PRODEX program managed by ESA in collaboration with the Belgian Federal Science Policy Office.
文摘Kinetic-scale magnetic holes(KSMHs)are structures characterized by a significant magnetic depression with a length scale on the order of the proton gyroradius.These structures have been investigated in recent studies in near-Earth space,and found to be closely related to energy conversion and particle acceleration,wave-particle interactions,magnetic reconnection,and turbulence at the kineticscale.However,there are still several major issues of the KSMHs that need further study—including(a)the source of these structures(locally generated in near-Earth space,or carried by the solar wind),(b)the environmental conditions leading to their generation,and(c)their spatio-temporal characteristics.In this study,KSMHs in near-Earth space are investigated statistically using data from the Magnetospheric Multiscale mission.Approximately 200,000 events were observed from September 2015 to March 2020.Occurrence rates of such structures in the solar wind,magnetosheath,and magnetotail were obtained.We find that KSMHs occur in the magnetosheath at rates far above their occurrence in the solar wind.This indicates that most of the structures are generated locally in the magnetosheath,rather than advected with the solar wind.Moreover,KSMHs occur in the downstream region of the quasi-parallel shock at rates significantly higher than in the downstream region of the quasi-perpendicular shock,indicating a relationship with the turbulent plasma environment.Close to the magnetopause,we find that the depths of KSMHs decrease as their temporal-scale increases.We also find that the spatial-scales of the KSMHs near the subsolar magnetosheath are smaller than those in the flanks.Furthermore,their global distribution shows a significant dawn-dusk asymmetry(duskside dominating)in the magnetotail.
基金the support of the Leverhulme Centre for Wildfires,Environment and Society through the Leverhulme Trust(RC-2018-023)Sibo Cheng,César Quilodran-Casas,and Rossella Arcucci acknowledge the support of the PREMIERE project(EP/T000414/1)+5 种基金the support of EPSRC grant:PURIFY(EP/V000756/1)the Fundamental Research Funds for the Central Universitiesthe support of the SASIP project(353)funded by Schmidt Futures–a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologiesDFG for the Heisenberg Programm Award(JA 1077/4-1)the National Natural Science Foundation of China(61976120)the Natural Science Key Foundat ion of Jiangsu Education Department(21KJA510004)。
文摘Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).
文摘The adsorption of CH3O and H on the (100) facet of gold was studied using self-consistent periodic density functional theory (DFT-GGA) calculations. The best binding site, energy, and structural parameter, as well as the local density of states, of each species were determined. CH3O is predicted to strongly adsorb on the bridge and hollow sites, with the bridge site as preferred one, with one of the hydrogen atoms pointing toward a fourfold vacancy (bridge-H hollow). The top site was found to be unstable, the CH3O radical moving to the bridge –H top site during geometry optimization. Adsorption of H is unstable on the hollow site, the atom moving to the bridge site during geometry optimization. The 4-layer slab is predicted to be endothermic with respect to gaseous H2 and a clean Au surface.
文摘The spectroscopy technique has many advantages over conventional analytical methods since it is fast and easy to implement and with no use of chemical extractants. The objective of this study is to quantify soil total Carbon (C), available Phosphorus (P) and exchangeable potassium (K) using VIS-NIR reflectance spectroscopy. A total of 877 soils samples were collected in various agricultural fields in Mali. Multivariate analysis was applied to the recorded soils spectra to estimate the soil chemical properties. Results reveal the over performance of the Principal Component Regression (PCR) compared to the Partial Least Square Regression (PLSR). For coefficient of determination (R2), PLSR accounts for 0.29, 0.42 and 0.57;while the PCR gave 0.17, 0.34 and 0.50, respectively for C, P and K. Nevertheless, this study demonstrates the potential of the VIS-NIR reflectance spectroscopy in analyzing the soils chemical properties.
文摘Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.
基金funded by Researchers Supporting Project number(RSPD2024R698),King Saud University,Riyadh,Saudi Arabia.
文摘The process of generating descriptive captions for images has witnessed significant advancements in last years,owing to the progress in deep learning techniques.Despite significant advancements,the task of thoroughly grasping image content and producing coherent,contextually relevant captions continues to pose a substantial challenge.In this paper,we introduce a novel multimodal method for image captioning by integrating three powerful deep learning architectures:YOLOv8(You Only Look Once)for robust object detection,EfficientNetB7 for efficient feature extraction,and Transformers for effective sequence modeling.Our proposed model combines the strengths of YOLOv8 in detecting objects,the superior feature representation capabilities of EfficientNetB7,and the contextual understanding and sequential generation abilities of Transformers.We conduct extensive experiments on standard benchmark datasets to evaluate the effectiveness of our approach,demonstrating its ability to generate informative and semantically rich captions for diverse images.The experimental results showcase the synergistic benefits of integrating YOLOv8,EfficientNetB7,and Transformers in advancing the state-of-the-art in image captioning tasks.The proposed multimodal approach has yielded impressive outcomes,generating informative and semantically rich captions for a diverse range of images.By combining the strengths of YOLOv8,EfficientNetB7,and Transformers,the model has achieved state-of-the-art results in image captioning tasks.The significance of this approach lies in its ability to address the challenging task of generating coherent and contextually relevant captions while achieving a comprehensive understanding of image content.The integration of three powerful deep learning architectures demonstrates the synergistic benefits of multimodal fusion in advancing the state-of-the-art in image captioning.Furthermore,this approach has a profound impact on the field,opening up new avenues for research in multimodal deep learning and paving the way for more sophisticated and context-aware image captioning systems.These systems have the potential to make significant contributions to various fields,encompassing human-computer interaction,computer vision and natural language processing.
文摘Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions entered.This growth in size is the main obstacle to the blockchain,which makes it unsuitable for resource-constrained IoT environments.Moreover,conventional consensus algorithms such as PoW,PoS are very computationally heavy.This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm.The Multi-Zone Direct Acyclic Graph(DAG)Blockchain(Multizone-DAG-Blockchain)framework is proposed for the fog-based IoT environment.In this context,fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes,thus preserving the energy consumption of the IoT devices.Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validationmechanism(DPUF-VM)inwhichmultiple authentication certificates are verified in the blockchain.Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm.In the cloud,sensitive data is stored as ciphertext.Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes.The fog node first performs a redundancy analysis using the Jaccard Similarity(JS)measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network(N2IN)algorithm.A lightweight proof-of-authentication(PoAh)algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization(BoSo)algorithm for transaction validation.The proposed work is modeled in Network Simulator 3.26(ns-3.26),and the performance is evaluated in terms of energy consumption,storage cost,response time,and throughput.
文摘Drug treatment, snail control, cercariae control, improved sanitation and health education are the effective strategies which are used to control the schistosomiasis. In this paper, we consider a deterministic model for schistosomiasis transmission dynamics in order to explore the role of the several control strategies. The global stability of a schistosomiasis infection model that involves mating structure including male schistosomes, female schistosomes, paired schistosomes and snails is studied by constructing appropriate Lyapunov functions. We derive the basic reproduction number R0 for the deterministic model, and establish that the global dynamics are completely determined by the values of R0. We show that the disease can be eradicated when R0?≤1;otherwise, the system is persistent. In the case where ?R0?>1, we prove the existence, uniqueness and global asymptotic stability of an endemic steady state. Sensitivity analysis and simulations are carried out in order to determine the relative importance of different control strategies for disease transmission and prevalence. Next, optimal control theory is applied to investigate the control strategies for eliminating schistosomiasis using time dependent controls. The characterization of the optimal control is carried out via the Pontryagins Maximum Principle. The simulation results demonstrate that the insecticide is important in the control of schistosomiasis.
