Insular biodiversity has aroused the curiosity of biologists in assessing ecological and evolutionary hypotheses concerning their mainland counterparts.Comparisons of particular life history traits between island and ...Insular biodiversity has aroused the curiosity of biologists in assessing ecological and evolutionary hypotheses concerning their mainland counterparts.Comparisons of particular life history traits between island and mainland populations have provided a broader view of phenotypic and behavioral plasticity at both intraspecific and interspecific levels(Meiri 2007).展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
With the rapid expansion of the Internet of Things(IoT),user data has experienced exponential growth,leading to increasing concerns about the security and integrity of data stored in the cloud.Traditional schemes rely...With the rapid expansion of the Internet of Things(IoT),user data has experienced exponential growth,leading to increasing concerns about the security and integrity of data stored in the cloud.Traditional schemes relying on untrusted third-party auditors suffer from both security and efficiency issues,while existing decentralized blockchain-based auditing solutions still face shortcomings in correctness and security.This paper proposes an improved blockchain-based cloud auditing scheme,with the following core contributions:Identifying critical logical contradictions in the original scheme,thereby establishing the foundation for the correctness of cloud auditing;Designing an enhanced mechanism that integrates multiple hashing with dynamic aggregate signatures,binding encrypted blocks through bilinear pairings and BLS signatures,and improving the scheme by setting parameters based on the Computational Diffie-Hellman(CDH)problem,significantly strengthening data integrity protection and anti-forgery capabilities;Introducing a random challenge mechanism and dynamic parameter adjustment strategy,effectively resisting various attacks such as forgery,tampering,and deletion,significantly improving the detection probability of malicious Cloud Service Providers(CSPs),and significantly reducing the proof generation overhead for CSPswhilemaintaining the same computational cost forDataOwners.Theoretical analysis and performance evaluation experiments demonstrate that the proposed scheme achieves significant improvements in both security and efficiency.Finally,the paper explores potential applications of the Enhanced Security Scheme in fields such as healthcare,drone swarms,and government office attendance systems,providing an effective approach for building secure,efficient,and decentralized cloud auditing systems.展开更多
Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphys...Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
The Pantone Color of the Year 2026,PANTONE 11-4201 Cloud Dancer,has been introduced as a soft,lofty white symbolizing calm and clarity in an increasingly noisy world.This gentle shade invites a sense of peace and spac...The Pantone Color of the Year 2026,PANTONE 11-4201 Cloud Dancer,has been introduced as a soft,lofty white symbolizing calm and clarity in an increasingly noisy world.This gentle shade invites a sense of peace and spaciousness,encouraging focus and creating room for creativity and reflection.Cloud Dancer embodies a desire for simplicity and renewal-a blank canvas that allows our minds to wander and new ideas to take shape.Its expansive presence fosters environments where tranquility meets inspiration,offering visual calm that supports wellbeing and mental lightness.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection.This study presents a thorough analysis of the phase-transition phenomena induced by shock pressur...Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection.This study presents a thorough analysis of the phase-transition phenomena induced by shock pressure as the shock wave propagates initially to the rear of the projectile.The shock wave that induces a phase-transition is commonly referred to as a macroscopic phase-transition wave,whereas the interface that separates the distinct phases is referred to as macroscopic phase-boundary.The contact interface between the spherical projectile and the thin plate,characterized by its curved surface,plays a significant role in the nonlinear propagation and evolution of wave systems.The pressure distribution along the central axis of a spherical projectile is derived in accordance with the linear decay law observed for axial pressure.On this basis,a quadratic function is employed to characterize the trend of changes in wave front pressure,thereby facilitating the establishment of a model for wave front pressure distribution.Using the phase-transition pressure criterion for materials,the wave front phase evolution process is derived,and the macroscopic phase-boundary is determined.Based on the geometric propagation model(GPM)and the pressure distribution of the wave front,a phase geometric propagation model(PGPM)is proposed.The phase distribution of a spherical projectile impacting a thin plate is obtained by theoretical methods.The accuracy of the PGPM is subsequently validated through a comparison of its results with those obtained from numerical simulations.展开更多
Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation with...Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.展开更多
E-commerce has transformed Caoxian into a hub of traditional Chinese attire.STEPPING inside Caoxian’s Youai Cloud Warehouse,a livestreaming base dedicated to hanfu(traditional clothing),it feels less like a clothing ...E-commerce has transformed Caoxian into a hub of traditional Chinese attire.STEPPING inside Caoxian’s Youai Cloud Warehouse,a livestreaming base dedicated to hanfu(traditional clothing),it feels less like a clothing warehouse and more like a time capsule.Thousands of intricately embroidered garments line the racks-from elegant horse-face skirts to modernised editions of traditional Chinese clothing.展开更多
Evaluating rock mass quality using three-dimensional(3D)point clouds is crucial for discontinuity extraction and is widely applied in various industrial sectors.However,the utilization of this method in geological sur...Evaluating rock mass quality using three-dimensional(3D)point clouds is crucial for discontinuity extraction and is widely applied in various industrial sectors.However,the utilization of this method in geological surveys remains limited.Notable limitations of current research include the scarcity of validation using simple geometric shapes for discontinuity extraction methods,and the lack of studies that target both planar and linear discontinuity.To address these gaps,this study proposes a workflow for identifying discontinuity planes and traces in rock outcrops from photogrammetric 3D modeling,employing the Compass and Facets plugins in the open-source CloudCompare software.Prior to field application,the efficacy of the extraction methods was first evaluated using experimental datasets of a cube and an isosceles triangular prism generated under laboratory-controlled conditions.This validation demonstrated exceptional accuracy,with the dip and dip direction(DDD)of extracted structures consistently within±2°of the actual values.Following this rigorous laboratory validation,this methodology was applied to a more complex natural rock outcrop(Miocene–Pliocene deposits in Japan),demonstrating its applicability in realistic geological settings for identifying structures.The results showed that the dip and dip direction trends of the extracted bedding planes and faults were consistent with field measurements,achieving a time reduction of approximately 40%compared to traditional methods.In conclusion,through strictly controlled initial verification and subsequent successful application to a complex natural setting,this study confirmed that the proposed workflow can effectively and efficiently extract discontinuous geological structures from point clouds.展开更多
Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstru...Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstructed from point clouds,they are usually too dense,leading to high computational costs.This paper presents an optimization method for converting dense meshes into optimal meshes,enabling fast and accurate computation of the electrostatic interaction by point clouds.First,the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators.Second,the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique,ultimately yielding an optimal mesh for computing the electrostatic interaction.Simulation results show that our method effectively optimizes dense meshes,making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.展开更多
This study investigates the vertical variations of aerosol size distribution (0.06–1µm) and cloud condensation nuclei(CCN) spectra over the Southern Ocean (SO) using aircraft observations from the SOCRATES campa...This study investigates the vertical variations of aerosol size distribution (0.06–1µm) and cloud condensation nuclei(CCN) spectra over the Southern Ocean (SO) using aircraft observations from the SOCRATES campaign.Results reveal a bimodal aerosol size distribution within the marine boundary layer (MBL),with peaks at diameters of~0.06µm and~0.65µm,dominated by sea-salt particles.Accumulation-mode aerosol concentrations decrease with altitude within the MBL,while Aitken-mode aerosol concentrations peak above the MBL (~2–3 km).Wind speed strongly correlates with coarse-mode aerosol concentration (R2=0.77),implicating sea spray as a major CCN source at low supersaturations (SS=0.1%).The altitudes of CCN concentration peaks shift from the MBL (<1 km,SS<0.4%) to the free troposphere (~2.5 km,SS>0.4%),suggesting new particle formation aloft,distinct from sea surface sources.These findings highlight the unique aerosol–CCN dynamics in the pristine SO,offering critical constraints for models simulating cloud–aerosol interactions in preindustrial-like environments.展开更多
Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudass...Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudassisted architecture faces two critical challenges:the untrusted cloud services and the separation of data ownership from control.Although Attribute-based Searchable Encryption(ABSE)provides fine-grained access control and keyword search over encrypted data,existing schemes lack of error tolerance in exact multi-keyword matching.In this paper,we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search(FCS-ABMSE)scheme that avoids computationally expensive bilinear pairing operations on the IoT device side.The scheme supportsmulti-keyword fuzzy search without requiring explicit keyword fields,thereby significantly enhancing error tolerance in search operations.It further incorporates forward-secure ciphertext search to mitigate trapdoor abuse,as well as offline encryption and verifiable outsourced decryption to minimize user-side computational costs.Formal security analysis proved that the FCS-ABMSE scheme meets both indistinguishability of ciphertext under the chosen keyword attacks(IND-CKA)and the indistinguishability of ciphertext under the chosen plaintext attacks(IND-CPA).In addition,we constructed an enhanced variant based on type-3 pairings.Results demonstrated that the proposed scheme outperforms existing ABSE approaches in terms of functionalities,computational cost,and communication cost.展开更多
The interactions between clouds and aerosols represent one of the largest uncertainties in assessing the Earth's radiation budget, highlighting the importance of research on the transition zone(TZ) within the clou...The interactions between clouds and aerosols represent one of the largest uncertainties in assessing the Earth's radiation budget, highlighting the importance of research on the transition zone(TZ) within the cloud-aerosol continuum.This study assesses the global distribution of TZ conditions, analyzes its optical characteristics, and determines the cloud or aerosol types most commonly associated with them, using the cloud-aerosol discrimination(CAD) score of the CloudAerosol Lidar with Orthogonal Polarization(CALIOP) instrument on the CALIPSO satellite. The CAD score classifies clouds and aerosols by the probability density functions of attenuated backscatter, total color ratio, volume depolarization ratio, altitude, and latitude. After applying several filters to avoid artifacts, the TZ was identified as those atmospheric layers that cannot be clearly classified as clouds or aerosols, layers within the no-confidence range(NCR) of the CAD score, and cirrus fringes. The optical characteristics of NCR layers exhibit two main clusters: Cluster 1, with properties between high-altitude ice clouds and aerosols(e.g., wispy cloud fragments), and Cluster 2, with properties between water clouds and aerosols at lower altitudes(e.g., large hydrated aerosols). Our results highlight the significant ubiquity of TZ conditions, which appear in 9.5% of all profiles and comprise 6.4% of the detected layers. Cluster 1 and cirrus-fringe layers predominate near the ITCZ and in mid-latitudes, whereas Cluster 2 layers are more frequent over the oceans along the central West African and East Asian coasts, where elevated smoke and dusty marine aerosols are common.展开更多
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva...Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
基金We are deeply thankful to Neftali Camacho(LACM),Addison Wynn(NMNH),Esther Langan(NMNH),Christina K.Sami(NMNH),David A.Kizirian(AMNH),and Lauren Vonnahme(AMNH)for logistic support to visiting each museum.Shai Meiri and anonymous reviewers provided insightful suggestions that improved the manuscript,and Louis W.Porras also improves the English of the manuscript.Finally,we thank Jose Gonzalez and Ismael Huerta for help in preparing the figure.
文摘Insular biodiversity has aroused the curiosity of biologists in assessing ecological and evolutionary hypotheses concerning their mainland counterparts.Comparisons of particular life history traits between island and mainland populations have provided a broader view of phenotypic and behavioral plasticity at both intraspecific and interspecific levels(Meiri 2007).
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金funded by the National Natural Science Foundation of China(New Design and Analysis of Fully Homomorphic Signatures,Grant No.62172436).
文摘With the rapid expansion of the Internet of Things(IoT),user data has experienced exponential growth,leading to increasing concerns about the security and integrity of data stored in the cloud.Traditional schemes relying on untrusted third-party auditors suffer from both security and efficiency issues,while existing decentralized blockchain-based auditing solutions still face shortcomings in correctness and security.This paper proposes an improved blockchain-based cloud auditing scheme,with the following core contributions:Identifying critical logical contradictions in the original scheme,thereby establishing the foundation for the correctness of cloud auditing;Designing an enhanced mechanism that integrates multiple hashing with dynamic aggregate signatures,binding encrypted blocks through bilinear pairings and BLS signatures,and improving the scheme by setting parameters based on the Computational Diffie-Hellman(CDH)problem,significantly strengthening data integrity protection and anti-forgery capabilities;Introducing a random challenge mechanism and dynamic parameter adjustment strategy,effectively resisting various attacks such as forgery,tampering,and deletion,significantly improving the detection probability of malicious Cloud Service Providers(CSPs),and significantly reducing the proof generation overhead for CSPswhilemaintaining the same computational cost forDataOwners.Theoretical analysis and performance evaluation experiments demonstrate that the proposed scheme achieves significant improvements in both security and efficiency.Finally,the paper explores potential applications of the Enhanced Security Scheme in fields such as healthcare,drone swarms,and government office attendance systems,providing an effective approach for building secure,efficient,and decentralized cloud auditing systems.
基金National Natural Science Foundation of China(42375153,42105153,42205157)Development of Science and Technology at Chinese Academy of Meteorological Sciences(2023KJ038)。
文摘Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
文摘The Pantone Color of the Year 2026,PANTONE 11-4201 Cloud Dancer,has been introduced as a soft,lofty white symbolizing calm and clarity in an increasingly noisy world.This gentle shade invites a sense of peace and spaciousness,encouraging focus and creating room for creativity and reflection.Cloud Dancer embodies a desire for simplicity and renewal-a blank canvas that allows our minds to wander and new ideas to take shape.Its expansive presence fosters environments where tranquility meets inspiration,offering visual calm that supports wellbeing and mental lightness.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by National Natural Science Foundation of China(Nos.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(No.12221002)National Natural Science Foundation of China(No.12302493)。
文摘Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection.This study presents a thorough analysis of the phase-transition phenomena induced by shock pressure as the shock wave propagates initially to the rear of the projectile.The shock wave that induces a phase-transition is commonly referred to as a macroscopic phase-transition wave,whereas the interface that separates the distinct phases is referred to as macroscopic phase-boundary.The contact interface between the spherical projectile and the thin plate,characterized by its curved surface,plays a significant role in the nonlinear propagation and evolution of wave systems.The pressure distribution along the central axis of a spherical projectile is derived in accordance with the linear decay law observed for axial pressure.On this basis,a quadratic function is employed to characterize the trend of changes in wave front pressure,thereby facilitating the establishment of a model for wave front pressure distribution.Using the phase-transition pressure criterion for materials,the wave front phase evolution process is derived,and the macroscopic phase-boundary is determined.Based on the geometric propagation model(GPM)and the pressure distribution of the wave front,a phase geometric propagation model(PGPM)is proposed.The phase distribution of a spherical projectile impacting a thin plate is obtained by theoretical methods.The accuracy of the PGPM is subsequently validated through a comparison of its results with those obtained from numerical simulations.
基金supported by National Natural Science Foundation of China(U1909201,62206062).
文摘Clouds are one of the leading causes of sun shading,which reduces the direct horizontal irradiance and curtails the photovoltaic(PV)power.It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon(second/minute).To achieve the precise forecasting of cloud cover,an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue.An optimal threshold estimation method is further designed to achieve higher cloud identification precision.Considering the cloud's meteorological properties,a random hypersurface model(RHM)based on the Gaussian mixture probability hypothesis density(GM-PHD)filter is applied to track the cloud.The GM-PHD can track the rotation and diffusion of clouds,which helps to estimate sun-cloud collision.Furthermore,a hybrid autoregressive integrated moving average(ARIMA)and backpropagation(BP)neural network-based model is applied for intra-hour PV power forecasting.The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power,improving forecasting precision.
文摘E-commerce has transformed Caoxian into a hub of traditional Chinese attire.STEPPING inside Caoxian’s Youai Cloud Warehouse,a livestreaming base dedicated to hanfu(traditional clothing),it feels less like a clothing warehouse and more like a time capsule.Thousands of intricately embroidered garments line the racks-from elegant horse-face skirts to modernised editions of traditional Chinese clothing.
文摘Evaluating rock mass quality using three-dimensional(3D)point clouds is crucial for discontinuity extraction and is widely applied in various industrial sectors.However,the utilization of this method in geological surveys remains limited.Notable limitations of current research include the scarcity of validation using simple geometric shapes for discontinuity extraction methods,and the lack of studies that target both planar and linear discontinuity.To address these gaps,this study proposes a workflow for identifying discontinuity planes and traces in rock outcrops from photogrammetric 3D modeling,employing the Compass and Facets plugins in the open-source CloudCompare software.Prior to field application,the efficacy of the extraction methods was first evaluated using experimental datasets of a cube and an isosceles triangular prism generated under laboratory-controlled conditions.This validation demonstrated exceptional accuracy,with the dip and dip direction(DDD)of extracted structures consistently within±2°of the actual values.Following this rigorous laboratory validation,this methodology was applied to a more complex natural rock outcrop(Miocene–Pliocene deposits in Japan),demonstrating its applicability in realistic geological settings for identifying structures.The results showed that the dip and dip direction trends of the extracted bedding planes and faults were consistent with field measurements,achieving a time reduction of approximately 40%compared to traditional methods.In conclusion,through strictly controlled initial verification and subsequent successful application to a complex natural setting,this study confirmed that the proposed workflow can effectively and efficiently extract discontinuous geological structures from point clouds.
基金supported by the National Natural Science Foundation of China(No.62003269).
文摘Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstructed from point clouds,they are usually too dense,leading to high computational costs.This paper presents an optimization method for converting dense meshes into optimal meshes,enabling fast and accurate computation of the electrostatic interaction by point clouds.First,the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators.Second,the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique,ultimately yielding an optimal mesh for computing the electrostatic interaction.Simulation results show that our method effectively optimizes dense meshes,making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.42430601,42175087)the Science and Technology Project of Gansu Province(Outstanding Youth Fund,Grant No.24JRRA386)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2024-jdzx04)。
文摘This study investigates the vertical variations of aerosol size distribution (0.06–1µm) and cloud condensation nuclei(CCN) spectra over the Southern Ocean (SO) using aircraft observations from the SOCRATES campaign.Results reveal a bimodal aerosol size distribution within the marine boundary layer (MBL),with peaks at diameters of~0.06µm and~0.65µm,dominated by sea-salt particles.Accumulation-mode aerosol concentrations decrease with altitude within the MBL,while Aitken-mode aerosol concentrations peak above the MBL (~2–3 km).Wind speed strongly correlates with coarse-mode aerosol concentration (R2=0.77),implicating sea spray as a major CCN source at low supersaturations (SS=0.1%).The altitudes of CCN concentration peaks shift from the MBL (<1 km,SS<0.4%) to the free troposphere (~2.5 km,SS>0.4%),suggesting new particle formation aloft,distinct from sea surface sources.These findings highlight the unique aerosol–CCN dynamics in the pristine SO,offering critical constraints for models simulating cloud–aerosol interactions in preindustrial-like environments.
文摘Internet of Things(IoT)interconnects devices via network protocols to enable intelligent sensing and control.Resource-constrained IoT devices rely on cloud servers for data storage and processing.However,this cloudassisted architecture faces two critical challenges:the untrusted cloud services and the separation of data ownership from control.Although Attribute-based Searchable Encryption(ABSE)provides fine-grained access control and keyword search over encrypted data,existing schemes lack of error tolerance in exact multi-keyword matching.In this paper,we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search(FCS-ABMSE)scheme that avoids computationally expensive bilinear pairing operations on the IoT device side.The scheme supportsmulti-keyword fuzzy search without requiring explicit keyword fields,thereby significantly enhancing error tolerance in search operations.It further incorporates forward-secure ciphertext search to mitigate trapdoor abuse,as well as offline encryption and verifiable outsourced decryption to minimize user-side computational costs.Formal security analysis proved that the FCS-ABMSE scheme meets both indistinguishability of ciphertext under the chosen keyword attacks(IND-CKA)and the indistinguishability of ciphertext under the chosen plaintext attacks(IND-CPA).In addition,we constructed an enhanced variant based on type-3 pairings.Results demonstrated that the proposed scheme outperforms existing ABSE approaches in terms of functionalities,computational cost,and communication cost.
基金funded through project NUBOLOSYTI (PID2023149972NB-100) of the Spanish Ministry of Science and Innovation (MICINN)supported by an IFUdG 2022 fellowship。
文摘The interactions between clouds and aerosols represent one of the largest uncertainties in assessing the Earth's radiation budget, highlighting the importance of research on the transition zone(TZ) within the cloud-aerosol continuum.This study assesses the global distribution of TZ conditions, analyzes its optical characteristics, and determines the cloud or aerosol types most commonly associated with them, using the cloud-aerosol discrimination(CAD) score of the CloudAerosol Lidar with Orthogonal Polarization(CALIOP) instrument on the CALIPSO satellite. The CAD score classifies clouds and aerosols by the probability density functions of attenuated backscatter, total color ratio, volume depolarization ratio, altitude, and latitude. After applying several filters to avoid artifacts, the TZ was identified as those atmospheric layers that cannot be clearly classified as clouds or aerosols, layers within the no-confidence range(NCR) of the CAD score, and cirrus fringes. The optical characteristics of NCR layers exhibit two main clusters: Cluster 1, with properties between high-altitude ice clouds and aerosols(e.g., wispy cloud fragments), and Cluster 2, with properties between water clouds and aerosols at lower altitudes(e.g., large hydrated aerosols). Our results highlight the significant ubiquity of TZ conditions, which appear in 9.5% of all profiles and comprise 6.4% of the detected layers. Cluster 1 and cirrus-fringe layers predominate near the ITCZ and in mid-latitudes, whereas Cluster 2 layers are more frequent over the oceans along the central West African and East Asian coasts, where elevated smoke and dusty marine aerosols are common.
基金supported in part by the National Key Research and Development Program of Chinaunder(Grant 2021YFB3101100)in part by the National Natural Science Foundation of Chinaunder(Grant 42461057),(Grant 62272123),and(Grant 42371470)+1 种基金in part by the Fundamental Research Program of Shanxi Province under(Grant 202303021212164)in part by the Postgraduate Education Innovation Program of Shanxi Province under(Grant 2024KY474).
文摘Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
