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
Background:Submarine personnel often experience insomnia and reduced psychological resilience due to extended deployments in confined,high-stress environments.Effective non-pharmacological interventions are needed to ...Background:Submarine personnel often experience insomnia and reduced psychological resilience due to extended deployments in confined,high-stress environments.Effective non-pharmacological interventions are needed to improve sleep quality and resilience in this population.This study aimed to investigate the effect of virtual reality(VR)combined with forest therapy interventions on psychological resilience and sleep quality among submarine personnel with insomnia symptoms.Methods:Using convenience sampling,92 submarine personnel with insomnia symptoms undergoing recuperation at a PLA sanatorium between July 2023 and May 2025 were randomly allocated to experimental and control groups(n=46 each).The control group received forest therapy intervention,while the intervention group received combined VR and forest therapy interventions.Pre-and post-intervention assessments were conducted using the Pittsburgh Sleep Quality Index(PSQI)and Connor-Davidson Resilience Scale(CD-RISC).Results:There is no significant differences between two groups before the intervention on sleep or psychological resilience.Both groups showed significant pre-to post-intervention improvements in sleep and resilience;however,mixed-ANOVA results showed that the intervention(VR+forest therapy)group achieved significantly better outcomes than the control group at post-intervention after Bonferroni correction,including lower PSQI total and key component scores(subjective sleep quality,sleep efficiency,daytime dysfunction)and higher CD-RISC resilience scores.Conclusions:The integration of virtual reality and forest therapy effectively improved sleep quality and psychological resilience among submarine personnel with insomnia symptoms.This combined intervention shows promise as a non-pharmacological approach in military healthcare settings;however,further studies are needed to validate and generalize these findings.展开更多
Detector and event visualization are crucial components of high-energy physics(HEP)experimental software.Virtual reality(VR)technologies and multimedia development platforms,such as Unity,offer enhanced display effect...Detector and event visualization are crucial components of high-energy physics(HEP)experimental software.Virtual reality(VR)technologies and multimedia development platforms,such as Unity,offer enhanced display effects and flexible extensibility for visualization in HEP experiments.In this study,we present a VR-based method for detector and event displays in the Jiangmen Underground Neutrino Observatory(JUNO)experiment.This method shares the same detector geometry descriptions and event data model as those in the offline software and provides the necessary data conversion interfaces.The VR methodology facilitates an immersive exploration of the virtual environment in JUNO,enabling users to investigate the detector geometry,visualize event data,and tune the detector simulation and event reconstruction algorithms.Additionally,this approach supports applications in data monitoring,physics data analysis,and public outreach initiatives.展开更多
Objectives This scoping review aimed to identify and summarize the current research on virtual reality(VR)technologies used for health education in cancer patients,as well as to identify key areas of application.Metho...Objectives This scoping review aimed to identify and summarize the current research on virtual reality(VR)technologies used for health education in cancer patients,as well as to identify key areas of application.Methods In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines,a comprehensive literature search was performed across 11 electronic databases and gray literature sources from inception to 12 September 2025.Studies employing immersive VR tools to improve health education outcomes in cancer patients were included.Data extraction and thematic synthesis were conducted to map evidence regarding VR modalities,educational applications,and outcome measures.Results Twenty-eight studies met the inclusion criteria.VR was applied across four primary educational scenarios,including radiotherapy,chemotherapy,surgery,and healthy behavior(including rehabilitation,smoking cessation,and self-management).Eight distinct VR modalities were identified,namely VR videos,virtual environments,virtual environment for radiotherapy training(VERT),VR interactions,3D models,VR games,VR non-player characters(VR NPCs),and virtual libraries.Among these,VR videos(50.0%),virtual environments(46.4%),and VR interactions(28.6%)were the most frequently employed.The interventions led to significant improvements in patient knowledge,skills,attitudes,health behaviors,and psychological well-being.A clear evolution in VR educational approaches has been observed,shifting from static environmental familiarization toward interactive,gamified,and intelligence-driven experiences.Nevertheless,notable gaps remain regarding safety protocols and data privacy protections,with only a minority of studies addressing these issues.Conclusions VR technologies demonstrate considerable promise as an innovative educational tool in oncology care,enhancing patient understanding,psychological preparedness,and engagement throughout the cancer journey.Future implementation must address infrastructural,ethical,and user-centered design barriers to facilitate the scalable and sustainable integration of this approach into clinical practice.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
In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In additi...In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.展开更多
The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pu...The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pursuit of advanced AR displays,particularly those capable of delivering immersive 3D experiences,is significantly hindered by the performance limitations of current hardware and the complexity of system integration.In this study,we present an innovative multi-focal plane AR display system that integrates a non-orthogonal polarization-multiplexing metasurface,freeform optical elements,and an OLED display screen.All optical elements are integrated into a single solid-state architecture,based on a joint optimization design approach of ray tracing and diffraction theory.The multi-focal plane AR visual effect is realized by the compact and multiplexing metasurface,which performs distinct phase functions across diverse polarization channels.Meanwhile,freeform surfaces offer ample design flexibility for the collaborative optimization of multi-focal plane imaging and the see-through systems.Followed by a mechanical design and prototype assembly,we demonstrate the system's capabilities in real-time and multi-focal plane display.The digital images at all virtual image distances seamlessly integrate with the real environment,fully exhibiting the system's high parallelism and real-time interactivity.With the innovative design concept and joint design method,we believe that our work will spur more innovative and compact intelligent solutions for AR displays and inject new vitality into hybrid optical systems.展开更多
Surgical navigation has evolved significantly through advances in augmented reality,virtual reality,and mixed reality,improving precision and safety across many clinical applications,including neurosurgery,maxillofaci...Surgical navigation has evolved significantly through advances in augmented reality,virtual reality,and mixed reality,improving precision and safety across many clinical applications,including neurosurgery,maxillofacial,spinal,and arthroplasty procedures.By integrating preoperative imaging with real-time intraoperative data,these systems provide dynamic guidance,reduce radiation exposure,and minimize tissue damage.Key challenges persist,including intraoperative registration accuracy,flexible tissue deformation,respiratory compensation,and real-time imaging quality.Emerging solutions include artificial intelligence-driven segmentation,deformation-field modeling,and hybrid registration techniques.Future developments will include lightweight,portable systems,improved non-rigid registration algorithms,and greater clinical adoption.Despite advances in rigid-tissue applications,soft-tissue navigation requires additional innovation to address motion variability and registration reliability,ultimately advancing minimally invasive surgery and precision medicine.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
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
Conventional surgical teaching techniques face several challenges,highlighting a necessity for ongoing innovation in ophthalmology education to align with the evolving demands of clinical practice.The recent rapid adv...Conventional surgical teaching techniques face several challenges,highlighting a necessity for ongoing innovation in ophthalmology education to align with the evolving demands of clinical practice.The recent rapid advancement of computer technology has enabled the integration of virtual reality(VR)into medical training,thereby revolutionizing ophthalmic surgical education through VRbased educational methods.VR technology offers a safe,risk-free environment for trainees to practice repeatedly,enhancing surgical skills and accelerating the learning curve without compromising patient safety.This research outlines the application of VR technology in ophthalmic surgical skills training,particularly in cataract and vitreoretinal surgery.Including assessing the effectiveness of intraocular surgery training systems,evaluating skills transfer to the operating room,comparing it with wet lab cataract surgery training,and enhancing non-dominant hand training for cataract surgery,among other aspects.Additionally,this paper will identify the limitations of VR technology in ocular surgical skills training,offer improvement strategies,and detail the advantages and prospects,with the objective of guiding subsequent researchers.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
The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation i...The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation inaccurate on the TP.This paper introduces an indirect estimation scheme for the LWC profile obtained using a monochromatic radiative transfer model(MonoRTM)and microwave radiometers(MWRs)on the TP.The LWC estimation method was improved using an optimization of the difference between the simulated and observed brightness temperature(TB)at specific microwave channels that are sensitive to liquid water.The accuracy of the LWC estimation method depends heavily on the value of the cloud-base environment humidity criterion(CBEHC).Our experiment confirmed that the default CBEHC value of 95%is unsuitable for the TP.For the rainfall scenarios,the optimization method suggested the use of CBEHC values of 81%,76%,and 83%for Mangya,Nagqu,and Qamdo stations,respectively.The new CBEHC values produced a 30 K improvement in the TB simulation when compared to that of 95%CBEHC under rainfall conditions.This demonstrates the robustness of the LWC estimation scheme and its significant improvement in LWC estimation on the TP.For no-rainfall scenarios,the original Karstens model remained suitable for Nagqu station.An adjustment of the CBEHC to 94%for Mangya station resulted in a 1 K improvement of its TB simulation.Qamdo station had a 2.5 K improvement when the CBEHC was adjusted to 98%.The relationship between the simulated TB simulation error and the maximum relative humidity of the radiosonde profiles weakened after CBEHC optimization.Thus,the innovative method proposed in this article provides a practical estimation method for LWC in the TP region.This LWC estimation method has a higher potential for rainfall days than no-rainfall days.Under no-rainfall conditions,the accuracy of the proposed LWC estimation method is sensitive to TB errors included in its measurement and simulation.An accurate estimation of LWC for no-rainfall conditions relies more on the equipment and radiation model.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
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
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.
文摘Background:Submarine personnel often experience insomnia and reduced psychological resilience due to extended deployments in confined,high-stress environments.Effective non-pharmacological interventions are needed to improve sleep quality and resilience in this population.This study aimed to investigate the effect of virtual reality(VR)combined with forest therapy interventions on psychological resilience and sleep quality among submarine personnel with insomnia symptoms.Methods:Using convenience sampling,92 submarine personnel with insomnia symptoms undergoing recuperation at a PLA sanatorium between July 2023 and May 2025 were randomly allocated to experimental and control groups(n=46 each).The control group received forest therapy intervention,while the intervention group received combined VR and forest therapy interventions.Pre-and post-intervention assessments were conducted using the Pittsburgh Sleep Quality Index(PSQI)and Connor-Davidson Resilience Scale(CD-RISC).Results:There is no significant differences between two groups before the intervention on sleep or psychological resilience.Both groups showed significant pre-to post-intervention improvements in sleep and resilience;however,mixed-ANOVA results showed that the intervention(VR+forest therapy)group achieved significantly better outcomes than the control group at post-intervention after Bonferroni correction,including lower PSQI total and key component scores(subjective sleep quality,sleep efficiency,daytime dysfunction)and higher CD-RISC resilience scores.Conclusions:The integration of virtual reality and forest therapy effectively improved sleep quality and psychological resilience among submarine personnel with insomnia symptoms.This combined intervention shows promise as a non-pharmacological approach in military healthcare settings;however,further studies are needed to validate and generalize these findings.
基金supported by the National Natural Science Foundation of China(Nos.12175321,W2443004,11975021,11675275,U1932101)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA10010900)+2 种基金National Key Research and Development Program of China(Nos.2023YFA1606000 and 2020YFA0406400)National College Students Science and Technology Innovation ProjectUndergraduate Base Scientific Research Project of Sun Yat-sen University。
文摘Detector and event visualization are crucial components of high-energy physics(HEP)experimental software.Virtual reality(VR)technologies and multimedia development platforms,such as Unity,offer enhanced display effects and flexible extensibility for visualization in HEP experiments.In this study,we present a VR-based method for detector and event displays in the Jiangmen Underground Neutrino Observatory(JUNO)experiment.This method shares the same detector geometry descriptions and event data model as those in the offline software and provides the necessary data conversion interfaces.The VR methodology facilitates an immersive exploration of the virtual environment in JUNO,enabling users to investigate the detector geometry,visualize event data,and tune the detector simulation and event reconstruction algorithms.Additionally,this approach supports applications in data monitoring,physics data analysis,and public outreach initiatives.
基金supported by a project supported by Scientific Research Fund of Zhejiang Provincial Education Department(Grant number Y202457058).
文摘Objectives This scoping review aimed to identify and summarize the current research on virtual reality(VR)technologies used for health education in cancer patients,as well as to identify key areas of application.Methods In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines,a comprehensive literature search was performed across 11 electronic databases and gray literature sources from inception to 12 September 2025.Studies employing immersive VR tools to improve health education outcomes in cancer patients were included.Data extraction and thematic synthesis were conducted to map evidence regarding VR modalities,educational applications,and outcome measures.Results Twenty-eight studies met the inclusion criteria.VR was applied across four primary educational scenarios,including radiotherapy,chemotherapy,surgery,and healthy behavior(including rehabilitation,smoking cessation,and self-management).Eight distinct VR modalities were identified,namely VR videos,virtual environments,virtual environment for radiotherapy training(VERT),VR interactions,3D models,VR games,VR non-player characters(VR NPCs),and virtual libraries.Among these,VR videos(50.0%),virtual environments(46.4%),and VR interactions(28.6%)were the most frequently employed.The interventions led to significant improvements in patient knowledge,skills,attitudes,health behaviors,and psychological well-being.A clear evolution in VR educational approaches has been observed,shifting from static environmental familiarization toward interactive,gamified,and intelligence-driven experiences.Nevertheless,notable gaps remain regarding safety protocols and data privacy protections,with only a minority of studies addressing these issues.Conclusions VR technologies demonstrate considerable promise as an innovative educational tool in oncology care,enhancing patient understanding,psychological preparedness,and engagement throughout the cancer journey.Future implementation must address infrastructural,ethical,and user-centered design barriers to facilitate the scalable and sustainable integration of this approach into clinical practice.
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.
基金supported by the 2024 Research Fund of University of Ulsan.
文摘In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.
基金funding provided by National Natural Science Foundation of China(U21A20140)National Key Research and Development Program of China(2021YFA1401200)+2 种基金Beijing Natural Science Foundation(JQ24028)Beijing Nova Program(20240484557)Synergetic Extreme Condition User Facility(SECUF).
文摘The advent of artificial intelligence(AI)has propelled augmented reality(AR)display technology to a pivotal juncture,positioning it as a contender for the next generation of mobile intelligent terminals.However,the pursuit of advanced AR displays,particularly those capable of delivering immersive 3D experiences,is significantly hindered by the performance limitations of current hardware and the complexity of system integration.In this study,we present an innovative multi-focal plane AR display system that integrates a non-orthogonal polarization-multiplexing metasurface,freeform optical elements,and an OLED display screen.All optical elements are integrated into a single solid-state architecture,based on a joint optimization design approach of ray tracing and diffraction theory.The multi-focal plane AR visual effect is realized by the compact and multiplexing metasurface,which performs distinct phase functions across diverse polarization channels.Meanwhile,freeform surfaces offer ample design flexibility for the collaborative optimization of multi-focal plane imaging and the see-through systems.Followed by a mechanical design and prototype assembly,we demonstrate the system's capabilities in real-time and multi-focal plane display.The digital images at all virtual image distances seamlessly integrate with the real environment,fully exhibiting the system's high parallelism and real-time interactivity.With the innovative design concept and joint design method,we believe that our work will spur more innovative and compact intelligent solutions for AR displays and inject new vitality into hybrid optical systems.
基金Supported by the National Natural Science Foundation of China(NSFC)under Grants 62025104,62422102,62331005,62301034,and U22A2052the Beijing Natural Science Foundation-Daxing Innovation Joint Fund(L256040).
文摘Surgical navigation has evolved significantly through advances in augmented reality,virtual reality,and mixed reality,improving precision and safety across many clinical applications,including neurosurgery,maxillofacial,spinal,and arthroplasty procedures.By integrating preoperative imaging with real-time intraoperative data,these systems provide dynamic guidance,reduce radiation exposure,and minimize tissue damage.Key challenges persist,including intraoperative registration accuracy,flexible tissue deformation,respiratory compensation,and real-time imaging quality.Emerging solutions include artificial intelligence-driven segmentation,deformation-field modeling,and hybrid registration techniques.Future developments will include lightweight,portable systems,improved non-rigid registration algorithms,and greater clinical adoption.Despite advances in rigid-tissue applications,soft-tissue navigation requires additional innovation to address motion variability and registration reliability,ultimately advancing minimally invasive surgery and precision medicine.
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.
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 Key Special Project of“Cutting-Edge Biotechnology”in the National Key Research and Development Program of China(No.2024YFC3406200)Sanming Project of Medicine in Shenzhen(No.SZSM202411007)Shenzhen Science and Technology Program(No.JCYJ20240813152704006).
文摘Conventional surgical teaching techniques face several challenges,highlighting a necessity for ongoing innovation in ophthalmology education to align with the evolving demands of clinical practice.The recent rapid advancement of computer technology has enabled the integration of virtual reality(VR)into medical training,thereby revolutionizing ophthalmic surgical education through VRbased educational methods.VR technology offers a safe,risk-free environment for trainees to practice repeatedly,enhancing surgical skills and accelerating the learning curve without compromising patient safety.This research outlines the application of VR technology in ophthalmic surgical skills training,particularly in cataract and vitreoretinal surgery.Including assessing the effectiveness of intraocular surgery training systems,evaluating skills transfer to the operating room,comparing it with wet lab cataract surgery training,and enhancing non-dominant hand training for cataract surgery,among other aspects.Additionally,this paper will identify the limitations of VR technology in ocular surgical skills training,offer improvement strategies,and detail the advantages and prospects,with the objective of guiding subsequent researchers.
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 Natural Science Foundation of China(Grant Nos.41975009 and U2442213).
文摘The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation inaccurate on the TP.This paper introduces an indirect estimation scheme for the LWC profile obtained using a monochromatic radiative transfer model(MonoRTM)and microwave radiometers(MWRs)on the TP.The LWC estimation method was improved using an optimization of the difference between the simulated and observed brightness temperature(TB)at specific microwave channels that are sensitive to liquid water.The accuracy of the LWC estimation method depends heavily on the value of the cloud-base environment humidity criterion(CBEHC).Our experiment confirmed that the default CBEHC value of 95%is unsuitable for the TP.For the rainfall scenarios,the optimization method suggested the use of CBEHC values of 81%,76%,and 83%for Mangya,Nagqu,and Qamdo stations,respectively.The new CBEHC values produced a 30 K improvement in the TB simulation when compared to that of 95%CBEHC under rainfall conditions.This demonstrates the robustness of the LWC estimation scheme and its significant improvement in LWC estimation on the TP.For no-rainfall scenarios,the original Karstens model remained suitable for Nagqu station.An adjustment of the CBEHC to 94%for Mangya station resulted in a 1 K improvement of its TB simulation.Qamdo station had a 2.5 K improvement when the CBEHC was adjusted to 98%.The relationship between the simulated TB simulation error and the maximum relative humidity of the radiosonde profiles weakened after CBEHC optimization.Thus,the innovative method proposed in this article provides a practical estimation method for LWC in the TP region.This LWC estimation method has a higher potential for rainfall days than no-rainfall days.Under no-rainfall conditions,the accuracy of the proposed LWC estimation method is sensitive to TB errors included in its measurement and simulation.An accurate estimation of LWC for no-rainfall conditions relies more on the equipment and radiation model.
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.
