A terrestrial relay-aided reconfigurable intelligent surface(RIS)system with decode,re-encode and forward(DRF)relaying scheme is presented where the RIS effectively contributes to both sourceto-destination and relay-t...A terrestrial relay-aided reconfigurable intelligent surface(RIS)system with decode,re-encode and forward(DRF)relaying scheme is presented where the RIS effectively contributes to both sourceto-destination and relay-to-destination signaling.While in the conventional decode and forward(DF)relaying scheme,the source signal is merely duplicated in the relay and the time intervals are equally allocated to the source and relay nodes,this paper considers DRF relaying scheme where versatile time-sharing is adopted for the source and relay nodes which can be optimized based on the relative coordinates of the involved nodes.Two protocols namely unidirectional connection(UC)and bidirectional connection(BC)are proposed based on the source awareness from the relay’s successful reception.The outage probability(OP)performance for both protocols and both DF and DRF relaying schemes is analyzed and tight approximations are obtained.The numerical results show the out-performance of the DRF over the DF relaying scheme in the both UC and BC protocols.Equipped with the obtained system OP,the system throughput is defined and the optimum system throughput is obtained by optimizing the system rate and the timesharing between the source and the relay.Analytical results are corroborated in the numerical examples.展开更多
In this study,the dynamic characteristics and microstructures of lacustrine soft clays were studied.Dynamic character tests were conducted on undisturbed,remolded,and saturated lacustrine soft clays,using a dynamic tr...In this study,the dynamic characteristics and microstructures of lacustrine soft clays were studied.Dynamic character tests were conducted on undisturbed,remolded,and saturated lacustrine soft clays,using a dynamic triaxial tester.A scanning electron microscope(SEM)was employed to assess the soil samples after dynamic testing.The results indicate that the dynamic characteristics of lacustrine soft clay were significantly affected by confining pressure and water content.A quantitative relationship was established among confining pressures,water content,and the dynamic shear modulus ratio.The dynamic characteristic parameters of undisturbed,remolded and saturated soil are obviously different,and the original structure can enhance the shear strength of soil.By comparing the results with those from other studies,we found that the dynamic characters of soft clays were considerably varied in different regions,and lacustrine soft clays had a larger dynamic shear modulus ratio and a smaller damping ratio when the dynamic shear strain was large.Using IPP software to process the microstructural images,we found that the soil was dominated by small pores and medium particles,and the roundness of pores and particles had an apparently positive correlation with the maximum diameter.Moreover,the pores and particles of the soil showed fractal characteristics and directionality,and the fractal dimensions and probability entropy were strongly correlated with the macrostructural parameters.Finally,we developed a prediction model for macrostructural and microstructural parameters.展开更多
In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s...In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.展开更多
In this study,the effects of laser fields that can be achieved in the near future on cluster penetration probability and half-life are quantitatively investigated.The calculation results show that extreme laser fields...In this study,the effects of laser fields that can be achieved in the near future on cluster penetration probability and half-life are quantitatively investigated.The calculation results show that extreme laser fields can slightly change the cluster-decay half-life by affecting the penetration probability within a narrow range.Subsequently,we discuss the correlation between the change rate of the penetration probability and the tunneling path.The results indicate that for different parent nuclei emitting the same cluster,nuclei with longer tunneling paths are more easily affected by the laser fields.The shell effect on this correlation is also observed.In addition,the impact of laser fields on the penetration probability in any direction is investigated.展开更多
We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensi...We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the re...Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.展开更多
Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle...Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms.展开更多
We introduce a minimal model consisting of a two-body system with stochastically broken reciprocity(i.e.random violation of Newton's third law)and then investigate its statistical behaviors,including fluctuations ...We introduce a minimal model consisting of a two-body system with stochastically broken reciprocity(i.e.random violation of Newton's third law)and then investigate its statistical behaviors,including fluctuations of velocity and position,time evolution of probability distribution functions,energy gain,and entropy production.The effective temperature of this two-body system immersed in a thermal bath is also derived.Furthermore,we heuristically present an extremely minimal model where the relative motion adheres to the same rules as in classical mechanics,while the effect of stochastically broken reciprocity only manifests in the fluctuating motion of the center of mass.展开更多
In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capabil...In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.展开更多
Reconfigurable Intelligent Surface(RIS)is envisioned as a promising technology to improve the system capacity of 6G network,by controlling the electromagnetic wave propagation.Most existing works use the Central Limit...Reconfigurable Intelligent Surface(RIS)is envisioned as a promising technology to improve the system capacity of 6G network,by controlling the electromagnetic wave propagation.Most existing works use the Central Limit Theorem(CLT)to analyze the performance of RIS-assisted systems for large number of reflective elements.However,the assumption of extremely large number of elements may not be practical in the actual situation.In addition,the CLT-based approximation yields an inaccurate scaling law of the outage probability when the transmit Signal-to-Noise Ratio(SNR)tends to infinity.Motivated by these limitations,in this paper,we investigate the performance of RIS-assisted cellular networks with multiple Device-to-Device(D2D)users under the general fading channels,i.e.,Nakagami-m fading channels.We propose a tractable solution to evaluate the outage probability and the ergodic achievable rate,which is accurate for any number of reflective elements,any network topology,as well as any SNR.In addition,the accurate approximations for the high SNR case and the large number of reflective elements case are further derived in simpler closed form.Numerical results verify the accuracy of our analytical results and analyze the performance between CLT and the proposed method.展开更多
The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifie...The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifies the influence of heat and drought on their likelihood using a copula-based Bayesian probability framework.The wildfires were characterized by burned area(BA)and intensity(fire radiative power,FRP).The criteria establishing the presence of“hot drought”conditions were identified using the 5-day Standardized Temperature Index(STI)and 75-day Standardized Precipitation Index(SPI),respectively.The wildfire outbreak began on 7 January 2025 and burned for more than six days,with the total burned area exceeding 245 km^(2) and the cumulative FRP exceeding 41060 MW.Based on satellite-derived active fire observations from 2001 to 2025,we estimate that such large and intense wildfires during LA’s rainy season represent a once-in-a-67-year event.The wildfires were largely driven by the combination of hot and dry conditions,which dried out soils and vegetation that had proliferated due to above-average precipitation in previous winter seasons,thereby providing abundant fuel.Our seasonal analysis reveals that extreme drought increased the probability of wildfires matching the 2025 intensity and BA by 54%and 75%,respectively.Hot drought further amplified these probabilities by 149%(intensity)and 210%(BA).These findings suggest an elevated risk of large wildfires under hot drought conditions,contributing to their expansion into the non-traditional fire season.展开更多
A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are sys...A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.展开更多
Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and env...Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.展开更多
Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely em...Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines,which often showstrong performance but limited generalizability,probabilistic reliability,and operational interpretability.This study proposes a stacked ensemble deep learning framework that integrates random forest,extreme gradient boosting,and a deep neural network as base learners,with CatBoost as the meta-learner.On the ToN-IoT Linux process dataset,the model achieved near-perfect discrimination(macro area under the curve=0.998),robust calibration,and superior F1-scores compared with standalone classifiers.Interpretability was achieved through SHapley Additive exPlanations–based feature attribution,which highlights actionable drivers ofmalicious behavior,such as command-line patterns,process scheduling anomalies,and CPU usage spikes,and aligns these indicators with MITRE ATT&CK tactics and techniques.Complementary analyses,including cumulative lift and sensitivity-specificity trade-offs,revealed the framework’s suitability for deployment in security operations centers,where calibrated risk scores,transparent explanations,and resource-aware triage are essential.These contributions bridge methodological rigor in artificial intelligence/machine learning with operational priorities in cybersecurity,delivering a scalable and explainable intrusion detection system suitable for real-world deployment in IoT environments.展开更多
Background:The level of premature deaths(deaths among those aged 30-69 years)caused by cancer is an important indicator of evaluating the level of cancer prevention and control.However,the current burden and temporal ...Background:The level of premature deaths(deaths among those aged 30-69 years)caused by cancer is an important indicator of evaluating the level of cancer prevention and control.However,the current burden and temporal trends in cancer-related premature deaths,and their impact on life expectancy at the global,regional,and national levels are not clear.Methods:Cancer mortality data for 185 countries were obtained from the GLOBOCAN 2022 database.High-quality cancer mortality data and national population statistics for 47 countries were extracted from the United Nations and national cancer registry databases,covering the period 2003-2022.Countries were classified based on the human development index(HDI).The death probability,the year of life lost(YLL),and the potential gain in life expectancy(PGLE)attributable to premature deaths from site-specific and all-cancers combined were calculated.Results:Globally,the probability of premature cancer deaths was 6.49%(95%UI 6.49-6.50).The YLLs caused by cancer-related premature death were 163.86 million(95%UI 163.70-164.03),constituting 65.58%of the total cancer-related YLLs.The PGLEs were 1.16 years(95%UI 1.16-1.16).The premature death probability increased with higher HDI levels in men,but decreased in women.Cancer-related premature deaths as a proportion of total cancer deaths varied from 18.31%(95%UI 18.20-18.43)in Japan to 84.44%(95%UI 76.10-91.16)in São Toméand Príncipe.Lung cancer was the leading cause of cancer-related premature deaths in men,and breast cancer ranked first in women.By eradicating premature deaths attributable to lung,liver,colorectal,and stomach cancer in men,and to breast,cervical,and lung cancer in women,0.55 years(95%UI 0.55-0.55)and 0.49 years(95%UI 0.49-0.49)of PGLEs could be achieved,accounting for 48.67%and 42.24%of the total PGLEs,respectively.Cancer-related premature deaths decreased significantly in 38 countries during 2003-2022(P<0.05).The probability of premature cancer-related deaths decreased by more than 15.50%from 2015 to 2022 in 16 countries.Conclusions:Cancer-related premature deaths declined in many countries,with 16 of them having achieved the expected reduction by 2022.The current burden of cancer-related premature deaths is profound but varies around the world.Eliminating premature deaths from major cancer types could substantially increase life expectancy,underscoring the importance of prevention and treatment efforts for these cancers.展开更多
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment mo...This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.展开更多
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
The fuzzy comfortability of a wind-sensitive super-high tower crane is critical to guarantee occupant health and improve construction efficiency.Therefore,the wind-resistant fuzzy comfortability of a super-high tower ...The fuzzy comfortability of a wind-sensitive super-high tower crane is critical to guarantee occupant health and improve construction efficiency.Therefore,the wind-resistant fuzzy comfortability of a super-high tower crane in the Ma’anshan Yangtze River(MYR)Bridge site is analyzed in this paper.First,the membership function model that represents fuzzy comfortability is introduced in the probability density evolution method(PDEM).Second,based on Fechner’s law,the membership function curves are constructed according to three acceleration thresholds in ISO 2631.Then,the fuzzy comfortability for the super-high tower crane under stochastic wind loads is assessed on the basis of different cut-set levelsλ.Results show that the comfortability is over 0.9 under the required maximum operating wind velocity.The low sensitivity toλcan be observed in the reliability curves of ISOⅡandⅢmembership functions.The reliability of the ISOⅠmembership function is not sensitive toλwhenλ<0.7,whereas it becomes sensitive toλwhenλ>0.7.展开更多
文摘A terrestrial relay-aided reconfigurable intelligent surface(RIS)system with decode,re-encode and forward(DRF)relaying scheme is presented where the RIS effectively contributes to both sourceto-destination and relay-to-destination signaling.While in the conventional decode and forward(DF)relaying scheme,the source signal is merely duplicated in the relay and the time intervals are equally allocated to the source and relay nodes,this paper considers DRF relaying scheme where versatile time-sharing is adopted for the source and relay nodes which can be optimized based on the relative coordinates of the involved nodes.Two protocols namely unidirectional connection(UC)and bidirectional connection(BC)are proposed based on the source awareness from the relay’s successful reception.The outage probability(OP)performance for both protocols and both DF and DRF relaying schemes is analyzed and tight approximations are obtained.The numerical results show the out-performance of the DRF over the DF relaying scheme in the both UC and BC protocols.Equipped with the obtained system OP,the system throughput is defined and the optimum system throughput is obtained by optimizing the system rate and the timesharing between the source and the relay.Analytical results are corroborated in the numerical examples.
基金National Natural Science Foundation of China under Grant No.52278340Natural Science Foundation of Hebei Province under Grant No.E2023202028。
文摘In this study,the dynamic characteristics and microstructures of lacustrine soft clays were studied.Dynamic character tests were conducted on undisturbed,remolded,and saturated lacustrine soft clays,using a dynamic triaxial tester.A scanning electron microscope(SEM)was employed to assess the soil samples after dynamic testing.The results indicate that the dynamic characteristics of lacustrine soft clay were significantly affected by confining pressure and water content.A quantitative relationship was established among confining pressures,water content,and the dynamic shear modulus ratio.The dynamic characteristic parameters of undisturbed,remolded and saturated soil are obviously different,and the original structure can enhance the shear strength of soil.By comparing the results with those from other studies,we found that the dynamic characters of soft clays were considerably varied in different regions,and lacustrine soft clays had a larger dynamic shear modulus ratio and a smaller damping ratio when the dynamic shear strain was large.Using IPP software to process the microstructural images,we found that the soil was dominated by small pores and medium particles,and the roundness of pores and particles had an apparently positive correlation with the maximum diameter.Moreover,the pores and particles of the soil showed fractal characteristics and directionality,and the fractal dimensions and probability entropy were strongly correlated with the macrostructural parameters.Finally,we developed a prediction model for macrostructural and microstructural parameters.
基金supported by the National Level Project of China(No.KJSP2023020201)the Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory of China(No.kjw6142210240202)+1 种基金the Beijing Institute of Technology Research Fund Program for Young Scholars of Chinathe Fundamental Research Funds for Central Universities of China。
文摘In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.
基金supported in part by the National Natural Science Foundation of China(Nos.12175100 and 11975132)the Construct Program of the Key Discipline in Hunan Province+5 种基金the Research Foundation of Education Bureau of Hunan Province,China(Nos.21B0402,18A237,22A0305)the Natural Science Foundation of Hunan Province,China(No.2018JJ2321)the Innovation Group of Nuclear and Particle Physics in USCthe Shandong Province Natural Science Foundation,China(No.ZR2022JQ04)the Opening Project of Cooperative Innovation Center for Nuclear Fuel Cycle Technology and Equipment,University of South China(No.2019KFZ10)the Hunan Provincial Innovation Foundation for Postgraduate(No.CX20251453)。
文摘In this study,the effects of laser fields that can be achieved in the near future on cluster penetration probability and half-life are quantitatively investigated.The calculation results show that extreme laser fields can slightly change the cluster-decay half-life by affecting the penetration probability within a narrow range.Subsequently,we discuss the correlation between the change rate of the penetration probability and the tunneling path.The results indicate that for different parent nuclei emitting the same cluster,nuclei with longer tunneling paths are more easily affected by the laser fields.The shell effect on this correlation is also observed.In addition,the impact of laser fields on the penetration probability in any direction is investigated.
文摘We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金supported in part by the Chongqing Postgraduate Research and Innovation Project(CYB22250)National Natural Science Foundation of China(62271096,U20A20157)+2 种基金Natural Science Foundation of Chongqing-China(CSTB2023NSCQ-LZX0134,CSTB2024NSCQ-LZX0124)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)。
文摘Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.
基金supported by the National Natural Science Foundation Joint Fund,No.U22A20309(to PY)the Natural Science Foundation of LiaoningProvince,No.2023-MS-07(to HuL)the Unveiling Key Scientific and Technological Projects of Liaoning Province,No.2021JH1/10400051(to HuL).
文摘Some patients with systemic lupus erythematosus experience neuropsychiatric symptoms.Although magnetic resonance imaging can detect abnormal signals in the white matter of the brain,conventional methods often struggle to accurately capture microstructural changes.Various diffusion models have been used to study white matter in systemic lupus erythematosus;however,comparative analyses of their sensitivity and specificity for detecting microstructural changes remain insufficient.To address this,our team designed a diagnostic trial that used multimodal diffusion imaging techniques to observe white matter microstructural changes in patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with an aim to identify key diagnostic biomarkers for these patients.Patients with active lupus who received treatment at the Department of Rheumatology and Immunology,The First Affiliated Hospital of China Medical University,from September 2023 to March 2024 were recruited.According to the standards of the American College of Rheumatology,patients with systemic lupus erythematosus who had neuropsychiatric symptoms were assigned to the systemic lupus erythematosus group,whereas those without neuropsychiatric symptoms were assigned to the non-systemic lupus erythematosus group.Additionally,healthy volunteers matched by region,sex,and age were recruited as controls.All three groups underwent the same diffusion magnetic resonance imaging examination protocol to compare differences in diffusion parameters.Advanced diffusion imaging models were able to sensitively detect microstructural changes in the white matter fibers of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,with specific diffusion parameters showing significant abnormalities in key brain regions.In the left superior longitudinal fasciculus subregion and the right thalamic radiations of patients with systemic lupus erythematosus who had neuropsychiatric symptoms,we also identified abnormal diffusion characteristics that were clearly correlated with disease activity,suggesting that microstructural changes in these areas may reflect the dynamic process of neuroinflammatory damage.The present study addresses critical challenges in the diagnosis of systemic lupus erythematosus by identifying specific white matter imaging biomarkers and elucidating the association between microstructural damage and clinical manifestations.The main contributions of our study include:1)establishing axial regression probability parameters from mean apparent propagator magnetic resonance imaging as sensitive biomarkers for systemic lupus erythematosus,particularly in the third subregion of the left superior longitudinal fasciculus;2)demonstrating that multimodal diffusion imaging may be superior to conventional diffusion tensor imaging for detecting white matter microstructural abnormalities in patients with systemic lupus erythematosus;and 3)integrating tract-based spatial statistics with clinically relevant analyses to link imaging findings to pathological mechanisms.
基金supported by the National Natural Science Foundation of China(Grant No.12475032)。
文摘We introduce a minimal model consisting of a two-body system with stochastically broken reciprocity(i.e.random violation of Newton's third law)and then investigate its statistical behaviors,including fluctuations of velocity and position,time evolution of probability distribution functions,energy gain,and entropy production.The effective temperature of this two-body system immersed in a thermal bath is also derived.Furthermore,we heuristically present an extremely minimal model where the relative motion adheres to the same rules as in classical mechanics,while the effect of stochastically broken reciprocity only manifests in the fluctuating motion of the center of mass.
基金supported by the National Natural Science Foundation of China(62271247)the Natural Science Foundation of Jiangsu Province(BK20240181)+4 种基金the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM01D001)the National Aerospace Science Foundation of China(20220055052001)the Qing Lan Project of Jiangsu Provincethe Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronauticsthe Key Laboratory of Radar Imaging and Microwave Photonics(Nanjing University of Aeronautics and Astronautics),Ministry of Education。
文摘In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.
基金supported in part by Jiangsu Provincial Key Research and Development Program(No.BE2023022-2)in part by National Natural Science Foundation of China(No.62471204,92367302)in part by Major Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(No.24KJA510003)。
文摘Reconfigurable Intelligent Surface(RIS)is envisioned as a promising technology to improve the system capacity of 6G network,by controlling the electromagnetic wave propagation.Most existing works use the Central Limit Theorem(CLT)to analyze the performance of RIS-assisted systems for large number of reflective elements.However,the assumption of extremely large number of elements may not be practical in the actual situation.In addition,the CLT-based approximation yields an inaccurate scaling law of the outage probability when the transmit Signal-to-Noise Ratio(SNR)tends to infinity.Motivated by these limitations,in this paper,we investigate the performance of RIS-assisted cellular networks with multiple Device-to-Device(D2D)users under the general fading channels,i.e.,Nakagami-m fading channels.We propose a tractable solution to evaluate the outage probability and the ergodic achievable rate,which is accurate for any number of reflective elements,any network topology,as well as any SNR.In addition,the accurate approximations for the high SNR case and the large number of reflective elements case are further derived in simpler closed form.Numerical results verify the accuracy of our analytical results and analyze the performance between CLT and the proposed method.
基金supported by the National Natural Science Foundation of China(Grant Nos.42471034,42330604)the Qing Lan Projectsupport from the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifies the influence of heat and drought on their likelihood using a copula-based Bayesian probability framework.The wildfires were characterized by burned area(BA)and intensity(fire radiative power,FRP).The criteria establishing the presence of“hot drought”conditions were identified using the 5-day Standardized Temperature Index(STI)and 75-day Standardized Precipitation Index(SPI),respectively.The wildfire outbreak began on 7 January 2025 and burned for more than six days,with the total burned area exceeding 245 km^(2) and the cumulative FRP exceeding 41060 MW.Based on satellite-derived active fire observations from 2001 to 2025,we estimate that such large and intense wildfires during LA’s rainy season represent a once-in-a-67-year event.The wildfires were largely driven by the combination of hot and dry conditions,which dried out soils and vegetation that had proliferated due to above-average precipitation in previous winter seasons,thereby providing abundant fuel.Our seasonal analysis reveals that extreme drought increased the probability of wildfires matching the 2025 intensity and BA by 54%and 75%,respectively.Hot drought further amplified these probabilities by 149%(intensity)and 210%(BA).These findings suggest an elevated risk of large wildfires under hot drought conditions,contributing to their expansion into the non-traditional fire season.
基金supported by the National Natural Science Foundation of China(72071106)Jiangsu Provincial Social Science Fund(23EYA001)+1 种基金Jiangsu Provincial Education Science Planning Fund(Ba/2024/08)Jiangsu Higher Education Association Fund(24FYHLX090)。
文摘A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.
基金supported in part by the National Natural Science Foundation of China(Key Program)under Grant No.62031021。
文摘Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks(UUSNs),significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring.Existing failure models primarily focus on power grids and traffic systems,and don't address the unique challenges of weak-communication UUSNs.In UUSNs,cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels,passive node drift,adversarial attacks,and network heterogeneity.To address these challenges,a directed weighted graph model of UUSNs is first developed,in which node positions are updated according to ocean-current-driven drift and link weights reflect the probability of successful acoustic transmission.Building on this UUSNs graph model,a cascading failure model is proposed that integrates a normal-failure-recovery state-cycle mechanism,multiple attack strategies,and routingbased load redistribution.Finally,under a five-level connectivity UUSNs scheme,simulations are conducted to analyze how dynamic topology,network load,node recovery delay,and attack modes jointly affect network survivability.The main findings are:(1)moderate node drift can improve survivability by activating weak links;(2)based-energy routing(BER)outperform based-depth routing(BDR)in harsh conditions;(3)node self-recovery time is critical to network survivability;(4)traditional degree-based critical node metrics are inadequate for weak-communication UUSNs.These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.
文摘Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines,which often showstrong performance but limited generalizability,probabilistic reliability,and operational interpretability.This study proposes a stacked ensemble deep learning framework that integrates random forest,extreme gradient boosting,and a deep neural network as base learners,with CatBoost as the meta-learner.On the ToN-IoT Linux process dataset,the model achieved near-perfect discrimination(macro area under the curve=0.998),robust calibration,and superior F1-scores compared with standalone classifiers.Interpretability was achieved through SHapley Additive exPlanations–based feature attribution,which highlights actionable drivers ofmalicious behavior,such as command-line patterns,process scheduling anomalies,and CPU usage spikes,and aligns these indicators with MITRE ATT&CK tactics and techniques.Complementary analyses,including cumulative lift and sensitivity-specificity trade-offs,revealed the framework’s suitability for deployment in security operations centers,where calibrated risk scores,transparent explanations,and resource-aware triage are essential.These contributions bridge methodological rigor in artificial intelligence/machine learning with operational priorities in cybersecurity,delivering a scalable and explainable intrusion detection system suitable for real-world deployment in IoT environments.
基金supported by the Capital’s Funds for Health Improvement and Research(CFH2024-2G-40214)the CAMS Innovation Fund for Medical Sciences(2021-I2M-1-011,2021-I2M-1-061).
文摘Background:The level of premature deaths(deaths among those aged 30-69 years)caused by cancer is an important indicator of evaluating the level of cancer prevention and control.However,the current burden and temporal trends in cancer-related premature deaths,and their impact on life expectancy at the global,regional,and national levels are not clear.Methods:Cancer mortality data for 185 countries were obtained from the GLOBOCAN 2022 database.High-quality cancer mortality data and national population statistics for 47 countries were extracted from the United Nations and national cancer registry databases,covering the period 2003-2022.Countries were classified based on the human development index(HDI).The death probability,the year of life lost(YLL),and the potential gain in life expectancy(PGLE)attributable to premature deaths from site-specific and all-cancers combined were calculated.Results:Globally,the probability of premature cancer deaths was 6.49%(95%UI 6.49-6.50).The YLLs caused by cancer-related premature death were 163.86 million(95%UI 163.70-164.03),constituting 65.58%of the total cancer-related YLLs.The PGLEs were 1.16 years(95%UI 1.16-1.16).The premature death probability increased with higher HDI levels in men,but decreased in women.Cancer-related premature deaths as a proportion of total cancer deaths varied from 18.31%(95%UI 18.20-18.43)in Japan to 84.44%(95%UI 76.10-91.16)in São Toméand Príncipe.Lung cancer was the leading cause of cancer-related premature deaths in men,and breast cancer ranked first in women.By eradicating premature deaths attributable to lung,liver,colorectal,and stomach cancer in men,and to breast,cervical,and lung cancer in women,0.55 years(95%UI 0.55-0.55)and 0.49 years(95%UI 0.49-0.49)of PGLEs could be achieved,accounting for 48.67%and 42.24%of the total PGLEs,respectively.Cancer-related premature deaths decreased significantly in 38 countries during 2003-2022(P<0.05).The probability of premature cancer-related deaths decreased by more than 15.50%from 2015 to 2022 in 16 countries.Conclusions:Cancer-related premature deaths declined in many countries,with 16 of them having achieved the expected reduction by 2022.The current burden of cancer-related premature deaths is profound but varies around the world.Eliminating premature deaths from major cancer types could substantially increase life expectancy,underscoring the importance of prevention and treatment efforts for these cancers.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413)。
文摘This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金The National Natural Science Foundation of China(No.52108274,52208481,52338011)State Scholarship Fund of China Scholarship Council(No.202306090285).
文摘The fuzzy comfortability of a wind-sensitive super-high tower crane is critical to guarantee occupant health and improve construction efficiency.Therefore,the wind-resistant fuzzy comfortability of a super-high tower crane in the Ma’anshan Yangtze River(MYR)Bridge site is analyzed in this paper.First,the membership function model that represents fuzzy comfortability is introduced in the probability density evolution method(PDEM).Second,based on Fechner’s law,the membership function curves are constructed according to three acceleration thresholds in ISO 2631.Then,the fuzzy comfortability for the super-high tower crane under stochastic wind loads is assessed on the basis of different cut-set levelsλ.Results show that the comfortability is over 0.9 under the required maximum operating wind velocity.The low sensitivity toλcan be observed in the reliability curves of ISOⅡandⅢmembership functions.The reliability of the ISOⅠmembership function is not sensitive toλwhenλ<0.7,whereas it becomes sensitive toλwhenλ>0.7.
