0 INTRODUCTION The lunar surface lacks an atmosphere and is continuously subjected to a combination of space weathering factors such as cosmic rays,solar wind,and micrometeorite impacts,forming a several-meter-thick l...0 INTRODUCTION The lunar surface lacks an atmosphere and is continuously subjected to a combination of space weathering factors such as cosmic rays,solar wind,and micrometeorite impacts,forming a several-meter-thick lunar regolith(Sorokin et al.,2020).展开更多
To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances...To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimiz...This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.展开更多
Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from...Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.展开更多
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this iss...Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.展开更多
Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion in...Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.展开更多
Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development...Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.展开更多
Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to pred...Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.展开更多
Aiming at reducing the dust pollution during the tunneling process and improving the application efficiency of air curtain dust prevention technology,according to the changes of radial jet velocity(v_(r)),axial extrac...Aiming at reducing the dust pollution during the tunneling process and improving the application efficiency of air curtain dust prevention technology,according to the changes of radial jet velocity(v_(r)),axial extraction velocity(v_(e))and extraction distance(L)in the formation process of air curtain,the numerical simulation method was used to analyze the rules of airflow structure evolution and the diffusion characteristics of dust particles in fully mechanized excavation tunnel.The results indicate that as v_(r) and v_(e) increase,the migration path of the wall jet of the air curtain changes into an axial direction;as L decreases,the migration distance increases accordingly.These phenomena make the airflow distribution in the working face tends to be uniform.The dust diffusion distance reduces as well,wherein,the range of the discrete area of dust particles decreases sharply,until all dust particles are concentrated in the accumulation area.On this basis,the v_(r),v_(e) and L were optimized and applied in the 63_(up) 08 fully mechanized working face.By the application of the optimal parameters,the average dust removal efficiency at the driver’s position increased by 71%.The dust concentration was reduced and the working environment had been improved effectively.展开更多
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of indivi...Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.展开更多
Due to the presence of nitro groups, the dust generated during the production and utilization of energetic materials may potentially lead to dust explosion even under low-oxygen or anaerobic conditions.Considering the...Due to the presence of nitro groups, the dust generated during the production and utilization of energetic materials may potentially lead to dust explosion even under low-oxygen or anaerobic conditions.Considering the high energy density of energetic materials, dust explosion can cause serious production safety accidents. Therefore, it is necessary to understand the dust explosion characteristics of energetic materials and the mechanism of dust explosion. According to the literature review, among various influencing factors, the physical and chemical properties of dust are the decisive factors affecting the explosion characteristics of dust. In addition to experimental studies, numerical simulation is another important tool. However, it is subjected to certain limitations. Moreover, it is essential but challenging to fully understand the underlying mechanism. In addition, given the safety hazards posed by dust explosion, explosion suppression has attracted extensive attention for research. Depending on the medium used, there are different forms of suppression, including powder explosion suppression, water spray explosion suppression, inert gas explosion suppression, porous material explosion suppression, and vacuum chamber explosion suppression. As for the selection of explosion suppression agent, consideration must be given to the characteristics of the material. Furthermore, the above research has laid a foundation for discussing the future progress in studying dust explosion of energetic materials, with nano dust and the constraints of existing technology as the focal point.展开更多
The large amount of harmful particles in coal dust not only pollutes the production environment,affects the production efficiency and resource utilization of enterprises,but also poses a risk to human health.Effective...The large amount of harmful particles in coal dust not only pollutes the production environment,affects the production efficiency and resource utilization of enterprises,but also poses a risk to human health.Effectively controlling coal dust is of great significance to clean production.Water-based dust suppressants are extensively employed to mitigate coal dust.This paper provides a comprehensive review of the water-based dust suppression materials for coal dust control.Accord-ing to the difference of mechanism,the dust suppressants are divided into wetting type,hygroscopic coalescence type,cohesive agglomeration type,and composite type.The evaluation methods for dust suppressants key properties such as wettability,permeability,moisture absorption and water retention,and consolidation are summarized.The review results show that coal dust suppressants are no longer limited to a single dust suppression function.For example,it is necessary to develop multi-functional coal suppressants to meet the needs for synergistic suppression both coal dust and coal spon-taneous combustion.Driven by the concept of green,low-carbon and sustainable development,attention should be paid to the development of bio-based environmentally friendly coal dust suppressants.In addition,the evaluation method system for the key performance of water-based dust suppressants should also be improved,and further research is necessary.展开更多
The occurrence of extreme weather events is becoming more frequent due to global climate change.A long-lasting dust outbreak in the spring of 2023 was triggered by Mongolia cyclones and cold fronts in the dust source ...The occurrence of extreme weather events is becoming more frequent due to global climate change.A long-lasting dust outbreak in the spring of 2023 was triggered by Mongolia cyclones and cold fronts in the dust source areas.In this study,we illustrate the spatial distribution,the transport path of the dust and its influence on the air quality of downstream cities utilizing ground-based and space-borne measurements.Results indicate a more complicated pollution,coexisting of polluted dust stage S1 and pure dust stage S2.In S1,the aerosol was characterized by a dual-layer vertical structure—high extinction coefficient of nearly 1.0 km^(−1)with a low particle depolarized ratio(PDR)of<0.1 under 500 m,and a small extinction coefficient of 0.3 km^(−1)with a high PDR of 0.15 above 500 m.Then the intrusion of the Mongolian cyclone carried new dust plumes on March 10,2023,suggesting the onset of pure dust period S2.The source transition was also confirmed by the MODIS,CALIPSO and Hysplit observations.The pure Asian dust evolved rapidly with one thickening layer and distributed homogeneously in the boundary layer.The PDR increased significantly to the peak of 0.35 and resulted in the peak PM10 value of>1300µg/m^(3).PM10 positively correlated with trace gases in S1 while varying inversely with the pollution gases in S2.The results help to shed light on the classification of different types of dust and also be useful in developing an effective strategy to forecast air pollution in downstream areas.展开更多
Silicomanganese dust contains large amounts of valuables,such as Si and Mn,which can be used as raw materials for the smelting of silicomanganese.However,the direct addition of dust to the submerged arc furnace can in...Silicomanganese dust contains large amounts of valuables,such as Si and Mn,which can be used as raw materials for the smelting of silicomanganese.However,the direct addition of dust to the submerged arc furnace can influence the permeability of burden due to the fine particle size of dust,which results in incomplete reduction reactions during the smelting process.In this paper,silicomanganese dust,graphite powder,and other additives were pressed to form carbon-containing dust briquettes,and the self-reduction process of the dust briquettes was investigated through the isothermal thermogravimetric method with different carbon–oxygen (C/O) molar ratios,contents of fluxing agents,and reduction temperatures.Various reduction kinetic models for dust briquettes at different temperatures were established.The results show that the reaction fraction of the dust briquettes was about 90%at a C/O molar ratio of 1.2 with optimal reduction efficiency.The addition of CaF_(2)contributed to the decrease in the melting point and viscosity of dust briquettes,which increased their reduction rate.As the reduction temperature increased,the reduction rate of dust briquettes increased.The reduction reaction rate of dust briquettes was controlled through gas-phase diffusion.Meanwhile,their reduction process was analyzed kinetically,with the reaction time of 5 min as the dividing line.The apparent activation energies for the two diffusion stages were 56.10 and 100.52 kJ/mol,respectively.The kinetic equations are expressed as[1-(1-f)^(1/3)]^(2)=0.69e^(-56100/(RT))t and [1-(1-f)^(1/3)]^(2)=2.06e^(-100520/(RT))t.展开更多
The effectiveness of fluopyram suspension concentrate against pine wilt disease(PWD)is limited by spraying efficiency and water dependence.A traditional dust formulation with strong dispersibility can overcome these s...The effectiveness of fluopyram suspension concentrate against pine wilt disease(PWD)is limited by spraying efficiency and water dependence.A traditional dust formulation with strong dispersibility can overcome these shortcomings.However,its efficacy against PWD remains uncertain.This study evaluated the translocation of fluopyram dust within tree tissues,soil and water degradation,and its effective control against PWD.Nursery tests showed effective prevention;field tests showed dust absorption and translocation into pine tissues.Thirty days following application,residual concentrations in soil were low at 0.09 mg kg^(−1);no detectable residues were found in water samples.Three years after applying fluopyram,its effectiveness increased to approximately 87%.Based on this study,fluopyram had a half-life of 346 d with persistence lasting up to three years.This provides valuable insight for managing PWD through dust applications.展开更多
基金supported by the National Major Scientific and Technological Infrastructure Project“Space Environment Simulation and Research Infrastructure”financially supported in part by the National Natural Science Foundation of China(No.52275241)the Fund for National Key Laboratory of Space Environment and Matter Behaviors(No.2023059)。
文摘0 INTRODUCTION The lunar surface lacks an atmosphere and is continuously subjected to a combination of space weathering factors such as cosmic rays,solar wind,and micrometeorite impacts,forming a several-meter-thick lunar regolith(Sorokin et al.,2020).
基金supported by the National Key R&D Program of China(No.2023YFB2603602)the National Natural Science Foundation of China(Nos.52222810 and 52178383).
文摘To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
文摘This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.
基金Sponsored by Beijing Youth Innovation Talent Support Program for Urban Greening and Landscaping——The 2024 Special Project for Promoting High-Quality Development of Beijing’s Landscaping through Scientific and Technological Innovation(KJCXQT202410).
文摘Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金supported by the National Natural Science Foundation of China(61903305,62073267)the Fundamental Research Funds for the Central Universities(HXGJXM202214).
文摘Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.
基金the financial support of Shanghai Science and Technology innovation action plan(19DZ2203600).
文摘Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.
基金National Key Research and Development Program of China(No.2023YFB3907103).
文摘Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.
基金supported by the National Natural Science Foundation of China(41977215)。
文摘Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.
基金financially supported by the Natural Science Foundation of Shandong Province(ZR2020QE124,ZR2023ME031 and ZR2023ME012)Innovation Achievement Cultivation Project of Qingdao University of Technology(CLZ2022-002)National Natural Science Foundation of China(52404222 and 52374209).
文摘Aiming at reducing the dust pollution during the tunneling process and improving the application efficiency of air curtain dust prevention technology,according to the changes of radial jet velocity(v_(r)),axial extraction velocity(v_(e))and extraction distance(L)in the formation process of air curtain,the numerical simulation method was used to analyze the rules of airflow structure evolution and the diffusion characteristics of dust particles in fully mechanized excavation tunnel.The results indicate that as v_(r) and v_(e) increase,the migration path of the wall jet of the air curtain changes into an axial direction;as L decreases,the migration distance increases accordingly.These phenomena make the airflow distribution in the working face tends to be uniform.The dust diffusion distance reduces as well,wherein,the range of the discrete area of dust particles decreases sharply,until all dust particles are concentrated in the accumulation area.On this basis,the v_(r),v_(e) and L were optimized and applied in the 63_(up) 08 fully mechanized working face.By the application of the optimal parameters,the average dust removal efficiency at the driver’s position increased by 71%.The dust concentration was reduced and the working environment had been improved effectively.
基金supported by the National Key Research and Devel-opment Program of China (Grant No.2022YFC3005503)the National Natural Science Foundation of China (Grant Nos.52322907,52179141,U23B20149,U2340232)+1 种基金the Fundamental Research Funds for the Central Universities (Grant Nos.2042024kf1031,2042024kf0031)the Key Program of Science and Technology of Yunnan Province (Grant Nos.202202AF080004,202203AA080009).
文摘Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.
基金the financial support of the Shanxi Fire & Explosion-Proofing Safety Engineering and Technology Research Center, North University of China。
文摘Due to the presence of nitro groups, the dust generated during the production and utilization of energetic materials may potentially lead to dust explosion even under low-oxygen or anaerobic conditions.Considering the high energy density of energetic materials, dust explosion can cause serious production safety accidents. Therefore, it is necessary to understand the dust explosion characteristics of energetic materials and the mechanism of dust explosion. According to the literature review, among various influencing factors, the physical and chemical properties of dust are the decisive factors affecting the explosion characteristics of dust. In addition to experimental studies, numerical simulation is another important tool. However, it is subjected to certain limitations. Moreover, it is essential but challenging to fully understand the underlying mechanism. In addition, given the safety hazards posed by dust explosion, explosion suppression has attracted extensive attention for research. Depending on the medium used, there are different forms of suppression, including powder explosion suppression, water spray explosion suppression, inert gas explosion suppression, porous material explosion suppression, and vacuum chamber explosion suppression. As for the selection of explosion suppression agent, consideration must be given to the characteristics of the material. Furthermore, the above research has laid a foundation for discussing the future progress in studying dust explosion of energetic materials, with nano dust and the constraints of existing technology as the focal point.
基金supported by the National Natural Science Foundation of China(52474226,52322404)Basic scientific research projects in higher education institutions of Liaoning Province(JYTZD2023079)。
文摘The large amount of harmful particles in coal dust not only pollutes the production environment,affects the production efficiency and resource utilization of enterprises,but also poses a risk to human health.Effectively controlling coal dust is of great significance to clean production.Water-based dust suppressants are extensively employed to mitigate coal dust.This paper provides a comprehensive review of the water-based dust suppression materials for coal dust control.Accord-ing to the difference of mechanism,the dust suppressants are divided into wetting type,hygroscopic coalescence type,cohesive agglomeration type,and composite type.The evaluation methods for dust suppressants key properties such as wettability,permeability,moisture absorption and water retention,and consolidation are summarized.The review results show that coal dust suppressants are no longer limited to a single dust suppression function.For example,it is necessary to develop multi-functional coal suppressants to meet the needs for synergistic suppression both coal dust and coal spon-taneous combustion.Driven by the concept of green,low-carbon and sustainable development,attention should be paid to the development of bio-based environmentally friendly coal dust suppressants.In addition,the evaluation method system for the key performance of water-based dust suppressants should also be improved,and further research is necessary.
基金supported by the Innovation and Development Special Project of China Meteorological Administration(Nos.CXFZ2024J011 and CXFZ2024J057)the Key Lab.of Environmental Optics and Technology,CAS(No.2005DP173065-2021-07).
文摘The occurrence of extreme weather events is becoming more frequent due to global climate change.A long-lasting dust outbreak in the spring of 2023 was triggered by Mongolia cyclones and cold fronts in the dust source areas.In this study,we illustrate the spatial distribution,the transport path of the dust and its influence on the air quality of downstream cities utilizing ground-based and space-borne measurements.Results indicate a more complicated pollution,coexisting of polluted dust stage S1 and pure dust stage S2.In S1,the aerosol was characterized by a dual-layer vertical structure—high extinction coefficient of nearly 1.0 km^(−1)with a low particle depolarized ratio(PDR)of<0.1 under 500 m,and a small extinction coefficient of 0.3 km^(−1)with a high PDR of 0.15 above 500 m.Then the intrusion of the Mongolian cyclone carried new dust plumes on March 10,2023,suggesting the onset of pure dust period S2.The source transition was also confirmed by the MODIS,CALIPSO and Hysplit observations.The pure Asian dust evolved rapidly with one thickening layer and distributed homogeneously in the boundary layer.The PDR increased significantly to the peak of 0.35 and resulted in the peak PM10 value of>1300µg/m^(3).PM10 positively correlated with trace gases in S1 while varying inversely with the pollution gases in S2.The results help to shed light on the classification of different types of dust and also be useful in developing an effective strategy to forecast air pollution in downstream areas.
基金financially supported by the Hubei Provincial Key Laboratory for New Processes of Ironmaking and Steelmaking (No. KF-20-3)Shandong Postdoctoral Science Foundation, China (No. SDCX-ZG-202301014)。
文摘Silicomanganese dust contains large amounts of valuables,such as Si and Mn,which can be used as raw materials for the smelting of silicomanganese.However,the direct addition of dust to the submerged arc furnace can influence the permeability of burden due to the fine particle size of dust,which results in incomplete reduction reactions during the smelting process.In this paper,silicomanganese dust,graphite powder,and other additives were pressed to form carbon-containing dust briquettes,and the self-reduction process of the dust briquettes was investigated through the isothermal thermogravimetric method with different carbon–oxygen (C/O) molar ratios,contents of fluxing agents,and reduction temperatures.Various reduction kinetic models for dust briquettes at different temperatures were established.The results show that the reaction fraction of the dust briquettes was about 90%at a C/O molar ratio of 1.2 with optimal reduction efficiency.The addition of CaF_(2)contributed to the decrease in the melting point and viscosity of dust briquettes,which increased their reduction rate.As the reduction temperature increased,the reduction rate of dust briquettes increased.The reduction reaction rate of dust briquettes was controlled through gas-phase diffusion.Meanwhile,their reduction process was analyzed kinetically,with the reaction time of 5 min as the dividing line.The apparent activation energies for the two diffusion stages were 56.10 and 100.52 kJ/mol,respectively.The kinetic equations are expressed as[1-(1-f)^(1/3)]^(2)=0.69e^(-56100/(RT))t and [1-(1-f)^(1/3)]^(2)=2.06e^(-100520/(RT))t.
基金supported by grants from the National Key R&D Program of China(grant number 2021YFD1400900)the National Natural Science Foundation of China(grant numbers U1905201,32171805)+6 种基金the Forestry Key Program of Science and Technology in Fujian Province(grant number 2021FKJ03)the Natural Science Foundation of Fujian Province,China(grant number 2021J01056)the Forestry Programs of Science and Technology in Fujian Province[grant number Mincaizhi(2020)601]the Science and Technology Program of Fujian Province(grant number 2018N5002)the Forestry Science Research Project of Fujian Forestry Department[grant number Minlinke(2017)03]the National Major Emergency Science and Technology Program of China(grant number ZD202001)the Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University(grant numbers 72202200205,71201800720).
文摘The effectiveness of fluopyram suspension concentrate against pine wilt disease(PWD)is limited by spraying efficiency and water dependence.A traditional dust formulation with strong dispersibility can overcome these shortcomings.However,its efficacy against PWD remains uncertain.This study evaluated the translocation of fluopyram dust within tree tissues,soil and water degradation,and its effective control against PWD.Nursery tests showed effective prevention;field tests showed dust absorption and translocation into pine tissues.Thirty days following application,residual concentrations in soil were low at 0.09 mg kg^(−1);no detectable residues were found in water samples.Three years after applying fluopyram,its effectiveness increased to approximately 87%.Based on this study,fluopyram had a half-life of 346 d with persistence lasting up to three years.This provides valuable insight for managing PWD through dust applications.
