A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Alignment between satellite and central galaxies in the EAGLE simulation:dependence on the large-scale environments

The alignment between satellite and central galaxies serves as a proxy for addressing the issue of galaxy formation and evolution, and has been investigated abundantly in observations and theoretical works.Most scenarios indicate that the satellites preferentially are located along the major axis of their central galaxy. Recent work shows that the strength of alignment signals depends on the large-scale environment in observations. We use the publicly-released data from EAGLE to figure out whether the same effect can be found in the associated hydrodynamic simulation. We found much stronger environmental dependency of alignment signals in the simulation. We also explore change of alignments to address the formation of this effect.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Effects of thinning on the understory light environment of different stands and the photosynthetic performance and growth of the reforestation species Phoebe bournei

Light levels determine regeneration in stands and a key concern is how to regulate the light environment of different stand types to the requirements of the understory.In this study,we selected three stands typical in south China(a Cryptomeria japonica plantation,a Quercus acutissima plantation,and a mixed stand of both)and three thinning intensities to determine the best understory light environ-ment for 3-year-old Phoebe bournei seedlings.The canopy structure,understory light environment,and photosynthe-sis and growth indicators were assessed following thin-ning.Thinning improved canopy structure and understory light availability of each stand;species composition was the reason for differences in the understory light environ-ment.Under the same thinning intensity,the mixed stand had the greatest light radiation and most balanced spectral composition.P.bournei photosynthesis and growth were closely related to the light environment;all three stands required heavy thinning to create an effective and sustained understory light environment.In a suitable understory light environment,the efficiency of light interception,absorption,and use by seedlings was enhanced,resulting in a higher carbon assimilation the main limiting factor was stomatal conductance.As a shade-avoidance signal,red/far-red radia-tion is a critical factor driving changes in photosynthesis and growth of P.bournei seedlings,and a reduction increased light absorption and use capacity and height:diameter ratios.The growth advantage transformed from diameter to height,enabling seedlings to access more light.Our findings suggest that the regeneration of shade-tolerant species such as P.bournei could be enhanced if a targeted approach to thinning based on stand type was adopted.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Design and Sizing of an Ecological Wastewater Treatment System in a School Environment: A Case Study of Ndiebene Gandiol 1 School

The primary objective of this study was to design and size a sustainable sanitation solution for the Ndiebene Gandiol 1 school located in the eponymous commune in northern Senegal. Field investigations led to the collection of wastewater samples. Their analysis revealed specific pollutant loads, including loads of BOD5 3.6966 kgO2/day and COD of 12.8775 kgO2/day, which were central to the design phase. Following a rigorous assessment of the existing sanitation infrastructure, constructed wetland (CWs) emerged as the most appropriate ecological solution. This system, valued for its ability to effectively remove contaminants, was tailored to the specific needs of the site. Consequently, the final design of the filter extends over 217.16 m2, divided into two cells of 108.58 m2 each, with dimensions of 12.77 m in length and 8.5 m in width. The depth of the filtering medium is approximately 0.60 m, meeting the standards while ensuring maximized purification. Typha, an indigenous and prolific plant known for its purification abilities, was selected as the filtering agent. Concurrently, non-crushed gravel was chosen for its proven filtration capacity. This study is the result of a combination of scientific rigor and design expertise. It provides a holistic view of sanitation for Ndiebene Gandiol. The technical specifications and dimensions of the constructed wetland filter embody an approach that marries indepth analysis and practical application, all aimed at delivering an effective and long-lasting solution to the local sanitation challenges. By integrating precise scientific data with sanitation design expertise, this study delivers a holistic solution for Ndiebene Gandiol. The detailed dimensions and specifications of the constructed wetland filter reflect a methodology that combines meticulous analysis with practical adaptation, aiming to provide an effective and sustainable response to the challenges of rural and school sanitation in the northern region of Senegal.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Synthesis and nitrogen content regulation of diamond in a high-pressure hydrogen-rich environment

The regulating nitrogen content of diamond in a hydrogen-rich high-temperature and high-pressure(HPHT) growth environment was systematically investigated in this work by developing three growth systems,namely, "FeNi+Ti", "FeNi+G_(3)N_(6)H_(6)",and "FeNi+Ti+C_(3)N_(6)H_(6)".Optical microscopy,infrared spectroscopy,and photoluminescence(PL)spectroscopy measurements were conducted to analyze the spectroscopic characteristics of diamonds grown in these three systems.From our analysis,it was demonstrated that the presence of hydrogen in the sp^(3) hybrid C-H does not directly affect the color of the diamond and facilitates the increase of the nitrogen-vacancy(NV) center concentration in a highnitrogen-content diamond.In addition,titanium plays an important role in nitrogen removal,while its impact on hydrogen doping within the diamond lattice is insignificant.Most importantly,by regulating the ratio of nitrogen impurities that coexist in the nitrogen and hydrogen HPHT environment,the production of hydrogenous Ⅱa-type diamond,hydrogenous Ib-type diamond,and hydrogenous high-nitrogen-type diamonds was achieved with a nitrogen content of less than 1 ppm to 1600 ppm.

Heterogeneous population distribution enhances resistance to wheat lodging by optimizing the light environment

Lodging is still the key factor that limits continuous increases in wheat yields today,because the mechanical strength of culms is reduced due to low-light stress in populations under high-yield cultivation.The mechanical properties of the culm are mainly determined by lignin,which is affected by the light environment.However,little is known about whether the light environment can be sufficiently improved by changing the population distribution to inhibit culm lodging.Therefore,in this study,we used the wheat cultivar“Xinong 979”to establish a low-density homogeneous distribution treatment(LD),high-density homogeneous distribution treatment(HD),and high-density heterogeneous distribution treatment(HD-h)to study the regulatory effects and mechanism responsible for differences in the lodging resistance of wheat culms under different population distributions.Compared with LD,HD significantly reduced the light transmittance in the middle and basal layers of the canopy,the net photosynthetic rate in the middle and lower leaves of plants,the accumulation of lignin in the culm,and the breaking resistance of the culm,and thus the lodging index values increased significantly,with lodging rates of 67.5%in 2020–2021 and 59.3%in 2021–2022.Under HD-h,the light transmittance and other indicators in the middle and basal canopy layers were significantly higher than those under HD,and the lodging index decreased to the point that no lodging occurred.Compared with LD,the activities of phenylalanine ammonia-Lyase(PAL),4-coumarate:coenzyme A ligase(4CL),catechol-O-methyltransferase(COMT),and cinnamyl-alcohol dehydrogenase(CAD)in the lignin synthesis pathway were significantly reduced in the culms under HD during the critical period for culm formation,and the relative expression levels of TaPAL,Ta4CL,TaCOMT,and TaCAD were significantly downregulated.However,the activities of lignin synthesis-related enzymes and their gene expression levels were significantly increased under HD-h compared with HD.A partial least squares path modeling analysis found significant positive effects between the canopy light environment,the photosynthetic capacity of the middle and lower leaves of plants,lignin synthesis and accumulation,and lodging resistance in the culms.Thus,under conventional high-density planting,the risk of wheat lodging was significantly higher.Accordingly,the canopy light environment can be optimized by changing the heterogeneity of the population distribution to improve the photosynthetic capacity of the middle and lower leaves of plants,promote lignin accumulation in the culm,and enhance lodging resistance in wheat.These findings provide a basis for understanding the mechanism responsible for the lower mechanical strength of the culm under high-yield wheat cultivation,and a theoretical basis and for developing technical measures to enhance lodging resistance.

Predict the evolution of mechanical property of Al-Li alloys in a marine environment

The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Sedimentary Environment and Mineralization of the Black Shale Polymetallic Layer in the Early Cambrian,SW China:Constraints from in situ LA-ICP-MS Analysis of Pyrite

A polymetallic layer is usually developed at the bottom of the early Cambrian black shale in Guizhou Province.The mineral that makes up the polymetallic layer is related to the sedimentary facies.To analyze the differentiation mechanism between polymetallic deposits(Ni-Mo and V),the Zhijin Gezhongwu profile located in the outer shelf and the Sansui Haishan V deposit located in the lower slope are selected to study the in situ sulfur isotopes and trace elements of pyrite.The results show that δ^(34)S values of pyrite vary widely from−7.8‰to 28‰in the Gezhongwu profile,while the δ^(34)S values are relatively uniform(from 27.8‰to 38.4‰)in the Haishan profile.The isotopic S composition is consistent with the transition that occurs in the sedimentary phase from the shelf to the deep sea on the transgressive Yangtze platform;this indicates that the δ^(34)SO_(4)^(2−)values in seawater must be differently distributed in depositional environments.The sulfur in the Ni-Mo layer is produced after the mixing of seawater and hydrothermal fluid,while the V layer mainly originates from seawater.Overall,the Ni-Mo and V deposits have been differentiated primarily on the basis of the combined effect of continental weathering and hydrothermal fluid.

Grain size and surface micro-texture characteristics and their paleoenvironmental significance of Holocene sediment in southern margin of the Gurbantunggut Desert,China

The southern margin of the Gurbantunggut Desert,China,is characterized by alternating layers of aeolian and alluvial deposits.Investigating the characteristics of arenaceous sediment in this area is of significant importance for understanding the interactive processes of wind and water forces,as well as the provenance of sediment.However,there are relatively few investigations on the characteristics of such sediment at present.In this study,we researched three aeolian-alluvial interactive stratigraphic profiles and different types of surface sediment on the desert-oasis transitional zone of southern margin of the Gurbantunggut Desert.Based on the optically stimulated luminescence(OSL)dating of aeolian sand and analyses of quartz sand grain size and surface micro-texture,we explored the aeolian-alluvial environmental change at southern margin of the desert in Holocene,as well as the provenance of sediment.The results indicated that the grain size characteristics of different types of sediment in the stratigraphic profiles were similar to those of modern dune sand,interdune sand,muddy desert surface soil,and riverbed sand.Their frequency curves were unimodal or bimodal,and cumulative probability curves were two-segment or three-segment,mainly composed of suspension load and saltation load.The quartz sand in the sediment at southern margin of the desert had undergone alternating transformation of various exogenic forces,with short transportation distance and time,and sedimentary environment was relatively humid.In Holocene,southern margin of the desert primarily featured braided river deposits,and during intermittent period of river activity,there were also aeolian deposits such as sand sheet deposits,stabilized dune deposits,and mobile dune deposits.The provenance for Holocene alluvial deposits at southern margin of the desert remains relatively constant,with the debris of the Tianshan Mountains being the primary provenance.Aeolian sand is mainly near-source recharge,which is formed by in situ deposition of fluvial or lacustrine materials in southern margin of the desert transported by wind erosion,and its provenance was still the weathered debris of the Tianshan Mountains.In addition,the sand in interior of the desert may be transported by northwest wind in desert-scale,thus affecting the development of dunes in southern margin of the desert.The results of this study provide a reference for understanding the composition and provenance changes of desert sand in the context of global climate change.

The chemical environment and structural ordering in liquid Mg-Y-Zn system:An ab-initio molecular dynamics investigation of melt for the formation mechanism of LPSO structure

In an effort to clarify the formation mechanism of LPSO structure in Mg-Y-Zn alloy,the chemical environment and structural ordering in liquid Mg-rich Mg-Y-Zn system are investigated with the aid of ab-initio molecular dynamics simulation.In liquid Mg-rich Mg-Y alloys,the strong Mg-Y interaction is determined,which promotes the formation of fivefold symmetric local structure.For Mg-Zn alloys,the weak Mg-Zn interaction results in the fivefold symmetry weakening in the liquid structure.Due to the coexistence of Y and Zn,the strong attractive interaction is introduced in liquid Mg-Y-Zn ternary alloy,and contributes to the clustering of Mg,Y,Zn launched from Zn.What is more,the distribution of local structures becomes closer to that in pure Mg compared with that in binary Mg-Y and Mg-Zn alloys.These results should relate to the origins of the Y/Zn segregation zone and close-packed stacking mode in LPSO structure,which provides a new insight into the formation mechanism of LPSO structure at atomic level.

Paleogene transgression process and environmental evolution in the deepwater area of the Baiyun Depression in the northern South China Sea

Multiple borehole samples are collected from the Baiyun Depression in deep-water area of the northern South China Sea(SCS)in an effort to reconstruct transgression processes during the Paleogene based on palynalgal analysis.This study indicates that the Baiyun Depression generated a large group of palynopore assemblages and fluvial/lacustrine-related algae during the early and middle Eocene when the Wenchang Formation was deposited.The entire depression was dominated by fluvial and lacustrine facies before transgression.Its eastern and southeastern sags transitioned to shallow marine environment by generating a large abundance of marine dinoflagellates during the Enping deposition of the late Eocene.Meanwhile,the southern uplift zone simply yielded fluvial/lacustrine-related palynopores and algae,and was dominated by the fluvial and lacustrine environment during the early stage of the Enping Formation,prior to shifting into transitional setting in the later period.Northwestern sags remained extensive fluvial and delta facies without existence of marine dinoflagellates.It was until the depositional stage of the Zhuhai Formation(Oligocene)that the overall depression was strongly impacted from transgression process.Both eastern and southeastern sags were mainly under deep marine setting on a continental slope while northwestern and southern areas developed transitional facies.Although distribution and accumulation patterns varied greatly among sub-sags,the overall Baiyun Depression was characterized by widespread development of marine dinoflagellates.It should be noted that the northwestern sag also partly generated large-scale river delta deposits.Due to the eustatic rise and change of SCS spreading axis,the overall Baiyun Depression was mostly influenced by the deep marine environment on a continental slope during the early Miocene.Both northwestern sag and southern uplift zone were found plentiful marine dinoflagellates.In summary,transgression initiated from the eastern and southeastern Baiyun Depression before subsequently progressing into the farther west.Evolution of transgression process is also greatly consistent with the gradual westward expansion of the SCS.

A CombustionModel for Explosive Charge Affected by a Bottom Gap in the Launch Environment

Launch safety of explosive charges has become an urgent problem to be solved by all countries in the world aslaunch situation of ammunition becomes consistentlyworse.However, the existing numericalmodels have differentdefects. This paper formulates an efficient computational model of the combustion of an explosive charge affectedby a bottom gap in the launch environment in the context of the material point method. The current temperatureis computed accurately from the heat balance equation, and different physical states of the explosive charges areconsidered through various equations of state. Microcracks in the explosive charges are described with respectto the viscoelastic statistical crackmechanics (Visco–SCRAM) model. Themethod for calculating the temperatureat the bottomof the explosive charge with respect to the bottomgap is described. Based on this combustionmodel,the temperature history of a Composition B (COMB) explosive charge in the presence of a bottom gap is obtainedduring the launch process of a 155-mm artillery. The simulation results show that the bottom gap thickness shouldbe no greater than 0.039 cm to ensure the safety of the COM B explosive charge in the launch environment. Thisconclusion is consistent with previous results and verifies the correctness of the proposed model. Ultimately, thispaper derives amathematical expression for themaximumtemperature of the COMB explosive chargewith respectto the bottomgap thickness (over the range of 0.00–0.039 cm), and establishes a quantitative evaluationmethod forthe launch safety of explosive charges.The research results provide some guidance for the assessment and detectionof explosive charge safety in complex launch environments.