A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Integration of Sequence Stratigraphic Analysis and 3D Geostatistical Modeling of Pliocene–Pleistocene Delta,F3 Block,Netherlands

This research is focused on the analysis of the sequence stratigraphic units of F3 Block,within a wave-dominated delta of Plio–Pleistocene age.Three wells of F3 block and a 3D seismic data,are utilized in this research.The conventional techniques of 3D seismic interpretation were utilized to mark the 11 surfaces on the seismic section.Integration of seismic sequence stratigraphic interpretation,using well logs,and subsequent 3D geostatistical modeling,using seismic data,aided to evaluate the shallow hydrocarbon traps.The resulting models were obtained using System Tract and Facies models,which were generated by using sequential stimulation method and their variograms made by spherical method,moreover,these models are validated via histograms.The CDF curve generated from upscaling of well logs using geometric method,shows a good relation with less percentage of errors(1 to 2 for Facies and 3 to 4 for System Tract models)between upscaled and raw data that complements the resulted models.These approaches help us to delineate the best possible reservoir,lateral extent of system tracts(LST and/or HST)in the respective surface,and distribution of sand and shale in the delta.The clinoform break points alteration observed on seismic sections,also validates the sequence stratigraphic interpretation.The GR log-based Facies model and sequence stratigraphy-based System Tract model of SU-04-2 showed the reservoir characteristics,presence of sand bodies and majorly LST,respectively,mainly adjacent to the main fault of the studied area.Moreover,on the seismic section,SU-04-2 exhibits the presence of gas pockets at the same location that also complements the generated Facies and System Tract models.The generated models can be utilized for any similar kind of study and for the further research in the F3 block reservoir characterization.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.

A SARS-CoV-2 neutralizing antibody discovery by single cell sequencing and molecular modeling

Although vaccines have been developed,mutations of SARS-CoV-2,especially the dominant B.1.617.2(delta)and B.1.529(omicron)strains with more than 30 mutations on their spike protein,have caused a significant decline in prophylaxis,calling for the need for drug improvement.Antibodies are drugs preferentially used in infectious diseases and are easy to get from immunized organisms.The current study combined molecular modeling and single memory B cell sequencing to assess candidate sequences before experiments,providing a strategy for the fabrication of SARS-CoV-2 neutralizing antibodies.A total of 128 sequences were obtained after sequencing 196 memory B cells,and 42 sequences were left after merging extremely similar ones and discarding incomplete ones,followed by homology modeling of the antibody variable region.Thirteen candidate sequences were expressed,of which three were tested positive for receptor binding domain recognition but only one was confirmed as having broad neutralization against several SARS-CoV-2 variants.The current study successfully obtained a SARS-CoV-2 antibody with broad neutralizing abilities and provided a strategy for antibody development in emerging infectious diseases using single memory B cell BCR sequencing and computer assistance in antibody fabrication.

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

Sedimentary architecture and distribution of intra-platform shoal in sequence framework of Permian Changxing Formation in central Sichuan Basin,SW China

Based on the comprehensive analysis of core, thin section, logging and seismic data, this study carried out the identification and comparison of Permian Changxing Formation sequences, clarified the typical sedimentary architectures of intra-platform shoal, investigated the vertical and horizontal development and distribution of intra-platform shoal in each sequence, and thus established the sedimentary evolution model of shoal body. The study results are reflected in four aspects.First, there are two complete third-order sequences(SQ1 and SQ2) in Changxing Formation in central Sichuan Basin. SQ1 is generally thick in the north and thin in the south, and SQ2 shows a thickness differentiation trend of “two thicknesses and three thinnesses”. Second, the Changxing Formation in central Sichuan Basin mainly develops intra-platform shoal, inter-shoal sea and intra-platform depression subfacies. In the vertical direction, the intra-platform shoal mainly presents two typical sedimentary sequences: stable superposed and high-frequency interbedded. Third, the stable superimposed sedimentary sequence is developed in the shoal belt at the edge of intra-platform depression, which is composed of two shoal-forming periods and located in the highstand systems tracts(HSTs) of SQ1 and SQ2. The high-frequency interbedded sedimentary sequence is developed in the southern shoal belt of intra-platform depression, which is composed of four shoal-forming periods and mainly located in the HST of SQ2. Fourth, during the SQ1 deposition, the intra-platform shoal was mainly developed at the edge of the intra-platform depression on the north side of the study area, and the inter-shoal sea subfacies was mainly developed on the south side. During the SQ2 deposition, the intra-platform shoal was widely developed in the area, forming two nearly parallel intra-platform shoal belts. The study results provide direction and ideas for exploration of Changxing Formation intra-platform shoal reservoirs in central Sichuan Basin.

Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

Chaos game representation(CGR)-walk model for DNA sequences

Chaos game representation （CGR） is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to determine the coordinates of their positions in a continuous space. This distribution of positions has two features： one is unique, and the other is source sequence that can be recovered from the coordinates so that the distance between positions may serve as a measure of similarity between the corresponding sequences. A CGR-walk model is proposed based on CGR coordinates for the DNA sequences. The CGR coordinates are converted into a time series, and a long-memory ARFIMA （p, d, q） model, where ARFIMA stands for autoregressive fractionally integrated moving average, is introduced into the DNA sequence analysis. This model is applied to simulating real CGR-walk sequence data of ten genomic sequences. Remarkably long-range correlations are uncovered in the data, and the results from these models are reasonably fitted with those from the ARFIMA （p, d, q） model.

A Climatic Sequence Stratigraphic Model in the Terrestrial Lacustrine Basin:A Case Study of Green River Formation,Uinta Basin,USA

In recent years, with the development of terrestrial sequence stratigraphy, more attention has been focused on the study of the terrestrial lacustrine sequence stratigraphic model globally. Different viewpoints are preferred by researchers. Under the guidance of the theory of sequence stratigraphy, the findings of this paper indicate that climate is a major factor controlling the formation of the fourth-order sequence, based upon the study of the sequence stratigraphy in the Green River Formation of the Uinta basin in the USA. It also divides the fourth-order sequence in the terrestrial lacustrine basin into two system tracts： the wet （rising） half-cycle and the dry （falling） half- cycle, establishing a new-style fourth-order sequence stratigraphic model for the terrestrial lacustrine basin, that is, the climate-genetic sequence stratigraphic model. As a result, the theory of sequence stratigraphy is greatly enriched.

On the Carboniferous Sequence Stratigraphy in the Tazhong Area, Xinjiang——A Model of the Sequence Stratigraphy Framework of Intracratonic Depressional Basins

The Carboniferous prototype sedimentary basin in the Tazhong (Central Tarimbasin) area is recognized as a compressive intracratonic depressional one. Three type Ⅰ sequenceboundaries and three type Ⅱ sequence boundaries can be identified in the CarboniferousSystem, which can accordingly be divided into five sedimentary sequences. These sequencespossess stratigraphic characters of the standard sequence and correspond to the depositionalstratigraphic unit of a third-order eustatic cycle. They can be regionally or globally correlatedwith each other. The framework of sequence stratigraphy of the intracratonict basin in thestudy area distinctly differs from that of the passive continental-margin basin in the lack ofdepositional systems of early-middle lowstand, poor development of the deeply incised valleyand condensed section of the maximum sea-flood, good development of type Ⅱ sequenceboundaries and coastal plain depositional systems coexisting with shelf-type fan deltas underwet climatic conditions, Which consequently led to the formation of a paralic lithofacies frame-work.

Chaos game representation walk model for the protein sequences

A new chaos game representation of protein sequences based on the detailed hydrophobic-hydrophilic （HP） model has been proposed by Yu et al （Physica A 337（2004） 171）. A CGR-walk model is proposed based on the new CGR coordinates for the protein sequences from complete genomes in the present paper. The new CCR coordinates based on the detailed HP model are converted into a time series, and a long-memory ARFIMA（p, d, q） model is introduced into the protein sequence analysis. This model is applied to simulating real CCR-walk sequence data of twelve protein sequences. Remarkably long-range correlations are uncovered in the data and the results obtained from these models are reasonably consistent with those available from the ARFIMA（p, d, q） model.

Microfacies models and sequence stratigraphic architecture of the Oligocene-Miocene Qom Formation, south of Qom City, Iran

The Oligocene-Miocene Qom Formation has different depositional models in the Central han, Sanandaj-Sirjan and Urunlieh-Dokhtar inagmatic arc provinces in han. The Kahak section of the Qom Formation in the Uiumieh-Dokhtar magmatic arc has been studied, in order to determinate its microfacies, depositional model and sequence stratigraphy. The textural analysis and faunal assemblages reveal ten microfacies. These microfacies are indicative of five depositional settings of open marine, patch reef, lagoon, tidal flat and beach of the inner and middle ramp. On the basis of the vertical succession architecture of depositional system tracts, four third-order sequences have been recognized in the Oligocene-Miocene Kahak succession of Qom Formation. Based on the correlation charts, the transgression of the Qom Sea started fiom the southeast and continued gradually towards the north. This resulted in widespread northward development of the lagoon paleoenvironment in the Aquitanian-Burdigalian stages. Also, the sequence stratigraphic model of the Oligocene Miocene Qom Formation has an architecture similar to those that have developed from Oligocene Miocene global sea level changes.

Classification and Facies Sequence Model of Subaqueous Debris Flows

Objective Debris flows are cohesive sediment gravity flows which occur in both subaerial and subaqueous settings. Compared to subaerial debris flows which have been well studied as a geological hazard, subaqueous debris flows showing complicated sediment composition and sedimentary processes were poorly understood. The main objective of this work is to establish a classification scheme and facies sequence models of subaqueous debris flows for well understanding their sedimentary processes and depositional characteristics.

EXTENSION OF SEQUENCE STRATIGRAPHIC MODELAND SEQUENCE STRATIGRAPHIC ANALYSIS IN LIMNIC DEPOSITIONAL BASIN

The architectural patterns of sedimentary succession are diverse in different depositionalbasins. The sedimentary architecture and geological condition of such basins asepicontinental sea, intraplate limnic basins, etc., differ cIearly from those of continentalmargin basin. Extension, complement and perfection of sequence stratigraphic models are needed in the studies of various depositional basins based on the classical sequence model. This paper, for this reason,expounds the thought, principles of sequence division, methodology and technology of the study of sequence stratigraphy in epicontinental and limnic basins.

Asymptotic normality of error density estimator in stationary and explosive autoregressive models

In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.

High-Resolution Sequence Stratigraphy and Coal Accumulating Models of the Permo-Carboniferous Coal Measures in Southwestern Shanxi

The Qinshui Basin in southeastern Shanxi Province is an important base for coalbed methane exploration and production in China.The methane reservoirs in this area are the Carboniferous and Permian coals and their thickness are strongly controlled by the depositional environments and the sequence stratigraphic framework.This paper analyzes the high-resolution sequence stratigraphy of the Permo-Carbo-

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

Brief Introduction to Self-Adapting Hidden Markov Model Program for Multiple Sequences Alignment

In this letter, we briefly describe a program of self adapting hidden Markov model (SA HMM) and its application in multiple sequences alignment. Program consists of two stage optimisation algorithm.

Complex Sequence Quality Evaluation Model for Concealed Water Supply Pipeline of Prefabricated Construction

The accurate assessment of the quality can materially affect the safety and life of the prefabricated construction.In this paper,we studied the overall module of concealed water supply pipeline,and a new complex sequence quality evaluation model was established.By analyzing the small evaluation index elements of the model,the weight of the larger evaluation index could be deduced.The total quality evaluation score of the overall module could be obtained by calculating the weight value of the evaluation index,and then the quality standard of the overall module could be obtained.

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.