Timing and Spectral Analysis of the Black Hole X-Ray Binary MAXI J1803-298 with Insight-HXMT Data

We present a comprehensive analysis of the 2021 outburst of MAXI J1803–298 utilizing observations of the Insight-Hard X-ray Modulation Telescope(Insight-HXMT)spanning from the low hard state to the high soft state.Within the Insight-HXMT data set,compared to the previous work,we identify a more prolonged presence of typeC quasi-periodic oscillations(QPOs)with centroid frequencies ranging from~0.16 to 6.3 Hz,which present correlations with the hardness ratio and the photon index of the Comptonized component.For QPO frequencies less than~2 Hz,the QPO phase lags are hard(photons of 10–19 keV arrive later than those of 1–4 keV),while at higher frequencies,the lags become soft at and above~4 Hz.Furthermore,the spectra in all Insight-HXMT observations consist of a multi-color blackbody component and a Comptonized component,as commonly observed in classical black hole X-ray binaries.We analyze state transitions and the evolution of accretion geometry in this work.The fitted inner disk radius increases abnormally during the low hard state,hypothesized to result from the corona condensing onto the inner disk.Additionally,two significant drops in flux are observed during the soft intermediate state,maybe implying changes in the corona/jet and the disk,respectively.

An Implementation of Galactic White Dwarf Binary Data Analysis for MLDC-3.1

The space-borne gravitational wave detectors will observe a large population of double white dwarf binaries in the Milky Way.However,the search for double white dwarfs in the gravitational wave data will be time-consuming due to the large number of templates involved and antenna response calculation.In this paper,we implement an iterative combinatorial algorithm to search for double white dwarfs in MLDC-3.1 data.To quickly determine the rough parameters of the target sources,the following algorithms are adopted in a coarse search process:(1)using the downsampling method to reduce the number of original data points;(2)using the undersampling method to speed up the generation of a single waveform template;(3)using the stochastic template bank method to quickly construct the waveform template bank while achieving high coverage of the parameter space;(4)combining the FFT acceleration algorithm with the stochastic template bank to reduce the calculation time of a single template.A fine search process is applied to further determine the parameters of the signals based on the coarse search,for which we adopt the particle swarm optimization.Finally,we detect O(10^(4))double white dwarf signals,validating the feasibility of our method.

Local binary pattern-based reversible data hiding

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

Research of Methods for Lost Data Reconstruction in Erasure Codes over Binary Fields

In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of information technology.A matrix decoding method is proposed in this paper.The method is a universal data reconstruction scheme for erasure codes over binary fields.Besides a pre-judgment that whether errors can be recovered,the method can rebuild sectors of loss data on a fault-tolerant storage system constructed by erasure codes for disk errors.Data reconstruction process of the new method has simple and clear steps,so it is beneficial for implementation of computer codes.And more,it can be applied to other non-binary fields easily,so it is expected that the method has an extensive application in the future.

ASSESSMENT AND OPTIMIZATION OF THE PHASE DIAGRAMS AND THERMODYNAMIC DATA OF SOME BINARY SYSTEMS CONTAINING PRASEODYMIUM CHLORIDES

In this paper a critical assessment and optimization of the phase diagrams and thermodynamic properties of the PrCl_3-MCl(M=Li,Na)and PrCl_3-MCl_2(M=Mg,Ca,Sr,Ba) binary systems have been per- formed.The assessed and optimized binary phase diagrams and thermodynamic data with self consistency are a better basis for constructing multicomponent phase diagrams.

Steganography in binary image by checking data-carrying eligibility of boundary pixels

We present a novel scheme for embedding secret data into a binary image without introducing noticeable artifacts. Unlike some block-based methods, the proposed scheme encodes the secret bits directly into boundary pixels by checking each pixel of the cover image in a pseudo-random order for embedding eligibility. A set of rules ensures correct identification of data-carrying pixels in blind extraction. The proposed scheme does not generate isolated dots, and can incorporate various coding methods such as matrix encoding to further improve the embedding performance. It is shown that up to one fourth of the boundary pixels may be used to carry secret data. Experimental results indicate that the method can achieve good visual quality with fairly large data capacity.

Phase-field simulation of dendritic growth in a binary alloy with thermodynamics data

This paper simulates the dendrite growth process during non-isothermal solidification in the Al-Cu binary alloy by using the phase-field model. The heat transfer equation is solved simultaneously. The thermodynamic and kinetic parameters are directly obtained from existing database by using the Calculation of Phase Diagram （CALPHAD） method. The effects of the latent heat and undercooling on the dendrite growth, solute and temperature profile during the solidification of binary alloy are investigated. The results indicate that the dendrite growing morphologies could be simulated realistically by linking the phase-field method to CALPHAD. The secondary arms of solidification dendritic are better developed with the increase of undercooling. Correspondingly, the tip speed and the solute segregation in solid-liquid interface increase, but the tip radius decreases.

An analysis of longitudinal binary data using generalized linear models

Objective: To analyze longitudinal binary data by using generalized linear models. The correlation between repeated measures were considered. The general method for analyzing longitudinal binary data was given. Methods: Generalized estimating equations (GEE) proposed by Zeger and Liang was used. For sevens covariance structures, one method was given for estimating regression and correlation parameters. Results: Regression and coerelation parameters were estimated simultaneously. A Set of program was finished and an example was illustrated. Conclusion: Longitudinal dsta often occur in medical researches and clinical trials. For solving the problem of correlation between repeated measures, it is necessary to use some special methods to cope with this Kind of data.

Transition Logic Regression Method to Identify Interactions in Binary Longitudinal Data

Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.

Estimation of Attributable Risk from Clustered Binary Data: The Case of Cross-Sectional and Cohort Studies

Effect sizes are estimated from several study designs when the subjects are individually sampled. When the samples are the aggregate cluster of individuals, the within cluster correlation must be accounted for to construct correct confidence intervals, and to conduct valid statistical inference. The purpose of this article is to propose and evaluate statistical procedures for the estimation of the variance of the estimated attributable risk in parallel groups of clusters, and in a design dividing each of k clusters into two segments creating multiple sub-clusters. The estimated variance is the first order approximation and is obtained by the delta method. We apply the methodology and propose a Wald type confidence interval on the difference between two correlated attributable risks. We also construct a test on the hypothesis of equality of two correlated attributable risks. We evaluate the power of the proposed test via Monte-Carlo simulations.

Photometric Investigation of Contact Binary DY Cet Based on TESS Data

We present a photometric analysis of the Transiting Exoplanet Survey Satellite(TESS)light curve of contact binary system DY Cet and the behavior of its orbital period variation.The light curve and published radial velocity data analysis was performed using the Wilson-Devinney code.As a result of simultaneous analysis of the light curve with radial velocity data,the masses and radii of the system’s components were determined as M_(1)=1.55±0.02 M_(⊙),M_(2)=0.55±0.01 M_(⊙) and R_(1)=1.51±0.02 R_(⊙),R_(2)=0.95±0.02 R_(⊙),respectively.The degree of contact(f)and mass ratio(q)of the system were determined as 23%and 0.355±0.012,respectively.Orbital period analysis of DY Cet was conducted for the first time in this study.It was observed that the orbital period has a sinusoidal change with decreasing parabola.To explain the orbital period change,mass transfer between components is proposed with the assumption of conservative mass,and the transfer rate was calculated to be dM/dt=1.1×10^(-7) M_(⊙)yr^(-1).A possible third component is suggested for explaining the sinusoidal change,and the mass of the unseen component was determined as 0.13 M_(⊙).The age of the DY Cet system was estimated as 3.77 Gyr.

Feasibility study on the binary-parameter retrieval model of ocean suspended sediment concentration based on MODIS data

This paper brought out a new idea on the retrieval of suspended sediment concentration, which uses both the water-leaving radiance from remote sensing data and the grain size of the suspended sediment. A principal component model and a neural network model based on those two parameters were constructed. The analyzing results indicate that testing errors of the models using the two parameters are 0.256 and 0.244, while the errors using only water-leaving radiance are 0,384 and 0.390. The stability of the models with grain size parameter is also better than the one without grain size. This research proved that it is necessary to introduce the grain size parameter into suspended sediment concentration retrieval models in order to improve the retrieval precision of these models.

Variable binaries and variables in binaries in the Binary star DataBase

The Binary star DataBase(BDB, http://bdb.inasan.ru) combines data from catalogs of binary and multiple stars of all observational types. There is a number of ways for variable stars to form or to be a part of binary or multiple systems. We describe how such stars are represented in the database.

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data:A COVID-19 Case Study

Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

Prediction of Vapor-Liquid Equilibrium Data from C—H Band Shift of IR Spectra in Some Binary Systems

Prediction of vapor-liquid equilibrium (VLE) is extremely necessary to separate liquid mixture in chemical production, especially when the required experimental data are difficult to measure, or the measurement is not economical. The infinite dilution activities can be used to predict VLE. However, it needs both the ends of the activities that are difficult to obtain for many systems. In the present study, a new model is proposed for correlating the frequency shift of C H stretching band of IR spectra over the whole concentration. Investigated mixtures in-clude water/2-propanol, water/N, N-dimethylformamide (DMF), water/methanol, water/ethanol, water/1, 4-dioxane, and water/dimethylsulfoxide (DMSO) systems. Simultaneous correlations of C H frequency shift and VLE data are made. Furthermore, the VLE data were predicted with satisfactory results by the parameters obtained from IR spectra coupled with one of the infinite dilution activity coefficients.

Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

Design and implementation of binary tree data structure based on DNA computing

The designing,encodings and an instance of simulation of a binary tree for DNA computer were proposed,which utilizes the method of biology to complete inserting and deleting of the binary tree. Firstly,DNA encodings for storage and all elements of the binary tree were completely given out. Then, the implementations of all bio-operations in DNA computer were described. Finally, to prove the feasibility of this method, an actual binary tree with detailed nucleotide encodings was introduced. The process of an algorithm implemented on this binary tree was demonstrated. Based on this method, more other data structures in DNA computer can be developed.

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics

This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].