Research on Big Data Coding System Based on the Classification of Artificial Materials and Mechanical Equipment in Construction Engineering

By analyzing and comparing the current application status and advantages and disadvantages of domestic and foreign artificial material mechanical equipment classification coding systems,and conducting a comparative study of the existing coding system standards in different regions of the country,a coding data model suitable for big data research needs is proposed based on the current national standard for artificial material mechanical equipment classification coding.This model achieves a horizontal connection of characteristics and a vertical penetration of attribute values for construction materials and machinery through forward automatic coding calculation and reverse automatic decoding.This coding scheme and calculation model can also establish a database file for the coding and unit price of construction materials and machinery,forming a complete big data model for construction material coding unit prices.This provides foundational support for calculating and analyzing big data related to construction material unit prices,real-time information prices,market prices,and various comprehensive prices,thus contributing to the formation of cost-related big data.

Modeling of the Multi-Target Locating and Tracking in the Field Artillery System

A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system.

Real-world cost-effectiveness of targeted temperature management in out-of-hospital cardiac arrest survivors: results from an academic medical center

BACKGROUND: Targeted temperature management(TTM) is a common therapeutic intervention, yet its cost-effectiveness remains uncertain. This study aimed to evaluate the real-world cost-effectiveness of TTM compared with that of conventional care in adult out-of-hospital cardiac arrest(OHCA) survivors using clinical patient-level data.METHODS: We conducted a retrospective cohort study at an academic medical center in the USA to assess the cost-effectiveness of TTM in adult non-traumatic OHCA survivors between 1 January, 2019 and 30 June, 2023. The primary outcome was survival to hospital discharge. Incremental cost-effectiveness ratios(ICERs) were calculated and compared with various decision makers' willingness to pay. Cost-effectiveness acceptability curves were utilized to evaluate the economic attractiveness of TTM. Uncertainty about the incremental cost and effect was explored with a 95% confidence ellipse.RESULTS: Among 925 non-traumatic OHCA survivors, only 30(3%) received TTM. After adjusting for potential confounders, the TTM group did not demonstrate a significantly lower cost(delta cost-$5,141, 95% confidence interval [95% CI]: $-35,347 to $25,065, P=0.79) and higher survival to hospital discharge(delta effect 6%, 95% CI:-11% to 23%, P=0.41). Additionally, a 95% confidence ellipse indicated uncertainty reflected by evidence that the true value of the ICER could be in any of the quadrants of the cost-effectiveness plane.CONCLUSION: Although TTM did not demonstrate a clear survival benefit in this study, its potential cost-effectiveness warrants further investigation with larger sample sizes. These findings highlight the need for additional research to optimize TTM use in OHCA care and inform resource allocation decisions.

Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Calculations of radioactivity and afterheat in the components of the CSNS target station

This paper shows the calculations of radioactivity and afterheat in the components of the China Spallation Neutron Source （CSNS） target station, with the Monte Carlo codes LAHET, MCNP4C and the multigroup code CINDER＇90. These calculations provide essential data for the detailed design and maintenance of the CSNS target station.

Flow Field and Thermal Analysis of the Divertor Target Plate for HL-2A Tokamak

In the initial phase of the physics experiment, the double-null divertor plates used consist of graphite armor tiles, Mo-alloy intermediate layers and Cu-alloy coolant tubes. In the later operating phase, tungsten will be used as armor tiles. A multi-physical field numerical analysis method is used in this paper. Its analysis model reflects more realistically the real divertor structure than other models. Two-dimensional （2D） and three-dimensional （3D） fluid flow field, temperature distribution and thermal stress analyses of the divertor plates are carried out by the ANSYS code. During the physics experimental phase with a heat flux of 1 MW/m2, a coolant velocity of 5.48 m/s, and a thermal stress of 750 kg/cm2, the graphite armor tiles successfully meet the requirements of temperature, thermal stress and sputtering erosion. The tungsten armor will be considered as a second candidate. The result of simulation can be used for upgrading the design parameters of the HL-2A poloidal divertor.

METHODS OF RADAR DATA COMPRESSION AND TARGET IDENTIFICATION BASED ON BIORTHOGONAL FDWT

In this paper, by using the biorthogonal quadrature filters, the biorthogonal mul-tiresolution analysis of finite dimension space equipped with inner product and the fast discrete wavelet transform (FDWT) are constructed. The dual transform method is proposed and the radar data storage is reduced by it. The method of choosing the wavelet coefficients, and the methods of correlation and nearest neighbor classification in wavelet domain based on the compressed data, are presented. The experimental results of the classification, using the high resolution range returns from six kinds of aircrafts, show that the methods of transform, compression and recognition are efficient.

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.

New Approaches to Targeted Poverty Alleviation in the Age of Big Data——On Improving the Results of Targeted Poverty Alleviation Programs(10)

Since the State Council issued the Action Plan on Promoting the Development of the Big Data Industry,big data-enabled information integration and processing applications have increasingly become the basic strategic resources for the building of a modern governance system in China.When it comes to poverty reduction,given that we are currently at a critical stage in the battle to eradicate poverty,it's important that we apply the big data way of thinking and big data technology to the development and integration of poverty alleviation resources.This paper examines the need to apply big data technology in targeted poverty alleviation and discusses how big data technology can be integrated into targeted poverty alleviation programs and how the big data way of thinking meshes with the idea of targeted poverty alleviation.It is believed that the application of big data technology can significantly improve the results of targeted poverty alleviation programs and that the building of big data-powered poverty alleviation platforms is a new approach to implementing the targeted poverty alleviation strategy.This paper calls for changing our way of thinking regarding targeted poverty alleviation and points out the directions for targeted poverty alleviation in the age of big data,with a view to promoting the extensive application of big data technology in the field of poverty reduction and improving the results of poverty alleviation and eradication programs.

Comparison and Analysis between Two Types of Sounding Data of China

The upper air weather forecast data used in current business and research and digital data of the recently finished upper air meteorological monthly report were comparatively analyzed in complete data and quality condition of data, and sounding curve change caused by the difference of complete data was also compared, which evaluated advantages and disadvantages of two types of data.

Point association analysis of vessel target detection with SAR, HFSWR and AIS

A space-borne synthetic aperture radar （SAR）, a high frequency surface wave radar （HFSWR）, and a ship automatic identification system （AIS） are the main remote sensors for vessel monitoring in a wide range. These three sensors have their own advantages and weaknesses, and they can complement each other in some situations. So it would improve the capability of vessel target detection to use multiple sensors including SAR, HFSWR, and A/S to identify non-cooperative vessel targets from the fusion results. During the fusion process of multiple sensors＇ detection results, point association is one of the key steps, and it can affect the accuracy of the data fusion and the efficiency of a non-cooperative target＇s recognition. This study investigated the point association analyses of vessel target detection under different conditions： space- borne SAR paired with AIS, as well as HFSWR, paired with AIS, and the characteristics of the SAR and the HFSWR and their capability of vessel target detection. Then a point association method of multiple sensors was proposed. Finally, the thresholds selection of key parameters in the points association （including range threshold, radial velocity threshold, and azimuth threshold） were investigated, and their influences on final association results were analyzed.

Recent Advances in China on the Predictability of Weather and Climate

This article summarizes the progress made in predictability studies of weather and climate in recent years in China,with a main focus on advances in methods to study error growth dynamics and reduce uncertainties in the forecasting of weather and climate.Specifically,it covers(a)advances in methods to study weather and climate predictability dynamics,especially those in nonlinear optimal perturbation methods associated with initial errors and model errors and their applications to ensemble forecasting and target observations,(b)new data assimilation algorithms for initialization of predictions and novel assimilation approaches to neutralize the combined effects of initial and model errors for weather and climate,(c)applications of new statistical approaches to climate predictions,and(d)studies on meso-to small-scale weather system predictability dynamics.Some of the major frontiers and challenges remaining in predictability studies are addressed in this context.

Data fusion of target characteristic in multistatic passive radar

Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.

Maneuvering target state estimation based on separate model-ing of target trajectory shape and dynamic characteristics

The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.

Emerging role of long non-coding RNA in the development of gastric cancer

Gastric cancer is a common, worldwide malignancy and has a poor prognosis due to late diagnosis. Long non-coding RNAs(lnc RNAs) are a significant subtype of RNA molecules with a length longer than 200 nucleotides(nt) that rarely encode proteins. In recent decades, deregulation of lnc RNAs has been shown to be involved in tumorigenesis and tumor progression in various human carcinomas, including gastric cancer. Accumulating evidence has shown that some lnc RNAs may function as diagnostic biomarkers or therapeutic targets for gastric cancer. Thus, exploring the specific functions of lnc RNAs will help both gain a better understanding of the pathogenesis and develop novel treatments for gastric cancer. In this review, we highlight the expression and functional roles of lncR NAs in gastric cancer, and analyze the potential applications of lncR NAs as diagnostic markers and therapeutic targets.

Using Grey Target Theory for Power Quality Evaluation Based on Power Quality Monitoring Data

Smart grid puts forward higher requirements for power quality.Power quality evaluation can provide a decision-making basis for quality improvement.Based on power quality monitoring data,a grey target method is proposed for power quality evaluation.The grey target is composed of power quality evaluation standard and power quality monitoring data to be evaluated.Combining with the characteristics of each power quality evaluation index,the target center of the whole grey target system is found.Then,the power quality monitoring data to be evaluated and the power quality standard mode are compared and analyzed to construct the power quality grey correlation difference information space.Finally,the target center coefficient and target degree of power quality are calculated to realize the comprehensive evaluation of power quality,and the evaluation grade of power quality monitoring data to be evaluated is obtained.Compared with the evaluation results of the existing literature,the effectiveness of the proposed method is verified,which shows that grey target theory is reasonable in the comprehensive evaluation of power quality.

ALGORITHMS AND ARCHITECTURE IMPLEMENTATIONS OF MIMO OFDM BASEBAND RECEIVER BASED ON THE SIMD DSP CORE

This letter presents a programmable single-chip architecture for Multi-lnput and Multi-Output （M1MO） OFDM baseband receiver. The architecture comprises a Single Instruction Multiple Data （SIMD） DSP core and three coprocessors that are used for synchronization, FFT and channel decoder. In this MIMO OFDM system, the Zero Correlation Zone （ZCZ） code is used as the synchronization word preamble of packet in the physical layer in order to avoid the interference from other transmitting antennas. Furthermore, a simple channel estimation algorithm is proposed which is appropriate tbr the SIMD DSP computation.

Cost-optimization based target reliabilities for design of structures exposed to fire

Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment.Cost-benefit analyses,however,are complicated and computationally challenging,and hence impractical for application to individual projects.To address this issue,design guidance proposes target reliability indices for normal design conditions,but no target reliability indices are defined for structural fire design.We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures.We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions.The optimum safety level is expressed as a function of a new dimensionless variable named“Damage-to-investment indicator”(DII).The cost optimization approach is validated for the target reliability indices for normal design condition.The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures.Two case studies are presented:(i)a one-way loaded reinforced concrete slab and(ii)a steel column under axial loading.This study thus provides a framework for deriving optimum(target)reliability index for structural fire design which can support the development of rational provisions in codes and standards.

New computing method of weighted coefficients for tracking a maneuvering target using PDAF in the presence of clutter

To avoid or reduce the influence of unpredictable motion mode on data association, a new computing method of weighted coefficients of measurements for PDAF is presented in which it is assumed that the current turn rate of a maneuvering target changes within a limited range and its turn may be in arbitrary direction during data association. Thus, the predicted center for computing the weighted coefficients is a curved surface in 3-D space, which differs from the predicted center for setting up a validation gate, namely, a point in 3-D space. The distance between a measurement and the curved surface is used to compute its weighted coefficient. To reduce the computational complexity of weighted coefficients, the formulas for computing the maneuvering direction angle and turn rate corresponding to a measurement are presented. Simulation results show the proposed method reduces the percentage of lost tracks and improves the state estimation accuracy in tracking a maneuvering target using PDAF in the presence of clutter.

THEORETICAL ANALYSIS OF IMPROVEMENT OF TRACK LOSS IN CLUTTER WITH MULTISENSOR DATA FUSION

The paper analyses the improvement of track loss in clutter with multisensor data fusion.By a determination of the transition probability density function for the fusion prediction error, one can study the mechanism of track loss analytically. With nearest-neighbor association algorithm. The paper we studies the fused tracking performance parameters, such as mean time to lose fused track and the cumulative probability of lost fused track versus the normalized clutter density, for track continuation and track initiation, respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fused tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.