Macro-Analysis on Across-Fault Crustal Deformation Measurement Data Along the Northern Edge of the Qinghai-Xizang Block

Based on the arrangement of the across-fault measurement data along the northern edge of the Qinghai-Xizang block,we divide the deformation into different types and probe the nature of various fault movements based on these types.The recent situation of tectonic movement of main structural belts and seismicity in this area are expounded.From the above,it is concluded that across-fault measurement can reflect not only the conditions of fault movement nearby but also the change of regional stress fields; not only is this a method to obtain regional seismogenic information and to conduct short-term prediction but it is also involved with large scale space-time prediction of moderate and strong earthquakes on the basis of the macro characteristics of fractures.

Data-driven methods for predicting the representative temperature of bridge cable based on limited measured data

Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.

On Human Autonomic Nervous Activity Related to Behavior, Daily and Regional Changes Based on Big Data Measurement via Smartphone

This research uses a large amount of autonomic nervous system data (approximately 100,000 entries) to investigate the relationship between human autonomic nervous activity and behaviors, daily and regional changes. Data were measured via a heart rate variability analysis system that utilizes the camera of smartphones. This system was developed by the authors during previous research. The relations between autonomic nervous system and behaviors, total power and sympathetic nervous activity were found to rise after waking, while during leisure time, the total power rises and sympathetic nervous activity is inhibited. Concerning the relationship between autonomic nervous system and day of the week, it was found that total power decreases from the middle through the latter part of the week (namely, Wednesday, Thursday, and Friday), while it rises on Saturday, while the sympathetic nervous activity is suppressed on Saturday. Regarding the relationship between autonomic nervous system and region, it was found that total power is lower in the Kanto region of Japan than in others. This study also shows statistical proof (using a large amount of measurement data) to ideas held by the public for years. Thus, the data can be considered meaningful to the society, and the authors hope that it helps to improve work-life balance.

On Human Autonomic Nervous Activity Related to Weather Conditions Based on Big Data Measurement via Smartphone

This research uses our previously-developed smartphone camera-based heart rate change analysis system to survey the correlation between weather patterns and the autonomic nervous activity across a big data set of approximately 200,000 entries. The results showed a trend in which a significant decrease was seen in sympathetic nervous activity in both males and females—the higher the temperature. In addition, a significant increase was seen in the sympathetic nervous system in both males and females—the higher the atmospheric pressure. Lastly, a significant decrease was seen in the sympathetic nervous system in both males and females—the more precipitation there was. These results accord with prior research and with human biological phenomena, and we were able to use a data set of approximately 200,000 entries to statistically demonstrate our hypothesis. We believe this represents a valuable set of reference data for use in the health care.

A comparative study of models for the different covariance structure analysis of repeated measurement data

In repeated measurement data, the variables are not independent, and a certain auto- correlation typically exists between different levels of repeated measurement factors. The random error is composed of at least two parts, i.e. the individual random effect and the intra-individual multi-repeated measurement effect. Traditional statistical analysis methods （such as the t-test and the one-way analysis of variance） are not applicable. The linear mixed model has been widely applied for the analysis and design of repeated measurement data. This paper focuses on medical examples and describes the selection of a covariance structure for the linear mixed model of repeated measurement in the modeling of different variance-eovariance structures. By selecting different covariance structures, we can perform the parameter estimation and statistical test for the fixed effect of repeated measurement data, the parameters of random effects, and the covari- ance matrix. The results are analyzed and compared to provide a reference for applying the linear mixed model of repeated measurement to medical research.

Block-based test data adequacy measurement criteria and test complexity metrics

On the basis of software testing tools we developed for programming languages, we firstly present a new control flowgraph model based on block. In view of the notion of block, we extend the traditional program\|based software test data adequacy measurement criteria, and empirically analyze the subsume relation between these measurement criteria. Then, we define four test complexity metrics based on block. They are J\|complexity 0; J\|complexity 1; J\|complexity \{1+\}; J\|complexity 2. Finally, we show the Kiviat diagram that makes software quality visible.

Effect of building energy efficiency standards on carbon emission efficiency in commercial buildings

The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,exploring the provincial variations in carbon emission efficiency(CEE)in the building sector and identifying the effect of BEESs on CEE is crucial.This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings.The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019.Furthermore,heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs.The results indicate that BEESs positively influence the CEE of commercial buildings.Specifically,a 1%increase in the intensity of BEESs causes a 0.1484%increase in the CEE of commercial buildings.Moreover,the impact of BEESs is particularly pronounced in the southern and western provinces.This study provides valuable scientific evidence for governments to enhance BEESs implementation.

Estimation of Extreme Coastal Wave Heightsfrom Time Series of Wave Data

A method is presented to extrapolate a time series of wave data to extreme wave heights. The 15-year time series of deepwater wave data collected for 34 min every hour from 1988 to 2002 in the South Pacific Ocean, Australia, is analyzed to generate a set of storm peak wave heights by use of the Peaks-Over-Threshold method. The probability distribution is calculated by grouping the observod storm peak wave heights into a number of wave height classes and assigning a probability to each wave height class. The observed probability distribution is then fitted to eight different probability distribution functions and found to be fitted best by the Weibull distribution （a = 1.17）, nearly best by the FT-Ⅰ, quite well by the exponential, and poorly by the lognormal function based on the criterion of the sum of squares of the errors, SSE （H）. The effect of the threshold wave height on the estimated extreme wave height is also studied and is found insignificant in this study. The 95 % prediction intervals of the best-fit FT-Ⅰ , exponential and Weibull functions are also derived.

A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.

Calorimetric power measurements in the EAST ECRH system

In this paper, the measurement method of calorimetric power for an electron cyclotron resonance heating（ECRH） system for EAST is presented. This method requires measurements of the water flow through the cooling circuits and the input and output water temperatures in each cooling circuit. Usually, the inlet water temperature stability is controlled to obtain more accurate results.The influence of the inlet water temperature change on the measurement results is analyzed for the first time in this paper. Also, a novel temperature calibration method is proposed. This kind of calibration method is accurate and effective, and can be easily implemented.

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.

ERROR ANALYSIS FOR PARABOLIC OPTIMAL CONTROL PROBLEMS WITH MEASURE DATA IN A NONCONVEX POLYGONAL DOMAIN

This paper considers the finite element approximation to parabolic optimal control problems with measure data in a nonconvex polygonal domain.Such problems usually possess low regularity in the state variable due to the presence of measure data and the nonconvex nature of the domain.The low regularity of the solution allows the finite element approximations to converge at lower orders.We prove the existence,uniqueness and regularity results for the solution to the control problem satisfying the first order optimality condition.For our error analysis we have used piecewise linear elements for the approximation of the state and co-state variables,whereas piecewise constant functions are employed to approximate the control variable.The temporal discretization is based on the implicit Euler scheme.We derive both a priori and a posteriori error bounds for the state,control and co-state variables.Numerical experiments are performed to validate the theoretical rates of convergence.

Simulation of fire emergency evacuation in a large-passenger-flow subway station based on the on-site measured data of Shenzhen Metro

Because of its large capacity,high efficiency and energy savings,the subway has gradually become the primary mode of transportation for citizens.A high density of passengers exists within a large-passenger-flow subway station,and the number of casualties and injuries during a fire emergency is substantial.In this paper,Pathfinder software and on-site measured data of Pingzhou station in Shenzhen(China)were utilized to simulate a fire emergency evacuation in a large-passenger-flow subway station.The Required Safe Egress Time(RSET),number of passengers and flow rates of stairs and escalators were analysed for three fire evacuation scenarios:train fire,platform fire and hall fire.The evacuation time of the train fire,which was 1173 s,was the longest,and 3621 occupants needed to evacuate when the train was fully loaded.Occupants could not complete the evacuation within 6 mins in all three fire evacuation scenarios,which does not meet the current standard requirements and codes.By changing the number of passengers and the number of stairs for evacuation,the flow rate capacity and evacuation time were explored,which have reference values for safety management and emergency evacuation plan optimization during peak hours of subway operation.

流场测量数据的预处理分析与实现

基于一组典型工业流场环境下流场数据分析和处理的要求,对检测数据进行一定预处理,使数据具有典型的随机过程的特征。研发数据处理系统,通过软件实现流场测量,从而使经过预处理的流场序列数据为进一步开发流场测量数据处理系统提供可靠的保障。

Urban Water Resource Utilization Efficiency in China

The efficient use of water resources directly affects environmental, social, and economic development; therefore, it has a significant impact on urban populations. A slacks-based measure for data envelopment analysis （SBM-DEA） has been widely used in energy efficiency and environmental efficiency analyses in recent years. Based on this model, data from 316 cities were examined and a category method was employed involving three different sorting techniques to empirically evaluate the efficiency of urban water re- source utilization in China between 2000 and 2012. The overall efficiency （OE） of urban water resource utilization in China was initially low, but has improved over the past decade. The scale efficiency （SE） was higher than the pure technological efficiency （PTE）; PTE is a major determining factor of OE, and has had an increasingly significant effect. The efficiency of water resource utilization varied ac- cording to the region, urban scale, and economic function. The OE score for the eastern China was higher than for the rest of the region, and the OE score for the western China was higher than for the central China. The OE score for urban water resource utilization has improved with urban expansion, except in the case of small cities. The SE showed an inverted U-shaped＇ trend with increasing urban expansion. The OE of urban water utilization in comprehensive functional cities was greater than in economic specialization cities, and was greater in heavy industry specialization cities than in other specialization cities. This study contributes to the field of urban water resource management by examining variations in efficiency with urban ~ezle

Back-n white neutron source at CSNS and its applications

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source(CSNS)that emit through the incoming proton channel were exploited to build a white neutron beam facility(the so-called Back-n white neutron source),which was completed in March 2018.The Back-n neutron beam is very intense,at approximately 29107 n/cm2/s at 55 m from the target,and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the slices.In addition,the excellent energy spectrum spanning from 0.5 eV to 200 MeV,and a good time resolution related tothe time-of-flight measurements make it a typical white neutron source for nuclear data measurements;its overall performance is among that of the best white neutron sources in the world.Equipped with advanced spectrometers,detectors,and application utilities,the Back-n facility can serve wide applications,with a focus on neutron-induced cross-sectional measurements.This article presents an overview of the neutron beam characteristics,the experimental setups,and the ongoing applications at Backn.

ON SINGULAR EQUATIONS INVOLVING FRACTIONAL LAPLACIAN

We study the existence and the regularity of solutions for a class of nonlocal equations involving the fractional Laplacian operator with singular nonlinearity and Radon measure data.

Asymptotic Calculation of the Wave Trough Exceedance Probabilities in A Nonlinear Sea

This paper concerns the calculation of the wave trough exceedance probabilities in a nonlinear sea. The calculations have been carried out by incorporating a second order nonlinear wave model into an asymptotic method. This is a new approach for the calculation of the wave trough exceedance probabilities, and, as all of the calculations are performed in the probability domain, avoids the need for long time-domain simulations. The proposed asymptotic method has been applied to calculate the wave trough depth exceedance probabilities of a sea state with the surface elevation data measured at the coast of Yura in the Japan Sea. It is demonstrated that the proposed new method can offer better predictions than the theoretical Rayleigh wave trough depth distribution model. The calculated results by using the proposed new method have been further compared with those obtained by using the Arhan and Plaisted nonlinear distribution model and the Toffoli et al.’s wave trough depth distribution model, and its accuracy has been once again substantiated. The research findings obtained from this study demonstrate that the proposed asymptotic method can be readily utilized in the process of designing various kinds of ocean engineering structures.

ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

Estimate the influence of snow grain size and black carbon on albedo

Estimation of the influence of snow grain size and black carbon on albedo is essential in obtaining the accurate albedo. In this paper, field measurement data, including snow grain size, snow depth and density was obtained. Black carbon samples were collected from the snow surface. A simultaneous observation using Analytical Spectral Devices was employed in the Qiyi Glacier located in the Qilian Mountain. Analytical Spectral Devices spectrum data were used to analyze spectral re- flectance of snow for different grain size and black carbon content. The measurements were compared with the results obtained from the Snow, Ice, and Aerosol Radiation model, and the simulation was found to correlate well with the ob- served data. However, the simulated albedo was near to 0.98 times of the measured albedo, so the other factors were as- sumed to be constant using the corrected Snow, Ice, and Aerosol Radiation model to estimate the influence of measured snow grain size and black carbon on albedo. Field measurements were controlled to fit the relationship between the snow grain size and black carbon in order to estimate the influence of these factors on the snow albedo.