A New Device for Gas-Liquid Flow Measurements Relying on Forced Annular Flow

A new measurement device,consisting of swirling blades and capsule-shaped throttling elements,is proposed in this study to eliminate typical measurement errors caused by complex flow patterns in gas-liquid flow.The swirling blades are used to transform the complex flow pattern into a forced annular flow.Drawing on the research of existing blockage flow meters and also exploiting the single-phase flow measurement theory,a formula is introduced to measure the phase-separated flow of gas and liquid.The formula requires the pressure ratio,Lockhart-Martinelli number(L-M number),and the gas phase Froude number.The unknown parameters appearing in the formula are fitted through numerical simulation using computational fluid dynamics(CFD),which involves a comprehensive analysis of the flow field inside the device from multiple perspectives,and takes into account the influence of pressure fluctuations.Finally,the measurement model is validated through an experimental error analysis.The results demonstrate that the measurement error can be maintained within±8%for various flow patterns,including stratified flow,bubble flow,and wave flow.

ZTE Communications Special Issue on Channel Measurement and Modeling for Heterogeneous 5G

While cellular networks have continuously evolved in re- cent years, the industry has clearly seen unprecedented chal- lenges to meet the exponentially growing expectations in the near future. The 5G system is facing grand challenges such as the ever-increasing traffic volumes and remarkably diver- sified services connecting humans and machines alike. As a result, the future network has to deliver massively increased capacity, greater flexibility, incorporated computing capabili- ty, support of significantly extended battery lifetime, and ac- commodation of varying payloads with fast setup and low latency, etc. In particular, as 5G requires more spectrum resource, higher frequency bands are desirable. Nowadays, millimeter wave has been widely accepted as one of the main communication bands for 5G.

ZTE Communications Special Issue on Channel Measurement and Modeling for Heterogeneous 5G

While cellular networks have continuously evolved in recent years, the industry has clearly seen unprecedented challenges to meet the exponentially growing expectations in the near future.

Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020

Improving cultivated land use eco-efficiency(CLUE)can effectively promote agricultural sustainability,particularly in developing countries where CLUE is generally low.This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model.In addition,given the undesirable outputs of agricultural production,we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure(GB-US-SBM)model,which combines global benchmark technology,undesirable output,super efficiency,and slacks-based measure.The results indicated that there was an upward trend in CLUE in China from 2000 to 2020,with an increase rate of 2.62%.The temporal evolution of CLUE in China could be classified into three distinct stages:a period of fluctuating decrease(2000-2007),a phase of gradual increase(2008-2014),and a period of rapid growth(2015-2020).The major grain-producing areas(MPAs)had a lower CLUE than their counterparts,namely,non-major grain-production areas(non-MPAs).The spatial agglomeration effect followed a northeast-southwest strip distribution;and the movement path of barycentre revealed a"P"shape,with Luoyang City,Henan Province,as the centre.In terms of influencing factors of CLUE,investment in science and technology played the most vital role in improving CLUE,while irrigation index had the most negative effect.It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs.Therefore,relevant departments should formulate policies to enhance the level of science and technology,improve irrigation condition,and promote sustainable utilization of cultivated land.

Measurement and Spatio-temporal Distribution of Urbanization Development Quality of Urban Agglomeration in China

Urbanization development quality(UDQ) of urban agglomeration(UA) is one of the important indexes to evaluate if the UA urbanization speed is reasonable,if the population urbanization process is sound,if the economic urbanization process is efficient,if the social urbanization process is harmonious and fair,which is generally composed of three parts,including economic urbanization development quality,social urbanization development quality and spa-tial urbanization security quality.With the research of evolution rules of UDQ,through the analysis on the interactive coordination relation between UDQ and urbanization level,the paper proposes three dimensional index ball and stan-dard value of comprehensive measures of UDQ,which is composed of three categories of indexes.By introducing At-kinson model,this paper constructs the sub-element measure model and segmental measure model of UDQ,and fur-ther makes an overall evaluation on the characteristic of UDQ of UAs in China.Results show that:1) UDQ of UAs lied at optimization and upgrading phase during 1995-2008 and the quality was low.Population urbanization level did not reflect the UDQ of UAs;2) the improving speed of the spatial urbanization security quality was higher than the so-cial urbanization development quality,and the improving speed of economic urbanization development quality was the lowest;3) the coordination between UDQ and urbanization level was not so good;4) there was a weak correlation re-lation between UDQ and UA scale,and it was not true that the bigger the UA was,the better the UDQ was.

Multiple Measurement Models of Articulated Arm Coordinate Measuring Machines

The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.

Integrating unascertained measurement and information entropy theory to assess blastability of rock mass

Due to the complex features of rock mass blastability assessment systems, an evaluation model of rock mass blastability was established on the basis of the unascertained measurement （UM） theory and the actual characteristics of the project. Considering a comprehensive range of intact rock properties and discontinuous structures of rock mass, twelve main factors influencing the evaluation blastability of rock mass were taken into account in the UM model, and the blastability evaluation index system of rock mass was constructed. The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively. Then, the UM function of each evaluation index was obtained based on the initial data for the analysis of the blastability of six rock mass at a highway improvement cutting site in North Wales. The index weights of the factors were calculated by entropy theory, and credible degree identification （CDI） criteria were established according to the UM theory. The results of rock mass blastability evaluation were obtained by the CDI criteria. The results show that the UM model assessment results agree well with the actual records, and are consistent with those of the fuzzy sets evaluation method. Meanwhile, the unascertained superiority degree of rock mass blastability of samples S1-$6 which can be calculated by scoring criteria are 3.428 5, 3.453 3, 4.058 7, 3.675 9, 3.516 7 and 3.289 7, respectively. Furthermore, the proposed method can take into account large amount of uncertain information in blastability evaluation, which can provide an effective, credible and feasible way for estimating the blastability of rock mass. Engineering practices show that it can complete the blastability assessment systematically and scientifically without any assumption by the proposed model, which can be applied to practical engineering.

Unascertained measurement classifying model of goal collapse prediction

Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are influential factors including over- burden layer type,overburden layer thickness,the complex degree of geologic structure, the inclination angle of coal bed,volume rate of the cavity region,the vertical goaf depth from the surface and space superposition layer of the goaf region.Unascertained mea- surement (UM) function of each factor was calculated.The unascertained measurement to indicate the classification center and the grade of waiting forecast sample was determined by the UM distance between the synthesis index of waiting forecast samples and index of every classification.The training samples were tested by the established model,and the correct rate is 100%.Furthermore,the seven waiting forecast samples were predicted by the UMC model.The results show that the forecast results are fully consistent with the ac- tual situation.

Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

Evaluation of the sustainable development level of countries along the Belt and Road and its impact factors:Empirical analysis based on the Super-efficiency slacks-based measure and Tobit measure models

Sustainable development is an important component of the Belt and Road Initiative(BRI)and is of great significance for evaluating the levels of sustainable development of countries along this route(henceforth,BRI countries).Therefore,this study aims to identify the factors that influence the levels of sustainable development of BRI countries in a reasonable and objective manner.Eventually,this study employs the super efficiency slacks-based measure(Super-SBM)model,which considers unexpected outputs to measure the level of sustainable development of BRI countries.The dynamic change and composition of the sustainable development level of these countries are calculated using the global Malmquist-Luenberger index.Furthermore,the Tobit model is used to identify the factors influencing the level of sustainable development of BRI countries in general and in various categories.The empirical results suggest the following points.(a)The overall level of sustainable development of BRI countries is low,whereas those of high-income and middle-and high-income countries are relatively high.(b)The overall sustainable development levels of BRI countries declined to a certain extent in 2008 owing to the effect of the financial crisis,.However,the sustainable development level of other countries,barring low-income countries,has gradually increased since 2011.(c)Since 2008,technological progress has replaced technical efficiency as the main driving force behind the improvement of the sustainable development level of BRI countries.(d)A U-shaped relationship is observed between the economic and sustainable development levels of these countries.(e)The level of science and technology and the proportion of renewable energy consumption can promote the sustainable development of these countries.Moreover,a negative correlation exists between the level of opening to the outside world and that of sustainable development of countries that mainly export resource-based products and are dominated by labor-intensive export industries.Barring low-income countries,the energy structure plays an effective role in improving the level of sustainable development.Finally,the study presents suggestions for China in the process of coping with the sustainable development of relevant countries during its promotion of the BRI.

Sub-external cavity effect and elimination method in laser self-mixing interference wave plate measurement system

Laser self-mixing interference（SMI） wave plate measurement method is a burgeoning technique for its simplicity and efficiency. But for the non-coated sample, the reflected light from the surface can seriously affect the measurement results.To analyze the reason theoretically, a self-consistent model for laser operation with a sub-external and an external cavity is established, and the sub-external cavity formed by the sample and a cavity mirror is proved to be the main error source.A synchronous tuning method is proposed to eliminate the sub-external cavity effect. Experiments are carried out on the synchronously tuning double external cavities self-mixing interference system, and the error of the system is in the range of -0.435°~0.387° compared with the ellipsometer. The research plays an important role in improving the performance and enlarging the application range of the laser self-mixing interference system.

Modeling Multisource-heterogeneous Information Based on Random Set and Fuzzy Set Theory

This paper presents a new idea, named as modeling multisensor-heterogeneous information, to incorporate the fuzzy logic methodologies with mulitsensor-multitarget system under the framework of random set theory. Firstly, based on strong random set and weak random set, the unified form to describe both data (unambiguous information) and fuzzy evidence (uncertain information) is introduced. Secondly, according to signatures of fuzzy evidence, two Bayesian-markov nonlinear measurement models are proposed to fuse effectively data and fuzzy evidence. Thirdly, by use of "the models-based signature-matching scheme", the operation of the statistics of fuzzy evidence defined as random set can be translated into that of the membership functions of relative point state variables. These works are the basis to construct qualitative measurement models and to fuse data and fuzzy evidence.

The Establishment of Measurement Model and Implementation Strategy for Service Productivity

As a key factor of economic growth, productivity has been valued in the academic community. Today, with the rapid development of service industry, the research for service productivity has also attracted wide attention. However, in the service industry, because of its own characteristics and properties, measurement of service productivity could not apply for the traditional productivity measurement methods simply. This research first has put out the constitution model of service productivity, and thus put out the measurement model of service productivity. And explains that service productivity is a function which contains internal efficiency, external efficiency, and capacity efficiency. In service productivity, external efficiency is the key one, internal efficie and capacity efficiency should also be given considerations. Eventually, the strategy of implementing measurement of service productivity have been proposed ncy the

Recent Developments of Image Based Measurement Methods for Application to Transonic Flows in Industrial Wind Tunnels

The experimental investigation of unsteady complex flow fields in wind tunnels requires advanced measurement techniques. The most important of such image based measurement techniques are those for the measurement of planar flow velocity fields, planar pressure distribution, model location and deformation, model temperature and quantitative high speed flow visualization. The applications as carried out by DLR range from low speed flows to transonic flows, from high lift configurations to propellers and rotors, from wake vortex investigations in catapult facilities and water towing tanks to investigations of vortex break down phenomena on delta wings. The capability to use image based measurement techniques in transonic flows requires dedicated technical developments and experienced scientists due to the special environment of a transonic wind tunnel. In this paper an overview of the state-of-the art of the application of image based measurement techniques in transonic flows as performed by DLR＇s Institute of Aerodynamics and Flow Technology will be given.

MEASUREMENT METHOD AND PHYSICAL MODEL OF VSC CONDUCTIVITY AND ITS APPLICATIONS IN CONDUCTING POLYMERS

Method of VSC (Voltage Shorted Compaction)can be used to determine the intrinsic temperature dependence ofconductivity ofpolycrystalline compaction. The experimental conditions and technical key for preparation of VSC device and its physical model as well as its applications in conducting polymers are discussed in detail.

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)applications.Edge devices enable limited computational capacity and energy availability that hamper end user performance.We designed a novel performance measurement index to gauge a device’s resource capacity.This examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services.In this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions.Consequently,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the network.Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation factor.Our system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

Characteristics of a Flow Transducer Based on Polarized Charge

In this paper we analyze the characteristics of a flow transducer based on polarized charge. The effects of the charged particles in pneumatic pipeline on the measurement pipe potential are discussed in detail and the equivalent circuits of the potential measurement are presented. On this bases, the relationships between mass flowrate and the electrical potential are obtained for different time constants of the measurement circuit. A satisfactory model is presented based on the characteristics of gas solid two phase flow. The linearity of the model is verified by the experiment results. The transducer, which is coaxially connected with the transport pipeline, does not disturb the flow state and has the features of ruggedness and durability, it is especially suitable for industry process control.

Can climate change influence agricultural GTFP in arid and semi-arid regions of Northwest China?

There are eight provinces and autonomous regions(Gansu Province,Ningxia Hui Autonomous Region,Xinjiang Uygur Autonomous Region,Inner Mongolia Autonomous Region,Tibet Autonomous Region,Qinghai Province,Shanxi Province,and Shaanxi Province)in Northwest China,most areas of which are located in arid and semi-arid regions(northwest of the 400 mm precipitation line),accounting for 58.74%of the country's land area and sustaining approximately 7.84×10^6 people.Because of drought conditions and fragile ecology,these regions cannot develop agriculture at the expense of the environment.Given the challenges of global warming,the green total factor productivity(GTFP),taking CO2 emissions as an undesirable output,is an effective index for measuring the sustainability of agricultural development.Agricultural GTFP can be influenced by both internal production factors(labor force,machinery,land,agricultural plastic film,diesel,pesticide,and fertilizer)and external climate factors(temperature,precipitation,and sunshine duration).In this study,we used the Super-slacks-based measure(Super-SBM)model to measure agricultural GTFP during the period 2000-2016 at the regional level.Our results show that the average agricultural GTFP of most provinces and autonomous regions in arid and semi-arid regions underwent a fluctuating increase during the study period(2000-2016),and the fluctuation was caused by the production factors(input and output factors).To improve agricultural GTFP,Shaanxi,Shanxi,and Gansu should reduce agricultural labor force input;Shaanxi,Inner Mongolia,Gansu,and Shanxi should decrease machinery input;Shaanxi,Inner Mongolia,Xinjiang,and Shanxi should reduce fertilizer input;Shaanxi,Xinjiang,Gansu,and Ningxia should reduce diesel input;Xinjiang and Gansu should decrease plastic film input;and Gansu,Shanxi,and Inner Mongolia should cut pesticide input.Desirable output agricultural earnings should be increased in Qinghai and Tibet,and undesirable output(CO2 emissions)should be reduced in Inner Mongolia,Xinjiang,Gansu,and Shaanxi.Agricultural GTFP is influenced not only by internal production factors but also by external climate factors.To determine the influence of climate factors on GTFP in these provinces and autonomous regions,we used a Geographical Detector(Geodetector)model to analyze the influence of climate factors(temperature,precipitation,and sunshine duration)and identify the relationships between different climate factors and GTFP.We found that temperature played a significant role in the spatial heterogeneity of GTFP among provinces and autonomous regions in arid and semi-arid regions.For Xinjiang,Inner Mongolia,and Tibet,a suitable average annual temperature would be in the range of 7℃-9℃;for Gansu,Shanxi,and Ningxia,it would be 11℃-13℃;and for Shaanxi,it would be 15℃-17℃.Stable climatic conditions and more efficient production are prerequisites for the development of sustainable agriculture.Hence,in the agricultural production process,reducing the redundancy of input factors is the best way to reduce CO2 emissions and to maintain temperatures,thereby improving the agricultural GTFP.The significance of this study is that it explores the impact of both internal production factors and external climatic factors on the development of sustainable agriculture in arid and semi-arid regions,identifying an effective way forward for the arid and semi-arid regions of Northwest China.

Methodology for production logging in oil-in-water flows under low flow rate and high water-cut conditions

This study aimed to obtain the production profiles of oil-in-water flow under low flow rate and high water-cut conditions in oil wells.A combination production profile logging composed of an arc-type conductance sensor(ATCS)and a cross-correlation flow meter(CFM)with a center body is proposed and experimentally evaluated.The ATCS is designed for water holdup measurement,whereas the CFM with a center body is proposed to obtain the mixture velocity.Then,a drift-flux model based on flow patterns is established to predict the individual-phase superficial velocity of oil-in-water flows.Results show that the ATCS possesses high resolution in water holdup measurement and that flow pattern information can be deduced from its signal through nonlinear time series analysis.The CFM can enhance the correlation of upstream and downstream signals and simplify the relationship between the cross-correlation velocity and mixture velocity.On the basis of the drift-flux model,individual-phase superficial velocities can be predicted with high accuracy for different flow patterns.

Singularity of Some Software Reliability Models and Parameter Estimation Method

According to the principle, “The failure data is the basis of software reliability analysis”, we built a software reliability expert system (SRES) by adopting the artificial intelligence technology. By reasoning out the conclusion from the fitting results of failure data of a software project, the SRES can recommend users “the most suitable model” as a software reliability measurement model. We believe that the SRES can overcome the inconsistency in applications of software reliability models well. We report investigation results of singularity and parameter estimation methods of experimental models in SRES.