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.

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.

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.

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.

Comparison of three measurement models of soil nitrate-nitrogen based on ion-selective electrodes

Ion-selective electrode(ISE)is a quick and low-cost method of soil nitrate nitrogen(N)detection.The measurement models of soil nitrate-N based on ISEs includes the linear regression model,multiple linear regression model and BP neural network model,and so on.Three models were analyzed in theory,measurement experiments of validation samples and soil nitrate-N concentrations were carried out in this study,and the measurement accuracies of the three models were compared.The results showed that,in the measurement experiments of validation samples and soil nitrate-N concentrations,BP neural network model had the highest accuracy(the average relative errors between results of the BP neural network model and the reference values were 5.07%and 8.81%,respectively)among the three models,multiple linear regression model had the second highest accuracy(the average relative errors between results of the multiple linear regression model and the reference values were 7.70%and 10.51%,respectively),linear regression model couldn’t exclude the interference of chloride ions so that it had the lowest accuracy(the average relative errors between results of the linear regression model and the reference values were 11.16%and 12.28%,respectively)among the three models.The BP neural network model can effectively restrain the interference of chloride ions,and it has a high accuracy for the measurement of soil nitrate-N concentration,so that the BP neural network model can be used to measure soil nitrate-N concentration accurately.

Spatio-temporal Evolution of the Agricultural Eco-efficiency Network and Its Multidimensional Proximity Analysis in China

As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for China from 1997 to 2019,this study divided these data into five periods according to the Five-Year Plan(FYP)of China,measured the agricultural eco-efficiency(AEE)values using the Super-SBM model,and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model.Finally,social network analysis(SNA)was used to further analyze the evolution process of AEE,and we de-veloped a framework of how multidimensional proximity,which includes geographical,economic,technological,cognitive,and institutional proximity,made an influence on the formation of AEE spatial relation network.The findings indicated that:1)in 1997−2019,the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale,and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE.2)The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects.The evolution of the AEE network has clearly observable trends of hierarchization and aggregation,and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west.The network structure has changed from monocentric radiation to a multicentric network,and network nodes select the more advantageous nodes with which to connect.3)Finally,the geographical proximity had a significant negative effect;the economic,technological,and institutional proximities were all observed to contribute to the AEE network formation,and cognitive proximity did not significantly influence this network formation.

Installation error calibration for MEMS angular measurement system in hypersonic wind tunnel

The accuracy of model attitude measurement has an important impact on wind tunnel test results. Microelectromechanical System Inertial Measurement Unit(MEMS IMU) provides a feasible way to measure model attitudes with high accuracy. However, the installation error between MEMS IMU coordinate system and the body coordinate system of test models can make the accuracy of the model attitude measurement decrease. In wind tunnel tests, the installation error depends on the relationship between the IMU and the model mechanism before tests. Therefore, infield calibration in wind tunnel tests is necessary to reduce installation errors. To improve attitude measurement accuracy, the least squares quaternion calibration method based on MEMS IMU and six-position calibration procedure are proposed. High-precision three-axis turntable tests are performed. The pitch accuracy after calibration is higher than that before calibration in the angle of attack sweeping tests. The Root-Mean-Square Errors(RMSE) in the roll and yaw are within0.01°, which are smaller than those before calibration. In the roll sweeping tests, RMSE of three attitude angles decrease significantly. In hypersonic wind tunnel tests, the pitch errors before and after calibration are within 0.05° and 0.02° in the angle of attack sweeping tests without wind. In five angle of attack sweeping tests with wind, the deviation between the mean of the pitch and the pitch after the elastic angle correction is within 0.03° and the standard deviation of five tests is within 0.01°. The proposed method is confirmed to enhance the accuracy of attitude measurement effectively, which is convenient for engineering applications.

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

Measurement investigation on the feasibility of shallow geothermal energy for heating and cooling applied in agricultural greenhouses of Shouguang City: Ground temperature profiles and geothermal potential

The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source of energy could be applied.In this study,a measurement model,the distribution profiles of temperature,and a preliminary assessment of the geothermal potential in the shallow zone at depths of 0.1 m to 3.6 m in Shouguang City,Shandong Province,eastern China were presented.The measurement results showed that the annual average temperature at depths of 0.1–3.6 m ranged from 13.1℃ to 17.6℃.Preliminary assessment results of the geothermal potential showed that the daily average temperature difference between the air and at depths of 1.5–3.6 m was mainly from 10℃ to 25℃ during the winter months and between-15℃ and-5℃ during the summer months.Therefore,the heating systems could operate during January,February,November,and December.In May,June,and July,the cooling systems could be applied.Moreover,the measurement model gave good stability results,and it could be used in combination with the monitoring of the groundwater table,a survey of the thermal conductivity of the soil,climate change studies,which helps reduce unnecessary time and costs.

Variable Selection in High-Dimensional Error-in-Variables Models via Controlling the False Discovery Proportion

Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to select the important variables among many variables.Performing variable selection in high-dimensional linear models with measurement errors is challenging.Both the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely biases.We consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection method.By constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables models.An extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.

Research frameworks, methodologies, and assessment methods concerning the adaptive reuse of architectural heritage: a review

With the growing trend towards preserving global architectural heritage, the adaptive reuse of built heritagebuildings is becoming increasingly popular;as commentators have noted, this popularity can in part be attributedto the economic, cultural, and social benefits they provide to urban communities. In considering adaptive reuse,urban developers and planners seek to reach an equilibrium in the battle between time and space. Bothacademically and practically, the adaptive reuse of heritage buildings requires compatible, appropriate, andscientific means for assessing built heritage assets;however, currently, research in this area is still relatively meagre.To address this gap, this paper investigates research frameworks, methodologies, and assessment methods thatconcern the adaptive reuse of architectural heritage. In this paper, we examine the current literature on theparadigms for applying mixed methodologies: the multi-criteria decision model (MCDM) and the preferencemeasurement model (PMM). Specifically, in examining the extant literature, we explore the ways in which thesemethods are discussed, compared, and evaluated, and the positive functions of these methods are also highlighted.In addition, this review examines a range of cases to better clarify the research frameworks, methodologies, andassessment methods used in the study of the adaptive reuse of architectural heritage.

Measuring County Resilience After the 2008 Wenchuan Earthquake

The catastrophic earthquake that struck Sichuan Province,China,in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China.In recent years,great attention has been paid to the resilience of the affected area.This study applied the resilience inference measurement(RIM) model to quantify and validate the community resilience of 105 counties in the impacted area.The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure,damage,and recovery conditions.The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's resilience.Analysis results show that counties located at the epicenter had the lowest resilience,but counties immediately adjacent to the epicenter had the highest resilience capacities.Counties that were farther away from the epicenter returned to normal resiliency quickly.Socioeconomic variables—including sex ratio,per capita GDP,percent of ethnic minority,and medical facilities—were identified as the most influential characteristics influencing resilience.This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.

A Stable and Energy-Efficient Routing Algorithm Based on Learning Automata Theory for MANET

The mobile Ad Hoc network(MANET)is a self-organizing and self-configuring wireless network,consisting of a set of mobile nodes.The design of efficient routing protocols for MANET has always been an active area of research.In existing routing algorithms,however,the current work does not scale well enough to ensure route stability when the mobility and distribution of nodes vary with time.In addition,each node in MANET has only limited initial energy,so energy conservation and balance must be taken into account.An efficient routing algorithm should not only be stable but also energy saving and balanced,within the dynamic network environment.To address the above problems,we propose a stable and energy-efficient routing algorithm,based on learning automata(LA)theory for MANET.First,we construct a new node stability measurement model and define an effective energy ratio function.On that basis,we give the node a weighted value,which is used as the iteration parameter for LA.Next,we construct an LA theory-based feedback mechanism for the MANET environment to optimize the selection of available routes and to prove the convergence of our algorithm.The experiments show that our proposed LA-based routing algorithm for MANET achieved the best performance in route survival time,energy consumption,energy balance,and acceptable per-formance in end-to-end delay and packet delivery ratio.

Mass transfer mechanisms in fixed-bed adsorption of erythromycin

The equilibrium and kinetic characteristics of the adsorption of erythromycin to Sepabeads SP825 were determined.The equilibrium data in a batch system was well described by a Langmuir isotherm.The separation performance was investigated in a fixed-bed system with respect to the adsorption superficial velocity,ionic strength and pH.A mathematical model was used to simulate the mass transfer mechanism,taking film mass transfer,pore diffusion and axial dispersion into account.The model predictions were consistent with the experi-mental data and were consequently used to determine the mass transfer coefficients.