Recursive Model Research Articles

Impact of regional accessibility on urban residents' income in China: A bidirectional-fixed panel model.

The income gap between regions and its expansion are the main manifestations of the imbalanced and inadequate economic development in China. High-speed railway (HSR) construction is regarded as an important method to drive domestic demand, drive the pulse of the economy, and promote the coordinated development of regions. Based on the opening of HSR and the acceleration of ordinary railways, we used the weighted average travel time model and accessibility coefficient to estimate the changes on accessibility in 286 cities at prefecture-level and above from 2000 to 2018. Then, the influence mechanisms of improving regional accessibility on urban residents' income were estimated by using the bidirectional-fixed effects panel model and the recursive model respectively. We found that: (1) The accessibility of urban areas has been greatly improved due to the opening of HSR and the acceleration of ordinary railway, among which the improvement of HSR cities is greater. (2) The improvement of regional accessibility significantly promoted the income growth of urban residents, and the increase of the regional accessibility coefficient by 1 unit led to an average increase of 2140 yuan in the per capita disposable income of urban residents. (3) There is regional heterogeneity in the impact of improving regional accessibility on urban residents' income, and it has a significant effect on the eastern and northeastern regions. It has a greater positive effect on improving the income of residents in central cities compared with peripheral cities. (4) Regional accessibility can promote urban income growth through regional employment and fixed asset investment. In the future, the transportation network should be further improved to facilitate the regional economic cycle, strengthen the coordination and complementarity of regional economies, and promote regional economic integration so as to promote the improvement of resident income level and the common prosperity of the people.

Tensor-Train Decomposition-Based Hybrid Beamforming for Millimeter-Wave Massive Multiple-Input Multiple-Output/Free-Space Optics in Unmanned Aerial Vehicles with Reconfigurable Intelligent Surface Networks

Unmanned aerial vehicles (UAVs) can support low-cost, highly mobile communications while making it possible to establish dedicated terrestrial networks. To overcome the pointing error (PE) and beam misalignment of millimeter-wave large-scale multiple-input multiple-output/free-space optics (MIMO/FSO) caused by UAV jitter, a millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is proposed. This approach is used for reconfigurable intelligent surface (RIS) network-assisted UAV millimeter-wave massive MIMO/FSO to improve system spectral efficiency. Firstly, the high-dimensional channel of RIS-assisted millimeter-wave massive MIMO/FSO in UAV is represented as a low-dimensional channel by tensor training decomposition. Secondly, the two-way gated recursive unit attention neural network model can effectively solve the FSO PE caused by UAV jitter, and the fast fading channel and Doppler frequency shift are estimated by the Fast Cyclic Tensor Power Method (FCTPM) based on tensor training decomposition. Finally, the RIS phase shift matrix is optimized by singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were estimated by using the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation results show that by using the proposed method, the spectrum utilization rate is improved by 23.6% compared with other methods.

Explicit potential function and fast algorithm for computing potentials in [formula omitted] conic surface resistor network

Resistor network research is of great importance, yet many resistor networks and their large-scale fast computations have not received sufficient attention. This paper proposes a new resistor network with idiosyncratic shape, i.e., a α×β conic surface (CS) resistor network that resembles the upper part of a three-dimensional Dirac function. Utilizing the Recursion Transform (RT-V) method of Tan, a recursive matrix equation model is constructed based on Kirchhoff’s law and nodal voltages, which contains the modified tridiagonal Toeplitz matrix. By using the orthogonal matrix transformation, the eigenvalues and eigenvectors of the modified tridiagonal Toeplitz are obtained. The discrete sine transform of the fourth type (DST−IV) is utilized to solve node voltages, while the explicit potential function is represented by the Chebyshev polynomials of the second kind. In addition, explicit potential functions for some special cases are provided, and the potential distribution is illustrated using dynamic three-dimensional graph. To achieve a rapid calculation of the potential, a fast algorithm based on the multiplication of DST-IV with a vector is proposed. In the end, analysis of computational efficiency for the explicit potential function and the fast algorithm are shown.

THE INFLUENCE OF FISCAL POLICY ON UNEMPLOYMENT RATE IN TÜRKİYE

In the context of ongoing economic discussion, the connection between government actions and economic performance remains an important issue. There exist diverse opinions and arguments regarding how fiscal policy affects the unemployment rate from both empirical and theoretical perspective. The objective of this research is to explore the response of unemployment rate to fiscal policy shocks, namely government expenditure and tax revenue shocks. To achieve this goal, we gather annual data spanning from 1990 to 2021 for Türkiye employing recursive Structural VAR model. The results show that a tax revenue shock, corresponding to a one standard deviation change, initially results in a temporary decline in the unemployment but it positively affects the unemployment rate in the long run. Furthermore, the reaction of the unemployment to a government spending shock follows a hump-shape pattern. Hence, we can conclude that the government expenditure shock increases unemployment rate temporarily but decreases it slightly over the long term.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements

Background and objectiveFor patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training. MethodsThis study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern. ResultsSix muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent. ConclusionThe proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Tired or Inspired: A Conceptual Model for Using Regenerative Artificial Intelligence to Create Context, User, and Time-Aware Individualized Shopping Guidance

This paper conceptualizes a consumer-centric, regenerative artificial intelligence (“ReGenAI”) model for the Fast-Moving Consumer Goods (“FMCG”) retailing channel. The system uses its awareness of context, time, and users to (re)generate customer touchpoints and other marketing communications. Its output provides deep insights into regular and altered FMCG customer journeys, such as shopping behaviors under stressors like lifestyle choices or cataclysmic socio-economic and weather events. The recursive model advances from current, generative AI systems. It uses “tired or inspired” as a simplified bifurcated grocery shopper taxonomy to operationalize customers’ purchasing and consumption behaviors into actionable data for demand planning and retail operations.

Mapping the ratoon rice suitability region in China using random forest and recursive feature elimination modeling

Mapping the ratoon rice suitability region in China using random forest and recursive feature elimination modeling

Fault classification and detection for photovoltaic plants using machine learning algorithms

<span>Using photovoltaic (PV) energy has increased in recently, due to new laws that aim to reduce the global use of fossil fuels. The efficiency of a PV system relies on many types of malfunctions which may cause significant energy loss during the system’s operation, besides the ecological factors. Consequently, a monitoring system (MS) capable of measuring both the environmental and electrical factors is described in order to gather real-time and historical data and estimate the plant efficiency metrics. Additionally, a recursive linear model for detecting problems in the system is presented, where the input is the irradiance and temperature of the PV module, whereas the output is the power, using the same MS. The achieved fault detection’s accuracy for the 5-kW power plant reached 93.09 percent, based on 16 days and 143 hours of failures under various situations. After detecting a defect, a machine-learning-based algorithm categorizes each defect problem as short circuit, partial shadowing, deterioration, or open-circuit. The performance of the four most prevalent supervised machine learning (ML) approaches for this assignment (Naïve Bias, decision tree, LDA, and KNN) was evaluated according to their results.</span>

Impact of carbon border adjustment mechanism on China's manufacturing sector: A dynamic recursive CGE model based on an evolutionary game

As the global carbon emission problem is getting more and more serious, the European Union has proposed the Carbon Border Adjustment Mechanism (CBAM) to prevent carbon leakage, which will have an impact on China's foreign trade. Based on this background and the problems of current research that ignores factors such as commodity prices, long-term impacts, intertemporal substitution analysis, and changes in national strategies for foreign exports, this paper propose a Computable General Equilibrium (CGE) model based on an evolutionary game as a dynamic recursive basis to study the impact of the CBAM on the Chinese manufacturing industry. For the industries mentioned in the CBAM, 18 affected sectors are collated, and a CGE model containing multiple blocks such as production, trade, and carbon emissions is built. Meanwhile, we establish an evolutionary game model with EU suppliers, manufacturers, and the government, allowing Chinese suppliers to compete with EU suppliers. Based on the above model, the impact of carbon tariff policies from 2020 to 2050 is studied. The results indicate that under the intra-EU competition condition, carbon tariffs will reduce the price of Chinese exports and slightly decrease China's real GDP, as well as the carbon emission intensity of 18 sectors and fossil energy. However, the impact of carbon tariffs on overall carbon emissions is small and is essentially a protective policy. In addition, the evolutionary game-based CGE model takes into account the dynamic strategies of both sides and is therefore less affected by carbon tariff shocks and recovers more quickly and more realistically. Finally, the article argues that the advantage of carbon tariffs for local suppliers is unsustainable and Chinese suppliers will still dominate, and local suppliers need more subsidies from the EU government. On the other hand, the Chinese government needs to implement a stricter carbon tax regime and export subsidies to improve the competitiveness of Chinese products in terms of green levels and price advantages. The purpose of this paper is to examine the quantitative impacts of CBAM on China's related manufacturing industries and strategies to maximize the benefits to both governments.

Analysing individual differences in car use to maintenance and discretionary leisure activities

In order to reach ambitious goals of CO2 reduction, a decrease in fossil-fuelled private transport is important. Research on car use for commuters dominates literature although transport for leisure purposes constitutes a substantial share of the total transported kilometres. This study contributes by its focus on car use to maintenance (e.g. grocery shopping) and discretionary (e.g. social visits) leisure activities. We analyse whether individual perceptions about a personal responsibility to contribute to a sustainable transport sector restricts car use, and we also control for other factors such as type of municipality, residential area and sociodemographic characteristics. The analysis is based on survey data from 1024 car users living in rural, urban or metropolitan municipalities in Sweden. Data is analysed by applying recursive bivariate probit models. The results show that those who feel a personal responsibility to contribute to a sustainable transport sector drive less when travelling, in particular to maintenance activities. As well, those who regularly commute by car are more likely to use the car also for leisure purposes, which indicates that measures aimed at reducing commuting by car may spill over to travel for leisure.

Does increased construction activity transmit into housing prices and rents? Evidence from the V4 countries and Austria

This study focuses on the housing market in Central European countries. We analyze the housing prices and their sensitiveness to selected variables, especially regarding construction activity in the V4 countries and Austria. By employing a specification of a recursive VAR model for each country, we show that the most responsive to changes in construction activity is housing market in Austria. Meanwhile housing prices in the V4 countries do not exhibit any strong association with construction activity, perhaps due to under-supply of housing in the countries. The paper provides discussion on the variables dependencies explanations.

Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data

The study of urban functional area identification is of great significance for urban function cognition, spatial planning, and economic development. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Aiming at the above problems, this paper proposes a multi-scale recursive recognition method based on interactive validation for urban functional areas using taxi trajectory data and point of interest (POI) data as the main data sources. First, the dynamic time warping (DTW) algorithm generates a time series similarity matrix, a CA-RFM model combining the clustering algorithm and random forest model is constructed. The model extracts significant feature regions as inputs through a K-medoid clustering algorithm, which are imported into the random forest model for urban functional zone (UFZ) identification. Then, to overcome the shortcomings of a single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area, the results show that the combination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the study demonstrates the superiority of the proposed method in two aspects, namely, faster delineation and improved accuracy in urban functional area identification.

Estimation of infiltration efficiency of ambient PM2.5 in urban residences of Beijing during winter

Fine particulate matter pollution has become a major public health concern in the urban areas of China. Ambient PM2.5 concentrations measured at fixed sites are commonly used as an exposure surrogate, although exposure to ambient PM2.5 primarily occurs in indoor environments. In this study, valid indoor and outdoor mass concentrations of PM2.5 during winter, were concurrently measured in 26 urban residences in Beijing. The infiltration factor (Finf), defined as the fraction of ambient PM2.5 which penetrates indoors and remains suspended, was estimated using a recursive model. The environmental conditions that significantly influenced Finf were screened using a general linear model and multiple regression analysis. Finf during winter was 0.52 ± 0.25 with large home-by-home variability. The results suggested that: (1) infiltration from outdoor to indoor environments remarkably reduced the concentrations of ambient PM2.5; and (2) using ambient PM2.5, as an exposure surrogate, can result in exposure misclassification. Improved ventilation was found to increase Finf, whereas an increase in indoor RH from 19.20 to 51.30% resulted in a reduction in Finf. The residences proximate to arterial roads were found to be prone for the infiltration of ambient PM2.5. There were remarkable seasonal differences in the infiltration behavior of ambient PM2.5.

Does ICTs Usage Facilitate Access to Market? An Empirical Evidence of Market Gardeners in Benin

ABSTRACT Imperfect information is a major obstacle for agricultural businesses in developing countries. The resulting information asymmetry increases transaction costs and affects the behavior of market stakeholders. In Benin, market gardeners’ participation is limited by geographical isolation and poor communication and transport infrastructures. Information and communication technologies (ICTs) now offer a fast and less costly means of accessing and disseminating information for more active market participation. The aim of this article is to measure the contribution of ICTs to market participation by testing whether they improve participation. Using a recursive bivariate probit model, our results indicate that market gardeners who use ICT to access information have a higher frequency of market participation than those who do not. Cell phones, radios, and televisions lead to a 35.3%, 25.6% and 8% higher probability of market participation respectively than those who don’t use them. These results suggest that a platform for disseminating knowledge via ICT could be set up to increase market participation.

Prediction of multi-bead profile of robotic wire and arc additive manufactured components recursively using axisymmetric drop shape analysis

ABSTRACT Dimension prediction of robotic wire and arc additive manufacturing (WAAM) part is fundamentally dependent on the modelling accuracy of single-bead profile and its subsequent overlapping ones. Current multi-bead overlapping models are still not capable of describing the flatten valley area of WAAM parts. This paper proposes a new recursive model, based on coordinate transformation and axisymmetric drop shape analysis (ADSA), to predict multi-bead overlapping profiles. First, a single-bead profile model for WAAM is established based on ADSA, followed by detailed description of conventional and proposed modified recursive ADSA profile model. The properties of developed recursive ADSA model are then investigated to reveal the effects of overlapping ratio and single-bead aspect ratio. Finally, multi-bead overlapping deposition experiment is carried out to validate the model feasibility. The results show that the modified recursive ADSA model is more accurate than the conventional one for its better accountability of valley areas. It is also indicated that the modified recursive ADSA model is suitable for the robotic WAAM process. The research outcome is beneficial to improving the forming accuracy of WAAM parts and geometry prediction of other additive manufactured products.

Exploring plausible contributions of end-use harvested wood products to store atmospheric carbon in China

The lateral transfer of carbon from forests to end-use Harvested Wood Products (HWPs), such as furniture and buildings through harvesting and industrial processing, creates an end-use HWPs carbon pool. This carbon pool serves as a substantial storage for atmospheric carbon dioxide, thus contributing to the mitigation of greenhouse gas effects. As new wood products enter this carbon pool and old products are disposed of upon reaching the end of their service lives, monitoring the carbon inflow and outflow allows for assessment of the size of HWPs carbon pool. Numerous factors, including climate change, population, and economy, significantly influence the supply and demand for wood products, consequently affecting the size of this carbon pool. Therefore, incorporating crucial factors to project plausible futures of carbon stock in HWPs is essential for predicting land-atmospheric carbon exchange and regional carbon budgets. In this study, we compiled and analyzed historical records of wood production and trade data, outlined their plausible futures using a recursive dynamic economic equilibrium model, and employed a product-type and service-life based accounting approach to quantify the HWPs carbon pool in China. Our findings indicate that from 1961 to 2020, the end-use HWPs carbon pool in China functioned as a net annual carbon sink, sequestering 14.9 Tg C of carbon per year and accumulating a total of 893 Tg C, with 76% stored in furniture products. The model predictions suggest that the HWPs carbon pool could potentially sequester carbon up to 2979 ± 806 Tg C by 2070.

Water–energy nexus: The coupling effects of water and energy policy applied in China based on a computable general equilibrium model

China has implemented a number of national and regional water and energy policies to achieve its goals of carbon emissions (CE) reduction, energy saving, and water conservation. Given its high energy and water resources interdependence, a framework is needed that can provide both a unified and systematic assessment of these. This study investigates the effects of coupling China's energy tax and water fee policies using a recursive dynamic computable general equilibrium model. Several possible scenarios are simulated. The simulation results show that the water fee promotes water conservation, but its effect is limited at China's current water fee level. The energy tax is shown to be more effective in improving water-saving efficiency. Under the maximum energy tax scenario (EH), total energy and industrial water demand fall by 10.45% and 3.82%, respectively, compared with 0.59% and 0.72%, respectively, under the maximum water fee scenario (W8). Furthermore, the energy tax is more conducive to China's energy conservation and CE reduction (CER) targets than the water fee, but raising both energy taxes and water fees can have a significant impact on energy conservation and CER. In the scenario going from a low energy tax (EL) to a high-energy tax (EH), the reduction in primary energy production increases from 8.36% to 19.16%, while CE also drop significantly, going from 5.59% to 16.71%; however, In the scenario going from the low water fee (W2) to the high-water fee (W8), the reduction in primary energy production only changes from 0.24% to 0.43%, and CO2 reduction from 0.35% to 0.61%. Meanwhile, the mixed water-energy scenario shows a spill over effect, with the CE and energy savings management goals requiring less GDP loss than water or energy policies implemented in isolation. Ultimately, the results show that even when combined, relying solely on existing water and energy tax policies will be insufficient to meet China's water-saving targets without other environmental policies. The key to overcoming energy and water constraints is to increase water and energy efficiency year after year. This will enable China to achieve its CER target and the 14th Five-Year Plan water savings target. Thus, the synergistic effect of water conservation and energy saving can be realised simultaneously when the CER and energy consumption structure improve, which is crucial for developing an effective environmental policy strategy.

A recursive inference method based on invertible neural network for multi-level model updating using video monitoring data

Likelihood-free inference methods have been widely adopted, but they face significant challenges in updating multi-level computational models that have hierarchically embedded sub-models. This difficulty arises from the lack of direct observations of the quantities of interest of the sub-models. In addition, recent advancements in sensing and image processing technologies allow for the collection of a substantial amount of video monitoring data through non-contact sensing techniques. The implicit and very complicated relationship between the uncertain model parameters and video monitoring data adds an additional layer of challenge to the updating of multi-level models. This paper overcomes these challenges by proposing an innovative Recursive Inference method based on Invertible Neural Networks (RINN) for multi-level models. The proposed RINN framework first compresses the high-dimensional video monitoring data into low-dimension latent-space data using a convolutional autoencoder. Using synthetic video monitoring data generated by considering various uncertainty sources in the computational simulation models, a likelihood-free inference model is then trained through a conditional invertible neural network (cINN) and a summary neural network. This model efficiently approximates the posterior distributions of uncertain model parameters without evaluating the computationally intractable likelihood function, given any latent-space data obtained from the autoencoder. To facilitate continuous monitoring and model updating over an extended monitoring period, this paper further proposes a recursive model updating strategy that integrates the cINN-based likelihood-free inference with the particle filtering method. The updating of a degradation model of a miter gate application is employed as an example throughout this paper to explain and demonstrate the efficacy of the proposed RINN framework. The results of the case study show that the RINN is able to effectively reduce uncertainty in the degradation model parameters through strain video monitoring data of a miter gate, and thereby increase the confidence in the remaining useful life (RUL) estimation using the updated degradation model.

How does carbon trading scheme affect carbon emissions and economic performance of electricity cross-subsidy reform: A recursive dynamic CGE model in China

How does carbon trading scheme affect carbon emissions and economic performance of electricity cross-subsidy reform: A recursive dynamic CGE model in China

The Impact of Carbon Emission Trading on Renewable Energy: A Comparative Analysis Based on the CGE Model

This study examines the effects of carbon emission trading on renewable energy consumption in China. The research applies the CEEEA2.0 model to simulate the economic, energy, and environmental impacts of carbon trading from 2018 to 2030. The CEEEA2.0 model is a recursive dynamic computable general equilibrium model that incorporates multiple households, sectors, and an energy and environment module. Four scenarios are considered: the Business as Usual (BaU) scenario, the Emission Trading Scheme (ETS)-benchmark scenario, and the ETS-strengthened and ETS-enhanced scenarios. The findings reveal that carbon emission trading positively influences electricity consumption, resulting in a higher preference for renewable energy due to reduced price disparities between renewable sources and fossil fuels. Consequently, electricity generation from renewable sources increases in all scenarios compared to the BaU scenario. However, the share of renewable energy is not substantially affected by carbon emission trading due to the complex interplay of factors, including substitution and income effects. The study further highlights that carbon trading significantly reduces coal usage and partially increases the overall proportion of renewable energy. These results underscore the significance of establishing ambitious carbon reduction targets and continual efforts to shift towards clean energy sources.