Will Decline in Foreign Trade Reshape Internal Economic Geography? Simulations in an Estimated Model of the Chinese Space-economy

This study evaluates the role of decline in foreign trade in shaping China’s internal economic Geography. In a first step, we develop a multi-region multi-industry economic geography model under Cournot competition, of which we estimate the parameter values from real regional and industry data to obtain a predictable model. Next, we set some scenarios reflecting a decline in export to simulate the evolution of industrial spatial pattern. The simulations indicate the evident impact of decreasing export on the spatial distribution of industries; besides, the degree of influence varies across different industries. Moreover, the decline in export of one industry not only influences its own location, but also the location of the forward or backward-linked industries. However, the general spatial pattern, observed after policy reforms and trade liberalization, will not be reversed due to export recession in China

Spatial Dynamics of Chinese Manufacturing Industries: Comparative Advantage versus New Economic Geography

This paper analyzes the evolving spatial distribution of manufacturing industries in China. Besides, we explore the substantial determinants by adopting a spatial panel model. The spatial pattern of Chinese manufacturing industries has changed significantly, which is featured by obvious trend of labor intensive industries and some of capital and tech intensive ones in Eastern China spread to Central and Western China. Generally, spatial dynamics of industries present an order to a certain degree. Central China has been the main region where industries spread to, and relative large range of sectors is involved. We test the determinants in terms of comparative advantage and new economic geography. It turns out that the factors driving industry spatial dynamics is not the enlarging regional cost differences of production factors but endogenous agglomeration externalities. Specifically, more and more fierce competition of local firms in same sector has become one of the principal reasons cause spatial relocation. More congestion happens in labor intensive industries compared to technology intensive industries and capital intensive industries. The findings of this study indicate that comparative advantage theory and new economic geography will be suitable in different spatial scape of economic issues. Geographic evolution of industries in one country is less determined by changing regional comparative advantage differences but the endogenous effect of varying agglomeration externalities

New insights into stress changes before and after the Wenchuan Earthquake using hydraulic fracturing measurements

AbstractThis paper summarizes in situ stress data by hydraulic fracturing method over the past 10years along the Longmenshan fault belt, and these data can be divided into three segments: northern, middle, and southern. The orientations of the maximum horizontal stress rotate from north-northwest in the northern to northwest in the middle, and to west-northwest in the southern. The stress magnitudes are characterized by higher values in the two ends and lower values in the middle segment. Furthermore, three stress measurement campaigns in two boreholes on the northern segment of the Longmenshan fault belt, before and after the great earthquake, show clear stress decrease of 23%–29% in the shallow crust after the earthquake. Analysis using the mathematical fitting method also indicates a decrease in regional stress state after the earthquake. Meanwhile, the frictional characteristic indexes based on the stress measurements further imply that the frictional strength of the Longmenshan fault belt is characterized by a strong southern segment, a weak middle segment, and a moderately strong northern segment. The analysis based on the stress data implies that the northern and southern segments of the fault belt are extremely important stress concentration and transformation nodes of the regional stress regime

Model predictive current control based on hybrid control set for permanent magnet synchronous motor drives

Abstract Model predictive current control (MPCC) has been widely recognized as a high‐performance scheme for permanent magnet synchronous motor drives because of its simple control structure. This article proposes a novel multivector MPCC (MMPCC) method based on a hybrid control set, which includes both original basic voltage vectors (VVs) and synthesized VVs. The two active VVs and one null VV are considered as an initial control set, and the exact combination of them depends on their corresponding durations, which are calculated by stator current slopes in dq frame with the use of the prediction formula. Contrary to the traditional cost function, an alternative hybrid control set can achieve superior voltage precision. This is due to the involved cost function that minimizes the maximum of prediction current errors, which typically occur at non‐integer sample instants during the control period. The performance of the proposed MMPCC method has been experimented to confirm its effectiveness, compared with the conventional MPCC (CMPCC) and duty‐cycle MPCC (DMPCC). The results indicated an anticipated enhancement in both dynamic and steady‐state performance at a low rotor speed, alongside a significant robustness against parameter mismatch

Multiple adaptive factors based interacting multiple model estimator

Abstract In the field of optoelectronic tracking, precisely modeling the motion equations of the tracked target is often challenging, and in some cases, they may even be entirely unknown. This necessitates the use of a robust state estimator for accurate state estimation. Additionally, atmospheric turbulence, variations in illumination, and intricate observation backgrounds may introduce a significant increase in observation noise for the tracked target. To address these challenges, one approach is to introduce adaptive factors, such as the Mahalanobis method, into the robust state estimator to enhance estimation accuracy. However, further exploration has revealed that adaptive factors designed using different methods offer unique advantages in scenarios with varying levels of noise amplification. In this paper, different adaptive factors are further combined using an interacting multiple model approach, allowing the designed state estimator to exhibit stronger adaptability to noise amplification. The stability and effectiveness of this algorithm are validated through program simulations, double reflection mirror experiment, and drone trace prediction, demonstrating its applicability and reliability in diverse scenarios

Intention inference‐based interacting multiple model estimator in photoelectric tracking

Abstract Aiming to improve the estimation and prediction accuracy of a target's position, this paper proposes a state estimation method for photoelectric tracking systems, based on the evaluation of the tracked target's motion intention. Traditional photoelectric tracking systems utilize external physical quantities such as the position, velocity, and acceleration of the target as the estimated states. While this method can output good results for pre‐modelled target positions, it struggles to maintain the accuracy when facing manoeuvering targets or complex motion patterns targets. Here, the relevant parameters of the tracked target's motion intention are directly estimated innovatively, like estimating the circling point position rather than the circular flying target's position and velocity. This approach enables recognizing the target's motion intention and leads to precise estimation, which specifically consists of an interacting multiple model approach, multiple unscented Kalman estimators, and a robust estimator. The effectiveness and stability of this estimator are validated through software simulations and experiments on a dual‐reflection mirror platform

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

This paper proposes a dynamic high-type control (DHTC) method based on an interval type-2 fuzzy logic controller (IT2FLC), which is used in the photoelectric tracking system to improve the steady-state accuracy and response speed. Adding integrators to the traditional multi-loop feedback control loop can increase the system type, thereby speeding up the response speed and improving the steady-state accuracy, but there is a risk of integral saturation. Switching the type dynamically according to the system state can avoid integral saturation while retaining the advantages of the high-type. Fuzzy logic control (FLC) can dynamically change the output value according to the input change and has the advantages of fast response speed and strong ability to handle uncertainties. Therefore, in this paper, the FLC is introduced into the high-type control system, and the output of the FLC is used as the gain of the integrator to control the on-off to achieve the goal of dynamic switching type, which is successfully verified in the experiment. IT2FLC introduces a three-dimensional membership function, which further improves the FLC’s ability to handle uncertainties. From the experimental results, compared with T1FLC, IT2FLC’s ability to handle uncertainties is significantly improved. In addition, in order to speed up the calculation speed of IT2FLC, this paper proposes an improved type-reduction algorithm, which is called weighted-trapezoidal Nie-Tan (WTNT). Compared with the traditional type-reduction algorithm, WTNT has faster calculation speed and better steady-state accuracy, and has been successfully applied to real-time control systems, which has good engineering application value. Finally, in order to reduce the interference of human factors and improve the automation level of the system, a multi-population genetic algorithm (MPGA) is used to iteratively optimize the parameters of the FLC, which improves the output accuracy. On the experimental platform of the flexible fast steering mirror (FFSM), the control effects of the traditional controller, T1FLC and IT2FLC are compared, which proves that the IT2FLC-DHTC system has a faster response performance, higher steady-state accuracy, and stronger ability to handle uncertainties

Extended State Kalman Filter-Based Model Predictive Control for Electro-Optical Tracking Systems with Disturbances: Design and Experimental Verification

A modified Extended State Kalman Filter (ESKF)-based Model Predictive Control (MPC) algorithm is introduced to tailor the enhanced disturbance suppression in electro-optical tracking systems. Traditional control techniques, although robust, often struggle in scenarios with concurrent internal, external disturbances, and sensor noise. The proposed algorithm effectively overcomes these limitations by precisely estimating system states and actively mitigating disturbances, thus significantly boosting noise and perturbation control resilience. The primary contributions of this study include the integration of ESKF for accurate system state and disturbance estimation in noisy environments, the embedding of an ESKF estimation-compensation loop to simulate an improved disturbance-free system, and a simplified modeling approach for the controlled device. This designed structure minimizes the reliance on extensive system identification, easing the predictive control model-based constraints. Moreover, the approach incorporates total disturbance estimation into the optimization problem, safeguarding against actuator damage and ensuring high tracking accuracy. Through rigorous simulations and experiments, the ESKF-based MPC has demonstrated enhanced model error tolerance and superior disturbance suppression capabilities. Comparative analyses under varying model parameters and external disturbances highlight its exceptional trajectory tracking performance, even in the presence of model uncertainties and external noise

Bismuth-Remaining Cupellation Fire Assay Preconcentration Combined with Inductively Coupled Plasma Mass Spectrometry for the Simultaneous Determination of Ultratrace Au, Pt, Pd, Ru, Rh, and Ir in Geologic Samples

In this work, a novel method of bismuth fire assay (Bi-FA) combined with inductively coupled plasma mass spectrometry (ICP-MS) simultaneous determination of ultratrace gold (Au), platinum (Pt), palladium (Pd), ruthenium (Ru), rhodium (Rh), and iridium (Ir) in geologic samples was established. Bismuth oxide (Bi2O3) was used as noble metal elements fire assay collector, and Bi-remaining protection cupellation was employed to generate Bi granule. After the Bi granule was microwave-digested by aqua regia (40%, v/v), 197Au, 195Pt, 106Pd, 101Ru, 103Rh, and 193Ir in the sample solution were determined by ICP-MS. Using Bi as cupellation protector, volatile Ru could be collected effectively and without volatilization loss during microwave digestion and decompression. Moreover, the toxicity of Bi was exceptionally low compared to toxic nickel oxide and lead oxide in nickel sulfide/lead fire assay; thus Bi-FA was a green environmental analysis method. The mineral composition and decomposition character of chromite, black shale, and polymetallic ore were investigated, and pretreatment procedures were optimized for such special samples. Besides, the influence of mass spectrum interference of coexisting elements was discussed. Under the optimal conditions, excellent curve fittings of Au, Pt, Pd, Ru, Rh, and Ir were obtained between 0.01 and 100 ng·mL−1, with the correlation coefficients exceeding 0.9995. The detection limits were from 0.002 ng·g−1 to 0.025 ng·g−1. The developed method was applied to analyze the Chinese Certified Reference Materials (CRMs) and the determined values were in good agreement with the certified values