ALBERT

Description: In urban environments, Global Navigation Satellite Systems (GNSS) signals are frequently attenuated, blocked or reflected, which degrades the positioning accuracy of GNSS receivers significantly. To improve the performance of GNSS receiver for vehicle urban navigation, a GNSS/INS deeply-coupled software defined receiver (GIDCSR) with a low cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) ICM-20602 is presented, in which both GPS and BDS constellations are supported. Two key technologies, that is, adaptive open-close tracking loops and INS aided pseudo-range weight control algorithm, are applied in the GIDCSR to enhance the signal tracking continuity and positioning accuracy in urban areas. To assess the performance of the proposed deep couple solution, vehicle field tests were carried out in GNSS-challenged urban environments. With the adaptive open-close tracking loops, the deep couple output carrier phase in the open sky, and improved pseudo-range accuracy before and after GNSS signal blocked. Applying the INS aided pseudo-range weight control, the pseudo-range gross errors of the deep couple decreased caused by multipath. A popular GNSS/INS tightly-coupled vehicle navigation kit from u-blox company, M8U, was tested side by side as benchmark. The test results indicate that in the GNSS-challenged urban areas, the pseudo-range quality of GIDCSR is at least 25% better than that of M8U, and GIDCSR’s horizontal positioning results are at least 69% more accurate than M8U’s.

Description: Urban sprawl has led to various economic, social, and environmental problems. Therefore, it is very significant to improve the efficiency of resource usage and promote the development of compact urban form. It is a common topic that measuring urban compactness is done with certain ways and methods as well. Presently, most urban compactness measurement methods are based on two-dimensional (2D) formats, but methods based on three-dimensional (3D) formats that can precisely describe the actual urban spatial conditions are still lacking. To measure the compactness of the 3D urban spatial form accurately, a 3D Compactness Index (VCI) was established based on the Law of Gravitation and the quantitative measurement model. In this model, larger 3D Compactness Index values indicate a more 3D-compact city. However, different urban scales may influence the discrepancy scale of different cities. Thus, the 3D Compactness Index model was normalized as the Normalized 3D Compactness Index (NVCI) to eliminate such discrepancies. In the Normalized 3D Compactness Index model, a sphere with the same volume of real urban buildings in the city was assumed as the most compact 3D urban form, and which was also calculated by 3D Compactness Index processing. The compactness value of the normalized 3D urban form is obtained by comparing the 3D Compactness Index with the most compact 3D urban form. In this study, 1149 typical communities in Xiamen, China, were selected as the experimental fields to verify the index. Some of communities have a quite different Normalized 3D Compactness Index, although they have a similar Normalized 2D Compactness Index (NCI), respectively. Moreover, comparing with the 2D Compactness Index (CI) and Normalized 2D Compactness Index (NCI), the 3D Compactness Index and Normalized 3D Compactness Index can describe and explain reality more precisely. The constructed 3D urban compactness model is expected to contribute to scientific study on urban compactness.

Description: Three-dimensional magnetic inversion allows the distribution of magnetic parameters to be obtained, and it is an important tool for geological exploration and interpretation. However, because of the redundancy of the data obtained from large-scale investigations or high-density sampling, it is very computationally intensive to use these data for iterative inversion calculations. In this paper, we propose a method for compressing magnetic data by using an adaptive quadtree decomposition method, which divides the two-dimensional data region into four quadrants and progressively subdivides them by recursion until the data in each quadrant meets the regional consistency criterion. The method allows for dense sampling at the abnormal boundaries with large amplitude changes and sparse sampling at regions with small amplitude changes, and achieves the best approximation to the original data with the least amount of data, thus retaining more anomalous information while achieving the purpose of data compression. In addition, assigning values to the data in the quadrants using the averaging method is essentially equivalent to average filtering, which reduces the noise of the magnetic data. Testing the synthetic model and applying the method to mineral exploration a prove that it can effectively compress the magnetic data and greatly improve the computational efficiency.

Description: Monochamus alternatus is an important insect pest in pine forests of southern China and the dispersing vector of the pine wood nematode, Bursaphelenchus xylophilus, which leads to pine wilt disease (PWD). Microbiome of M. alternatus may contribute to survival of larvae in the host pine trees. In order to investigate the intestinal bacterial structure of M. alternatus during the larvae and pupae stages in host trees, and infer the function of symbiotic bacteria, we used 16S rRNA gene Illumina sequencing to obtain and compare the bacterial community composition in the foregut, midgut, and hindgut of larvae, pupal intestines, larval galleries, and pupal chambers of M. alternatus. The diversity of the bacterial community in larval intestines and pupal intestines were similar, as well as was significantly greater in larval galleries and pupal chambers. Although there were differences in bacterial compositions in different samples, similar components were also found. Proteobacteria and Firmicutes were the two most dominant phyla in all samples, and genera Enterobacter, Raoultella, Serratia, Lactococcus, and Pseudomonas were dominant in both the intestinal samples and plant tissue samples. Enterobacter was the most abundant genus in larval intestines, and Serratia was dominant in pupal intestine. The functions of these dominant and specific bacteria were also predicted through metagenomic analyses. These bacteria may help M. alternatus degrade cellulose and pinene. The specific role of symbiotic bacteria in the infection cycle of PWD also warrants further study in the future.

Description: Self-healing of smart distribution networks with distributed generators, which are usually operated as independent islands after fault, can improve power-supply reliability. As a hot research topic, a self-healing scheme is usually treated as the output of a nonlinear optimizuoation model. However, existing strategies have two main shortcomings. The first, high-optimization dimension, results in low-optimization efficiency. The second, the effects of power-quality issues, which are more serious on islands and may further threaten the safe operation of islands, is usually neglected. To quickly obtain a reliable self-healing scheme, a novel strategy is proposed. As the first step, the distribution network after a fault occurrence can be divided into several trouble-free subnets via the connectivity analysis; each subnet is called an initial island. Further, for each initial island, a two-step optimization model of self-healing, which consists of load-shedding optimization and network reconfiguration optimization, is proposed to obtain the self-healing strategy with lower searching space as well as higher solving efficiency. In detail, in load-shedding optimization, by means of heuristic differential evolution algorithm, larger total recovery capacity is achieved by considering the droop characteristic of distributed generators (DGs) within the permissible change in frequency. In network-reconfiguration optimization, based on the improved hybrid particle swarm optimization algorithm, a comprehensive set of power-quality constraints, including constraint of change in frequency, uncertain constraints of node voltage total harmonic distortion (THD), and negative sequence components of DGs, is developed to guarantee the reliability of each island. To evaluate whether the constraints are satisfied during the optimization procedure, an improved flexible power-flow algorithm is developed to calculate the power flow of each island under change in frequency. Further, 2m+1-point estimate method is employed for uncertainty analyses of the distributions of harmonic and negative sequence components caused by the uncertainty of corresponding sources. Finally, via a 94-node practical distribution network, the effectiveness and advantages of the proposed strategy in safety, recovery capacity, and optimization efficiency are verified.

Description: A novel non-planar coding metasurface optimized by discrete particle swarm algorithm (DPSO) is proposed in terms of the property of wideband radar cross-section (RCS) and diffuse scattering. The design consists of two unit cells, “0” and “1”, which have a 180° ± 37° phase difference for phase interference cancellation. The 10 dB monostatic RCS reduction frequency range of the metasurface is from 6.4 to 29.6 GHz, and its bandwidth ratio is 4.62:1, under normal incidence of the two polarizations. Compared to the planar surface, the non-planar surface has a greater bandwidth with respect to the monostatic and bistatic RCS reduction. The results declare its properties of ultra-wideband, angle insensitivity, and polarization insensitivity. Finally, the theoretical analysis, simulation, and experimental results match perfectly, indicating that the metasurface can be used in the RCS reduction or other microwave applications with wider RCS reduction and diffuse scattering.

Description: In previous studies, there were few portfolio models involving investors’ psychological states, market ambiguity and entropy. Some entropy can make the model have the effect of diversifying investment, which is very important. This paper mainly studies four kinds of entropy. First, we obtained four definitions of entropy from the literature, and gave the function of fuzzy entropy in different psychological states through strict mathematical proof. Then, we construct a fuzzy portfolio entropy decision model based on the investor’s psychological states, and compared it with the possibilistic mean–variance model. Then we presented a numerical example and compared the five different models established. By comparing the results, we find that: (a) The possibilistic mean–Shannon entropy model solves the problem of the possibility of excessive concentration in the possibilistic mean–variance model, but the dispersion is not enough. Conversely, the possibilistic mean–Yager entropy is over–emphasized due to the definition of its own function, such that it gave an investment pattern of equal weight distribution or approximate average distribution. (b) The results of possibilistic mean–proportional entropy can be said to be the middle status of the portfolios of possibilistic mean–Shannon entropy and possibilistic mean–Yager entropy. This portfolio not only achieves a certain rate of return, but also disperses the risk to some extent. (c) The lines of satisfaction for portfolios derived from different models are approximately U–shaped with the increase in return preference. (d) The possibilistic mean–Shannon entropy model tends to have the highest portfolio satisfaction with the same psychological state of the investor.

Description: Unbalanced regional development is widespread, and the imbalance of regional development in developing countries with rapid urbanization is increasingly apparent. This threatens the sustainable development of the region. Promoting the coordinated development of the region has become a hot spot of scientific research and a major practical need. Taking 99 counties of Jiangsu Province China, a typical coastal plain region, as the basic research unit, this paper explores the unbalanced development characteristics of the regional urban spatial form using three indicators: urban spatial expansion size, development intensity, and distribution aggregation degree. Then, their driving mechanisms were evaluated using spatial autocorrelation analysis, Pearson correlation analysis, linear regression, and geographically weighted regression. Our results found that the areas with larger urban spatial expansion size and development intensity were mainly concentrated in southern Jiangsu, where there was a positive spatial correlation between them. We found no agglomeration phenomenon in urban spatial distribution aggregation degree. From the perspective of driving factors: economics was the main driving factor of urban spatial expansion size; urbanization level and urbanization quality were the main driving factors of urban spatial development intensity. Natural landform and urbanization level are the main driving factors of urban spatial distribution aggregation degree. Finally, we discussed the optimization strategy of regional coordinated development. The quality of urbanization development and regional integration should be promoted in Southern Jiangsu. The level of urbanization development should be improved relying on rapid transportation to develop along the axis in central Jiangsu. The economic size should be increased, focusing on the expansion of the urban agglomeration in northern Jiangsu. This study will enrich the perspective of research on the characteristics and mechanisms of regional urban spatial imbalance, and helps to optimize and regulate the imbalance of regional urban development from multiple perspectives.

Description: Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.