Online Detection and Classification of Moldy Core Apples by Vis-NIR Transmittance Spectroscopy

Apple moldy core disease is a common internal fungal disease. The online detection and classification of apple moldy core plays a vital role in apple postharvest processing. In this paper, an online non-destructive detection system for apple moldy core disease was developed using near-infrared transmittance spectroscopy in spectral range of 600–1100 nm. A total of 120 apple samples were selected and randomly divided into a training set and a test set based on the ratio of 2:1. First, basic parameters for detection of apples with moldy core were determined through detection experiments of samples in a stationary state. Due to the random distribution of the diseased tissue inside diseased apples, stationary detection cannot accurately identify the diseased tissue. To solve this problem, the spectra of apples in motion state transmitted forward by the transmission line were acquired. Three placement orientations of the apple in the carrying fruit cup were tested to explore the influence of fruit orientation on spectral characteristics and prediction. According to the performance of the model, the optimal preprocessing method and modeling method were determined (fixed orientation model and arbitrary orientation model). SPA was used to select the characteristic wavelengths to further improve the online detection speed. The overall results showed that the multi-spectra model using mean spectra of three orientations was the best. The prediction accuracies of multi-spectra model using SPA for three orientations were 96.7%, 97.5% and 97.5% respectively. As a conclusion, the arbitrary orientation model was beneficial to improve the online detection of apple moldy core disease

The Geographical Distribution and Influencing Factors of COVID-19 in China

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design

Evolution Mode, Influencing Factors, and Socioeconomic Value of Urban Industrial Land Management in China

(1) Background: Accurate measurement of the matching relationship between urban industrial land change and economic growth is of great value for industrialized and re-industrialized countries to perform land resource management in territorial spatial planning. (2) Methods: Based on the combination of the Boston Consulting Group matrix, Geodetector, and decoupling model, we constructed a new method integrating “model evolution + driving mechanism + performance evaluation + policy design” in this paper, and conducted an empirical study on the economic value of urban industrial land management in the Yangtze River Delta. (3) Results: The evolution modes of urban industrial land in the Yangtze River Delta are divided into four types: stars, cows, dogs, and question, distributed in structures ranging from an “olive” shape to a “pyramid” shape, with high spatial heterogeneity and agglomeration and low autocorrelation. The government demand led by driving economic growth and making large cities bigger is the key factor driving the change in urban industrial land and the influence of transportation infrastructure and the business environment has remained stable for a long time. The mechanisms of industrialization, globalization, and innovation are becoming increasingly complicated. Industrial land change and value-added growth in most cities have long been in a state of strong and weak decoupling, with progressive decoupling occurring alongside the unchanged stage and regressive decoupling. The government outperforms the market in terms of urban industrial land management, and the degradation of the synergy between urban industrial land and corporate assets emerges as a new threat to sustainable and high-quality development of the region. (4) Conclusions: This paper establishes a technical framework for zoning management and classification governance of urban industrial land to divide the Yangtze River Delta into reduction-oriented transformation policy zoning, incremental high-quality development zoning, incremental synchronous growth zoning, and reduction and upgrading development zoning. It also proposes an adaptive land supply governance strategy for quantitative and qualitative control, providing a basis for territorial spatial planning and land resource management

Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China

The urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of integrated urban–rural development planning. In this paper, we conduct an empirical study on 84 county-level cities in Gansu Province by using various analysis tools, such as GIS, GeoDetector and Boston Consulting Group Matrix. The findings show that: (1) The urban–rural income gap in Gansu province is at a high level in spatial correlation and agglomeration, leading to the formation of a stepped and solidified spatial pattern. (2) Different factors vary greatly in influence, for example, per capita Gross Domestic Product, alleviating poverty policy and urbanization rate are the most prominent, followed by those such as floating population, added value of secondary industry and number of Internet users. (3) The driving mechanism becomes increasingly complex, with the factor interaction effect of residents’ income dominated by bifactor enhancement, and that of the urban–rural income gap dominated by non-linear enhancement. (4) The 84 county-level cities in Gansu Province are classified into four types of early warning zones, and differentiated policy suggestions are made in this paper

Evolution Mode, Influencing Factors, and Socioeconomic Value of Urban Industrial Land Management in China

(1) Background: Accurate measurement of the matching relationship between urban industrial land change and economic growth is of great value for industrialized and re-industrialized countries to perform land resource management in territorial spatial planning. (2) Methods: Based on the combination of the Boston Consulting Group matrix, Geodetector, and decoupling model, we constructed a new method integrating “model evolution + driving mechanism + performance evaluation + policy design” in this paper, and conducted an empirical study on the economic value of urban industrial land management in the Yangtze River Delta. (3) Results: The evolution modes of urban industrial land in the Yangtze River Delta are divided into four types: stars, cows, dogs, and question, distributed in structures ranging from an “olive” shape to a “pyramid” shape, with high spatial heterogeneity and agglomeration and low autocorrelation. The government demand led by driving economic growth and making large cities bigger is the key factor driving the change in urban industrial land and the influence of transportation infrastructure and the business environment has remained stable for a long time. The mechanisms of industrialization, globalization, and innovation are becoming increasingly complicated. Industrial land change and value-added growth in most cities have long been in a state of strong and weak decoupling, with progressive decoupling occurring alongside the unchanged stage and regressive decoupling. The government outperforms the market in terms of urban industrial land management, and the degradation of the synergy between urban industrial land and corporate assets emerges as a new threat to sustainable and high-quality development of the region. (4) Conclusions: This paper establishes a technical framework for zoning management and classification governance of urban industrial land to divide the Yangtze River Delta into reduction-oriented transformation policy zoning, incremental high-quality development zoning, incremental synchronous growth zoning, and reduction and upgrading development zoning. It also proposes an adaptive land supply governance strategy for quantitative and qualitative control, providing a basis for territorial spatial planning and land resource management

Improving Chinese word segmentation using partially annotated sentences

Conference Name:12th China National Conference on Chinese Computational Linguistics, CCL 2013 and 1st International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2013. Conference Address: Suzhou, China. Time:October 10, 2013 - October 12, 2013.Manually annotating is important for statistical NLP models but time-consuming and labor-intensive.We describe a learning task that can use partially annotated data as the training data. Traditional supervised learning task is a special case of such task. Particularly, we adapt the perceptron algorithm to train Chinese word segmentation models.We mix conventional fully segmented Chinese sentences with partially annotated sentences as the training data. Partially annotated sentences can be automatically generated from the heterogeneous segmented corpora as well as naturally annotated data such as markup language sentences like wikitexts without any additional manual annotating. The experiments show that our method improves the performances of both supervised model and semi-supervised models. ? Springer-Verlag 2013