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Urban Agglomeration (UA) is regarded as an emerging complex urban system in China. The development of UA demands a reasonable scale structure, which can be investigated by Zipf's law. However, few studies have been conducted to quantify the optimal scale of UA and how its development deviates from the optimal scale. With the continuous urban expansion, the problem of UAs' scale structure has received increasing attention. In this study, we propose a method based on Zipf's law for estimating the theoretical optimal scale of UAs in China and assessing the deviation rate from their optimal scales. Twelve typical UAs in China are selected, and their development is assessed via urban impervious surface data from 2000 to 2018. The results show that the average deviation rate of the investigated UAs decreased from 3.40% in 2000 to 2.32% in 2018, demonstrating that these UAs are on a positive evolution trajectory. Furthermore, according to the development stage, we make recommendations on "large cities vs. medium/small-sized cities and promoting vs. restraining" to each UA based on its size. The conceptual and analytical knowledge, as well as the results from this study, are expected to offer valuable insights and new references for regulating and managing UAs' development in China. [ABSTRACT FROM AUTHOR]

A traffic crash is becoming one of the major factors that leads to unexpected death in the world. Short window traffic crash prediction in the near future is becoming more pragmatic with the advancements in the fields of artificial intelligence and traffic sensor technology. Short window traffic prediction can monitor traffic in real time, identify unsafe traffic dynamics, and implement suitable interventions for traffic conflicts. Crash prediction being an important component of intelligent traffic systems, it plays a crucial role in the development of proactive road safety management systems. Some near future crash prediction models were put forward in recent years; further improvements need to be implemented for actual applications. This paper utilizes traffic accident data from the study Freeway in China to build a time series-based count data model for daily crash prediction. Lane traffic flow, weather information, vehicle speed, and truck to car ratio were extracted from the deployment of non-intrusive detection systems with support of the Bridge Management Administration study and were input into the model as independent variables. Different types of prediction models in machine learning and time series forecasting methods such as boosting, ARIMA, time-series count data model, etc. are compared within the paper. Results show that integrating time series with a count data model can capture traffic accident features and account for the temporal structure for variable serial correlation. A prediction error of 0.7 was achieved according to Root Mean Squared Deviation. [ABSTRACT FROM AUTHOR]

In order to promote the economic development of China's provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China's provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction. [ABSTRACT FROM AUTHOR]

Urban–rural land transition and the coordination of coupled human–environmental systems are two important issues in the process of global urban–rural development. Although existing studies have explored the coupling coordination degree (CCD) of human–environmental interactions under the context of urbanization, few studies have taken land transitions into consideration. In this study, we investigated the dynamics of CCD in China from 2001 to 2018 using multisource remote sensing data and quantified the CCD changes in land transitions among urban construction land (UCL), rural residential land (RRL), and non-construction land (NCL). The CCD alterations mainly occurred in the decline in NCL stock, the increase in UCL stock, and especially the losses during RRL to NCL transfers. We urge academics and government decision-makers to pay more attention to the CCD transfers and losses during urban–rural transitions. This study provides scientific guidance for the development of urban–rural integration and is expected to assist the coordinated evaluation of human–environmental interactions in the process of sustainable development. [ABSTRACT FROM AUTHOR]

Background: Cushing's syndrome has been described as a complex endocrine disorder characterized with high cortisol concentration. Correct and early diagnosis of Cushing's syndrome is challenging. According to the latest guideline, bilateral inferior petrosal sinus sampling (BIPSS) is considered to be the gold standard for the differential diagnosis. However, in some unusual cases, this method may be false positive. Here, we presented a rare case of orbital neuroendocrine tumor secreting adrenocorticotrophic hormone with false positive inferior petrosal sinus sampling. Case presentation: A 48-year-old woman was admitted to West China Hospital of Sichuan University, presenting with fatigue, whole body edema for 1 year, alopecia and skin pigmentation for 5 months. Hormonal profiles including plasma cortisol and adrenocorticotrophic hormone (ACTH) measurements and low-dose dexamethasone inhibition test suggested that the patient had Cushing's syndrome. However, during tumor location phase, the results of high-dose dexamethasone inhibition test (HDDST) contradicted desmopressin (DDAVP) stimulation test. Thus, BIPSS was employed, and its results indicated a pituitary origin. Interestingly, MRI of sellar region showed an innocent pituitary but caught a serendipitous lesion in the lateral rectus muscle of left eye, which was later proved to be an orbital neuroendocrine tumor secreting ACTH by pathological and immunohistochemical results. ACTH level of the patients was < 0.1 ng/L and cortisol level was 51.61 nmol/L 1 week after surgery. At 24 months follow-up, the patient appeared stable with no complaints nor any symptoms of Cushing's syndrome, including moon face, purple striate and central obesity. The patient's life quality also improved significantly. Conclusion: We reported a rare case of endogenous Cushing's syndrome due to ectopic ACTH secreting from an orbital neuroendocrine tumor. This unique case of orbital EAS suggests that orbital venous blood backflow, owning to abnormal anatomic structures, may possibly lead to false positive BIPSS results. [ABSTRACT FROM AUTHOR]

In Shanxi Province, China, groundwater is a major problem and exploration of groundwater potential zones (GWPZs) is a great necessity. This paper contributed to integrate RS-GIS to delineate GWPZ and applied Fuzzy-AHP method in a single platform. The main objective includes delineation GWPZs with RS and geo-environmental factors using Fuzzy-AHP method. Fuzzy-AHP method was employed to calculate weight of factors. RS-GIS was used to create maps and discover groundwater availability. GWPZs were classified in five separate classes. Results indicated that 13.26%, 27.02%, 26.35%, 23.64%, and 9.71% area classified as very good, good, moderate, poor, and very poor GWPZs. The validated analytical results revealed 82.5%, 12.3%, 3.5%, and 1.7% existing water wells exhibited in very good, good, moderate, and poor/very poor GWPZs. This indicates Fuzzy-AHP model generated findings were in very good agreement with ground-truth data. This RS-GIS based Fuzzy-AHP method is proficient and efficient in identification and delineation of GWPZs. Image 1 • Groundwater potential zones (GWPZs) in semi-arid area delineated with RS-GIS. • Seven geo-environmental factors are used for GWPZ. • RS-GIS for GWPZ and Fuzzy-AHP model are used in a single platform. • Fuzzy-AHP method was employed to calculate the weight of the factors. • GWPZs are classified as very good, good, moderate, poor, and very poor GWPZ. [ABSTRACT FROM AUTHOR]