Illegal Radio Station Localization with UAV-Based Q-Learning

This paper proposes a novel unmanned aerial vehicle(UAV)-based illegal radio station(IRS) localization scheme, where the transmit power of the IRS, the channel model and the noise model are unknown to the UAV. A direction-aware Q-learning algorithm is developed to process received signal strength(RSS) values collected by a directional antenna, as well as directions corresponding to the RSS values. This algorithm determines the direction the UAV flies towards and thereby finds the IRS. The proposed scheme is compared to two baseline schemes. One baseline locates the IRS by a UAV equipped with an omnidirectional antenna, where conventional Q-learning is exploited to process the measured RSS and determine the UAV's trajectory. The other baseline locates the IRS by a directional-antenna UAV, where the UAV flies towards the direction with respect to the maximum RSS value. Numerical results show that, especially for a low receive SNR, the proposed scheme can outperform the two baselines in terms of the localization efficiency, providing a smoother trajectory for the UAV.

State Classification via a Random-Walk-Based Quantum Neural Network

In quantum information technology,crucial information is regularly encoded in different quantum states.To extract information,the identification of one state from the others is inevitable.However,if the states are nonorthogonal and unknown,this task will become awesomely tricky,especially when our resources are also limited.Here,we introduce the quantum stochastic neural network(QSNN),and show its capability to accomplish the binary discrimination of quantum states.After a handful of optimizing iterations,the QSNN achieves a success probability close to the theoretical optimum,no matter whether the states are pure or mixed.Other than binary discrimination,the QSNN is also applied to classify an unknown set of states into two types:entangled ones and separable ones.After training with four samples,it can classify a number of states with acceptable accuracy.Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.

Urban water system theory and its model development and application

The urban water system theory is an extension of the basin water system science on an urban scale, providing a new systematic solution for the unbalanced human-water relationship and severe water challenges, such as waterlogging, black and odorous water, and ecological degradation caused by urbanization. Most existing studies on urban water systems have focused on individual water cycle processes linked with water supply and sewage treatment plants, but mutual feedback between the water cycle and its associated material circulation and water ecology, as well as human processes, still needs further exploration. In this paper, the concept, theory, and technical methodology of the urban water system were developed based on the water cycle and basin water system science. The Urban Water System 5.0(UWS 5.0) model was developed by integrating the Time Variant Gain rainfall-runoff Model with Urban water system(TVGM_Urban) in different underlying surface conditions for analyzing the natural-social water cycle processes and their associated water environmental and ecological processes and the influence of multiscale sponge measures. Herein, five major simulation functions were realized: rainfall-runoff-nonpoint source pollutant load,water and pollutant transportations through the drainage network system, terminal regulation and purification, socioeconomic water cycle, and water system assessment and regulation. The location for the case study used in this paper was Wuhan City. The findings showed that the entire urban water system should consider the built-up area and its associated rivers and lakes as the research object and explore the integrations among the urban natural-social water cycle and river regulations inside and outside of the city as well as the effects of socioeconomic development and sponge measures on the water quantity-quality-ecology processes. The UWS 5.0 model efficiently simulated the urban rainfall-runoff process, total nitrogen(TN) and total phosphorus(TP) concentrations in water bodies, and characteristic indicators of socioeconomic development. For the rainfall-runoff simulations, the correlation coefficient and Nash-Sutcliffe efficiency(NSE) fall under the excellent and good classes, respectively. For the TN and TP concentration simulations, results exhibited good bias and the correlation coefficients exceeded 0.90 for 78.1% of the sampled sites. The simulation of 18 socioeconomic indicators provided excellent bias, correlation coefficient, and NSE values of 100%, 83.3%, and 69.4% to total indicators, respectively. Based on the well-calibrated UWS 5.0 model, the source sponge,artificial enhancement, and source reduction-path interception-terminal treatment measures were optimized, which considerably mitigated waterlogging, black and odorous water, and lake eutrophication, respectively. The mitigation performance revealed that the maximum inundated area for a once-in-10-year rainfall event was reduced by 32.6%, the removal ratio of the black and odorous water area was 65%, the comprehensive trophic state index of water bodies was reduced by 37%, and the green development level of Wuhan City in 2020 increased from 0.56 to 0.67. This study is expected to advance the intersection and development of multidisciplinary fields(e.g., urban hydrology, environmental science, and ecology) and offer an important theoretical and technical basis for solving urban complex water issues and promoting green development of cities.

高生物量经济植物修复重金属污染土壤研究进展

植物修复是重金属污染土壤修复的重要方法之一。利用高生物量经济植物修复重金属污染土壤,能够兼顾生态和经济效益,具有很大的应用前景。本文系统分析了植物修复现状及存在的问题,提出利用高生物量经济植物修复重金属污染土壤的优势,总结了近年来利用高生物量经济植物吸收重金属的研究进展,探讨了改善高生物量经济植物修复重金属污染土壤效率的方法,以期为提高植物修复经济效益、促进植物修复广泛应用提供参考。

The development of China's Yangtze River Economic Belt:how to make it in a green way?

The Yangtze River is one of the largest and longest rivers in Asia.The river originates in the Tibet-Qinghai Plateau(headwater reach),passes through the mountainous provinces of Sichuan,Yunnan and Chongqing(upper reach),flows into the Central Plain(middle reach)and Lower Plain(lower reach),and finally empties into the East China Sea in Shanghai(estuary).The Yangtze River Economic Belt(YREB;Fig.1)has a surface area of 2.1

Regulation characteristics of underlying surface on runoff regime metrics and their spatial differences in typical urban communities across China

The regulation and spatial differences of urban runoffs are of great concern in contemporary hydrological research.However,owing to a shortage of basic data sources and restrictions on urban hydrological simulation functions,simulating and investigating the regulation mechanism behind rainfall-runoff processes remain significantly challenging.In this study,the Time Variant Gain Model(TVGM),a hydrological nonlinear system model,was extrapolated to the hydrodynamic model of an urban drainage network system by integrating it with the widely used Stormwater Management Model(SWMM)to adequately simulate urban runoff events while considering various underlying surfaces and runoff routing modes,such as surface,drainage network and river runoff,in urban regions(i.e.,TVGM-SWMM).Moreover,runoff events were characterized using the following four runoff regime metrics:runoff coefficient,capture ratio of annual runoff volume,standardized flood timescale,and the ratio of occurrence time differences between flow and rainfall peak to event duration(peak flow delay time).The characteristics and spatial differences of urban runoff regulations were investigated,and the key impact factors and their relative contributions were identified using multivariate statistical analyses.Four communities were selected as our study areas,consisting of communities from Beijing,Shenzhen,Wuhan,and Chongqing.Our results showed that the TVGM-SWMM performed considerably better than SWMM alone.The comprehensive simulation accuracy of 60%of the events(12/20)improved by 4-86%,with the bias improving the most,followed by the efficiency coefficient.Barring the runoff coefficient,significant spatial differences were identified at the patch scale for the runoff regime metrics,with differences of 0.43,0.22,and 0.16(p<0.05).The key impact factors were the pipe length(r=0.51)in the drainage network system and the forest area ratios(r=0.56),sponge measures(r=0.52),grassland(r=0.48),and impervious surface(r=0.46)in the underlying surfaces.The contributions of the drainage network system and the underlying surfaces were 4.27%and 37.83%,respectively.Regulation in the Beijing community,dominated by grassland regulation,delayed and reduced the peak flow and total runoff volume.In the Shenzhen community,sharp and thin runoff events were mainly generated by impervious surfaces and were not adequately regulated.Forest regulation was the dominant regulation type in the Wuhan community,which reduced the total runoff volume and delayed the peak flow.Waterbody regulation was the primary regulation type in the Chongqing community,which reduced the total runoff volume and peak flow.This study aims to introduce a comprehensive theoretical and technical assessment of the hydrological effects of urbanization and the performance of sponge city construction and provide a reference for urban hydrological model improvements in China.

Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.

Small water bodies in China:Spatial distribution and influencing factors

Inland water bodies,including ponds,lakes,reservoirs,and rivers,provide extensive ecosystem services for human beings.Among these,small water bodies(SWBs),such as ponds and small reservoirs,are more common landscape features and important biogeochemical reactors.SWBs can significantly influence biogeochemical processes and hydrologic cycles.However,due to their small size,SWBs(<10 ha)have been largely ignored in natural resource surveys,leading to a limited understanding of their spatial distribution in China.Insufficient geospatial datasets of SWBs limit the accurate assessments of resource utilization and fluxes of biogenic elements in both aquatic and terrestrial ecosystems.To address this,in this study,we applied a convolutional neural network and a visual interpretation approach to extract SWBs from high-resolution satellite images from Google Earth.The spatial distribution of SWBs in China was mapped,and drivers of the spatial pattern of SWBs were also identified.As a result,a total of 5.18 million water bodies with a surface area larger than 0.1 ha,including ponds,lakes,and reservoirs,were identified.These water bodies(>0.1 ha)cover approximately 179300 km^(2),which is approximately 1.8%of the land area in China.In addition,the combined shoreline length of the water bodies was approximately 2157400 km.Of these water bodies,96.85%were SWBs,accounting for 17.85%of the total water area and 76.4% of the total shoreline length.Precipitation,terrain,and human activity cumulatively explained 45% of the variance in SWB distribution,with precipitation being the strongest climatic explanatory factor.Our results provide important data for determining the roles of SWBs in biogeochemical cycles,habitat protection,and hydrological cycles.

Chitinase-3-like protein 1 as a predictor for the progression or regression of liver fibrosis

Liver fibrosis is a wound-healing response of liver cells to chronic injuries caused by viral infections, including hepatitis B virus (HBV), hepatitis C virus (HCV), toxins, and alcohol abuse. The ability to stage diseases for treatment na?ve patients to initiate proper medical procedures and predict the clinical causes of the disease or the treatment response is important given the increased prevalence of liver fibrosis caused by HBV, HCV and fatty liver diseases. CHI3L1 (chitinase-3-like protein 1, also known as YKL-40), which belongs to the chitinase family but lacks chitinolytic activity and is highly expressed in the liver, seems to fulfill this role. CHI3L1 is a non-invasive staging marker for liver fibrosis caused by HBV, HCV and non-alcoholic fatty liver disease as well as a predictor of the clinical causes and fibrotic changes after treatments. CHI3L1 predicts histological progression of liver fibrosis and fibrosis progression rate (fibrosis unit/year), rapid fibrosis progression after liver transplantation and response to interferon and recent direct acting antiviral therapy in chronic HCV patients. CHI3L1 also predicts response to antiviral therapy in chronic HBV patients.