Population is one of the key problematic factors that are restricting China’s economic and social development. Previous studies have used nighttime light imagery to calculate population density. This study analyzes the spatiotemporal evolution of the population in Northeast China based on linear regression analyses of NPP-VIIRS NTL imagery and statistical population data from 36 cities in Northeast China from 2012 to 2017. Based on a comparison of the estimation results in different years, we observed the following. The population of Northeast (...) China showed an overall decreasing trend from 2012–2017, with population changes of +31,600, −960,800, −359,800, −188,000, and −1,127,600 in the respective years. With the overall population loss trend in Northeast China, the population increased in only three cities, namely, Shenyang, Dalian, and Panjin, with an average increase during the six-year period of 24,200, 6,500, and 2,000 people, respectively. The four major urban agglomerations in Northeast China have annual populations far exceeding 4 million people. A correct appreciation of the population dynamics is vital to resource management and comprehensive management efforts. Making full use of natural resources and regional advantages could effectively improve and potentially solve the urban population loss problem and would be of great innovative significance for supporting the realization of the Millennium Development Goals. (shrink)

Background and PurposeTranscranial direct current stimulation is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS by analyzing the ctDCS-induced functional network alterations.MethodsEither the active 5-day, −1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were (...) compared employing graph theoretical analysis based on the functional magnetic resonance imaging data. A support vector machine prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers.ResultsOur results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS. Our prediction model achieved the mean prediction accuracy of 68.3% after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance.ConclusionThe study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome. (shrink)

BackgroundExercise is increasingly recognized as a key component of Parkinson’s disease treatment strategies, but the underlying mechanism of how exercise affects PD is not yet fully understood.ObjectiveThe activation likelihood estimation method is used to study the mechanism of exercise affecting PD, providing a theoretical basis for studying exercise and PD, and promoting the health of patients with PD.MethodsRelevant keywords were searched on the PubMed, Cochrane Library, and Web of Science databases. Seven articles were finally included according to the screening criteria, (...) with a total sample size of 97 individuals. Using the GingerALE 3.0.2 software, an ALE meta-analysis was performed using seven studies that met the requirements, and the probability of the cross-experiment activation of each voxel was calculated.ResultsThe meta-analysis produced seven clusters, and major activations were found in the cerebellum, occipital lobe, parietal lobe, and frontal lobe brain regions.ConclusionExercise for PD mainly results in the enhanced activation of the cerebellum, occipital lobe, parietal lobe, and frontal lobe. Exercise for PD does not cause a change in the activation of a single brain area, and the observed improvement may result from coordinated changes in multiple brain areas. (shrink)

In the process of rapid urbanization, urban heat island effect has been showing more and more significant impacts on human well-being. Therefore, a more detailed understanding of the impact of three-dimensional building morphology on UHI effect across a continuum of spatial scales will be necessary to guide and improve the human settlement.This study selected 31 provincial capital cities of mainland China to analyze the impacts of the 3D building morphology, including the number, area, height, volume, and the surface area of (...) the buildings, on the land surface temperature. By exploring how the influence of 3D building morphology on LST changes with the increase of spatial scale, this study finally recognized which 3D building morphology index is the most significant index affecting LST in different cities, and which spatial scale these 3D building morphology indexes have the most significant impact on LST. The results showed that the building area is the most important 3D building morphology parameter affecting the LST, while the building height has the slightest influence on the LST. These effects are more significant in the spatial scale of 150 m–540 m, and the spatial scale increases with the increase of building areas in developed cities. These results highlight the necessity of considering fine-grained management in the governance and alleviating of the urban thermal environment through urban planning and urban renewal strategies. (shrink)

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking (...) based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform. (shrink)

Air quality in China is characterized by significant spatial and temporal differences, which are directly related to local meteorological conditions. This study used air quality monitoring data, namely, the air pollution index and air quality index between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of (...) PM2.5, PM10, SO2, CO, NO2, and O3 were identified. The AQI has the advantages of facilitating higher index values than the API. The air quality showed an improvement from 2005 to 2018. Wind direction and precipitation were the most important meteorological factors affecting the air quality in northern and southern China, respectively, which to some extent reflected the causes and degradation mechanisms of air pollution in the two regions. There were significant spatial and temporal differences in the effects of meteorological factors on the concentrations of different pollutants. The influence of atmospheric pressure on pollutant concentration differed between the east and west. Precipitation and relative humidity in most cities had significant impacts on PM2.5 and PM10. The influence of relative humidity was most significant for SO2 and it also had a great influence on O3, while wind speed had a great influence on NO2. The results of the study confirm the meteorological sensitivity of air quality and provide support for the implementation of regional air pollution prevention and control initiatives. (shrink)

Land use in the Yangtze River Delta in 2000 and 2017 was classified by the visual interpretation of Landsat satellite images. Then, these images were overlain with economic and physical geographical data to analyze the urban spatial expansion pattern and its physical constraints and socioeconomic influence factors by employing a combination of transition matrix analysis, expansion intensity indices, and equal-fan analysis. The results showed that from 2000 to 2017, there was a significant increase in built-up areas in the region, with (...) rapid expansion in the core area. The northern and southern parts of the Yangtze River Delta experienced different urban spatial expansions, with a higher scale and rate in the cities along the Yangtze River and the coast in Jiangsu Province in the north than in Zhejiang Province in the south. Cities expanded towards megacities or hubs along the Yangtze River or the coast, indicating that urban expansion is influenced by preferential policies and urban planning factors in addition to the spillover effects of neighboring cities and the adjacency to seas or large rivers. Finally, urban expansion is significantly constrained by elevation, with cities at lower elevations or in flat terrain undergoing more rapid urban expansion and development. (shrink)

ObjectiveTo explore the relationships between dispositional mindfulness and their post-traumatic stress symptoms of emergency nurses, and the mediating effects of coping styles and emotional exhaustion.MethodsA cross-sectional survey study was conducted to collect data on DM, coping styles, EE, and PTSS among 571 emergency nurses from 20 hospitals in Chongqing, China. Correlation and structural equation models were used to evaluate the relationship among variables.ResultsEmergency nurses with lower dispositional mindfulness, higher emotional exhaustion and preference for negative coping revealed more PTSS. The effect (...) of NC on PTSS was partially mediated by emotional exhaustion. Negative coping and emotional exhaustion played concurrent and sequential mediating roles between dispositional mindfulness and PTSS.ConclusionThis study has made a significant contribution to existing literature. It was suggested to develop interventions aimed at enhancing mindfulness, reducing negative coping strategies, and alleviating emotional exhaustion, which may be effective at reducing or alleviating post-traumatic stress symptoms of emergency nurses. (shrink)

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed (...) restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods. (shrink)

Because shale gas content plays a very important role in the evaluation of gas shale potential, its calculation and prediction become obligatory. We used two predictive models, namely, the Langmuir and Ambrose models, to calculate the shale gas content. The parameters involved in these two models are calculated by various experiments and analytic methods, including indirect prediction, the isothermal adsorption test, X-ray diffraction analysis, total organic carbon measurement, pyrolysis, and porosity measurement. Then, a new calculation model that is applicable to (...) shales in the Kuqa Depression, Tarim Basin, is established. Further research on influential factors of gas content in well YN2 is implemented. The result indicates that the gas content of terrestrial shales is more influenced by TOC abundance than by the content of clay minerals and quartz. The main parameters in the new calculation model are the TOC, depth, porosity, and gas saturation. The Jurassic shale gas in well YN2 is speculated to be mainly adsorption gas, with a dominant proportion of 75%–90% in the total gas content. As the formation depth increases, the free-gas content rises continuously, whereas the adsorption gas content first increases and then approaches the equilibrium value or even tends to decrease slightly. Based on the foregoing results, the target layer, the Yengisar Formation, is predicted to possess an enormous amount of shale gas potential, with an average total gas content of [Formula: see text]. (shrink)

Data gathering is the basis of monitoring applications in an underwater sensor network, and excellent network coverage and data transmission reliability are the guarantees for the quality of monitoring tasks. However, the energy consumption of the nodes is too fast due to the heavy load of the cluster heads closer to the sink when data is transmitted between cluster heads and the sink by multihop, which leads to an energy hole problem in an underwater sensor network of clustering technology. Aiming (...) to address this problem, we propose a dynamic hierarchical clustering data gathering algorithm based on multiple criteria decision making in a 3D underwater sensor network. Firstly, the entire monitoring network is divided into many layers. For selecting a cluster head in each layer, multiple criteria decision making of an intuitionistic fuzzy Analytic Hierarchy Process and hierarchical fuzzy integration is adopted. Furthermore, a sorting algorithm is used to form a clustering topology algorithm to solve the problem that there is the only node in one cluster. Then, an energy-balanced routing algorithm between clusters is proposed according to the residual energy of the node, the depth, and the number of neighbor nodes. Finally, the simulation results show that DHCDGA can not only effectively balance the energy consumption of the network and prolong the network lifetime but also improve network coverage and data gathering reliability. (shrink)

Due to huge amount of greenhouse gases emission, freight has been adversely affecting the global environment in facilitating the global economy. Therefore, green vehicle routing problem, aiming to minimize the total carbon emissions in the transportation, has become a hot issue. In this paper, an adaptive large neighborhood search algorithm is proposed to solve large-scale instances of GVRP. The core of ALNS algorithm is destroy operators and repair operators. In the destroy operators, a new removal heuristic applying to the characteristics (...) of GVRP is proposed. The heuristic can quickly remove customers who bring a large amount of carbon emissions with pertinence, and these customers may be arranged more properly in future repair operators. In the repair operators, a fast insertion method is developed. In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. Computational experiments were performed on Solomon benchmark with 100 customers and Homberger benchmark instances with up to 1000 customers. Given the same computational time, the proposed ALNS improves the average accuracy by 8.49% compared with the classic ALNS. In the optimal situation, the improvement can achieve 33.61%. (shrink)

In this paper, a finite-time simultaneous stabilization problem is investigated for a set of stochastic port-controlled Hamiltonian systems over delayed and fading noisy channels. The feedback control signals transmitted via a communication network suffer from both constant transmission delay and fading channels which are modeled as a time-varying stochastic model. First, on the basis of dissipative Hamiltonian structural properties, two stochastic PCH systems are combined to form an augmented system by a single output feedback controller and then sufficient conditions are (...) developed for the semiglobally finite-time simultaneous stability in probability of the resulting closed-loop systems. The case of multiple stochastic PCH systems is also considered and a new control scheme is proposed for the systems to save costs and achieve computational simplification. Finally, an example is provided to verify the feasibility of the proposed simultaneous stabilization method. (shrink)

This article focuses on the multidimensional construction of the multimedia network public opinion supervision mechanism, puts the research on the background of the era of big data, and based on the analysis and definition of the difference between network public opinion and network public opinion, deeply summarizes the network public opinion in the era of big data. New features analyze the opportunities and challenges faced by online public opinion in the era of big data. Based on the rational construction of (...) the index system, this paper studies the multimedia network public opinion evaluation and prediction algorithm. Existing network public opinion assessment and prediction algorithms have shortcomings in capturing the characteristics of data sequences and the long-term dependence of data sequences, and the problems of overfitting and gradient disappearance may occur during training. Because of the above problems, based on the long-term and short-term memory network model, a regularized method is used to construct a multimedia network public opinion prediction model algorithm. This paper builds a multimedia network public opinion threat rating evaluation model based on the public opinion supervision prediction model and conducts analysis. The model constructed this time can not only improve the accuracy of public opinion assessment and prediction but also better avoid the problem of gradient disappearance and overfitting. (shrink)