DESIGN AND IMPLEMENTATION OF A WEB INTERACTIVE THEMATIC CARTOGRAPHY METHOD BASED ON A WEB SERVICE CHAIN

There is a tendency toward the creation of thematic maps on the web in the ongoing development of cartographic technology. However, automatically creating maps through the web and creating interactive web maps are two challenges associated with this field. To solve these problems, a web thematic cartography method based on a web service chain is presented here. Automated cartography is performed through knowledge-based web cartographic services, and interacting with the services is possible. The method is implemented in the construction of a urban thematic atlas in Shenzhen, China that is dedicated to supporting decision making for urban planning and public participatory thematic map making on the web.

Urban nighttime leisure space mapping with nighttime light images and POI data

Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km(2), which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures

Practical m

In collaborative data publishing (CDP), an m-adversary attack refers to a scenario where up to m malicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributed k-anonymization scheme, m-k-anonymization, designed to defend against m-adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset

The dynamic investigation of intrinsic vibration characteristics of a stranding machine by the finite element method

In response to the design problems of violent vibration and noise when a stranding machine is running at high speed, this project completed a motion simulation and vibration analysis based on the prototype FB-650C-2 bow-type stranding machine produced by Fuchuan Mechanical and Electrical Technology Co. The modal analysis was carried out in ANSYS to obtain the first eight orders of inherent frequencies and vibration patterns, combined with excitation force analysis to verify whether the rotating parts could avoid the resonant frequency when operating. Harmonic response analysis was carried out based on the modal state to calculate the steady-state forced vibration of the structure, and the variation curve of response value (usual deformation) with frequency and the cloud diagrams of stress distribution of each component at the rotation frequency were obtained. Suggestions for improving vibration and reducing noise were made based on the experimental and analytical results

Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique – singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods

Robust information hiding in low-resolution videos with quantization index modulation in DCT-CS domain

Video information hiding and transmission over noisy channels leads to errors on video and degradation of the visual quality notably. In this paper, a video signal fusion scheme is proposed to combine sensed host signal and the hidden signal with quantization index modulation (QIM) technology in the compressive sensing (CS) and discrete cosine transform (DCT) domain. With quantization based signal fusion, a realistic solution is provided to the receiver, which can improve the reconstruction video quality without requiring significant extra channel resource. The extensive experiments have shown that the proposed scheme can effectively achieve the better trade-off between robustness and statistical invisibility for video information hiding communication. This will be extremely important for low-resolution video analytics and protection in big data era

The Gravity Environment of Zhouqu Debris Flow of August 2010 and Its Implication for Future Recurrence

This study investigates the geological background of the August 7-8, 2010 Zhouqu debris flows in the northwestern Chinese province of Gansu, and possible future occurrence of such hazards in the peri-Tibetan Plateau (TP) regions. Debris flows are a more predictable type of landslide because of its strong correlation with extreme precipitation. However, two factors affecting the frequency and magnitude of debris flows: very fine scale precipitation and degree of fracture of bedrock, both defy direct observations. Annual mean Net Primary production (NPP) is used as a surrogate for regional precipitation with patchiness filtered out, and gravity satellite measured regional mass changes as an indication of bedrock cracking, through the groundwater as the nexus. The GRACE measurements indicate a region (to the north east of TP) of persistent mass gain (started well before the 2008 Wenchuan earthquake), likely due to increased groundwater percolation. While in the neighboring agricultural region further to the north east, there are signal of decreased fossil water reservoir. The imposed stress fields by large scale increase/decrease groundwater may contribute to future geological instability of this region. Zhouqu locates right on the saddle of the gravity field anomaly. The region surrounding the Bay of Bangle (to the southeast of TP) has a similar situation. To investigate future changes in extreme precipitation, the other key player for debris flows, the “pseudo-climate change” experiments of a weather model forced by climate model provided perturbations on the thermal fields are performed and endangered locations are identified. In the future warmer climate, extreme precipitation will be more severe and debris will be more frequent and severe

Diagnostic Performance of Neurofilaments in Chinese Patients With Amyotrophic Lateral Sclerosis: A Prospective Study

Several studies have attempted to reduce diagnostic delay and identify biomarkers for drug development in amyotrophic lateral sclerosis (ALS). In this study, we aimed to evaluate the diagnostic accuracy for ALS of cerebrospinal fluid (CSF) neurofilament (Nf), Tau protein, and inflammatory factors such as interleukin (IL)-2, IL-6, IL-10, IL-15, and granulocyte-macrophage colony-stimulating factor (GMCSF) in Chinese patients. Our prospective study measured the concentration of phosphorylated Nf heavy chain (pNfH), Nf light chain (NfL), Tau, pTau, and inflammatory factors in the CSF of 85 patients. Detailed clinical data and laboratory, neuroimaging, and neurophysiological findings were recorded. The concentrations of pNfH and NfL were higher in the ALS group than in the control group. At the 1104 pg/mL pNfH cutoff, the specificity was 68.8%, the sensitivity 100%, and the area under the curve (AUC) 0.907. At the 1,139 pg/mL NfL cutoff, the specificity was 56.3%, the sensitivity 96.2%, and the AUC 0.775. There was no significant difference in the concentrations of Tau, pTau, IL-2, IL-6, IL-10, IL-15, and GMCSF between the ALS and control groups (p > 0.05). In the ALS group, the concentration of pNfH in the CSF was correlated with disease duration (r = −0.475, p < 0.001). This is the first prospective study to confirm the diagnostic value of Nf for ALS in Chinese patients

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude ( alpha ), mean ( mu ), and standard deviation ( sigma ) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma , alpha and mu values. The paper concludes with a number of open questions and outlines future research directions