Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families.ISPRS Int. J. Geo-Inf. 2015, 4 2307We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

With the advancement of GPS and remote sensing technologies, large amounts of geospatial and spatiotemporal data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or continuous) at a set of locations, the problem aims to learn a model that can predict the response variable based on explanatory features. The problem is important with broad applications in earth science, urban informatics, geosocial media analytics and public health, but is challenging due to the unique characteristics of spatiotemporal data, including spatial and temporal autocorrelation, spatial heterogeneity, temporal non-stationarity, limited ground truth, and multiple scales and resolutions. This paper provides a systematic review on principles and methods in spatial and spatiotemporal prediction. We provide a taxonomy of methods categorized by the key challenge they address. For each method, we introduce its underlying assumption, theoretical foundation, and discuss its advantages and disadvantages. Our goal is to help interdisciplinary domain scientists choose techniques to solve their problems, and more importantly, to help data mining researchers to understand the main principles and methods in spatial and spatiotemporal prediction and identify future research opportunities. Index Terms-Spatial and spatiotemporal prediction, survey, classification and regression, spatial big data, deep learning ! 1.1 Societal Applications Spatial prediction is of great importance in societal applications related to various agencies, such as the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), the Department of Defense, the Department of Transportation, the Department of Homeland Security, the United States Department of Agriculture (USDA), and the Environmental Protection Agency (EPA). Here we categorize application examples into four major areas including earth science, urban informatics, geosocial media analytics, and public health. Earth science: Earth science is a major application area for spatial prediction [3]. Remote sensors from satellites, airplanes, and unmanned aerial vehicles (UAVs) have collected petabytes of geo-referenced earth imagery. Particularly, the recent deployment of CubeSat fleets by commercial companies (e.g., Planet Labs Inc.) help collect high-resolution imagery that covers the entire earth surface almost every day. In addition, numerous ground sensors deployed on

Carbon nanotube (CNT) films, easily drawn from super-aligned CNT arrays with a large area and a good compatibility with semiconductor technology, have been used as light sensitive materials for infrared (IR) detection. A bolometric CNT detector made from one layer of super-aligned CNT film shows a 15.4% resistance change under 10 mW mm(-2) of IR illumination and a fast characteristic response time of 4.4 ms due to its ultra-small heat capacity per unit area in vacuum at room temperature. Besides the power intensity detection, the anisotropic property of the super-aligned CNT films makes them ideal materials to detect the polarization of IR light simultaneously, which provides great potential in infrared imaging polarimetry. Theoretical analyses have been carried out to investigate the influences of CNT film properties on the responsivity and response time of the detector.

Background There are many shortcomings in traditional prefabricated rehabilitation insoles for symptomatic flatfoot patients. This study investigated the effects of customized 3-dimensional (3D) printed insoles on pressure and comfort of the plantar foot in symptomatic flatfoot patients. Material/Methods Eighty patients with bilateral flatfoot participated in this study. At week 0, patients were randomly assigned into 1 of 2 groups. In the control group, the patients wore standardize shoes with prefabricated insoles; and in the experimental group the patients wore standardize shoes and customized insoles. The Footscan ® system recorded peak pressure, peak force, and peak contact area in 10 areas of the sole at weeks 0 and at week 8. Patients used visual analogue scale scores at week 0 and at week 8 to assess overall comfort of insoles. Results At week 0, compared with the control group, the peak pressure in the metatarsal was significantly lower in the experimental group ( P <0.05) while the peak pressure in the mid-foot was significantly higher than the control group ( P <0.05). At week 8, in the experimental group, the peak pressures in the mid-foot were significantly higher than the control group ( P <0.05). The comfort scores (measured by pain scale) reported by the experimental group were significantly lower than those reported by the control group ( P <0.05). Conclusions Customized 3D printed insoles reduced the pressure on the metatarsals by distributed it over the mid-foot area, thus reduced the damage from symptomatic flatfoot. Customized 3D printed insoles were more effective than prefabricated insoles and offered better comfort for patients with symptomatic flatfoot.

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TEAS is a safe noninvasive adjunctive intervention for anesthesia management among patients undergoing VATS lobectomy. TEAS at 2/100 Hz can reduce intraoperative opioid dosage and slow the decrease of PaO during one-lung ventilation. It can also effectively reduce pain score, extubation time, and PACU stay immediately after surgery. Further, 100 Hz TEAS can reduce PONV morbidity.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research.