Cephalometric Pharyngeal Morphology in Adults with Unoperated Cleft Palate.

OBJECTIVE: The aim of this study was to cephalometrically evaluate the pharyngeal morphology in adults with unoperated Submucous Cleft Palate (SMCP), adults with unoperated Overt Cleft Palate (OCP), and adults without clefts. DESIGN: This study employed a retrospective cross-sectional design. Lateral cephalometric radiography was performed on three groups of adults: 1) 29 with unrepaired SMCP; 2) 41 with unrepaired OCP; and 3) 39 without clefts, who served as controls. One-way ANOVA and rank-sum tests were used for intergroup comparisons. P value was set at .05. RESULTS: The soft palate length and the ratio of soft palate length to pharyngeal depth were significantly lower in subjects with unoperated SMCP and OCP than in non-cleft controls. Significant differences were also observed in pharyngeal depth, nasopharyngeal depth, and posterior pharyngeal wall thickness between subjects with unoperated OCP and non-cleft controls. CONCLUSIONS: Pharyngeal morphology differs significantly between individuals with and without clefts, particularly in soft palate length and the ratio of soft palate length to pharyngeal depth.

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic.

Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lightweight pedestrian detection network to accurately detect pedestrians by human head detection in real-time and then calculate the social distancing between pedestrians on UAV images. In particular, our network follows the PeleeNet as backbone and further incorporates the multi-scale features and spatial attention to enhance the features of small objects, like human heads. The experimental results on Merge-Head dataset show that our method achieves 92.22% AP (average precision) and 76 FPS (frames per second), outperforming YOLOv3 models and SSD models and enabling real-time detection in actual applications. The ablation experiments also indicate that multi-scale feature and spatial attention significantly contribute the performance of pedestrian detection. The test results on UAV-Head dataset show that our method can also achieve high precision pedestrian detection on UAV images with 88.5% AP and 75 FPS. In addition, we have conducted a precision calibration test to obtain the transformation matrix from images (vertical images and tilted images) to real-world coordinate. Based on the accurate pedestrian detection and the transformation matrix, the social distancing monitoring between individuals is reliably achieved.

Evaluating the Potential of LJ1-01 Nighttime Light Data for Modeling Socio-Economic Parameters.

The LJ1-01 satellite is the first dedicated nighttime light remote sensing satellite in the world and offers a higher spatial resolution than the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) satellites of the United States. This study compared the LJ1-01 nighttime light data with NPP/VIIRS data in the context of modeling socio-economic parameters. In the eastern and central regions of China, 10 parameters from the four aspects of gross regional product (annual average population, electricity consumption, and area of land in use) were selected to build linear regression models. The results showed that the LJ1-01 nighttime light data offered better potential for modeling socio-economic parameters than the equivalent NPP/VIIRS data; the former can be an effective tool for establishing models for socio-economic parameters. There were significant positive correlations between the two types of nighttime light data and the 10 socio-economic parameters; that for the gross regional product was the highest.

Analysis and Reduction of Solar Stray Light in the Nighttime Imaging Camera of Luojia-1 Satellite.

As one of the experimental payloads on Luojia-1 satellite, the nighttime imaging camera works with a high sensitivity to acquire nighttime light on earth. Solar stray light is a fatal problem for optical satellite works in the polar orbit, even for nighttime scene imaging, resulting in image saturation and light signal detection failure. To solve this problem, an analysis of the range of solar incident angles was conducted firstly. Based on the result, a special-shaped baffle was designed to avoid direct sunlight incidence. Moreover, the capability of stray light elimination of the lens was enhanced by an order of magnitude via optimizing the internal structure. An evaluation of secondary scattering stray lights into the camera from surrounding parts was performed based on a real satellite model. The results showed that the stray light elimination reaches a 10-10 order, meeting design requirements. Utilizing on-orbit images, the ability of satellites in illuminated areas to obtain artificial lights in dawn-dusk area was verified, proving the effectiveness of the stray light elimination design.

Thermal Stability Optimization of the Luojia 1-01 Nighttime Light Remote-Sensing Camera's Principal Distance.

The instability of the principal distance of the nighttime light remote-sensing camera of the Luojia 1-01 satellite directly affects the geometric accuracy of images, consequently affecting the results of analysis of nighttime light remote-sensing data. Based on the theory of optical passive athermal design, a mathematical model of optical-passive athermal design for principal distance stabilization is established. Positive and negative lenses of different materials and the mechanical structures of different materials are matched to optimize the optical system. According to the index requirements of the Luojia 1-01 camera, an image-telecentric optical system was designed under the guidance of the established mathematical model. In the temperature range of -20 °C to +60 °C, the principal distance of the system changes from -0.01 µm to +0.28 µm. After on-orbit testing, the geometric accuracy of the designed nighttime light remote-sensing camera is better than 0.20 pixels and less than index requirement of 0.3 pixels, which indicating that the principal distance maintains good stability on-orbit and meets the application requirements of nighttime light remote sensing.

High Sensitive Night-time Light Imaging Camera Design and In-orbit Test of Luojia1-01 Satellite.

Luojia1-01 satellite is the first scientific experimental satellite applied for night-time light remote sensing data acquisition, and the payload is an optical camera with high sensitivity, high radiation measurement accuracy and stable elements of interior orientation. At the same time, a special shaped hood is designed, which significantly improved the ability of the camera to suppress stray light. Camera electronics adopts the integrated design of focal plane and imaging processing, which greatly reduces the volume and weight of the system. In this paper, the design of the optical camera is summarized, and the results of in-orbit imaging performance tests are analyzed. The results show that the dynamic modulation transfer function (MTF) of the camera is better than 0.17, and the SNR is better than 35 dB under the condition of 10 lx illuminance and 0.3 reflectivity and all indicators meet the design requirements. The data obtained have been widely applied in many fields such as the process of urbanization, light pollution analysis, marine fisheries detection and military.

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras.

The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigation like Global Navigation Satellite System (GNSS) solutions in outdoor environments. Positioning in indoor scenes by using smartphone cameras has its own advantages: no additional needed infrastructure, low cost and a large potential market due to the popularity of smartphones, etc. However, existing methods or systems based on smartphone cameras and visual algorithms have their own limitations when implemented in relatively large indoor spaces. To deal with this problem, we designed an indoor positioning system to locate users in large indoor scenes. The system uses common static objects as references, e.g., doors and windows, to locate users. By using smartphone cameras, our proposed system is able to detect static objects in large indoor spaces and then calculate the smartphones' position to locate users. The system integrates algorithms of deep learning and computer vision. Its cost is low because it does not require additional infrastructure. Experiments in an art museum with a complicated visual environment suggest that this method is able to achieve positioning accuracy within 1 m.

On-Orbit Relative Radiometric Calibration of the Night-Time Sensor of the LuoJia1-01 Satellite.

The LuoJia1-01 satellite can acquire high-resolution, high-sensitivity nighttime light data for night remote sensing applications. LuoJia1-01 is equipped with a 4-megapixel CMOS sensor composed of 2048 × 2048 unique detectors that record weak nighttime light on Earth. Owing to minute variations in manufacturing and temporal degradation, each detector's behavior varies when exposed to uniform radiance, resulting in noticeable detector-level errors in the acquired imagery. Radiometric calibration helps to eliminate these detector-level errors. For the nighttime sensor of LuoJia1-01, it is difficult to directly use the nighttime light data to calibrate the detector-level errors, because at night there is no large-area uniform light source. This paper reports an on-orbit radiometric calibration method that uses daytime data to estimate the relative calibration coefficients for each detector in the LuoJia1-01 nighttime sensor, and uses the calibrated data to correct nighttime data. The image sensor has a high dynamic range (HDR) mode, which is optimized for day/night imaging applications. An HDR image can be constructed using low- and high-gain HDR images captured in HDR mode. Hence, a day-to-night radiometric reference transfer model, which uses daytime uniform calibration to calibrate the detector non-uniformity of the nighttime sensor, is herein built for LuoJia1-01. Owing to the lack of calibration equipment on-board LuoJia1-01, the dark current of the nighttime sensor is calibrated by collecting no-light desert images at new moon. The results show that in HDR mode (1) the root mean square of mean for each detector in low-gain (high-gain) images is better than 0.04 (0.07) in digital number (DN) after dark current correction; (2) the DN relationship between low- and high-gain images conforms to the quadratic polynomial mode; (3) streaking metrics are better than 0.2% after relative calibration; and (4) the nighttime sensor has the same relative correction parameters at different exposure times for the same gain parameters.

Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery.

Luojia 1-01 satellite, launched on 2 June 2018, provides a new data source of nighttime light at 130 m resolution and shows potential for mapping urban extent. In this paper, using Luojia 1-01 and VIIRS nighttime light imagery, we compared several methods for extracting urban areas, including Human Settlement Index (HSI), Simple Thresholding Segmentation (STS) and SVM supervised classification. According to the accuracy assessment, the HSI method using LJ1-01 data had the best performance in urban extent extraction, which presented the largest Kappa Coefficient value, 0.834, among all the results. For the urban areas extracted by VIIRS based HSI method, the largest Kappa Coefficient value was 0.772. In contrast, the largest Kappa Coefficient values obtained by STS method were 0.79 and 0.7512 respectively when using LJ1-01 and VIIRS data, while for SVM method the values were 0.7829 and 0.7486 when using Landsat-LJ and Landsat-VIIRS composite data respectively. The experimented results demonstrated that the utilization of nighttime light imagery can largely improve the accuracy of urban extent extraction and LJ1-01 data, with a higher resolution and more abundant spatial information, can lead to better identification results than its predecessors.

Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite.

A low Earth orbiter (LEO)-based navigation signal augmentation system is considered as a complementary of current global navigation satellite systems (GNSS), which can accelerate precise positioning convergence, strengthen the signal power, and improve signal quality. Wuhan University is dedicated to LEO-based navigation signal augmentation research and launched one scientific experimental satellite named Luojia-1A. The satellite is capable of broadcasting dual-frequency band ranging signals over China. The initial performance of the Luojia-1A satellite navigation augmentation system is assessed in this study. The ground tests indicate that the phase noise of the oscillator is sufficiently low to support the intended applications. The field ranging tests achieve 2.6 m and 0.013 m ranging precision for the pseudorange and carrier phase measurements, respectively. The in-orbit test shows that the internal precision of the ephemeris is approximate 0.1 m and the clock stability is 3 × 10-10. The pseudorange and carrier phase measurement noise evaluated from the geometry-free combination is about 3.3 m and 1.8 cm. Overall, the Luojia-1A navigation augmentation system is capable of providing useable LEO navigation augmentation signals with the empirical user equivalent ranging error (UERE) no worse than 3.6 m, which can be integrated with existing GNSS to improve the real-time navigation performance.

Rapid Texture Optimization of Three-Dimensional Urban Model Based on Oblique Images.

Seamless texture mapping is one of the key technologies for photorealistic 3D texture reconstruction. In this paper, a method of rapid texture optimization of 3D urban reconstruction based on oblique images is proposed aiming at the existence of texture fragments, seams, and inconsistency of color in urban 3D texture mapping based on low-altitude oblique images. First, we explore implementing radiation correction on the experimental images with a radiation procession algorithm. Then, an efficient occlusion detection algorithm based on OpenGL is proposed according to the mapping relation between the terrain triangular mesh surface and the images to implement the occlusion detection of the visible texture on the triangular facets as well as create a list of visible images. Finally, a texture clustering algorithm is put forward based on Markov Random Field utilizing the inherent attributes of the images and solve the energy function minimization by Graph-Cuts. The experimental results display that the method is capable of decreasing the existence of texture fragments, seams, and inconsistency of color in the 3D texture model reconstruction.

Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach.

After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.

A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching.

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.

Impact of global urban expansion on the terrestrial vegetation carbon sequestration capacity.

Continuous urban expansion has a negative impact on the potential of terrestrial vegetation. Till now, the mechanism of such impact remains unclear, and there have been no systematic investigations. In this study, we design a theoretical framework by laterally bridging urban boundaries to explain the distress of regional disparities and longitudinally quantify the impacts of urban expansion on net ecosystem productivity (NEP). The findings demonstrate that global urban expanded by 37.60 × 104 km2 during 1990-2017, which is one of the causes of vegetation carbon loss. Meanwhile, certain climatic changes (e.g., rising temperature, rising CO2, and nitrogen deposition) caused by urban expansion indirectly boosted vegetation carbon sequestration potential through photosynthetic enhancement. The direct decrease in NEP due to the urban expansion (occupying 0.25 % of the Earth's land area) offsets the 1.79 % increase due to the indirect impact. Our findings contribute to a better understanding of the uncertainty associated with urban expansion towards carbon neutrality and provide a scientific reference for sustainable urban development worldwide.

Reducing the Core Losses of Fe-Si-B Amorphous Alloy Ribbons by High Cooling Rate Planar Flow Casting.

In the planar flow casting (PFC) process, the cooling rate significantly affects the structure and properties of a cast ribbon. The influence of the thermal conductivity of the cooling wheel substrate on cooling rate was simulated by a numerical method, and it is shown that a higher thermal conductivity of the cooling wheel substrate leads to a higher cooling rate in the PFC process. Two copper-beryllium (Cu-2Be) rings with thermal conductivities of 175.3 W/m·K and 206.5 W/m·K were manufactured and installed onto a wheel core as the substrate of the cooling wheel. The effects of cooling rate on the soft magnetic properties of Fe-Si-B amorphous ribbons were investigated by pragmatic ribbon casting. The results show that the increment in the thermal conductivity of the cooling wheel substrate from 175.3 W/m·K to 206.5 W/m·K lowered the coercive force of amorphous ribbon from 2.48 A/m to 1.92 A/m and reduced the core losses at 1.4 T and 50 Hz by up to 22.1%.

A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification.

Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast patch-free global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information. However, FPGA has difficulty in extracting the most discriminative features when the sample data are imbalanced. In this article, a spectral-spatial-dependent global learning (SSDGL) framework based on the global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.

Structure-dependent high-TC ferromagnetism in Mn-doped GeSe.

Layered IV-VI diluted magnetic semiconductors (DMSs) have exhibited fascinating ferromagnetism down to atomic layers, but their relatively low Curie temperature (TC, ≤200 K) significantly hinders their practical application. In this work, Mn-doped GeSe (GeMnSe) DMSs with high-TC ferromagnetism (FM) are synthesized by chemical vapor deposition. As the Mn concentration varies, the obtained samples exhibit various structures including single-crystalline nanocombs (SC-NCs), polycrystalline nanoparticles (PC-NPs) and amorphous nanoaggregates (a-NAs). All the samples exhibit FM, and their TC and saturation magnetization (MS) are correlated to their structures. Notably, GeMnSe SC-NCs show a record high TC of 309 K and a record strong magnetic moment of 4.37µB/Mn compared to all the previously-reported IV-VI DMSs. Further analysis shows that the FM originates from the synergetic effect of the Ruderman-Kittel-Kasuya-Yoshida (RKKY) interaction, the crystalline order and the shape anisotropy in the samples. Our results provide a panorama of nanostructure-dependent FM in GeSe-based DMSs and suggest a peculiar playground for investigating fundamental physics and spintronic applications in IV-VI compounds.

Civil war hinders crop production and threatens food security in Syria.

Assessing the impact of violent conflict on Syrian agriculture is challenging given data limitations and attributability issues. Using satellite data at 30 m spatial resolution, we found that the extent of productive cropland showed greater interannual variability and spatial heterogeneity after the start of the civil war in 2011. Using changes in satellite-based night-time light as a proxy for war impact intensity, we also found that cropland close to severely impacted urban settlements faced greater disruption. Fixed-effects models revealed the relationship between productive cropland and precipitation for the pre-war period, whereas a counterfactual scenario constructed for the period 2012-2019 showed substantial variation at the regional level. While the ongoing conflict promoted cropland cultivation in safer zones, cropland reduction took place in the country's northwest and southeast regions. Our study demonstrated the combined utility of daytime and night-time satellite data to assess food insecurity in extreme environments and can help guide distribution of food and aid in Syria.

Proton pump inhibitors suppress DNA damage repair and sensitize treatment resistance in breast cancer by targeting fatty acid synthase.

Human fatty acid synthase (FASN) is the sole cytosolic enzyme responsible for de novo lipid synthesis. FASN is essential for cancer cell survival and contributes to drug and radiation resistance by up-regulating DNA damage repair but not required for most non-lipogenic tissues. Thus, FASN is an attractive target for drug discovery. However, despite decades of effort in targeting FASN, no FASN inhibitors have been approved due to poor pharmacokinetics or toxicities. Here, we show that the FDA-approved proton pump inhibitors (PPIs) effectively inhibit FASN and suppress breast cancer cell survival. PPI inhibition of FASN leads to suppression of non-homologous end joining repair of DNA damages by reducing FASN-mediated PARP1 expression, resulting in apoptosis from oxidative DNA damages and sensitization of cellular resistance to doxorubicin and ionizing radiation. Mining electronic medical records of 6754 breast cancer patients showed that PPI usage significantly increased overall survival and reduced disease recurrence of these patients. Hence, PPIs may be repurposed as anticancer drugs for breast cancer treatments by targeting FASN to overcome drug and radiation resistance.

Urban drought challenge to 2030 sustainable development goals.

In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving; 2) Fostering flexible reliable, and integrated urban water supply; 3) Improving efficiency of urban water management; 4) Investing in sustainability science research for urban drought; and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.