Miracle-dz/UniFAR-FTUs huggingface dataset:
A latent profile analysis of moral injury appraisals in refugees

ABSTRACT Background: Refugees often exhibit reactions to traumatic events that may be conceptualized as moral injury (i.e. the impact of events that violate important moral values). There have been two types of moral injury appraisals found in refugees: transgressions by others and transgressions by oneself. Objective: To examine whether these types of moral injury appraisals co-occur or whether one form is usually predominant. Additionally, to investigate what types of events (trauma, living difficulties) and outcomes (PTSD, depression, anger, suicidality) were associated with each moral injury appraisal profile. Method: Participants included 221 refugees and asylum seekers residing in Australia. Data was collected online, and via pen and paper. A latent profile analysis was used to identify profiles of moral injury appraisals. Results: Results indicated a three-profile solution: Moral Injury-Other (MI-O; 37.8%), Moral Injury Other + Self (MI-OS; 35.2%), and no moral injury (No-MI; 26.9%). MI-O and MI-OS were predicted by both trauma experience and living difficulties. MI-O and MI-OS were also associated with greater psychopathology across all outcome variables compared to No-MI. MI-OS was also associated with greater anger and depression, compared to the MI-O profile. Conclusions: The association between the moral injury appraisal profiles and traumatic events, living difficulties and psychopathology, will have important clinical implications. HIGHLIGHTS • Three moral injury appraisal profiles were found in a diverse refugee sample• One profile appraised moral violations as committed by others• One profile appraised moral violation as committed by others and themselves• Both profiles were predicted by both greater trauma and living difficulties• Both profiles were associated with greater psychopathology.
Clinical and radiological features of knee osteoarthritis in patients attending the university hospital of Kinshasa, Democratic Republic of Congo

Introduction The aim of the present study was to describe the clinical and radiological features of knee osteoarthritis in Congolese outpatients attending the University Hospital of Kinshasa (UHK). Methods A cross-sectional study was performed in the rheumatology unit of the UHK from January to August 2012. Patients were consecutively recruited. The diagnosis of Osteoarthritis (OA) was based on the criteria of the American College for Rheumatology. Demographic, clinical and x-rays data were collected. The X-rays severity was assessed according to Kellgren and Lawrence's method. Results 1049 patients attended the Rheumatology unit of the UHK during the study period. An accurate diagnosis was reported for 839 patients, of whom 376 (44.8%) suffered from OA. Knee OA was diagnosed in 118 patients (31.4% of all OA patients). 101 patients accepted to be included in the study, 78 women (77.2%) and 23 men (22.8%). Their average age was 58.9 ± 10 years. A body mass index (BMI) ≥ 25kg/m2 was observed in 68 patients of whom 28 were obese (BMI ≥ 30kg/m2). The main symptoms were a mechanical pain (100%), swelling (40.6%), crepitus (79.2%) and mobility reduction (X%). Knee deformities were observed in some patients. At baseline, radiological damages > stage 2 of Kellgren-Lawrence were found in 70 patients. Conclusion Knee OA is a common disease among outpatients who attend the unit of Rheumatology of the UHK. Its clinical profile is the same as what is reported in the literature. Obesity and skeletal abnormalities are encountered in the majority of patients.
Assessing Facilitating Factors and Barriers to Body Mass Index Screening among School Nurses

Childhood obesity and overweight are epidemic in the United States (U.S.), but are not new phenomena. An overabundance of obesity research has explored children from the perspective of disease treatment. A systematic review of 14 Pub Med identified studies was conducted to identify the current body of scientific knowledge as it applies to obesity prevention intervention programs for school age children. Inclusion criteria were published in January 1998 through June 2008, children ages 5-12, public elementary schools, and obesity prevention intervention programs (1,288 studies). Six systematic reviews/meta-analyses, 7 randomized control trials (RCTs), and 1 integrative research (IR) review were critically reviewed. Thirty-four school-based obesity prevention intervention programs were identified and analyzed according to established clinical benchmarks for 1. ) daily dietary intake servings; 2. ) 11,000 to 12,000 steps per week represented 60 minutes per day of moderate/vigorous physical activity; and, 3. ) fasting capillary glucose serum level of 80-100 mg/dl. Those programs meeting the clinical
Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.
Application of DInSAR-PSI Technology for Deformation Monitoring of the Mosul Dam, Iraq

On-going monitoring of deformation of dams is critical to assure their safe and efficient operation. Traditional monitoring methods, based on in-situ sensors measurements on the dam, have some limitations in spatial coverage, observation frequency, and cost. This paper describes the potential use of Synthetic Aperture Radar (SAR) scenes from Sentinel-1A for characterizing deformations at the Mosul Dam (MD) in NW Iraq. Seventy-eight Single Look Complex (SLC) scenes in ascending geometry from the Sentinel-1A scenes, acquired from 03 October 2014 to 27 June 2019, and 96 points within the MD structure, were selected to determine the deformation rate using persistent scatterer interferometry (PSI). Maximum deformation velocity was found to be about 7.4 mm·yr−1 at a longitudinal subsidence area extending over a length of 222 m along the dam axis. The mean subsidence velocity in this area is about 6.27 mm·yr−1 and lies in the center of MD. Subsidence rate shows an inverse relationship with the reservoir water level. It also shows a strong correlation with grouting episodes. Variations in the deformation rate within the same year are most probably due to increased hydrostatic stress which was caused by water storage in the dam that resulted in an increase in solubility of gypsum beds, creating voids and localized collapses underneath the dam. PSI information derived from Sentinel-1A proved to be a good tool for monitoring dam deformation with good accuracy, yielding results that can be used in engineering applications and also risk management.
A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction

High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.
Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy

Forests have potential economic value and play a significant role in maintaining ecological balance. Considering its outdated and incomplete forest statistics, the Kyrgyzstan Republic urgently needs a forest cover map for assessing its current forest resources and assisting national policies on improving rural livelihood and sustainability. This study adopted a hybrid fusion strategy to develop a forest cover map for the Kyrgyzstan Republic with improved accuracy. The fusion strategy uses the merits of the GlobeLand30 in 2010 and the USGS TreeCover2010, the benefits of auxiliary geographic information, and the advantages of the stacking learning method in classification. Additionally, we explored the influence of different forest definitions, based on the tree cover percentage value in the USGS TreeCover2010, on the accuracy of forest cover. Results suggested that the accuracy of our model can be improved significantly by including auxiliary geographic features and feeding the optimal size of training samples. Thereafter, using our model, forest cover maps were derived at different tree cover threshold values in the USGS TreeCover2010. Importantly, the forest cover map at the tree cover threshold value of 40% was determined as the most accurate one with the kappa value of 0.89, whose spatial extent constitutes about 2.4% of the entire territory. This estimated forest cover percentage suggests a low estimation of forest resources based on rigorous definition, which can be valuable for reviewing and amending the current national forest policies.
Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F1-score of 84.2%.
Spatiotemporal Fusion of Satellite Images via Very Deep Convolutional Networks

Spatiotemporal fusion provides an effective way to fuse two types of remote sensing data featured by complementary spatial and temporal properties (typical representatives are Landsat and MODIS images) to generate fused data with both high spatial and temporal resolutions. This paper presents a very deep convolutional neural network (VDCN) based spatiotemporal fusion approach to effectively handle massive remote sensing data in practical applications. Compared with existing shallow learning methods, especially for the sparse representation based ones, the proposed VDCN-based model has the following merits: (1) explicitly correlating the MODIS and Landsat images by learning a non-linear mapping relationship; (2) automatically extracting effective image features; and (3) unifying the feature extraction, non-linear mapping, and image reconstruction into one optimization framework. In the training stage, we train a non-linear mapping between downsampled Landsat and MODIS data using VDCN, and then we train a multi-scale super-resolution (MSSR) VDCN between the original Landsat and downsampled Landsat data. The prediction procedure contains three layers, where each layer consists of a VDCN-based prediction and a fusion model. These layers achieve non-linear mapping from MODIS to downsampled Landsat data, the two-times SR of downsampled Landsat data, and the five-times SR of downsampled Landsat data, successively. Extensive evaluations are executed on two groups of commonly used Landsat–MODIS benchmark datasets. For the fusion results, the quantitative evaluations on all prediction dates and the visual effect on one key date demonstrate that the proposed approach achieves more accurate fusion results than sparse representation based methods.
Computational Modeling Methods for Deployable Structure Based on Alternatively Asymmetric Triangulation

Asymmetric triangulation is an interesting method combined with concentric pleating to obtain a 3D shape without stretching or tearing. There exists some geometric properties in the process of folding to help realize extension and contraction, which can be used in parametric modeling of different regular polygons. To facilitate design and modeling, adequate computational modeling methods are indispensable. This paper proposes a new mathematical idea and presents a feasible way to build the parameterized models in the digital environment of Rhinoceros, utilizing the Kangaroo plugin in Grasshopper. Designers can directly observe the models’ kinematic deployment and calculate the folding efficiency. It is concluded that the tendencies of folding efficiency in different regular polygons are not the same. To realize rigid folding, each polygon has a limited folding angle.
Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.
Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.
Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening

In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening). However, the small-scale data and the gradient vanishing problem have been preventing the existing CNN-based fusion approaches from leveraging deeper networks that potentially have better representation ability to characterize the complex nonlinear mapping relationship between the input (source) and the targeting (fused) images. In this paper, we introduce a very deep network with dense blocks and residual learning to tackle these problems. The proposed network takes advantage of dense connections in dense blocks that have connections for arbitrarily two convolution layers to facilitate gradient flow and implicit deep supervision during training. In addition, reusing feature maps can reduce the number of parameters, which is helpful for reducing overfitting that resulted from small-scale data. Residual learning is explored to reduce the difficulty for the model to generate the MS image with high spatial resolution. The proposed network is evaluated via experiments on three datasets, achieving competitive or superior performance, e.g. the spectral angle mapper (SAM) is decreased over 10% on GaoFen-2, when compared with other state-of-the-art methods.
Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables

Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.
Internet of Things Meets Vehicles: Sheltering In-Vehicle Network through Lightweight Machine Learning

An internet of vehicles allows intelligent automobiles to interchange messages with other cars, traffic management departments, and data analysis companies about vehicle identification, accident detection, and danger warnings. The implementation of these features requires Internet of Things system support. Smart cars are generally equipped with many (hundreds or even thousands of) sensors and microcomputers so that drivers gain more information about travel. The connection between the in-vehicle network and the Internet can be leveraged by the attackers in a malicious manner and thus increases the number of ways the in-vehicle network can now be targeted. Protecting increasingly intelligent vehicle systems becomes more difficult, especially because a network of many different devices makes the system more vulnerable than ever before. The paper assumes a generic threat model in which attackers can access the controller area network (CAN) bus via common access points (e.g., Bluetooth, OBD-II, Wi-Fi, physical access, and cellular communication, etc). A machine learning based simplified attention (SIMATT)-security control unit (SECCU) symmetry framework is proposed towards a novel and lightweight anomaly detecting mechanism for the in-vehicle network. For this framework, we propose two new models, SECCU and SIMATT, and obtain state-of-the-art anomaly detecting performance when fusing the former to the latter. Regardless of the training phase or the detection phase, we strive to minimize the computational cost and thereby obtain a lightweight anomaly detection method. In particular, the SECCU model has only one layer of 500 computing cells and the SIMATT model has been improved to reduce its computational costs. Through substantial experiment comparisons (with various classical algorithms, such as LSTM, GRU, GIDS, RNN, or their variations), it is demonstrated that the SIMATT-SECCU framework achieves an almost optimal accuracy and recall rate.