Miracle-dz/UniFAR-FTUs huggingface dataset:
Ranking the Meso Level Critical Factors of Electronic Medical Records Adoption Using Fuzzy Topsis Method

As Electronic Medical Records (EMRs) have a great possibility for rising physician’s performance in their daily work which improves quality, safety and efficiency in healthcare, they are implemented throughout the world (Boonstra and Broekhuis, 2010). In physician practices the rate of EMRs adoption has been slow and restricted (around 25%) according to Endsley, Baker, Kershner, and Curtin (2005) in spite of the cost savings through lower administrative costs and medical errors related with EMRs systems. The core objective of this research is to identify, categorize, and analyse meso-level factors introduced by Lau et al, 2012, perceived by physicians to the adoption of EMRs in order to give more knowledge in primary care setting. Finding was extracted through questionnaire which distributed to 350 physicians in primary cares in Malaysia to assess their perception towards EMRs adoption. The findings showed that Physicians had positive perception towards some features related to technology adoption success and emphasized EMRs had helpful impact in their office. The fuzzy TOPSIS physician EMRs adoption model in meso-level developed and its factors and sub-factors discussed in this study which provide making sense of EMRs adoption. The related factors based on meso-level perspective prioritized and ranked by using the fuzzy TOPSIS. The purpose of ranking using these approaches is to inspect which factors are more imperative in EMRs adoption among primary care physicians. The result of performing fuzzy TOPSIS is as a novelty method to identify the critical factors which assist healthcare organizations to inspire their users in accepting of new technology.
Treatment of fungal pyelonephritis and ureterolithiasis with a subcutaneous ureteral bypass system and systemic antifungal medication in a cat

Purpose We describe a case of ureteral obstruction and fungal pyelonephritis in a cat. The case was managed successfully with a subcutaneous ureteral bypass (SUB) device and systemic antifungal medication. Case summary A 9-year-old, male, neutered, domestic shorthair cat was referred for severe azotemia, ureteral obstruction, and sonographically identified progressive pyelectasia of the left kidney. A SUB device was placed. Cytology of the left kidney subsequently identified fungal organisms, and the cat responded clinically to treatment with fluconazole and supportive care. The cat survived for 11 months with intermittent fluconazole treatment. Conclusion Fungal pyelonephritis should be considered as a possible concurrent diagnosis in cats with ureteral obstruction. The successful clinical outcome in this case suggests that treatment with SUB placement, antifungal medication, and supportive care could be an effective treatment strategy.
Going Forward with Pokemon Go

Pokemon Go is an augmented reality (AR) game which combines the use of smart mobile technology with physical exploration in the real world. It was a global phenomenon that rocked the world since 2016. Across boundaries and nations, the young and seniors were actively downloading and playing, joining the intrend gaming community. Was it a fleeting fad or a more sustainable activity? This paper discusses the literature currently available on this interesting phenomenon: its effect on physical and mental health as well as some documented hazards and distractions. From the review, Pokemon Go demonstrates that cleverly implemented AR games can reach millions of people and trigger substantial behavioral changes. AR games can help increase physical activities and exercise provided people's interest can be sustained.
Sensor-based wearable system for the detection and automatic treatment of nocturnal hypoglycaemia

Diabetic patients are prone to daily and severe health-related risks, namely hyper and hypoglycaemia. Hypoglycaemia phenomenon happens when the glucose level in patient's blood is lower than a well-determined sill. It may induce serious impacts, such as functional brain failure or even the death. Hypoglycaemia is especially dangerous when it occurs during the night while the patient is asleep because it becomes difficult to be detected by the patient itself or other persons around him. While all existing sensor-based solutions are detection-only driven, the proposed solution goes beyond and attempts to treat autonomously, and at low cost, the nocturnal hypoglycaemia. The presented system detects the nocturnal hypoglycaemia phenomenon based on accelerated heart-rate symptom and a progressive detection algorithm. The system treats then the detected nocturnal hypoglycaemia throughout safe and automatic injection of glucagon.
Circulating inflammatory biomarkers are related to cerebrovascular disease in older adults

Objective This investigation aimed at examining whether circulating inflammatory biomarkers C-reactive protein (CRP), interleukin-6 (IL6), and alpha 1-antichymotrypsin (ACT) were related to cerebrovascular disease (CVD) assessed by MRI. Methods The study included nondemented elderly participants of a community-based, multiethnic cohort, who received baseline MRI scans and had CRP (n = 508), ACT (435), and IL6 (N = 357) measured by ELISA. Silent brain infarcts and white matter hyperintensities (WMH) were derived from all available MRI scans at baseline, approximately 4.4 years after blood sample collection for inflammatory biomarkers. Repeated assessments of infarcts and WMH, as well as microbleeds assessment, were performed at follow-up MRI visits around 4.5 years later. Cross-sectional and longitudinal relationship between inflammatory biomarkers and CVD were analyzed using appropriate logistic regression models, generalized linear models, or COX models. Results After adjusting for age, sex, ethnicity, education, APOE genotype, and intracranial volume, 1 SD increase in log10IL6 was associated with infarcts on MRI {odds ratio [OR] (95% confidence interval [CI]) = 1.28 [1.02–1.60], p = 0.033}, and 1 SD increase in log10CRP and log10ACT was associated with microbleeds (OR [95% CI] = 1.46 [1.02–2.09], p = 0.041; and 1.65 [1.11–2.46], p = 0.013; respectively). One SD increase in log10ACT was also associated with larger WMH at the follow-up MRI (b = 0.103, p = 0.012) and increased accumulation of WMH volume (b = 0.062, p = 0.041) during follow-up. The associations remained significant after additional adjustment of vascular risk factors and excluding participants with clinical stroke. Conclusions Among older adults, increased circulating inflammatory biomarkers were associated with the presence of infarcts and microbleeds, WMH burden, and progression of WMH.
The umbilical cord, preeclampsia and the VEGF family

Introduction The VEGF family has been identified as abnormal in preeclampsia (PE). Hypertensive disorders of pregnancy (HDP) are major contributors to maternal and neonatal morbidity and mortality worldwide; likewise, umbilical cord anatomical abnormalities (UCAA) are linked to poor neonatal outcomes. Based on the relationship described between PE and UCAA and the role of the VEGF family in PE, this study explored VEGF expression in placental and UC tissued from patients with PE and with UCAA. Methods We performed an observational, analytical study on placentas, comparing protein and mRNA expression in four groups: patients with PE, patients with UC abnormalities, patients with both, and patients with none of them. Using immunohistochemistry, we studied VEGF A, VEGF R1 (FLT1), MMP1, and PLGF. With quantitative reverse transcription polymerase chain reaction we described mRNA expression of PLGF, VEGF and sFLT1, and sFLT1/PLGF ratio. Results Forty newborns were included. Sixty-seven percent of mothers and 45% of newborns developed no complications. Immunohistochemistry was performed on UC and placental disc paraffin-embedded tissue; in the latter, the mRNA of the VEGF family was also measured. Statistically significant differences were observed among different expressions in both HDP and UCAA groups. Interestingly, the UCAA group exhibited lower levels of sFLT1 and VEGF-A in comparison with other groups, with significant P-value for sFLT1 (P=0000.1). Conclusion The origin of UCAA abnormalities and their relation with HDP are still unknown. VEGF family alterations could be involved in both. This study provides the first approach related to molecules linked to UCAA.
New Context-Adaptive Arithmetic Coding Scheme for Lossless Bit Rate Reduction of Parametric Stereo in Enhanced aacPlus

SUMMARY We propose a new context-adaptive arithmetic coding (CAAC) scheme for lossless bit rate reduction of parametric stereo (PS) in enhanced aacPlus. Based on the probability analysis of stereo parameters indexes in PS, we propose a stereo band-dependent CAAC scheme for PS. We also propose a new coding structure of the scheme which is simple but e ﬀ ective. The proposed scheme has normal and memory-reduced versions, which are superior to the original and conventional schemes and guarantees signiﬁcant bit rate reduction of PS. The proposed scheme can be an alternative to the original PS coding scheme at low bit rate, where coding e ﬃ ciency is very important.
Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography

In tree Aboveground Biomass (AGB) estimation, the traditional harvest method is accurate but unsuitable for a large-scale forest. The airborne Light Detection And Ranging (LiDAR) is superior in obtaining the point cloud data of a dense forest and extracting tree heights for AGB estimation. However, the LiDAR has limitations such as high cost, low efficiency, and complicated operations. Alternatively, the overlapping oblique photographs taken by an Unmanned Aerial Vehicle (UAV)-loaded digital camera can also generate point cloud data using the Aerial Triangulation (AT) method. However, limited by the relatively poor penetrating capacity of natural light, the photographs captured by the digital camera on a UAV are more suitable for obtaining the point cloud data of a relatively sparse forest. In this paper, an electric fixed-wing UAV loaded with a digital camera was employed to take oblique photographs of a sparse subalpine coniferous forest in the source region of the Minjiang River. Based on point cloud data obtained from the overlapping photographs, a Digital Terrain Model (DTM) was generated by filtering non-ground points along with the acquisition of a Digital Surface Model (DSM) of Minjiang fir trees by eliminating subalpine shrubs and meadows. Individual tree heights were extracted by overlaying individual tree outlines on Canopy Height Model (CHM) data computed by subtracting the Digital Elevation Model (DEM) from the rasterized DSM. The allometric equation with tree height (H) as the predictor variable was established by fitting measured tree heights with tree AGBs, which were estimated using the allometric equation on H and Diameter at Breast Height (DBH) in sample tree plots. Finally, the AGBs of all of the trees in the test site were determined by inputting extracted individual tree heights into the established allometric equation. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted individual tree heights were 0.92 and 1.77 m, and the R2 and RMSE of the estimated AGBs of individual trees were 0.96 and 54.90 kg. The results demonstrated the feasibility and effectiveness of applying UAV-acquired oblique optical photographs to the tree AGB estimation of sparse subalpine coniferous forests.
Patch Optimization for Surface Light Field Reconstruction

Surface light field advances conventional light field rendering techniques by utilizing geometry information. Using surface light field, real-world objects with complex appearance could be faithfully represented. This capability could play an important role in many VR/AR applications. However, an accurate geometric model is needed for surface light field sampling and processing, which limits its wide usage since many objects of interests are difficult to reconstruct with their usually very complex appearances. We propose a novel two-step optimization framework to reduce the dependency of accurate geometry. The key insight is to treat surface light field sampling as a multi-view multi-texture optimization problem. Our approach can deal with both model inaccuracy and image to model misalignment, making it possible to create high-fidelity surface light field models without using high-precision special hardware. key words: surface light field, appearance reconstruction, photo-realistic rendering
Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.
Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation

Different kinds of radiative transfer models, including a relative sunshine-based model (BBM), a physical-based model for tropical environment (PBM), an efficient physical-based model (EPP), a look-up-table-based model (LUT), and six artificial intelligence models (AI) were introduced for modeling the daily photosynthetically-active radiation (PAR, solar radiation at 400–700 nm), using ground observations at twenty-nine stations, in different climatic zones and terrain features, over mainland China. The climate and terrain effects on the PAR estimates from the different PAR models have been quantitatively analyzed. The results showed that the Genetic model had overwhelmingly higher accuracy than the other models, with the lowest root mean square error (RMSE = 0.5 MJ m−2day−1), lowest mean absolute bias error (MAE = 0.326 MJ m−2day−1), and highest correlation coefficient (R = 0.972), respectively. The spatial–temporal variations of the annual mean PAR (APAR), in the different climate zones and terrains over mainland China, were further investigated, using the Genetic model; the PAR values in China were generally higher in summer than those in the other seasons. The Qinghai Tibetan Plateau had always been the area with the highest APAR (8.668 MJ m−2day−1), and the Sichuan Basin had always been the area with lowest APAR (4.733 MJ m−2day−1). The PAR datasets generated by the Genetic model, in this study, could be used in numerous PAR applications, with high accuracy.
Retrieval of the Fine-Mode Aerosol Optical Depth over East China Using a Grouped Residual Error Sorting (GRES) Method from Multi-Angle and Polarized Satellite Data

The fine-mode aerosol optical depth (AODf) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AODf directly. However, the currently operational algorithm of Laboratoire d’Optique Atmosphérique (LOA) has a poor AODf retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AODf retrieval using the traditional look-up table (LUT) approach and then the AODf retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AODf at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AODf and PARASOL AODf product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AODf at 865 nm = 0.283). An application using the GRES method for total AOD (AODt) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.
Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.
Tropical Deforestation and Recolonization by Exotic and Native Trees: Spatial Patterns of Tropical Forest Biomass, Functional Groups, and Species Counts and Links to Stand Age, Geoclimate, and Sustainability Goals

We mapped native, endemic, and introduced (i.e., exotic) tree species counts, relative basal areas of functional groups, species basal areas, and forest biomass from forest inventory data, satellite imagery, and environmental data for Puerto Rico and the Virgin Islands. Imagery included time series of Landsat composites and Moderate Resolution Imaging Spectroradiometer (MODIS)-based phenology. Environmental data included climate, land-cover, geology, topography, and road distances. Large-scale deforestation and subsequent forest regrowth are clear in the resulting maps decades after large-scale transition back to forest. Stand age, climate, geology, topography, road/urban locations, and protection are clearly influential. Unprotected forests on more accessible or arable lands are younger and have more introduced species and deciduous and nitrogen-fixing basal areas, fewer endemic species, and less biomass. Exotic species are widespread—except in the oldest, most remote forests on the least arable lands, where shade-tolerant exotics may persist. Although the maps have large uncertainty, their patterns of biomass, tree species diversity, and functional traits suggest that for a given geoclimate, forest age is a core proxy for forest biomass, species counts, nitrogen-fixing status, and leaf longevity. Geoclimate indicates hard-leaved species commonness. Until global wall-to-wall remote sensing data from specialized sensors are available, maps from multispectral image time series and other predictor data should help with running ecosystem models and as sustainable development indicators. Forest attribute models trained with a tree species ordination and mapped with nearest neighbor substitution (Phenological Gradient Nearest Neighbor method, PGNN) yielded larger correlation coefficients for observed vs. mapped tree species basal areas than Cubist regression tree models trained separately on each species. In contrast, Cubist regression tree models of forest structural and functional attributes yielded larger such correlation coefficients than the ordination-trained PGNN models.
Towards a 20 m Global Building Map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.