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Predicting doctor ratings is a critical task in the healthcare industry. A patient usually provides ratings to a few doctors only, leading to the data sparsity issue, which complicates the rating prediction task. The study attempts to improve the prediction methodologies used in the doctor rating prediction systems. The study proposes a novel deep learning (DL) model for online doctor rating prediction based on a hierarchical attention bidirectional long short‐term memory (ODRP‐HABiLSTM) network. A hierarchical self‐attention bidirectional long short‐term memory (HA‐BiLSTM) network incorporates a textual review's word and sentence level information. A highway network is used to refine the representations learned by BiLSTM. The resulting latent patient and doctor representations are utilized to predict the online doctor ratings. Experimental findings based on real‐world doctor reviews from Yelp.com across two medical specialties demonstrate the proposed model's superior performance over state‐of‐the‐art benchmark models. In addition, robustness analysis is used to strengthen the findings. [ABSTRACT FROM AUTHOR]

Extracting meaningful attributes of radar scatterers from SAR images, PAZ in our case, facilitates a better understanding of SAR data and physical interpretation of deformation processes. The attribute categories and attribute extraction method are not yet thoroughly investigated. Therefore, this study recognizes three attribute categories: geometric, physical, and land-use attributes, and aims to design a new scheme to extract these attributes of every coherent radar scatterer. Specifically, we propose to obtain geometric information and its dynamics over time of the radar scatterers using time series InSAR (interferometric SAR) techniques, with SAR images in HH and VV separately. As all InSAR observations are relative in time and space, we convert the radar scatterers in HH and VV to a common reference system by applying a spatial reference alignment method. Regarding the physical attributes of the radar scatterers, we first employ a Random Forest classification method to categorize scatterers in terms of scattering mechanisms (including surface, low-, high-volume, and double bounce scattering), and then assign the scattering mechanism to every radar scatterer. We propose using a land-use product (i.e., TOP10NL data for our case) to create reliable labeled samples for training and validation. In addition, the radar scatterers can inherit land-use attributes from the TOP10NL data. We demonstrate this new scheme with 30 Spanish PAZ SAR images in HH and VV acquired between 2019 and 2021, covering an area in the province of Friesland, the Netherlands, and analyze the extracted attributes for data and deformation interpretation. [ABSTRACT FROM AUTHOR]

In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R2) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB. [ABSTRACT FROM AUTHOR]

This article deals with closed-cell aluminum alloy LM13-cenosphere hybrid foams (ACHFs) with cell anisotropy prepared through stir casing technique. The compressive deformation behavior of hybrid foams in both transverse and longitudinal directions was measured at a strain rate of 0.01/s. The hybrid foam loaded in longitudinal direction (LD) shows higher plastic collapse stress than that in transverse direction (TD). The stress drop ratio is also observed to be higher in the LD. The plastic collapse stress and energy absorption capacity of ACHFs follow the power law relationship with relative density in both the directions. The plateau stress is higher in case of LD and densification strains are marginally higher in case of LD than that in TD. [ABSTRACT FROM AUTHOR]

Pediatric adrenocortical carcinoma and adrenocortical adenoma are two rare diseases affecting children. Molecular analyses were performed to identify commonalities in gene expression between the diseases. Differentially expressed genes were identified for the pediatric adrenocortical adenoma and carcinoma tissues, as compared with normal tissues, using the expression dataset. Protein‑protein interaction (PPI) networks were constructed for adenoma and carcinoma disease models, and common modules among the diseases were identified. A total of two common modules with 14 nodes and 20 nodes were revealed among the adenoma and carcinoma networks, respectively. Genes of the common modules were also identified to be the common hub genes of the disease models. Enrichment of the genes of the common modules suggested associations with steroid biosynthesis, the proteasome, cell cycle and metabolic pathways. Modularity, topological and functional analysis of the PPI networks revealed common modules among pediatric adenoma and carcinoma disease models, which provided insight into the underlying disease mechanisms and suggesting prospective targets for future study. [ABSTRACT FROM AUTHOR]

Pediatric adrenocortical carcinoma (ACC) is a rare malignancy with a poor outcome. Molecular mechanisms of pediatric ACC oncogenesis and advancement are not well understood. Accurate and timely diagnosis of the disease requires identification of new markers for pediatric ACC. Differentially expressed genes (DEGs) were identified from the gene expression profile of pediatric ACC and obtained from Gene Expression Omnibus. Gene Ontology functional and pathway enrichment analysis was implemented to recognize the functions of DEGs. A protein- protein interaction (PPI) and gene-gene functional interaction (GGI) network of DEGs was constructed. Hub gene detection and enrichment analysis of functional modules were performed. Furthermore, a gene regulatory network incorporating DEGs-microRNAs-transcription factors was constructed and analyzed. A total of 431 DEGs including 228 upregulated and 203 downregulated DEGs were screened. These genes were largely involved in cell cycle, steroid biosynthesis, and p53 signaling pathways. Upregulated genes, CDK1, CCNB1, CDC20, and BUB1B, were identified as the common hubs of PPI and GGI networks. All the four common hub genes were also part of modules of the PPI network. Moreover, all the four genes were also present in the largest module of GGI network. A gene regulatory network consisting of 82 microRNAs and 100 transcription factors was also constructed. CDK1, CCNB1, CDC20, and BUB1B may serve as potential biomarker of pediatric ACC and as potential targets for therapeutic approach, although experimental studies are required to authenticate our findings. [ABSTRACT FROM AUTHOR]

Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sinkhole pattern detector, called the Sinkhole Scanner. The Sinkhole Scanner includes a spatio-temporal mathematical model such as a 2-dimensional time evolving Gaussian function as a kernel, which moves over the study area using a sliding window approach. The scanner attempts to fit the model over deformation time series of Constantly Coherent Scatterers (CCS) intersected by the window and returns the posterior variance as a measure of goodness of fit. In this way, the scanner searches for subsiding regions resembling sinkhole shapes over a sinkhole prone area. It is designed to detect large sinkholes with a high efficiency, and small sinkholes with a lower efficiency. It is tested at four different spatial scales, and on a simulated and real set of deformation data. Real data were obtained from Sentinel-1A SLC data in IW mode, over Ireland where a large sinkhole occurred on 24 September 2018. The Sinkhole Scanner was able to identify a pattern of low posterior variance zones consistent with the simulated set. In case of the real data, it is able to identify significantly low posterior variance zones near the sinkhole area with the lowest value being 51.1% of the maximum value. The results from Sinkhole Scanner over the real sinkhole site were compared with Multiple Hypothesis Testing (MHT), which identifies Breakpoint and Heaviside temporal anomalies in the deformation time series of CCS. MHT was able to identify high likelihood for Heaviside anomalies in deformation time series of CCS near the sinkhole site about 10 epochs before the sinkhole occurrence. We show that the Sinkhole Scanner is efficient in monitoring a large area and search for sinkholes and that MHT can be used successively to identify temporal anomalies in the vicinity of areas detected by the Sinkhole Scanner. Future research may address other Sinkhole shapes whereas the underlying stochastic model may be adjusted. We conclude that the Sinkhole Scanner is important to be applied at different levels of scale to converge on potential sinkhole centers. [ABSTRACT FROM AUTHOR]