Hybrid discrete (H TN) approximations to the equation of radiative transfer

The linear kinetic transport equations are ubiquitous in many application areas, including as a model for neutron transport in nuclear reactors and the propagation of electromagnetic radiation in astrophysics. The main computational challenge in solving the linear transport equations is that solutions live in a high-dimensional phase space that must be sufficiently resolved for accurate simulations. The three standard computational techniques for solving the linear transport equations are the (1) implicit Monte Carlo, (2) discrete ordinate(S$_N$), and (3) spherical harmonic(P$_N$) methods. Monte Carlo methods are stochastic methods for solving time-dependent nonlinear radiative transfer problems. In a traditional Monte Carlo method when photons are absorbed, they are reemitted in a distribution which is uniform over the entire spatial cell where the temperature is assumed constant, resulting in loss of information. In implicit Monte Carlo(IMC) methods, photons are reemitted from the place where they were actually absorbed, which improves the accuracy. Overall, IMC method improves stability, flexibility, and computational efficiency \cite{fleck}. The S$_N$ method solves the transport equation using a quadrature rule to reconstruct the energy density. This method suffers from so-called ray effect , which are due to the approximation of the double integral over a unit sphere by a finite number of discrete angular directions \cite{chai}. The P$_N$ approximation is based on expanding the part of the solution that depends on velocity direction (i.e., two angular variables) into spherical harmonics. A big challenge with the P$_N$ approach is that the spherical harmonics expansion does not prevent the formation of negative particle concentrations. The idea behind my research is to develop on an alternative formulation of P$_N$ approximations that hybridizes aspects of both P$_N$ and S$_N$. Although the basic scheme does not guarantee positivity of the solution, the new formulation allows for the introduction of local limiters that can be used to enforce positivity

Spatial Analytics with Hospitality Big Data: Examining the Impact of Locational Determinants on Customer Satisfaction in the U.S. Hotel Market

Although hotel location has been recognized as one of the important factors affecting hotel selection and guest satisfaction, relatively few studies have examined guest satisfaction with hotel location and its locational determinants at a macro level. This study aims to identify the locational determinants of hotel guest satisfaction through big data spatial analytics via a case study of 5,302 hotels in 151 cities in the U.S. Based on the framework of hotel location satisfaction, we classified all location-related factors into three categories: accessibility to points of interest, transport convenience, and surrounding environment. Our findings indicated that hotel property’s proximity to city area, landmark, park, shopping center, and highway as well as, attraction-driven tourism industry specialization, and hotel industry agglomeration were significant determinants. Furthermore, the impacts of these factors were spatially heterogeneous. These findings can provide geographical insights that are critical for developing a customer service experience and satisfaction model

PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling

Despite the recent development of deep learning-based point cloud upsampling, most MLP-based point cloud upsampling methods have limitations in that it is difficult to train the local and global structure of the point cloud at the same time. To solve this problem, we present a combined graph convolution and transformer for point cloud upsampling, denoted by PU-EdgeFormer. The proposed method constructs EdgeFormer unit that consists of graph convolution and multi-head self-attention modules. We employ graph convolution using EdgeConv, which learns the local geometry and global structure of point cloud better than existing point-to-feature method. Through in-depth experiments, we confirmed that the proposed method has better point cloud upsampling performance than the existing state-of-the-art method in both subjective and objective aspects. The code is available at https://github.com/dohoon2045/PU-EdgeFormer.Comment: Accepted to ICASSP 202

TRansPose: Large-Scale Multispectral Dataset for Transparent Object

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using a FLIR A65 thermal infrared (TIR) camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. TRansPose dataset can be accessed from the following link: https://sites.google.com/view/transpose-datasetComment: Under revie

Changes in Body Water Caused by Sleep Deprivation in Taeeum and Soyang Types in Sasang Medicine: Prospective Intervention Study

Background. There is a negative relationship between sleep deprivation and health. However, no study has investigated the effect of sleep deprivation on individuals with different body composition. The aim of this study was to determine the differential effect of sleep deprivation in individuals with different body compositions (fluid) according to Soyang type (SY) and Taeeum type (TE). Methods. Sixty-two cognitively normal, middle-aged people with normal sleep patterns were recruited from the local population. The duration of participants’ sleep was restricted to 4 h/day during the intervention phase. To examine the physiological changes brought on by sleep deprivation and recovery, 10 ml of venous blood was obtained. Results. Total Body Water (TBW) and Extracellular Water (ECW) were significantly different between the groups in the intervention phase. Physiological parameters also varied from the beginning of the resting phase to the end of the experiment. Potassium levels changed more in SY than TE individuals. Conclusion. Participants responded differently to the same amount of sleep deprivation depending on their Sasang constitution types. This study indicated that SY individuals were more sensitive to sleep deprivation and were slower to recover from the effects of sleep deprivation than TE individuals

Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke

Hybrid discrete (H TN) approximations to the equation of radiative transfer

The linear kinetic transport equations are ubiquitous in many application areas, including as a model for neutron transport in nuclear reactors and the propagation of electromagnetic radiation in astrophysics. The main computational challenge in solving the linear transport equations is that solutions live in a high-dimensional phase space that must be sufficiently resolved for accurate simulations. The three standard computational techniques for solving the linear transport equations are the (1) implicit Monte Carlo, (2) discrete ordinate(S$_N$), and (3) spherical harmonic(P$_N$) methods. Monte Carlo methods are stochastic methods for solving time-dependent nonlinear radiative transfer problems. In a traditional Monte Carlo method when photons are absorbed, they are reemitted in a distribution which is uniform over the entire spatial cell where the temperature is assumed constant, resulting in loss of information. In implicit Monte Carlo(IMC) methods, photons are reemitted from the place where they were actually absorbed, which improves the accuracy. Overall, IMC method improves stability, flexibility, and computational efficiency \cite{fleck}. The S$_N$ method solves the transport equation using a quadrature rule to reconstruct the energy density. This method suffers from so-called "ray effect", which are due to the approximation of the double integral over a unit sphere by a finite number of discrete angular directions \cite{chai}. The P$_N$ approximation is based on expanding the part of the solution that depends on velocity direction (i.e., two angular variables) into spherical harmonics. A big challenge with the P$_N$ approach is that the spherical harmonics expansion does not prevent the formation of negative particle concentrations. The idea behind my research is to develop on an alternative formulation of P$_N$ approximations that hybridizes aspects of both P$_N$ and S$_N$. Although the basic scheme does not guarantee positivity of the solution, the new formulation allows for the introduction of local limiters that can be used to enforce positivity.</p

Effectiveness a herbal medicine (Sipjeondaebo-tang) on adults with chronic fatigue syndrome: A randomized, double-blind, placebo-controlled trial

Background: Sipjeondaebo-tang (SJDBT, Shi-quan-da-bu-tang in Chinese) is a widely prescribed herbal medicine in traditional Korean medicine. This study aimed to evaluate the effectiveness and safety of SJDBT for treating chronic fatigue syndrome (CFS). Methods: Ninety-six eligible participants were randomly allocated to either the SJDBT or placebo groups in a 1:1 ratio. Nine grams of SJDBT or placebo granules were administered to the patients for 8 weeks. The primary outcome was the response rate, defined as the proportion of participants with a score of 76 or higher in the Checklist Individual Strength assessment. Other measurements for fatigue severity, quality of life, and qi/blood/yin/yang deficiency were included. Safety was assessed throughout the trial. Results: At week 8, the response rate did not significantly differ between the groups (SJDBT: 35.4%; placebo: 54.2%; P =  0.101, effect size [95% confidence interval] = 0.021 [-0.177, 0.218]). However, the scores of the visual analogue scale (P =  0.001, -0.327 [-0.506, -0.128]), Fatigue Severity Scale (P =  0.020, 0.480 [0.066, 0.889]), and Chalder fatigue scale (P =  0.004, -0.292 [-0.479, -0.101]) for the SJDBT group showed significant improvements in fatigue severity at the endpoint. Quality of life was not significantly different. Furthermore, SJDBT significantly ameliorated the severity of qi deficiency compared to that in the placebo group. No serious adverse events were observed. Conclusion: This trial failed to show a significant improvement in fatigue severity, as assessed by the CIS-deprived response rate. It merely showed that SJDBT could alleviate the severity of fatigue and qi deficiency in patients with CFS. However, the further study is needed to confirm the details