Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy

Purpose Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry. Participants and Methods Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems. Results Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05). Conclusion Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.
The onset of centrifugal instability in an open cavity flow

This study deals with the experimental investigation of the onset of instability in an open cavity flow, in the incompressible limit. Far below the development of self-sustained oscillations, the first instability is three-dimensional on the inner recirculating flow. We address the case of a cavity of a large span-wise aspect ratio in which the side-wall effects are negligible. We study the pattern selection at threshold as the stream-wise aspect ratio is varied and, for large aspect ratios, we report the development of a second mode, which is quasi-symmetric to the primary mode.
An Unified Mathematical Expression for Ideal Peppas Model: Prospective Estimation of percent deviation

The aim of this study was to design and evaluate controlled-release formulations of a model drug, Nicorandil using gastroretentive approach. The dissolution test was employed using 0.1N HCL (pH 1.2) to measure the in vitro release behaviour of Nicorandil formulations. The best formulation of MCS4 is selected based on the identical drug release profile with theoretical release data which follows zero order and Higuchi model based on their higher square of correlation coefficient (R2) and adjusted coefficient of determination (R2). In order to evaluate the modeling of drug release from swellable polymeric systems, the drug release data was fitted into Korsmeyer-Peppas equation and the model showed higher R2 and R2 (0.9974 and 0.9971, respectively) with the diffusional exponent value (n) of 0.87. Further, the drug release of best formulation is checked for its deviation from an ideal release profile of Peppas model and confirmed that the test formulation is slightly deviate the ideal Peppas model.
Traveling wave solutions of a nonlocal dispersal SIRS model with spatio-temporal delay

This paper is mainly concerned with the existence and nonexistence of traveling wave solutions of a nonlocal dispersal SIRS model with nonlocal delayed transmissions. We find that the existence and nonexistence of traveling wave solutions are determined by the critical wave speed c∗. More specifically, we establish the existence of traveling wave solutions for every wave speed c > c∗ and R0 > 1 by means of upper-lower solutions and Schauder’s fixed point theorem. Nonexistence of traveling wave solutions is obtained by Laplace transform for any wave speed c ∈ (0,c∗) and R0 > 1.
Hepatic venous pressure gradient and rebleeding risk of patients with nonalcoholic steatohepatitis cirrhosis after variceal bleeding

Background and aims Hepatic venous pressure gradient (HVPG) has a strong predictive value for variceal rebleeding in cirrhotic patients, but the accuracy of HVPG may be compromised in nonalcoholic steatohepatitis (NASH) cirrhosis. This study aimed to evaluate the accuracy of HVPG and portal pressure gradient (PPG) for predicting rebleeding in NASH cirrhosis after acute variceal bleeding. Patients and methods Thirty-eight NASH cirrhosis patients and 82 hepatitis B virus (HBV) cirrhosis patients with acute variceal bleeding were included in this study. All patients recived transjugular intrahepatic portalsystemic shunt (TIPS). The prognostic value of HVPG and PPG for variceal rebleeding was evaluated. Results Compared with HBV cirrhosis, NASH cirrhosis demonstrated a lower HVPG (15.3 ± 3.8 vs. 18.0 ± 4.8; p = 0.003) and lower PPG (18.0 ± 3.7 vs. 20.0 ± 3.4; p = 0.005). HVPG (AUC = 0.82; p = 0.002) and PPG (AUC = 0.72; p = 0.027) had promising prognostic value among NASH cirrhosis patients. The optimal threshold of HVPG and PPG for predicting rebleeding in NASH cirrhosis was 17 mmHg and 20 mmHg. At multivariate analysis, HVPG ≥17 mmHg was a significant predictor of variceal rebleeding (HR 9.40; 95% CI 1.85–47.70; p = 0.007). Conclusion In the patients with cirrhosis and vairceal bleeding, the levels of HVPG and PPG were found to be low in NASH cirrhosis than HBV cirrhosis. However, the prevalence of rebleeding was similar between two groups. HVPG measurement is still an accurate way to assess the risk of variceal rebleeding in NASH cirrhosis.
Ondes scélérates complexes dans les fibres optiques

Ce manuscrit de these presente l’etude d’instabilites non-lineaires et la generation d’ondes scelerates complexes liees a la propagation de la lumiere dans des fibres optiques standards des telecommunications optiques. Un rappel est tout d’abord presente sur les phenomenes physiques lineaires et non-lineaires impliques et qui peuvent presenter une analogie directe avec le domaine de l’hydrodynamique. Les differentes formes d’ondes scelerates liees au processus d’instabilite de modulation, aussi appelees « breathers », sont alors presentees, elles sont obtenues par la resolution de l’equation de Schrodinger non-lineaire. A partir de ces solutions exactes, divers systemes experimentaux sont alors concus par simulation numerique a partir de deux methodes d’excitation d’ondes scelerates. La premiere est une generation exacte a partir des solutions analytiques en effectuant une mise en forme spectrale en intensite et en phase d’un peigne de frequence optique. La seconde methode est basee sur des conditions initiales approchees avec des ondes continues modulees sinusoidalement. Les mesures experimentales realisees avec ces deux methodes demontrent parfaitement la generation d’ondes scelerates complexes (solutions d’ordre superieur du systeme) issues de la superposition non-lineaire ou collisions de « breathers » de premier ordre. Enfin, nous avons egalement etudie un systeme non-lineaire equivalent au modele de Manakov, qui fait intervenir la propagation de deux ondes distinctes avec des polarisations orthogonales dans une fibre optique. L’analyse de stabilite et des simulations numeriques de ce systeme multi-variable mettent en evidence un nouveau regime d’instabilite de modulation vectorielle ainsi que de nouvelles solutions d’ondes scelerates noires et couplees en polarisation. Un nouveau systeme experimental mis en place a permis de confirmer ces predictions theoriques avec un excellent accord quantitatif.
Extracting binary features from speech production errors and perceptual confusions using Redundancy-Corrected Transmission

We develop a mutual information-based feature extraction method and apply it to English speech production and perception error data. The extracted features show different phoneme groupings than conventional phonological features, especially in the place features. We evaluate how well the extracted features can define natural classes to account for English phonological patterns. The features extracted from production errors had performance close to conventional phonological features, while the features extracted from perception errors performed worse. The study shows that featural information can be extracted from underused sources of data such as confusion matrices of production and perception errors, and the re-sults suggest that phonological patterning is more closely related to natural production errors than to perception errors in noisy speech.
Full and folded forms: a compact review of the formulation of tensegrity structures

This piece of work presents a simple and compact overview of the design problem of tensegrity structures in two and three dimensions. The main aim of this study is to present the design and calculation of tensegrity structures in their simplest form, avoiding unnecessary simplifications that can rule out solutions, as has happened up until now. As a result of the simplicity of the procedure, two types of tensegrity structures are obtained for the same initial topology: full and folded forms. Several examples are shown.
Estimating the Direction and Distance of an Unknown Radiation Source Using RMC

Rotating modulation collimator(RMC) is a remote sensing technique for a radiation source. This paper introduces an RMC system model and its image reconstruction algorithm based on Kowash’s research. The reconstructed image can show the direction of a source. However, the distance to the source cannot be recovered. Moreover, the RMC image suffers from 180° ambiguity. In this paper, we propose a distance estimation method using two RMCs together with a solution to the ambiguity. We also demonstrate its performance using simulated RMC data.
Towards an accurate model of redshift-space distortions: a bivariate Gaussian description for the galaxy pairwise velocity distributions

Abstract As a step towards a more accurate modelling of redshift-space distortions (RSD) in galaxy surveys, we develop a general description of the probability distribution function of galaxy pairwise velocities within the framework of the so-called streaming model. For a given galaxy separation , such function can be described as a superposition of virtually infinite local distributions. We characterize these in terms of their moments and then consider the specific case in which they are Gaussian functions, each with its own mean μ and variance σ2. Based on physical considerations, we make the further crucial assumption that these two parameters are in turn distributed according to a bivariate Gaussian, with its own mean and covariance matrix. Tests using numerical simulations explicitly show that with this compact description one can correctly model redshift-space distorsions on all scales, fully capturing the overall linear and nonlinear dynamics of the galaxy flow at different separations. In particular, we naturally obtain Gaussian/exponential, skewed/unskewed distribution functions, depending on separation as observed in simulations and data. Also, the recently proposed single-Gaussian description of redshift-space distortions is included in this model as a limiting case, when the bivariate Gaussian is collapsed to a two-dimensional Dirac delta function. More work is needed, but these results indicate a very promising path to make definitive progress in our program to improve RSD estimators.
Capacitor Voltage Increment Suppression Control for CTB-HMMCs With Bypassed UFB-SMs Under DC Fault

The crossing thyristor branches (CTBs) based hybrid modular multilevel converter (HMMC) can effectively protect high-voltage direct current system under dc-line short-circuit fault with advantages of low percentage of unipolar full-bridge (UFB) submodules (SMs), short dc current interruption time, and high efficiency. However, the faulty UFB-SMs being bypassed would affect dc fault current interruption performance of the CTB-HMMC, which may cause large capacitor voltage increment of UFB-SMs and deteriorate CTB-HMMC. This article first analyzes the capacitor voltage increment of the CTB-HMMC with bypassed UFB-SMs under dc-line short-circuit fault. And then, this article proposes a capacitor voltage increment suppression control (CVISC) for the CTB-HMMC with bypassed UFB-SMs under dc-line short-circuit fault, where the capacitor voltage increment of UFB-SMs during dc-line short-circuit fault can be significantly suppressed through bypassing suitable number of UFB-SMs according to the proportion of faulty UFB-SMs being bypassed in each arm, and therefore the proposed CVISC effectively protects the CTB-HMMC under dc-line short-circuit fault. The simulation and experiment are conducted, and their results verify the effectiveness of the proposed CVISC.
Multi-channel EEG emotion recognition through residual graph attention neural network

In this paper, a novel EEG emotion recognition method based on residual graph attention neural network is proposed. The method constructs a three-dimensional sparse feature matrix according to the relative position of electrode channels, and inputs it into the residual network to extract high-level abstract features containing electrode spatial position information. At the same time, the adjacency matrix representing the connection relationship of electrode channels is constructed, and the time-domain features of multi-channel EEG are modeled using graph. Then, the graph attention neural network is utilized to learn the intrinsic connection relationship between EEG channels located in different brain regions from the adjacency matrix and the constructed graph structure data. Finally, the high-level abstract features extracted from the two networks are fused to judge the emotional state. The experiment is carried out on DEAP data set. The experimental results show that the spatial domain information of electrode channels and the intrinsic connection relationship between different channels contain salient information related to emotional state, and the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.
Effects of Community Environment, Leisure, and Social Activities on Health Status of Older Adults with Diabetes in South Korea

This study investigates the effects of community environment, leisure, and social activities on the health status of older adults with diabetes, a serious disease in modern society. Data from the 2020 National Survey of Older Koreans were analyzed. Descriptive statistics were used to assess participants’ characteristics, and regression analyses were conducted to assess the effects of community environment, leisure, and social activities. Mediating effects were tested using hierarchical regression analysis and bootstrapping. The key results are as follows. (a) Community environmental satisfaction affected participation in leisure and social activities. (b) Community accessibility had a negative effect on subjective health, while community environmental satisfaction had a positive effect on subjective health, cognitive function, and chronic diseases. (c) Leisure activities had a positive effect on cognitive health, while social activities influenced subjective health, cognitive function, and chronic diseases. (d) Analysis of the mediating effect of leisure and social activities on the relationship between the community environment and health status of older adults with diabetes confirmed a partial mediating effect. To improve older adults’ mental and physical health, mere quantitative increases in the community environment will not be sufficient. It is necessary to cultivate and manage professionals to increase opportunities for participation by increasing social exchanges and systematically managing older adults’ health.
Impact of embedded voids on thin-films with high thermal expansion coefficients mismatch

Using technology to reduce defects at heterogeneous interfaces of thin-films is at a high-priority for modern semiconductors. The current work utilizes a three-dimensional multiple-slip crystal-plasticity model and specialized finite-element formulations to study the impact of the embedded void approach (EVA) to reduce defects in thin-films deposited on a substrate with a highly mismatched thermal expansion coefficient, in particular, the growth of an InGaN thin-film on a Si substrate, where EVA has shown a remarkable reduction in stresses on the side of the embedded voids.
Three-Dimensional Source Localization Based on 1-D AOA Measurements: Low-Complexity and Effective Estimator

Three-dimensional (3-D) source localization based on 1-D angles of arrival (AOA) measurements from multiple linear arrays have attracted much attention due to the low cost of linear arrays and the easy access to 1-D AOAs. However, most existing methods are restricted to specific array deployments, and the convex optimization methods applicable to the linear array with arbitrary deployments have drawbacks such as high computational complexity and nonportability to small industrial equipment. In this article, we propose two 1-D AOA-based source localization methods. The first method uses the iterative reweighted least square (IRLS) to obtain a coarse solution and a deviation refinement (DR) procedure to refine the estimation. Each step in IRLS-DR has a closed-form solution; thus, it is computationally efficient. Moreover, it applies to arbitrary interarray deployments, and theoretical analysis shows that its performance can approach the Cramér–Rao lower bound (CRLB) at small noise levels. The second method addresses the maximum likelihood estimation (MLE). A modified Levenberg–Marquardt (MLM) method is proposed, which focuses on designing a new selection strategy of the regularization parameter to realize a high convergence probability and localization accuracy. Experiments based on computer simulations and SWellEx96-S59 ocean acoustic measurements are carried out to verify the performance and advantages of the proposed methods.
A Characteristics Finite Element Algorithm for Computational Open Channel Flow Analysis

For the reliable determination of open channel ows, a minimally dissipative ux jacobian decomposition nite element algorithm is developed for the oneand two-dimensional inviscid open channel ow equation systems. The modifed form of the parent kinetic ux divergence is biased by the characteristic speeds, i.e. eigenvalues of the kinetic ux divergence jacobian, to induce along all wavelike propogation directions a dissipation level proportional to the propogation speeds of the solution to the hyperbolic problem statement. The analysis rigorously investigates the inviscid open channel equation system via non-linear, wave-like solutions and reveals linear dependency issues for the momentum equations in steady state ow for all values of the Froude number, which is subsequently eliminated by the modi ed equation. The modi ed equation is developed on the continuum level via decomposition of the kinetic ux divergence into components which physically correspond to convection and celerity propagation. These decomposition components are then combined to satisfy the demanding conditions that the eigenvalues of the resulting matrix within the dissipative ux divergence, hence dissipation level, correlate with the eigenvalues of the hyperbolic problem statement, for algorithm accuracy, while remaining positive and real, for algorithm stability. In both one and two dimensions, for suband supercritical ows induced by various dam-break veri cations and benchmarks, the algorithm is veri ed to yield reliable determination of the depthaveraged momentum and height elds by generating accurate and essentially non-oscillatory numerical solutions in the presence of hydraulic jumps while remaining second-order accurate in both space and time.
Combinatorial and compositional aspects of bilingual aligned corpora

The subject of investigation of this thesis is the building blocks of translation in Statistical Machine Translation (SMT). We find that these building blocks, namely phrase-level dictionary entries, which are extracted from bilingual aligned corpora (training data), admit richer structure than previously known. A rigorous explanation of the extraction mechanism shows that the resulting set of building blocks is amenable to mathematical investigation with the potential of developing tools and new frameworks for translation. To this end we bridge previously unseen gaps between graph theory and probability theory within SMT in order to derive probability mass functions for phrase-level sentence segmentations and rules of translation. For the latter, experimental results support the claim of a statistical (principle of) compositionality of translation rules which fosters future work on data generation. In addition, since the constituents of composition are the original building blocks of translation, as extracted from the training process, we investigate whether they generalize monolingual building blocks (phrases), and if so, of what type. This leads to identifying the role of pointwise mutual information as the distance metric on segmentation refinements. Experiments show that such a partially ordered framework is more appropriate than a standard language model approach for finding the 'natural' building blocks of monolingual corpora.
A Tree Point Cloud Simplification Method Based on FPFH Information Entropy

LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring.