Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructural Dynamics of Polymer Melts during Stretching: Radial Size Distribution

The transient elongational viscosity ηe(t) of the polymer melt is known to exhibit strain hardening, which depends on the strain rate ε˙. This phenomenon was elucidated by the difference of chain stretching in the entanglement network between extension and shear. However, to date, the microscopic evolution of polymer melt has not been fully statistically analyzed. In this work, the radial size distributions P(Rg,t) of linear polymers are explored by dissipative particle dynamics during the stretching processes. In uniaxial extensional flow, it is observed that the mean radius of gyration R¯g(t) and standard deviation σ(t) remain unchanged until the onset of strain hardening, corresponding to linear viscoelasticity. Both R¯g and σ rise rapidly in the non-linear regime, and bimodal size distribution can emerge. Moreover, the onset of strain hardening is found to be insensitive to the Hencky strain (ε˙Ht) and chain length (N).
Research of image reconstruction of Compressed Sensing using basis pursuit algorithm

Theory of compressed sensing releases us from the restriction defined by Nyquist sampling frequency.This paper focuses on research and realization of a new compressing and sampling scheme for two dimensional image data based on this theory.Sparseness processing is realized eby using wavelet transform in this scheme,and the random measurement matrix is generated using standard pseudo-random number matrix of uniformly distributed sequences and two dimension central Fourier transform,also weighted sampling is implemented for wavelet transformed high frequency sub-bands.Compressed Sensing reconstruction of two dimensional image is solved using improved basis pursuit algorithm.Simulation experiment shows that better PSNR of objective evaluation of the reconstructed image is achieved.
Multiple Classifier Fusion Method based on k-Nearest Templates

In this paper, the k-nearest templates method is proposed to combine multiple classifiers effectively. First, the method decomposes training samples of each class into several subclasses based on the outputs of classifiers to represent a class as multiple models, and estimates a localized template by averaging the outputs for each subclass. The distances between a test sample and templates are then calculated. Lastly, the test sample is assigned to the class that is most frequently represented among the k most similar templates. In this paper, C-means clustering algorithm is used as the decomposition method, and k is automatically chosen according to the intra-class compactness and inter-class separation of a given data set. Since the proposed method uses multiple models per class and refers to k models rather than matches with the most similar one, it could obtain stable and high accuracy. In this paper, experiments on UCI and ELENA database showed that the proposed method performed better than conventional fusion methods.
Distributed Linear Network Operators Based on Successive Graph Shift Matrices

In the context of graph signal processing, the existing distributed approaches for implementing linear network operators rely on the notion of graph shift matrix, which captures the local structure of the graph. Most of the existing approaches consider only a restricted set of linear network operators. However, in this paper, we focus on approximating general linear network operators as fast as possible after a finite number of local exchanges, with a negligible error. We propose a new distributed successive method based on designing a sequence of different graph shift matrices, which are optimized to approximate the desired network operator in an approximately minimal number of iterations. We also consider the robustness of the distributed computation of linear operators against graph perturbations. For this, we first analyze the effect of graph perturbations on our successive method and then, we incorporate the effect of graph perturbations in our design by proposing an online kernel-based estimator, which enables the nodes of the network to estimate the missing values caused by graph perturbations across iterations via available information received from neighbor nodes. Our numerical results demonstrate the superior performance of our methods over the existing state-of-the-art approaches.
Analysis of Stationary Fluid Queue Driven by State-Dependent Birth-Death Process Subject to Catastrophes

- This paper investigates an infinite buffer fluid queueing model driven by a state-dependent birth-death process prone to catastrophes.We use the Laplace-Stieltjes transform and continued fraction approaches to establish precise expression for the joint probability of the content of the buffer and the number of customers in an / /1 M M queueing model. The importance of the proposed system is that, in numerous practical situation, the service facility has defence mechanisms in place to deal with long waits. Under the strain of a significant backlog of work, the servers may improve their service rate. Therefore,considering the state-dependent character of queueing systems is of relevance. For example, congestion control technologies prevent long queues forming in computer and communication systems by adjusting packet transmission speeds based on the length of the queue (of packets) at the source or destination. Theoretical results are supported by numerical illustrations.
Platform Integrated Multiband MIMO Antennas for 5G Mobile Communications

To fulfil different requirements in complex wireless communication scenarios for internet of things (IoT), terminal devices enabled with multiband and MIMO antennas are in demand. In this article, a method for designing multiband and multiport platform integrated terminal antennas is proposed using N-port characteristic mode (CM) approach. An example of handset antenna system is taken to illustrate the design procedure. N ports are firstly added along the handset, leading to a N×N impedance matrix. The status of the ports, i.e., short or open, is optimized based on the modal significances of the sub-impedance matrix at the target bands. Consequently, the optimized structure, with proper locations of breakings and groundings along the frames, is obtained. The feedings of the antennas are then selected from the grounding points based on the local modal currents of the desired modes. At the same time, low correlations between MIMO antennas are ensured by selecting the least correlated ports. As a result, six antennas are designed on the mobile handset, with two antennas covering the cellular bands of GSM900 (880–960 MHz) and DCS1800 (1710–1900 MHz) and other four antennas operating at the 5G bands of 2500-2690 MHz and 3300-3600 MHz. The proposed method is also applied to a transparent vehicular antenna for low profile and dual-band operations, showing its wide application to different terminal antennas.
Biomass-Derived Sustainable Electrode Material for Low-Grade Heat Harvesting

The ever-increasing energy demand and global warming caused by fossil fuels push for the exploration of sustainable and eco-friendly energy sources. Waste thermal energy has been considered as one of the promising candidates for sustainable power generation as it is abundantly available everywhere in our daily lives. Recently, thermo-electrochemical cells based on the temperature-dependent redox potential have been intensely studied for efficiently harnessing low-grade waste heat. Despite considerable progress in improving thermocell performance, no attempt was made to develop electrode materials from renewable precursors. In this work, we report the synthesis of a porous carbon electrode from mandarin peel waste through carbonization and activation processes. The influence of carbonization temperature and activating agent/carbon precursor ratio on the performance of thermocell was studied to optimize the microstructure and elemental composition of electrode materials. Due to its well-developed pore structure and nitrogen doping, the mandarin peel-derived electrodes carbonized at 800 °C delivered the maximum power density. The areal power density (P) of 193.4 mW m−2 and P/(ΔT)2 of 0.236 mW m−2 K−2 were achieved at ΔT of 28.6 K. However, KOH-activated electrodes showed no performance enhancement regardless of activating agent/carbon precursor ratio. The electrode material developed here worked well under different temperature differences, proving its feasibility in harvesting electrical energy from various types of waste heat sources.
Modelling And Simulation Of A Savonius RotorUsing The Boundary Element Method

The Boundary Element Method (BEM) is a very effective technique for solving complex domain and moving boundary problems, because it only involves calculations on the boundary thus reducing the processing time. For nonlinear and time dependent problems the BEM method presents some domain integrals that can be transformed into equivalent boundary integrals by the dual reciprocity approach applied in a subdomain basis; this method is known as Multi Domain Dual Reciprocity MD-DRM. In this paper some flow examples with moving boundarieswill be solved using the MD-DRM method to predict particle trajectories and in particular the simulation of a Savonius rotor with a more complex geometry will be carried out to predict the velocity field, tractions and torque in this equipment.
Adaptive Decomposition of Multicomponent Signals and Estimation of Phase Synchronization

The decomposition of a nonlinear and non-stationary signal into intrinsic modes is challenging, and algorithms, such that of Empirical Mode Decomposition (EMD) used successfully in a broad range of applications, have limitations regarding their sensitivity to noise and sampling. To address these drawbacks a mode decomposition algorithm, which we name Maximal Spectral Overlap Wavelet Transform (MSO-WT), is proposed. MSO-WT relies on the adaptive decomposition of a signal into a series of components using wavelet packets, and the clustering of the components for the synthesis of the modes. The effectiveness of the algorithm is demonstrated using several synthetic signals. In addition, this algorithm is used for the development of a new phase synchronization index, named Multi-Component Mean Phase Coherence (MCMPC) generalizing the Mean Phase Coherence (MPC) index. A comparison between the two indices is performed by applying them for the detection of phase synchronization between two coupled Rössler and Mackey-Glass systems. Finally, the MCMPC index is used for the construction of a brain connectivity network from multichannel electroencephalogram data and the detection of an epileptic seizure.
Burnout in Nurses of an Intensive Care Unit during COVID-19: A Pilot Study in Portugal

Background: This study aimed to evaluate burnout in intensive care unit nurses and describe the relationship between burnout dimensions and sociodemographic and professional variables. Methods: A pilot study was carried out with 29 intensive care nurses during the COVID-19 pandemic. The data were collected using an online questionnaire consisting of a sociodemographic characterization and the Maslach Burnout Inventory Human Services Survey. In the comparative statistical analysis, the nonparametric Mann–Whitney and Kruskal–Wallis tests were used (p < 0.05). Results: The Emotional Exhaustion dimension represented an average of 27.9 ± 10.4, and the Personal Fulfillment of 30.8 ± 7.0 was considered high. Regarding Depersonalization, medium-level results (8.1 ± 6.0) were found. There was a prevalence of 41.3% of high levels of burnout. There was no statistically significant difference in the comparison between the three dimensions and sociodemographic and professional variables (p > 0.05). Conclusions: The global results point to high levels of burnout in nurses, with greater expression in the Emotional Exhaustion and Professional Fulfillment domains. There were no differences between groups, demonstrating homogeneity in the team’s level of burnout. The incidence of burnout in this study was higher than that identified in other studies carried out in Portugal with health professionals. This pilot study will allow the defining of strategies for reducing burnout and, consequently, a proposed extension of the study to other organization wards.
Segment duration and the mora in Japanese

Linguists have made various claims for the mora as a durational unit in Japanese. One of the strongest of these claims is that the mora has such a basic phonetic reality that, for example, vowel lengths are adjusted to compensate for the intrinsic durations of tautomoraic consonants [M. S. Han, Onsei no kenkyuu 10, 81–100 (1962)]. Examination of a preliminary corpus of utterances used in the development of synthesis rules for Japanese, however, did not reveal this tendency toward moraic isochrony. This discrepancy between our findings and previous claims has led us to examine segment durations in a larger set of utterances (including readings by several native speakers of a long narrative passage) in order to determine whether a tendency to equalize mora durations might become apparent after other factors influencing duration are accounted for.
DESIGNING FROM A SEQUENCE OF I.I.D. EXPERIMENTS

We consider a regression problem, with observations Yk = ·(µ;Xk) + †k, where (†k) is a sequence of independent measurement errors and where the experimental conditions Xk form a sequence of independent random variables distributed with a probability measure ", independent of (†k) and observed sequentially. The length of the sequence (Xk) is N but only n 1 (2, 3). We propose here a difierent solution, based on the construction of an optimal constrained design measure, and show that it is asymptotically optimal (n = bfiNc, fi 2 (0;1), N ! 1). As a byproduct, we obtain a procedure that asymptotically samples from an optimal constrained measure » ⁄ • "=fi, without requiring neither the determination of » ⁄ fi nor the knowledge of the measure ".
Random bisexual forests: Intersections between gender, sexuality, and race in /s/ production

The ideology of “the gay lisp” has inspired numerous quantitative studies examining the relationship between /s/ production and sexuality in American English (e.g. Linville 1998; Munson et al. 2006a; Zimman 2013). There are two key gaps in this literature. First, research in this area typically focuses on monosexual (i.e. lesbian, gay, and straight) speakers to the exclusion of bisexuality. Second, work in this area rarely considers the intersection of sexuality with factors outside of gender or sex, and to a lesser extent geographic location (e.g. Campbell-Kibler 2011; Podesva & Hofwegen 2014). This article addresses these disparities by (1) centralizing bisexual speakers and (2) attending to social factors such as race, place, age, and their intersections in the analysis. To that end, we build upon previous work by Willis (forthcoming) and apply a random forest (Breiman 2001) to /s/ center of gravity measurements. In doing so, we follow Tagliamonte and Baayen (2012) in demonstrating the utility of random forests as an approach to quantitative sociolinguistic analysis. Ultimately, the analysis underscores the need to attend to power structures and biases within research practice: the monosexist ideologies of sexuality and gender normativity that obfuscate bisexuality, and the privileging of whiteness that permeates quantitative studies of sexuality and the voice.
Infrared Image Segmentation Using Two-Dimensional Fisher Linear Optimal Discriminant Analysis and Particle Swarm Optimization

Two-dimensional(2-D) Fisher linear optimal discriminant analysis,which considers the gray information and spatial neighbor information between pixels in the image simultaneously,overcome especially if the histogram of images in reality has no distinct sharp valleys or the valley is flat and broad,the proposed is an efficient image segmentation method.However,finding the optimal threshold vector using exhaustive searching is expensive for 2-D fisher criterion function thresholding method.In this paper,an optimization method,i.e.,particle swarm optimization(PSO) is used to find the optimal 2-D threshold vector,in which each particle represents a possible 2-D threshold vector and the best 2-D threshold is obtained through the cooperation among particles.To show the validity of the proposed method,this paper uses several infrared images to segment.Analysis and experimental results show that the proposed method can not only obtain ideal segmentation results but also decrease the computation cost reasonably,and it is suitable for real-time applications.
An Examination of Tropical and Extratropical Gust Factors and the Associated Wind Speed Histograms

Abstract A gust factor, defined as the ratio between a peak wind gust and mean wind speed over a period of time, can be used along with other statistics to examine the structure of the wind. Gust factors are heavily dependent on upstream terrain conditions (roughness), but are also affected by transitional flow regimes (specifically, changes in terrain and the distance from the upstream terrain change to the measuring device), anemometer height, stability of the boundary layer, and, potentially, the presence of deep convection. Previous studies have yielded conflicting results regarding differences in gust factors that might exist between winds generated by tropical cyclones and those generated by extratropical systems. Using high-resolution wind speed data collected from both landfalling tropical cyclones and extratropical systems, two databases of wind characteristics were developed. Gust factors from tropical cyclone and extratropical winds were examined, summarized, and compared. Further analysis was .
Microfluidics: Insights into Intestinal Microorganisms

Microfluidics is a system involving the treatment or manipulation of microscale (10−9 to 10−18 L) fluids using microchannels (10 to 100 μm) contained on a microfluidic chip. Among the different methodologies used to study intestinal microorganisms, new methods based on microfluidic technology have been receiving increasing attention in recent years. The intestinal tracts of animals are populated by a vast array of microorganisms that have been established to play diverse functional roles beneficial to host physiology. This review is the first comprehensive coverage of the application of microfluidics technology in intestinal microbial research. In this review, we present a brief history of microfluidics technology and describe its applications in gut microbiome research, with a specific emphasis on the microfluidic technology-based intestine-on-a-chip, and also discuss the advantages and application prospects of microfluidic drug delivery systems in intestinal microbial research.
The Gut Microbiome Responds Progressively to Fat and/or Sugar-Rich Diets and Is Differentially Modified by Dietary Fat and Sugar

Describing diet-related effects on the gut microbiome is essential for understanding its interactions with fat and/or sugar-rich diets to promote obesity-related metabolic diseases. Here, we sequenced the V3-V4 hypervariable region of the bacterial 16S rRNA gene to study the composition and dynamics of the gut microbiome of adult mice fed diets rich in fat and/or sugar, at 9 and 18 weeks of diet. Under high-fat, high-sugar diet, the abundances of Tuzzerella and Anaerovorax were transiently increased at 9 weeks, while Lactobacillus remained elevated at 9 and 18 weeks. The same diet decreased the abundances of Akkermansia, Paludicola, Eisenbergiella, and Butyricicoccus at 9 and 18 weeks, while Intestinimonas and UCG-009 of the Butyricicoccaceae family responded only at 18 weeks. The high-fat diet decreased the abundances of UBA1819 at 9 weeks, and Gastranaerophilales, Clostridia UCG-014, and ASF356 at 9 and 18 weeks. Those of Marvinbryantia, Harryflintia, Alistipes, Blautia, Lachnospiraceae A2, Eubacterium coprostanoligenes group, and Eubacterium brachy group were lowered only at 18 weeks. Interestingly, these genera were not sensitive to the high-sugar diet. The mouse gut microbiome was differentially affected by diets rich in fat or fat and sugar. The differences observed at 9 and 18 weeks indicate a progressive microbiome response.
Determination of Optimum Velocity for Various Nanofluids Flowing in a Double-Pipe Heat Exchanger

Piping is one of the most important issues in the cost of process factories. It is known that 80% of bought equipment cost or 20% of overhead capital can belong to piping cost in a fluid-process factory. Pipe diameter and therefore flow velocity strongly affect the existing value of the factory regarding the consumed electric energy and fitting cost of pipes, pumps, and valves. We give a detailed cost analysis model for the pure fluids of water, motor oil, glycerin, ammonia, methanol, ethanol, ethylene glycol, and propane and their nanofluid mixtures with Ti and TiO2 particles in liquid phase flowing in the tube side of a double-pipe heat exchanger. Pressure drop and pumping power values increase with flow velocity but total cost values show an arc with it. The clear outcome is that there is a minimum cost value as a result of the analyses for each investigated fluids. Moreover, validation of the model is performed by plotting the calculated items in figures such as total heat transfer coefficient versus Reynolds number, pressure drop versus Reynolds number, and friction factor versus mass flow rate. Characteristics of the trend lines in these figures are seen as they should be.
Các ma trận MDS truy hồi hiệu quả cho thực thi từ mã Reed-Solomon và phép lũy thừa trực tiếp

Tóm tắt— Hiện nay, có nhiều mã khối sử dụng các ma trận của mã tách khoảng cách cực đại (MDS) như một phần quan trọng trong tầng khuếch tán của chúng. Tuy nhiên, các ma trận MDS luôn có một mô tả lớn, gây ra các thực thi phần cứng/phần mềm tốn chi phí. Các ma trận MDS truy hồi cho phép giải quyết bài toán này vì chúng có thể là lũy thừa của một ma trận Companion đơn giản, do đó chúng có thể phù hợp cho cả các môi trường bị ràng buộc, hạn chế. Trong bài báo này, phương pháp xây dựng các ma trận MDS truy hồi hiệu quả cho thực thi từ mã Reed-Solomon sẽ được trình bày. Sau đó, khả năng tạo ra các ma trận MDS truy hồi hiệu quả cho thực thi từ một ma trận MDS truy hồi hiệu quả ban đầu, nhờ phép biến đổi lũy thừa trực tiếp sẽ được chỉ ra. Các ma trận MDS truy hồi hiệu quả cho thực thi sẽ có ý nghĩa trong thực thi phần cứng. Chúng có thể được sử dụng cho tầng khuếch tán của nhiều mã khối và hàm băm, đặc biệt là các mã khối và hàm băm hạng nhẹ nhằm tiết kiệm tài nguyên và chi phí thực thi. Abstract— Nowaday, many block ciphers have used MDS matrices for their diffusion layer. However, the MDS matrices are always the components that cause large implementation cost for ciphers. Recursive MDS matrices will help to deal with this problem because they can be the power of a simple Companion matrix that is very sparse. In this paper, the ability to generate different efficient recursive MDS matrices for implementation from an original efficient recursive MDS matrix by direct exponential transformation is shown. These recursives MDS matrices are meaningful in hardware implementation. These matrices can be used in the diffusion layer of some block ciphers and hash functions ciphers and hash functions especially lightweight block ciphers and hash functions to save resources and implementation cost.