Miracle-dz/UniFAR-FTUs huggingface dataset:
Dispersion of a passive solute in non-ergodic transport by steady velocity fields in heterogeneous formations

An inert solute is convected by a steady random velocity field, which is associated with flow through a heterogeneous porous formation. The log conductivity and the velocity are stationary random space functions. The log conductivity Y is assumed to be normal, with an isotropic two-point correlation of variance σY2 and of finite integral scale I. The solute cloud is of a finite input zone of lengthscale l. The transport is characterized with the aid of the spatial moments of the solute body. The effective dispersion coefficient is defined as half of the rate of change with time of the second spatial moment with respect to the centroid. Under the ergodic hypothesis, which is bound to be satisfied for l/I [Gt ] 1, the centroid moves with the mean velocity U and the longitudinal dispersion coefficient [dscr ]L tends to its constant, Fickian, limit. Under a Lagrangian first-order analysis in σY2 it has been found that [dscr ]L = σY2 UI. This study addresses the computation of the effective longitudinal dispersion coefficient for a finite input zone, for which ergodic conditions may not be satisfied. In this case the centroid trajectory and the second spatial moments are random variables. In line with a previous work (Dagan 1990) the effective dispersion coefficient DL is defined as half the rate of change of the expected value of the second spatial moment for large transport time. The aim of the study is to derive DL and its dependence upon l/I and in particular to determine the conditions under which it tends to the ergodic limit [dscr ]L. The computation is carried out separately for a thin body aligned with the mean flow and one transverse to it. In the first case it is found that DL is equal to zero, i.e. the streamlined body does not disperse in the mean. This result is explained by the correlation between the trajectories of the leading and trailing edges, respectively, once the latter reaches the position of the first. The relatively modest increase of the mean second spatial moment is effectively computed. In the case of a thin body initially transverse to the mean flow, DL may reach the ergodic limit [dscr ]L for a ratio l/I of the order 102. For smaller values, DL is found to be bounded from above, and its maximum depends on l but not on I. The uncertainty caused by the randomness of the velocity field is manifested in the trajectory of the centroid rather than in the effective dispersion.
Effect on glucose monitoring of pressure exerted by fiber-optic probe: skin model and simulation

In the research of optical diagnosis, such as noninvasive measurement of blood glucose by near-infrared diffuse-reflectance spectroscopy, the fiber-optic probe are widely used to deliver light to the interested tissue site and collect the light from the tissue. In order to minimize motion artifacts and specula reflectance, the fiber-optic probe contact to tissue site with certain pressure. The spacing between tissue components decreased due to water displacement, while the volumes of cells and elastic fibers are reduced which in turn increase the concentration of the scatter and absorber inside the tissue. In our research a three-layer skin model is established and the Finite Element Method is employed to simulate the deformation of skin tissue caused by fiber-optic probe with different pressure. The change of water volume inside the tissue as well as the concentration and scattering cross section of scatters are considered. The Monte Carlo simulation is utilized to establish the diffuse-reflectance spectroscopy of three-layer skin model before and after compression. The result indicated that the pressure of fiber-optics probe should be controlled less the 50kpa to reduce the influence on measurement.
Two-dimensional polymer photonic crystal band-edge lasers fabricated by nanoimprint lithography

We report on the fabrication and characterization of two-dimensional polymer photonic crystal band-edge lasers operating in the visible range. The components have been fabricated in a dye chromophore-loaded polymer matrix by nanoimprint lithography. High-symmetry band-edge modes are used to generate laser emission. The experimental lasing frequencies are in good agreement with those calculated using a two-dimensional plane wave algorithm. These results demonstrate the potential of nanoimprint lithography for the fabrication of two-dimensional planar photonic crystal structures in an active medium in a one-step process.
A C*-algebra formulation of the quantization of the electromagnetic field

A presentation of the Fermi, Gupta–Bleuler, and radiation gauge methods for quantizing the free electromagnetic field is given in the Weyl algebra formalism for quantum field theory first introduced by Segal. The abstract Weyl algebra of the vector potential is defined using the formalism of Manuceau. Then the Fermi and Gupta–Bleuler methods are given as schemes for constructing representations of the algebra. The algebra of the physical photons is shown to be a factor algebra of a certain subalgebra of the original algebra of the vector potential. In this formalism, the application of the supplementary condition in the Fermi method, and the supplementary condition and indefinite metric in the Gupta–Bleuler method, can be interpreted as the means by which a representation of this factor algebra is obtained. The Weyl algebra of the physical photons is the Weyl algebra associated with the radiation gauge method. It is also shown that in the Fock representation of the Weyl algebra given by the Fermi method, .
The WD perspective on the PN binary hypothesis

There is no solid theoretical precept through which we can understand the amazing shapes of planetary nebulae (PN). The only plausible theoretical explanation of most PN shapes is that a companion interacts with the mass-losing progenitor of the central star of PN. Plausibility, however, is not sufficient to prove reality and efforts are ongoing to test the PN binary hypothesis observationally. Here, a short review is presented of the problem, but this time set within the context of binarity in the white dwarf (WD) population. We find that observationally the PN and WD binary populations agree. Most noticeably, both populations point to a deep flaw with current theories of the common envelope binary interaction. However, unexplained issues remain, several of which point to a sizable population of as yet undetected intermediate period binaries. Unfortunately, today the WD binaries do not constrain sufficiently the PN binary hypothesis.
Search for Gluino and Squark Production in Multi-jets plus Missing Transverse Energy Final States at the Tevatron using the CDF Detector

El model estandar (ME) es actualment el marc teoric mes complet que descriu la fisica de les particules elementals. En ell s'hi descriuen les particules de materia (fermions) i les seves interaccions (bosons). Fins ara no s'ha trobat cap desviacio significativa entre l'experiment i les prediccions del model. Aixi i tot, el ME no pot ser la teoria final que descriu la fisica de particules perque pateix dificultats teoriques importants a mes altes energies. Una de les possibles extensions del ME amb mes acceptacio es la Supersimetria (SUSY) que relaciona masses i acoblaments entre bosons i fermions. D'aquesta forma, per cada boso existent al ME ha d'existir una "super-parella" fermionica i a l'inreves. A mes, una altra simetria anomenada "paritat R" s'hi acostuma a introduir per tal d'evitar la violacio del nombre barionic i leptonic. Si aixo es aixi, aquestes noves particules, que apareixerien a l'escala energetica dels TeV, s'haurien de formar per parelles i no podrien desintegrar-se completament en particules del ME. Aixo implica l'existencia d'una particula supersimetrica que sigui la mes lleugera de totes (LSP), la qual sera estable i un candidat a explicar la presencia de materia fosca freda a l'univers. Precisament l'escala energetica dels TeV es el rang d'energies accessible per al col·lisionador proto-antiproto Tevatron (Fermilab). Tevatron i els seus dos experiments, els detectors CDF i D0 van ser preparats per l'anomenat "Run II" en el que les col·lisions se succeeixen cada 396 ns amb un centre de masses d'1.96 TeV. Degut a que les noves particules son molt massives, es desintegren en cascades amb multiples raigs de particules (jets) d'alt moment transversal. Quan la paritat R es conserva, dos LSP son produits que escapen del detector deixant un important desequilibri en el balanc energetic del succes. Aquesta tesi tracta de la recerca de les super-parelles dels quarks (squarks) i gluons (gluinos) en multiples jets i energia transversa mancant, amb les dades recollides amb el detector CDF durant gran part del Run II (371 pb-1 de lluminositat). Per aquest motiu, s'ha dut a terme un estudi curos de les condicions del feix i dels diferents processos del ME que tenen estats finals similars al senyal. Diferents talls per reduir la seva presencia tot potenciant el possible senyal s'han estudiat i optimitzat. Tambe s'han considerat les diferents fonts d'errors experimentals i teorics per tal de fer una bona caracteritzacio de les dades. Observant les dades, no s'ha trobat cap desviacio significativa de les prediccions del ME. Degut a aquests resultats, s'han posat limits inferiors a la massa squarks i gluinos (menor de 387 GeV/c2) en un determinat escenari del model teoric anomenat mSUGRA. Aquest estudi es pioner al Run II de CDF i els resultats presenten millores significatives respecte a anteriors limits. En aquest sentit, el present treball constitueix la base i sera la referencia per a futures extensions amb mes dades.
Biplane and Tail Effects in Flapping Flight

A numerical investigation is performed on the interference effects in single or biplane flapping airfoil propulsion in the presence of a stationary downstream tail. At a Reynolds number of 1000, this corresponds to the regime of small micro aerial vehicles. The objective of this study is to provide insight into the complex wing–tail interaction effects occurring in flapping-wing propulsion configurations. The effect of the relative distance between the airfoils, as well as the positioning and incidence angle of the tail, is investigated. Adding a tail behind a single flapping airfoil increases the efficiency and average thrust by up to 10 and 25%, respectively. For the biplane flapping airfoils without tail, overall efficiency and average thrust per airfoil increase up to 17 and 126%, respectively, with respect to the single airfoil due to the formation of a strong momentum jet. The effect of adding a tail behind the biplane flapping airfoils depends on the tail’s orientation and location. Increasing the .
Preference-aware Graph Attention Networks for Cross-Domain Recommendations with Collaborative Knowledge Graph

Knowledge graphs (KGs) can provide users with semantic information and relations among numerous entities and nodes, which can greatly facilitate the performance of recommender systems. However, existing KG-based approaches still suffer from severe data sparsity and may not be effective in capturing the preference features of similar entities across domains. Therefore, in this article, we propose a Preference-aware Graph Attention network model with Collaborative Knowledge Graph (PGACKG) for cross-domain recommendations. Preference-aware entity embeddings with some collaborative signals are first obtained by exploiting the graph-embedding model, which can transform entities and items in the collaborative knowledge graph into semantic preference spaces. To better learn user preference features, we devise a preference-aware graph attention network framework that aggregates the preference features of similar entities within domains and across domains. In this framework, multi-hop reasoning is employed to assist in the generation of preference features within domains, and the node random walk based on frequency visits is proposed to gather similar preferences across domains for target entities. Then, the final preference features of entities are fused, while a novel Cross-domain Bayesian Personalized Ranking (CBPR) is proposed to improve cross-domain recommendation accuracy. Extensive empirical experiments on four real-world datasets demonstrate that our proposed approach consistently outperforms state-of-the-art baselines. Furthermore, our PGACKG achieves strong performance in different ablation scenarios, and the interaction sparsity experiments also demonstrate that our proposed approach can significantly alleviate the data sparsity issue.
Lane Occupancy Prediction: A Deep Model Based on Dilated Convolution and Graph Convolution Fusion

Due to the growing population and substantially increased motor vehicle usage, traffic congestion and insufficient allocation of transportation hardware resources have become a very serious problem. To solve this problem, this work proposes a deep model based on the fusion of dilated convolution and graph convolution network (DGCN) to predict the lane occupancy rate. Among them, the graph convolution layer is used to extract the spatial correlation of traffic data. The dilated convolution layer is used to extract temporal correlations of traffic data. And this work adds traffic flow and vehicle speed as two implicit factors into the model, which are fused with the previously extracted multi modal features. In this way, the non-linearity of traffic data and the influence of many implicit factors are solved, and then the task of predicting the lane occupancy rate is completed. Experiments are carried out on the data set published by California Transportation Bureau, compared with other models. The analysis of the experimental results shows that the average error rate of the model is the lowest and the error growth rate is lower than that of the contrast model.
ECG Signals Classification Method for Wireless Body Area Network Based on Quantized Residual Network

In recent years, rapidly developing deep neural network methods have been widely used for ECG signals classification. However, deep neural networks generally have problems such as too many network redundant parameters, high computational complexity, and large model storage, which are not suitable for application in resource-constrained wireless body area network (WBAN). To address the above problems, this paper proposes a quantized residual network-based ECG signals classification method for WBAN. The method firstly proposes a full-precision residual network, which is activated by adding the input and output of the residual unit through the form of a hopping layer connection and using a one-dimensional convolutional layer as the residual information to enhance the characteristics of the residual block. The test of the MIT-BIH arrhythmia database shows that the accuracy of ECG classification is 99.18%. Secondly, the parameters of the above full-precision residual network are fixed-point binary quantized, and the same database test shows that the final model memory occupation is compressed by 32 times, and the classification accuracy is 99.07%. The proposed method effectively solves the problems of many redundant parameters and large model storage in deep neural networks while ensuring high classification accuracy, and provides some reference for the research of low power consumption in WBAN.
Analysis on Characteristics of Bus Passenger Flow in Big Data Environment

Analysis of passenger flow characteristics is an effective way to optimize travel structure and relieve traffic pressure. Through the idea of "big data", using multi-source data fusion technology, the passenger boarding station is matched, and the passenger travel OD matrix is derived based on the passenger getting off station model. The characteristics of bus passenger flow were analyzed from two levels of time and space. The travel data of different periods, weekdays and weekends were extracted from the time level, and the periodicity of passenger travel was analyzed visually. At the spatial level, OD matrix is combined to analyze the passenger flow distribution and site correlation. Based on this, the travel mode and distribution of residents are obtained, which is helpful for decision makers to carry out targeted planning and management.
Corporate Digital Transformation and Total Factor Productivity: Internal Mechanisms and External Conditions

This paper conducted a research on the impact of corporate digital transformation on total factor productivity, by means of data from 2010∼2021 of A-share listed companies in the Shanghai and Shenzhen exchange stock. This paper measured the degree of corporate digital transformation based on the crawler text recognition function offered by Python and the annual reports of public listed companies, and empirically tested the impact and internal mechanisms of corporate digital transformation on total factor productivity. This paper measured the degree of the support of local government based on the crawler text recognition function offered by Python and the annual work reports of government, and empirically tested the external conditions on corporate digital transformation. The main conclusions are as follows. First, corporate digital transformation has significantly improved the level of total factor productivity. Second, corporate digital transformation can increase total revenue, reduce management expense and capital cost, and contribute to the improvement of the total factor productivity. Third, the external conditions of the support of the local government and the loose monetary policy of the central government have a positive effect on corporate digital transformation.
3D Skull Similarity Evaluation Based on Siamese Network with Residual Structure

The similarity evaluation of skull data is a research work of great significance and has been widely used in forensic, archaeology, and other fields. In this paper, we propose a 3D skull similarity evaluation method based on Siamese Network. Firstly, the original 3D skull data are mapped onto 2D depth images, which maintains the original information of the 3D data to the greatest extent. Then, we introduce the residual structure into the Siamese network to extract the main features of any pair of skull depth image data and map it to a new space for representation. Finally, a mapping function is proposed to calculate a reasonable similarity value of 0-1 between the two input skull data through the sigmoid layer. We conducted objective experiments on 3D skull models and compared them with the subjective skull similarity. The experimental results show that the 3D skull similarity evaluation method based on Siamese network with residual structure can effectively evaluate the similarity of skulls. The results are basically consistent with people's subjective evaluation, providing an effective direction for craniofacial reconstruction and identification.
Research on Teaching Evaluation Enabled by Big Data and Artificial Intelligence in Classroom

The educational evaluation enabled by big data and artificial intelligence (AI) is a breakthrough and innovation of traditional educational evaluation, which leads to the reform of educational evaluation form. The diagnostic evaluation, formative evaluation and summative evaluation can be enabled by big data and AI in different links, which can promotes the reform of educational evaluation. The teaching evaluation enabled by big data and AI in classroom is discussed in this paper, and to optimizes the evaluation of mathematics teaching process in classroom in middle school by using big data and AI. Using practical teaching cases, the possibility of teaching evaluation enabled by big data and AI in classroom is studied in practice.
Semantic Recognition and Representation of Management Business Processes

Within a company or research institute, management business process specifications are usually recorded in the form of unstructured textual information. So far, it is a time-consuming and costly operation to obtain business processes from the text data of process descriptions. To solve this problem, a system for automatic extraction and management of business processes is designed. Based on various management of business normative text data, the management business process elements and their semantic relationships are identified by using named entity recognition, relation extraction and other technologies. The results show that the system can reduce the workload of manually extracting out management business processes to a certain extent.
Rotation of Doppler Features in the Solar Photosphere

Les magnetogrammes et Dopplergrammes solaires sont analyses. Les amplitudes de correlation des magnetogrammes et des Dopplergrammes, comme fonction de la latitude et des decalages en longitude, sont determinees. Le profil de rotation differentielle de la caracteristique Doppler est etabli.
Does in Vitro and in Vivo Exposure To Medicinal Herbs Cause Structural Cuticular Changes in Haemonchus Contortus?

Summary The health and production of small ruminants in constantly menaced by parasitic infections, especially those caused by the blood-sucking gastrointestinal nematode Haemonchus contortus. The aim of this study was to assess the structural cuticular changes in adult H. contortus induced by the use of extracts from local medicinal plants and to examine their ovicidal activity. Previous studies have confirmed the beneficial effect of herbal mixtures in preventing haemonchosis in lambs by lowering fecal egg production and improving immunocompetence. We exposed adult H. contortus to Herbmix (a mixture of medicinal plants) under in vivo and in vitro conditions for observation by scanning electron microscopy (SEM). For the in vivo observations, adult worms were isolated from the abomasa of experimentally infected lambs from a Herbmix group and a control group. Surface structure did not differ significantly between the exposed and control groups. The ovicidal activity of an aqueous Herbmix extract was assessed in vitro, establishing the inhibition of hatching with an ED50 of 6.52 mg/mL. Adult worms for in vitro examination were isolated from experimentally infected lambs and incubated in Herbmix aqueous extracts for 24 h. SEM observations indicated that none of the worms had prominent ultrastructural changes on their cuticles. This study suggests that previously demonstrated antiparasitic effects of medicinal plants did not negatively affect adult parasites by damaging their external structures.
Self‐Powered Wearable Piezoelectric Monitoring of Human Motion and Physiological Signals for the Postpandemic Era: A Review

As society advances, the shift from passive medical care to health management and preventive medical care has become an important issue, with the realization of wearable monitors becoming desirable. In light of the COVID‐19 pandemic, the number of patients who are in urgent need of the monitoring of biological information is increasing. This review focuses on piezoelectric materials and composites that convert kinetic energy into electrical energy to realize self‐powered wearable monitoring sensors, outlining the recent research activity on sensors for use in healthcare monitoring. First, a general description of the principles of piezoelectric monitoring sensors is given. Next, the development status of piezoelectric materials and composites aimed at the application of detecting tiny motions of the human body is introduced, and then the research trends on the detection of larger human body movements are highlighted. Finally, after presenting the performance of current piezoelectric sensors and future research guidelines for developing multifunctional systems in the post COVID‐19 era, the achievements are summarized. Overall, this review will provide guidance to researchers who are seeking to design and develop highly sensitive self‐powered piezoelectric sensors that monitor human motion and physiological signals.