Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal; nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model
Systematic study of Ge1−xSnx absorption coefficient and refractive index for the device applications of Si-based optoelectronics

The absorption coefficient and refractive index of Ge1−xSnx alloys (x from 0% to 10%) were characterized for the wavelength range from 1500 to 2500 nm via spectroscopic ellipsometry at room temperature. By applying physical models to fit the obtained data, two empirical formulae with extracted constants and coefficients were developed: (1) Absorption coefficient. The absorption regarding Urbach tail, indirect and direct bandgap transitions were comprehensively taken into account; (2) refractive index. The Sellmeier coefficients associated with dispersion relationship were extracted. In these formulae, the Sn composition and strain percentage were the input parameters, by inputting which the spectral absorption coefficient and spectral refractive index can be obtained. Since the absorption coefficient is key information to determine the performance of the photodetectors including operation wavelength range, responsivity, and specific detectivity, and the refractive index is very useful for the design of the anti-reflection coating for photodetectors and the layer structure for waveguides, the developed formulae could simplify the optoelectronic device design process due to their parameter-based expressions.
Sampling theorem in fractional Fourier domains

The standard Fourier transform operator F is a particular case of the fractional order Fourier transform operator Fα (it is obtained for α = 1). In the same way that has been done for F, a sampling theorem for Fα is proposed for signals whose fractional Fourier transforms of order α have finite supports. An interpolation formula for fractional bandlimited functions is also deduced.
Integrating Spatial Details With Long-Range Contexts for Semantic Segmentation of Very High-Resolution Remote-Sensing Images

This letter presents a cross-learning network (i.e., CLCFormer) integrating fine-grained spatial details within long-range global contexts based upon convolutional neural networks (CNNs) and transformer, for semantic segmentation of very high-resolution (VHR) remote-sensing images. More specifically, CLCFormer comprises two parallel encoders, derived from the CNN and transformer, and a CNN decoder. The encoders are backboned on SwinV2 and EfficientNet-B3, from which the extracted semantic features are aggregated at multiple levels using a bilateral feature fusion module (BiFFM). First, we used attention gate (ATG) modules to enhance feature representation, improving segmentation results for objects with various shapes and sizes. Second, we used an attention residual (ATR) module to refine spatial features’s learning, alleviating boundary blurring of occluded objects. Finally, we developed a new strategy, called auxiliary supervise strategy (ASS), for model optimization to further improve segmentation performance. Our method was tested on the WHU, Inria, and Potsdam datasets, and compared with CNN-based and transformer-based methods. Results showed that our method achieved state-of-the-art performance on the WHU building dataset (92.31% IoU), Inria building dataset (83.71% IoU), and Potsdam dataset (80.27% MIoU). We concluded that CLCFormer is a flexible, robust, and effective method for the semantic segmentation of VHR images. The codes of the proposed model are available at https://github.com/long123524/CLCFormer.
Spectral (Bin) microphysics coupled with a Mesoscale Model (MM5). Part I: Model description and first results

Abstract Considerable research investments have been made to improve the accuracy of forecasting precipitation systems in cloud-resolving, mesoscale atmospheric models. Yet, despite a significant improvement in model grid resolution and a decrease in initial condition uncertainty, the accurate prediction of precipitation amount and distribution still remains a difficult problem. Now, the development of a fast version of spectral (bin) microphysics (SBM Fast) offers significant potential for improving the description of precipitation-forming processes in mesoscale atmospheric models. The SBM Fast is based on solving a system of equations for size distribution functions for water drops and three types of ice crystals (plates, columns, and dendrites), as well as snowflakes, graupel, and hail/frozen drops. Ice processes are represented by three size distributions, instead of six in the original SBM code. The SBM uses first principles to simulate microphysical processes such as diffusional growth and collision.
Three unfinished works on the optimal storage capacity of networks

The optimal storage properties of three different neural network models are studied. For two of these models the architecture of the network is a perceptron with +or-J interactions, whereas for the third model the output can be an arbitrary function of the inputs. Analytic bounds and numerical estimates of the optimal capacities and of the minimal fraction of errors are obtained for the first two models. The third model can be solved exactly and the exact solution is compared to the bounds and to the results of numerical simulations used for the two other models.
Shifts in the Thermal Dependence of Locomotor Performance across an Altitudinal Gradient in Native Populations of Xenopus laevis

Ectothermic species are dependent on temperature, which drives many aspects of their physiology, including locomotion. The distribution of the native populations of Xenopus laevis is characterized by an exceptional range in latitude and altitude. Along altitudinal gradients, thermal environments change, and populations experience different temperatures. In this study, we compared critical thermal limits and thermal performance curves of populations from the native range across an altitudinal gradient to test whether optimal temperatures for exertion differ depending on altitude. Data on exertion capacity were collected at six different temperatures (8°C, 12°C, 16°C, 19°C, 23°C, and 27°C) for four populations spanning an altitudinal gradient (60, 1,016, 1,948, and 3,197 m asl). Results show that the thermal performance optimum differs among populations. Populations from cold environments at high altitudes exhibit a lower optimal performance temperature than populations from warmer environments at lower altitudes. The ability of this species to change its optimal temperature for locomotor exertion across extremely different climatic environments within the native range may help explain its exceptional invasive potential. These results suggest that ectothermic species capable of adapting to broad altitudinal ranges may be particularly good at invading novel climatic areas, given their ability to cope with a wide range of variation in environmental temperatures.
Energy-Efficient Distributed Cluster-Tree Based Routing Protocol for Applications IoT-Based WSN

4 With the growth of wireless sensor network (WSN) technologies, the applications of IoT-based WSNs allow the interconnection of smart objects or sensors through the Internet. However, energy constraint is a major obstacle in WSN, which directly affects the lifespan of the network. Hence, many researchers have focused on how to program routing protocols to maximize energy conservation in WSNs. The clustering mechanism is demonstrated that separating the network into clusters may significantly decrease energy consumption. In this paper, we propose distributed tree-based clustering routing protocol for IoT applications (EE-DTC). In order to enhance efficient energy, EE-DTC chooses cluster head nodes based on the remaining energy, the location, and the density of nodes. In addition, to lengthen the network lifespan, we create multi-hop routes with short communication links intra-clusters by building the minimum spanning tree (MST) using the Kruskal algo-rithm. Our experiment results exhibit that the performance of EE-DTC overcomes the TBC and LEACH-VA protocols in terms of increasing network lifespan, reducing energy consumption, and improving efficient energy
Conditional Specification with Exponential Power Distributions

The problem of modeling Bayesian networks with continuous nodes deals with discrete approximations and conditional linear Gaussian models. In this article we have considered the possibility of using the exponential power family as conditional probability densities. It will be shown that for some platikurtic conditional distributions\ in this family, conditional regression functions are constant. These results give conditions to avoid compatibility problems when distributions with lighter tails than the normal are used in the description of conditional densities to specify joint densities, like in Bayesian networks.
L to H mode transition: on the role of Zeff

In this paper, the nature of the primary instability present in the pedestal forming region prior to the transition into H mode is analysed using a gyrokinetic code on JET-ILW profiles. The linear analysis shows that the primary instability is of resistive nature, and can therefore be stabilized by increased temperature, hence power. The unstable modes are identified as being resistive ballooning modes. Their growth rates decrease for temperatures increasing towards the experimentally measured temperature at the L–H transition. The growth rates are larger for lower effective charge Zeff. This dependence is shown to be in qualitative agreement with recent and past experimental observations of reduced Zeff associated with lower L–H power thresholds.
Range Particle Algorithm Application in the Solving Inverse Problem of Line Source

Now mature and effective algorithms for the numerical solution of inverse problem of line source are few, so the researching of algorithms for the problem is urgent and necessary. Firstly, a brief introduction to the characteristics of solving the inverse problem of the line source is given, and the mathematical solution model of inverse problem is established based on the Line source equation, Secondly a new algorithm (Range Particle algorithm) based on range searching algorithm is proposed for the numerical solving the inverse problem of the line source particle and the basic implementation steps and parameter adjustment of the algorithm also be discussed. Finally, simulated and measured data were used to test the effect of the algorithm. The results show that the range particle algorithm is an algorithm of high precision, fast convergence and computational stability for the solving the inverse problem of the line source and it do can be applied in Engineering.
Characterization of Triatoma infestans (Klug, 1834) (Hemiptera: Heteroptera, Reduviidae) from Residual Foci in the States of Bahia and Rio Grande do Sul, Brazil, Using Geometric Morphology

Simple Summary Foci of Triatoma infestans were found in the states of Rio Grande do Sul and Bahia, Brazil. The objective of the present work is to evaluate the morphometric differences between populations of residual and wild focus of T. infestans through head geometric morphometry. It is possible to show significant morphometric differences between the studied populations. Furthermore, it is possible to establish a morphometric relationship between these populations and to list hypotheses about the distribution and maintenance of residual foci of T. infestans in Brazil. Abstract Triatoma infestans is one of the main vectors of Chagas disease in Latin America. Although the species is under control in most Latin countries, it is still necessary to maintain epidemiological surveillance. The present study aims to characterize T. infestans populations from residual foci in Bahia and Rio Grande do Sul, Brazil, comparing them with natural populations in Argentina and Bolivia. For this, we adopt the geometric morphometry of the heads. It is possible to report the morphometric variety of the studied populations. In addition, we show that the size of the heads contributes to the differentiation between populations, while the shape has less power to discriminate groups. Furthermore, we show that some natural populations have morphometric proximity to residual populations, suggesting a relationship between these triatomines. Our data do not support the origin of residual populations but demonstrate the importance of new studies with other techniques to understand the dynamics of distribution and reintroduction of these vectors in Brazilian territory.
Metagenomics Provides a Deeper Assessment of the Diversity of Bacterial Communities in Polar Soils Than Metabarcoding

The diversity of soil bacteria was analyzed via metabarcoding and metagenomic approaches using DNA samples isolated from the biocrusts of 12 different Arctic and Antarctic sites. For the metabarcoding approach, the V3-4 region of the 16S rRNA was targeted. Our results showed that nearly all operational taxonomic units (OTUs = taxa) found in metabarcoding analyses were recovered in metagenomic analyses. In contrast, metagenomics identified a large number of additional OTUs absent in metabarcoding analyses. In addition, we found huge differences in the abundance of OTUs between the two methods. The reasons for these differences seem to be (1) the higher sequencing depth in metagenomics studies, which allows the detection of low-abundance community members in metagenomics, and (2) bias of primer pairs used to amplify the targeted sequence in metabarcoding, which can change the community composition dramatically even at the lower taxonomic levels. We strongly recommend using only metagenomic approaches when establishing the taxonomic profiles of whole biological communities.
Genome-Wide Identification of the Rose SWEET Gene Family and Their Different Expression Profiles in Cold Response between Two Rose Species

Sugars Will Eventually be Exported Transporter (SWEET) gene family plays indispensable roles in plant physiological activities, development processes, and responses to biotic and abiotic stresses, but no information is known for roses. In this study, a total of 25 RcSWEET genes were identified in Rosa chinensis ‘Old Blush’ by genome-wide analysis and clustered into four subgroups based on their phylogenetic relationships. The genomic features, including gene structures, conserved motifs, and gene duplication among the chromosomes of RcSWEET genes, were characterized. Seventeen types of cis-acting elements among the RcSWEET genes were predicted to exhibit their potential regulatory roles during biotic and abiotic stress and hormone responses. Tissue-specific and cold-response expression profiles based on transcriptome data showed that SWEETs play widely varying roles in development and stress tolerance in two rose species. Moreover, the different expression patterns of cold-response SWEET genes were verified by qRT-PCR between the moderately cold-resistant species R. chinensis ‘Old Blush’ and the extremely cold-resistant species R. beggeriana. Especially, SWEET2a and SWEET10c exhibited species differences after cold treatment and were sharply upregulated in the leaves of R. beggeriana but not R. chinensis ‘Old Blush’, indicating that these two genes may be the crucial candidates that participate in cold tolerance in R. beggeriana. Our results provide the foundation for function analysis of the SWEET gene family in roses, and will contribute to the breeding of cold-tolerant varieties of roses.
The Monte Carlo technique as applied to the fundamentals of EPMA and SEM

A Monte Carlo technique was applied to study the fundamental phenomena in the electron probe microanalyzer (EPMA) and the scanning electron microscope (SEM), which are brought about by scattering of the primary electrons in a specimen. The calculation procedure was based on Lewis' multiple scattering theory; however, some attempts were made to improve it by introducing the substeps to each step which had been used in previous calculations. The main purpose is to investigate the applicability of this improvement through the comparison with the experimental results with respect to backscattered electrons obtained by Kanter, and next to give a theoretical explanation for the experimental results of Wells as well as to obtain more detailed information on the electron backscattering. The results show that the fidelity of the present Monte Carlo simulation for the electron backscattering is satisfactory.
ACOUSTIC MEASUREMENTS OF SCATTERING BY OBJECTS OF IRREGULAR SHAPE

Although many scatterers in nature are irregular in shape, the data on the scattering of sound by asymmetrical bodies is relatively limited. Our understanding of the scattering of underwater sound by nonregular shaped targets is therefore significantly less developed than that due to scattering by scatterers of symmetry. In the present work, measurements are reported on the backscattering from irregularly shaped solid elastic particles in both the time and frequency domains. Backscatter data have been collected for a variety of irregularly shaped particles at varying orientations. These observations show a number of characteristics which are comparable with scattering by targets of symmetry, particularly the property of the scattered echo being significantly longer in duration than the insonifying pulse. This pulse elongation for symmetrical targets is normally interpreted using resonance scattering theory, RST, as arising from radiation due to surface eigen waves on the body of the scatterer. In the present work the interpretation of the scattering by irregular shaped targets has been guided by resonance scattering studies, and an understanding of the response formulated within this framework.
A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy (IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy. The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.
Pairwise Comparison and Ranking in Tournaments

Since, however, other ranking procedures have been proposed, e.g., by WeiKendall [3], the problem arises how to characterize the "goodness" of any such procedure. In this paper we give such a characterization in terms of the "underlying probability structure" and then exhibit a class of such structures for which the usual ranking procedure by scores si is optimal. In order to keep what follows as intuitive as possible we shall from now on use the terminology referring to chess tournaments, i.e., "player" for "item," "game" for "comparison" and "won," "lost" or "drawn" for the possible results of any comparison.
High dimensional correlation matrices: the central limit theorem and its applications

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, the paper establishes a new central limit theorem for a linear spectral statistic of high dimensional sample correlation matrices for the case where the dimension p and the sample size n are comparable. This result is of independent interest in large dimensional random‐matrix theory. We also further investigate the sample correlation matrices of a high dimensional vector whose elements have a special correlated structure and the corresponding central limit theorem is developed. Meanwhile, we apply the linear spectral statistic to an independence test for p random variables, and then an equivalence test for p factor loadings and n factors in a factor model. The finite sample performance of the test proposed shows its applicability and effectiveness in practice. An empirical application to test the independence of household incomes from various cities in China is also conducted.
Chebyshev Fuzzy Neural Network Super-Twisting Terminal Sliding-Mode Control for Active Power Filter

To suppress the harmonic pollution, a global fast super-twisting terminal sliding-mode controller (GFSTTSMC) using an adaptive recurrent Chebyshev fuzzy neural network (ARCFNN) is proposed to eliminate the current distortion for an active power filter (APF) with unknown model nonlinearities. First, a global fast terminal sliding-mode controller (GFTSMC) is adopted due to its advantages in finite-time convergence and faster convergence rate of tracking error. Second, a super-twisting sliding-mode control (STSMC) algorithm having an edge in terms of weakening the chattering and smoothening the input signals, is designed to promote the dynamic tracking capability of APF. Third, by combining the fuzzy neural network and Chebyshev polynomials, the ARCFNN is utilized to estimate the unknown model of the APF system due to its strong generalization and approximation ability. Ultimately, the availability of the ARCFNN-GFSTTSMC scheme has been fully numerically and experimentally verified in an APF prototype, showing the improvement of characteristic performance than other strategies.