Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of Translation Reliability in the Era of Translation Tools: Analysis of Translations from the Serbian Language

: This paper aims to explore how the use of the internet and machine tools facilitates the process of translation. Translation tools such as Google Translate, Yandex Translate, and ChatGPT were used in the research. The original text in the Serbian language was translated into five foreign languages: Russian, German, Greek, English, and French. After comparing translations into these languages using three translation tools, it was shown how internet tools can help solve translation dilemmas, as well as what their reliability and accuracy are. Thanks to translation tools and an abundance of updated information, adequate application of translation methods and procedures is enabled, contributing to the acceleration of the translation process and the improvement of translation quality.
Conversion of the UTD Method to an Exact Method at All Frequencies for 2-D Conducting Half-Plane Problems

In addition to two recently discovered series, we discover a new asymptotic series for the exact solution of two-dimensional conducting half-plane problems. These series are compared with the ones used in the conventional uniform theory of diffraction (UTD) method solutions of the problem. By attaching some coefficients, which are not complex, to the terms of those series we convert the UTD method to an exact method at all frequencies, including low ones, regarding the given problem type. Schemes for calculations of these series with 7 digits of accuracy or 2 digits of accuracy for high-speed calculations are described.
A dual‐channel ensembled deep convolutional neural network for facial expression recognition in the wild

Facial expression recognition (FER) in the wild is an active and challenging field of research. A system for automatic FER finds use in a wide range of applications related to advanced human–computer interaction (HCI), human–robot interaction (HRI), human behavioral analysis, gaming and entertainment, etc. Since their inception, convolutional neural networks (CNNs) have attained state‐of‐the‐art accuracy in the facial analysis task. However, recognizing facial expressions in the wild with high confidence running on a low‐cost embedded device remains challenging. To this end, this study presents an efficient dual‐channel ensembled deep CNN (DCE‐DCNN) for FER in the wild. Initially, two DCNNs, namely the DCNNG$$ {\mathrm{DCNN}}_G $$ and DCNNS$$ {\mathrm{DCNN}}_S $$ , are trained separately on the grayscale and Scharr‐convolved vertical gradient facial images, respectively. The proposed network later integrates the two pre‐trained DCNNs to obtain the dual‐channel integrated DCNN (DCI‐DCNN). Finally, all three neural networks, namely the DCNNG$$ {\mathrm{DCNN}}_G $$ , DCNNS$$ {\mathrm{DCNN}}_S $$ , and DCI‐DCNN, are jointly fine‐tuned to get a single dual‐channel‐multi‐output model. The multi‐output model produces three prediction scores for the given input facial image. The prediction scores are thus fused using the max‐voting ensemble scheme to obtain the DCE‐DCNN with the final classification label. On the FER2013, RAF‐DB, NCAER‐S, AffectNet, and CKPlus benchmark FER datasets, the proposed DCE‐DCNN consistently outperforms the two individual DCNNs and numerous state‐of‐the‐art CNNs. Moreover, the network achieves competitive recognition accuracy on all four FER in the wild datasets with reduced memory storage size and parameters. The proposed DCE‐DCNN model with high throughput on resource‐limited embedded devices is suitable for applications that seek real‐time classification of facial expressions in the wild with high confidence.
Encrypted Domain Secret Medical-Image Sharing With Secure Outsourcing Computation in IoT Environment

In existing secret medical-image sharing (SMIS) schemes, to protect and manage secret medical-images (SMIs), the sharing and recovery of each SMI are implemented by local servers of medical institutions. However, since a lot of SMIs are produced by personal smart terminal devices in Internet of Things (IoT) environment, directly implementing the sharing and recovery processes will cause excessive communication and computing burden for those local servers, which makes the existing SMIS schemes not suitable for IoT environment. To address the above issue, we propose an encrypted domain SMIS (Enc-SMIS) scheme with secure outsourcing computation for protecting and managing medical images in IoT environment. In the proposed scheme, the medical images are first encrypted using fully homomorphic encryption (FHE) and then outsourced to a cloud server for generating a set of image shares. Subsequently, these shares are stored separately in different local servers of medical institutions. Furthermore, the recovery process is also outsourced to the cloud server when doctors need to observe the patients’ medical images. Compared with the existing SMIS schemes, the proposed Enc-SMIS scheme alleviates the computing and communication burden on local servers significantly with secure outsourcing computation in the semi-honest model, and thus supports the storage and management of medical images well in IoT environment.
Stress management on underlying GaN-based epitaxial films: A new vision for achieving high-performance LEDs on Si substrates

High-performance blue GaN-based light-emitting diodes (LEDs) on Si substrates have been achieved by applying a suitable tensile stress in the underlying n-GaN. It is demonstrated by simulation that tensile stress in the underlying n-GaN alleviates the negative effect from polarization electric fields on multiple quantum wells but an excessively large tensile stress severely bends the band profile of the electron blocking layer, resulting in carrier loss and large electric resistance. A medium level of tensile stress, which ranges from 4 to 5 GPa, can maximally improve the luminous intensity and decrease forward voltage of LEDs on Si substrates. The LED with the optimal tensile stress shows the largest simulated luminous intensity and the smallest simulated voltage at 35 A/cm2. Compared to the LEDs with a compressive stress of −3 GPa and a large tensile stress of 8 GPa, the improvement of luminous intensity can reach 102% and 28.34%, respectively. Subsequent experimental results provide evidence of the sup.
ON A CERTAIN CLASS OF KÄHLERIAN MANIFOLDS

where R^j^» an(* ® 8 X 8 curvature tensor, Ricci tensor and the scalar curvature, respectively (for the geometric meaning of the Bochner tensor see Blair [1], Yano [9]). Kahlerian manifolds with parallel or vanishing Bochner curvature tensor were investigated by Chen and Yano [3]> Matsumoto [4], Matsumoto and Tanno [5]» Tachibana and Liu [7]. In this paper we consider a class of Kahlerian manifolds in which Bochner curvature tensor satisfies the condition
Atlantic Masters: Three Early Modern Afro-Brazilian Artists

Brazil received the largest number of Africans enslaved into the Americas: nearly five million by some estimates. Thus, Brazil became the world’s largest slavocracy. But slavery was not the only experience available to Africans and Brazilians of African descent in slavery-era Brazil. Numerically, Afro-Brazilians dominated the arts in colonial Brazil. However, very few of those artists and artisans, many of whom were enslaved, are known by name today. Free Afro-Brazilian artists, such as Aleijadinho, Mestre Valentim, and Teófilo de Jesus, on the other hand, fared far better. In this article, I turn to these three mixed-race artists’ works and what little is known of their lives, not only as exemplary Afro-Brazilian artists but also as some of the most important artists of Brazil’s late colonial period, where they had the greatest impact on the artistic developments in their home regions. These artists’ careers thus illustrate how artists of African descent contributed to and defined urban and sacred spaces in the early modern Atlantic. This is therefore an invitation to look at Afrodescendants’ role in early modern art beyond the anonymity of slavery and static representation.
A novel vision-based mold monitoring system in an environment of intense vibration

Mold monitoring has been more and more widely used in the modern manufacturing industry, especially when based on machine vision, but these systems cannot meet the detection speed and accuracy requirements for mold monitoring because they must operate in environments that exhibit intense vibration during production. To ensure that the system runs accurately and efficiently, we propose a new descriptor that combines the geometric relationship-based global context feature and the local scale-invariant feature transform for the image registration step of the mold monitoring system. The experimental results of four types of molds showed that the detection accuracy of the mold monitoring system is improved in the environment with intense vibration.
Moisture Sorption Isotherm of C. nutans Herbal Leaves

The moisture sorption isotherms of Clinacanthus nutans (C. nutans) herbal leaves were established at three different temperatures of 30°C, 40°C, and 50°C, and water activity between 0.3–0.9 using a static gravimetric method. The results showed that equilibrium moisture content of C. nutans increased with increasing water activity at a constant temperature for both desorption and adsorption isotherms. In contrast, the equilibrium moisture content decreased when the temperature increased at a constant water activity for all isotherms. The sorption isotherms exhibited Type III behaviour or J-shaped curve according to Brunauer-Emmett-Teller (BET) classification. It was found that Guggenheim-Anderson-de Boer (GAB) and Peleg models were the best-fit to the experimental moisture isotherms. The maximum net isosteric heat of sorption values were found to be 70 kJ/mol and 13 kJ/mol for desorption and adsorption respectively. The moisture sorption isotherms established are essential for optimum design of C. nutans dryer and storage conditions.
Printed ZnO nanoparticle seed layers to grow ZnO nanowires on flexible substrates

Zinc oxide (ZnO) nanowires (NWs) are excellent candidates for the fabrication of energy harvesters, mechanical sensors, piezotronic and piezo-phototronic devices thanks to the interplay between piezoelectric and semiconducting properties. The growth of ZnO NWs on flexible substrates would further broaden their possible applications. However, such a growth requires low temperature synthesis to prevent any damage to the flexible polymer. Another difficulty lies in the fact that the deposition of patterned ceramic thin films on flexible substrates is challenging, especially under vacuum free conditions. In this framework, printing technologies like inkjet and gravure printing have a noteworthy potential since they allow to deposit thin films onto flexible substrates and offer several other advantages like cost efficiency, use of low temperatures, vacuum-free processing, high throughput and the possibility of patterning during the deposition process. In this work, we report the chemical bath deposition (CBD) growth of high quality ZnO NWs on polyethylene terephthalate (PET) starting from inkjet printed seed layer constituted of ZnO nanoparticles and a comparison with that obtained with seed layer deposited by gravure technology. Using Piezoresponse Force Microscopy (PFM), we observed that the Zn-polar domains are homogeneously distributed at the top surface of the grown ZnO NWs. This work demonstrates the key benefit of the printing techniques over conventional methods (e.g. Atomic Layer Deposition, ALD) to deposit seed layers at low temperature on flexible substrates. This opens the possibility of manufacturing completely vacuum-free solution-based flexible piezoelectric devices.
Implementation of the Problem-Based Learning Model and Its Effect on Accounting Learning Outcomes During the COVID-19 Pandemic

The transition from face-to-face learning to distance learning has decreased student learning outcomes. Departing from this phenomenon, this research is presented as an effort to overcome various obstacles experienced by teachers and students during the COVID-19 pandemic. The researcher offers a model that can be used in virtual learning activities. Beginning with finding out the extent of the influence of the problem-based learning (PBL) model on accounting learning outcomes in class X Accounting 1 students at SMK YPKK Limbung. Research data obtained from the results of the test, observation, and documentation. Then analyzed using t-test which consists of simple linear regression analysis and product moment correlation. The findings in this experimental study resulted in a simple linear regression equation model with a value of Y=33,012+4.283X and a correlation coefficient value of 0.580. After students received learning using the PBL model through distance learning and were given a post-test, their learning outcomes also increased. In other words, the implementation of the PBL model has a significant and positive effect on accounting learning outcomes during the COVID-19 pandemic.
Properties of inverse probability of adherence weighted estimator of the per-protocol effect for sustained treatment strategies under different data-generating mechanisms and adherence patterns

Inverse Probability (of Adherence) Weighted per-protocol (IPW-PP) estimators are get- ting popular in addressing medication non-adherence while analyzing pragmatic trial data. However, their finite sample properties under different data generating mechanisms (DGMs) have not been investigated comprehensively. In the current work, we investigated the finite sample performances of such estimators in the context of a pragmatic random- ized controlled trial. We compared the performances of IPW-PP estimators with commonly used naive and baseline-adjusted per-protocol estimators, under different DGMs emulating pragmatic trials, comparing two sustained treatment strategies, possibly with a non-null effect. DGMs include (i) different roles of a baseline variable; whether future time-varying prognostic factors are impacted by past adherence; and whether the baseline variable is measured, (ii) whether adherence patterns observed in two arms are differential, and when we have access to measurements of adherence and confounders that are recorded infre- quently (sparsely). When baseline confounders are adjusted, we generally obtain unbiased estimates, but if some necessary variables are not measured, the IPW-PP estimator may still be preferable. High non-adherence patterns might negatively impact IPW-PP effect estimators, particularly when DGMs include confounding that may be influenced by previ- ous adherence history. We used the above estimators to analyze a case study from the Lipid Research Clinics Coronary Primary Prevention Trial data in the presence of non-adherence. Journal of Statistical Research 2022, Vol. 56, No. 2, pp.134-154
Maximum likelihood estimation of variance components

In this thesis, the Maximum Likelihood and Restricted Maximum Likelihood methods of estimating variance components are investigated for the one-way model. Expressions for the estimators and their variances are obtained, and algorithms for finding the estimates are tested by means of a Monte Carlo study. The quantitative effects of non-normality on the variability of estimates are discussed. Finally, diagnostic tests for identifying outliers and non-normality are proposed, and illustrated with data concerning soybean plant growth. CONTENTS Statement of Originality i Acknowledgements ii Abstract iii
Dark breather using symmetric Morse, solvent and external potentials for DNA breathing

We analyze the dynamics and the quantum thermodynamics of DNA in Symmetric-Peyrard-Bishop-Dauxois model (S-PBD) with solvent and external potentials and describe the transient conformational fluctuations using dark breather and the ground state wave function of the associate Schrodinger differential equation. We used the S-PBD, the Floquet theory, quantum thermodynamic and finite difference methods. We show that for lower coupling dark breather is present. We estimate the fluctuations or breathing of DNA. For the S-PBD model we have the stability of dark breather for k<0.004 and mobile breathers with coupling k=0.004. The fluctuations of the dark breather in the S-PBD model is approximately zero with the quantum thermodynamics. The viscous and external potential effect is direct proportional to hydrogen bond stretching.
An Global Spatial Similarity based on Fuzzy C-means Clustering for Image Segmentation

The segmentation results of the traditional FCM based image segmentation algorithms are only determined by the distribution of pixel intensity in the feature space, and they does not take the spatial distribution of pixels into consideration, which make the segmentation results discrete in the spatial distribution. To solve this problem, a global spatial similarity metric and a global intensity similarity metric are proposed, and introduced to a new distance metric which is used to calculate the difference between pixels and cluster centers. In addition, a maximal similarity based class merging mechanism is employed to achieve more accurate image segmentation. The experiments demonstrate that, comparison with the FCM and KFCM based image segmentation algorithms, the proposed method produces more accurate and applicable segmentation results.
Large time behavior of solutions of local and nonlocal nondegenerate Hamilton-Jacobi equations with Ornstein-Uhlenbeck operator

We study the well-posedness of second order Hamilton-Jacobi equations with an Ornstein-Uhlenbeck operator in R N and R N ×[0, +∞). As applications, we solve the associated ergodic problem associated to the stationary equation and obtain the large time behavior of the solutions of the evolution equation when it is nondegenerate. These results are some generalizations of the ones obtained by Fujita, Ishii & Loreti (2006) by considering more general diffusion matrices or nonlocal operators of integro-differential type and general sublinear Hamiltonians. Our work uses as a key ingredient the a-priori Lipschitz estimates obtained in Chasseigne, Ley & Nguyen (2017).
Endmember initialization method for hyperspectral data unmixing

Abstract. Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of the hyperspectral image scene. One important issue in hyperspectral data unmixing is the initialization of endmembers. Most unmixing methods initialize their endmembers by randomly selecting a specified number of pixels from the data or by vertex component analysis, which limits their performance in practice. We propose an endmember initialization method for hyperspectral data unmixing. Our initial endmembers include some of the true endmembers, which improves the accuracy of hyperpspectral unmixing effectively. The experimental results on both synthetic and real hyperspectral data illustrate the superiority of the proposed method compared with other state-of-the-art approaches.
Extended Reversible and Irreversible Thermodynamics: A Hamiltonian Approach with Application to Heat Waves

Abstract A new version of extended irreversible thermodynamics (EIT) satisfying a Hamiltonian structure is proposed. For pedagogical purpose, the simple problem of linear heat conduction in a rigid body is investigated to illustrate the general framework. In contrast with earlier versions of EIT wherein the heat flux was upgraded to the status of state variables, we select here its conjugate dual and higher order fluxes as new independent variables. Their time–evolution equations are formed of reversible and irreversible terms but they cannot take any arbitrary form. Restrictions are placed on the reversible terms by imposing a Hamiltonian structure while the irreversible contribution is subject to the requirement to satisfy the second law of thermodynamics. Explicit expressions of the temperature and heat flux waves are also derived.
Characterization of rectangular and cylindrical piezoceramics using three dimensional modelling

ABSTRACT This paper is devoted to piezoceramic characterization using 3D-modelling. It is applied on Navy III and Navy II elements. The influence of coupling on resonance frequencies is analysed. It is shown that velocities are independent of the element dimensions and coupling is asymmetrical because it is generated by elastic constants . Characterization is described and the determination of six elastic constants (), two piezoelectric coefficients () and permittivity is explained. Calculation needs only one element: parallelepiped or cylindrical. The error made on resonance frequencies using 1D-models is deduced and can reach 20% when certain element shapes are employed.
Inhibition of P2X4 and P2X7 receptors improves histological and behavioral outcomes after experimental traumatic brain injury in rats

Release of large amounts of adenosine triphosphate (ATP), a gliotransmitter, into the extracellular space by traumatic brain injury (TBI) is considered to activate the microglia followed by release of inflammatory cytokines resulting in excessive inflammatory response that induces secondary brain injury. The present study investigated whether antagonists of ATP receptors (P2X4 and/or P2X7) on microglia are beneficial for reducing the post-injury inflammatory response that leads to secondary injury, a prognostic aggravation factor of TBI. Adult male Sprague-Dawley rats were subjected to cortical contusion injury (CCI) and randomly assigned to injury and drug treatment conditions, as follows: i) No surgical intervention (naïve group); ii) dimethyl sulfoxide treatment after CCI (CCI-control group); iii) 5-BDBD (antagonist of P2X4 receptor) treatment after CCI (CCI-5-BDBD group); iv) CCI-AZ11645373 (antagonist of P2X7 receptor) treatment after CCI (CCI-AZ11645373 group); v) or 5-BDBD and AZ11645373 treatment after CCI (CCI-5-BDBD + AZ11645373 group). In the CCI-5-BDBD, CCI-AZ11645373, and CCI-5-BDBD + AZ11645373 groups, expression of activated microglia was suppressed in the ipsilateral cortex and hippocampus 3 days after the CCI. Western blotting with ionized calcium-binding adaptor molecule 1 antibody revealed that administration of CCI-5-BDBD and/or CCI-AZ11645373 suppressed expression of microglia and reduced expression of inflammatory cytokine mRNA 3 days after the CCI. Furthermore, the plus maze test, which reflects the spatial memory function and involves the hippocampal function, showed improvement 28 days after secondary injury to the hippocampus. These findings confirmed that blocking the P2X4 and P2X7 receptors, which are ATP receptors central in gliotransmission, suppresses microglial activation and subsequent cytokine expression after brain injury, and demonstrates the potential as an effective treatment for reducing secondary brain injury.
Analysis of high Reynolds numbers effects on a wind turbine airfoil using 2D wind tunnel test data

The aerodynamic behaviour of a wind turbine airfoil has been measured in a dedicated 2D wind tunnel test at the DNW High Pressure Wind Tunnel in Gottingen (HDG), Germany. The tests have been performed on the DU00W212 airfoil at different Reynolds numbers: 3, 6, 9, 12 and 15 million, and at low Mach numbers (below 0.1). Both clean and tripped conditions of the airfoil have been measured. An analysis of the impact of a wide Reynolds number variation over the aerodynamic characteristics of this airfoil has been performed.