Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Cryptography-Based Architecture to Achieve Secure Energy Trading in Microgrid

With the advancement of distributed generation and information technology, energy trading in micro grid (MG) becomes popular in recent years. However, due to the lack of authority management in the MG, the traditional energy trading method with trusted system operator (SO) and market operator (MO) is no longer suitable. This paper proposes a novel, secure and privacy-preserving energy trading architecture with the state-of-the-art cryptosystem. Firstly, a zero-knowledge-proof based non-interactive protocol is proposed to authenticate prosumers. Next, a novel energy data evaluation method is proposed to achieve privacy-preserving power pricing and dispatching with the homomorphic encryption. Moreover, a lightweight tree-chained trading book is proposed to protect the transaction information. Then, we analysis the security performance and emphasize the advantages of our proposed architecture. At last, we exam the performance of our proposed methods in the IEEE benchmark systems. We observe that, our proposed architecture could achieve the privacy-preserving and secure energy trading in a low-time-cost but high-accuracy way.
The lipid composition of extracellular vesicles: applications in diagnostics and therapeutic delivery

Extracellular vesicles (EVs), including exosomes, with nanoscale sizes, biological origins, various functions, and unique lipid and protein compositions have been introduced as versatile tools for diagnostic and therapeutic medical applications. Numerous studies have reported the importance of the lipid composition of EVs and its influence on their mechanism of action. For example, changes in the lipidomic profile of EVs have been shown to influence the progression of various diseases, including ovarian malignancies and prostate cancer. In this review, we endeavored to examine differences in the lipid content of EV membranes derived from different cell types to characterize their capabilities as diagnostic tools and treatments for diseases like cancer and Alzheimer’s disease. We additionally discuss designing functionalized vesicles, whether synthetically by hybrid methods or by changing the lipid composition of natural EVs. Lastly, we provide an overview of current and potential biomedical applications and perspectives on the future of this growing field.
Description of resonant processes in the dipole moment interaction

We analyze the resonant interaction between cumulus of nano-particles distributed on a two-dimensional array controlling the polarization states on the illumination, this allows controlling the dipole moment induced in a tunable-way obtaining an analytic expression for the refractive index. The resonant effects depend on the parameters that characterize the spatial distribution of the particle arrangement. We present two cases, firstly the interaction is described using a linear polarization on a linear particle array, and secondly it is obtained using circular polarization inducing resonant interaction between ring-particle kind structures. The refractive index associated to both configurations is implemented in the Fresnel equations for the study of the reflectivity and transmittance of optical fields. As a main result of the analysis is that we can to identify and control the parameters required for the synthesis of metamaterials. Computer simulations are presented.
AOHSR: attention mechanism-based optical image-guided hyperspectral image super-resolution

ABSTRACT With the rapid development of hyperspectral imaging technology, hyperspectral image (HSI) has been applied in various fields. However, due to the constraint of hyperspectral imaging devices, the spatial resolution of HSI remains too low to accurately meet the needs of target detection and recognition, which shackles the further application of HSI. In this paper, an optical image-guided HSISR algorithm based on the attention mechanism (AOHSR) was proposed in order to effectively improve the resolution of HSI. With the help of the attention mechanism, the proposed algorithm can effectively retain the spectral information of HSI and the spatial information of the optical image. Even without high spatial resolution optical imaging in the test stage, favourable reconstruction results can still be achieved. Firstly, the framework of the proposed optical image-guided HSI SR algorithm based on the attention mechanism was introduced. The algorithm consists of three modules: spectral reservation module (SRM), optical image guidance module (OIGM) and spatial-spectral recovery module (SSRM). The SRM uses an improved channel attention mechanism to extract spectral features from low-resolution (LR) HSIs to preserve spectral information. Through an improved spatial attention mechanism, the OIGM extracts spatial information from high-resolution (HR) optical images, supplementing the information extracted from LR HSI. The SSRM is used to fuse the spectral information on HSI and the spatial information on optical images to reconstruct HR HSI. Then, a joint training algorithm for reconstruction loss and optical image content loss was also proposed, which can effectively guide to learn the reconstruction of detailed information of the spatial texture while ensuring the preservation of spectral information. The results of experiments on three open datasets reveal that the HSIs reconstructed by the proposed network performed better than the existing algorithms regardless of whether optical images in the test stage exist.
Stabilization of the Two-Axis Gimbal System Based on an Adaptive Fractional-Order Sliding-Mode Controller

ABSTRACT This paper introduces an adaptive fractional-order sliding-mode controller for stabilization of a two-axis gimbal platform in the presence of the torque disturbance effects. To tend the angular velocities of the inner gimbal in the elevation and azimuth axes to zero, an adaptive fractional-order sliding-mode approach is utilized. To achieve this goal, fractional-order sliding surfaces in both azimuth and elevation axes and the corresponding adaptive controllers with adjustable parameters tuned according to Lyapunov-based adaptation mechanisms are employed. The optimal value of the fractional order is obtained through an integral square error performance index minimization. The moments products uncertainties are incorporated in the design procedure. The numerical simulations demonstrate that the proposed control approach is robust against the moments products variations.
SOLPS-ITER Modeling of the Alcator C-Mod Divertor Plasma

SOLPS-ITER is a new edge code package that will be developed and maintained at the ITER Organization [X. Bonnin et al., Plasma Fusion Res. 11, 1403102 (2016)], in close collaboration with the wider SOLPS community, and will be used to support the design of the ITER divertor [A.S. Kukushkin et al., Fusion Eng. Des. 86, 2865 (2011)]. In this paper, we report on the first application of the code to the modeling of the Alcator C-Mod divertor. With its high density, high magnetic field, and strong ITER-like target shaping, C-Mod is of particular interest to ITER in terms of plasma and neutral parameters in the divertor. We show that with a fluid neutral model, we can qualitatively reproduce the observed particle fluxes to inner and outer targets under partially detached conditions. However, simulated electron temperatures in the divertor are much too low. A number of physics and numerical reasons are proposed to resolve this issue and serve as a guideline for further development of the code.
Pixelated multi-spectral filters assessment for space applications

This papers reports on the integration of pixelated multispectral filters on a COTS CMOS detector CMV4000 from CMOSIS. This paper covers several aspects: from de filter conception, to its characterization before and after its integration on the detector. Studies on demosaicing and inter-pixel crosstalk correction will also be presented. The last domain covered by this paper is a space assessment carried out on some assembly in order to check any weakness concerning space environment particularities.
Characterization of Tunneled Wide Band Gap Mixed Conductors: The Na2O-Ga2O3-TiO2 System

This article focuses on the Na2O-Ga2O3-TiO2 system, which is barely explored in the study of transparent conductive oxides (TCOs). NaxGa4+xTin−4−xO2n−2 (n = 5, 6, and 7 and x ≈ 0.7–0.8) materials were characterized using neutron powder diffraction and aberration-corrected scanning transmission electron microscopy. Activation energy, as a function of different structures depending on tunnel size, shows a significant improvement in Na+ ion conduction from hexagonal to octagonal tunnels. New insights into the relationship between the crystal structure and the transport properties of TCOs, which are crucial for the design and development of new optoelectronic devices, are provided.
Planning Experiments for Discriminating between Models

SUMMARY Results in the theory of optimum regression designs are used to develop experimental designs for discriminating between several regression models. An extension of the Kiefer-Wolfowitz general equivalence theorem is used to establish properties of the procedures. A sequential data-dependent strategy is described and its properties investigated for specific examples. In conclusion, some difficulties in the Bayesian analysis of discrimination experiments are considered.
Higher Education Admission Capacity Planning Using a Linearized Integer Goal Programming Model

This paper proposes a linearized integer goal programming model (LIGPM) for solving the general problem of admission capacity planning at the national and/or institutional level and for a given long-term planning horizon. The model aims to satisfy the required key objectives of the Ministry of Education and/or 5-year national plan related to the higher education enrolment problem. The model is described in a general form that can be adopted for any country taking into considerations its special requirements. The national plans for higher education together with relevant data for Saudi Arabia are used as a case study. Many relative objectives of national admission capacity planning have been taken into consideration. The considered objectives are as follows: Annual growth rate for enrolled students, Percentage of the total number of students enrolled in science and engineering programs, Percentage of total enrolment in higher education regardless of age to the total population in the age group of 18-23 years, Accepted percentage in higher education from high school graduates, Percentage of the total number of students in each discipline of education to the total faculty members, Percentage of the total number of girls to the total number of boys in higher education, Not violating the available resources, Annual growth rate for graduated students and Ratio of graduated students to those enrolled 5 years ago.
Approximation of controls for linear wave equations: A first order mixed formulation

This paper deals with the numerical approximation of null controls for the wave equation posed in a bounded domain of \begin{document}$ \mathbb{R}^n $\end{document} . The goal is to compute approximations of controls that drive the solution from a prescribed initial state to zero at a large enough controllability time. In [Cindea & Munch, A mixed formulation for the direct approximation of the control of minimal \begin{document}$ L^2 $\end{document} -norm for linear type wave equations], we have introduced a space-time variational approach ensuring strong convergent approximations with respect to the discretization parameter. The method, which relies on generalized observability inequality, requires \begin{document}$ H^2 $\end{document} -finite element approximation both in time and space. Following a similar approach, we present and analyze a variational method still leading to strong convergent results but using simpler \begin{document}$ H^1 $\end{document} -approximation. The main point is to preliminary restate the second order wave equation into a first order system and then prove an appropriate observability inequality.
Pyrrolizidine Alkaloids in Food on the Italian Market

Pyrrolizidine alkaloids (PAs) are secondary metabolites produced by over 6000 plant species worldwide. PAs enter the food chain through accidental co-harvesting of PA-containing weeds and through soil transfer from the living plant to surrounding acceptor plants. In animal studies, 1,2-unsaturated PAs have proven to be genotoxic carcinogens. According to the scientific opinion expressed by the 2017 EFSA, the foods with the highest levels of PA contamination were honey, tea, herbal infusions, and food supplements. Following the EFSA’s recommendations, data on the presence of PAs in relevant food were monitored and collected. On 1 July 2022, the Commission Regulation (EU) 2020/2040 came into force, repealed by Commission Regulation (EU) 2023/915, setting maximum levels for the sum of pyrrolizidine alkaloids in certain food. A total of 602 food samples were collected from the Italian market between 2019 and 2022 and were classified as honey, pollen, dried tea, dried herbal infusions, dried herbs, and fresh borage leaves. The food samples were analyzed for their PA content via an in-house LC-MS/MS method that can detect PAs according to Regulation 2023/915. Overall, 42% of the analyzed samples were PA-contaminated, 14% exceeded the EU limits, and the items most frequently contaminated included dried herbs and tea. In conclusion, the number of food items containing considerable amounts of PAs may cause concern because they may contribute to human exposure, especially considering vulnerable populations—most importantly, children and pregnant women.
Data Analysis of Upper Atmosphere Temperature Detected by Sounding Rockets in China

AbstractSounding rockets launched by China have collected data on the upper atmosphere for nearly 50 years. In this work, the data accuracy and variable characteristics of upper atmosphere temperature data, gathered at heights of 20–60 km over Jiuquan, China, during 1974–2014, were analyzed. The relative accuracy of sounding rocket temperature data was determined by comparing the data with Mass Spectrometer and Incoherent Scatter (MSIS) model data by season, and with Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) from the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) satellite by year. The sounding rocket temperature data showed differences from MSIS in every season, with the minimum difference occurring in summer, the next smallest difference in winter, and the maximum difference occurring in autumn. The sounding rocket data showed smaller differences from the SABER data, although the deviation still fluctuated depending on the date and hour of the observati.
Functional data analysis in hyperspectral remote sensing : application to the study of agri-forest landscape

In hyperspectral imaging, each pixel is associated with a spectrum derived from observed reflectance in d measurement points (i.e., wavelengths). We are often facing a situation where the sample size n is relatively low compared to the number d of variables. This phenomenon called "curse of dimensionality" is well known in multivariate statistics. The mored increases with respect to n, the more standard statistical methodologies performances are degraded. Reflectance spectra incorporate in their spectral dimension a continuum that gives them a functional nature. A hyperspectrum can be modelised by an univariate function of wavelength and his representation produces a curve. The use of functional methods allows to take into account functional aspects such as continuity, spectral bands order, and to overcome strong correlations coming from the discretization grid fineness. The main aim of this thesis is to assess the relevance of the functional approach in the field of hyperspectral remote sensing for statistical analysis. We focused on the nonparametric fonctional regression model, including supervised classification. Firstly, the functional approach has been compared with multivariate methods usually involved in remote sensing. The functional approach outperforms multivariate methods in critical situations where one has a small training sample size combined with relatively homogeneous classes (that is to say, hard to discriminate). Secondly, an alternative to the functional approach to overcome the curse of dimensionality has been proposed using parsimonious models. This latter allows, through the selection of few measurement points, to reduce problem dimensionality while increasing results interpretability. Finally, we were interested in the almost systematic situation where one has contaminated functional data. We proved that for a fixed sample size, the finer the discretization, the better the prediction. In other words, the larger dis compared to n, the more effective the functional statistical methodis.
Einstien-Multidimensional Extrapolation methods

In this paper, we present a new framework for the recent multidimensional extrapolation methods: Tensor Global Minimal Polynomial (TG-MPE) and Tensor Global Reduced Rank Extrapolation (TG-RRE) methods. We develop a new approach to the one presented in \cite{17}. The proposed framework highlights, in addition their polynomial feature, the connection of TG-MPE and TG-RRE with nonlinear Krylov subspace methods. A unified algorithm is proposed for their implemention. Theoretical results are given and some numerical experiments on linear and nonlinear problems are considered to confirm the performance of the proposed algorithms.
A hybrid stochastic-deterministic gradient descent algorithm for image reconstruction in cone-beam computed tomography

There is a growing interest in algorithms for computed tomography (CT) reconstruction from a small number of projection measurements. In this paper, we propose an algorithm that is based on variance-reduced stochastic gradient descent (SGD). Variance-reduced SGD methods are a new class of stochastic optimization algorithms that have proved highly successful in solving large-scale optimization problems. These algorithms store a copy of the full gradient direction or stochastic gradient directions and use them in building update directions. We propose an algorithm for CT image reconstruction that starts off with variance-reduced SGD updates and gradually increases the batch size. As the algorithm approaches the solution and the batch size grows, the algorithm turns into a limited-memory quasi-Newton algorithm to exploit the curvature information in the vicinity of the solution. We apply the proposed algorithm on simulated and real cone-beam CT projections and compare it with several other algorithms. Our results show that the proposed algorithm is very fast and is able to reconstruct very high-quality images in a small number of iterations.
A parallel Navier–Stokes solver using spectral discretisation in time

ABSTRACT We investigate the performance of a multi-harmonic space–time approach to solve time-periodic flow problems. It employs a Fourier spectral discretisation of the time domain. The resulting large system of nonlinear equations is solved iteratively and in parallel. For illustration, a three-dimensional channel flow disturbed by an oscillating force is simulated.
Error Bounds for General Variational Inclusion Involving Difference of Operators

In this paper, we introduce some new classes of merit functio s f r general variational inclusion involving difference of two monotone operators. Using these merit functions, we obt ain the error bounds for the solution of the general variatio nal inclusion. Several special cases are also investigated. Results prove d in this paper continue to hold for these cases. Results obta ined in this paper can be viewed as significant contribution in this field and may otivate further research.
V-System and Expression of Geometric Group Information in Frequency Domain

The V-system is a new class of complete orthogonal functions system in L2(0,1), which is composed of piecewise kth-order polynomials. There are continuous functions as well as discontinuous functions in V-system. It can be used for signal processing and global expression of a geometric graph group. Moreover, the information of geometric modeling in CAGD can be reconstructed precisely by finite terms of V-system without Gibbs phenomenon, so global feature analysis of the complicated modeling can be implemented. This paper shows that 3-dimension complicated geometric model can be reconstructed by the V-system over triangulated domain. The experiment results indicate that V-system is an effective tool used to reconstruct complicated geometric information with both continuous and discontinuous signals. This is the essential difference among V-system, the classical complete orthogonal system with continuous functions and Walsh and Haar system which include intense
Bond Strength of Orthodontic Brackets to Temporary Crowns: In vitro Effects of Surface Treatment

Background: To evaluate bond strength of orthodontic brackets to temporary crowns. Materials and Methods: A bis-acrylic composite was used to create a total of 25 discs, which were then divided into five groups based on how their surfaces were treated with black, blue, green, and sandblasted diamond burs, in addition to a control group. Software called SPSS was used to analyze the outcome. Results: The average pressures measured for the green bur, black bur, and sandblasting, respectively, were 12.05 MPa, 12.87 MPa, and 19.25 MPa. Comparing the control and blue groups, a substantial variation in shear bond strength was only noticed with reference to sandblasting. Conclusion: The binding strength of orthodontic brackets is increased by sandblasting temporary crowns.