Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Cyber Attack Localization in Distribution Systems Using Digital Twin Reference Model

Identifying the locations of cyber attacks in power distribution systems is critical to take immediate and effective mitigation actions. This article introduces a novel real-time attack localization strategy for power distribution systems utilizing a Digital Twin (DT) as a cyber-physical real-time reference model that mimics the cyber-physical dynamic characteristics of the actual system. The proposed approach takes advantage of the DT-based reference model as well as the physical characteristics of the radial distribution network to compute a new metric, namely Residual Rate of Change (RRC), which allows differentiating False Data Injection (FDI) attacks on load measurements and protective devices. The RRC enables the determination of the attack's location by constructing a directed graph that characterizes the physical dependencies of protective devices. The proposed attack localization method is implemented on a testbed consisting of a digital real-time simulator, feeder protection devices, a real-time automation controller, and advanced communication infrastructure for a power distribution system. The provided experimental results demonstrate the capability of the proposed method to localize cyber attacks in various operation scenarios of the power distribution system. The results also illustrate that the introduced method outperforms a learning-based strategy in localizing different types of attacks in the test system.
On The Block Decomposition and Spectral Factors of {\lambda}-Matrices

In this paper we factorize matrix polynomials into a complete set of spectral factors using a new design algorithm and we provide a complete set of block roots (solvents). The procedure is an extension of the (scalar) Horner method for the computation of the block roots of matrix polynomials. The Block-Horner method brings an iterative nature, faster convergence, nested programmable scheme, needless of any prior knowledge of the matrix polynomial. In order to avoid the initial guess method we proposed a combination of two computational procedures . First we start giving the right Block-Q. D. (Quotient Difference) algorithm for spectral decomposition and matrix polynomial factorization. Then the construction of new block Horner algorithm for extracting the complete set of spectral factors is given.
Dietary artemisinin boosts intestinal immunity and healthy in fat greenling (Hexagrammos otakii)

Introduction Artemisinin (ART) is very common as a diet additive due to its immunoregulatory activities. Nonetheless, the immunoregulatory mechanism of ART in marine fish remains unknown. This study comprehensively examined the effects and explored the potential mechanism of ART ameliorating intestinal immune disease (IID) in fat greenlings (Hexagrammos otakii). Methods and results The targets of ART were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Here, eight putative targets of ART were collected and identified with the Uniprot database, and 1419 IID-associated target proteins were filtered through the Drugbank, Genecards, OMIM, and PHARMGKB Databases. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways point out that ART may have immunoprotective effects by regulating cellular responses to stress, hypoxia, inflammation, and vascular endothelial growth factor stimulus through the hypoxia-inducible factor 1 (HIF-1) signaling pathway. The findings of molecular docking indicated that ART contains one active ingredient and three cross-targets, which showed a kind combination with hypoxia-inducible factor 1-alpha (HIF1-a), transcription factor p65 (RELA), and vascular endothelial growth factor A (VEGF-A), respectively. Furthermore, an ART feeding model was established to assess the ART’s immunoprotect effect on the intestine of H.otakii in vivo. The D48 group showed smaller intestinal structural changes after being challenged by Edwardsiella tarda. The supplementation of ART to the diet improved total superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) and reduced the malondialdehyde (MDA) in intestine of H. otakii. The expression of transcription factor p65, HIF1-α, VEGF-A, cyclin D1, matrix metalloprotease 9 (MMP9), monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) was decreased after dietary ART in the intestinal of H. otakii. Discussion The present results demonstrated that dietary ART improved antioxidants and immunity, optimized the intestinal structure, and increased resistance to E. tarda through the SOD2/nuclear-factor-kappa- B (NFkB)/HIF1-a/VEGF-A pathway in the intestinal tract of H.otakii. This study integrated pharmacological analysis and experimental validation and revealed the mechanism of ART on IID, which provides insight into the improvement of IID in H. otakii.
SNR Impact of Magnetic Capping Layer in Granular FePt-L10 Media

In this work, we present a micromagnetic modeling study on a scheme to enhance the medium signal-to-noise ratio (SNR) in heat-assisted magnetic recording (HAMR). The micromagnetic model that incorporates the perpendicular L10 ordering with the Fe/Pt superlattice is employed in this study, which enables the modeling of media with separate magnetic properties for each individual layer. By adding a relatively thin Co-based magnetic capping layer on top of FePt-L10 granular thin film, the simulation study finds that the recording performance can be significantly improved, especially at high linear densities, provided the capping layer has significantly higher Curie temperature than that of FePt-L10 material. The study also found that the enhancement of adding the capping layer reaches saturation at 2 nm thickness. The SNR gain also increases monotonically as the capping layer anisotropy field decreases from 3 to 10 kOe.
Convective processes in higt resolution models: impact of the lead time of the simulation

This work is funded by the Spanish government through grant BES-2016-078158 and MINECO/FEDER co-funded projects INSIGNIA (CGL2016-79210-R) and MULTI-SDM (CGL2015-66583-R). UCAN simulations have been carried out on the Altamira Supercomputer at the Instituto de Fisica de Cantabria (IFCA, CSIC-UC), member of the Spanish Supercomputing Network.
Global stabilisation of discrete‐time linear systems using event‐triggered bounded controls

This study deals with the problem of global stabilisation of discrete-time linear systems using event-triggered bounded controls. The system dynamics is decomposed into single input subsystems, which explicitly display various dynamic characteristics. Event-triggered bounded control laws, both of the state feedback type and the output feedback type, are constructed based on the dynamics of the subsystems. Event-triggering strategies are designed for updating the control input of the two control laws, respectively. Global asymptotic stabilisation is shown to be achieved under these control laws. Numerical simulation is given to verify the effectiveness of the theoretical results.
Comparative evaluation of the protective effects of garlic and ginger against cisplatin induced-nephrotoxicity in a rat model.

Keywords Cisplatin is one of the most frequently used drugs for cancer treatment. However, its use declined due to its dangerous side effects including nephrotoxicity. The aim of this study is to examine the garlic and/or ginger ameliorative impact on cisplatin-caused nephrotoxicity in rats. Seven groups of rats (n=10) were used. Group I was administered 0.5 ml normal saline, group II was administered garlic (200 mg /kg), and Group III was administered ginger (310 mg/kg) orally once time per day for 21 consecutive days. Group IV was administered 0.5 ml normal saline orally once daily and injected cisplatin on 16 th day (10 mg/kg, intraperitoneal). Group V was received garlic plus cisplatin, group VI was administered ginger plus cisplatin, and group VII was received garlic and ginger plus cisplatin. Twenty-four hours following the last dose, the blood and tissue samples were collected for the biochemical, antioxidant, and histopathological examinations. Administration of cisplatin significantly increased serum urea level, creatinine level, and uric acid concentration. Furthermore, cisplatin significantly increased MDA level and induced a significant decrease in CAT, GSH, and SOD levels in renal tissue. Histologically, cisplatin induced marked degeneration of the renal tubular epithelium with lymphocytic cellular infiltration of the renal parenchyma. Treatment with ginger and/or garlic restores the levels of renal biochemical markers towards normalcy, improves oxidative stress and histological picture.
In search of a democratic school culture: an analysis from the lenses of critical realism

ABSTRACT Critical realism is a valuable philosophical stance in providing an in-depth analysis of multi-dimensional and multi-layered relationships in social science. Considering the scarcity of studies on this philosophical framework in social science, this study aimed, from multiple viewpoints, to examine the multi-faceted and multi-stratified experiences, events, structures, and mechanisms underlying democratic school climate in primary school education in Northern Cyprus, through a critical realist philosophical stance. The analysis of the data obtained from teachers, headteachers, and students in five state schools revealed that certain factors limited the opportunities for achieving a democratic school culture. The inadequacy of the education system manifested itself in various forms at the empirical level and therefore affected the climates of the participating schools. The fragmented governance, the pressure of the college entrance examination, and the lack of integration of the newly arrived migrants were found to be influencing the climate in the participating schools.
Communication Base Station Site Planning Based on Improved Simulated Annealing Algorithm

With the sharp development of mobile communication technology, the coverage area of existing base stations cannot meet the increasing demand of users, so it is significant to establish a suitable model to reasonably plan the new base station site with the objective of meeting the service volume of weak coverage points. In this paper, we reduce redundancy in urban weak coverage data. Initially, existing data is preprocessed and weak coverage points near existing base stations are removed to avoid duplication. A nonlinear programming model is then created, considering over 90% coverage and minimizing construction costs. We employ a simulated annealing algorithm to determine the number of new base stations needed. After rigorous analysis, our optimal solution suggests deploying 131 micro and 19 macro base stations, with a total cost of 321.
Robust Optimal Control of Uncertain Nonlinear Switched System using Approximate Dynamic Programming

— This paper presents robust optimal control of an uncertain nonlinear switched system with forced subsystems. The uncertainties include external disturbance and parametric uncertainties. Switching signal and control input are designed to minimize a given cost function. Approximate dynamic programming (ADP) has been efficiently applied to certain switched systems as an optimal control strategy. Since approximate dynamic programming method is model based, there would seem to be some difficulties to apply approximate dynamic programming to uncertain switched system. To overcome these mentioned problems, this paper presents an appropriate model. In order to apply proposed control approach, robust time-delay controller is added with ADP control. At first uncertainties are compensated by robust time-delay controller. Then the switching signal and the control input are design by approximate dynamic programming that provides a feedback solution for unspecified initial conditions. The discussing boundedness of states and simulation results verify the effectiveness of the proposed control approach.
Research on Adaptive Control Method of Quadruped Robot's Load

In order to improve the load adaptability of quadruped robot in varied topography, a load estimation and adaptive control method of quadruped robot is studied. First, this paper sets up mechanical balance equation to achieve weight estimation of the load, moreover, it builds the desired body action force and torque based on the virtual model control method, integrates the actual force information of the ground to calculate the torque error of body action, obtains the center-of-mass deviation in combination with the load weight, and builds the optimal control strategy of force distribution. Finally, the feasibility of the method is verified experimentally, and the fused estimated center-of-mass and mass information can be used for effective robot position and attitude control.
Application of the Finite Cell Method to Tunnel Engineering Simulation

The Finite Cell Method (FCM) is an embedded domain method, in which the simulation domain is divided into a physical domain and a fictitious domain. The adaptive quadrature scheme employs the quad‐tree (oct‐tree in 3D) data structure to generate the quadrature points up to the specified level around the material boundary. In this paper, the FCM is applied for machine driven tunnel advancement simulations [1]. The geometry of the excavation volume is embedded into a background mesh by employing the B‐Rep concept. The mesh for the soil volume is structured and is fixed during the tunnel advancement. The tunnel lining is embedded in the soil mesh as a surface mesh, which incorporates the shell element formulation. In terms of the material description, the soil is represented as a saturated two‐phase medium [2], which consists of the soil matrix and the pore water. The effective stress of the soil is governed by means of the elastoplastic Mohr‐Coulomb constitutive model. Selected numerical benchmarks are performed to demonstrate the performance of the proposed method.
Observer-based H∞ finite-time control for switched linear systems with interval time-delay

In this work, interval time-delay switched systems having completely unstable and mixed stable matrices of the state vector are considered. An observer-based controller is designed for finite-time boundedness and H∞-control of these systems. New sufficient conditions on the existence of a desired observer are developed and new average dwell-time bounds are introduced separately in case of unstable and mixed stable subsystems. An algorithm is presented for the calculation of unknown constants in the average dwell-time bounds which depend on nonlinear matrices in terms of the cone complementarity linearization method. Finally, numerical examples are given for the effectiveness and validity of the proposed solutions.
An Algorithm for Symbolic Computing of Singular Limits of Dynamical Systems

An Algorithm for Symbolic Computing of Singular Limits of Dynamical Systems Dane Jordan Bjork Department of Computer Science, BYU Master of Science The manifold boundary approximation method, MBAM is a new technique used in approximating systems of equations using parameter reduction. This method and other approximation methods are introduced and described. Several current issues in performing MBAM are discussed in further detail. These issues significantly slow down the process of MBAM and create a barrier of entry for those wishing to use the method without a strong background in mathematics. A solution is proposed to automatically reparameterize models and evaluate specific types of variables approaching limits – significantly speeding up the process of MBAM. An implementation of the solution is discussed.
A New Perspective on SPME and SPME Arrow: Formaldehyde Determination by On-Sample Derivatization Coupled with Multiple and Cooling-Assisted Extractions

Formaldehyde (FA) is a toxic compound and a human carcinogen. Regulating FA-releasing substances in commercial goods is a growing and interesting topic: worldwide production sectors, like food industries, textiles, wood manufacture, and cosmetics, are involved. Thus, there is a need for sensitive, economical, and specific FA monitoring tools. Solid-phase microextraction (SPME), with O-(2,3,4,5,6-pentafluorobenzyl)-hydroxylamine (PFBHA) on-sample derivatization and gas chromatography, is proposed for FA monitoring of real-life samples. This study reports the use of polydimethylsiloxane (PDMS) as a sorbent phase combined with innovative commercial methods, such as multiple SPME (MSPME) and cooling-assisted SPME, for FA determination. Critical steps, such as extraction and sampling, were evaluated in method development. The derivatization was performed at 60 °C for 30 min, followed by 15 min sampling at 10 °C, in three cycles (SPME Arrow) or six cycles (SPME). The sensitivity was satisfactory for the method’s purposes (LOD-LOQ at 11-36 ng L−1, and 8-26 ng L−1, for SPME and SPME Arrow, respectively). The method’s linearity ranges from the lower LOQ at trace level (ng L−1) to the upper LOQ at 40 mg L−1. The precision range was 5.7–10.2% and 4.8–9.6% and the accuracy was 97.4% and 96.3% for SPME and SPME Arrow, respectively. The cooling MSPME set-up applied to real commercial goods provided results of quality comparable to previously published data.
Data reconciliation of nonnormal observations with nonlinear constraints

ABSTRACT This paper presents a new method for the reconciliation of data described by arbitrary continuous probability distributions, with the focus on nonlinear constraints. The main idea, already applied to linear constraints in a previous paper, is to restrict the joint prior probability distribution of the observed variables with model constraints to get a joint posterior probability distribution. Because in general the posterior probability density function cannot be calculated analytically, it is shown that it has decisive advantages to sample from the posterior distribution by a Markov chain Monte Carlo (MCMC) method. From the resulting sample of observed and unobserved variables various characteristics of the posterior distribution can be estimated, such as the mean, the full covariance matrix, marginal posterior densities, as well as marginal moments, quantiles, and HPD intervals. The procedure is illustrated by examples from material flow analysis and chemical engineering.
Multi-user Power Load Forecasting Based on K-means and Deep Neural Network

Short-term power load forecasting is an important issue in power system management and planning, and is of great importance for enhancing the efficiency and stability of the power system. Traditional short-term power load forecasting systems are mostly based on historical load data of a single region or user which cannot meet the demand for load forecasting of multiple users with different electricity consumption characteristics. This paper proposes a method that integrates big data distributed computing, clustering algorithms, and deep neural networks(DNN) for preprocessing, feature extraction, classification and prediction of multi-user power load data. Firstly, we use the Spark distributed computing engine to preprocess and extract features from historical load data. Then, we use the K-means clustering algorithm to classify users into different classes based on their characteristics. Finally, we design deep neural network models for different categories, then we train, validate, and test the models with a real user load dataset from a region of China. The results show that the proposed method has high accuracy and can meet the demand for multi-user power load forecasting.
Combining Unsupervised and Supervised Deep Learning for Alzheimer's Disease Detection by Fractional Anisotropy Imaging

We propose a new approach for Alzheimer's disease (AD) detection using diffusion tensor imaging, specifically fractional anisotropy (FA) images, based on a combination of unsupervised and supervised deep learning techniques. Our method involves training a 3D convolutional autoencoder to learn low-dimensional representations of FA images in an unsupervised manner and using the learned representations to pre-train a supervised 3D convolutional classifier to predict the presence or absence of AD. Unsupervised pre-training can improve the classifier's performance, especially when difficult-to-collect labeled data are limited. We evaluate our approach on the OASIS-3 dataset and demonstrate promising performance.
Accurate Segmentation of Skin Injury Based on Multimodal Attention Mechanism

Medical image segmentation can separate relevant important features from medical images and provide reliable basis for clinical diagnosis. In medical images, some parts are embedded in each other, and there is a problem of blurred borders or margins. Because of the difference of skin lesions in size, location, texture and color, it has become an important challenge in image segmentation in medical field. The multi-modal attention mechanism proposed in this paper enhances the recognition ability of the network by incorporating the channel attention mechanism, enriches the feature information at different scales, improves the accuracy of segmentation, and realizes the accurate segmentation of skin injury areas in dermatological medical images. Compared with many methods, our method achieves excellent performance.
Analysis of STEAM Content on the Module of Pancasila Student Profile Strengthening Project (P5) Based on Sasak Heritage

This study aims to analyze the STEAM content on the P5 module-based on Sasak Heritage. This research uses content analysis research and literature review. Content analysis was used to analyze the integration between STEAM and P5 in the modules that have been developed. Furthermore, this analysis was also strengthened by conducting a literature review on the STEAM topic from articles in international journals published starting in 2017-2022. The articles analyzed were 7 articles. The results obtained are that STEAM content at the P5 module stage can be made in 5 ways in implementing STEAM, namely by inter-discipline STEAM, several STEAM disciplines, as well as all STEAM disciplines at each stage of the planned project. STEAM integration can also be carried out starting at the basic education unit level with the application of P5
Does Attitudes Towards Money Matter? A Mediational Analysis of Job Crafting and Job Satisfaction in School Teachers

The current study aimed to find out relationship between job crafting and job satisfaction in teachers and also to determine whether a teacher's attitude towards money had a mediating role between job satisfaction and job crafting. The following hypotheses were proposed: a) job satisfaction, attitude towards money and co-variances would likely have a significant relationship b) attitude towards money would likely significantly predict job satisfaction; and c) attitude towards money would likely act as a mediator between job crafting and job satisfaction. The sample consisted of 150 school teachers (N=150) with an age range of 20 to 55 years employed via non-probability purposive sampling approach. Assessment measures included Job Crafting Questionnaire (Slemp & Vella-Brodrick, 2013), Money Attitude Questionnaire (Lay & Furnham, 2018) and Job Satisfaction Scale (Macdonald & Macintyre, 1997) with . 91, . 91 and . 89 alpha reliability coefficient respectively. The data was collected online via Google form from the potential participants. Results revealed that the association between money attitude and job satisfaction is not correlated, however, there is a highly significant and positive relationship between job crafting and satisfaction with one's job. Job crafting exhibited a favorable and highly significant prediction of job satisfaction, however, money attitude has no significant results and so do not suggest any prediction of job satisfaction. Moreover, the link between job satisfaction and money attitude was completely mediated by the money attitude. For young adults, researchers, counselors, and educational psychologists; these indigenous results would have substantial implications for awareness, policy making, and future research endeavors.