Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Privacy-Preserving Consensus Strategy for AC Microgrids Based on Output Mask Approach and Node Decomposition Mechanism

The technology of distributed cooperative consensus has been widely used in inverter-based microgrids. However, the traditional information interaction will lead to the problem of data disclosure. Although privacy issues have been widely researched in the tertiary control layer of microgrids, the issues of secondary control layer have not been properly addressed. To fill this gap, taking voltage restoration as the object, the privacy preserving consensus problem for secondary control layer of nDGs islanded microgrids is considered in this paper. Output mask approach, the basic idea of which is to insert a dynamic mask to the transmitted information, is adopted to achieve an accurate consensus rather than a pre-specified convergence accuracy level by differential private method. After that, a newly state decomposition mechanism is proposed, the DG agent is decomposed into two subagents, called <inline-formula> <tex-math notation="LaTeX">$x^{\alpha} _{i}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$x^{\beta} _{i}$ </tex-math></inline-formula>. Among the agent pairs <inline-formula> <tex-math notation="LaTeX">$(x^{\alpha} _{i}, x^{\beta} _{i})$ </tex-math></inline-formula>, choose only one subagent of pair <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> arbitrarily connected to one subagent of its neighbor agent pair. Furthermore, only the communication lines among the agent pairs are masked. Compared with the existing literature, the constraint on the communication topology can be first removed without additional computational burden. Moreover, the consensus and privacy analysis of the controlled system are carried out. In the end, the simulation results verify the effectiveness of the proposed privacy preserving mechanism in the MATLAB/Simulink environment. Note to Practitioners—With the rapid development of information and intelligent technology, the information system and the physical system of MGs have been deeply integrated to get a higher efficiency of energy utilization. However, the traditional information interaction will lead to the problem of data disclosure. Although privacy issues have been widely researched in the tertiary control layer of microgrids, the issues of secondary control layer have not been properly addressed. Motivated by this, an improved privacy-preserving consensus strategy is proposed for secondary voltage control of islanded microgrids based on a newly node decomposition mechanism and the output mask method in this paper. And the theoretical results of this paper can be easily extended to other multi-agent systems.
Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.
A comparative study for parameter estimation of the Weibull distribution in a small sample size: An application to spring fatigue failure data

Abstract The Weibull distribution is the most widely applied model in reliability analysis. The main objective of this paper is to present a simple method called the minimum discrepancy method that is applicable to both complete and censored data for the parameter estimation of the Weibull distribution and a detailed comparison in a small sample size of thirteen methods in terms of several criteria by a simulation study. Additionally, parameter estimation methods are applied to the spring fatigue failure data. By extensive simulations and comparisons, the generalized least square 1, the weighted least square 1, the weighted Maximum likelihood estimation and the minimum discrepancy method are recommended for parameter estimation with small samples.
Comparisons of Image Classification Using LBP with CNN and ANN

In image classification techniques CNN (convolutional neural networks) has a strong feature extraction capability, which has been used to extract the feature from different class image datasets. Various techniques have been applied to compare the images and to gain the accuracy level. Convolutional neural network and many other algorithms can be for feature extraction like Decision tree, K-Nearest neighbor, and nearest clustering algorithms. In this paper, we performed image classification using CNN and an artificial neural network (ANN) with local binary pattern (LBP) feature extraction technique. One-dimensional (1-D) CNN is implemented to process the feature extraction. The network is trained using the Kaggle dataset of birds and airplanes. The trained classifier model can classify the images into either birds or airplanes. The experimental outcome shows that the proposed technique can provide 77.50 and 62.42 classification accuracy, and the proposed method can also achieve better performance using different datasets.
Do you see what I see? Using questions and answers to align representations of robotic actions

: We introduce the task of learning to answer questions about a robotic agent’s past actions using natural language alone. Humans represent much of our experience using language so training agents to answer questions about their experiences is one way of ensuring that humans and robotic agents see the world in similar ways. We develop a method to automatically generate questions and answers about objects, actions, and the temporal order in which they appeared in particular episodes by making use of an existing dataset and virtual environment. We then combine a convolutional network with a pretrained language transformer model to answer the questions from multimodal input, viz. questions embedded in the transformer model’s embedding space concatenated with egocentric video frame features. The model achieves a high level of accuracy (90%+ accuracy on thirteen of eighteen evaluation sets).
An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor

This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties.
A Maturity-Based Adaptive Ant Colony Optimization Algorithm

In this paper, for the problems of low convergence rate and getting trapped in local optima easily, the average path similarity (APS) was proposed to present the optimization maturity by analyzing the relationship between parameters of local pheromone updating and global pheromone updating, as well as the optimizing capacity and convergence rate. Furthermore, the coefficients of pheromone updating adaptively were adjusted to improve the convergence rate and prevent the algorithm from getting stuck in local optima. The adaptive ACS has been applied to optimize several benchmark TSP instances. The solution quality and convergence rate of the algorithm were compared comprehensively with conventional ACS to verify the validity and the effectiveness.
Paternal obesity induces epigenetic aberrations and gene expression changes in placenta and fetus

Paternal epigenome regulates placental and fetal growth. However, the effect of paternal obesity on placenta and its subsequent effect on the fetus via sperm remains unknown. We previously discovered abnormal methylation of imprinted genes involved in placental and fetal development in the spermatozoa of obese rats. In the present study, elaborate epigenetic characterization of sperm, placenta, and fetus was performed. For 16 weeks, male rats were fed either control or a high‐fat diet. Following mating studies, sperm, placenta, and fetal tissue were collected. Significant changes were observed in placental weights, morphology, and cell populations. Methylation status of imprinted genes—Igf2, Peg3, Cdkn1c, and Gnas in spermatozoa, correlated with their expression in the placenta and fetus. Placental DNA methylating enzymes and 5‐methylCytosine levels increased. Furthermore, in spermatozoa, DNA methylation of a few genes involved in pathways associated with placental endocrine function—gonadotropin‐releasing hormone, prolactin, estrogen, and vascular endothelial growth factor, correlated with their expression in placenta and fetus. Changes in histone‐modifying enzymes were also observed in the placenta. Histone marks H3K4me3, H3K9me3, and H4ac were downregulated, while H3K27me3 and H3ac were upregulated in placentas derived from obese male rats. This study shows that obesity‐related changes in sperm methylome translate into abnormal expression in the F1‐placenta fathered by the obese male, presumably affecting placental and fetal development.
From primal sketch to 2.1D sketch with contour reorganization

This paper proposed a model for 2.1D sketch based on primalsketch [6,7] and a corresponding algorithm based on SWC(Swendsen-Wang Cut)[1,2]. Given a primal sketch graph, all boundaries' color and curvature information can be gotten from the dictionary of image primitives of the primal sketch. Suppose all curves in the sketch graph can be combined into closed contours and then contour can be combined into regions. Calculating boundaries similarity energy of two regions by comparing the comparability using the color and curvature information of boundaries, then the energy is regarded as the proposal of SWC. All regions are regarded as nodes of graph space, then the 2.1D sketch problem can be looked as graph partition problem. In each step, all curves will be reorganized into contours and then contours generate regions. Some experiment results based on above model and algorithm are given in the paper.
Scanning Hall Probe Microscopy with Shear Force Distance Control

We describe a new type of scanning Hall probe microscope operating at room temperature for quantitative and noninvasive measurements of magnetic stray fields. The probe-sample distance is controlled by piezoelectrical detection of the shear forces acting on an oscillating cantilever. The Hall probes are manufactured from prepatterned GaAs wafers overgrown with a GaAs/AlGaAs heterostructure containing a high-mobility two-dimensional electron gas a few ten nm below the surface. The active Hall area is defined by optical and electron-beam lithography with a junction width of 0.6 μm yielding in a resolution of approximately 0.4 μm. The Hall coefficient of the sensor at room temperature is 0.23 Ω/G with a noise level of ∼0.1 G/Hz1/2. We show measurements of the stray field pattern of bits written on a magnetic hard disk.
Nonlinear stimulated Brillouin scattering in inhomogeneous plasmas

The combined effects of ion-acoustic nonlinearity and plasma inhomogeneity on a stimulated Brillouin scattering (SBS) are considered in the model of coupled Korteweg–de Vries (KdV) and electromagnetic wave equations. In the regime of linear ion-acoustic waves the results are in good agreement with the existing linear theories of SBS and demonstrate the regimes of both convective and absolute instabilities. New features of SBS saturation due to ion-acoustic nonlinearity and pump depletion in inhomogeneous plasmas are found. They include the scaling for saturated reflectivity, the transition from convective to absolute growth, and the temporal pulsations of the reflectivity coefficient. The autoresonance process is analyzed and its relevance to SBS for realistic plasma parameters is discussed.
DEEP LEARNING APPROACH TO CLASSIFYING SOLAR RADIO BURSTS

Solar radio bursts have a profound effect on the environment and the technological infrastructure of society. Due to the effects that solar radio bursts have, it is important to be able to correctly classify them in an efficient and timely manner. Recently, there has been great success in object classifying by using convolutional neural networks. Therefore, this paper attempts to classify type II, and type III solar radio bursts, and spectrograms containing no bursts, using a convolutional neural network. An in-depth study was conducted to understand the nature of type II and type III bursts and how convolutional neural networks can be implemented to classify these bursts. Data samples, in the form of FITS files, were collected from the e-Callisto network and processed into images. A convolutional neural network was created with the Python packages TensorFlow and Keras. This model was then trained and tested using the only raw data collected from the e-Callisto network. While the model was not able to reach a very high accuracy the paper shows that such a model can make correct predictions.
High-Efficiency Automated Nanomanipulation With Parallel Imaging/Manipulation Force Microscopy

The atomic force microscope (AFM) has been widely used to manipulate nanoparticles, nanowires, and nanotubes for applications such as nanostructure building, nanocharacterization, and biomanipulation. However, conventional AFM-based nanomanipulation is inefficient because of the serial scan-manipulation-scan process involved. In this paper, high-efficiency automated nanomanipulation with a parallel imaging/manipulation force microscope (PIMM) is presented. With the PIMM, image scan and nanomanipulation can be performed in parallel through the collaboration between two cantilevers: one cantilever acts as an imaging sensor and the other is used as a manipulating tool. Two automated manipulation schemes were introduced for normal- and high-speed image scanning, respectively. An automated parallel manipulation task is managed by system control software with multithread through a procedure of dynamic image processing, task planning, two-tip collaboration, and a controlled pushing manipulation with amplitude feedback from the cantilevers. The efficiency of automated parallel nanomanipulation with normal-speed image scanning was validated by building nanoparticle patterns.
Electrostatic turbulence and transport in a simple magnetized plasma

Gradient driven electrostatic instabilities are investigated in TORPEX [A. Fasoli, B. Labit, M. McGrath, S. H. Muller, M. Podesta, and F. M. Poli, Bull. Am. Phys. Soc. 48, 119 (2003)], a toroidal device (R=1m, a=0.2m) in which plasmas are produced by microwaves (P⩽20kW) with frf=2.45GHz, in the electron cyclotron frequency range. Typical density and temperature are ne⩽1017m−3 and Te≃5eV, respectively. The magnetic field is mainly toroidal (⩽0.1T), with a small vertical component (⩽4mT). Instabilities that can be generally identified as drift-interchange waves are observed and characterized for different levels of collisionality with neutrals. The frequency spectrum and the spatial profile of the fluctuation-induced flux are measured. An 86-tip probe is used to reconstruct the spatio-temporal evolution of density structures across the plasma cross section. The measured structures are characterized statistically, and related quantitative observables are constructed.
Agronomic practices for sustainable diseases management in rice: A review

Rice is globally the most important food crop and there is a dire need to feed the ever-increasing population by improving its productivity. It has been realised that diseases are the major impediment towards enhancing the productivity of this crop. Despite the advent of modern effective disease control measures such as use of chemicals, bioagents and resistant varieties; agronomic practices still play a vital role in disease management in rice. Optimum use of different agro-techniques can be exploited for efficient control of various devastating diseases like rice blast, sheath blight, bakanae and many more by providing a favourable environment to better crop survival. Besides, appropriate selection of a variety, use of quality seed, method of establishment, planting time, nutrient, water and weed management practices can be well exploited to control various diseases. This manuscript entails to review the work pertaining to use of agronomic practices for exploiting the potential of crop environment interaction through reduced disease infection and to bridge the yield gap for ensuring sustained food security.
Personal Values as Regulators of Teacher Burnout

The aim was to identify the means of correction and prevention of burnout syndrome in college teachers who switched to online teaching during the period of quarantine caused by the COVID-19 pandemic. Descriptive and experimental research designs were used to achieve the aim. Causal relationships were defined using one-way analysis of variance (ANOVA) and correlational analysis. The Fisher’s angular transform criterion and the Mann–Whitney U test (STATISTICA 10.0) were used to determine the psychological characteristics of teachers with different levels of burnout syndrome. Boyko’s Emotional Burnout Inventory and Rokeach Value Survey (RVS) were used for diagnosis. The practical recommendations for college administrations were developed based on the causal relationships revealed in the results. It was found that the system of personal values could be an effective regulator of the personal state in difficult situations. © 2022, North American Business Press. All rights reserved.
Large-eddy simulations of 90° pipe bend flows

Oscillatory phenomena in separated turbulent flows through 90° pipe bends are numerically investigated using large-eddy simulations. Curvature ratios of one and three pipe diameters are considered and the Reynolds numbers range from 5000 up to 27 000. The analysis of the simulations shows that the results are in good agreement with the experimental data. Spectra of the overall forces exhibit a distinct peak in the high-frequency range, related to vortex shedding at the inner side of the bends, and show a low-frequency oscillation at higher Reynolds numbers, which is due to an alternating domination of one of the Dean vortices. The Strouhal number of the low-frequency oscillation matches the so-called swirl switching frequency earlier found in experimental measurements. This article was chosen from selected Proceedings of the Eighth European Turbulence Conference (Advances in Turbulence VIII (Barcelona, 27-30 June 2000) (Barcelona: CIMNE) ed C Dopazo. ISBN: 84-89925-65-8).
Fitting probabilistic automata via the em algorithm

Probabilistic automata (PA) or stochastic sequential machines are very popular, because they are general enough to approximate an arbitrary process and at the same time they posses an attractive property of conditional independence which simplifies their applications. Therefore, it is important to develop an algorithm for approximating an arbitrary stochastic process with the PA. Standard fitting techniques are not robust and are difficult to implement, because of the large number of model parameters. The EM algorithm proved to be robust for maximum likelihood parameter estimation from experimental data of special cases of PA. We demonstrate that the EM algorithm can be used for fitting the PA to an arbitrary stochastic process if Kullback-Leibler discrimination information is used as a distance between the processes. We show that the EM algorithm can be used to calculate derivatives which allows us to combine it with other optimization algorithms. We develop also the modified forms of the algorithm for f.
The structure of regulated functions

It is shown that there is a nontrivial class of regulated functions each of which is a representable as the sum of a continuous function and a uniformly convergent series of jump functions whose jumps are those of the given function. The set of regulated functions is the union of the classes of functions of bounded -variation for convex $. The regulated functions on a closed interval are those functions whose right and left limits exist at each point. Every regulated function is bounded, has a countable set of discontinuities, and is the limit of a uniformly convergent sequence of step functions. The regulated functions are of importance in the theory of stochastic processes and in the theory of everywhere convergence of Fourier series. Functions of bounded variation are regulated, as are the functions of bounded 4>-variation (1) and the functions of bounded A-variation (3). In §1 we shall show that each regulated function is of bounded -variation for some 4>. A function of bounded variation has a canonical representation as the sum of a continuous function of bounded variation and a sum of jump functions, the jumps being those of the given function of bounded variation. In §2 we shall investigate the possibility that a regulated function have a representation as the sum of a continuous function and a uniformly convergent series of jump functions whose jumps are those of the given function. We shall see that, although we can characterize a nontrivial class of functions for which this representation is possible, we can construct functions which have no such representation. 1. Let (0) = 0, $(x) > 0 for x > 0. A function / defined on an interval / is said to be of bounded $- variation ( - BV) if the «^-variation of/,
Principles of Igneous and Metamorphic Petrology

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.