Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly sensitive proximity and tactile sensor based on composite with dielectric elastomer and carbon microcoils

This work presents a dual purpose sensor for collecting proximity and tactile information by using a composite with dielectric elastomer (DE) and Carbon Micro Coils (CMC). CMC is a coil-like carbon microstructure with the size of several hundred micrometers, and its electrical characteristics change with the distance between the object or via physical contact. Especially, the impedance change of the composite depending on the distance can be used as the principle for proximity sensing. We present a method to process the materials by using dielectric materials and additives. A prototype of the sensor is fabricated and its feasibility is experimentally validated.
Personal continuous route pattern mining

In the daily life, people often repeat regular routes in certain periods. In this paper, a mining system is developed to find the continuous route patterns of personal past trips. In order to count the diversity of personal moving status, the mining system employs the adaptive GPS data recording and five data filters to guarantee the clean trips data. The mining system uses a client/server architecture to protect personal privacy and to reduce the computational load. The server conducts the main mining procedure but with insufficient information to recover real personal routes. In order to improve the scalability of sequential pattern mining, a novel pattern mining algorithm, continuous route pattern mining (CRPM), is proposed. This algorithm can tolerate the different disturbances in real routes and extract the frequent patterns. Experimental results based on nine persons’ trips show that CRPM can extract more than two times longer route patterns than the traditional route pattern mining algorithms.
The symmetry of the electron-electron interaction operator in the dipole approximation

The operator for the interaction of a charged quantum particle with an excited hydrogen-like atom or ion is considered in the dipole approximation. A new additional integral of motion and related symmetry group are found. A broader approximate symmetry group and expressions for the eigenvalues and eigenfunctions are proposed in the semiclassical limit of large total orbital momentum. The qualitative peculiarities of the spectrum are discussed and limiting cases are considered. A comparison with the results of other authors and with analytical and numerical data is made. The results are applicable to the doubly excited states of two-electron atoms, scattering of charged particles on hydrogen-like atoms or ions, and spectral line broadening etc.
Cloud Particles Evolution Algorithm

Many evolutionary algorithms have been paid attention to by the researchers and have been applied to solve optimization problems. This paper presents a new optimization method called cloud particles evolution algorithm (CPEA) to solve optimization problems based on cloud formation process and phase transformation of natural substance. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The cloud is composed of descript and independent particles in this algorithm. The cloud particles use phase transformation of three states to realize the global exploration and the local exploitation in the optimization process. Moreover, the cloud particles not only realize the survival of the fittest through competition mechanism but also ensure the diversity of the cloud particles by reciprocity mechanism. The effectiveness of the algorithm is validated upon different benchmark problems. The proposed algorithm is compared with a number of other well-known optimization algorithms, and the experimental results show that cloud particles evolution algorithm has a higher efficiency than some other algorithms.
Canonical Sobolev projections of weak type (1,1)

Introduction and notation Some properties of weak type multipliers and canonical projections of weak type $(1,1)$ A class of weak type $(1,1)$ rational multipliers A subclass of $L^\infty(\mathbb{R}^2)\backslash M_1^{(w)}(\mathbb{R}^2)$ induced by $L^\infty(\mathbb{R})$ Some combinatorial tools Necessity proof for the second order homogeneous case: A converse to Corollary (2.14) Canonical projections of weak type $(1,1)$ in the $\mathbb{T}^n$ model: Second order homogeneous case The non-homogeneous case Reducible smoothnesses of order $2$ the canonical projection of every two-dimensional smoothness is of weak type $(1,1)$ References.
On Optimal Support Reaction in Viscoelastic Vibrating Structures

ABSTRACT For a viscoelastic beam or frame structure that undergoes forced vibrations, the conditions for optimal support reaction are derived. Translation or rotation of supports are allowed and a functional expressed in terms of stress or strain amplitudes is minimized. Optimality conditions are derived using the concept of an adjoint structure and an illustrative example is presented for the case of beam vibration.
Robust Representation Learning for Group Shifts and Adversarial Examples

Despite the high performance achieved by deep neural networks on various tasks, extensive research has demonstrated that small tweaks in the inputs can lead to failure in the model’s predictions. This issue affecting deep neural networks has led to a number of methods to improve model robustness, including adversarial training and distributionally robust optimization. Although both of these two methods are geared towards learning robust models, they have essentially different motivations: adversarial training attempts to train deep neural networks against perturbations, while distributional robust optimization aims to improve model performance on the most difficult “uncertain distributions". In this work, we propose an algorithm that combines adversarial training and group distribution robust optimization to improve robust representation learning. Experiments on three image benchmark datasets illustrate that the proposed method achieves superior results on robust metrics without sacrificing much of the standard measures.
Some canonical forms for linear multivariable systems

Six different canonical forms for the triple A, B, C of a linear multivariable system are discussed, namely (i) the column-companion form, (ii) the row-companion form, (iii) the output identifiable form, (iv) the input-identifiable form, (v) the controllable canonical form and (vi) the observable canonical form. Simple algorithms for transforming any system into these forms are proposed and illustrated by means of examples. Practical applications of the various forms are discussed.
Multi-disciplinary approach to teaching numerical methods to engineers using Matlab.

Teaching Numerical Methods to first year engineering students is improved by a collaboration between engineers, computer scientists and mathematicians. Our approach has been to teach a problem based course with six, fortnightly assignments, each revolving around an engineering problem solved by a specific numerical method. The collaborative delivery, engineering problem focus, and intensive tutorials all contribute to make this a very successful course that gives the students a sound foundation to take into later years and hopefully their careers.
Two noniterative algorithms for locating inclusions using one electrode measurement of electric impedance tomography

The aim of electric impedance tomography is to gather information on the conductivity inside a physical body from boundary measurements of current and voltage. In many situations of practical importance, the investigated object has known constant background conductivity, but is contaminated by embedded inhomogeneities. In this work, we test numerically two noniterative algorithms for locating such inclusions using only one electrode measurement pair of impedance tomography. The first method is based on the concept of convex source support and has its origins in the works of Kusiak and Sylvester. The other one is the effective dipole method of Hanke. We show that these algorithms are useful in the framework of electrode measurements, but also point out some restrictions to their applicability. Our measurements are simulated using the complete electrode model, which is in good agreement with real-life data.
Incipient mixing by Marangoni effects in slow viscous flow of two immiscible fluid layers

Ignoring inertia, a deformable interface separating two fluid films is considered, subject to non-uniform tension driven by the solutal Marangoni effect in the presence of a scalar concentration field. Detailed description of adsorption kinetics is abrogated by a simple ansatz directly relating interfacial tension and bulk solute concentration. Consequently, the formal mathematical treatment and some of the results share features in common with the Rayleigh–Benard–Marangoni thermocapillary problem. Normal mode perturbation analysis in the limit of small interface deformations establishes the existence of an unstable response for low wavenumber excitation. In the classification of Cross & Hohenberg (1993, Pattern formation outside of equilibrium. Rev. Mod. Phys., 65, 851–1112), both type I and type II behaviour are observed. By considering the zero wavenumber situation exactly, it is proved that all eigenvalues are purely imaginary with non-positive imaginary part; hence, a type III instability is not possible. For characteristic timescales of mass diffusion much shorter than the relaxation time of interfacial fluctuations (infinite crispation number): the response growth rate is obtained explicitly; only a single excitation mode is available, and a complete stability diagram is constructed in terms of the relevant control parameters. Otherwise, from a quiescent base state, an infinite discrete spectrum of modes is observed that exhibit avoided crossing and switching phenomena, as well as exceptional points where stationary state pairs coalesce into a single oscillatory standing wave pattern. A base state plane Poiseuille flow, driven by an external pressure gradient, generally exaggerates the response: growth rates of instabilities are enhanced, and stable decay is further suppressed with increasing base flow speed, but the inherent symmetry breaking destroys stationary and standing wave response. Results are obtained in this most general situation by implementing a numerical Chebyshev collocation scheme. The model was motivated by hydrodynamic processes supposed to be involved in gastric digestion of humans.
New and Confirmed Triple Systems with Luminous Cool Primaries and Hot Companions

In the course of comparing parameters of evolved cool star plus hot main-sequence star binaries with theoretical isochrones, some discrepancies are found between implied stellar masses and the spectroscopic binary mass function or the measured angular separation. These are naturally explained if there is a third star in the system. Multiplicity is also required to explain some comparisons of "cool plus hot binary" IUE and optical spectral energy distribution analysis with measured flux ratios, especially Tycho's two-color photometry of separate components. Out of a sample of 136 cool-plus-hot binary star systems under study, measurements are now indicating several systems considered double (HD 5373, 23089, 26673, 29094, 49126, 71129, 149379, 179002, 187299), and probably a few others (including HD 136415), to have at least three stellar components. Several other cases of suspected triple systems are confirmed. For comparison, there are eight known triples included in the project. In all, about 25% of the systems contain three or more components within a few arcseconds. Estimated separations are provided, which may be of use when not known from interferometry. In general, the triple systems have one post–main-sequence component and two upper main-sequence components, usually revolving around each other. One new triple system, HD 149379, has as its middle component an F giant in the brief first crossing of the Hertzsprung gap.
Saddle-node bifurcations for hyperbolic sets

Hyperbolic sets are robust under perturbations: they persist on an open set of the parameter space. In this paper we investigate the boundary of this open set. Generalizing the theory of fixed points we define saddle-node bifurcations for hyperbolic sets K with one-dimensional unstable directions. In this bifurcation the geometrical splitting of the tangent space is preserved but the expansion in the unstable direction degenerates near a periodic orbit. The compact set K can be followed on a closed half-space bounded by a codimension-one manifold \mathcal{O}^0. On \mathcal{O}^0 the saddle-node bifurcation occurs. On one side of \mathcal{O}^0, K is hyperbolic and on the other side, it has disappeared.
A method for affine invariant curve smoothing

This paper proposes a new curve smoothing method invariant to affine transformation. Curve smoothing is one of the important challenges in computer vision as a procedure for noise suppression in shape analysis such as Curvature Scale Space (CSS). Currently, Gaussian filtering is widely used among a lot of smoothing methods. However Gaussian filtering is not affine invariant. This paper proposes a new method for curve smoothing that is invariant under affine transformation such that area of any region in the image does not change. Specifically, we introduce an affine invariant evaluate function with a metric tensor. The original curve is smoothed by minimizing the evaluation function. We mathematically prove that this method is affine invariant. Further, experimental results show that the proposed method is almost never affected by affine transformation different from usual Gaussian filtering. In the proposed method, processing results are expected to be not affected much by variation of the viewpoint.
Citrate-Capped AuNP Fabrication, Characterization and Comparison with Commercially Produced Nanoparticles

Gold nanoparticles (AuNPs) were synthesized using citrate reduction, also known as the Turkevich method. The AuNPs were compared with the commercially available product and later subjected to characterization. The AuNPs were 13 nm in diameter with a 2.7 × 108 M−1cm−1 extension coefficient. The calculated concentration was 5.1 nM through the Beer–Lambert law using UV–vis absorbance spectra. Further detailed characterization was applied, such as scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), elemental analysis using electro and DLS instruments, energy-dispersive X-ray spectroscopy (EDS), XRD, and Zeta potential. The synthesized AuNPs had a higher UV-absorbance peak of 0.93 in comparison to commercially available nanoparticles at 5.8 identical conditions. The characterization confirmed successful fabrication of colloidal-citrate-capped AuNPs and their dispersed and aggregated state with induced salt concentration. The shape and morphology were confirmed through XRD, showing a face-centered cubic lattice of {111}, confirmed at 38.1 round shape, and a crystalline lattice. AuNPs tend to be applied in sensing, detection, drug delivery, pharmaceuticals, and other applications in the environment and materials. Other applications include environmental contaminant detection, colorimetric sensors, antimicrobial applications, biosensing and drug delivery, tissue engineering, nanomedicines, optoelectronics, and catalysts.
Artificial Intelligence in Decision Making System

There is a way to transmute in Medical Management by implementing Artificial Intelligence techniques. The latest techniques are being incorporated into the research technologies. It shows a way for the experts to deliver accurate, consistent, and knowledgeable results. It incorporates the liability as well as clarity reasoning utilized by AI-associated systems dynamically. The main issues to appear with these moderations include accountability as well as a clear concept made by AI-based systems. These are also been elevated from scientific bias. These can be well-adjusted and can be stable against the necessities of making community benefit in a systematic way. AI can be employed in numerous organized and unorganized data in medical analysis. The techniques include different methods like support vector machines and some other networks for those unorganized data. For this, some AI tools must be employed. It will be effective in medical analysis massively, in connection with detailed calculations, and meaningful analysis on the basis of the outcomes. The study enforces the corresponding approach for Big Data together with healthcare for exploration based on these complications (Xafis et al. 2019). The exploration spread over in terms of appropriate standards. Depending upon this the analyzer can interpret data for spreading and employing AI-based systems in medical analysis with the experimentation properly and accurately. However, Artificial Intelligence plays a significant role in acting as black boxes that is characteristics as well as the quantity, computation, and techniques would be moderated properly by the experts. Reaching out to this point of measure, black-box medicine will point out this. In this research paper, how the black box deals with the patient-centered medicine that needs to be executed.
Exploiting Variable Correlation with Masked Modeling for Anomaly Detection in Time Series

Online anomaly detection in multi-variate time series is a challenging problem particularly when there is no supervision information. Autoregressive predictive models are often used for this task, but such detection methods often overlook correlations between variables observed in the most recent step and thus miss some anomalies that violate normal variable relations. In this work, we propose a masked modeling approach that captures variable relations and temporal relations in a single predictive model. Our method can be combined with a wide range of predictive models. Our experiment shows that our new masked modeling method improves detection performance over pure autoregressive models when the time series itself is not very predictable.
inter com Comparative analysis between the early months of 2020 and 2021 in the process of monothematization of the news coverage during the COVID-19 pandemic in Jornal Nacional

This paper aims to understand the journalistic processes in the coverage of the COVID-19 pandemic by Jornal Nacional on TV Globo in the 2020-2021 period, directing journalistic attention to a comparative analysis in the monothematic coverage. It is considered that, since the outbreak of the disease and its consequences, the attention of journalists was divided among three political crises generated by the federal government. Such facts divide the times of TV news coverage with the biggest health crisis, on a global scale, in the 21 st century. As research methods, bibliographical research, exploratory research, and the Content Analysis technique were employed to compare, from the news, how the sanitary and political crises intermingled and became a political crisis of public health.
Clinical Characteristics of TZAP (ZBTB48) in Hepatocellular Carcinomas from Tissue, Cell Line, and TCGA

Background and Objectives: ZBTB48 is a telomere-related protein that has been renamed telomeric zinc finger-associated protein (TZAP). It favorably binds to elongated telomeres to regulate their appropriate length. However, TZAP expression has not been investigated in hepatocellular carcinomas (HCC). Materials and Methods: The clinical significance of TZAP expression in 72 HCC was investigated. Additionally, its findings were supported by open big data and cancer cell lines. Results: TZAP expression level was not associated with the clinical parameters of HCC. TZAP expression induced a poorer survival result (overall survival, p = 0.020; disease-free survival, p = 0.012). TCGA data showed TZAP expression was more frequently found in HCCs with hepatitis C infection (p = 0.023). However, TCGA data revealed that TZAP expression did not predict HCC prognosis. In a cell line study, TZAP inhibition via siRNA suppressed PLC/PRF/5 cell growth; however, cell viability was increased in HepG2 cells. Conclusions: We presented the clinical and prognostic values of TZAP expression in HCC tissues and cancer cell lines. Additionally, the TCGA results also revealed a significant role for TZAP expression. TZAP expression may involve HCC progression and its prognosis.
Optimizing multisample anti-aliasing for deferred renderers

Deferred renderers are popular in computer graphics because they allow using a larger number of light sources, but they have some drawbacks too. One of these is the inability to work together with traditional hardware-based multisample anti-aliasing. Multiple solutions exist to this problem, but their common drawback is the increased memory and bandwidth requirements. We propose a novel approach that eliminates unnecessary memory usage and improves performance while maintaining image quality. Our method is based on a new G-Buffer structure that uses per-pixel linked lists to store the samples. By limiting the number of pre-allocated blocks in the G-Buffer we can also satisfy strict requirements about memory usage and processing time. Similarly to variable rate shading, our method enables to selectively apply anti-aliasing either on preferred parts of the screen or on a per-object basis. We measured the new method using a Vulkan based renderer on scenes with different geometry complexity and characteristics while comparing performance and memory usage to the traditional techniques.
Liquid Crystalline Nanoparticles Conjugated with Dexamethasone Prevent Cisplatin Ototoxicity In Vitro

The conjugation of drugs with nanoparticles represents an innovative approach for controlled and targeted administration of therapeutic agents. Nanoparticle-based systems have been tested for the inner ear therapy, increasing the drug diffusion and being detected in all parts of the cochlea when locally applied near the round window. In this study, glycerol monooleate liquid crystalline NanoParticles were conjugated with Dexamethasone (NPD), a hydrophobic drug already used for inner ear treatments but defective in solubility and bioavailability. NPD has been tested in vitro in the cell line OC-k3, a model of sensory cells of the inner ear, and the therapeutic efficacy has been evaluated against cisplatin, a chemotherapeutic compound known to induce ototoxicity. After comparing the physical chemical characteristics of NPD to the equivalent naïve nanoparticles, an initial investigation was carried out into the nanoparticle’s uptake in OC-k3 cells, which takes place within a few hours of treatment without causing toxic damage up to a concentration of 50 µg/mL. The NPD delivered the dexamethasone inside the cells at a significantly increased rate compared to the equivalent free drug administration, increasing the half-life of the therapeutic compound within the cell. Concerning the co-treatment with cisplatin, the NPD significantly lowered the cisplatin cytotoxicity after 48 h of administration, preventing cell apoptosis. To confirm this result, also cell morphology, cell cycle and glucocorticoids receptor expression were investigated. In conclusion, the NPD system has thus preliminarily shown the potential to improve the therapeutic efficacy of treatments delivered in the inner ear and prevent drug-induced ototoxicity.