Miracle-dz/UniFAR-FTUs huggingface dataset:
Scattering of Massless Scalar Waves by a Schwarzschild

This paper investigates the scattering and absorption of scalar waves satisfying the equation φ;μ;μ=0 in the Schwarzschild metric. This problem has been previously considered by Hildreth. We find, for a Schwarzschild mass m, the following cross sections in the zero‐frequency limit for s‐waves: σ(absorption) = 0, dσ/dΩ ≃ [c + ⅓(2m) ln (2mω)]2, where c is a constant of order m. These results disagree with the previous calculation. We exhibit a method of solution for the equation. Its limiting (Newtonian) form, with suitable identification of the coefficients, is the problem of Coulomb scattering in non‐relativistic quantum mechanics. By demanding coordinate conditions which for large l allow the usual Coulomb results in a partial‐wave expansion, we are able to define a partial‐wave cross section. The (summed) differential cross section for small frequencies inherits the logarithmic behavior of the s‐wave part, which is the only contribution explicitly calculated. (The l ≠ 0 contributions and the behavior of.
Well posedness and finite element approximability of two-dimensional time-harmonic electromagnetic problems involving non-conducting moving objects with stationary boundaries

A set of sufficient conditions for the well-posedness and the convergence of the finite element approximation of two-dimensional time-harmonic electromagnetic boundary value problems involving non-conducting moving objects with stationary boundaries is provided for the first time to the best of authors’s knowledge. The set splits into two parts. The first of these is made up of traditional conditions, which are not restrictive for practical applications and define the usual requirements for the domain, its boundary, its subdomains and their boundaries, the boundary conditions and the constitutive parameters. The second part consists of conditions which are specific for the problems at hand. In particular, these conditions are expressed in terms of the constitutive parameters of the media involved and of the velocity field. It is shown that it is not difficult to check the validity of these conditions and that they hold true for broad classes of practically important problems which involve axially moving media.
Effect of adjunctive intermittent theta-burst repetitive transcranial magnetic stimulation as a prophylactic treatment in migraine patients: A double-blind sham-controlled study

Background: The left dorsolateral prefrontal cortex (DLPFC) seems to exert a bilateral control of chronic pain states such as migraine. Repetitive transcranial magnetic stimulation (rTMS) is known to modulate brain excitability, neurotransmitters, and endogenous opioids involved in pathophysiology of migraine. Aim: This study was designed to assess the efficacy of adjunctive intermittent theta-burst stimulation (iTBS) to the left DLPFC, as a prophylactic treatment in migraine. Materials and Methods: The study was a double-blind, sham-controlled experiment. Patients with migraine were allotted to active (n = 20) or sham (n = 21) rTMS, respectively. Each patient received ten sessions of iTBS over the left DLPFC. Patients were rated at baseline and at 2, 4, 6, and 12 weeks after receiving the treatment. Scores were obtained from the headache diary and by applying the Migraine Disability Assessment Scale (MIDAS). Results: There was a significant decrease in frequency, duration, and severity of migraine in the active group than the sham group over the study period. The effect was more pronounced during the initial 2 weeks. The MIDAS score reduced significantly in the active group than the sham group at 12 weeks. There were no significant adverse effects observed during the entire period of study. Conclusion: Compared to sham stimulation, adjunctive active iTBS over the left DLPFC was safe and effective in reducing the frequency, duration, and severity of migraine headache and in reducing disability associated with the illness.
Soft Magnetic Fingertip With Particle-Jamming Structure for Tactile Perception and Grasping

Typically, the fingertips of a dexterous robotic hand are designed to be rigid and equipped with a variety of sensors to provide tactile perception. However, this design scheme renders the grasping of small objects difficult, because rigid fingertips usually have a small contact area with the objects. In this article, we propose a novel fingertip design that combines soft tactile skin and a particle-jamming structure. Benefiting from the particle-jamming structure, the soft skin and fluid-state jamming particle material ensure that the fingertip encloses the object as much as possible when in contact with it. Then, the application of a vacuum turns the particle material into a high-stiffness solid-state, allowing it to hold objects. Soft tactile skin integrated with permanent magnetic particles estimates contact information between the fingertip and objects. Thus, utilizing the soft tactile skin and particle-jamming structure the fingertip can perceive and grasp objects simultaneously. The proposed soft fingertip module is fabricated by molding, which reduces the processing cycle time and cost. In addition, a series of experiments are conducted to demonstrate the performance of this novel fingertip design. The results validate that the proposed robotic fingertip can grasp tiny objects while perceiving contact position and force information.
Real-Time Detection of Acute Cognitive Stress Using a Convolutional Neural Network From Electrocardiographic Signal

As stress is related to many mental and physical health problems, monitoring stress and its management is getting increasingly important in modern societies. Because of the advantage of convolutional neural network (CNN) in automatic feature learning, this study is proposed to use CNN to achieve accurate and fast detection of acute cognitive stress from heart rate variability (HRV). The traditional mental arithmetic calculation was adopted as the stressor for a total of twenty participants, during which one-lead electrocardiogram (ECG) was acquired. Six conventional HRV methods for inferring cognitive stress were extracted from the ECG signals, and their performance in identifying acute cognitive stress was compared with the proposed CNN-based method. The experimental results showed that with a super-short (10 s) time window, the detection error rate of CNN was 17.3%, which is significantly better than the performance of all six conventional HRV methods ( $> 7.2{\%}, p < 0.01$ ). Further analysis showed that the improvement achieved by the proposed CNN methods mainly came from the decrease in false stress sample detection. This study demonstrated the possibility of super-short windows and the advantage of CNN on acute cognitive stress detection. Its outcome would benefit practical applications of real-time stress detection via HRV.
Immobilized copper nanoparticles on biodegradable magnetic starch composite: investigation of its ovarian cancer, cytotoxicity, and antioxidant effects

Abstract In recent days, the green synthesized nanomagnetic biocomposites have been evolved with tremendous potential as the future catalysts. This has encouraged us to design and synthesis of a novel Cu NPs fabricated potato starch functionalized magnetic nanomaterial (Fe3O4@starch/Cu nanocomposite). The prepared nanocomposite were characterized using advanced analytical techniques like FT-IR, FESEM, TEM, EDX, elemental mapping and ICP-OES. The biogenic synthesized Fe3O4@starch/Cu nanocomposite was explored in the anti-ovarian cancer investigations against ovarian cancer cell lines (PA-1, Caov-3, SW 626, and SK-OV-3). The material exhibited significant cytotoxicities against the ovarian cancer cell lines. However, it was considerably inactive against the normal HUVEC cell line. The antioxidant potential of Fe3O4@starch/Cu nanocomposite was also investigated by DPPH method. The Fe3O4@starch/Cu nanocomposite revealed the significant antioxidant potentials according to the IC50 value.
Quantum energy decays and decoherence in discrete baths

The quantum average energy decay and the purity decay are studied for a system particle as a function of the number of constituents of a discrete bath model. The system particle is subjected to two distinct physical situations: the harmonic oscillator (HO) and the Morse potential. The environment (bath) is composed by a {\it finite} number N of uncoupled HOs, characterizing the structured bath, which in the limit $N\to\infty$ is assumed to have an ohmic, sub-ohmic or super-ohmic spectral density. For very low values of N the mean energy and purity remain constant in time but starts to decay for intermediate values (10<N<20), where two distinct time regimes are observed: two exponential decays for relatively short times and a power-law decay for larger times. In this interval of N decoherence occurs for short times and a non-Markovian dynamics is expected for larger times. When $N$ increases, energy and coherence decay very fast and a Markovian dynamics is expected to occur. Wave packet dynamics is used to determine the evolution of the particle inside the system potentials.
Contact Dynamics and Control for Tethered Space Net Robot

Maneuverable tethered space net robot (TSNR) is promising for active debris removal due to its flexibility and controllability. However, the contact dynamics of TSNR is not completely understood. Therefore, the contact dynamics is analyzed and the net does not close completely after contact. A super-twisting sliding mode control scheme is designed to make the net completely closed and avoid the risk of tether cut. The efficacy of the controller is validated by simulation results.
Hesitant Fuzzy Linguistic Analytic Hierarchical Process With Prioritization, Consistency Checking, and Inconsistency Repairing

Analytic hierarchy process (AHP), as one of the most important methods to tackle multiple criteria decision-making problems, has achieved much success over the past several decades. Given that linguistic expressions are much closer than numerical values or single linguistic terms to a human way of thinking and cognition, this paper investigates the AHP with comparative linguistic expressions. After providing the snapshot of classical AHP and its fuzzy extensions, we propose the framework of hesitant fuzzy linguistic AHP, which shows how to yield a decision for qualitative decision-making problems with complex linguistic expressions. First, the comparative linguistic expressions over criteria or alternatives are transformed into hesitant fuzzy linguistic elements and then the hesitant fuzzy linguistic preference relations (HFLPRs) are constructed. Considering that HFLPRs may be inconsistent, we conduct consistency checking and improving processes after obtaining priorities from the HFLPRs based on a linear programming method. Regarding the consistency-improving process, we develop a new way to establish a perfectly consistent HFLPR. The procedure of the hesitant fuzzy linguistic AHP is given in stepwise. Finally, a numerical example concerning the used-car management in a lemon market is given to illustrate the efficiency of the proposed hesitant fuzzy linguistic AHP method.
Floating-capacitor based inverter for open-ended winding induction motor drive with fault-tolerance

ABSTRACT Floating-capacitor-based nine-level inverter topology for open-ended winding induction motor (OWIM) drive is presented in this paper. The proposed multilevel inverter topology (PMLIT) essentially consists of four three-phase two-level voltage source inverters (VSI), three isolated DC sources and a floating-capacitor (FC). Out of four inverters, three inverters are fed from DC sources, and the fourth inverter is connected with the FC. The advantage of the PMLIT is that the maximum output voltage will be twice the source voltage with nine levels across each phase winding. The PMLIT employs least number of components which makes the inverter cost-effective and has lowered control complexity. The PMLIT can deliver seven, five or three-level output voltage with a decrease in modulation index. Conventional sinusoidal pulse-width modulation (PWM) techniques can be employed to generate switch-gate pulses. The intermediate-voltage levels are realised by proper charging and discharging of the FC. The performance of the PMLIT under possible switch faults is investigated, and modified switching logics are proposed to deliver a balanced supply for the OWIM drive in post-fault operation. Simulations are performed in the MATLAB/Simulink environment, and an experimental setup is designed to validate the simulations, and the results are presented.
Characterization of Tilt Stability via Subgradient Graphical Derivative with Applications to Nonlinear Programming

This paper is devoted to the study of tilt stability in finite dimensional optimization via the approach of using the subgradient graphical derivative. We establish a new characterization of tilt-stable local minimizers for a broad class of unconstrained optimization problems in terms of a uniform positive definiteness of the subgradient graphical derivative of the objective function around the point in question. By applying this result to nonlinear programming under the metric subregularity constraint qualification, we derive a second-order characterization and several new sufficient conditions for tilt stability. In particular, we show that each stationary point of a nonlinear programming problem satisfying the metric subregularity constraint qualification is a tilt-stable local minimizer if the classical strong second-order sufficient condition holds.
General Analytical Solutions for Merton'S-Type Consumption-Investment Problems

We solve analytically the Merton's problem of an investor with time additive power utility. For general state dynamics, we prove existence of two power series representations of the relevant optimal policies and value functions, which hold for all admissible risk aversion parameters. We characterize all terms in the power series by a recursive formula, allowing analytical computations to arbitrary order. Some applications to explicit model settings highlight a very satisfactory accuracy of finite order approximations provided by our power series solution approach.
Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling

Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper aims to develop and evaluate an IMC-based approach to estimate back loading during manual material handling (MMH) tasks. During various representative workplace MMH tasks performed by nine participants, this approach was evaluated by comparing the results with the ones computed from optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found between both computations on indicators based on L5/S1 peak and cumulative flexion moments, while lower correlations were found on indicators based on asymmetric moments. Since no force measurement or load kinematics measurement is needed, this study shows the potential of using only the handler’s kinematics measured by IMC to estimate kinetics variables. The assessment of workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics evaluation and will improve the ecological validity compared to laboratory studies, where the situations are often simplified and standardized.
Therapeutic Potential of Small Molecules Targeting Oxidative Stress in the Treatment of Chronic Obstructive Pulmonary Disease (COPD): A Comprehensive Review

Chronic obstructive pulmonary disease (COPD) is an increasing and major global health problem. COPD is also the third leading cause of death worldwide. Oxidative stress (OS) takes place when various reactive species and free radicals swamp the availability of antioxidants. Reactive nitrogen species, reactive oxygen species (ROS), and their counterpart antioxidants are important for host defense and physiological signaling pathways, and the development and progression of inflammation. During the disturbance of their normal steady states, imbalances between antioxidants and oxidants might induce pathological mechanisms that can further result in many non-respiratory and respiratory diseases including COPD. ROS might be either endogenously produced in response to various infectious pathogens including fungi, viruses, or bacteria, or exogenously generated from several inhaled particulate or gaseous agents including some occupational dust, cigarette smoke (CS), and air pollutants. Therefore, targeting systemic and local OS with therapeutic agents such as small molecules that can increase endogenous antioxidants or regulate the redox/antioxidants system can be an effective approach in treating COPD. Various thiol-based antioxidants including fudosteine, erdosteine, carbocysteine, and N-acetyl-L-cysteine have the capacity to increase thiol content in the lungs. Many synthetic molecules including inhibitors/blockers of protein carbonylation and lipid peroxidation, catalytic antioxidants including superoxide dismutase mimetics, and spin trapping agents can effectively modulate CS-induced OS and its resulting cellular alterations. Several clinical and pre-clinical studies have demonstrated that these antioxidants have the capacity to decrease OS and affect the expressions of several pro-inflammatory genes and genes that are involved with redox and glutathione biosynthesis. In this article, we have summarized the role of OS in COPD pathogenesis. Furthermore, we have particularly focused on the therapeutic potential of numerous chemicals, particularly antioxidants in the treatment of COPD.
Deception Attacks on Remote Estimation With Disclosure and Disruption Resources

The problem of deception attacks utilizing both disclosure and disruption resources in the multiple types of sensors setting against remote state estimation is investigated in this article. A group of sensors, including reliable and unreliable ones, measure system states and transmit innovations to a remote fusion center via wireless channels, wherein a malicious attacker can modify the data packets of unreliable sensors. By virtue of disclosure resources of reliable sensors and disruption resources of unreliable ones, a stealthy linear attack strategy is given, which can deceive the anomaly detectors and degrade the estimation performance. In this article, we obtain feasible criteria under which such attack policies can bypass existing detectors. The evolution of estimation error covariance at the remote end under the attacks is analyzed. Then, a closed-form expression of the optimal linear deception attack maximizing estimation error covariance is derived. Moreover, extended results on the designed attack strategies and attackers with extra measurements are provided. Finally, two illustrative examples with stable and unstable processes are given to verify the effectiveness of proposed methods.
Development of a passive microwave global snow depth retrieval algorithm for Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) data

This paper describes research conducted to develop an integrated snow monitoring algorithm at global and regional scales for the Special Sensor Microwave Imager (SSM/I) and the planned Advanced Microwave Scanning Radiometer‐EOS. Methods to detect global snow cover are well advanced and have been applied routinely at local, regional, and global scales. Current snow cover retrieval methods tend to underestimate snow volume because important fractional forest cover and snowpack physical properties are not well parameterized in simple snow parameter retrieval schemes. Building on a static approach to snow depth retrieval, a spatially dynamic algorithm is described that incorporates information about fractional forest cover and snowpack physical development. SSM/I brightness temperatures of land surfaces are calibrated with ground measurements of snow depth through empirical statistical and geostatistical models to produce globally distributed snow depth estimates. The calibration is based on a 4‐year and a one winter season record of daily snow depth and SSM/I observations. The biasing effect of forest cover on microwave estimates is quantified and short‐term fluctuations of microwave‐estimated snow depth (caused by physical changes in the pack) are reduced by time smoothing daily estimated snow depth values. For the data sets used the estimated snow depth from the new algorithm gives global seasonal errors of 13.7 and 22.1 cm for the two data sets. Further major improvements in snow depth retrieval methods will only be achieved through enhancements to forest and snow metamorphism parameterizations.
An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing

With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.
Lógica básica con afirmación alterna

El lenguaje del sistema extiende el lenguaje de la logica clasica al incluir un operador para la nocion de afirmacion alterna (en contraste con la afirmacion clasica o afirmacion usual), y tambien operadores de incompatibilidad y determinabilidad entre la pareja de operadores negacion versus afirmacion alterna. El sistema esta caracterizado por una semantica de valuaciones, con la cual se muestra la no equivalencia entre los dos operadores afirmacion. Como es de esperarse, el sistema colapsa en la logica clasica si se pide esta equivalencia. Se generan dos sistemas intermedios cuando se pide por un lado que la afirmacion alterna implique la clasica y por otro lado la implicacion reciproca.
Composed SoD - Uma Proposta para Auxiliar Modelos de Controle de Acesso

Uma das principais fontes de ataque ao sistema interno de empresas, ainda são os próprios funcionários. Os modelos de controle de acesso atuais não cobrem grupos organizados de funcionários mal intencionados. Este artigo propõe uma maneira de identificar estes grupos de usuários e verificar suas tarefas executadas e, desta forma, impedir maiores danos a empresa.