Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification Models of Action Research Arm Test Activities in Post-Stroke Patients Based on Human Hand Motion

The Action Research Arm Test (ARAT) presents a ceiling effect that prevents the detection of improvements produced with rehabilitation treatments in stroke patients with mild finger joint impairments. The aim of this study was to develop classification models to predict whether activities with similar ARAT scores were performed by a healthy subject or by a subject post-stroke using the extension and flexion angles of 11 finger joints as features. For this purpose, we used three algorithms: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The dataset presented class imbalance, and the classification models presented a low recall, especially in the stroke class. Therefore, we implemented class balance using Borderline-SMOTE. After data balancing the classification models showed significantly higher accuracy, recall, f1-score, and AUC. However, after data balancing, the SVM classifier showed a higher performance with a precision of 98%, a recall of 97.5%, and an AUC of 0.996. The results showed that classification models based on human hand motion features in combination with the oversampling algorithm Borderline-SMOTE achieve higher performance. Furthermore, our study suggests that there are differences in ARAT activities performed between healthy and post-stroke individuals that are not detected by the ARAT scoring process.
Effects of Circadian Rhythm and Feeding Modes on Rumen Fermentation and Microorganisms in Hu Sheep

All organisms have a biological clock system which is strongly tied to how well an organism digests food and develops. This study aimed to understand the effects of circadian rhythm and feeding modes on rumen fermentation and microorganisms in Hu sheep. Forty-five healthy Hu sheep were randomly divided into three treatment groups of 15 sheep in each group, wherein they were fed the same concentrate and roughage. Under the condition that the nutrient-feeding amount was consistent throughout the day, the concentrate-to-forage ratio was dynamically adjusted during the day and night. Rumen fluid collected after the feeding experiment was used to determine the study parameters; the results showed a connection between rumen fermentation and the circadian clock. Volatile fatty acids (VFAs), pH, and NH3-N were significantly influenced by the fermentation duration (p < 0.05). The activities of digestive enzymes also showed a relationship with nutrition and circadian rhythm, and there were differences in the digestive enzyme activities of amylase, lipase, and cellulase (p < 0.05). Dominant microorganisms, such as Saccharomycetes and Mucor, were more abundant in the daytime of the high-concentrate fed group. The correlation among the study objectives was evident from the differences in enzyme activity and microbial diversity among the treatment groups. On the basis of the circadian rhythm characteristics of Hu sheep, changes in the feeding mode of Hu sheep and only adjusting the proportion of concentrate and forage in the morning and evening showed that feeding diets with the high-concentrate ratio in the day significantly reduced rumen PH and increased NH3-N concentration (p < 0.05). Under this feeding pattern, the activities of major digestive enzymes in the rumen, such as amylase and lipase, were significantly increased (p < 0.05), and the microbial diversity was also improved.
Rate of Decay of Auditory Sensation

The rate of decay of auditory sensation was investigated by measuring the minimum silent interval that must be introduced between two noise pulses to be perceived. The value of this critical time Λt was determined for different intensity levels of both the first and the second pulse. It is shown that in this case the sensation level of the second pulse may be considered as a very good approximation of the level of auditory sensation after Λt, due to the first pulse. From the experiments, we may conclude (1) expressed in dB as a function of log t, the decay of sensation is represented by a straight line; (2) independent of the sensation level of the stimulating sound, the hearing threshold is reached at the same time of about 200–300 msec. These results are compared with other experiments and the differences are discussed.
Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles. Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced; second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced; then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is increased by 0.32%, which improves the practicability of the system.
Adaptive neural network tracking control of an omnidirectional mobile robot

The trajectory tracking control problem of an omnidirectional mobile robot is studied in this article. The omnidirectional mobile robot is adsorbed by suction cups for aircraft skin inspection, which is a typical nonholonomic system. In the control design part, a novel adaptive neural network control scheme is presented in the presence of uncertainty and external disturbance. The adaptive neural network has the characteristics of weights online updating. The neural network is applied to estimate uncertainty online to obtain the desired tracking performance. The weights online updating algorithm contains a correction term, which is an improved algorithm to ensure robustness. On the basis of Lyapunov theory, the closed-loop system can converge to an arbitrarily small domain containing origin. This illustrates that the closed-loop system is globally asymptotically bounded stable. Excellent control performance can be obtained by selecting design parameters reasonably. A simulation example of tracking an eight-shape trajectory is given to verify the effectiveness of the proposed control scheme.
Toroidal Compactifications and Dimension Formulas for Spaces of Modular Forms for Orthogonal Shimura Varieties

In this paper we describe the general theory of constructing toroidal compactifications of locally symmetric spaces and using these to compute dimension formulas for spaces of modular forms. We focus explicitly on the case of the orthogonal locally symmetric spaces arising from quadratic forms of signature $(2,n)$, giving explicit details of the constructions, structures and results in these cases. This article does not give explicit cone decompositions, compute explicit intersection pairings, or count cusps and thus does not give any complete formulas for the dimensions. This article is still `in preparation'.
Exact Solution of a Spin-$\frac{1}{2}$ Particle for a Linear Potential

The problem of a spin-$\frac{1}{2}$ particle moving in a linear potential field in two-dimensions is searched to obtain for nonzero energy eigenvalues and the corresponding normalized eigenfunctions. The zero-mode ($E=0$) eigenfunctions are also studied and it is seen that they are normalizable. The variation of the non-zero eigenfunctions and also eigenvalues are according to the position and potential parameter $\gamma$, respectively, given in the text.
Effects of cash transfers on food expenditure patterns in northern Kenya

Abstract Most households in the ASALs lack access to sufficient and nutritious food, among factors contributing to this include; conflicts, droughts, invasion of desert locusts, increase in food prices, crop failure, and livestock diseases. Cash transfers have risen rapidly over the years in both emergency and developmental contexts as a means of responding to food security and livelihood threats. To understand whether cash transfers are effective in addressing food insecurity we need to know how cash transfers affect beneficiaries’ food expenditure patterns. This paper adopted the Quadratic Almost Ideal Model (QUAIDS) to understand how food expenditure patterns changes in presence of cash transfers. The findings indicate that households diversified their diet to some high-value foods, the diet was not only based on starch but also some proteins. The paper also adopted a difference-in-difference model to determine the effects of cash transfers on household food expenditure. The findings indicate that cash transfers increased the food expenditure of the beneficiaries.
Preconditioned Recycling Krylov subspace methods for self-adjoint problems

The authors propose a recycling Krylov subspace method for the solution of a sequence of self-adjoint linear systems. Such problems appear, for example, in the Newton process for solving nonlinear equations. Ritz vectors are automatically extracted from one MINRES run and then used for self-adjoint deflation in the next. The method is designed to work with arbitrary inner products and arbitrary self-adjoint positive-definite preconditioners whose inverse can be computed with high accuracy. Numerical experiments with nonlinear Schr\"odinger equations indicate a substantial decrease in computation time when recycling is used.
Hodge–Tate Conditions for Landau–Ginzburg Models

We give a sufficient condition for a class of tame compactified Landau-Ginzburg models in the sense of Katzarkov-Kontsevich-Pantev to satisfy some versions of their conjectures. We also give examples which satisfy the condition. The relations to the quantum D-modules of Fano manifolds and the original conjectures are explained in Appendices.
Squared envelope-based SNR estimation

Two novel squared envelope (SQE)-based and nondata-aided (NDA) signal-to-noise ratio (SNR) estimators over slow fading channel are proposed, which are robust to carrier offsets and can be applied to both nonconstant modulus (NCM) and constant modulus (CM) constellations. First, the moment-based first-and-second moments (M1M2) estimator is proposed because of its simple computation. The NDA approach has some difficulties when it is applied to NCM constellations, mainly due to self-noise introduced by the variability in the amplitude of transmitted symbols. Therefore, an expectation-maximization (EM) estimator, to solve the problem, instead of using the maximum-likelihood estimation, which is too complex for practical implementation, is proposed. For comparison purposes, the normalized Cramér–Rao lower bound is numerically evaluated as the performance benchmark for those estimators. Simulation results have verified that the two novel SQE and NDA SNR estimators are better at estimating the corresponding SNR range for different constellations. Besides, two hybrid schemes combining M1M2 with EM estimators are suggested to reduce deficiencies caused by these methods when they are employed independently to estimate the SNR.
Controlling the magnetic and electric responses of dielectric nanoparticles via near-field coupling

Among the two materials families used in nanophotonics, the fundamental mode for metal nanostructures is electric, while that for dielectric nanostructures is magnetic. Consequently, the optical properties of hybrid dimers that incorporate both materials have prompted signiﬁcant research. Here, we study the optical response of such hybrid dimers with the coupled electric and magnetic dipoles method, and demonstrate how their electromagnetic interactions can be used to modulate the electric and magnetic responses. The conditions for the complete suppression of the magnetic dipole in the visible range for high refractive index dielectric nanoparticles are described. The electric and magnetic responses can be enhanced or reduced by positioning the dielectric particle in the nodes of the standing wave formed by the metallic particle. This controlled near-ﬁeld interaction provides a handle on the near- and far-ﬁeld responses of the system, with possible applications such as sensing and optical switches.
p-Coumaric Acid Nanoparticles Ameliorate Diabetic Nephropathy via Regulating mRNA Expression of KIM-1 and GLUT-2 in Streptozotocin-Induced Diabetic Rats

Diabetic nephropathy (DN) has become a leading cause of end-stage renal failure worldwide. The goal of the current study was to examine the protective effects of chitosan-loaded p-Coumaric acid nanoparticles (PCNPs) in nephrotoxicity induced by streptozotocin (STZ). Because of the antidiabetic, anti-inflammatory, and antioxidant properties of PCNPs, the development of DN may be considerably decreased. In this study, the rats received a single intraperitoneal injection (i.p.) of STZ (45 mg/kg) to induce DN. PCNPs were given orally 80 mg/kg b.w to the rats for a duration of four weeks. Body weight, kidney weight, blood glucose, and insulin levels were measured at the end of the experiment. Serum and urine parameters were also examined, along with the histological, immunobiological, and tumor necrosis factor (TNF) and interleukin-6 (IL-6) expression of the nephrotic rats. To comprehend the impact of PCNPs, the expression patterns of the kidney injury molecule (KIM-1) and glucose transporter-2 (GLUT-2) were evaluated. Administration of PCNPs significantly increased body weight, decreased kidney weight and also ameliorated blood glucose levels in the nephropathic rats. The administration of PCNPs also reverted the levels of urea, serum creatinine, urinary NAG, β-glucuronidase and albumin to near-normal levels. The administration of PCNPs also caused the levels of serum and urine parameters to return to near-normal levels. Additionally, the PCNP-treated rats had markedly reduced TNF-α, IL-6, and KIM-1 expressions as well as enhanced GLUT-2 mRNA expression. Our findings clearly showed that PCNP administration prevents the onset of DN in rats by lowering hyperglycemia, decreasing inflammation, and improving the expression of GLUT-2 mRNA in nephropathic rats.
FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY

ABSTRACT: Fractional differencing is a tool for modeling time series which have long-term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box-Jenkins ARIMA(p, d, q) models to take nonintegral values; they permit the simultaneous modeling of the long-term and short-term behavior of an observed time series. This paper discusses the usefulness of fractional differencing to time-series modeling, with emphasis on hydrologic applications. A methodology for fitting fractionally differenced ARIMA models is described, and examples are presented.
(Digital Presentation) H2o and CO2 Surface Contamination of the Lithium Garnet Li7La3Zr2O12 Solid Electrolyte

Garnet-type Li7La3Zr2O12 is a promising solid-state electrolyte for lithium (Li)-ion batteries, and it readily reacts with H2O and CO2 when exposed to ambient air. Such reactions form a contamination layer on Li7La3Zr2O12, which is detrimental to the battery operation. Here, using first-principles calculations, we investigate the adsorption and reactions of H2O and CO2 on a Li7La3Zr2O12 surface. We show that H2O reacts through the exchange of protons and Li+ and produces metal hydroxide species. At high H2O coverage, half of the H2O molecules dissociate, while the other half remain intact. CO2 reacts with the Li7La3Zr2O12 surface directly to produce carbonate species. We clarify that the individual reactions of H2O and CO2 with Li7La3Zr2O12 are thermodynamically more favorable than the co-adsorption of H2O and CO2. Finally, we demonstrate that low temperature and high partial pressure promote the reactions of H2O and CO2 with Li7La3Zr2O12. Our study provides guidance on processing conditions for the garnet Li7La3Zr2O12 solid electrolyte to minimize its surface contamination.
Using SOS for Analysis of Zeno Stability in Hybrid systems with Nonlinearity and Uncertainy

Hybrid systems exhibit phenomena which do not occur in systems with continuous vector fields. One such phenomenon - Zeno executions - is characterized by an infinite number of discrete events or transitions occurring over a finite interval of time. This phenomenon is not necessarily undesirable and may indeed be used to capture physical phenomena. In this paper, we examine the problem of proving the existence and stability of zero executions. Our approach is to develop a polynomial-time algorithm - based on the sum-of-squares methodology - for verifying the stability of a Zeno execution. We begin by stating Lyapunov-like theorems for local Zeno stability based on existing results. Then, for hybrid systems with polynomial vector fields, we use polynomial Lyapunov functions and semialgebraic geometry (Positivstellensatz results) to reduce the local Lyapunov-like conditions to a convex feasibility problem in polynomial variables. The feasibility problem is then tested using an algorithm for sum-of-squares programming - SOSTOOLS. We also extend these results to hybrid system with parametric uncertainty, where the uncertain parameters lie in a semialgebriac set. We also provide several examples illustrating the use of our technique.
Sensitivity Analysis of Factors Influencing the Anchorage Force of Soil Anchor Rods Based on the Orthogonal Experiment – ANN

Based on the BP algorithm of artificial neural network (ANN), an intelligent model which is used to predict the anchorage force of soil anchor rods is established in this article, under the comprehensive consideration of the anchor rod diameter, strength of the grouting body, length of anchoring segment, bore diameter, inclination angle of the anchor rod, and other factors. Combining with the in-situ test data of the anchor rod reinforcement engineering, the network is trained and the errors are analyzed. Thus it verifies the applicability and feasibility of the method. Extended calculations are conducted by using the well-trained intelligent forecast model. Through application of orthogonal table test theory, 25 groups of tests are designed to analyze the sensitivity of influencing factors on the anchorage force of anchor rods in the soil. The results show that: the sensitivity of influencing factors on the anchorage force of soil anchor rods, in a descending order, is the length of anchoring segment, strength of the grouting body, anchor rod diameter, bore diameter, inclination angle of anchor rod. The results can provide reference for the practical application of the same reinforcement engineering.
Quadrature uncertainty and information entropy of quantum elliptical vortex states

We study the quadrature uncertainty of the quantum elliptical vortex state using the associated Wigner function. Deviations from the minimum uncertainty states were observed due to the absence of Gaussianity. We further observed that there exists an optimum value of ellipticity which gives rise to the maximum entanglement of the two modes of the quantum elliptical vortex states. In our study of entropy, we noticed that with increasing vorticity, entropy increases for both the modes. A further increase in ellipticity reduces the entropy thereby resulting in a loss of information carrying capacity. We check the validity of the entropic inequality relations, namely the subaddivity and the Araki–Lieb inequality. The latter was satisfied only for a very small range of the ellipticity of the vortex, while the former seemed to be valid at all values.
EXACT MEAN INTEGRATED SQUARED ERROR

An exact and easily computable expression for the mean integrated squared error (MISE) for the kernel estimator of a general normal mixture density, is given for Gaussian kernels of arbitrary order. This provides a powerful new way of understanding density estimation which complements the usual tools of simulation and asymptotic analysis. The family of normal mixture densities is very flexible and the formulae derived allow simple exact analysis for a wide variety of density shapes. A number of applications of this method giving important new insights into kernel density estimation are presented. Among these is the discovery that the usual asymptotic approximations to the MISE can be quite inaccurate, especially when the underlying density contains substantial fine structure and also strong evidence that the practical importance of higher order kernels is surprisingly small for moderate sample sizes. 1. Introduction. Substantial research has been devoted to kernel density estimation. This is because it provides a simple, yet appealing, context in which to study problems and issues that arise in all types of nonparametric curve estimation. This includes regression, spectral density and hazard estimation, and also a variety of other estimators, including histograms, splines and orthogonal series. Three important and useful tools for understanding the behavior of nonparametric curve estimators are asymptotic analysis, simulation and numerical calculation of error criteria. Each of these methods provides many useful insights into the complicated structure present in the study of curve estimation. However, each has its limitations as well. rhe strength of asymptotic analysis is that it frequently allows simultaneous study of many different specific examples, through general results applying to entire classes of settings. The weakness of asymptotics is that they only describe behavior in the limit. This is still very useful in many situations because the asymptotics describe the actual situation quite well. However, it is less useful when the asymptotics have not yet kicked in (that is, in studying situations where the asymptotically dominant effect has not taken over yet). Perhaps the biggest drawback to asymptotics is that it is very difficult to determine in a given situation which of these possibilities is occurring.