Miracle-dz/UniFAR-FTUs huggingface dataset:
Dipole-disk antenna with beam-forming ring

A method of improving the radiation characteristics of a dipole-disk antenna by using a beam-forming ring (BFR) is suggested. The radiation patterns of the dipole-disk antenna are calculated by the moment methods (MM) and the uniform geometrical theory of diffraction (UTD) and are compared with experimental results.
Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data

Abstract Transcriptome-Wide Association Studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we’d like to identify causal genes for Low-Density Lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (two-sample) two-stage least squares (2SLS) using multiple correlated SNPs as instrumental variables (IVs); it is closely related to typical (two-sample) Mendelian randomization (MR) using independent SNPs as IVs, which is expected to be impractical and lower-powered for TWAS (and some other) applications. However, often some of the SNPs used may not be valid IVs, for example, due to the widespread pleiotropy of their direct effects on the outcome not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). Building on recent advances in sparse regression, we propose a robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood (2ScML), an extension of 2SLS. We first develop the proposed method with individual-level data, then extend it both theoretically and computationally to GWAS summary data for the most popular two-sample TWAS design, to which almost all existing robust IV regression methods are however not applicable. We show that the proposed method achieves asymptotically valid statistical inference on causal effects, demonstrating its wider applicability and superior finite-sample performance over the standard 2SLS/TWAS (and MR). We apply the methods to identify putative causal genes for LDL by integrating large-scale lipid GWAS summary data with eQTL data. Supplementary materials for this article are available online.
Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

High Impedance Fault detection in a solar photovoltaic (PV) and wind generator integrated power system is described in this paper using discrete wavelet transform and a neural network with radial basis function (NNRBF). For this paper, the integration of solar photovoltaic and wind systems was modelled in a MATLAB/Simulink environment to create an IEEE 13-bus system. Microgrids (MG’s) are mostly powered by renewable energy. Uncertainty about renewables has shifted attention to ensuring a steady supply and long-term viability. It has been addressed in the paper whether or not a small-scale distant end source connection may be made at the terminal of a radial distribution feeder. Some typical power system problems compromise the reliability of the grid’s power supply. To solve this problem, this study suggests a criterion algorithm based on the neural network with radial basis function (NNRBF), and a defect detection method based on the discrete wavelet transform (DWT). The MATLAB/Simulink model of the system is then used to produce fault and travelling wave signals. The db4 wavelet is used to deconstruct the travelling wave signals into detail and approximate signals, which are then combined with the data from the two-terminal travelling wave localization approach for fault detection. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). The results show that both the criterion algorithm and the fault detection algorithm are reliable in their assessments of whether or not faults exist in the power system, and that neither algorithm is particularly sensitive to variations in fault type, fault detection, fault initial angle, or transition resistance. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). Overhead distribution system faults are simulated in Matlab/Simulink, and the technique is rigorously validated across a wide range of system situations. It has been shown through simulations that the proposed method can be relied upon to successfully and dependably protect high impedance fault (Hi-Z).
Pseudo Maximum Likelihood Methods: Applications to Poisson Models

Pseudo maximum likelihood techniques are applied to basic Poisson models and to Poisson models with specification errors. In the latter case it is shown that consistent and asymptotically normal estimators can be obtained without specifying the p.d. f. of the disturbances. These estimators are compared both from the finite sample and the asymptotic point of view. Quasi generalized PML estimators, which asymptotically dominate all PML estimators, are also proposed. Finally, bivariate and panel data Poisson models are discussed. THE ANALYSIS OF ECONOMIC BEHAVIOR often leads to the study of characteristics taking a small number of positive values. The classical linear model is not adapted to explain how such discrete variables depend on other quantitative or qualitative variables. The reasons are similar to those usually given in the case of an endogenous qualitative variable: the shape of the observation set does not correspond to a linear model, the assumption of normality of the disturbances cannot be made, since the endogenous variables take a small number of values with strictly positive probabilities, and the prediction formulae which are deduced from a linear model give impossible values. In the models considered in the literature to describe discrete variables (Cox and Lewis [2], El. Sayyad [3], Frome, Kutner, and Beauchamp [4], Gilbert [5], Hausman, Hall, and Griliches [8]; see also Lancaster [12]) the endogenous variable is assumed to have a Poisson distribution conditional upon the exogenous variables. The parameter of this distribution is a function of the values of the exogenous variables. The choice of such a model is justified if the dependent variable counts the occurrence of a given event during a fixed period and if the usual assumptions of the Poisson process are satisfied. For instance, the model is adapted to describe daily numbers of oil tankers' arrivals in a port, the number of accidents at work by factory, or the number of patents applied for and received by firms (Hausman, Hall, and Griliches [8]).
Implementation of the total and reduced magnetic scalar potential technique using hierarchical finite elements

The total and reduced scalar potential technique is a method for introducing source currents into magnetic scalar potential field models. If required, the magnetic scalars can be coupled to other field vectors in different regions in order to model eddy currents or may be used on their own in magnetostatics problems. This contribution shows how hierarchical finite elements which use shape functions based on Legendre‐type polynomial expansions may be implemented in total and reduced scalar potential schemes.
Willis covered stent treatment for blood blister-like aneurysm: A meta-analysis of efficacy and safety

Background Blood blister-like aneurysm (BBA) is a rare and special type of intracranial aneurysm with extremely high rates of rupture, morbidity, mortality, and recurrence. Willis Covered Stent (WCS) is a new device that is specifically designed for the treatment of intracranial complex aneurysms. However, the efficacy and safety of WCS treatment for BBA remain controversial. Thus, a high level of evidence is required to prove the efficacy and safety of WCS treatment. Methods A systematic literature review was performed using a comprehensive literary search in Medline, Embase, and Web of Science databases to identify studies related to WCS treatment for BBA. A meta-analysis was then conducted to incorporate the efficacy and safety outcomes, including intraoperative situation, post-operative situation, and follow-up data. Results Eight non-comparative studies containing 104 patients with 106 BBAs met the inclusion criteria. In the intraoperative situation, the technical success rate was 99.5% [95% confidence interval (CI), 0.958, 1.000], the complete occlusion rate was 98.2% (95% CI, 0.925, 1.000), and the side branch occlusion rate was 4.1% (95% CI, 0.001, 0.114). Vasospasm and dissection occurred in 9.2% (95% CI, 0.000, 0.261) and 0.1% (95% CI, 0.000, 0.032) of the patients, respectively. In the post-operative situation, the rebleed and mortality rates were 2.2% (95% CI, 0.000, 0.074) and 1.5% (95% CI, 0.000, 0.062), respectively. In the follow-up data, recurrence and parent artery stenosis occurred in 0.3% (95% CI, 0.000, 0.042) and 9.1% (95% CI, 0.032, 0.168) of the patients, respectively. Ultimately, 95.7% (95% CI, 0.889, 0.997) of the patients had a good outcome. Conclusions Willis Covered Stent could be effectively and safely applied for BBA treatment. The results provide a reference for clinical trials in the future. Well-designed prospective cohort studies must be conducted for verification.
How “Berry Phase” Analysis of Non-Adiabatic Non-Hermitian Systems Reflects Their Geometry

There is currently great interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a “phase” parameter that characterises the way “exceptional points” (singularities of various sorts) determine the system. These systems are briefly reviewed here with an emphasis on their geometrical thermodynamics properties.
Ohmic ion temperature and thermal diffusivity profiles from the JET neutron emission profile monitor

The JET neutron emission profile monitor was used to study ohmically heated deuterium discharges. The radial profile of the neutron emissivity is deduced from the line-integral data. The profiles of ion temperature, Ti, and ion thermal diffusivity, chi i, are derived under steady-state conditions. The inferred chi i, for 0.3 < p = r/a < 0.5, is in the range 0.5-2.5m2s-1. The ion thermal diffusivity is higher than, and its scaling with plasma current opposite to, that predicted by neoclassical theory.
Automatic Calibration of Conceptual Rainfall-Runoff Models: Optimization Algorithms, Catchment Conditions, and Model Structure

From 32 CRR-catchment cases (combinations from four conceptual rainfall-runoff models (CRR) and eight catchments) calibrated with either two or three optimization methods, (1) the shuffle complex evolution method (SCE-UA), (2) the multiple start Simplex (MSX), and (3) the local Simplex, it seems that all three methods produced parameter sets of comparable, local-optimum quality. Even with comparable performance among the models, some parameter values derived by the three optimization methods for the same CRR-catchment cases are surprisingly different from each other. In addition, parameter sets of SCE-UA or MSX, which often produce marginally better results than the local Simplex at the calibration stage, could end up with worse results at the validation stage. Apparently, given the inherent limitations of calibration data, model inadequacies, and identifiability problems, it is impossible to achieve global convergence in the parameter search. However, other than those for dry catchments such as Ihimbu or Bird Creek, the parameter sets obtained are generally feasible. Both SCE-UA and the local Simplex are viable optimization tools, while MSX is inefficient computationally. SCE-UA can complete the parameter search in one run, while the local Simplex often requires multirun operations to get good results.
Evaluations of the Disease Surveillance Centre network in Scotland: What parts has it reached?

Regular evaluation is a prerequisite for systems that provide surveillance of animal populations. Scotland's Rural College Veterinary vices' Disease Surveillance Centre (DSC) network plays an integral part in surveillance to detect new and re-emerging threats within animal populations, predominantly livestock. In ronse to surveillance reviews and proposed changes to the network, an initial evaluation of diagnostic submissions data in 2010 to mid-2012 established a baseline “footprint”, while highlighting challenges with the data. In this recenaluation for the period 2013–2018, we developed a new denominator using a combination of agricultural census and movement data, to identify relevant holdings more accurately. Iterative discussions between those processing submissions data ahose involved in collection at source took place to understand the intricacies of the data, establish the most appropriate dataset, and develop the processes required to optimise the data extraction and cleansing. The subsequent descriptive analysis identifies the number of diatic submissions, the number of unique holdings making submissions to the network and shows that both the surrounding geographic region of, and maximum dise to the closest DSC vary greatly between centres. Analysis of those submissions classed as farm animal post-mortems also highlights the effect of distance to the closest DSC. Whether specific differences between the time periods are due to changes in the behavior of the submitting holdior the data extraction and cleaning processes was difficult to disentangle. However, with the improved techniques producing better data to work with, a new baseline foot prior the network has been created. This provides information that can help policy makers and surveillance providers make decisions about service provision and evaluate the impact of future changes. Additionally, thtputs of these analyses can provide feedback to those employed in the service, providing evidence of what they are achieving and why changes to data collection processes and ways of working are being made. In a different setting, er data will be available and different challenges may arise. However, the fundamental principles highlighted in these evaluations and the solutions developed should be of interest to any surveillance providers generating similar diagnostic data.
On Dubins Paths to Intercept a Moving Target at a Given Time

Abstract In this work we examine the problem of intercepting a moving target with a pursuer that only moves forwards at constant speed and whose radius of turn is bounded from below. We assume that the target is moving on a straight line at constant speed and that the target's velocity is known to the pursuer with no measurement error. We analyze both the problem of minimum-time interception, as well as the problem of interception at a predefined time. We establish lower and upper bounds on the minimum time to interception that are easy to compute. We examine the relation between shortest paths and minimum time interception paths, give conditions for the two types of paths to coincide and show cases where they differ. Finally, we propose two algorithms for the elongation of an admissible path and provide conditions that guarantee continuous elongation. The above analysis is also conducted in scenarios where the target is located near or inside the circles of minimum turning radius that correspond to the pursuer's initial configuration.
Relativistic calculations on the alkali atoms by a modified Hartree-Fock method

A relativistic Hartree-Fock method, being a slight modification of the conventional formulation and closely related to the approach of Cowan and Griffin (1976) is examined in calculations for the Li, Na, K, Rb and Cs isoelectronic series. Relativistic corrections to the total and orbital energies, expectation values of rn, excitation energies, fine-structure splittings (calculated as energy differences) and oscillator strengths are very close to the corresponding values obtained by means of the Dirac-Fock method. A comparison with existing experimental data shows that the method gives reliable estimates of the transition energies and oscillator strengths, especially for highly ionised atoms.
On Inversion Principles

The idea of an ‘inversion principle’, and the name itself, originated in the work of Paul Lorenzen in the 1950s, as a method to generate new admissible rules within a certain syntactic context. Some fifteen years later, the idea was taken up by Dag Prawitz to devise a strategy of normalization for natural deduction calculi (this being an analogue of Gentzen's cut-elimination theorem for sequent calculi). Later, Prawitz used the inversion principle again, attributing it with a semantic role. Still working in natural deduction calculi, he formulated a general type of schematic introduction rules to be matched – thanks to the idea supporting the inversion principle – by a corresponding general schematic Elimination rule. This was an attempt to provide a solution to the problem suggested by the often quoted note of Gentzen. According to Gentzen ‘it should be possible to display the elimination rules as unique functions of the corresponding introduction rules on the basis of certain requirements’. Many people have since worked on this topic, which can be appropriately seen as the birthplace of what are now referred to as “general elimination rules”, recently studied thoroughly by Sara Negri and Jan von Plato. In this study, we retrace the main threads of this chapter of proof-theoretical investigation, using Lorenzen's original framework as a general guide.
Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results demonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.
Impact of Functional Training on Functional Movement and Athletic Performance in College Dragon Boat Athletes

Functional training has become a popular training method in different sports, yet limited studies have focused on paddle sports. The purpose of this study was to evaluate the effects of functional training on functional movement and athletic performance in college dragon boat athletes. A total of 42 male athletes were divided into 2 groups: a functional training (FT) group (n = 21, 21 ± 1.47 years) and a regular training (RT) group (n = 21, 22 ± 1.50 years). The FT group participated in an 8-week (16-session) functional-training program, while the RT group trained with strength-training sessions. Functional movement screen (FMS), Y-balance test (YBT) and athletic performance evaluations were conducted before and after the intervention. Repeated measure ANOVA and t-test evaluations were employed to examine differences for both groups. The FT group was significantly improved in FMS scores (F = 0.191, p < 0.001) and YBT scores (F = 2.59, p = 0.027), and it also showed significantly improved muscular fitness (pull-ups: F = 0.127, p < 0.001; push-ups: F = 1.43, p < 0.001) and rowing speed (F = 4.37, p = 0.004). It is recommended to include functional training as a part of training and routine exercise, as it appears to be an effective way of improving FMS and athletic performance in paddle sports.
Soft computing methods in the analysis of elastic wave signals and damage identification

Abstract An outline of a Structure Health Monitoring (SHM) system is given. A non-destructive technique of elastic wave propagation was used in the presented approach. For the analysis of time signals recorded by piezoelectric transducers, signal processing algorithm has been developed and artificial neural networks have been used. As a consequence, two levels of the damage identification problem have been realized: novelty detection and damage assessment. The systems accuracy and reliability have been verified during laboratory tests of strip specimens made of various materials. It has been proved that the system can be used for the analysis of simple as well as complex signals. Moreover, the system can operate online as an automatic SHM system.
A Comparative Overview of Various Fungal Menaces Amidst Corona Virus Disease

The disastrous Coronavirus Disease outbreak declared as a global pandemic, has become a potential hazard to public health. The second wave of COVID-19 in India has been strongly linked to the rising cases of various fungal infections including mucormycosis, aspergillosis, candidiasis, and mucor septicus. These fungal infections have been a cause for alarm for the general public. The color-coding of the fungal infections is primarily based on the symptoms observed in the infected patients and not based on the color of the fungi itself. For busting the myths behind fungal infections, a comprehensive and deeper understanding of the facts is needed to overcome this challenge. Rapid diagnosis, reversal of underlying predispositions, surgical excision or debridement, and optimal antifungal therapy are some of the crucial factors in combating these fungal infections. This article provides a comparative review of literature on various fungal infections during COVID-19, that have been threatening worldwide, predominantly in India. A well-established databases literature search was conducted.