Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of user's car parking behaviour through twitter hashtags

Due to its ease and popularity, social media has recently become an essential source of data for researchers and various stakeholder groups that seek a reliable assessment of their policies based on a comprehensive understanding of users' feedback and inputs, which are reflected in their posts and discussions. In this study, we investigate the issue of users' car parking behaviour through a comprehensive analysis of related hashtags collected from the Twitter social media platform. For this purpose, we adopted a two‐step strategy where in the first stage, a surface‐level analysis of the identified hashtags involving inductive reasoning, sentiment analysis, and user interaction in terms of engagement and diversity scores is performed. In the second phase, a tweet content analysis is performed using sentiment analysis and Empath categorization with respect to the most frequent wordings (assimilating to separate topics), in the same spirit as aspect‐sentiment analysis, to gain further insights regarding the occurrence of negative and positive posts. A quantitative evaluation of the coherence of the Empath categorization indicates that achieved 0.83% coherence score and outperformed both LDA and LSA that had a score of 0.77% and 0.76% respectively. Furthermore, the common word technique assimilated to aspect sentiment compared to the state‐of‐the‐art model for aspect sentiment‐based deberta‐v3‐base model. Besides, the influence of bots or spammers is evaluated using engagement/diversity measures and Botometer API. The results provide valuable insights in terms of discriminating between positive and negative posts and the correlation of surface‐level analysis with content‐based analysis, as well as the impact of various categorizations. The results expect to enable urban planners and policymakers to advance evidence‐based policing in the future design of intelligent parking systems.
Brownian dynamics with hydrodynamic interactions

A method for simulating the Brownian dynamics of N particles with the inclusion of hydrodynamic interactions is described. The particles may also be subject to the usual interparticle or external forces (e.g., electrostatic) which have been included in previous methods for simulating Brownian dynamics of particles in the absence of hydrodynamic interactions. The present method is derived from the Langevin equations for the N particle assembly, and the results are shown to be consistent with the corresponding Fokker–Planck results. Sample calculations on small systems illustrate the importance of including hydrodynamic interactions in Brownian dynamics simulations. The method should be useful for simulation studies of diffusion limited reactions, polymer dynamics, protein folding, particle coagulation, and other phenomena in solution.
Prevention of seminal vesicle damage by Mucuna pruriens var. pruriens seed extract in chronic unpredictable mild stress mice

Abstract Context Thai Mucuna pruriens (L.) DC. var. pruriens (Fabaceae) or T-MP seed extract has been shown to improve sexual performance and sperm quality. Objective This study investigates the preventive effects of T-MP against seminal vesicle damage, apoptotic and Nrf2 protein expression in mice under chronic unpredictable mild stress (CUMS). Materials and methods Forty-eight male ICR mice were divided into four groups: control, CUMS, T-MP300 + CUMS and T-MP600 + CUMS. Mice in control and CUMS groups received distilled water, while those in treated groups were pretreated with T-MP extract (300 or 600 mg/kg BW) for 14 consecutive days. The CMUS and co-treated groups were exposed to one random stressor (of 12 total) each day for 43 days. Components and histopathology of the seminal vesicle were examined, along with localization of androgen receptor (AR) and caspase 3. Expression of seminal AR, tyrosine phosphorylated (TyrPho), heat shock protein 70 (Hsp70), caspases (3 and 9) and nuclear factor erythroid 2-related factor 2 (Nrf2) proteins was investigated. Results T-MP extract at a dose of 600 mg/kg BW improved seminal epithelial damage and secretion of fluid containing essential substances and proteins in CUMS mice. It also increased the expression of AR and TyrPho proteins. Additionally, T-MP increased expression of Nrf2 and inhibited seminal vesicular apoptosis through the suppression of Hsp70 and caspase expression. Conclusion T-MP seeds have an antiapoptotic property in chronic stress seminal vesicle. It is possible to apply this extract for the enhancement of seminal plasma quality.
Low power state assignment and flipflop selection for finite state machine synthesis: a genetic algorithmic approach

Current renewed emphasis for more aggressive logic designs with lesser area, delay, and power demands exploration of alternative avenues that could lead to better designs, albeit at the higher cost of computation. The author explores the avenue of genetic algorithm for a holistic view for synthesis of finite state machine (FSM) targeting power reduction by incorporating both state assignment and sequential element selection. Exhaustive experimentation performed on a large suite of benchmarks has established the fact that this tool results in state encodings with on average 46.08% reduction in power requirement over NOVA, without any significant increase in the number of product terms. The effectiveness of judicious flipflop selection has been demonstrated by showing that the approach with all D-flipflops as the sequential element requires 33.78% more power than the approach with a choice of D and T flipflop types. Moreover, as compared to the technique presented in LPSA, the algorithm presented here requires 38.96% less power when using a mix of D and T flipflops. The quality of the solution obtained and the high rate of convergence have established the effectiveness of the genetic algorithm in solving this particular NP-complete problem. Further, the inherent parallelism of genetic algorithm makes the proposed scheme ideal for solving the problem in a multiprocessor environment.
FAST TIMING ANALYSIS OF CYGNUS X-1 USING THE SPECTROMETER ON THE INTERNATIONAL GAMMA-RAY ASTROPHYSICS LABORATORY

We report for the first time the high-frequency analysis of Cyg X-1 up to hard X-ray using the spectrometer on International Gamma-Ray Astrophysics Laboratory (INTEGRAL). After analyzing the possible contribution from the background, and using the INTEGRAL archive from 2005 March to 2008 May, power density spectra were obtained up to 130 keV. First, we show that their overall shape is very similar to that observed at lower energies as they are well described by sets of Lorentzians. The strength of this fast variability (up to 40 Hz) does not drop at high energy since we show that it remains at ∼25% rms, even in the highest energy bands. Second, the hard X-ray variability patterns of Cyg X-1 are state dependent: the softer the spectrum (or the lower the hardness ratio), the lower the total fractional variability and the higher the typical frequencies observed. The strength of the total variability as a function of energy and state is then investigated. By comparison with simultaneous and published RXTE/Proportional Counter Array data, we show that in the hard state it remains quite constant in the 2–130 keV energy range. In the softer state it is also flat up to 50 keV and may increase at higher energy. The implications of this behavior on the models are then discussed.
Automation of 3D sewing of two different shaped parts by robot arm - Evaluation and verification of the proposed method through experiments -

To realize the task of stitching the edges of semi-circular and rectangular parts together in three dimensions with a robot arm, we have proposed and studied several methods to feed the edges of the parts accurately including a guide-set or a semi-circular robot hand. To investigate the conditions suitable for automatic stitching of the target parts in three dimensions, we conducted some experiments of the combination of different proposed methods. The results showed that the guide-set was effective in assisting the sending of parts with regular sewing lines, and that when the robot hand gripped the part, the gripping surface should have the same shape as the sewing line of the part. Through a series of experiments and verifications, we were able to realize three-dimensional sewing of two different shaped parts by the robot arm. In this report, we describe the results of verification and evaluation of the proposed methods by experiments.
Applicability of machine learning models using a neural network for predicting the parameters of the development of food markets

Forecasting the parameters of the food market is a difficult task due to the volatility of demand, which depends on many factors. In this study, the authors attempted to implement a machine learning model based on multiple data on the food market. A boxed recurrent neural network was chosen as a prediction technique. The information basis was made up of data from 3,200 US cities for 2010-2012, reflecting characteristics that may be directly or indirectly related to the price of dairy products. The following models were used for data preprocessing, anomaly search, dimensionality reduction: AdaBoost, LogisticRegression, SVM. As a result of analytical actions, a neural network architecture has been formed for use in market forecasting: two competitive neural networks. First: 2 layers with Bidirectional GRU+Dropout. Second: 3 layers of LSTM+Dropout + Attention with skip-layers. Its use makes it possible to obtain a prediction model of the desired parameters with qualitative indicators of the validation sample - R^= 0.86. The applicability of the constructed machine learning model is considered on the example of classical agricultural production with the presentation of the stages of deployment of such a model at the enterprise level.
Spectroscopy of Hot Stars in the Galactic Halo. III. Analysis of a Large Sample of Field Horizontal-Branch and Other A-Type Stars

We present results from an analysis of medium-resolution spectroscopy and UBV photometry for a sample of 1121 A-type stars in the halo (and disk) of the Galaxy. A previously developed calibration technique is used to assign estimates of effective temperature, surface gravity, and stellar metal abundance, as parameterized by [Fe/H]. Radial velocities are reported with an accuracy of ∼10 km s-1. Distance estimates are obtained for the stars with well-determined luminosity classes. Note that although we refer to "A-type" stars, which dominate the present sample, the present data set includes roughly 100 stars of later spectral types, as a result of the temperature range we have chosen to explore in this paper (6000 K ≤ Teff ≤ 10,000 K). Included in the hot star sample are 444 stars we classify as field horizontal-branch stars, 416 we classify as main-sequence–gravity A-type (or slightly later) stars (including stars that are likely members of the blue metal-poor population, the so-called BMPs), 140 stars we classify as likely metallic-line (Am) or peculiar (Ap) stars, and 121 stars that cannot be unambiguously classified based on the present data. Examination of the distributions in metallicity and velocity indicates that the field horizontal-branch and main-sequence A-type samples are quite distinct; hence we expect only a modest amount of cross-contamination between the subsamples. We identify 58 RR Lyrae candidates among the hot star sample, based on incompatibilities in their photometric and spectroscopic data. There are 19 stars in the sample that have been previously classified as RR Lyrae variables, and one additional star that had been previously suggested as a variable, though not necessarily of the RR Lyrae class. There are 115 stars in the sample that were previously classified as BMPs by Preston, Beers, & Shectman, most of which fall into the main-sequence A-type category, but 10 of which are found among the Am/Ap classifications. Furthermore, 53 of stars previously considered to be BMPs are shown by our analysis to have metal abundances [Fe/H] > -1, which calls their identification with this population somewhat into question.
Anisotropy Invariant Reynolds Stress Model and Its Application to Fully Developed Channel Flows

The paper is concerned with a second‐moment closure for the Reynolds‐averaged Navier‐Stokes equations which relies on the anisotropy invariant theory. It is shown how the anisotropy invariant map can be used to derive reasonable closure assumptions for turbulent flows. Furthermore, a first highly encouraging application for an inhomogeneous flow is discussed. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Zernike polynomials: a guide

In this paper we review a special set of orthonormal functions, namely Zernike polynomials which are widely used in representing the aberrations of optical systems. We give the recurrence relations, relationship to other special functions, as well as scaling and other properties of these important polynomials. Mathematica code for certain operations are given in the Appendix.
CONVERGENCE ANALYSIS OF BROADBAND, INTERNET AND INFORMATION FLOW FOR 43 AFRICAN COUNTRIES FOR 1991-2015

In the field of information technology, broadband, internet and information flow have been found to be the primary elements to indicate access to information in this current information age. We examine whether the country difference in Broadband use, Internet use and information flow existing in 1991 has been reduced and if so how fast. Convergence framework had been utilised to answer these questions for 43 African countries. The results indicated that all the three chosen information technology indicators for 43 African countries are converging toward a reduction of dispersion and a catch-up process during the period 1991-2015. However, there are marked differences exhibited in the convergence processes for the Total groups and for the subgroups of countries by income level.
Sequential Change-Point Detection Procedures That are Nearly Optimal and Computationally Simple

Abstract Sequential schemes for detecting a change in distribution often require that all of the observations be stored in memory. Lai (1995, Journal of Royal Statistical Society, Series B 57: 613–658) proposed a class of detection schemes that enable one to retain a finite window of the most recent observations, yet promise first-order optimality. The asymptotics are such that the window size is asymptotically unbounded. We argue that what's of computational importance isn't having a finite window of observations, but rather making do with a finite number of registers. We illustrate in the context of detecting a change in the parameter of an exponential family that one can achieve eventually even second-order asymptotic optimality through using only three registers for storing information of the past. We propose a very simple procedure, and show by simulation that it is highly efficient for typical applications.
A comparison of "clutter-agnostic" PHD filters

This paper describes a general approach for deriving PHD/CPHD filters that must estimate the background clutter process, rather than being provided with it a priori. I first derive general time- and measurementupdate equations for clutter-agnostic PHD filters. I then consider two different Markov motion models. For the Uncoupled Motion (UM) model, targets can transition only to targets, and clutter generators can transition only to clutter generators. For the Coupled Motion (CM) model, targets can transition to clutter generators and vice-versa. I demonstrate that R. Streit's "multitarget intensity filter" (MIF) is actually a PHD filter with a CM model. Streit has made the following claims for the MIF: it subsumes the conventional PHD filter as a special case, and can estimate both the clutter rate λk+1 and the target-birth rate Bk+1|k. I exhibit counterexamples to these claims. Because of the CM model, the MIF (1) does not subsume the conventional PHD filter as a special case; (2) cannot estimate Bk+1|k when there are no clutter generators; and (3) cannot estimate λk+1 when the target birth-rate and target death-rate are "conjugate. " By way of contrast, PHD filters with UM models do include the PHD filter as a special case, and can estimate the clutter intensity function κk+1(z). I also show that the MIF is essentially identical to the UM-model PHD filter when the target birth-rate and death-rate are both small.
A Self-Adaptive High/Low Beam Spotlight Filter in Capturing Local Structure Information for Object Contour Extraction

Contour extraction is a method in exactly obtaining an object’s contour from images. It is considered as one of the most important pre-processing for image processing applications. In this study, a self-adaptive high/low beam spotlight filter (SAHLBSF) is designed to capture local structure information for object contour extraction. The proposed SAHLBSF is inspired from the users’ experiences in car driving, where when the road is very straight and clear, a low beam light is applied, while a high beam light will be utilized when the road is winding and/or the environment is dark. Utilizing the SAHLBSF, the local structural information between two pre-selected initial contour points are captured and the candidate contour points are then determined. The spotlight filter continues for all pairs of initial points of an object such that a broadband of the object’s contour is constructed. A thinning process is then applied to obtain the final one-pixel-wide exact object contour. Experimental results using artificial and real medical images showed that better contour extraction performance can be obtained the proposed SAHLBSF than other existing methods.
Femtosecond laser-assisted photobleaching of single-wall carbon nanotubes

We present femtosecond laser induced photobleaching of individually isolated single-wall carbon nanotubes. Monodispersed single-wall carbon nanotubes fixed in aqueous gel were exposed under tightly focused femtosecond laser light. We measured photoluminescence spectra of single-wall carbon nanotubes before/after the laser irradiation. Because of unique excitonic band structures of single-wall carbon nanotubes, we clearly observed strong selectivity in the chirality and the orientation of carbon nanotubes depending on the wavelength and polarization of laser light. We also observed the difference from the case of continuous wave laser irradiation in the chirality selectivity and the efficiency of photobleaching.
Approximations for Standard Errors of Estimators of Fixed and Random Effects in Mixed Linear Models

Abstract Best linear unbiased estimators of the fixed and random effects of mixed linear models are available when the true values of the variance ratios are known. If the true values are replaced by estimated values, the mean squared errors of the estimators of the fixed and random effects increase in size. The magnitude of this increase is investigated, and a general approximation is proposed. The performance of this approximation is investigated in the context of (a) the estimation of the effects of the balanced one-way random model and (b) the estimation of treatment contrasts for balanced incomplete block designs.
Mathematical Modeling for the Detection of Fish by an Airborne Laser

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Special C-equivalences of finite one-side invertible quasigroups

In this paper we prove that for any natural n and any substitution h which is an involution with at least one fixed point there exists a left (right) invertible quasigroup of order n with the identity h(x)(xy) y ((xy)h(y) x). We give conditions under which such a quasigroup has a special C-equivalence and conditions under which any special C-equivalence of an invertible quasigroup is a congruence.
Path-Following Algorithms for Posynomial Geometric Programming Problems

Abstract Geometric programming (GP) is a class of optimization problems with a nonlinear objective function subject to a set of nonlinear constraints. The problems considered in this paper are posynomial GP problems, whose solutions can be obtained by solving a dual linear programing problem. The primary purpose of this paper is to investigate the quality of solutions found by, the application of a path-following algorithm, which is one of interior point methods. Two majorr approches to solve posynomial GP problems are investigated. One is dual GP as Generalized Lmear Programming (GLP) and the other is the traditional dual GP. The test results suggest that dual GP as GLP approach is advantageous over the posynomial one in the event that high a~curacy of the solution is required or critically important.
Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications

AbstractThis paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this m.