Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic Scattering Analysis for Typical Satellite in the Lower Terahertz Region

In order to meet the requirements of terahertz radar for space target detection, the radar cross sections (RCS) of typical satellite targets in terahertz low frequency region is studied. A typical satellite target with rough body is established by using half edge data structure. The RCS distributions at different heights, root-mean-square, correlation lengths and different frequencies are calculated by using the hybrid method of Physical Optics method (PO), Shooting and Bouncing Ray method (SBR) and Truncated-wedge Incremental-length Diffraction Coefficients (TWILDCs). Based on the principle of turntable imaging, the scattering center distribution of satellite target with different roughness at 220GHz and 0.05m imaging resolution is simulated and analyzed. The results show that RCS and image are more sensitive to the fluctuation of the target surface, the frequency and incident angle in terahertz band.
Facial Expression Recognition based on a Dual Graph Convolutional Network

The facial expression recognition on non-laboratory database is difficult and challenging. To improve the recognition performance, we propose a dual graph convolutional network, based on which two feature extractors are employed to facial images. The experimental results on RAF-DB and SFEW database show that our proposed method improves the accuracy of facial expression recognition on non-laboratory datasets.
A Novel Reconstruction Filter for the Azimuth Multi-Channel SAR

The compensation of channel imbalances plays a vital role in signal processing of the azimuth multi-channel (AMC) synthetic aperture radar (SAR). In the operational AMC SAR system, the channel imbalance is usually considered to be a constant in both the time and frequency domain for one image. However, since the antenna patterns of all channels cannot be strictly consistent, the channel imbalances may vary with the Doppler frequency. This paper establishes the signal model of AMC SAR with channel imbalances changing with Doppler frequency and proposes a novel compensation method during the reconstruction for the echo signals received by all channels. This method has been verified by simulations and real data acquired by Gaofen-3 (GF-3). The result shows that this method has excellent potential for improving the image quality for AMC SAR.
Selected Pseudo-Labelling With Class Confusion Regularization For Semi-supervised SAR Target Recognition

SAR images are highly sensitive to imaging conditions, e.g., depression angle, polarization mode and wave band. It is notable that existing enhanced structure in deep learning method does not consider domain discrepancy between training and testing data. In this paper, we employ the target domain selective pseudo-labeling to align the data distribution. To alleviate the mis-labelling issue, we introduce a novel regularization method, class confusion, the probability that neural networks misclassify the prediction between correct and ambiguous classes on target samples. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset can achieve state-of-the-art performance compared to other domain adaptation and model-based methods.
Fault Reconstruction of Medium Voltage DC Power System in Ships Based on Simulated Annealing Particle Swarm Algorithm

The fault reconstruction of ship medium voltage direct current(MVDC) power system is essentially a multi-objective multi-constrained nonlinear combinatorial optimization problem. A multi-objective fault reconstruction model of the MVDC power system of a ship is established with load recovery amount, number of switching operations and generator equalization efficiency as the objective functions for the ship's MVDC power system faults. A simulated annealing binary particle swarm algorithm is proposed to solve the fault location and reconstruction problem of the ship MVDC power system, and the adaptive weight method is introduced to balance the global search capability; the roulette selection method is introduced to optimize the selection of the adaptation degree value. The results of the test cases show that the proposed method has a high search effect and optimal search capability, and can obtain the reconfiguration scheme of the ship MVDC power system in a short time, which reflects a good optimization performance.
Complete Invariant Characterization of Scalar Linear (1+1) Parabolic Equations

Abstract We obtain a complete invariant characterization of scalar linear (1+1) parabolic equations under equivalence transformations for all the four canonical forms. Firstly semi- invariants under changes of independent and dependent variables and the construction of the relevant transformations that relate the two parabolic equations are given. Then necessary and sufficient conditions for a (1+1) parabolic equation, in terms of the coefficients of the equation, to be reducible via local equivalence transformations to the one-dimensional classical heat equation and the Lie canonical equation u t = u xx + Au/x 2, A a nonzero constant, are presented. These invariant conditions provide practical criteria for reduction to the respective canonical equations. Also the construction of the transformation formulas that do the reductions are provided. We further show how one can transform a (1+1) parabolic equation to the third and fourth Lie canonical forms thus providing invariant criteria for a parabolic equation to have two and one nontrivial symmetries as well. Ample examples are given to illustrate the various results.
Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases

Misogyny is a serious social problem that affects the mental and physical health of women and can even lead to femicide. This problem is visible and prevalent in different communication channels, such as music and social networks, encouraging and reinforcing this harmful behavior. Given this situation, the automatic detection of misogynistic content on social networks is a task of increasing interest. In this regard, most current computational approaches employ a supervised machine learning strategy. The main challenge is to capture the diversity and complexity of offensive language directed at women. Accordingly, the size and quality of training data play a fundamental role in the results of the methods. In this paper, we propose a novel data augmentation approach that takes advantage of song lyrics to increase the generalization capability of methods and improve their performance. Hence, we present a methodology for automatically compiling a corpus of song phrases that show abusive and explicit words against women. The proposed approach was evaluated using English and Spanish benchmark datasets, obtaining results that outperform conventional transfer learning techniques and achieve high competitiveness compared with state-of-the-art methods.
A geopolitical framework theory on "Culture II" in the peaceful societies that have no kings : "Rational" choice theory modified with miss-match theory of BioDarwinism

of the research Dec.2011~Jan.2012. 3 steps snowball sampling from students of a university in a southern part of Japan. Number of attacks was 172 trios. Number of the responses was 73 trios. The response rate was 42.4%. I analyzed all responses of the trios.
Ecology and coexistence of Aedes aegypti (Linnaeus 1762) and Aedes (Ste.) albopictus (Skuse 1894) in two state parks in Cuiabá, MT, Brazil

Scientific investigation of disease vectors is indispensable for knowledge of its Ecology, as they affect the health of human population. In this work, we present the results of the distribution and abundance of Aedes aegypti and Ae. albopictus in Massairo Okamura and Mãe Bonifácia state parks, Cuiabá-Mato Grosso, Brazil, to understand how changes in the rainy season interfere with their proliferation in natural wild areas located in urban regions. The focus was to investigate the coexistence of these species in the capture sites. The collections were carried out in eight campaigns within a period of 11 months of the year 2019, through ovitraps, with a total of 10 traps for each park. The results of the variables abundance (n) and relative humidity (%) were analyzed by the Statistic 7.0 program using factorial ANOVA. The 7117 quantified larvae produced 1462 adults for the two parks, with significant variability in mean abundance values between months and between sampling points. The record of the co-occurrence of Ae. aegypti and Ae. albopictus resulted in their coexistence and use of similar breeding sites under advantageous conditions for their colonization. The results presented showed that the parks have vulnerabilities in relation to the proliferation of vectors, and the environments must go through constant epidemiological surveillance. Faced with a serious situation in relation to arboviruses, it is essential to adopt strategies with greater investments in adequate methods, which provide sustainability to the actions established by surveillance networks.
The Burden of Rheumatoid Arthritis: Findings from the 2019 Global Burden of Diseases Study and Forecasts for 2030 by Bayesian Age-Period-Cohort Analysis

Background: Rheumatoid arthritis (RA) is a key health issue worldwide. Due to early identification and effective treatment strategies, the disease pattern of RA has also changed. However, the most comprehensive and up-to-date information about the burden of RA and its trends in subsequent years is lacking. Objective: this study aimed to report the global burden of RA by sex, age, region, and forecast for 2030. Method: Publicly available data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 were used in this study. The trends in the prevalence, incidence, and disability-adjusted life years (DALYs) of RA from 1990 to 2019 were reported. The global burden of RA in 2019 was reported by a sex, age, and sociodemographic index (SDI). Finally, the trends in the following years were predicted by Bayesian age-period-cohort (BAPC) models. Results: Globally, the age-standardized prevalence rate increased from 207.46 (95% UI:189.99 to 226.95) in 1990 to 224.25 (95% UI: 204.94 to 245.99) in 2019, with an estimated annual percent change (EAPC) of 0.37% (95% CI: 0.32 to 0.42). Regarding the incidence, the age-standardized incidence rate (ASR) increased from 12.21 (95% UI: 11.13 to 13.38) to 13 (95% UI: 11.83 to 14.27) per 100,000 people from 1990 to 2019, with an EAPC of 0.3% (95% CI: 11.83 to 14.27). The age-standardized DALY rate also increased from 39.12 (95% UI: 30.13 to 48.56) per 100,000 people in 1990 to 39.57 (95% UI: 30.51 to 49.53) in 2019, with an EAPC of 0.12% (95% CI: 0.08% to 0.17%). There was no significant association between the SDI and ASR when the SDI was lower than 0.7, while there was a positive association between the SDI and ASR when the SDI was higher than 0.7 The BAPC analysis showed that the ASR was estimated to be up to 18.23 in females and approximately 8.34 per 100,000 people in males by 2030. Conclusion: RA is still a key public health issue worldwide. The global burden of RA has increased over the past decades and will continue to increase in the coming years, and much more attention should be given to early diagnosis and treatment to reduce the burden of RA.
Impact of subsidy phase-out and credit trading policy tools on the competitive strategies of Chinese passenger car enterprises

In the context of subsidy phase-out and the tightening of credit trading policy tools, enterprises producing internal combustion engine passenger cars have also started to produce new energy passenger cars, creating a competitive market with new energy passenger car enterprises. To explore the operation strategies and profit fluctuations of two enterprises in passenger car market and credit trading market, this paper develops a duopoly competition game model. Based on solving a multi-objective equilibrium problem, and performing numerical simulations with real data on Chinese passenger car market, this paper analyzes the interactive impact of policy tools on market competition. The conclusions point out that the numerical relationship between the price of positive new energy credits and credit deficit penalty is decisive to enterprises' pricing strategies. As the subsidy recedes, the competitiveness of new energy passenger car enterprise will decline. The policy tools at corporate average fuel consumption credit regulation level only have a negative interactive effect on hybrid energy passenger car enterprise's pricing and production strategy. There is a threshold for the difference between corporate average fuel consumption up-to-standard value and actual value, below which hybrid energy passenger car enterprise will produce more new energy passenger cars than internal combustion engine passenger car. The tightening of policy tools at new energy credit regulation level will drive both enterprises to raise price and reduce yield.
Optimization of Computational Resources for Real-Time Product Quality Assessment Using Deep Learning and Multiple High Frame Rate Camera Sensors

Human eyes generally perform product defect inspection in Indonesian industrial production lines; resulting in low efficiency and a high margin of error due to eye tiredness. Automated quality assessment systems for mass production can utilize deep learning connected to cameras for more efficient defect detection. However, employing deep learning on multiple high frame rate cameras (HFRC) causes the need for much computation and decreases deep learning performance, especially in the real-time inspection of moving objects. This paper proposes optimizing computational resources for real-time product quality assessment on moving cylindrical shell objects using deep learning with multiple HFRC Sensors. Two application frameworks embedded with several deep learning models were compared and tested to produce robust and powerful applications to assess the quality of production results on rotating objects. Based on the experiment results using three HFRC Sensors, a web-based application with tensorflow.js framework outperformed desktop applications in computation. Moreover, MobileNet v1 delivers the highest performance compared to other models. This result reveals an opportunity for a web-based application as a lightweight framework for quality assessment using multiple HFRC and deep learning.
TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK

Recognizing road signs is one of the most important steps drivers can take to help prevent accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of the road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors' solutions were analyzed. In addition, in this work, a convolutional neural network (CNN) was used for an autonomous traffic and road sign detection and recognition system. The proposed system works in real-time on the recognition of road signs images. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs. In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems.
Ultrasound-guided versus computed tomography-controlled periradicular injections of the first sacral nerve: a prospective randomized clinical trial.

AIM To compare ultrasound (US)-guided versus computed tomography (CT)-controlled periradicular injections of the first sacral spinal (S1) nerve in a prospective randomized clinical trial. MATERIALS AND METHODS Thirty-nine patients with S1-radiculopathy were consecutively enrolled for 40 periradicular injections and assigned to an US or CT guided group. Needle position after US-assisted placement was controlled by a low-dose CT-scan. Accessibility, accuracy, and intervention time were compared. The overall effect on pain was matched evaluating the visual analog scale (VAS) decrease before and one month after the intervention. RESULTS The mean intervention time was lower in the US-group compared to the CT-group: 4.4±3.46 min (1.3-13.2) vs. 6.5±3.03 min (2.4-12.5). Using CT-controlled infiltration the mean number of needle passes was with 1.15 higher than utilizing US-guidance. The therapeutic effect (mean difference between pre- and post-intervention, VAS scores) for the CT-group was 4.85±2.52 and for the US-group 4.55±2.74 with no significant difference between the two groups (p=0.7). CONCLUSION US-controlled infiltrations of the first sacral nerve show a similar therapeutic effect to the time consuming, and ionizing CT-controlled injections and result in a significant reduction of procedure expenditure and avoidance of radiation.
Electron Localization Probed by Temperature-dependent Tunneling Spectroscopy in 1T-TaS2

In order to present a clear picture of the density of states at both sides of the Fermi level (E-F) in 1T-TaS2 near the nearly commensurate to commensurate transition (similar to 187 K) and at low temperatures, we have done a temperature-dependent tunneling spectroscopy study using a scanning tunneling microscope. We observed an abrupt transition from a charge-density-wave induced depletion to an opening of a deep pseudo gap near the E-F of the nearly commensurate to commensurate transition within similar to 1 K. The measured pseudo gap turned out to be deep enough to form localized states at E-F. In contrast to an inverse photoelectron spectroscopy study, our tunneling results indicate the gap structures of two Hubbard subbands resulting from band splitting of the Ta 5d band due to the electron correlation effect.
Physics of Neutron Stars

Since the first identification of neutron stars, in pulsars, a decade ago, theoretical and observational knowledge of these unusual objects has grown at a rapid rate. In this article we describe developments that have taken place since our 1975 review article on neutron stars (Baym & Pethick 1975, referred to hereafter as BP), as well as review several of their more astrophysical aspects not discussed there. The most striking observational fact about neutron stars is their existence: at present 321 pulsars, which are generally accepted to be rapidly rotating neutron stars, have been observed in our galaxy (Manchester et al. 1978, Taylor & Manchester 1977, Manchester & Taylor 1977, Smith 1976). In addition, most of the 16 pulsating compact X-ray sources so far discovered are likely to be accreting neutron stars in close binaries (for a review see Lamb 1977). The association of the Crab and Vela pulsars with supernova remnants provides evidence for the formation of neutron stars in supernovae, a picture supported to a limited extent by comparison of pulsar populations and lifetimes with estimated supernova rates (reviewed in Manchester & Taylor 1977). Optical and X-ray observations of binary X-ray sources provide the possibility of determining the masses of the neutron stars in these objects
Advanced Appendiceal Cancer with Systematic Metastasis without Gastrointestinal Symptoms Found by Subcutaneous Tumor

An 86-year-old woman with a subcutaneous nodule in her left axilla visited our hospital. She had no gastrointestinal symptoms, but contrast-enhanced computed tomography revealed a cecal mass and systemic metastasis, including cutaneous, bone, peritoneal dissemination and ascites. Colonoscopy revealed a circumferential, elevated cecal lesion. She underwent right hemicolectomy to prevent colon obstruction. The pathological diagnosis was poorly differentiated adenocarcinoma (por1＞tub2＞muc) arising from the appendix with a BRAFV600E mutation and microsatellite instability-high. Chemotherapy was administered, and she is currently still alive and undergoing chemotherapy. We describe a rare case of advanced appendiceal cancer without gastrointestinal symptoms diagnosed due to cutaneous metastasis.
The Urgency of Halal Label in Cosmetics for Muslim Women in Pemalang City

This study focuses on the orientation of the actions of Muslim women in the city of Pemalang towards the selection of cosmetics labeled halal and the meaning that underlies these actions. The method in this study is to use a qualitative method with a phenomenological approach. The findings of this study are Muslim women in Pemalang city are aware of religious values. Halal labels found on cosmetic products with halal labels become a symbolic representation of Islam. There is an externalization process in the form of implications for consumers considering halal-labeled cosmetic products as a form of Islamic symbolic representation. The objectification process is marked by changes in behavior that are even more selective in using cosmetic products, the internalization process with the influence of environmental factors who use cosmetic products labeled halal so that it affects consumer decisions to buy cosmetic products labeled halal. After this perception, people's beliefs and attitudes arise with the existence of a halal label, and in the end these beliefs and attitudes affect people's purchases.
Shock Waves in Nonequilibrium Gases and Plasmas

Abstract : An analysis and assessment of three mechanisms describing plasma/shock wave interactions was conducted under conditions typically encountered in a weakly ionized glow discharge. The mechanisms of ion-acoustic wave damping, post-shock energy addition and thermal inhomogeneities were examined by numerically solving the Euler equations with appropriate source terms adapted for each mechanism. Ion-acoustic wave damping was examined by modeling the partially ionized plasma as two fluids in one spatial dimension using the Riemann problem as a basis. Post-shock energy addition in the form of nonequilibrium vibrational energy relaxation was also examined in one spatial dimension using the Riemann problem as a basis. The influence of thermal inhomogeneities on shock wave propagation was examined in two spatial dimensions for both a Riemann shock and a shock generated by a spark discharge. Shocks were propagated through realistic thermal profiles with the resulting shock structure examined through the numerical application of various optical diagnostic techniques. Results from shock simulations indicate that ion-acoustic wave damping has an insignificant effect on the neutral flow at fractional ionization levels typical of glow discharges. Post-shock vibrational energy relaxation is also unable to effect the shock structure on the time scales of interest. An analysis of the effects of thermal inhomogeneities reveals that many of the observed plasma/shock anomalies can be explained based solely on this mechanism.