Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews

The African vulture optimization algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.
LONG-WAVELENGTH UNSTABLE MODES IN THE FAR UPSTREAM OF RELATIVISTIC COLLISIONLESS SHOCKS

The growth rate of long-wavelength kinetic instabilities arising due to the interaction of a collimated beam of relativistic particles and a cold unmagnetized plasma are calculated in the ultrarelativistic limit. For sufficiently culminated beams, all long-wavelength modes are shown to be Weibel-unstable, and a simple analytic expression for their growth rate is derived. For large transverse velocity spreads, these modes become stable. An analytic condition for stability is given. These analytic results, which generalize earlier ones given in the literature, are shown to be in agreement with numerical solutions of the dispersion equation and with the results of novel particle in cell simulations in which the electromagnetic fields are restricted to a given k-mode. The results may describe the interaction of energetic cosmic rays, propagating into the far upstream of a relativistic collisionless shock, with a cold unmagnetized upstream. The long-wavelength modes considered may be efficient in deflecting particles and could be important for diffusive shock acceleration. It is shown that while these modes grow in relativistic shocks propagating into electron–positron pair plasmas, they are damped in relativistic shocks propagating into electron–proton plasmas with moderate Lorenz factors Γsh ≲ (mp/me)1/2. If these modes dominate the deflection of energetic cosmic rays in electron–positron shocks, it is argued that particle acceleration is suppressed at shock frame energies that are larger than the downstream thermal energy by a factor of ≳ Γsh.
A PROPELLER MODEL FOR THE SUB-LUMINOUS STATE OF THE TRANSITIONAL MILLISECOND PULSAR PSR J1023+0038

The discovery of millisecond pulsars switching between states powered either by the rotation of their magnetic field or by the accretion of matter has recently proved the tight link shared by millisecond radio pulsars and neutron stars in low-mass X-ray binaries. Transitional millisecond pulsars also show an enigmatic intermediate state in which the neutron star is surrounded by an accretion disk and emits coherent X-ray pulsations, but is sub-luminous in X-rays with respect to accreting neutron stars, and is brighter in gamma-rays than millisecond pulsars in the rotation-powered state. Here, we model the X-ray and gamma-ray emission observed from PSR J1023+0038 in such a state based on the assumptions that most of the disk in-flow is propelled away by the rapidly rotating neutron star magnetosphere, and that electrons can be accelerated to energies of a few GeV at the turbulent disk–magnetosphere boundary. We show that the synchrotron and self-synchrotron Compton emission coming from such a region, together with the hard disk emission typical of low states of accreting compact objects, is able to explain the radiation observed in the X-ray and gamma-ray bands. The average emission observed from PSR J1023+0038 is modeled by a disk in-flow with a rate of 1–3 × 10−11 M⊙ yr−1, truncated at a radius ranging between 30 and 45 km, compatible with the hypothesis of a propelling magnetosphere. We compare the results we obtained with models that assume that a rotation-powered pulsar is turned on, showing how the spin-down power released in similar scenarios is hardly able to account for the magnitude of the observed emission.
A Novel Hybrid Deep Learning Approach to Code Generation Aimed at Mitigating the Real-Time Network Attack in the Mobile Experiment Via GRU-LM and Word2vec

As the use of devices in mobile environments increases, network attacks such as DDoS have a malicious attempt to flood the network's regular traffic to overload the target and surrounding infrastructure. This research proposed machine learning and deep learning approaches to dealing with DDoS attacks, and the results are described as follows. First, this research successfully detected DDoS attacks through an LGBM with a 100% accuracy score. Second, the proposed model (GRU-LM), which consists of a trained Word2vec layer with the Python dataset, is far more effective than the standard GRU model. Since Python is quite similar to English, language model-based GRU yields superior results. Various preprocessing steps were performed through the NLTK package, and each number was assigned to the tokenized one for constructing the GRU language model. The result reveals that the proposed model achieved an accuracy score of 87% for predicting the following words in the source code, while the rest achieved below 30% accuracy. This conclusion is significant because its relatively simple and light structure overcomes tradeoff problems between time and accuracy and is adaptable to the mobile setting. Discovering traffic patterns for the underlying data of DDOS assaults and retrieving them using statistical data analysis is the value of this research. Furthermore, since public cloud application vulnerability assaults are rising due to expanding cloud infrastructure, this finding could be used in such attacks.
Developing a Culture of Academic Integrity in Examinations in a Distance Learning Environment

The article deals with the issues of observing the culture of academic integrity in examinations in a distance learning environment. The purpose of this study is to provide an overview of the solutions that are currently used in our organization to monitor educational achievement, while maintaining an appropriate level of culture of academic integrity by technologization of the assessment process as one of the necessary ways. The authors, based on their research, which was carried out for two years, give recommendations on the organization of online exams, consider various types of control and the corresponding digital technologies that allow maintaining academic integrity. Identified possible violations during the online exam, analyzed the causes and proposed solutions. Types of exam that develop critical thinking and have zero tolerance for any violation of the code of academic integrity are the open book written exam, project and practical exams, as well as testing and closed book written exam using a proctoring system. Identified principles that should become the norm to strengthen the culture of academic integrity.
Surface abundances of ON stars

Context. Massive stars burn hydrogen through the CNO cycle during most of their evolution. When mixing is efficient or when mass transfer in binary systems occurs, chemically processed material is observed at the surface of O and B stars. Aims. ON stars show stronger lines of nitrogen than morphologically normal counterparts. Whether this corresponds to the presence of material processed through the CNO cycle is not known. Our goal is to answer this question. Methods. We performed a spectroscopic analysis of a sample of ON stars with atmosphere models. We determined the fundamental parameters as well as the He, C, N, and O surface abundances. We also measured the projected rotational velocities. We compared the properties of the ON stars to those of normal O stars. Results. We show that ON stars are usually rich in helium. Their CNO surface abundances are fully consistent with predictions of nucleosynthesis. ON stars are more chemically evolved and rotate - on average - faster than normal O stars. Evolutionary models including rotation cannot account for the extreme enrichment observed among ON main sequence stars. Some ON stars are members of binary systems, but others are single stars as indicated by stable radial velocities. Mass transfer is therefore not a simple explanation for the observed chemical properties. Conclusions. We conclude that ON stars show extreme chemical enrichment at their surface, consistent with nucleosynthesis through the CNO cycle. Its origin is not clear at present.
Transient gamma-ray emission from Cygnus X-3

The high-mass microquasar Cygnus X-3 has been recently detected in a flaring state by the gamma-ray satellites Fermi and Agile. In the present contribution, we study the high-energy emission from Cygnus X-3 through a model based on the interaction of clumps from the Wolf-Rayet wind with the jet. The clumps inside the jet act as obstacles in which shocks are formed leading to particle acceleration and non-thermal emission. We model the high energy emission produced by the interaction of one clump with the jet and briefly discus the possibility of many clumps interacting with the jet. From the characteristics of the considered scenario, the produced emission could be flare-like due to discontinuous clump penetration, with the GeV long-term activity explained by changes in the wind properties.
LICK INDICES AND SPECTRAL ENERGY DISTRIBUTION ANALYSIS BASED ON AN M31 STAR CLUSTER SAMPLE: COMPARISONS OF METHODS AND MODELS

Application of fitting techniques to obtain physical parameters—such as ages, metallicities, and α-element to iron ratios—of stellar populations is an important approach to understanding the nature of both galaxies and globular clusters (GCs). In fact, fitting methods based on different underlying models may yield different results and with varying precision. In this paper, we have selected 22 confirmed M31 GCs for which we do not have access to previously known spectroscopic metallicities. Most are located at approximately one degree (in projection) from the galactic center. We performed spectroscopic observations with the 6.5 m MMT telescope, equipped with its Red Channel Spectrograph. Lick/IDS absorption-line indices, radial velocities, ages, and metallicities were derived based on the EZ_Ages stellar population parameter calculator. We also applied full spectral fitting with the ULySS code to constrain the parameters of our sample star clusters. In addition, we performed fitting of the clusters’ Lick/IDS indices with different models, including the Bruzual & Charlot models (adopting Chabrier or Salpeter stellar initial mass functions and 1994 or 2000 Padova stellar evolutionary tracks), the galev, and the Thomas et al. models. For comparison, we collected their UVBRIJK photometry from the Revised Bologna Catalogue (v.5) to obtain and fit the GCs’ spectral energy distributions (SEDs). Finally, we performed fits using a combination of Lick/IDS indices and SEDs. The latter results are more reliable and the associated error bars become significantly smaller than those resulting from either our Lick/IDS indices-only or our SED-only fits.
Soil Organic Carbon Storage in Australian Wheat Cropping Systems in Response to Climate Change from 1990 to 2060

It is important to examine the effects of climate change on temporal variations in SOC storage, in order to optimize management practices for sustainable grain production. Using the denitrification–decomposition (DNDC) model to simulate biogeochemical processes in agro-ecosystems, SOC variability was evaluated in the Australian wheat cropping system from 1990 to 2060, under the Representative Concentration Pathway 85 (RCP85) climate change scenario. We analyzed the impacts of temperature and precipitation on SOC variability and further simulated six management scenarios for wheat cultivation over 71 years, which included wheat cropping under common nitrogen fertilizer (N-fertilizer) application rate (12 kg N/ha), adequate N-fertilizer application rate (50 kg N/ha), and legume–wheat rotation with N fertilizer application rates at 0, 12, and 50 kg N/ha. The results indicated that the DNDC model provided a good simulation of biogeochemical processes associated with wheat growth; the normalized root mean square error (NRMSE) of wheat yield was 15.16%, and the NRMSE of SOC was 13.21%. The SOC (0–30 cm) decreased from 3994.1 kg C/ha in 1990 to 2848.0 kg C/ha in 2060, an average decrease of 0.4% per year. Temperature and precipitation were the important factors affecting SOC storage, with contributions of 13% and 12%, respectively. Furthermore, adding a legume phase increased SOC and wheat yield in the low N-fertilizer scenario. In contrast, adding a legume phase in the adequate N-fertilizer scenario decreased SOC and wheat yield.
Analysis on Related Factors of Hospitalization Days of Patients with Novel Coronavirus Pneumonia in Fangcang Shelter Hospitals

Objective: To study the factors influencing the length of stay in patients with COVID-19. Methods: The clinical data of 110 patients with COVID-19 who were infected with Omicron virus for more than 10 days from April to May 2022 were collected. Their gender, age, smoking status, clinical manifestations related to pneumonia, whether they were combined with other basic diseases and vaccination status were analyzed. The above influencing factors were analyzed by single factor regression analysis, followed by Cox regression model analysis. Results: The age of patients had an effect on the length of hospital stay. The median length of hospital stay of patients that are < 50 years old, 50-64 years old and ? 65 years old were 14, 14, and 17 days respectively (? 2 = 9.346, P = 0.009). The presence of coronavirus-related symptoms also affects the length of hospital stay (? 2 = 4.840, P = 0.028). The results of multivariate regression model showed that age (? 2 = 8.669, P = 0.006) and coronavirus-related symptoms (? 2 = 5.424, P = 0.020) were independent factors affecting the length of hospital stay. Conclusion: During the development of COVID-19, the age of patients and whether they have coronavirus-related symptoms are positively correlated with the length of stay. Gender, habit of smoking, presence of other basic diseases and vaccination are not related to the length of stay.
Combining the COM-B Model and Habit Theory to Leverage Understanding of Adolescents’ Tooth-Brushing Behavior

ABSTRACT Although brushing teeth twice a day is not difficult, a surprisingly large proportion of adolescents do not behave accordingly. This paper brings together the COM-B model and the habit formation theory in order to create a basis for a more comprehensive, customer-oriented, theory-based understanding of the issue. A total of nine focus group interviews were conducted among adolescents to further understand underlying aspects and develop solutions. In findings, barriers related to adolescents tooth brushing behavior were identified in all areas of COM-B model. Especially the role of automatic motivation was highlighted as in mornings tooth brushing seemed not to have a stable place in daily routines. To further understand this issue and develop suitable solutions, habit formation theory together with identified enablers provided effective starting point. This also demonstrated how these two theories can complement each other. The study provides actionable insights for public sector marketers to understand and assists adolescents’ tooth-brushing behavior.
Establishment of the Mouse Model of Social Avoidance Induced by Female-Directed Female Aggression

Background Most preclinical research on the effects of stress has been done on male subjects, even though women are more prone than men to experience stress-related problems. Chronic social defeat stress (CSDS) is a rodent model of psychosocial stress. However, this model has been challenged in female mouse studies since neither male nor female resident mice attack intruder females. A female-to-female CSDS model is needed to investigate the physiological and behavioral aspects. Methods The intruders were either male or female C57BL/6J mice, whereas the residents were male or ovariectomized (OVX) female CD-1 mice. The CD-1 aggressor mice had direct physical contact with the C57BL/6J mice for 10 min before initiating sensory contact with them for 24 h. Jump escape and freezing were evaluated during the social defeat of days 1 and 12. Experimental C57BL/6J mice underwent a social interaction test after suffering social defeat for 12 days. Results We found that the number of attack bites and attack latency had a significant negative correlation during the selection of aggressors. In the single-housed OVX mice, 34% of mice met the criterion of the selection of aggressors. However, single-housed OVX mice did not show sustained aggressive behavior (eg, attack bites) through the 12-day CSDS. As a result, we did not find susceptible mice during the social interaction test. In contrast, during the selection of aggressors, 42% of OVX mice housed with partners satisfied the criterion and displayed consistently aggressive behavior. CSDS produced susceptible (50%) and resilient (50%) phenotypes during the social interaction test. Notably, male and OVX female CD-1 mice housed with partners had similar amounts of attack bites and attack rates over the 12-day CSDS. Finally, we found that chronically socially defeated male and female mice displayed different coping behaviors (eg, active vs passive) with social defeat. Conclusions Our study demonstrates that OVX CD-1 mice housed with mates exhibited territorial aggression toward female intruders, producing susceptibility and resilience to social avoidance. Additionally, socially defeated male and female mice displayed different behavioral susceptibility to social defeat.
Generalized second law at linear order for actions that are functions of Lovelock densities

In this article we consider the second law of black holes (and other causal horizons) in theories where the gravitational action is an arbitrary function of the Lovelock densities. We show that there exists an entropy which increases locally, for linearized perturbations to regular Killing horizons. In addition to a classical increase theorem, we also prove a generalized second law for semiclassical, minimally-coupled matter fields.
Experimental investigation of aerodynamics behavior of a new generic of unmanned air vehicle (heliplane)

The aeronautics studies and particularly the development of drones represent an important field, in which there is a very large number of research and studies.This article aims to present an experimental aerodynamic study of an autonomous surveillance unmanned aerial vehicle (UAV) that is capable of combining the advantages of both categories, namely the fixed-wing drones and the rotary-wing drones. To achieve this objective, a wind tunnel was used to study the flow around this drone in order to better understand the aerodynamic phenomena and to obtain some initial estimates of the lift, drag and moment coefficients, at three different Reynolds numbers of 4.02×104, 6.03×104 and 8.04×104, and for different angles of attack, from [−45°, +45°] with step of 1°. The experimental results obtained in this work show an casi-symetrical variation bertwen the negatif and positif incidence angles of the lift coeffecient, wiche indicate that the heliplane can fly in an inverted position, whatever the angle of incidence. Moreover, the minimum mean value of drag coeffecient according 1° incidence angle is 0.0478 then the drag due to geometry and pitching moment can never be canceled.
Day Ahead Electricity Price Forecasting Based on the Deep Belief Network

With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China’s electricity sales companies to predict electricity prices.
Depth-Included Curvature Inpainting for Disocclusion Filling in View Synthesis

Depth-image-based-rendering (DIBR) is the commonly used for generating additional views for 3DTV and FTV using 3D video formats such as video plus depth (V+D) and multi view-video-plus-depth (MVD). The synthesized views suffer from artifacts mainly with disocclusions when DIBR is used. Depth-based inpainting methods can solve these problems plausibly. In this paper, we analyze the influence of the depth information at various steps of the depth-included curvature inpainting method. The depth-based inpainting method relies on the depth information at every step of the inpainting process: boundary extraction for missing areas, data term computation for structure propagation and in the patch matching to find best data. The importance of depth at each step is evaluated using objective metrics and visual comparison. Our evaluation demonstrates that depth information in each step plays a key role. Moreover, to what degree depth can be used in each step of the inpainting process depends on the depth distribution.
Bulletin of Electrical Engineering and Informatics

Received Apr 11, 2022 Revised Jul 25, 2022 Accepted Aug 29, 2022 Vehicle routing problem with time windows (VRPTW) is a special kind of vehicle routing with adding time windows constraints and has a variety of applications in logistics. Many researchers have attacked the VRPTW by approximate solutions. Ant colony optimization (ACO) is a classical method to solve the VRPTW problem but the constraints of VRPTW are not used to consider customer selection. Most ACO-based optimization algorithms can suffer from the complexity of the VRPTW such as trapping in local optimum. In this paper, we present a novel ACO-based optimization method for VRPTW by using customer selection in order to decrease or solve the inefficiency of the customer selection of the ACO process. Moreover, we enhance performance searching of ACO in order to eliminate these small routes from the ACO process. Finally, we proposed the re-initialization technique in order to decrease or solve trapping in local optimum. Experiments conducted on fifty-six maps dataset have shown that the proposed method achieves encouraging performance compared to other ACOs.