Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Object Detection Based on Improved Frame Difference Algorithm

An improved algorithm for getting more precise boundary of moving object base on frame difference algorithm is listed in this paper.The situation of imprecise value of boundary because of moving object shape variation on video was solved by the improved algorithm.It performs simulation of detecting distance and velocity of moving object with object central point calculating by the precise value in matlab.Experiment results show that the proposed method has closer to realistic value than frame difference algorithm.It is suitable for moving object detection when shape of moving object varies greatly in video.
POWER SYSTEM TRANSIENT STABILITY ASSESSMENT BASED ON STATISTICAL LEARNING THEORY

This paper presents a method of model construction for the power system transient stability assessment based on statistical learning theory integrated with the bagging and the approximate reasoning. Support vector machines (SVM) operate on the principle of structure risk minimization. This paper takes full advantage of its ability to solve the problem with small sample, nonlinear and high dimension. Hence better generalization ability is guaranteed. The multi-class identification for power system transient stability assessment is solved by the data set reconstruction. The assessment model uses the data set regulation, bagging and approximate reasoning to improve the training speed, the accuracy and stability of the estimation result. The IEEE 39-Bus test system is employed to demonstrate the validity of the proposed approach.
Becoming a Target: Journalists’ Perspectives on Anti-Press Discourse and Experiences with Hate Speech

ABSTRACT In many parts of the world, journalists work in increasingly hostile environments. To better understand the characteristics and implications of hostility against the press in a hybrid regime, this study explores how journalists in Serbia perceive and experience anti-press hate speech. Based on 20 interviews with diverse reporters, we describe hate speech against journalists as an ad hominem attack triggered by the very nature of journalistic conduct. We discuss two identified mechanisms of hate speech propagation. The first, machinery of hate speech, refers to orchestrated attacks on independent journalists by ruling party officials, pro-government news outlets, and troll armies. In contrast, the lone-wolf-haters mechanism refers to authentic and sporadic expressions of hate by extreme partisan audiences to which all journalists are susceptible, regardless of their affiliation. Describing their personal experiences, the participants report various responses to encountering hate speech, ranging from immediate emotions to less immediate psychological reactions to physical health problems. To prevent hateful acts from interfering with their journalistic performance, they rely on a variety of coping strategies and mechanisms, including psychological and professional strategies, as well as social and institutional support.
Lifestyle Changes during the SARS-CoV-2 Pandemic as Predictors of BMI Changes among Men and Women in Poland

Background: Social isolation during the SARS-CoV-2 pandemic affected people’s body weight, therefore, this study was designed to evaluate the association between lifestyle elements and the change in BMI during lockdown. Methods: This retrospective observational study involved 290 questionnaires completed by adult participants divided into three groups according to BMI change during isolation. The structured questionnaire included a general description of the study objective and collected data regarding sociodemographics, anthropometrics, physical activity, sedentary behaviour, sleep duration, and food intake pre- and during COVID-19 lockdown. Results: A decrease or increase in BMI was found in 23.6% and 47.8% of women and 18.5% and 42.6% of men, respectively. Among those who lost weight, 46.5% of women and 40% of men followed a diet of their own choice, 30.2% of women and 25% of men changed their product mix and reduced their intake, 40% of men stopped eating outside the home. An increase in BMI was associated with increased food intake (32.2% of women and 28.3% of men), increased sleep duration on weekdays (49.2% of women and 43.5% of men) and, in more than 50% of subjects, decreased physical activity. In women, increased BMI was associated with the highest frequency of snacking (p = 0.0003), the highest intake of sweets (p = 0.0021), and in men with the highest intake of alcohol (p = 0.0017). Conclusions: The observed changes in BMI during social isolation were the result of lifestyle modifications including dietary behaviour and differed by gender.
Risk Factors and Prognostic Implications of New-Onset Paroxysmal Atrial Fibrillation in Patients Hospitalized with Intracerebral Hemorrhage

Objective We aimed to assess the prevalence and risk factors of new-onset paroxysmal atrial fibrillation (PAF) in patients hospitalized with ICH and determine whether the new-onset PAF had influenced functional outcomes. Methods We analyzed a database of all consecutive patients with ICH from October 2013 to May 2022. Univariate and multivariable regression analyses were performed to identify risk factors for new-onset PAF in patients with ICH. Multivariate models were also constructed to assess whether the new-onset PAF was an independent predictor of poor functional outcome, as measured using the modified Rankin scale. Results This study included 650 patients with ICH, among whom 24 patients had new-onset PAF. In the multivariable model, older age (OR per 10-y increase, 2.26 [95% CI, 1.52–3.35]; P<0.001), hematoma volume (OR per 10-mL increase, 1.80 [95% CI, 1.26–2.57]; P=0.001), and heart failure (OR, 21.77 [95% CI, 5.52–85.91]; P<0.001) were independent risk factors for new-onset PAF. In a sensitivity analysis restricted to 428 patients with N-terminal pro-B-type natriuretic peptide (NT-proBNP), older age, larger hematoma volume, heart failure, and increased NT-proBNP were associated with new-onset PAF. After adjusting for baseline variables, new-onset PAF was an independent predictor of poor functional outcome (OR, 10.35 [95% CI, 1.08–98.80]; P=0.042). Conclusion Older age, larger hematoma volume, and heart failure were independent risk factors for new-onset PAF after ICH. Increased NT-proBNP is correlated with higher risks for new-onset PAF when their information is available at admission. Furthermore, new-onset PAF is a significant predictor of poor functional outcome.
Trade-Wind Clouds and Hawaiian Rainbands

Abstract Two case studies of rainband evolution off the windward coast of the Big Island of Hawaii are presented along with an overview of the complete radar and satellite dataset from the Hawaiian Rainband Project conducted in the summer of 1990. These studies reveal that radar-observed rainbands and cells offshore of the windward coastline are nearly always embedded within larger-scale stratocumulus cloud patches and/or cloud lines moving in from the northeastern Pacific Ocean in the easterly trade winds. These cloud patches develop in the trade-wind flow downstream of the large shallow stratocumulus cloud mass that covers much of the northeastern Pacific Ocean between Hawaii and California. Tropical cyclones originating in the intertropical convergence zone sometimes move north through this stratocumulus cloud mass, producing, in their wake, cloud-free regions that then advect toward the Hawaiian Islands. These regions contribute to the variability of the organization of cloud patterns and rainband occ.
Unmanned aerial vehicle navigation in underground structure inspection: A review

Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.
Association of hsa-miR-5571-5p Expression with Clinicopathological Factors besides Identification of Its Hub Target Genes and Key Pathway in Breast Cancer

Background: miRNAs are small non-coding RNAs; regulate gene expression using RNA degradation or translation repression. Dysregulation of miRNAs is involved in the initiation and progression of many cancers. We aimed to determine the relationship between miR-5571-5p expression and clinical factors and regulatory mechanisms in breast cancer. Methods: Histopathologic sections approximately with 25 microns thick from FFPE tissues were achievement of Al-Zahra Hospital (Isfahan, Iran) in 2020–2021 years by Pathologist. miR-5571-5p expression, determined using real-time PCR. For miRNA target genes prediction, integrated miRNA target prediction tools, were used. Gene ontology and KEGG pathway analysis were accomplished to identify the biological function. A PPI network was constructed to display key target genes. For hub genes validation, GEPIA databases were used. Results: miR-5571-5p was upregulated in breast tumor tissues, and its increase was significantly related to a poor prognosis in breast cancer (P<0.0001). At first, 324 target genes were predicted, and then 110 genes with a decrease in expression were selected. GO analysis showed that genes were mainly enriched in the regulation of the ERBB2 and EGFR signaling pathway. KEGG pathway analysis suggested that downregulated genes were enriched in glioma, the ErbB signaling pathway, and breast cancer. Finally, the ten hub genes (EGF, PIK3R1, SOS1, PTEN, SHC1, CBLB, LIFR, LEP, PDE1C, and NT5C2) were detected from the PPI network. Conclusion: miR-5571-5p up-regulation is associated with breast cancer progression and worse survival. The current study identified ten genes associated with breast cancer, which might help to provide candidate targets for the treatment.
Inferences with an unknown noise level in a Bernoulli process

Inferences about a proportion p are often based on data generated from dichotomous processes, which are generally modeled as processes that are Bernoulli in p. In reality, the assumption that a data-generating process is Bernoulli in p is often violated due to the presence of noise. The level of noise is usually unknown and, furthermore, dependent on the unknown proportion in which one is interested. A specific model which takes into account the existence of noise is developed. Any arguments about p based exclusively on a likelihood analysis can lead to difficulties. A Bayesian approach is used, which also helps us to formalize a priori dependence between the proportion and the noise level. Empirical data are used to illustrate the model and provide some flavor of the implications of our uncertainty about the noise for inferences about a proportion.
A Randomized Controlled Study of Robot-Assisted versus 3D Laparoscopic Radical Prostatectomy in Patients with Carcinoma Prostate

Materials and Methods A prospective randomized comparative study was performed from 1st January 2020 to 30th June 2021. All patients included were diagnosed with localized/locally advanced ca prostate. 60 patients fulfilling the inclusion and exclusion criteria were randomized into 2 groups. Groups A and B included patients who underwent robot-assisted radical prostatectomy and 3D laparoscopic transperitoneal radical prostatectomy, respectively. Various demographic, intraoperative, postoperative, and follow-up parameters were collected. Outcomes were evaluated in the form of the trifecta (continence, potency, and BCR-free status) and pentafecta rates (trifecta with no perioperative complications and negative surgical margins) in between the two groups. Results The mean operative time in Group A was 137.83 mins ± 17.27 compared to 148.20 mins ± 26.16 in Group B. Trifecta rates in Group A and Group B were 43.3%, 63.3%, and 76.6% and 40%, 53.3%, and 70% at 1, 3, and 6 months. Pentafecta rates in Group A and Group B were 36.6%, 53.3%, and 70% and 33.3%, 40%, and 53.3% at 1, 3, and 6 months. Complication rates were 10% in Group A and 13.3% in Group B, respectively. Only one patient in our study (Group B) had a positive surgical margin. Conclusions We conclude from our comparative study, that both robot-assisted and 3D laparoscopic transperitoneal radical prostatectomy are feasible and efficacious treatment modalities for achieving acceptable trifecta and pentafecta rates in managing ca prostate with earlier continence and shorter urethrovesical anastomosis time in the robotic arm.
Identification and Characterization of Dmct: A Cation Transporter in Yarrowia lipolytica Involved in Metal Tolerance

Yarrowia lipolytica is a dimorphic fungus used as a model organism to investigate diverse biotechnological and biological processes, such as cell differentiation, heterologous protein production, and bioremediation strategies. However, little is known about the biological processes responsible for cation concentration homeostasis. Metals play pivotal roles in critical biochemical processes, and some are toxic at unbalanced intracellular concentrations. Membrane transport proteins control intracellular cation concentrations. Analysis of the Y. lipolytica genome revealed a characteristic functional domain of the cation efflux protein family, i.e., YALI0F19734g, which encodes YALI0F19734p (a putative Yl-Dmct protein), which is related to divalent metal cation tolerance. We report the in silico analysis of the putative Yl-Dmct protein’s characteristics and the phenotypic response to divalent cations (Ca2+, Cu2+, Fe2+, and Zn2+) in the presence of mutant strains, Δdmct and Rdmct, constructed by deletion and reinsertion of the DMCT gene, respectively. The absence of the Yl-Dmct protein induces cellular and growth rate changes, as well as dimorphism differences, when calcium, copper, iron, and zinc are added to the cultured medium. Interestingly, the parental and mutant strains were able to internalize the ions. Our results suggest that the protein encoded by the DMCT gene is involved in cell development and cation homeostasis in Y. lipolytica.
Research on expression recognition algorithm based on improved convolutional neural network

The accuracy of traditional convolutional neural networks in expression recognition is relatively low, this paper makes improvements on the basis of ResNet network. On the one hand, the two 3*3 convolutional layers of ResNet residual block are replaced by two 1*1 convolutional layers plus a 3*3 convolutional layer, which not only maintains the accuracy of the network model but also reduces the amount of computation at the same time, the ReLU activation function is replaced with Mish. On the other hand, the convolutional attention mechanism is added to the network model to suppress the useless feature information, so as to improve the recognition accuracy of facial expressions. The experimental results show that the improved network model has significant improvement over the original network in RAF-DB and FERPlus data sets respectively.
Removing Unwanted Fluctuations in the AVHRR Thermal Calibration Data Using Robust Techniques

The study deals with analysis of thermal calibration of the Advanced Very High Resolution Radiometer (AVHRR) aboard National Oceanic and Atmospheric Administration (NOAA) spacecrafts. In particular, the effects caused by various types of contamination or corruption of the thermal calibration data are investigated. These phenomena lead to perturbations of the true signal, referred to here as unwanted fluctuations. They must be removed or corrected to maximum possible extent to reduce the error in the calibrated data. It is shown that methods currently employed in operational practice at NOAA and the Canada Centre for Remote Sensing (CCRS) frequently fail to remove some of the unwanted fluctuations in calibration data that may lead to biases in brightness temperature exceeding 1 K. A complex method for removing unwanted fluctuations in the thermal calibration data specifically designed for the AVHRR radiometers is proposed. The procedure is based on combining robust statistical procedures and Fourier transform filtering techniques. Application of the method is considered for various components of calibration data: temperature sensors, blackbody, and space count, as well as gain in all thermal channels. High Resolution Picture Transmission (HRPT) data and Global Area Coverage (GAC) data are analyzed. Power spectra analysis of the calibration data has been conducted to estimate impact of various frequency harmonics. The method proposed may be useful for the development of calibration techniques for similar radiometers and the future National Polar-Orbiting Operational Environmental Satellite System.
DBD plasma‐assisted coating of metal alkoxides on sulfur powder for Li–S batteries

Sulfur particles coated by activation of metal alkoxide precursors, aluminum–sulfur (Alu–S) and vanadium–sulfur (Van–S), were produced by dielectric barrier discharge (DBD) plasma technology under low temperature and ambient pressure conditions. We report a safe, solvent‐free, low‐cost, and low‐energy consumption coating process that is compatible for sustainable technology up‐scaling. NMR, XPS, SEM, and XRD characterization methods were used to determine the chemical characteristics and the superior behavior of Li–S cells using metal oxide‐based coated sulfur materials. The chemical composition of the coatings is a mixture of the different elements present in the metal alkoxide precursor. The presence of alumina Al2O3 within the coating was confirmed. Multi‐C rate and long‐term galvanostatic cycling at rate C/10 showed that the rate capability losses and capacity fade could be highly mitigated for the Li–S cells containing the coated sulfur materials in comparison to the references uncoated (raw) sulfur. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) confirm the lower charge‐transfer resistance and potential hysteresis in the electrodes containing the coated sulfur particles. Our results show that the electrochemical performance of the Li–S cells based on the different coating materials can be ranked as Alu‐S > Van‐S > Raw sulfur.
Manipulating plasma turbulence in cross-field plasma sources using unsteady electrostatic forcing

Unsteady electrostatic forcing is investigated as a method for manipulating turbulent plasma behaviour within Hall-effect thrusters and similar cross-field plasma devices using a simplified one-dimensional three-velocity azimuthal electrostatic particle-in-cell simulation. A wide range of axial electric field forcing frequencies from 1 MHz up to 10 GHz at amplitudes of 10 V cm−1, 50 V cm−1 and 100 V cm−1 are applied to the plasma and the response is evaluated against a baseline case defined by the community benchmark LANDMARK Test Case 1. ‘Tailoring’ of plasma parameters, such as the electron cross-field mobility, is demonstrated via manipulation of the electron drift instability using unsteady forcing. Excitation of the unstable electron cyclotron modes by the electron drift instability is shown to be able to produce a reduction of the resultant electron cross-field mobility of the plasma by up to 50% compared to the baseline value. Additionally, forcing at the electron cyclotron frequency appears to be capable of increasing cross-field mobility by up to 2000%. Implications of the results for direct drive electric propulsion systems and improved current utilization efficiencies for Hall-effect thrusters are discussed.
Bio remedial Process: A Review on Removal of Fluoride from the Waste Water

Ground water has always been an important and most dependable source of water since prehistoric ages, but with rapid industrialization to meet the needs of growing population has stressed the ground water reservoir. Stress to ground water reservoir is just one problem; industrial waste has also introduced lots of new chemical substances in water bodies. Fluorides are one such pollutant that undermines living life forms especially human beings . There are primarily two sources of fluoride contamination in ground water. Geogenic sources and anthropogenic sources. Talking about the status of fluoride contamination it can be seen in more than 25 countries worldwide. In India itself 19 states and at least 132 districts have witnessed the problem of water contamination, Fluoride contamination is also impacting the health of people adversely. Diseases like skeletal fluorosis, non –skeletal fluorosis, and dental fluorosis are the most common health problem from fluoride contamination . Due to fluoride contamination diseases in plants like Chlorosis, Necrosis is impacting plant body. Thus, to remove fluorine contamination from water we can use bioremediation process. Bioremediation strategies can be classified mainly in two categories in – situ techniques and ex –situ techniques. Its mode of action primarily includes use of microbes in processing the fluoride contamination. This shows that bioremediation has lots of advantages like it’s a natural process, cost effective etc. But it too have some disadvantages like they are highly specific and it takes longer time etc. Considering all the merits and demerits of bioremediation, it is the most effective technological tool that holds great value for the future as scientists learn more about its capabilities and the curiosity to find more appropriate methods are still going on. This review article deals with ground water pollution due to fluoride concentration. We have thoroughly reviewed on its impact on health of plants and animals and the bioremediation processes to cure the water contamination