Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulator Pollution Degree Identification Method Based on Sampling Cloth and Layered Idea

The insulator that plays an insulating role at one end of the high-voltage line is exposed to the air environment, and long-term pollution accumulation in a humid environment will cause flashover between the insulators, which will affect the power supply line and require greater cost to detect and repair. At present, the contact measurement method is used to detect the pollution on insulator surface by cleaning, and the non-contact measurement method uses the idea of characteristic band imaging, which can guide the regular cleaning of insulator surface. In this paper, a method is proposed to use layering ideas and machine learning algorithms on hyperspectral image of a sampling cloth wiped clean from the insulator surface to achieve insulator pollution detection. Hyperspectral data is a three-dimensional data cube with a large amount of data. Firstly, the idea of stratification is used to identify the interval where the ratio of non-soluble deposit density (NSDD) and equivalent salt deposit density (ESDD) of insulators is located, and then XGBoost machine learning algorithm is used to establish the regression detection model of salt density and grey density in the corresponding interval. The method has been proven to have high accuracy and feasibility, and can guide the regular cleaning of insulator surface pollution in practical applications.
Drones Do Not Drift between Nests in a Wild Population of Apis cerana

Simple Summary In honey bees, drones disperse to specific locations in order to mate before returning to their natal nests. However, in apiaries of Apis mellifera, drones have been witnessed returning to non-natal nests; this behavior may also sometimes occur at natural nests. This behavior could be due to the unnaturally high density of nests in apiaries. In this study, we genotyped colonies of the Apis cerana honey bee collected in its invasive range of Far North Queensland, Australia, to determine whether there is evidence of drone drift between colonies. We found no such evidence of drone drift. Some drones that do not match the resident queen’s genotype are instead likely due to queen turnover or worker reproduction. Abstract The modes through which individuals disperse prior to reproduction has important consequences for gene flow in populations. In honey bees (Apis sp.), drones (males) reproduce within a short flight range of their natal nest, leaving and returning each afternoon within a narrow mating window. Drones are assumed to return to their natal nests as they depend on workers to feed them. However, in apiaries, drones are reported to regularly make navigation errors and return to a non-natal nest, where they are accepted and fed by unrelated workers. If such a “drone drift” occurred in wild populations, it could facilitate some further degree of dispersal for males, particularly if drones drift into host nests some distance away from their natal nest. Here, we investigated whether drone drift occurs in an invasive population of the Asian honey bee (Apis cerana). Based on the genotypes of 1462 drones from 19 colonies, we found only a single drone that could be considered a candidate drifter (~0.07%). In three other colonies, drones whose genotypes differed from the inferred queen were best explained by recent queen turnover or worker-laying. We concluded that drone drift in this population is low at best, and A. cerana drones either rarely make navigation errors in wild populations or are not accepted into foreign nests when they do so. We therefore confirm that drone dispersal distance is limited to the distance of daily drone flights from natal nests, a key assumption of both colony density estimates based on sampling of drone congregation areas and population genetic models of gene flow in honey bees.
A novel variational Bayesian method for spatiotemporal decomposition of resting-state fMRI

Introduction We apply a new variational Bayesian factor partition (VBFP) method to the sparse spatiotemporal decomposition of resting state fMRI data. The VBFP method estimates sources with sparse distributions in both spatial and temporal domains and incorporates automatic relevance determination in a fully Bayesian inference framework. Hence it achieves dimension reduction as an integrated part of the inference. We apply VBFP to resting state fMRI data and compare it with a maximum likelihood independent component analysis (ICA) algorithm [Bell and Sejnowski, 1995] and show that VBFP identifies similar functionally coherent brain networks and their temporal fluctuations. The potential advantages of VBFP on the integrated inference of the noise model and on robustness for small sample sizes motivate further investigation.
A Magnetic-Geared Machine With Improved Magnetic Circuit Symmetry for Hybrid Electric Vehicle Applications

Magnetic-geared machine (MGM) has been widely investigated as the power split device for hybrid electric vehicles (HEVs) with the inherently high integration of multimechanical ports. It is revealed that the magnetic circuits of armature winding in MGM are essentially asymmetrical for different phases under the magnetic field modulation effect. This inevitably results in unbalanced phase back electromotive force (EMF) waveforms, severe torque ripples, acoustic noise, and difficulty with precise control. To alleviate the asymmetry problem, a generic winding design model is presented for MGMs in this article, which allows for consideration of all potential winding configurations. First, a novel back-EMF harmonic factor is proposed to account for both the harmonic content and asymmetric level in MGM. The genetic algorithm (GA) is further employed to optimize the winding in terms of minimizing the back-EMF harmonic factor, armature magnetomotive force (MMF) harmonic contents, and phase winding resistance. The proposed winding layout exhibits superior filtering capability for three-phase asymmetric magnetic field harmonics. As compared with the conventional integral-slot distributed winding, the MGM with the proposed winding exhibits improved back-EMF waveform symmetry, suppressed torque ripple, and reduced core losses. Finally, an experimental prototype is manufactured to validate the effectiveness of asymmetric magnetic field suppression for MGMs. The results show the investigated winding design method is an effective solution to the asymmetric issue of MGMs, paving the way to research opportunities for further improvements.
Self-Reported Hearing-Aid Use Patterns in an Adult Danish Population

The retrospective reporting of users’ hearing aid (HA) usage can provide insight into individualized HA usage patterns. Understanding these HA usage patterns can help to provide a tailored solution to meet the usage needs of HA users. This study aims to understand the HA usage pattern in daily-life situations from self-reported data and to examine its relationship to self-reported outcomes. A total of 1537 participants who responded to questions related to situations where they always took off or put on the HAs were included in the study. A latent class analysis was performed to stratify the HA users according to their HA usage pattern. The results showed distinct usage patterns in the latent classes derived for both scenarios. The demographics, socio-economic indicators, hearing loss, and user-related factors were found to impact HA usage. The results showed that the HA users who reported using the HAs all the time (regular users) had better self-reported HA outcomes than situational users, situational non-users, and non-users. The study explained the underlying distinct HA usage pattern from self-reported questionnaires using latent class analysis. The results emphasized the importance of regular use of HAs for a better self-reported HA outcome.
Photovoltaic System based Interleaved Converter for Grid System

The MPPT controller of a photovoltaic (PV) system fluctuates, increasing solar irradiance, and has complex voltage-current properties. To balance the solar PV system towards the load and design the solar energy system on MPPT, a two-cell interleaved DC-DC boost connected with an inverter is utilized. Using just load voltage statistics, a voltage control technique is created, excluding array current monitoring. When compared to non-coupled pair interleaved conversions, the current conversion design has lower ripple contents from the loads and supply sides, better efficiency, as well as lower switch stress. Consequently, a decreased array capacitance value is enough to stabilize the array voltage output. For maximum power point functioning, analytical formulas for the solar supply and interleaved boost converter are constructed. The power interleaved converter is functioning using the control technique of MPPT, the Perturb and Observe (P&O) algorithm is employed, and the power is supplied to the alternating voltage conversion, and it is connected to the voltage source converter. The modeling and experimental findings are presented here to demonstrate how well suited that particular kind of power converter is for the application. In addition, a comparison of coupled and non-coupled interleaved boost converters for solar applications is performed.
Self-Organizing Coalition Formation Based on Non-Cooperative Games in Social Networks

In multi-agent systems, the individual agent must form coalitions to accomplish complex tasks. However, the centralized management model is not flexible in a dynamic environment. To overcome the restriction caused by central control, the paper presents a self-organizing dynamic coalition mechanism based on game theory. Firstly, we adopt a distributed network to communicate among agents, allowing agents to solve the real-time task assignment problem autonomously. Next, a non-cooperative game negotiation model is introduced to find the optimal strategy for each agent. Finally, the effectiveness of our mechanism is validated by comparing it with the traditional command model in three distributed networks. Experimental results indicate the proposed mechanism is capable to improve the system utility.
Multi-Scale Spectral-Spatial Attention Network for Hyperspectral Image Classification Combining 2D Octave and 3D Convolutional Neural Networks

Traditional convolutional neural networks (CNNs) can be applied to obtain the spectral-spatial feature information from hyperspectral images (HSIs). However, they often introduce significant redundant spatial feature information. The octave convolution network is frequently utilized instead of traditional CNN to decrease spatial redundant information of the network and extend its receptive field. However, the 3D octave convolution-based approaches may introduce extensive parameters and complicate the network. To solve these issues, we propose a new HSI classification approach with a multi-scale spectral-spatial network-based framework that combines 2D octave and 3D CNNs. Our method, called MOCNN, first utilizes 2D octave convolution and 3D DenseNet branch networks with various convolutional kernel sizes to obtain complex spatial contextual feature information and spectral characteristics, separately. Moreover, the channel and the spectral attention mechanisms are, respectively, applied to these two branch networks to emphasize significant feature regions and certain important spectral bands that comprise discriminative information for the categorization. Furthermore, a sample balancing strategy is applied to address the sample imbalance problem. Expansive experiments are undertaken on four HSI datasets, demonstrating that our MOCNN approach outperforms several other methods for HSI classification, especially in scenarios dominated by limited and imbalanced sample data.
SARS-CoV-2 Infection and Clinical Signs in Cats and Dogs from Confirmed Positive Households in Germany

On a global scale, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a serious threat to the health of the human population. Not only humans can be infected, but also their companion animals. The antibody status of 115 cats and 170 dogs, originating from 177 German households known to have been SARS-CoV-2 positive, was determined by enzyme-linked immunosorbent assay (ELISA), and the results were combined with information gathered from a questionnaire that was completed by the owner(s) of the animals. The true seroprevalences of SARS-CoV-2 among cats and dogs were 42.5% (95% CI 33.5–51.9) and 56.8% (95% CI 49.1–64.4), respectively. In a multivariable logistic regression accounting for data clustered in households, for cats, the number of infected humans in the household and an above-average contact intensity turned out to be significant risk factors; contact with humans outside the household was a protective factor. For dogs, on the contrary, contact outside the household was a risk factor, and reduced contact, once the human infection was known, was a significant protective factor. No significant association was found between reported clinical signs in animals and their antibody status, and no spatial clustering of positive test results was identified.
Controlled generation of neutral, negatively-charged and positively-charged excitons in the same single quantum dot

We report the controlled generation of neutral, negatively-charged and positively-charged excitons in the same single InAs quantum dot. The control parameters are a vertical electric field applied to a capacitor-like structure, in which the quantum dots are embedded, and optical pump power. The strong Coulomb blockade in quantum dots can be exploited to control the charge of excitons containing one hole, the neutral exciton, X0, and singly negatively charged exciton, X1−. We show here how this concept can be extended to excitons containing two holes, the biexciton, 2X0, and significantly the singly positively charged exciton, X1+. We support all these assignments with a Coulomb blockade model. For all dots, the emission from the X1− is redshifted relative to the neutral exciton, but surprisingly we observe blueshifts as well as small redshifts for X1+.
Emission-line nebulae around central cluster galaxies in cooling flows

We present new optical spectra of the emission-line nebulae at the centres of three cooling flows. One, associated with the cluster A496, is of class I in the line-ratio scheme of Heckman et al., and the second, in A2597, is of class II. The third, in Sersic 159-03, shows line ratios in the nucleus region which lie between those of classes I and II; a filament at 20 kpc radius shows ratios of extreme class II. We find trends of line ratio and line luminosity with linewidth. Previous work on the source of ionization of these nebulae has been inconclusive, since any mechanism is required to be distributed throughout the nebula
A fast and accurate method for evaluating the nonrelativistic free-free Gaunt factor for hydrogenic ions

A fast method for obtaining a two-dimensional Chebyshev expansion of the nonrelativistic hydrogenic free-free Gaunt factor is presented which gives a maximum relative error of 0.7 percent over a wide range of temperatures and frequencies. The expansion derived by Karzas and Latter (1961) is used to derive primary values of the free-free Gaunt factor as a function of the initial and final electron energies. Thermal averages have been numerically computed and are presented in tabular form in intervals of 0.25 dex.
The impact of the OSIRIS grating efficiency on radiance and trace-gas retrievals

The optical spectrograph and infrared imaging system (OSIRIS), launched in 2001, is a UVvisible diffraction-grating instrument designed to measure light scattered from the Earth's limb. Laboratory measurements of the OSIRIS diffraction-grating efficiency reveal a sensitivity to polarization including an anomalous structure of width 2030 nm introduced into light polarized in a direction perpendicular to the grooves of the grating. A vector radiative-transfer model was used to generate synthetic OSIRIS spectra in an effort to examine the effect of this on radiances and trace-gas retrievals. Radiances that included grating effects were found to deviate by nearly 10% from those that did not and also contained the anomalous structure. Performing differential optical absorption spectroscopy (DOAS) on these spectra revealed errors in ozone apparent column densities of up to 80 DU. The size of the error was controlled mainly by the difference in polarization between the two DOAS spectra. Two possible correction.
On the spatial structure of global modes in wake flow

Numerical simulations of wake flow behind an equilateral triangular obstacle are presented. The form of global modes and their dependence on the Reynolds number found in this study are in accordance with recent experimental results of Goujon–Durand et al. [Phys. Rev. E 50, 308 (1994)]. A scaling law of the amplitude oscillating with the fundamental frequency corresponding to the maximum of the global mode is found to agree with the Landau model in a range of Reynolds Re numbers larger than in previous studies. The position of the maximum amplitude of the fundamental modes scales as (Re−Rec)−1/2. The amplitude of the second harmonic of the longitudinal component of the velocity as well as the correction to the mean flow have different critical behavior than the velocity components oscillating with fundamental frequency. During linear growth the position of the maximum of the global modes is constant and moves only in the nonlinear regime. The effects of the blockage and the boundary conditions on the side .
Report on Evolution of Inconel 718 Following HFIR Irradiation

The report presents the microstructure and mechanical properties of 3D printed Inconel 718 after irradiation in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) to assess its potential use as a structural material. The structural components near the outlets of several proposed reactor cores will experience significant neutron fluxes and outlet coolant temperatures ranging from the hot standby temperature of 300 °C to nearly 550 °C at the center of the part. These components must support the core in appropriate loading conditions and require structural analysis at relevant temperatures. In FY21, three heat treatments were designed and conducted to simplify the microstructure and to determine how each precipitating phase contributed to the overall strength. In FY21, baseline mechanical properties were measured from uniaxial tensile tests on subsize SS-J2 specimens at room temperature and at elevated temperatures of 300, 450, and 600 °C to serve a comparison to the irradiated properties. Four capsules containing 3D printed Inconel 718 were inserted into HFIR in FY21 for a matrix of two temperatures and two doses. The lower of the two doses was available for characterization in FY22. Multiple heat treatments of Inconel 718 irradiated to nominal conditions of 2 displacements per atom (dpa) at either 300 or 600 °C were strained with uniaxial tensile tests at the Irradiated Material Examination and Testing (IMET) Facility to discern the mechanical properties. Transmission electron microscopy was performed to correlate the observed mechanical properties with nanoscale features. The initially homogenous AM718-HM increased in strength at both irradiation temperatures based on a high density of nanometer-scale radiation-induced cavities at lower temperature and nucleation and growth of γ ʺ precipitates at higher temperatures. The precipitate-hardened AM718-HT2 showed very small differences in strength before and after irradiation: the contribution to strength from γ ʺ precipitates was replaced with dislocation loops. Because the radioactivity of the nickel superalloys from neutron activation limited the scope of the analysis, a feasibility study examined the possibility of an ultra-miniature specimen geometry, colloquially SS-Tiny (SS-T), for mechanical property determination using the nonirradiated Inconel 718. This study found an overestimation of ductility from the SS-T geometry with yield strength and ultimate tensile strength slightly above the SS-J2 geometry: this could be contributed to a reduction in specimen thickness.
Evaluation of the Expression Levels of miR-21-5p and miR-429 Genes in Biopsy Samples from Patients with Oral Squamous Cell Carcinoma

Introduction: MicroRNAs (miRs) are a group of endogenous, non-coding, 18-24 nucleotide length single-strand RNAs that mediate gene expression at the post-transcriptional level through mRNA degradation or translational repression. They are involved in regulating diverse cellular biological processes such as cell cycle, differentiation, and apoptosis. The deregulation of miRs affects normal biological processes, leading to malignancies, including oral squamous cell carcinoma (OSCC). This study evaluates the expression level of miR-21-5p and miR-429 genes in biopsy samples from patients with OSCC and performs a comparison with controls. Materials and Methods: In this study, tissue samples were obtained from 40 individuals (20 OSCC patients and 20 healthy controls) to determine miR-21-5p and miR-429 expression using the ΔCT method and analyzed by the Mann–Whitney test. Results: The mean age of subjects in the control and patient groups was 47.15 and 53.8 years, respectively. According to the Mann–Whitney test, significant differences were observed in miR-21-5p (p < 0.0001) and miR-429 (p = 0.0191) expression levels between the two groups (p < 0.05). Conclusions: The expression of miR-21-5p, miR-429, and combined miRNAs in the OSCC group was significantly higher compared to the control group. As a result, changes in the expression of these biomarkers in cancerous tissues could potentially be considered as a marker for the early diagnosis of OSCC.
A New Image Recognition Combining Transfer Learning Algorithm and MobileNet V2 Model for Palm Vein Recognition

Among human biometrics, palm vein recognition is characterised by high security and privacy as an internal human biometric identification technology. Despite the breakthroughs associated with palm vein technology, there are various factors such as lighting variations, ambient temperature, and angular hand position that make it difficult to extract palm vein features from the original image. Also, due to the lack of sufficient palm vein datasets, the models will not have enough samples to differentiate the features and will easily lead to overfitting of the data. To address the above issues, we propose a MobileNet V2 convolutional neural network model combining a transfer learning algorithm named SEM. It consists of proposing a region of interest (ROI) segmentation method that contains more vein information, and transferring the pre-trained model weights from the palm print dataset to the palm vein recognition domain. Furthermore, the SENet module is embedded into the MobileNet V2 network to enhance the perceptual field of the network, and the important features are reinforced to improve the accuracy. Finally, the proposed SEM model is trained on the dataset, and our proposed method has a very high accuracy and high robustness, with an accuracy of 99.35%.