Miracle-dz/UniFAR-FTUs huggingface dataset:
Defect Detection Algorithm of Periodic Texture by Multi-metric-Multi-module Image Voting Method

Appling deep learning network models to train and detect defects on periodic texture background images requires a large number of standard datasets. However, in the field of texture fabric defect detection, there is lack of public standard datasets, and it is pretty time-consuming and laborious to prepare a high-quality training dataset. In this study, we propose a comprehensive method combining with the characteristics of periodic texture images, which uses multiple metrics and multiple mathematical models of an image to vote and score the splitted sub-images of the image, so as to detect the locations of defects on the periodic texture image. Central to our method is subimage segmentation, Zero-Slope-RANSac(ZS-RANSac) method, Multi-metric-Multi-model Image Voting strategy, which utilizes the local consistency of image metrics existing in periodic texture by cutting images into sub-images of the same size. To obtain the basic scoring matrix of each sub-image under each model, we take the difference of the standard value of the non-defect background calculated by ZS-RANSac and all measurements of sub-image, and then combine the matrix and multiple numeration model. According to the order of the scores, a certain proportion of polymer image points are considered as outer points, which are the defect sub-images. This method completely relies on statistical strategy to make use of the periodic texture characteristics of the image, and can detect the non-lattice texture image without training data. It has a wide application prospect for the textile industry, which requires real time and lacks high-quality training datasets.
The role of imperforate tracheary elements and narrow vessels in wood capacitance of angiosperm trees

There is a broad diversity of imperforate tracheary elements (ITEs) — libriform fibers, fiber-tracheids, true tracheids and vasicentric/vascular tracheids — described thoroughly by Sherwin Carlquist. However, in a quantitative sense, the functional meaning of different ITE types present in the wood of vessel-bearing angiosperms remains unclear because very few structure–function studies measure ITEs’ properties. ITEs with abundant pits and wide pit borders — vascular tracheids, vasicentric tracheids, and true tracheids sensu Carlquist — have been shown to conduct water and, thanks to this conductive ability and the multitude of pits, they could also contribute to wood capacitance. A dataset of 30 temperate angiosperm tree species was reanalysed to record the presence/absence of true, vasicentric, and vascular tracheids including data on conduits15 fraction and vessel-conduit15 contact fraction (conduits15 were defined as cells resembling vessels and with a maximum lumen diameter of 15 μm. They encompassed narrow vessels, vasicentric tracheids, and vessel tails). The presence of tracheids, conduits15 fraction, and contact fraction had no effect on wood capacitance, except, per given wood volumetric lumen water content, species with true tracheids tended to have lower capacitance. These results suggest that the presence of tracheids or conduits15 properties do not limit wood capacitance, but the results do not exclude the potential role these cells may play in internal water dynamics.
Pitfalls in Measuring Ultra Low Power Energy Harvesting Wireless Sensor Networks

Applications and evaluations in the area of ultra-low power energy harvesting Wireless Sensor Networks (WSNs) verge toward utilizing state-of-the-art hardware with extremely low power consumption. For the evaluation of these networks, real-world deployments are a common approach where special performance logging hardware is often used in conjunction with the sensor nodes. This hardware needs to be kept simple and cost-effective to allow for scalability. At the same time, it needs to be ensured that the measurement hardware does not interfere with the performance of the sensor node, thereby distorting the evaluation results. Herein lies an especially prominent challenge with ultra-low power sensor nodes. We outline some pitfalls that should be considered when designing or selecting this hardware.
Outbreak of Pathogenic Streptococcus equi subsp. zooepidemicus in Guinea Pigs Farms of The Andean Region

Streptococcus zooepidemicus is an emerging zoonotic pathogen involved in septicemic infections in humans and livestock. Raising guinea pigs in South America is an important economic activity compared to raising them as pets in other countries. An outbreak of severe lymphadenitis was reported in guinea pigs from farms in the Andean region. S. zooepidemicus was isolated from multiple cervical and mandibular abscesses. Isolate was characterized by multilocus sequence typing and phylogenetic analysis. This is the first molecular characterization of a highly pathogenic strain, showing major important virulence factors such as the M-like protein genes szP and mlpZ, the fimbrial subunit protein gene fszF, and the protective antigen-like protein gene spaZ. Additionally, this guinea pig strain was phylogenetically related to equines but distant from zoonotic and pig isolates reported in other countries.
An Improved YOLOv5-based Method for Surface Defect Detection of Steel Plate

In the digital manufacturing industry, since various features of steel products cannot quantify through traditional algorithms of quality evaluation, deep learning has been introduced into the field of detecting defects increasingly. However, there are still challenges to be overcome. Most steel plate surface defects are of various types and small, and the detection speed needs to meet the requirements of real-time. In this paper, a new network structure called CI-YOLOv5 be proposed, which is based on YOLOv5 and improves the precision of defect detection while meeting the real-time requirements by improving the network structure of YOLOv5. Two attention mechanisms and involution operator are embedded in CI-YOLOv5 to enhance the ability of feature learning, suppress unimportant information during feature extraction and aggregate richer semantic information. And then the extracted features are directly used to predict the location and classify the defects. Compared with the YOLOv5s, the evaluation metrics of CI-YOLOv5 performance have been improved. Specifically, it has achieved 96% mAP and improved by 5% over YOLOv5s, precision improved by 9% to 94%, recall improved by 1%, and F1 improved by 5% while maintaining the real-time detection of defects.
Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.
A Color-Caused Optical Illusion on a Statistical Graph

Abstract Despite the great increase in the use of color in statistical graphics, we know very little about how color affects people's perception of the quantitative information on graphical displays. Perceptual psychologists have already demonstrated that color can cause optical illusions of various kinds. We ran a simple experiment to see if this can happen with a statistical map and found that an illusion did occur.
The optimal size of choice sets in choice experiments

In this paper, we establish the optimal size of the choice sets in generic choice experiments for asymmetric attributes when estimating main effects only. We give an upper bound for the determinant of the information matrix when estimating main effects and all two-factor interactions for binary attributes. We also derive the information matrix for a choice experiment in which the choice sets are of different sizes and use this to determine the optimal sizes for the choice sets.
Refining the Intraoperative Identification of Suspected High-Grade Glioma Using a Surgical Fluorescence Biomarker: GALA BIDD Study Report

Background. Improving intraoperative accuracy with a validated surgical biomarker is important because identifying high-grade areas within a glioma will aid neurosurgical decision-making and sampling. Methods. We designed a multicentre, prospective surgical cohort study (GALA-BIDD) to validate the presence of visible fluorescence as a pragmatic intraoperative surgical biomarker of suspected high-grade disease within a tumour mass in patients undergoing 5-aminolevulinic acid (5-ALA) fluorescence-guided cytoreductive surgery. Results. A total of 106 patients with a suspected high-grade glioma or malignant transformation of a low-grade glioma were enrolled. Among the 99 patients who received 5-ALA, 89 patients were eligible to assess the correlation of fluorescence with diagnosis as per protocol. Of these 89, 81 patients had visible fluorescence at surgery, and 8 patients had no fluorescence. A total of 80 out of 81 fluorescent patients were diagnosed as high-grade gliomas on postoperative central review with 1 low-grade glioma case. Among the eight patients given 5-ALA who did not show any visible fluorescence, none were high-grade gliomas, and all were low-grade gliomas. Of the seven patients suspected radiologically of malignant transformation of low-grade gliomas and with visible fluorescence at surgery, six were diagnosed with high-grade gliomas, and one had no tissue collected. Conclusion. In patients where there is clinical suspicion, visible 5-ALA fluorescence has clinical utility as an intraoperative surgical biomarker of high-grade gliomas and can aid surgical decision-making and sampling. Further studies assessing the use of 5-ALA to assess malignant transformation in all diffuse gliomas may be valuable.
Antioxidant and Lipid-Lowering Effects of Buriti Oil (Mauritia flexuosa L.) Administered to Iron-Overloaded Rats

The indiscriminate use of oral ferrous sulfate (FeSO4) doses induces significant oxidative damage to health. However, carotene-rich foods such as buriti oil can help the endogenous antioxidant defense and still maintain other body functions. This study aimed to assess the effects of buriti oil intake in iron-overloaded rats by FeSO4 administration. Buriti oil has β-carotene (787.05 mg/kg), α-tocopherol (689.02 mg/kg), and a predominance of monounsaturated fatty acids (91.30 g/100 g). Wistar rats (n = 32) were subdivided into two control groups that were fed a diet containing either soybean or buriti oil; and two groups which received a high daily oral dose of FeSO4 (60 mg/kg body weight) and fed a diet containing either soybean (SFe) or buriti oil (Bfe). The somatic and hematological parameters, serum lipids, superoxide dismutase (SOD), and glutathione peroxidase (GPx) were determined after 17 days of iron overload. Somatic parameters were similar among groups. BFe showed a decrease in low-density lipoprotein (38.43%) and hemoglobin (7.51%); an increase in monocytes (50.98%), SOD activity in serum (87.16%), and liver (645.50%) hepatic GPx (1017.82%); and maintained serum GPx compared to SFe. Buriti oil showed systemic and hepatic antioxidant protection in iron-overloaded rats, which may be related to its high carotenoid, tocopherol, and fatty acid profile.
Fixed-time Super-Twisting-Like Attitude Tracking Control for A Quadrotor UAV

In this paper, a practical fixed-time controller is designed to deal with the tracking problem of a quadrotor unmanned aerial vehicle (UAV) attitude control system subject to external disturbances. Firstly, a novel improved super-twisting-like algorithm (ISTLA) is proposed. More specifically, the signum function is substituted by the continuous function in the integral term of the ISTLA, which can further suppress the control chattering phenomenon. Then the practical fixed-time stability of the attitude tracking system is rigorously proved by homogeneous theory and Lyapunov method. Finally, the superiority and effectiveness of the proposed strategy are verified by numerical simulation results.
Heterogeneously grown tunable group-IV laser on silicon

Tunable tensile-strained germanium (epsilon-Ge) thin films on GaAs and heterogeneously integrated on silicon (Si) have been demonstrated using graded III-V buffer architectures grown by molecular beam epitaxy (MBE). epsilon-Ge epilayers with tunable strain from 0% to 1.95% on GaAs and 0% to 1.11% on Si were realized utilizing MBE. The detailed structural, morphological, band alignment and optical properties of these highly tensile-strained Ge materials were characterized to establish a pathway for wavelength-tunable laser emission from 1.55 μm to 2.1 μm. High-resolution X-ray analysis confirmed pseudomorphic epsilon-Ge epitaxy in which the amount of strain varied linearly as a function of indium alloy composition in the InxGa1-xAs buffer. Cross-sectional transmission electron microscopic analysis demonstrated a sharp heterointerface between the epsilon-Ge and the InxGa1-xAs layer and confirmed the strain state of the epsilon-Ge epilayer. Lowtemperature micro-photoluminescence measurements confirmed both direct and indirect bandgap radiative recombination between the Γ and L valleys of Ge to the light-hole valence band, with L-lh bandgaps of 0.68 eV and 0.65 eV demonstrated for the 0.82% and 1.11% epsilon-Ge on Si, respectively. The highly epsilon-Ge exhibited a direct bandgap, and wavelength-tunable emission was observed for all samples on both GaAs and Si. Successful heterogeneous integration of tunable epsilon-Ge quantum wells on Si paves the way for the implementation of monolithic heterogeneous devices on Si.
Hybrid IoT Cyber Range

The use of IoT devices has increased rapidly in recent times. While the development of new devices is moving quickly, and as prices are being forced down, the costs of developing such devices also needs to be reduced. IoT devices are now trusted with more critical tasks, and it is important that they behave as intended and that the information they process is protected. It is not always the IoT device itself that is the target of a cyber attack, but rather, it can be a tool for another attack. Home consumers, in particular, expect these devices to be easy to use and set up. However, to reduce costs, complexity, and time, security measures are often cut down. To increase awareness and knowledge in IoT security, education, awareness, demonstrations, and training are necessary. Small changes may result in significant security benefits. With increased awareness and knowledge among developers, manufacturers, and users, they can make choices that can improve security. To increase knowledge and awareness in IoT security, a proposed solution is a training ground for IoT security, an IoT cyber range. Cyber ranges have received more attention lately, but not as much in the IoT field, at least not what is publicly available. As the diversity in IoT devices is large with different vendors, architectures, and components and peripherals, it is difficult to find one solution that fits all IoT devices. To some extent, IoT devices can be emulated, but it is not feasible to create emulators for all types of devices. To cover all needs, it is necessary to combine digital emulation with real hardware. A cyber range with this combination is called a hybrid cyber range. This work surveys the requirements for a hybrid IoT cyber range and proposes a design and implementation of a range that fulfills those requirements.
An Intelligent Evaluation Method of Root Canal Therapy Quality Based on Deep Learning

Dentists judge the quality of root canal therapy for each patient very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause judgment errors. At the same time, the traditional method of extracting the root canal image features based on experience is difficult to accurately extract the filling area of the root canal and the tooth area where it is located, resulting in low accuracy of root canal target segmentation, and thus affecting the accuracy of root canal treatment quality evaluation. In this paper, for the real root canal treatment images of patients, the CenterNet network was used to detect the root canal target of root canal images. Then, according to the test results, the U-Net full convolution neural network was used to segment the root canal filling region and its tooth region. Finally, the segmented images were quantitatively evaluated according to the professional evaluation index of doctors. The experimental results show that the coincidence rate between the evaluation results of this method and the evaluation results of professional doctors is 83.3 %, which can better complete the quality evaluation of root canal therapy for patients, effectively improve the work efficiency of doctors, and has reference significance for the application of artificial intelligence in medical field.
Closed-Form Solution to the Noncoplanar P4P Problem for Uncalibrated Camera

This paper presents a closed-form solution to determination of the position and orientation of a perspective camera with two unknown effective focal lengths for the noncoplanar perspective four point (P4P) problem. Given four noncoplanar 3D points and their correspondences in image coordinate, we convert perspective transformation to affine transformation, and formulate the problem using invariance to 3D affine transformation and arrive to a closed-form solution. We show how the noncoplanar P4P problem is cast into the problem of solving an eighth degree polynomial equation in one unknown. This result shows the noncoplanar P4P problem with two unknown effective focal lengths has at most 8 solutions. Last, we confirm the conclusion by an example. Although developed as part of landmark-guided navigation, the solution might well be used for landmark-based tracking problem, hand-eye coordination, and for fast determination of interior and exterior camera parameters. Because our method is based on closed-form solution, its speed makes it a potential candidate for solving above problems.
On the mean meridional circulation and the flux of angular momentum over the northern hemisphere

The mean meridional circulation north of the latitude 15° N has been determined by an indirect method, which uses the angular momentum and continuity equations under the assumption that the vertical eddy flux of the zonal momentum is negligible in the free atmosphere. The method requires a knowledge of the (time-space) mean zonal motion and the mean meridional eddy flux of angular momentum. Data for these quantities have been derived for four seasons of the year (December-February, March-May, June-August and September-November from the upper winds statistics charts as published by CRUTCHER (1959). For each season an essentially three-cell circulation pattern is obtained with the strongest cell intensities occurring in winter and the weakest in summer. Except in summer, the direct cell in the lower latitudes is stronger than the indirect cell in the middle latitudes. The direct cell in the high latitudes is found to be very weak in all seasons. The poleward transport of angular momentum and its different mechanisms are discussed. The role of the mean meridional circulation in the net flux across different latitudinal circles is found to be small except in the low latitudes in winter and spring. The transient eddies are in every season the main agency of the flux, but the standing eddies are also important, particularly in winter. The mean zonal stress of the atmosphere on the earth is derived from the convergence of the angular momentum flux. DOI: 10.1111/j.2153-3490.1967. tb01453.x
Ferroptosis in non-alcoholic liver disease: Molecular mechanisms and therapeutic implications

Ferroptosis refers to a novel modality of regulated cell death characterized by excessive iron accumulation and overwhelming lipid peroxidation, which takes an important part in multiple pathological processes associated with cell death. Considering the crucial roles of the liver in iron and lipid metabolism and its predisposition to oxidative insults, more and more studies have been conducted to explore the relationship between ferroptosis and various liver disorders, including non-alcoholic fatty liver disease (NAFLD). With increased morbidity and high mortality rates, NAFLD has currently emerged as a global public health issue. However, the etiology of NAFLD is not fully understood. In recent years, an accumulating body of evidence have suggested that ferroptosis plays a pivotal role in the pathogenesis of NAFLD, but the precise mechanisms underlying how ferroptosis affects NAFLD still remain obscure. Here, we summarize the molecular mechanisms of ferroptosis and its complicated regulation systems, delineate the different effects that ferroptosis exerts in different stages of NAFLD, and discuss some potential effective therapies targeting ferroptosis for NAFLD treatment, which putatively points out a novel direction for NAFLD treatment.
COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models

Since December 2019, many statistical spatial–temporal methods have been developed to track and predict the spread of the COVID-19 pandemic. In this paper, we analyzed the COVID-19 dataset which includes the number of biweekly infected cases registered in Ontario from March 2020 to the end of June 2021. We made use of Bayesian Spatial–temporal models and Area-to-point (ATP) and Area-to-area (ATA) Poisson Kriging models. With the Bayesian models, spatial–temporal effects and government intervention effects on infection risk are considered while the ATP Poisson Kriging models are used to display the spread of the pandemic over space.