Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Change with the Ratio of Training Data A Case Study on the Binary Classification of COVID-19 Chest X-Ray by using Convolutional Neural Networks

One of the features of artificial intelligence/machine learning-based medical devices resides in their ability to learn from real-world data. The performance may change after the market introduction. There are many aspects that contribute to the performance change relative to the real-world training data, such as the number and disease ratio. In actual clinical practice, the ratio of obtained training data varies from country to country, from region to region within each country, and from one hospital to another. Therefore, establishing a pre-change control plan at a premarket stage is essential to achieve safety and effectiveness through total product life cycles. In our previous work, we evaluated the performance change on the binary classification of coronavirus disease 2019 (COVID-19) and normal with the number of training data using two publicly large available chest X-ray (CXR) images. However, these results were obtained with the same ratio in the training data. Thus, this study aims to evaluate the performance change with a non-uniform ratio of COVID-19 CXR images based on the results of previous studies. We used the AlexNet and ResNet34 with and without the fine-tuning method as convolutional neural network (CNN) models. A total of 500, 1000, and 2000 CXR images were selected as training and validation datasets. These datasets represent states in which the performance change improves rapidly and those in which an equilibrium state is reached. Each dataset was divided into seven datasets, and the area under the curve was employed to evaluate the performance change for each dataset through independent 1000 test datasets with the same ratio. Our result shows that all performances indicate that there is an upward convex relationship to the ratio of COVID-19 CXR images, and the vertex is where the ratio is the same. This trend was remarkable for the rapidly improving state and the CNNs without a fine-tuning method. Moreover, the visual explanations technique called Grad-CAM for interpreting classification results of CNN models support these results.
Haemodynamic Effects of Pimobendan during General Anaesthesia in Healthy Senior Dogs: A Prospective, Randomised, Triple-Blinded, Placebo-Controlled Clinical Study

Simple Summary Pimobendan is a drug that improves cardiac output and contractility in dogs with mitral valve disease and dilated cardiomyopathy, as well as in healthy adult dogs. The intravenous injectable formulation has shown immediate cardiovascular effects in dogs without pro-arrhythmogenic effects. Our study aims to assess the haemodynamic effects of the intravenous administration of pimobendan at a dose of 0.15 mg/kg intravenously in healthy senior dogs undergoing general anaesthesia for surgical procedures. The results of this study showed that pimobendan improves cardiac function parameters measured with an oesophageal Doppler monitor in senior dogs with no previous heart disease. Pimobendan could be used during general anaesthesia in senior dogs when there is concern about the possible presence of impaired cardiac function. There were no significant changes in blood pressure or heart rate. In the present study, continuous haemodynamic monitoring showed that the effects appeared from the first minute and continued for at least 20 min. Abstract Pimobendan is an inotropic and vasodilator drug with no sympathomimetic effects. This study aimed to evaluate the haemodynamic effects of pimobendan during anaesthesia in healthy senior dogs. A prospective, randomised, triple-blinded, placebo-controlled clinical study was conducted. Thirty-three dogs (median [range]: 9 [7, 12] years) were anaesthetised for surgical procedures. The dogs were randomly allocated into two groups: eighteen dogs received intravenous pimobendan at a dose of 0.15 mg/kg (PIMOBENDAN), and fifteen dogs received intravenous saline solutions at a dose of 0.2 mL/kg (PLACEBO). Data were recorded before, 1 min, 10 min, and 20 min after injection. Velocity-time integral (VTI), peak-velocity (PV), and mean-acceleration (MA) were measured using an oesophageal Doppler monitor (ODM). Heart rate and mean arterial pressure were also registered. The data were analysed using a two-way ANOVA for trimmed means. Statistical differences were considered if p < 0.05. Twenty minutes after injection, the VTI (13.0 cm [10.4, 22.3]), PV (95.0 [83.0, 160] m/s), and MA (12.6 [9.40, 17.0] m/s2) were significantly higher in the PIMOBENDAN group compared to the PLACEBO group (VTI: 10.5 [6.50, 17.4] cm, PV: 80.0 [62.0, 103] m/s and MA: 10.2 [7.00, 16.0] ms2). No significant differences were observed in the rest of the variables. Using pimobendan during anaesthesia increases VTI, PV, and MA, as measured by an ODM.
Combined Effects of Radiation and Hall Current on MHD Flow Past an Exponentially Accelerated Vertical Plate in the Presence of Rotation

The theoretical solution of Magneto Hydro Dynamic flow past an exponentially accelerated vertical plate in the presence of Hall current and thermal radiation effect relative to a rotating fluid with uniform temperature and mass diffusion is presented. The dimensionless equations are solved using Laplace method. The temperature and concentration fields, axial and transverse velocity profiles are studied for different parameters such as Schmidt number, Prandtl number, Hall parameter (m), Hartmann number (M), Rotation parameter (Ω),thermal Grashof number (Gr) and mass Grashof number (Gc). The effects of all these parameters on the axial and transverse velocity profiles are shown graphically.
Design of a H-slot Based Microstrip Patch Antenna

A series of Microstrip Patch Antennas are put together in this paper in order to enhance the performance of the radiating antenna, called an Array Microstrip Patch antenna, in terms of its parameters like reflection coefficient, Gain,/Co-polarization, Cross-polarization, radiation pattern. The use of Microstrip Patch Antennas are constantly increasing in the field of communications due to its compact size and robust nature. Firstly a single Microstrip patch antenna is designed and optimized, this single antenna is operating in the frequency of 8.76 GHz at which the reflection coefficient is approximating up to -15.83 dB and the VSWR is 1.38 and to energize the patch microstrip line feed is used, Then multiple of these antennas are linearly connected to obtain the proposed array antenna.
Performance, Intake, Feed Efficiency, and Carcass Characteristics of Young Nellore Heifers under Different Days on Feed in the Feedlot

Simple Summary Brazilian feedlot operators subjectively decide how long animals must stay in the feedlot based on a visual evaluation of weight and fatness. Typically, remuneration for commodity meat producers is based on carcass weight, and the lack of a metric to define the ideal slaughter endpoint often leads to erroneous decision-making, mainly due to not knowing the actual carcass gain of the animals. The use of “days on feed” (DOF) is a metric that allows the producer to precisely determine when the animal’s gain drops and, consequently, the feed efficiency in the feedlot. Thus, this study aimed to evaluate the effect of DOF on the performance, intake, feed efficiency, and carcass characteristics of Nellore heifers that received high supplementation during the growing phase. This study generated information that can help to determine the biological slaughter endpoint for young heifers finished in the feedlot. Our results suggest a relationship between the proportion of ether extract of the shrunk body weight, backfat thickness, and body weight can be used as a reference point to make decisions about the biological endpoint of slaughter in the feedlot based on days on feed. Abstract Two studies evaluated the productive characteristics of young Nellore heifers receiving different days on feed (DOF) to determine the biological slaughter endpoint. In Experiment 1 (Exp. 1), fifty-one Nellore heifers [324 ± 19.3 kg of body weight (BW); 16 ± 1 months] were split into three DOF lengths (45, 75, or 105 days), while in Experiment 2 (Exp. 2), thirty-six Nellore heifers (362 ± 25.5 kg of BW; 18 ± 1 months of age) were split into three DOF lengths (45, 90, or 135 days). In both studies, all animals were distributed in complete randomized blocks according to initial BW and stratified via carcass ultrasound. The diet was supplied ad libitum, allowing 3% of refusals. The point at which the animals achieved 25% of ether extract of shrunk body weight (EESBW) was defined as the biological endpoint. Thus, relationships were made between some characteristics obtained in both studies. Positive linear relationships were found between backfat thickness (BFT) vs. EESBW (p < 0.001, r = 0.84) and BFT vs. body weight (p < 0.001, r = 0.77). Our results suggest that the biological slaughter endpoint for young Nellore heifers is 6.97 mm of backfat thickness or 402 kg shrunk body weight, corresponding to around 75 DOF.
First full dynamic range calibration of the JUNGFRAU photon detector

The JUNGFRAU detector is a charge integrating hybrid silicon pixel detector developed at the Paul Scherrer Institut for photon science applications, in particular for the upcoming free electron laser SwissFEL. With a high dynamic range, analogue readout, low noise and three automatically switching gains, JUNGFRAU promises excellent performance not only at XFELs but also at synchrotrons in areas such as protein crystallography, ptychography, pump-probe and time resolved measurements. To achieve its full potential, the detector must be calibrated on a pixel-by-pixel basis. This contribution presents the current status of the JUNGFRAU calibration project, in which a variety of input charge sources are used to parametrise the energy response of the detector across four orders of magnitude of dynamic range. Building on preliminary studies, the first full calibration procedure of a JUNGFRAU 0.5 Mpixel module is described. The calibration is validated using alternative sources of charge deposition, including laboratory experiments and measurements at ESRF and LCLS. The findings from these measurements are presented. Calibrated modules have already been used in proof-of-principle style protein crystallography experiments at the SLS. A first look at selected results is shown. Aspects such as the conversion of charge to number of photons, treatment of multi-size pixels and the origin of non-linear response are also discussed.
Research on target detection-based autofocus system

A target detection-based autofocus system is built to address the problem of unreasonable focus window planning in today's autofocus systems. A window planning strategy based on the HOG+SVM algorithm and an improved hill-climbing search algorithm are proposed for pedestrian targets. The two algorithms are validated separately, and the results show that the new window planning strategy improves the focusing accuracy and anti-interference ability of the system, so that the system will not be interfered by the background and occlusion when focusing on the target; the improved hill-climbing search algorithm adopts the way of recording first and then comparing to reduce the influence of local peaks. The experimental results prove that the focusing accuracy of the system is above 95% and the average running time is about 2.2s, which meets the demand of auto-focusing.
Large-Eddy Simulation of Shallow Cumulus over Land: A Composite Case Based on ARM Long-Term Observations at Its Southern Great Plains Site

AbstractBased on long-term observations by the Atmospheric Radiation Measurement program at its Southern Great Plains site, a new composite case of continental shallow cumulus (ShCu) convection is constructed for large-eddy simulations (LES) and single-column models. The case represents a typical daytime nonprecipitating ShCu whose formation and dissipation are driven by the local atmospheric conditions and land surface forcing and are not influenced by synoptic weather events. The case includes early morning initial profiles of temperature and moisture with a residual layer; diurnally varying sensible and latent heat fluxes, which represent a domain average over different land surface types; simplified large-scale horizontal advective tendencies and subsidence; and horizontal winds with prevailing direction and average speed. Observed composite cloud statistics are provided for model evaluation. The observed diurnal cycle is well reproduced by LES; however, the cloud amount, liquid water path, and shortw.
A Method for Generating Subsynchronous Oscillation Data of Power System Based on Wasserstein Generative Adversarial Network

With a large number of power electronic devices connected to the power grid, the problem of subsynchronous oscillation caused by the interaction between the converter and the power grid seriously threatens the safe and stable operation of the power system. The existing research methods of subsynchronous oscillation, especially the artificial intelligence methods, mostly rely on a large amount of oscillation data, but in practice, such data is very scarce. Therefore, this paper proposes a method to generate subsynchronous oscillation data in power systems based on Wasserstein generative adversarial network (WGAN). In order to improve the quality of the generated samples and solve the problem of training instability, the JS distance is replaced by Wasserstein distance. Taking the oscillation data in the direct-driven permanent-magnet wind farm system as a practical example, the generated data are analyzed by dynamic time warping (DTW) and frequency to damping ratio methods. The results show that the data generated by the proposed method is consistent with the characteristics of oscillation data and has certain advantages in data quality.
Temporal-Frequency Co-training for Time Series Semi-supervised Learning

Semi-supervised learning (SSL) has been actively studied due to its ability to alleviate the reliance of deep learning models on labeled data. Although existing SSL methods based on pseudo-labeling strategies have made great progress, they rarely consider time-series data's intrinsic properties (e.g., temporal dependence). Learning representations by mining the inherent properties of time series has recently gained much attention. Nonetheless, how to utilize feature representations to design SSL paradigms for time series has not been explored. To this end, we propose a Time Series SSL framework via Temporal-Frequency Co-training (TS-TFC), leveraging the complementary information from two distinct views for unlabeled data learning. In particular, TS-TFC employs time-domain and frequency-domain views to train two deep neural networks simultaneously, and each view's pseudo-labels generated by label propagation in the representation space are adopted to guide the training of the other view's classifier. To enhance the discriminative of representations between categories, we propose a temporal-frequency supervised contrastive learning module, which integrates the learning difficulty of categories to improve the quality of pseudo-labels. Through co-training the pseudo-labels obtained from temporal-frequency representations, the complementary information in the two distinct views is exploited to enable the model to better learn the distribution of categories. Extensive experiments on 106 UCR datasets show that TS-TFC outperforms state-of-the-art methods, demonstrating the effectiveness and robustness of our proposed model.
EFFECT OF COOKING METHODS ON TOTAL PHENOLICS AND ANTIOXIDANT ACTIVITY OF SELECTED WILD EDIBLE PLANTS

Objective: The target of this appraisal was to explore the impacts of various cooking techniques, for example, boiling and microwave cooking on total phenolics and antioxidant activity of Zanthoxylum acanthopodium, Viburnum foetidum, Houttuynia cordata, Sonchus arvensis and Oenanthe linearis, widely consumed by the common individuals of the North-Eastern area of India. Methods: The antioxidant activities of the plants were determined by using 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity, ABTS radical scavenging ability, reducing power capacity, estimation of total phenolic content, flavonoid content and flavonol content. Results: Total phenolics content (TPC) of fresh vegetables ranged from 108.28 to 253.99 mg/100 g (as gallic acid equivalent) on a dry weight basis. Total antioxidant capacity of fresh plants (IC50 mg dry extract) determined by DPPH and ABTS ranged from 0.37-1.23 and 0.29-0.89, respectively. Boiling caused the highest losses of TPC, resulting in a reduction of the TPC on dry weight (DW) basis ranging from 9.37% in O. linearis up to 25.97% in Z. acanthopodium whereas microwave cooking enhanced TPC ranging from 4.09% to 10.38%. Similarly, boiling treatment decreased the DPPH radical scavenging activities ranging from 10.65 to 29.77% and ABTS radical scavenging activities ranging from 5.88-16.35%, whereas microwave cooking increased the DPPH and ABTS radical scavenging activities ranging from 8.02-24.20% and 9.86 to 19.70% respectively, in the studied plants. Conclusion: The results suggest that the best cooking method for increasing the concentration of polyphenols and antioxidants was microwave cooking while boiling was the least recommended method.
Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps.
Cardan’s Coupling Shaft as a Dynamic Evolutionary System

The authors have long been concerned with the problem of dimensioning the long articulated shafts of trucks. The impulse for launching the research was a failure on real vehicles - permanent bending deformation of the Cardan coupling shafts. At present, this issue is solved in the framework of grant TA04010579 of the Czech Republic’s Technology Agency focusing on gear pumps. Regarding the shaft system with Hooke’s joints, it is necessary to respect the relative transverse oscillation of the shaft, which introduces to the system the frequency spectrum dependence on the angular velocity of the rotation. The paper describes the mathematical model derivation of the problem. The proposed model is extensively solved using Transfer-matrix Method and the Finite Element Method, and successfully used to detect the causes of failures that occurred on real vehicles.
Topological Materials: Quantum Anomalous Hall System

The quantum anomalous Hall effect (QAHE) is a quantized Hall effect that occurs at zero magnetic field. Its mechanism and properties are different from those of conventional quantum Hall effects (QHEs) induced by magnetic field. The first theory of a QHE without a magnetic field was proposed in 1988. Yet, it has taken 25 years to be experimentally realized, which has now happened thanks to the development of the topological insulator—a conceptually new topological state of matter. Here, we review QAHE, beginning with the original theoretical idea and concluding with its final implementation using experimentally accessible materials. The current status and future direction of the field are also discussed.
Comparing the “Rim” Versus the “Filled” Rectangular Array Grids—Their Direction-Finding Cramér-Rao Bounds

A rectangular array of sensors is often used in direction finding, due to the geometric regularity in its spatial rectangular grid. The sensor positions may be confined to the rectangle's perimeter (as in a “rim” array), or may span over the rectangle's entire interior as well (as in a “filled” array). This paper compares these two array grids by their precision in direction finding, by pioneering Cramér-Rao bound expressions for both array grids above, in closed forms and explicitly in terms of the array parameters.
Optimizing Lithium-ion Battery Performance with RC Equivalent Circuit Model Analysis

This paper investigates a lithium-ion battery’s charging and discharging behavior using the RC equivalent circuit model. The study aims to analyze the relationship between the battery’s open circuit voltage and model parameters and to evaluate the accuracy of the model’s predictions of the battery’s performance. The research seeks to optimize the battery’s performance by improving our understanding of its behavior and identifying ways to enhance its charging and discharging characteristics.
Differential diagnoses and the mutational landscape of myelodysplastic/myeloproliferative neoplasm with neutrophilia: A case report

Myelodysplastic/myeloproliferative neoplasm with neutrophilia (MDS/MPN-N; previously referred to as atypical chronic myeloid leukemia) is a type of myelodysplastic syndrome/myeloproliferative neoplasm. A molecular genetic precondition for diagnosis is BCR::ABL negativity; further diagnostic criteria include clinicopathological assessments, such as peripheral blood leukocyte counts, the number of neutrophils and their precursors, and the presence of dysgranulopoiesis. The present case report highlights the importance of differential diagnoses with a stringent diagnostic workup according to the 5th Edition of the World Health Organization Classification of Hematolymphoid Tumors. A systematic review of the literature from 2013 to 2022 covering the mutational landscape of MDS/MPN-N was also performed to highlight recent improvements in the molecular genetic diagnostics of this disease.