Miracle-dz/UniFAR-FTUs huggingface dataset:
Resistive wall mode feedback control in EXTRAP T2R with improved steady-state error and transient response

Experiments in the EXTRAP T2R reversed field pinch [P. R. Brunsell, H. Bergsaker, M. Cecconello et al., Plasma Phys. Control. Fusion 43, 1457 (2001)] on feedback control of m=1 resistive wall modes (RWMs) are compared with simulations using the cylindrical linear magnetohydrodynamic model, including the dynamics of the active coils and power amplifiers. Stabilization of the main RWMs (n=−11,−10,−9,−8,+5,+6) is shown using modest loop gains of the order G∼1. However, other marginally unstable RWMs (n=−2,−1,+1,+2) driven by external field errors are only partially canceled at these gains. The experimental system stability limit is confirmed by simulations showing that the latency of the digital controller ∼50μs is degrading the system gain margin. The transient response is improved with a proportional-plus-derivative controller, and steady-state error is improved with a proportional-plus-integral controller. Suppression of all modes is obtained at high gain G∼10 using a proportional-plus-integral-plus-deriv.
High frequency SAW devices on AlGaN: fabrication, characterization and integration with optoelectronics

In this paper, the technology and properties of surface acoustic wave (SAW) devices on AlN and GaN films are reviewed. The excellent characteristics of these materials for high frequency applications are demonstrated by the fabrication of SAW filters with central frequencies higher than 2.2 GHz. The thermal behavior of these filters has been analyzed. Finally, the integration of a SAW generator with a metal-semiconductor-metal photodetector is described, showing the important synergy resulting from such integration.
Subspace spanning graph topological spaces of graphs

A collection of spanning subgraphs TS, of a graph G is said to be a spanning graph topology if it satisfies the three axioms: Nn, K0 ∈ TS where, n = |V (G)|, the collection is closed under any union and finite intersection. Let (X, T) be a topological space in point set topology and Y ⊆ X then, TY = {U ∩ Y : U ∈ T} is a topological space called a subspace topology or relative topology defined by T on Y . In this paper we discusses the subspace spanning graph topology defined by the graph topology TS on a spanning subgraph H of G.
Remarks on the Mittleman max–min variational method for the electron-positron field

Barbaroux, Esteban and Séré have investigated the relation between Mittleman's max–min principle and the solutions of the Dirac–Fock equations. Their comparison is valid in the non-relativistic limit, but it does not contain quantitative estimates. We generalize their result of non-agreement in the case of one electron and show that this non-agreement holds for the physical value of the fine structure constant if Z < 42 (molybdenum).
Research on Network Attack Detection Model Based on BiGRU-Attention

The BiGRU model does not consider the weight of features when extracting features. In order to solve this problem, this paper adds the Attention mechanism to the BiGRU hidden layer, uses the feature vector obtained from the BiGRU as the input of the Attention layer, and uses the attention score as the weight of the feature vector, so that the most important features are retained to the greatest extent. In this paper, BiGRU-Attention model is applied to network attack traffic detection, and CIDDS data set is used for model training and testing. Experiments show that BiGRU-Attention model designed in this paper has high accuracy and F1 value.
Cumulative advantage is a double-edge sword for cooperation

The Matthew effect emphasizes the influence of early advantage on shaping long-term development by amplifying it over time, and its implications for public cooperation are yet to be fully understood. In this letter, we propose and study a spatial public goods game driven by cumulative advantage, where players who achieve high payoffs in a given round receive greater allocations in the next. The simulation results show that the Matthew effect leads to an irreversible polarization of individual wealth on the network. Such polarization makes moderate cooperation levels more prevalent, which helps to explain the widespread coexistence of prosocial and antisocial behavior. Meanwhile, heterogeneous networks may restrict the polarization of wealth, but also inhibit the evolution of cooperation, requiring a reconsideration of the commonly held view that heterogeneous networks enhance cooperation.
ENTANGLEMENT MODULATION IN A SPIN CHAIN BY A LOCAL IMPURITY

We study the ground-state entanglement properties of an XX spin 1/2 chain in transverse field, in its quasi-long-ranged ordered phase, with a magnetic impurity, represented in terms of an additional transverse magnetic field located at one precise site. For such a system, we show that a control of the ground state entanglement can be achieved by acting on the impurity field. To demonstrate this possibility, we evaluate exactly the nearest neighbor and next-nearest neighbor concurrence in the presence of the impurity. It turns out that either an enhancement or a quenching of entanglement between selected spin pairs can be obtained by acting on the intensity of the impurity. For specific values of the magnetic field a spatial modulation of concurrence along the chain is also obtained.
An Implicit Salienct Guided Infrared And Visible Image Fusion Method

The aim of image fusion is to synthesize the information from multiple images to provide richer information for understanding human image content. Since the visual saliency of the images is the basis for understanding the content of the images, understanding and emphasizing image fusion information of different modes is the key of multi-modal image fusion. In the field of infrared and visible image fusion, the fusion logic of current research relies principally on understanding the results of fusion, subjectively emphasizing the strong contrast region in the infrared image and the texture detail in the visible image. The emphais on content, based on people's subjective understanding, reflects people's demands and understanding of the fusion results, but this subjective emphasis is not sufficiently complete to pay attention to the content of the source images, and ignores certain details, which affects the fusion effect. Therefore, this article proposes to learn the human understanding of the saliency features of the image through deep neural networks to obtain more complete fusion features than subjective emphasis. These features implicitly reflect human vision's perception of the importance of different modal features in fusion. ASNet has a great performance in salient target inference from the human gaze, we therefore introduce it to obtain salient features more coherent with human visual understanding in source images, which are used as attention prior information.
An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens

Pathogens found in food are believed to be the leading cause of foodborne illnesses; and they are considered a serious problem with global ramifications. During the last few decades, a lot of attention has been paid to determining the microorganisms that cause foodborne illnesses and developing new methods to identify them. Foodborne pathogen identification technologies have evolved rapidly over the last few decades, with the newer technologies focusing on immunoassays, genome-wide approaches, biosensors, and mass spectrometry as the primary methods of identification. Bacteriophages (phages), probiotics and prebiotics were known to have the ability to combat bacterial diseases since the turn of the 20th century. A primary focus of phage use was the development of medical therapies; however, its use quickly expanded to other applications in biotechnology and industry. A similar argument can be made with regards to the food safety industry, as diseases directly endanger the health of customers. Recently, a lot of attention has been paid to bacteriophages, probiotics and prebiotics most likely due to the exhaustion of traditional antibiotics. Reviewing a variety of current quick identification techniques is the purpose of this study. Using these techniques, we are able to quickly identify foodborne pathogenic bacteria, which forms the basis for future research advances. A review of recent studies on the use of phages, probiotics and prebiotics as a means of combating significant foodborne diseases is also presented. Furthermore, we discussed the advantages of using phages as well as the challenges they face, especially given their prevalent application in food safety.
Inter-Turn Fault Localization in Transformer Winding based on Nanosecond Pulse Method

The early detection and localization of the inter-turn fault in the transformer winding are necessary for reducing severe damage to the transformer. This paper presents the localization of the inter-turn fault in transformer winding based on the nanosecond pulse method. In this method, the nanosecond range pulse is injected from one end of the faulted experimental winding, and the time domain response signal is measured from another end of the winding. The ladder network based simulation model of the winding is developed, and reference response signals are generated for each faulty section. The faulty winding response is correlated with the reference simulation responses. The section corresponding to the maximum correlation with simulation responses provides the location of the faulty section of the winding.
Image Convolution to Obtain Color ROI after Segmentation Process with Fuzzy Cmeans

Image segmentation is still an important concern in terms of digital image processing. Segmentation refers to dividing an image into several parts based on similar characteristics or uniformity. Its use is quite important, especially related to the analysis and application of digital image processing. The challenge faced is separating the object image from its background in images with complex backgrounds. The aim of this research is to separate tomatoes from simple to complex backgrounds. This paper proposes a convolution method of segmented binary images and RBG images all based on contours using Fuzzy C-means and reconstruction operations to obtain the foreground from an image with a complex background. This method has been tested on ripe tomatoes with various backgrounds. This method has Indicated Performance Achievement Sc = 99.2%, Fpe = 0.6% and FNe = 0.4%. This shows that the method is suitable and robust for the dataset used in this study, especially if it will be continued for further work related to the classification of tomato maturity assessment.
CORDIC-Based Computation of Arcsine and Arccosine Functions on FPGA

This brief presents a novel CORDIC-based approach for computing arcsine and arccosine functions. Previous approaches based on CORDIC either calculate double iterations, which increases the complexity of the stages, or have a high approximation error. By contrast, the proposed approach presents a novel compensation of the gain in the rotations that allows for an accurate computation of the arcsine and arccosine that does not increase the complexity of the stages. The proposed approach has been implemented on an FPGA to demonstrate its benefits.
Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis

Simple Summary It is important to be able to effectively predict the likelihood of breast cancer metastasis to potentially help make treatment plans for a patient. We developed a type of deep learning models called feedforward neural network (FNN) models to predict breast cancer metastasis using clinical data. We found that overfitting can affect the prediction performance negatively, and overfitting and model performance can be greatly affected by hyperparameter settings. In this research, we conducted grid search experiments to study how each of the 11 hyperparameters of our FNN models is related to overfitting and model performance. Our experiment results show that the top five hyperparameters that have a significant impact on overfitting are iteration-based decay, learning rate, batch size, L2, and L1. The experiment results illustrate that different hyperparameters have a different impact on overfitting, and hyperparameter tuning with grid search can help improve the prediction performance of the FNN models. Abstract Overfitting may affect the accuracy of predicting future data because of weakened generalization. In this research, we used an electronic health records (EHR) dataset concerning breast cancer metastasis to study the overfitting of deep feedforward neural networks (FNNs) prediction models. We studied how each hyperparameter and some of the interesting pairs of hyperparameters were interacting to influence the model performance and overfitting. The 11 hyperparameters we studied were activate function, weight initializer, number of hidden layers, learning rate, momentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show that most of the single hyperparameters are either negatively or positively corrected with model prediction performance and overfitting. In particular, we found that overfitting overall tends to negatively correlate with learning rate, decay, batch size, and L2, but tends to positively correlate with momentum, epochs, and L1. According to our results, learning rate, decay, and batch size may have a more significant impact on both overfitting and prediction performance than most of the other hyperparameters, including L1, L2, and dropout rate, which were designed for minimizing overfitting. We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs.
Harmonic Compensation Method for Low-voltage Small-scale Islanded Microgrids

This paper presents a harmonic compensation method for low-voltage small-scale islanded microgrids, which is implemented using a harmonic observer. The proposed system includes a harmonic observer which helps to decompose the load current harmonics of the non-linear loads. In addition to the load current harmonic decomposition approach, a feed-forward control method is also proposed to reduce the voltage distortion in the islanded microgrid. The main feature of the proposed control system is the estimation of load-induced disturbances and their compensation. Analysis and simulation results are presented to verify the performance of the proposed compensation method.
Solid–liquid–air coupling in multiphase porous media

This paper addresses various issues concerning the modelling of solid–liquid–air coupling in multiphase porous media with an application to unsaturated soils. General considerations based on thermodynamics permit the derivation and discussion of the general form of field equations; two cases are considered: a three phase porous material with solid, liquid and gas, and a two phase porous material with solid, liquid and empty space. Emphasis is placed on the presentation of differences in the formulation and on the role of the gas phase. The finite element method is used for the discrete approximation of the partial differential equations governing the problem. The two formulations are then analysed with respect to a documented drainage experiment carried out by the authors. The merits and shortcomings of the two approaches are shown. Copyright © 2003 John Wiley & Sons, Ltd.
Distribution of Process Induced Traps in Ozone Annealed Indium-Gallium-Zinc-Oxide Film Determined From Noise Characterization

Space-energy distribution of process induced traps was extracted from random telegraph noise (RTN) characterization on ozone (<inline-formula> <tex-math notation="LaTeX">$\text{O}_{{3}}{)}$ </tex-math></inline-formula> annealed Indium-Gallium-Zinc-Oxide (IGZO) metal-oxide-metal (MOM) capacitors. It was found that, <inline-formula> <tex-math notation="LaTeX">$\text{O}_{{3}}$ </tex-math></inline-formula> anneal increases both deeper and shallower traps in IGZO all the way to conduction band edge (CB) compared to the as-deposited film, and results in higher aggregate trap density as found from low frequency noise measurement. Extracted trap distribution was benchmarked with that in as-deposited and thermal annealed films, and was found convenient to interpret low field current degradation in IGZO due to <inline-formula> <tex-math notation="LaTeX">$\text{O}_{{3}}$ </tex-math></inline-formula> anneal.
Near-infrared spectra of Seyfert nuclei. I - The reddening problem

IR emission-line fluxes of various species from recent J and H band observations of five active galactic nuclei are given. These include a quasar and a Seyfert type 1, 1.5, 1.9, and 2. The infrared-to-optical ratios are used to derive the reddening appropriate to the regions emitting the broad and narrow emission lines, and comparison is made with other methods. The line ratio He II 3203 A to He II of 1.0123 microns is proposed as a useful new reddening diagnostic and is used to determine the A(v) in NGC 1068 and NGC 4151. The broad line extinction in NGC 2922 is inferred for the first time, and considered in relation to its X-ray properties. 45 references.
Electrospun Materials Based on Polymer and Biopolymer Blends—A Review

This review covers recent developments and progress in polymer and biopolymer blending and material preparation by electrospinning. Electrospinning is a technique that is used to produce nanofibers to improve the quality of membranes. Electrospun nanofibers are highly applicable in biomedical sciences, supercapacitors, and in water treatment following metal ion adsorption. The key affecting factors of electrospinning have been checked in the literature to obtain optimal conditions of the electrospinning process. Future research directions and outlooks have been suggested to think about innovative ideas for research in this field.
Biological control of weeds in Australia: the last 120 years

The development of the field of biological control of weeds in Australia is described, from the first attempts in 1903 to the present day. The interest sparked by the obvious success of prickly pear program, apparent from 1930 to 1935, resulted in several programs during the next 20 years, followed by a decline in activity until the 1970s when activity increased enormously following the success of the skeleton weed program and the effective use of a plant pathogen for the first time. This momentum was maintained until the beginning of the present century with several successes and was marked by several important advances in genetic profiling, host‐specificity testing, economic evaluation, conflict of interest resolution and the ecology of insect/plant interactions, including evaluation of the effectiveness of individual introductions. Biological control has proved to be a valuable and effective approach to weed management in Australia with 39% of all programs considered to produce complete or near‐complete control, 30.5% partial control and an average benefit–cost ratio of 23:1. Funding for research has been variable with a decline from the late 1990s but with a significant increase again since 2020.
Prognostic Impact of Baseline Six-Minute Walk Distance following Trans-Catheter Aortic Valve Replacement

Background: The six-minute walk test is a practical tool for assessing functional capacity in patients with a variety of etiologies including pulmonary disease and heart failure. Six-minute walk distance (6MWD) is associated with mortality and morbidity in patients with a variety of comorbidities. We aimed to assess the prognostic impact of baseline 6MWD in patients with severe aortic stenosis undergoing trans-catheter aortic valve replacement (TAVR). Methods: Patients with severe aortic stenosis who underwent a six-minute walk test after index admission and underwent TAVR between 2015 and 2022 were included in this retrospective study. Patients were followed up for two years or until November 2022 following TAVR. The impact of baseline 6MWD on the primary composite outcome, defined as all-cause death and all-cause readmission during the 2-year observation period following index discharge, was assessed. Results: A total of 299 patients (median age 86 years old, 85 men) were included. They received a 6-min walk test prior to TAVR, underwent successful TAVR procedures, and were discharged alive. The median baseline 6MWD was 204 (143, 282) meters. Shorter baseline 6MWD was associated with higher cumulative incidence of the primary outcome with an adjusted hazard ratio of 0.76 (95% confidence interval 0.58–1.01, p = 0.055) with a cutoff 6MWD of 178 m during the 2-year observation period after index discharge. Conclusions: Overall, functional capacity was impaired in the elderly patients with severe aortic stenosis prior to TAVR. Patients with severe aortic stenosis having shorter baseline 6MWD tended to have higher rates of morbidity and mortality after successful TAVR. The clinical implication of aggressive cardiac rehabilitation to improve patients’ functional capacity and 6MWD-guided optimal patient selection remain the future concerns.
PMNet: A Point-to-Mesh Network for 3-D Semantic Instance Reconstruction

Semantic instance reconstruction attracts increasing attention in several areas such as mobile mapping, scene reconstruction, and robot navigation. Although much progresses have been made in recent years, the reconstruction performance is highly sensitive to occlusions and noises. To address these issues, we incorporate point cloud completion into a novel semantic instance reconstruction network PMNet, which consists of a 3-D object detection module, a point cloud completion module, and a mesh generation module. Based on the candidate instance proposals and their proposal features obtained in the object detection module, a point encoder layer is proposed to learn the local geometric features from the point cloud belonging to the detected instances, and a feature transformation layer is utilized to align the proposal features with the local geometric features. These two types of features are then fused and fed into the point cloud decoder to predict the complete point cloud of each instance. The mesh is finally reconstructed for each instance by the mesh generation module. Quantitative and qualitative experiments conducted on the ScanNetv2 dataset demonstrate that the proposed PMNet achieves the best reconstruction performance on real-world point clouds.
Association of Urinary Biomarkers of Renal Tubular Injury with Cognitive Dysfunction in Older Patients with Chronic Kidney Disease: A Cross-Sectional Observational Study

Epidemiological data suggest that individuals in all stages of chronic kidney disease (CKD) have higher risks of developing cognitive impairment. The relationship between CKD and cognition has been assessed exclusively using glomerular function markers; however, kidney tubule injury has not been assessed. We assessed the association between urinary biomarkers of renal tubular injury and cognitive dysfunction in older patients with CKD Stages 3–4. According to the Montreal Cognitive Assessment, participants were divided into cognitive dysfunction and control groups. Compared with the control group, the cognitive dysfunction group had significantly higher percentages of smokers, noticeably lower average education, and higher mitochondrial DNA (mtDNA) levels in the peripheral blood. Spearman correlation analysis showed that higher urine neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and beta-2 microglobulin (β2M) levels were significantly associated with lower cognitive scores. Multivariate logistic regression analysis showed that only increased urinary β2M levels were independently associated with cognitive worsening in CKD after adjusting for confounders. Logistic regression identified a promising role of urinary β2M combined with smoking and education for predicting cognitive impairment in CKD. Urinary β2M and cognitive function negatively correlated with mtDNA content, suggesting that mitochondrial dysfunction is a common pathophysiological mechanism linking CKD and cognitive dysfunction.