Miracle-dz/UniFAR-FTUs huggingface dataset:
The power dependence of the maximum achievable H-mode and (disruptive) L-mode separatrix density in ASDEX Upgrade

In future fusion reactors disruptions must be avoided at all costs. Disruptions due to the density limit (DL) are typically described by the power-independent Greenwald scaling. Recently, a power dependence of the disruptive DL was predicted by several authors (Zanca et al 2019 Nucl. Fusion 59 126011; Giacomin et al 2022 Phys. Rev. Lett. 128 185003; Singh and Diamond 2022 Plasma Phys. Control. Fusion 64 084004; Stroth et al 2022 Nucl. Fusion 62 076008; Brown and Goldston 2021 Nucl. Mater. Energy 27 101002). It is investigated whether this increases the operational range of the tokamak. Increasing the heating power in the L-mode can induce an L-H transition, and therefore a power-dependent DL and the L-H transition cannot be considered independently. The different models are tested on a data base for separatrix parameters at the separatrix of ASDEX Upgrade and compared with the concept (SepOS) presented in Eich and Manz (2021 Nucl. Fusion 61 086017). The disruptive separatrix density scales with the power ne∝P0.38±0.08 in good agreement to all models. Also the back transition from high to low (H-L) confinement shows an approximately Greenwald scaling with an additional power dependence ne∝P0.4 according to the SepOS concept. For future devices operating at much higher heating power such a power scaling may allow operation at much higher separatrix densities than are common in H-mode operation. Preconditions to extrapolation for future devices are discussed.
Fuzzy comprehensive evaluation model based on Vague sets

The conception of Vague linear shift is defined based on literature~([1]) by Zhang Xiaping.The one-to-one correspondence among vague relationship, vague map and vague linear shift is farther demonstrated, so the theoretical basis for fuzzy decision-making problems is offered. In addition, a ranking method about interval numbers is introduced, a fuzzy comprehensive evaluation model based on vague sets is established and an applied example is given.
COMPUTATION OF EARTHQUAKE RESPONSES OF AN ECCENTRIC STRUCTURE USING GREEN'S FUNCTIONS RETRIEVED FROM MICROTREMORS.

The objective of our study is to retrieve Green’s functions from transfer functions of buildings that satisfy the causality by transforming phase spectra to satisfy the causality. It is often the case that when we perform reverse Fourier transforms directly to transfer functions retrieved from micro-tremors in order to obtain Green’s functions, we encounter the situation where the Green’s function does not satisfy causality. We estimated optimal solutions of Green’s functions using Generic Algorithm so that transfer functions of theoretical equation of motions fit transfer functions of observed micro-tremors. In addition, we identified the stiffness of buildings by using the identified variable of the Green’s functions. We checked the condition of buildings through the identified stiffness of buildings, to identify the damaged point of buildings for each frame.
Improved Generative Adversarial Networks With Filtering Mechanism for Fault Data Augmentation

Few-shot fault diagnosis (i.e., fault diagnosis with few samples) is a challenging issue in practice because fault samples are scarce and difficult to obtain. Data augmentation based on generative adversarial networks (GANs) has proven to be an effective solution. However, it often encounters problems such as difficult model training and low quality of generated samples. In this article, an improved GAN with filtering mechanism is developed for fault data augmentation, which introduces the self-attention mechanism and instance normalization (IN) into the GAN structure and utilizes a filtering mechanism. The implementation process comprises two parts, sample generation and abnormal sample filtering. The self-attention mechanism and IN adopted in sample generation can make the generative model easy to train and have better quality of the generated samples. For abnormal sample filtering, the isolated forest (IF) algorithm is used for detecting low-quality generated samples. The effectiveness of the proposed fault data augmentation method is verified using two public datasets for fault diagnosis purposes, and the results show that the proposed method can have better performance over the state-of-art GAN-based data augmentation methods.
Realization factors and sensitivity analysis of queueing networks with state-dependent service rates

The paper studies the sensitivity of the throughput with respect to a mean service rate in a closed queueing network with exponentially distributed service requirements and state-dependent service rates. The study is based on perturbation analysis of queueing networks. A new concept, the realization factor of a perturbation, is introduced. The properties of realization factors are discussed, and a set of equations specifying the realization factors are derived. The elasticity of the steady state throughput with respect to a mean service rate equals the product of the steady state probability and the corresponding realization factor. This elasticity can be estimated by applying a perturbation analysis algorithm to a sample path of the system. The sample path elasticity of the throughput with respect to a mean service rate converges with probability 1 to the elasticity of the steady state throughput. The theory provides an analytical method of calculating the throughput sensitivity and justifies the application of perturbation analysis.
Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach

Objective. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an appropriate treatment plan. Accurate identification requires clinical diagnosis by neuroscientists and microbiological analysis, which are time-consuming and expensive. In this study, we develop a domain enriched AI method for computerized tomography (CT)-based infection diagnosis in hydrocephalic imagery. State-of-the-art (SOTA) convolutional neural network (CNN) approaches form an attractive neural engineering solution for addressing this problem as pathogen-specific features need discovery. Yet black-box deep networks often need unrealistic abundant training data and are not easily interpreted. Approach. In this paper, a novel brain attention regularizer is proposed, which encourages the CNN to put more focus inside brain regions in its feature extraction and decision making. Our approach is then extended to a hybrid 2D/3D network that mines inter-slice information. A new strategy of regularization is also designed for enabling collaboration between 2D and 3D branches. Main results. Our proposed method achieves SOTA results on a CURE Children’s Hospital of Uganda dataset with an accuracy of 95.8% in hydrocephalus classification and 84% in pathogen classification. Statistical analysis is performed to demonstrate that our proposed methods obtain significant improvements over the existing SOTA alternatives. Significance. Such attention regularized learning has particularly pronounced benefits in regimes where training data may be limited, thereby enhancing generalizability. To the best of our knowledge, our findings are unique among early efforts in interpretable AI-based models for classification of hydrocephalus and underlying pathogen using CT scans.
IDENTIFICATION OF BEST PRACTICES IN MANAGING BANKING HR MANAGEMENT IN THE ERA OF AUTOMATION: WHAT EVIDENCE SHOWS

This study aims to identify best practices in managing human banking resources in the digital era through scientific evidence. So the next step we took was to complete data from various relevant sources to answer this problem. After collecting more than 100 complications, we found around 70 publication sources that we saw were appropriate for us to use as study support for findings. We conducted data coding and thorough analysis and concluded using a phenomenological approach. Based on the discussion of the results and available data, we can conclude that many strategies have been carried out by banks in this digital era, including establishing partnerships with other parties, providing appropriate incentives to staff, responding to real-time data to consumers and banks having a vision for the future. to collaborate for development and progress. It is hoped that these findings will be helpful in other studies.
Natural Convection Heat Transfer From Horizontal Rectangular Ducts

Experimental investigations have been reported on steady state natural convection from the outer surface of horizontal ducts in air. Five ducts have been used with aspect ratios (Γ=duct height/duct width) of 2, 1, and 0.5. The ducts are heated using internal constant heat flux heating elements. The temperatures along the surface and peripheral directions of the duct wall are measured. Longitudinal (circumference averaged) heat transfer coefficients along the side of each duct are obtained for laminar and transition regimes of natural convection heat transfer. Total overall averaged heat transfer coefficients are also obtained. Longitudinal (circumference averaged) Nusselt numbers are evaluated and correlated using the modified Rayleigh numbers for transition regime using the axial distance as a characteristic length. Furthermore, total overall averaged Nusselt numbers are correlated with the modified Rayleigh numbers, the aspect ratio, and area ratio for the laminar and transition regimes. The longitudinal or total averaged heat transfer coefficients are observed to decrease in the laminar region and to increase in the transition region. Laminar regimes are obtained only at very small heat fluxes, otherwise, transitions are observed.
LncRNA antigens- a novel resource to improve immunotherapy efficacy predictions in Melanoma

Background: ICI (immune checkpoint inhibitor) therapy is one of the most promising treatments for melanoma. ICI response however varies among patients, emphasizing the importance of identifying genomic biomarkers to predict likely therapeutic efficacy in advance of treatment. We hypothesised that a lncRNA based immunogencity (lnc-IM) score could be used to predict individual response to ICI treatment, and that this could complement the existing criterion for ICI selection based on tumor mutation burden (TMB). Methodology: The TCGA-SKCM (n=101) and the ICI treated UCLA (n=25), MSKCC (n=16) and DFCI (n=40) melanoma cohorts were used in this study, involving both clinical and transcriptomic data. Each patient was assigned an lnc-IM score based on the number of lncRNA sORF derived peptides predicted to be presented by their tumor's MHC-I genotype. For the ICI treated cohorts, a combined antigen score was defined as a sum of neo-antigen load (derived from TMB) and lnc-IM score. A logistic regression-based classifier was used to predict ICI responses based on these combined antigen scores. Results: Survival analysis showed improved overall survival among patients with low lnc-IM scores (HR= 0.39, p=0.009) in the TCGA-SKCM cohort. We also observed a negative association between tumor immune cell concentration and lnc-IM scores, with low lnc-IM groups showing higher anti-tumor immune cell concentrations . Using the ICI treated cohorts, we demonstrated that a classifier based on combined antigen scoring improved the prediction of immunotherapy outcomes as compared to using TMB alone, yielding an area under the curve (AUC) of 0.71 with an accuracy of 0.54 and recall of 1. We also demonstrated a reduced rate of false negatives (14%) by using a combined antigen score as compared to the use of TMB alone (33%) in ICI treated cohorts. Conclusion: Our findings suggest that the use of combined antigen scores (using lnc-IM scores along with TMB derived neoantigen load) have potential in improving immunotherapy efficacy predictions. Prospective validation in larger cohort sizes is warranted.
Deep Stroop: Using eye tracking and speech processing to characterize people with neurodegenerative disorders while performing the Stroop Test

Although many neurodegenerative diseases affect different neural circuits, they often express complex and overlapping symptom profiles making them difficult to differentiate precisely. Current methods of analyzing patients are limited to bedside examination, patient self-rating scales, semiquantitative clinician-rating scales, and other observational evidence, which are often non-specific, resulting in open multiple interpretations and ambiguity in diagnosis and treatment plans. We present a method to analyze patient symptom profiles using multimodal analysis of subjects performing the Stroop Test. We use high-sample-rate eye tracking and speech recording tools to record subject behavior while completing the Stroop Test and simultaneously analyze multiple traits of their interaction with the test. We compare the performance of healthy controls to patients with Parkinsons Disease, Alzheimers Disease, and other neurodegenerative diseases with clinical parkinsonism. We automatically extract metrics based on eye motor behavior, gaze characteristic uttered responses, and the temporal relationship between gaze and uttered responses. We identify many that have clinical relevance through high correlations with existing MoCA and MDS-UPDRS, many of which have significantly different distributions between groups. We present here our analysis approach, provide freely available source code to replicate it and demonstrate the potential of multi-modal recording and analysis of patients throughout their execution of neuro-psychological tests like the Stroop Test.
Analytical validation of HepatoPredict kit to assess hepatocellular carcinoma prognosis prior to a liver transplantation.

Background: The best curative treatment for hepatocellular carcinoma (HCC) is liver transplant (LT), but the limited number of organs available for LT dictates strict eligibility criteria. Despite this patient selection stringency, current criteria often fail in pinpointing patients at risk of HCC relapse and in identifying good prognosis patients that could benefit from a LT. HepatoPredict kit was developed and clinically validated to forecast the benefit of LT in patients diagnosed with HCC. By combining clinical variables and a gene expression signature in an ensemble of machine learning algorithms, HepatoPredict stratifies HCC patients according to their risk of relapse after LT. Methods: Aiming at the characterization of the analytical performance of HepatoPredict kit in terms of sensitivity, specificity and robustness, several variables were tested which included reproducibility between operators and between RNA extractions and RT-qPCR runs, interference of input RNA levels or varying reagent levels. The described methodologies, included in the HepatoPredict kit, were tested according to analytical validation criteria of multi-target genomic assays described in guidelines such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. Furthermore, a new retrained version of the HepatoPredict algorithms is also presented and tested. Results: The results of the analytical performance demonstrated that the HepatoPredict kit performed within the required levels of robustness (p > 0.05), analytical specificity (inclusivity [≥] 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The introduced operator, equipment, input RNA and reagents into the assay had no significant impact on HepatoPredict classifier results. As demonstrated in a previous clinical validation, a new retrained version of the HepatoPredict algorithm still outperformed current clinical criteria, in the accurate identification of HCC patients that more likely will benefit from a LT. Conclusions: Despite the variations in the molecular and clinical variables, the prognostic information obtained with HepatoPredict kit and does not change and can accurately identify HCC patients more likely to benefit from a LT. HepatoPredict performance robustness also validates its easy integration into standard diagnostic laboratories.
Transient increased risk of influenza infection following RSV infection in South Africa; findings from the PHIRST study, South Africa, 2016-2018.

Large-scale prevention of respiratory syncytial virus (RSV) infection may have ecological consequences for co-circulating pathogens, including influenza. We assessed if and for how long RSV infection alters the risk for subsequent influenza infection. We analysed a prospective longitudinal cohort study conducted in South Africa between 2016 and 2018. For participating households, nasopharyngeal samples were taken twice weekly, irrespective of symptoms, across three respiratory virus seasons, and real-time polymerase chain reaction (PCR) was used to identify infection with RSV and/or influenza. We fitted an individual-level hidden markov transmission model in order to estimate RSV and influenza infection rates and their interdependence. Of a total of 122113 samples collected, 1265 (1.0%) were positive for influenza and 1002 (0.8%) positive for RSV, with 15 (0.01%) samples from 12 individuals positive for both influenza and RSV. We observed 2.25-fold higher incidence of co-infection than expected if assuming infections were unrelated. We estimated that infection with influenza is 2.13 (95% CI 0.97 - 4.69) times more likely when already infected with, and for a week following, RSV infection, adjusted for age. This equates to 1.4% of influenza infections that may be attributable to RSV in this population. Due to the local seasonality (RSV season precedes the influenza season), we were unable to estimate changes in RSV infection risk following influenza infection. RSV infection was associated with an increased risk for influenza infection for a short period after infection. However, the impact on population-level transmission dynamics of this individual-level synergistic effect was not measurable in this setting.
RP-Bert: An approach to detect and classify network intrusions based on a combination of Transfer learning and rules

We investigate the model generalization problem that exists for network traffic intrusion detection. Existing works have begun to apply some advanced algorithms and models from NLP domain to network security domain, but have some challenges. One of the biggest problem is that the amount of data for some existing network intrusions is not sufficient to train the current oversized models. For this purpose, we improve on ET-Bert and propose a method to detect and classify network intrusion based on the combination of migration learning and rules (RP-Bert). And we test and validate it on a large open source dataset BoT-loT, which Recall is achieved, Precision is achieved, F1 Score is achieved, Accuracy is achieved.
Research on Att-NFM Recommendation Algorithm Based on Attention Mechanism

Attentional mechanisms play an important role in artificial intelligence, and inspired by other work, we apply them in the field of recommendation algorithms. By combining factorization machine and neural network together through parallel structure, the linear model of factorization machine is used for binary feature interaction, and the nonlinear relationship between features is explored in using neural network. We propose the Att-NFM model to learn attention coefficients in neural networks to characterize the importance between features. Through experiments, we use AUC and LogLoss as evaluation metrics, and the results show that the evaluation metrics are improved by 3% and 2%, respectively.
Att GGO-Net:A Semantic Segmentation Method of Lung CT Images with Self and Cross Attention Mechanism

Chest computed tomography imaging (CT) has become a diagnostic method for many lung diseases due to its high sensitivity and low missed rate of infection. Convolutional neural network based semantic segmentation model that can assist physicians in auxiliary diagnosis. The existing semantic segmentation models do not work well for lesion segmentation of lung CT images, small lesions are not sensitive, and the contour segmentation is coarse, for which this paper designs a network named Att GGO-Net specifically for lung CT images ground glass opacity(GGO) and Lung consolidation segmentation.The basic encoder-decoder network structure used in this study is the U-Net++ model. After feature mapping is finished at each level of U-Net++, cross attention mechanism is introduced to optimize the semantic information in the decoder layer. Finally, self attention mechanism is added to complete the overall network structure.The extensive attention mechanism will cause the network to have many parameters. To solve the issue of large network parameters, the research substitutes depthwise separable convolution for traditional convolution. The data collection of lung CT scans with ground glass opacity and lung consolidation was used for the experiments. The segmentation performance was compared between Att GGO-Net and three classical medical image segmentation networks, namely, U-Net, U-Net++, and Attention U-Net (Att U-Net). The experimental findings demonstrate that the strategy used in this research has a superior segmentation effect than the comparison method.
Wheeled Robot Visual Odometer Based on Two-dimensional Iterative Closest Point Algorithm

According to the two-dimensional motion characteristics of planar motion wheeled robot, the visual odometer was dimensionally reduced in this study. In the feature point matching part of visual odometer, the contour constraint was used to filter out the mismatched feature point pairs (abbreviated as FPP). This method could also filter out the matched FPP, and the feature of FPP was correct color image matches, however, their depth image error was large. This offered higher quality matched FPP for the subsequent interframe motion estimation. Dimension reduction was performed in the interframe motion estimation part, and the two-dimensional Iterative Closest Point (ICP) algorithm was used for camera motion estimation. The experiments indicated that the proposed algorithm effectively improved the computational speed and precision of planar motion wheeled robot visual odometer. This research indicates that the dimension reduction processing of ICP algorithm can effectively improve the operation speed and calculation accuracy of planar motion wheeled robot visual odometry, which provides a good reference and data support for the subsequent research of wheeled robot visual odometry in the future.
Design of a composite collimator for the hard X-ray camera in HL-2M tokamak

A new hard X-ray (HXR) camera is being designed for the HuanLiu-2M (HL-2M) tokamak to measure the spatial-temporal evolution of the fast electrons in the energy range of 20–200 keV during radio frequency (RF) drive or heating experiments. The new camera integrates 63 channels of independent detectors consisting of 63 lutetium yttrium oxyorthosilicate scintillators (LYSO) coupled to silicon photomultipliers (SiPMs). A composite collimator (pinhole + collimating tube) is designed for the HXR camera to arrange the 63 channels in a compact space and reduce background rays. This paper introduces the design of composite collimator and the calculation of geometric parameters of the pinhole and collimating tube. The geometric factor uniformity (GFU) and effective count rate (ECR) of the composite collimator were evaluated by Geant4 simulation for different pinhole inner wall thicknesses T 1. The results show that when the energy is 200 keV, GFU increases and ECR increases and then decreases when reducing T 1. The optimal T 1 is 2 mm, and the resulting GFU is 0.83 and ECR is 78.2% for 200 keV.