Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Performance Raman Distributed High-temperature Sensing System Based on Single Crystal YAG Fiber

We present a high-performance Raman distributed temperature sensing (DTS) system with a single-crystal YAG fiber. The sensing system was demonstrated from room temperature up to 1400 °C with an unprecedent record of 7 cm spatial resolution in a 1m long YAG fiber. In this system, a 2D image restoration was used to boost the signal to noise ratio of sensor. Empowered by the algorithm, the average temperature standard deviation was improved from 33.8 °C to 7.8 °C based on a single frame trace of the raw Raman data. A new record of temperature resolution of 0.62 °C was obtained in only 1 second frame data traces.
Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

The lower-limb exoskeleton for human performance augmentation (LEHPA) in sensitivity amplification control (SAC) is vulnerable to model parameter uncertainties and unmodeled dynamics due to its large sensitivity to external disturbances resulting from the positive feedback by the inverse dynamic model of the exoskeleton. This paper firstly proposes to combine SAC with deep reinforcement learning (DRL) to reduce the dependence on the model accuracy and tackle the ever-changing human-exoskeleton interaction (HEI) dynamics. The sensitivity adjustment is interpreted as finding the optimal policy for a Markov Decision Process (MDP) and solved using deep reinforcement learning algorithms. To train the policy safely and efficiently, a multibody simulation environment is created to implement the training process, accompanied by a novel hybrid inverse-forward dynamics simulation method to carry out the simulation. For comparison purposes, the SAC controller is introduced as a benchmark. A novel performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to evaluate the control effect of the trained SADRL controller quantitatively. The SADRL controller is compared with the SAC controller at five specified walking speeds, resulting in a lumped HEI force ratio as low as 0.54. The total decrease of HEI forces demonstrates the superior control effect of the SADRL strategy.
S-Estimators for Functional Principal Component Analysis

Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in identifying potential outliers among high-dimensional or functional observations. In this article, we propose a new class of estimators for principal components based on robust scale estimators. For a fixed dimension q, we robustly estimate the q-dimensional linear space that provides the best prediction for the data, in the sense of minimizing the sum of robust scale estimators of the coordinates of the residuals. We also study an extension to the infinite-dimensional case. Our method is consistent for elliptical random vectors, and is Fisher consistent for elliptically distributed random elements on arbitrary Hilbert spaces. Numerical experiments show that our proposal is highly competitive when compared with other methods. We illustrate our approach on a real dataset, where the robust estimator discovers atypical observations that would have been missed otherwise. Supplementary materials for this article are available online.
Bayesian regression versus application of least squares—an example

Regression is an important task in metrology and least-squares methods are often applied in this context. Bayesian inference provides an alternative that can take into account available prior knowledge. We illustrate similarities and differences of the two approaches in terms of a particular nonlinear regression problem. The impact of prior knowledge utilized in the Bayesian regression depends on the amount of information contained in the data, and by considering data sets with different signal-to-noise ratios the relevance of the employed prior knowledge for the results is investigated. In addition, properties of the two approaches are explored in the context of the particular example.
Large-scale Augmented Granger Causality (lsAGC) for discovery of causal brain connectivity networks in schizophrenia patients using functional MRI neuroimaging

The literature suggests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Augmented Granger Causality (lsAGC) can capture such alterations using restingstate fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsAGC to accurately identify schizophrenia patients from fMRI data using a subset of 31 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsAGC as features for classification. After feature extraction, we perform feature selection by Kendall’s tau rank correlation coefficient followed by classification using a support vector machine. For reference, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity, and several other standard methods. Using 30 different training/test data splits with 10-fold cross-validation we obtain mean/std f1-scores of 82.89% ± 17.25% and mean Area Under the receiver operating characteristic Curve (AUC) values of 93.33% ± 12.81% across all tested numbers of features for lsAGC, which is significantly better than the results obtained with cross-correlation (AUC=78.33% ± 25.60%, f1-score=66.22% ± 24.73%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsAGC as a potential imaging biomarker for schizophrenia.
Electrocatalytic Oxygen Reduction Reaction of Graphene Oxide and Metal-Free Graphene in an Alkaline Medium

Graphene is a well-known two-dimensional material with a large surface area and is used for numerous applications in a variety of fields. Metal-free carbon materials such as graphene-based materials are widely used as an electrocatalyst for oxygen reduction reactions (ORRs). Recently, more attention has been paid to developing metal-free graphenes doped with heteroatoms such as N, S, and P as efficient electrocatalysts for ORR. In contrast, we found our prepared graphene from graphene oxide (GO) by the pyrolysis method under a nitrogen atmosphere at 900 °C has shown better ORR activity in aqueous 0.1 M potassium hydroxide solution electrolyte as compared with the electrocatalytic activity of pristine GO. At first, we prepared various graphene by pyrolysis of 50 mg and 100 mg of GO in one to three alumina boats and pyrolyzed the samples under a N2 atmosphere at 900 °C. The prepared samples are named G50-1B to 3B and G100-1B and G100-2B. The prepared GO and graphenes were also analyzed under various characterization techniques to confirm their morphology and structural integrity. The obtained results suggest that the ORR electrocatalytic activity of graphene may differ based on the pyrolysis conditions. We found that G100-1B (Eonset, E1/2, JL, and n values of 0.843, 0.774, 4.558, and 3.76) and G100-2B (Eonset, E1/2, and JL values of 0.837, 0.737, 4.544, and 3.41) displayed better electrocatalytic ORR activity, as did Pt/C electrode (Eonset, E1/2, and JL values of 0.965, 0.864, 5.222, and 3.71, respectively). These results display the wide use of the prepared graphene for ORR and also can be used for fuel cell and metal–air battery applications.
A Resolution Control Loop for TDC-Based Phase Detectors in ADPLLs

This paper proposes a resolution control loop that runs in background to control the time resolution of a mid-rise Time to Digital Converter (TDC) used as a phase detector in All-Digital Phase Locked Loops (ADPLLs). The proposed resolution control loop minimizes the TDC resolution until the TDC linear dynamic range equals the range of the input time error. Consequently, PLL in-band phase noise is reduced due to reduction of the Power Spectral Density (PSD) of the TDC quantization noise. Moreover, the linearity of the TDC transfer function across the range of the input time error is guaranteed. On contrary, the counterpart resolution control loop based on Lloyd-Max algorithm does not guarantee the linearity of the TDC transfer function across the range of the input time error. Furthermore, the proposed resolution control loop achieves TDC quantization noise with a lower variance compared to that achieved by the counterpart resolution control loop when applied with a TDC with more than 3 bits. Finally, the hardware implementation of the proposed resolution control loop is more area and power efficient compared to the implementation of the counterpart resolution control loop.
Hybrid Differential Evolution with Biogeography-based Optimization for Absolute Value Equation

In this paper, we use a recently proposed algorithm named hybrid Differential Evolution with Biogeography-based Optimization (DEBBO) for solving NP-hard Absolute Value Equation (AVE) Ax− |x| = b, where A is an arbitrary square matrix whose singular values exceed one. DEBBO accepts good qualities of DE and BBO, and numerical experiments show that the DEBBO algorithm can avoid precocious phenomena to a certain extent and it has higher rate of convergence than the other BBOvariants algorithms.
OLD PROBLEMS AND NEW SOLUTIONS FOR MULTIPHASE FLOW IN POROUS MEDIA

The existing macroscopic equations of motion for multiphase flow in porous media are unsatisfactory in two general respects. On the one hand characteristic experimental features, such as relationships between capillary pressure and saturations, cannot be predicted. On the other hand the theoretical derivation of the equations from the well-known laws of hydrodynamics has not yet been accomplished. In this paper we discuss these deficiencies and present an alternative description which is based on energy balances. Our
Fatty Liver Disease in Patients with Prediabetes and Overweight or Obesity

Non-alcoholic fatty liver disease (NAFLD) is a global health problem associated with liver morbimortality, obesity, and type 2 diabetes mellitus. This study aimed to analyze the prevalence of NAFLD (defined as a fatty liver index [FLI] ≥ 60) and its association with other cardiovascular risk (CVR) factors in patients with prediabetes and overweight/obesity. The present cross-sectional analysis uses baseline data from an ongoing randomized clinical trial. Sociodemographic and anthropometric characteristics, CVR (assessed by the REGICOR-Framingham risk equation), metabolic syndrome (MetS), and FLI-defined NAFLD (cut-off value of ≥60) were assessed. The prevalence of FLI-defined NAFLD was 78% overall. Men exhibited a worse cardiometabolic profile as compared to women, specifically, with higher values of systolic blood pressure (137.02 ± 13.48 vs. 131.22 ± 14.77 mmHg), diastolic blood pressure (85.33 ± 9.27 vs. 82.3 ± 9.12 mmHg), aspartate aminotransferase (AST) (27.23 ± 12.15 vs. 21.23 ± 10.05 IU/L), alanine aminotransferase (ALT) (34.03 ± 23.31 vs. 21.73 ± 10.80 IU/L), and higher CVR (5.58 ± 3.16 vs. 3.60 ± 1.68). FLI-defined NAFLD was associated with elevated AST, ALT, and the presence of MetS (73.7%) and CVR for the whole sample. People with prediabetes present a high burden of comorbidities related to CVR, despite clinical follow-up, and it is recommended to actively begin working with them to reduce their risks.
A Corpus of Encoded Malware Byte Information as Images for Efficient Classification

Malware has become an increasing problem in today’s world. In an attempt to combat this ever-changing problem, this research presents a machine-learning approach for analyzing malware using image classification. This paper discusses the idea of using image-based machine learning (ML) in malware analysis. Ways of applying this technique into the real-world are also presented and discussed. The highest accuracy achieved with the dataset used for training was 98.21%. Other datasets were also tested and achieved up to 93.22%. This shows that not only is this an effective method, but also that it can be used on other datasets and that a pre-trained model is more effective. The pre-trained model, dataset, and application are made available here to the wider research community.
Establishment and Functional Characterization of Murine Monoclonal Antibodies Recognizing Neuritin

Neuritin represents a neurotrophic factor that is not only important in neuronal development and plasticity but also impacts endothelial angiogenesis, cell migration, tumor growth and the production of antibodies by B cells. We established monoclonal mouse anti-mouse neuritin antibodies by immunizing knock-out mice with two different neuritin-derived peptides. Because neuritin is well conserved between species, these new monoclonal antibodies recognize the neuritin of a wide variety of species, including human. Moreover, they not only recognize specifically surface-bound neuritin expressed by murine follicular regulatory T cells but also the block binding of recombinant neuritin to germinal center B cells. This suggests that these newly generated tools will be of great use in studying neuritin expression and function.
License Plate Character Recognition with Lightweight Convolutional Neural Networks

Character recognition is a crucial step in license plate (LP) recognition. Traditional methods based on template matching and human feature extraction are not robust enough. The Convolutional Neural Networks (CNN) based algorithm can automatically extract the character features through the deep network and significantly improve the correct recognition rate. This paper investigated data augmentation and a lightweight CNN-based algorithm for character recognition. The lightweight network is proposed to enhance character feature extraction by optimizing the convolutional network. In addition, various data enhancement methods are applied to increase the number of data samples to improve the robustness of recognition and enhance the model generalization ability. The experimental results showed that the character recognition accuracy of the proposed approach reached 99.8% with high precision, recall, and specificity. The recognition time for a single character was 6.9 ms on CPU and 0.46 ms on GPU, which meets the demand for real-time LP recognition and has specific reference significance in intelligent transportation.
A PPM-based UNet for Tumour and Kidney Segmentation in CTScans

ABSTRACT Kidney cancer is one of the leading causes of cancer death worldwide. The high rates of mortality and occurrences of cancer show the importance of research and the development of means for the early diagnosis. The correct segmentation of kidney becomes an essential step to help with the case’s analysis and degree of severity. Computational methods have been used for this purpose, emerging as alternatives to manual segmentation and as mechanisms to reduce fatigue during diagnosis. Fully convolutional neural networks have presented great prominence in automatic segmentation in medical images. This work aims to build a novel network based on the U-Net architecture, using the concept of Pyramid Pooling Module (PPM) blocks that we call PPM-UNet. We propose this model to force learning features in multiple resolutions during their encoding phase and emphasise differences between similar regions. The hypothesis is that in cases where the texture of the images is very similar, the PPM block will be able to represent information with greater precision. In the experiments, we verified that the PPM-UNet obtained promising results of 0.9466 of Dice for the kidney and 0.9383 of Dice for tumour regions when the method was applied to the Kits19 database.
Social Media and Influencer Marketing for Promoting Sustainable Tourism Destinations: The Instagram Case

Instagram is a critical tool for the selection of tourism destinations. Instagram travel influencers seem to play a significant role in this process, often using geo-location data to make their posts even more impactful. However, there is no evidence of them performing such a role in sustainable destinations, although these destinations are increasing and to a large extent are the future in tourism. This paper aspires to shed light in this area focusing on sustainable destinations. Specifically, the purpose of this paper is to examine (a) the role of Instagram travel influencers in shaping users’ opinions of a sustainable destination and (b) the importance of geo-location data used by influencers to the users’ selection of such a destination. Thirty sustainable tourism destinations, as posted between 2017 and 2019, were selected for examination by using 10 Instagram travel macro-influencers’ power derived from their followers’ interactions on posts. The study used a mixed method approach combining cross-sectional and quantitative data analysis. Panel data and a multiple hierarchical analysis using SPSS Statistics were implemented to test the hypotheses. Finally, a regression model was used to try to identify the relationship between geo-location data and the selection of sustainable tourism destinations. The findings show that Instagram travel influencers, as social media communicators, are positively related to the selection of a sustainable destination. However, the use of geo-location data by Instagram travel influencers does not enhance travelers’ intention for such a choice.
Genome Mining and Screening for Secondary Metabolite Production in the Endophytic Fungus Dactylonectria alcacerensis CT-6

Endophytic fungi are a treasure trove of natural products with great chemical diversity that is largely unexploited. As an alternative to the traditional bioactivity-guided screening approach, the genome-mining-based approach provides a new methodology for obtaining novel natural products from endophytes. In our study, the whole genome of an endophyte, Dactylonectria alcacerensis CT-6, was obtained for the first time. Genomic analysis indicated that D. alcacerensis CT-6 has one 61.8 Mb genome with a G+C content of 49.86%. Gene annotation was extensively carried out using various BLAST databases. Genome collinearity analysis revealed that D. alcacerensis CT-6 has high homology with three other strains of the Dactylonectria genus. AntiSMASH analysis displayed 45 secondary metabolite biosynthetic gene clusters (BGCs) in D. alcacerensis CT-6, and most of them were unknown and yet to be unveiled. Furthermore, only six known substances had been isolated from the fermented products of D. alcacerensis CT-6, suggesting that a great number of cryptic BGCs in D. alcacerensis CT-6 are silent and/or expressed at low levels under conventional conditions. Therefore, our study provides an important basis for further chemical study of D. alcacerensis CT-6 using the gene-mining strategy to awaken these cryptic BGCs for the production of bioactive secondary metabolites.
Computational Modeling of the Unsteady Wake behind Gurney-Flaps

The effect of small micro-tabs (Gurney-flaps) mounted at the lower trailing edge of a HQ17 airfoil has been studied using numerical simulation. The focus of attention has been placed on the unsteady flow structures in the wake of the Gurney-flap, as these are responsible for increased induced drag. At a Reynolds number of 106, the flow is investigated using steady and unsteady simulations based on the Reynolds-averaged Navier-Stokes equations (URANS) as well as Detached Eddy Simulations (DES). In order to reduce the occuring unsteady flow structures, further simulations have been performed using alternative trailing edge shapes. The results show that the unsteadyness can successfully be suppressed and the drag can be reduced substantially using advanced flap concepts.
Does cram school participation bring about negative emotions? Causal inference based on Chinese Education Panel Survey (CEPS) data

This study focuses on the causal relationship between teenagers’ participation in cram schools and their emotional well-being. Our analysis is based on Chinese Education Panel Survey data (CEPS 2013–2015). We construct instrumental variables and introduce lagged variables to mitigate possible endogeneity problems. The major findings are as follows. First, the participation of socioeconomically disadvantaged teenagers in cram schools significantly exacerbates their negative emotions. Teenagers of advantaged family socioeconomic status (SES) show better ability in emotion regulation and in avoiding specific negative emotions. Second, we explore class heterogeneity in emotion regulation. Specifically, we propose a preliminary theoretical framework based on a typology of parenting styles. We conjecture that parents of different social classes may respond differently to their children's negative emotions. And for children who attend cram schools, there might be intergroup disparities in their own emotion-regulation ability by their class origins. Empirically, we carry out a counterfactual analysis to further illustrate the association between class difference in emotion regulation and that in the depressive effect of cram school participation, providing supporting evidence for our theoretical framework. In sum, our study is helpful to further our understanding of the implications of emotional health inequality which may derive from the “shadow education” system.
Effects of a Losartan-Antioxidant Hybrid (GGN1231) on Vascular and Cardiac Health in an Experimental Model of Chronic Renal Failure

Drugs providing antihypertensive and protective cardiovascular actions are of clinical interest in controlling cardiovascular events and slowing the progression of kidney disease. We studied the effect of a hybrid compound, GGN1231 (derived from losartan in which a powerful antioxidant was attached), on the prevention of cardiovascular damage, cardiac hypertrophy, and fibrosis in a rat model of severe chronic renal failure (CRF). CRF by a 7/8 nephrectomy was carried out in male Wistar rats fed with a diet rich in phosphorous (0.9%) and normal calcium (0.6%) for a period of 12 weeks until sacrifice. In week 8, rats were randomized in five groups receiving different drugs including dihydrocaffeic acid as antioxidant (Aox), losartan (Los), dihydrocaffeic acid+losartan (Aox+Los) and GGN1231 as follows: Group 1 (CRF+vehicle group), Group 2 (CRF+Aox group), Group 3 (CRF+Los group), Group 4 (CRF+Aox+Los group), and Group 5 (CRF+GGN1231 group). Group 5, the CRF+GGN1231 group, displayed reduced proteinuria, aortic TNF-α, blood pressure, LV wall thickness, diameter of the cardiomyocytes, ATR1, cardiac TNF-α and fibrosis, cardiac collagen I, and TGF-β1 expression. A non-significant 20% reduction in the mortality was also observed. This study showed the possible advantages of GGN1231, which could help in the management of cardiovascular and inflammatory processes. Further research is needed to confirm and even expand the positive aspects of this compound.
The Distribution of the Inverse Square Root Transformed Error Component of the Multiplicative Time Series Model

The probability density function, mean and variance of the inverse square-root transformed left-truncated   2 1, N  error component * 1 t t e e      of the multiplicative time series model were established. A comparison of key-statistical properties of * t e and t e confirmed normality with mean 1 but with