Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Scale Convolutional Few-Shot Learning Networks for Online P300-Based Brain–Computer Interface and Its Application to Patients With Disorder of Consciousness

P300 brain–computer interfaces (BCIs) have significant potential for detecting and assessing residual consciousness in patients with disorders of consciousness (DoC) but are limited by insufficient data collected from them. In this study, a multiple scale convolutional few-shot learning (MSCNN-FSL) network was proposed to detect and recognize small sample P300 signals. A multiple scale convolutional neural network (MSCNN) was developed to learn different scale features from different scales of receptive fields to obtain more information from electroencephalograms (EEGs). Then, a prototypical network with cosine distance was introduced as a classifier to classify small sample P300 signals. The MSCNN-FSL was evaluated in two independent online BCI experiments. In the first P300 speller experiment, the presented network achieves good character recognition performance with average accuracies of 98.02% ± 1.70%. In the second experiment, eight healthy controls achieved photograph recognition performance with average accuracies of 98.75% ± 1.49%, and three of the 12 patients with DoC achieved more than 64% online accuracies with significance. Our results indicated that the proposed MSCNN-FSL could correctly assess the three patients who may have residual consciousness but were misdiagnosed by the coma recovery scale-revised (CRS-R). Clinical evaluation after two months did prove our BCI assessment results for the three patients.
Development of an Integrated Continuous Manufacturing Process for the rVSV-Vectored SARS-CoV-2 Candidate Vaccine

The administration of viral vectored vaccines remains one of the most effective ways to respond to the ongoing novel coronavirus disease 2019 (COVID-19) pandemic. However, pre-existing immunity to the viral vector hinders its potency, resulting in a limited choice of viral vectors. Moreover, the basic batch mode of manufacturing vectored vaccines does not allow one to cost-effectively meet the global demand for billions of doses per year. To date, the exposure of humans to VSV infection has been limited. Therefore, a recombinant vesicular stomatitis virus (rVSV), which expresses the spike protein of SARS-CoV-2, was selected as the vector. To determine the operating upstream process conditions for the most effective production of an rVSV-SARS-CoV-2 candidate vaccine, a set of critical process parameters was evaluated in an Ambr 250 modular system, whereas in the downstream process, a streamlined process that included DNase treatment, clarification, and a membrane-based anion exchange chromatography was developed. The design of the experiment was performed with the aim to obtain the optimal conditions for the chromatography step. Additionally, a continuous mode manufacturing process integrating upstream and downstream steps was evaluated. rVSV-SARS-CoV-2 was continuously harvested from the perfusion bioreactor and purified by membrane chromatography in three columns that were operated sequentially under a counter-current mode. Compared with the batch mode, the continuous mode of operation had a 2.55-fold increase in space–time yield and a reduction in the processing time by half. The integrated continuous manufacturing process provides a reference for the efficient production of other viral vectored vaccines.
Effect of Teachers’ Charismatic Leadership Perceived by College Students on Their Entrepreneurial Intentions: The Mediating Role of Entrepreneurial Passion

The present research examined the perceived teachers’ charismatic leadership and entrepreneurial intentions of college students. Based on the theory of emotional contagion, this study proposes a mediating model of entrepreneurial passion between college students’ perception of teachers’ charismatic leadership and entrepreneurial intentions. A questionnaire survey was conducted in 420 college students in Jiangsu Province, China. The results showed that college students’ perceived teachers’ charismatic leadership had a significant positive effect on entrepreneurial passion. Moreover, perceived teachers’ charismatic leadership of college students was found to have a significant positive predictive effect on entrepreneurial intentions, and entrepreneurial passion had a significant positive effect on entrepreneurial intentions. The mediation model analysis indicated that entrepreneurial passion played a partial mediating role between college students’ perception of teachers’ charismatic leadership and entrepreneurial intentions. The study results deepened our understanding of the inner influence mechanism of college students’ perception of teachers’ charismatic leadership and entrepreneurial intentions, in addition to providing a practical basis for college educators.
Infinite densities in strong anomalous diffusion: going beyond the central limit theorem

Strong anomalous diffusion, where $\langle |x(t)|^q \rangle \sim t^{q \nu(q)}$ with a nonlinear spectrum $\nu(q) \neq \mbox{const}$, is wide spread and has been found in various nonlinear dynamical systems and experiments on active transport in living cells. Using a stochastic approach we show how this phenomena is related to infinite densities, i.e., the asymptotic states of these systems are described by non-normalizable distribution functions. Our work shows that the concept of infinite densities plays an important role in the statistical description of open systems exhibiting multi-fractal anomalous diffusion, as it is complementary to the central limit theorem.
Reliability systems in random environment

A parallel reliability system is subject to shocks which are generated by a renewal point process. The shocks cause the components to fail with certain probabilities. First we study the case when the components fail independently of each other with the same probability. For this case simple closed expressions for the Laplace-Stieltjes transform and expectation of time to system failure are derived. Then a case of dependent components is studied: the probability of failure of a component depends on the number of functioning components. Some extensions are mentioned.
Measuring The Relative Efficiency of Tournaments

The efficiency of various tournament structures such as knock-outs and round robins has been considered by several authors, and it has been noted that the round robin structure has a higher probability that the best player wins than do the other structures, but it is at the cost of playing a larger number of matches. On the other hand the knock-out tournament requires relatively few matches, but has a smaller value for the probability that the best player wins. Thus, the compromise between the number of matches played in a tournament and the probability that the best player wins that tournament has remained to some extent an unsolved problem. The Masters Tennis tournament, being a combination of two round robins and a knock-out, is one attempt at such a compromise. In this paper the balance between the number of matches played and the probability that the best player wins is addressed, and the efficiency of several different tournament structures is evaluated. It is noted that although the efficiency has been evaluated for just some of the commonly used tournament structures, the approach outlined in this paper can be used for a wide range of other structures.
A Systematic Review and Meta-Analysis: Volatile Organic Compound Analysis in the Detection of Hepatobiliary and Pancreatic Cancers

Simple Summary Hepatobiliary cancers are notoriously difficult to detect, frequently leading to diagnosis in later stages of disease when curative treatment is not an option. The currently used biomarkers such as AFP (alpha-fetoprotein) and CA19.9 lack sensitivity and specificity. Hence, there is an unmet need for an alternative biomarker. Volatile organic compounds are produced by numerous tissues in the human body and subsequently excreted in breath, urine, blood, faeces, bile and saliva. Various analytical methods can be deployed to measure the concentration of these compounds in the bodily fluids. These are non-invasive and several studies have shown high patient acceptability. In light of this, we systematically reviewed the evidence available so far on the usefulness of volatile organic compounds in the detection of hepatobiliary and pancreatic cancers. Our review confirms that volatile organic compounds can be used, either alone or in combination with other biomarkers for the early diagnosis of hepatobiliary and pancreatic cancers. Abstract Background: Hepatobiliary cancers are notoriously difficult to detect, frequently leading to diagnosis in later stages of disease when curative treatment is not an option. The currently used biomarkers such as AFP (alpha-fetoprotein) and CA19.9 lack sensitivity and specificity. Hence, there is an unmet need for an alternative biomarker. Aim: To evaluate the diagnostic accuracy of volatile organic compounds (VOCs) for the detection of hepatobiliary and pancreatic cancers. Methods: A systematic review of VOCs’ use in the detection of hepatobiliary and pancreatic cancers was performed. A meta-analysis was performed using the software R. Heterogeneity was explored through meta-regression analysis. Results: A total of 18 studies looking at 2296 patients were evaluated. Pooled sensitivity and specificity of VOCs for the detection of hepatobiliary and pancreatic cancer were 0.79 (95% CI, 0.72−0.85) and 0.81 (97.5% CI, 0.76−0.85), respectively. The area under the curve was 0.86. Meta-regression analysis showed that the sample media used contributed to heterogeneity. Bile-based VOCs showed the highest precision values, although urine and breath are preferred for their feasibility. Conclusions: Volatile organic compounds have the potential to be used as an adjunct tool to aid in the early diagnosis of hepatobiliary cancers.
The relationship between sex and functional outcome in first-episode schizophrenia: the role of premorbid adjustment and insight

Abstract Background Studies that examined sex differences in first-episode patients consistently show that males compared to females have poor premorbid adjustment, earlier age of onset, worse clinical characteristics, and poorer outcomes. However, little is known about potential mediators that could explain these sex differences. Methods Our sample consisted of 137 individuals with first episode schizophrenia (males, n = 105; 77%) with a mean age of 22.1(s.d. = 4.1) years and mean education of 12.5(s.d. = 1.7) years. At entry, patients were within 2 years of their first psychotic episode onset. Baseline assessments were conducted for premorbid adjustment, symptoms, cognitive functioning, insight, and at 6-months for role and social functioning. Results Males as compared to females had poorer premorbid adjustment across several key developmental periods (p < 0.01), an earlier age of onset [M = 20.3(3.3) v. 22.8(5.6), p = 0.002], more negative symptoms (p = 0.044), poorer insight (p = 0.031), and poorer baseline and 6-month role (p = 0.002) and social functioning (p = 0.034). Several of these variables in which males showed impairment were significant predictors of 6-month role and social functioning. Premorbid adjustment and insight mediated the relationship between sex and role and social functioning at 6-months, but not negative symptoms. Discussion Males compared to females were at lower levels across several key premorbid and clinical domains which are strongly associated with functional outcome supporting the hypothesis that males might have a more disabling form of schizophrenia. The relationship between sex with role and social functioning was mediated through premorbid adjustment and insight suggesting pathways for understanding why females might have a less disabling form of schizophrenia.
Symmetry-Tunable Full-Duty Triangular-Shaped Waveform Generation Using a Simple Single-Drive Mach-Zehnder Modulator

A novel and simple photonic approach for generating symmetry-tunable full-duty triangular-shaped microwave waveforms is proposed and experimentally demonstrated. In this approach, a simple commercial single-drive Mach-Zehnder modulator (MZM) is adopted and driven by the radio frequency (RF) input. By properly presetting the RF modulation index and simply adjusting the direct current (DC) bias voltage of MZM, the full-duty triangular-shaped microwave waveforms with self-defined symmetrical coefficients can be obtained. A complete theoretical analysis on operation principle is presented and a proof-of-concept experiment is carried out. The full-duty triangular-shaped waveforms with tunable symmetrical coefficients at the repetition rate of 5 GHz are successfully generated. This approach features simple architecture, less variables, excellent reconfigurability and integrable capability, which are highly desired for the high-performance triangular-shaped waveform generator.
Multi-functional liquid crystal devices based on random binary matrix algorithm

ABSTRACT Multi-functional diffraction gratings have found broad applications in laser processing, medicine, beam manipulation, etc. However, based on the existing schemes there are still some challenges in fabricating the diffraction gratings with the controllable and arbitrary diffraction intensity of each order, multiple phase distribution, and polarisation of the output. Here, we propose a new phase mixing algorithm based on the random binary matrix and describe the theoretical foundation and experimental realisation of the proposed scheme. Using the new algorithm, several phase-mixing devices were fabricated, and the experiment verified the relationship of the light intensity, polarisation, phase and the algorithm. Our approach is practical to mix the phases of several diffraction gratings to realise the controllable diffraction orders, offering an innovative idea for the research of the liquid crystal phase mixing devices. GRAPHICAL ABSTRACT
Empreendedorismo Internacional: Mapeamento Temático e Proposta de Agenda de Pesquisa

Resumo O objetivo deste estudo foi realizar uma análise bibliométrica da produção sobre Empreendedorismo Internacional (EI). Para tanto, a partir de perspectivas da estrutura temática do campo, foi utilizada a técnica de mapeamento temático para modelar o conhecimento produzido no campo de estudos de EI. Os dados da produção científica sobre o tema foram coletados a partir de consulta nas bases da Scopus e da Web of Science. Emergiram do estudo três mapas temáticos representando os períodos 1995-2004, 2005- 2014 e 2015-2022. O estudo conclui que o EI é uma área de pesquisa que cresceu bastante a partir do segundo período, apresentando um aumento explosivo nas publicações, passando de 127 no primeiro período para 1.042 no segundo. Esse aumento se manteve constante chegando a 1.934 no terceiro e último período. Os temas que mais se destacam e são mais representativos na pesquisa sobre EI foram: desempenho das empresas no EI, inovação, conhecimento, empresas empreendedoras, orientação empreendedora, gestão, estratégia, modelo e comportamento exportador . Nota-se também uma variação conceitual dos temas, em que se destaca o tema desempenho, mantendo uma alta representatividade nos três períodos e com deslocamentos entre os temas básicos – transversais – e os temas-motor. Outro destaque é o tema capabilities , que, com o tempo, derivou no tema dynamic capabilities . Já como temas emergentes aparecem desempenho, orientação empreendedora, empreendedorismo, impacto e gestão. A partir dos resultados e das conclusões deste estudo, é proposta uma agenda de pesquisa.
Development of Vertex-Centered High-Order Schemes and Implementation in FUN3D

Many production and commercial unstructured computational fluid dynamics codes provide no better than second-order spatial accuracy. Unlike structured-grid procedures, in which there is an implied structured connectivity between the neighboring grid points, for unstructured grids, it is more difficult to compute higher derivatives due to a lack of explicit connectivity beyond the first neighboring cells. In this study, a modular high-order scheme with low-dissipation flux-difference splitting is developed that can be integrated into the existing computational fluid dynamics codes for use in improving the solution accuracy and to enable better prediction of complex physics and noise mechanisms and propagation. The salient features of the present approach include 1) high-resolution schemes with physics-based low-dissipation flux-difference splitting, 2) low-memory requirements and small overhead, and 3) modular structure for easy integration into an existing computational fluid dynamics code. Initially, fou.
Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines

In order to resolve the problem that the sample of image for internal detection of DN100 buried gas pipeline microleakage is single and difficult to identify, a recognition method of microleakage image of the pipeline internal detection robot is proposed. First, nongenerative data augmentation is used to expand the microleakage images of gas pipelines. Secondly, a generative data augmentation network, Deep Convolutional Wasserstein Generative Adversarial Networks (DCWGANs), is designed to generate microleakage images with different features for detection in the pipeline of gas pipelines to achieve sample diversity of microleakage images of gas pipelines. Then, a bi-directional feature pyramid network (BiFPN) is introduced into You Only Look Once (YOLOv5) to retain more deep feature information by adding cross-scale connecting lines in the feature fusion structure; finally, a small target detection layer is constructed in YOLOv5 so that more shallow feature information can be retained to achieve small-scale leak point recognition. The experimental results show that the precision of this method for microleak identification is 95.04%, the recall rate is 94.86%, the mAP value is 96.31%, and the minimum size of identifiable leaks is 1 mm.
Experimental and numerical performance assessment of a hybrid parabolic dish collector with photovoltaic for a distiller

ABSTRACT Environmental pollution has been rising recently, and so clean water needing has also increased. People could have easy access to pure water in all environments and conditions. This study is the solar tracking parabolic dish collector with produced pure water independent of electricity (off-grid). This system is fully renewable and autonomous. This system possesses a receiver, a boiler, a condenser, six 1000 mm diameter parabolic dish collectors, and 1 kW photovoltaic (PV) collectors. Thermal oil uses as the working fluid in the experiments. The experiment measured solar radiation, air temperature, air velocity, oil inlet and outlet temperatures, oil flow rate, receiver side surface temperatures, and instantaneous amount of pure water. Numerical and thermal analysis of the receiver in the experimental setup carry out. ANSYS Fluent 18.1 uses for numerical analysis. Energy and exergy analysis calculates the receiver for pure water production rate. The time interval when the system operates at high performance determines. In this study investigates the behavior of some exergetic indicators on the performance of the experimental setup, the cost of the experimental setup, and the enviro-economic impact of the parabolic dish-type collector. For this purpose, some exergetic factors such as the exergy effect, environmental impact factor, waste exergy rate, improvement potential, and exergetic sustainability index examine. The receiver’s improvement potential is between 1700 and 3000 W. In addition, the sustainability index, waste exergy rate, and environmental impact factor range from 0.32 to 0.36, 0.90 to 0.94, and 2.6 to 3.0, respectively.
Kansei engineering and its application on intelligent nursing bed design

With the development of industrial design, traditional requirements of reasonable function and aesthetic appearance are insufficient for nowadays customers' needs, which leads to the proposition of Kansei Engineering (KE). This paper introduces Kansei and six types of KE methods, and provides a design application based on Hybrid KE system and computer data analysis to evaluate and improve an intelligent home nursing bed by principal component analysis and cluster analysis, of which accurately and efficiently reflected the performance of the design scheme in six pairs of key indicators. The open challenges and future directions of KE are discussed as well, suggestion of its future study is offered for better knowledge and quick understanding of KE by relevant scholars.
Towards Lattice-Boltzmann Prediction of Turbofan Engine Noise

The goal of the present paper is to report verification and validation studies carried out by Exa Corporation in the framework of turbofan engine noise prediction through the hybrid Lattice-Boltzmann/Ffowcs-Williams & Hawkings approach (LB)-(FW-H). The underlying noise generation and propagation mechanisms related to the jet flow field and the fan are addressed separately by considering a series of elementary numerical experiments. As far as fan and jet noise generation is concerned, validation studies are performed by comparing the LB solutions with literature experimental data, whereas, for the fan noise transmission through and radiation from the engine intake and bypass ducts, LB solutions are compared with finite element solutions of convected wave equations. In particular, for the fan noise propagation, specific verification analyses are carried out by considering tonal spinning duct modes in the presence of a liner, which is modelled as an equivalent acoustic porous medium. Finally, a capability overview is presented for a comprehensive turbofan engine noise prediction, by performing LB simulation for a generic but realistic turbofan engine configuration.
Measurement Method and Experiment of Hydraulic Support Group Attitude and Straightness Based on Binocular Vision

Hydraulic supports are important equipment used in coal mining. Detecting the real-time attitude and straightness of hydraulic support group in unmanned working faces of coal mines in complex geological conditions is difficult. To address this issue, a method that measures the position and attitude of the hydraulic support using binocular vision detection-aided mark points was proposed. First, a combination of binocular vision positioning and a convolutional neural network (CNN) object detection is used to achieve the 3-D location of mark points. Then, the attitude calculation model of the hydraulic support based on the dual quaternion method is established. This method achieves the space attitude detection between multiple hydraulic supports and the straightness detection of the hydraulic support group in 3-D space. In the laboratory environment, the position and attitude detection test of the hydraulic support is obtained through the working face equipment position detection test bench. This method can adapt to the worse working environment in coal mine production and can provide early data for the automatic control of the straightness of the working face.