Miracle-dz/UniFAR-FTUs huggingface dataset:
An Assessment of Pulse Detonation Engine Performance Estimation Methods Based On Experimental Results

A number of Pulse Detonation Engine (PDE) performance modeling methodologies have been advanced over the last 15 years, varying widely in approach. In an effort to evaluate the effectiveness of several of these approaches, two data points from a single-tube, cyclically operating, direct-connect pulse detonation combustor and nozzle operating at simulated flight conditions were used as input to each of three PDE performance codes. One classical thermodynamics code, one CFD-based method, and one hybrid, unsteady thermodynamic/ computational/empirical cycle deck were selected for this study. After the codes were anchored to the experimental data, each code was used to simulate the performance of four different idealized engine cycles utilizing an optimized version of the experimental PDE combustor operating at the conditions tested. Specific impulse and specific thrust were calculated for each engine cycle at each experimental combustor operating condition. Utilizing the same data corrections and cycle assumptions, the three performance codes showed good agreement. The largest total variation from the mean for any cycle was 4% for specific thrust and 7% for specific impulse.
A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images

An error-related potential (ErrP) occurs when people’s expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain–computer interfaces.
Food-Related Behaviours of Female and Male Tourists before and during the COVID-19 Pandemic

The literature contains scant information on the relationship between tourists’ gender and their attitudes toward food while vacationing. The present study had as its main aim to examine the role of gender in key behaviours concerning tourists’ food choices and eating habits; in addition, our study evaluated the hypothesis that gender interacts with the “pandemic effect” in modifying tourists’ food-related behaviour. By implementing a quantitative, exploratory, and descriptive methodology, an online questionnaire was developed based on existing literature. To this end, data derived from 474 female and 370 male tourists visiting Greece in 2019 (before) or in 2020 (during the COVID-19 pandemic) were analysed and evaluated. The results indicate that, compared to men, women were more motivated to taste local food, especially with respect to obtaining cultural experience and excitement, promoting interpersonal relations, and as a result of health concerns. In addition, they dined at facilities at their place of stay, as well as at Greek restaurants and taverns at a higher rate compared to men. Women also reported being more satisfied than men by their food experiences during their trips, and was found that during the pandemic they spent, on average, more on food than before. The study showed that the pandemic affected both genders; however, the most significant of the differences observed relates to the choice of dining facilities, in all likelihood, as a result of women being more sensitive to the risks of COVID-19. Overall, during the pandemic, women appeared more likely than men to adopt behaviours that are in line with the principles of sustainability, such as a positive attitude and motivation to consume foods produced locally in the country. Differences detected between genders can be used by various stakeholders as a benchmark to further improve the food services offered at tourist destinations.
Physics informed neural networks for phase field fracture modeling enhanced by length-scale decoupling degradation functions

The paper proposed a novel framework for efficient simulation of crack propagation in brittle materials. In the present work, the phase field represents the sharp crack surface with a diffuse fracture zone and captures the crack path implicitly. The partial differential equations of the phase field models are solved with physics informed neural networks (PINN) by minimizing the variational energy. We introduce to the PINN-based phase field model the degradation function that decouples the phase-field and physical length scales, whereby reducing the mesh density for resolving diffuse fracture zones. The numerical results demonstrate the accuracy and efficiency of the proposed algorithm.
Analysis of the fabrication process of Nb/Al-AlN/sub x//Nb tunnel junctions with low R/sub n/A values for SIS mixers

We characterize the fabrication process of superconductor-insulator-superconductor junctions (SIS) based on a Nb/Al-AlN/sub x//Nb tri-layer. Utilization of the AlN/sub x/ tunnel barrier, produced by Al nitridation in a nitrogen glow discharge, enables us to produce high quality SIS junctions with low R/sub n/A values (the product of junction resistance and area). Analyzing the correlation of junction resistance and plasma properties, it is concluded that the mechanism of tunnel barrier formation is based on nitrogen implantation into the Al layer with subsequent diffusion of nitrogen, stimulated by plasma heating. The latter process plays a dominant role since R/sub n/A values are well correlated with the power dissipated on the substrate surface. An SIS mixer using this technology and electron-beam lithography has been successfully fabricated.
STRUCTURAL PARAMETERS FOR 10 HALO GLOBULAR CLUSTERS IN M33

In this paper, we present the properties of 10 halo globular clusters (GCs) with luminosities L ≃ 5–7 × 105 L⊙ in the Local Group galaxy M33 using images from the Hubble Space Telescope WFPC2 in the F555W and F814W bands. We obtained the ellipticities, position angles, and surface brightness profiles for each GC. In general, the ellipticities of the M33 sample clusters are similar to those of the M31 clusters. The structural and dynamical parameters are derived by fitting the profiles to three different models combined with mass-to-light ratios (M/L values) from population-synthesis models. The structural parameters include core radii, concentration, half-light radii, and central surface brightness. The dynamical parameters include the integrated cluster mass, integrated binding energy, central surface mass density, and predicted line of sight velocity dispersion at the cluster center. The velocity dispersions of the four clusters predicted here agree well with the observed dispersions by Larsen et al. The results here showed that the majority of the sample halo GCs are better fitted by both the King model and the Wilson model than the Sérsic model. In general, the properties of the clusters in M33, M31, and the Milky Way fall in the same regions of parameter spaces. The tight correlations of cluster properties indicate a “fundamental plane” for clusters, which reflects some universal physical conditions and processes operating at the epoch of cluster formation.
Identification of cuproptosis hub genes contributing to the immune microenvironment in ulcerative colitis using bioinformatic analysis and experimental verification

Instruction Ulcerative colitis (UC) can cause a variety of immune-mediated intestinal dysfunctions and is a significant model of inflammatory bowel disease (IBD). Colorectal cancer (CRC) mostly occurs in patients with ulcerative colitis. Cuproptosis is a type of procedural death that is associated with different types of diseases to various degrees. Methods We used a combination of bioinformatic prediction and experimental verification to study the correlation between copper poisoning and UC. We used the Gene Expression Omnibus database to obtain disease gene expression data and then identified relevant genes involved in various expression levels in normal and UC samples. The Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to cluster the genes that are highly responsible and find the central interaction in gene crosstalk. Notably, DLD, DLAT, and PDHA1 were present in high-scoring PPI networks. In addition, hub gene expression information in UC tissues was integrated to estimate the relationship between UC copper poisoning and the immune environment. Results In our study, the expression of DLD, DLAT, and PDHA1 in UC tissues was lower than that in normal tissues. The key genes associated with cuproptosis have therapeutic effects on immune infiltration. We verified the expression of DLD, DLAT, and PDHA1 using real-time quantitative polymerase chain reaction in mouse models of UC induced by DSS. Discussion Notably, this study clearly indicates that bioinformatic analysis performed to verify the experimental methods provides evidence that cuproptosis is associated with UC. This finding suggests that immune cell infiltration in UC patients is associated with cuproptosis. The key genes associated with cuproptosis can be helpful for discovering the molecular mechanism of UC, thus facilitating the improvement of UC treatment and preventing the associated CRC.
Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution

We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method’s potential through its application toward delineating chemical traits of Alzheimer’s disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.
The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC). Materials and methods A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis. Results Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability. Conclusion AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.
Passive OFE WDM-over-POF Gigabits per Second Performance Comparison of Spatial Diversity and Spatial Multiplexing

We present a novel indoor communication network using 1-mm core diameter step-index plastic optical fibres as feeder line, and using the light emitted by the POF-end for wireless data transmission, creating a fully passive access point (AP). This has the advantage of not requiring electrical powering, resulting in low maintenance. Due to the large numerical aperture, the light emitted by the POF is extremely divergent, thus a lens must be placed in front of the POF-end to adjust the size of the coverage area to enable users movement or to increase the number of users served by the wireless link. We present a theoretical model for both multiple-input multiple-output (MIMO) transmission modes, spatial diversity (SD) and spatial multiplexing (SM), using orthogonal frequency division multiplexing (OFDM). We report the system throughput, that is estimated using the singular value decomposition (SVD) of the experimentally measured channel matrix $\mathbf {H}(f)$. We then present experimental results applying both SD and SM concepts and compare with the theoretical results. We demonstrate that this passive AP concept using OFDM achieves throughputs of around 5.2 Gbps when SM is applied, and 3.8 Gbps when SD is applied. Furthermore, we use eye-safe visible light laser for the POF feeder which, due to optical losses by POF and components, limited the wireless link to 1 m only and the coverage area to 45 cm diameter. We believe the proposed system represents a low-cost, high-capacity, highly secure, interference free wireless system, suitable for next generation for industrial and home applications
Computational Convective And Conductive Heat Transfer

In this paper a brief description of numerical computation methods for convective and conductive heat transfer problems is presented. In particular the finite volume method (FVM) is considered. Various discretization schemes, methods for handling the pressure-velocity coupling, turbulence modeling, solution procedure etc. are described. A note on complex geometries is also included.
CARD11 gain-of-function mutation drives cell-autonomous accumulation of PD-1+ ICOShigh activated T cells, T-follicular, T-regulatory and T-follicular regulatory cells

Introduction Germline CARD11 gain-of-function (GOF) mutations cause B cell Expansion with NF-κB and T cell Anergy (BENTA) disease, whilst somatic GOF CARD11 mutations recur in diffuse large B cell lymphoma (DLBCL) and in up to 30% of the peripheral T cell lymphomas (PTCL) adult T cell leukemia/lymphoma (ATL), cutaneous T cell lymphoma (CTCL) and Sezary Syndrome. Despite their frequent acquisition by PTCL, the T cell-intrinsic effects of CARD11 GOF mutations are poorly understood. Methods Here, we studied B and T lymphocytes in mice with a germline Nethyl-N-nitrosourea (ENU)-induced Card11M365K mutation identical to a mutation identified in DLBCL and modifying a conserved region of the CARD11 coiled-coil domain recurrently mutated in DLBCL and PTCL. Results and discussion Our results demonstrate that CARD11.M365K is a GOF protein that increases B and T lymphocyte activation and proliferation following antigen receptor stimulation. Germline Card11M365K mutation was insufficient alone to cause B or T-lymphoma, but increased accumulation of germinal center (GC) B cells in unimmunized and immunized mice. Card11M365K mutation caused cell-intrinsic over-accumulation of activated T cells, T regulatory (TREG), T follicular (TFH) and T follicular regulatory (TFR) cells expressing increased levels of ICOS, CTLA-4 and PD-1 checkpoint molecules. Our results reveal CARD11 as an important, cell-autonomous positive regulator of TFH, TREG and TFR cells. They highlight T cell-intrinsic effects of a GOF mutation in the CARD11 gene, which is recurrently mutated in T cell malignancies that are often aggressive and associated with variable clinical outcomes.
Evaluating historic and modern optical techniques for monitoring phytoplankton biomass in the Atlantic Ocean

Traditional measurements of the Secchi depth (zSD ) and Forel-Ule colour were collected alongside modern radiometric measurements of ocean clarity and colour, and in-situ measurements of chlorophyll-a concentration (Chl-a), on four Atlantic Meridional Transect (AMT) cruises. These data were used to evaluate historic and modern optical techniques for monitoring Chl-a, and to evaluate remote-sensing algorithms. Historic and modern optical measurements were broadly consistent with current understanding, with Secchi depth inversely related to Forel-Ule colour and to beam and diffuse attenuation, positively related to the ratio of blue to green remote-sensing reflectance and euphotic depth. The relationship between Secchi depth and Forel-Ule on AMT was found to be in closer agreement to historical relationships when using data of the Forel-Ule colour of infinite depth, rather than the Forel-Ule colour of the water above the Secchi disk at half zSD . Over the range of 0.03-2.95 mg m-3, Chl-a was tightly correlated with these optical variables, with the ratio of blue to green remote-sensing reflectance explaining the highest amount of variance in Chl-a (89%), closely followed by the Secchi depth (85%) and Forel-Ule colour (71-81%, depending on the scale used). Existing algorithms that predict Chl-a from these variables were evaluated, and found to perform well, albeit with some systematic differences. Remote sensing algorithms of Secchi depth were in good agreement with in-situ data over the range of values collected (8.5 - 51.8 m, r 2>0.77, unbiased root mean square differences around 4.5 m), but with a slight positive bias (2.0 - 5.4 m). Remote sensing algorithms of Forel-Ule agreed well with Forel-Ule colour data of infinite water (r 2>0.68, mean differences <1). We investigated the impact of environmental conditions and found wind speed to impact the estimation of zSD , and propose a path forward to include the effect of wind in current Secchi depth theory. We discuss the benefits and challenges of collecting measurements of the Secchi depth and Forel-Ule colour and propose future directions for research. Our dataset is made publicly available to support the research community working on the topic.
Dynamic product development

Time-to-market has become the most critical factor in the success or failure of any new product introduction, particularly in today's increasingly global economy. The quest for speed is nothing new, but the technologies available, and the manner in which they can be applied, are constantly evolving. In this paper a new quantitative process model for product development is presented, which focuses on the representation of design iterations and their effects on the total product development process. The relations between the individual process steps are represented in design structure matrices and separated into dependent, independent and interdependent processes. By introduction of activity duration and transition probabilities for each process step and by application of the z-transform a quantitative model for coupled processes is created. This model is called Dynamic Product Development because it allows dynamic coupling of all product development functions. The resulting model is examined by analysis methods of discrete dynamic systems for its timing behavior. Probability distribution, expected value and variance of the lead time as well as sensitivities concerning individual development steps are determined. On the basis of a state space representation further participation factors and dominant modes are determined. The results supply the possibility for analyzing complex dependencies and time reduction of development process steps and provide a decision support tool for the product development process.
Decreased Functional Connectivity of Brain Networks in the Alpha Band after Sleep Deprivation Is Associated with Decreased Inhibitory Control in Young Male Adults

Sleep deprivation leads to reduced inhibitory control in individuals. However, the underlying neural mechanisms are poorly understood. Accordingly, this study aimed to investigate the effects of total sleep deprivation (TSD) on inhibitory control and their neuroelectrophysiological mechanisms from the perspective of the time course of cognitive processing and brain network connectivity, using event-related potential (ERP) and resting-state functional connectivity techniques. Twenty-five healthy male participants underwent 36 h of TSD (36-h TSD), completing Go/NoGo tasks and resting-state data acquisition before and after TSD; their behavioral and electroencephalogram data were recorded. Compared to baseline, participants’ false alarms for NoGo stimuli increased significantly (t = −4.187, p < 0.001) after 36-h TSD. ERP results indicated that NoGo-N2 negative amplitude increased and latency was prolonged (t = 4.850, p < 0.001; t = −3.178, p < 0.01), and NoGo-P3 amplitude significantly decreased and latency was prolonged (t = 5.104, p < 0.001; t = −2.382, p < 0.05) after 36-h TSD. Functional connectivity analysis showed that the connectivity of the default mode and visual networks in the high alpha band was significantly reduced after TSD (t = 2.500, p = 0.030). Overall, the results suggest that the negative amplitude increase in N2 after 36-h TSD may reveal that more attention and cognitive resources are invested after TSD; the significant decrease in P3 amplitude may indicate the impairment of advanced cognitive processing. Further functional connectivity analysis indicated impairment of the brain’s default mode network and visual information processing after TSD.