Miracle-dz/UniFAR-FTUs huggingface dataset:
Applicability of General Correlations for Critical Heat Flux in Conventional Tubes to Mini/Micro Channels

There is presently great interest in use of mini/micro channels with boiling in heat exchangers and for heat dissipation from microelectronic systems. Design of such devices requires the ability to predict critical heat flux (CHF). It is therefore desirable to know the applicability of general correlations for CHF in conventional tubes to minichannels. The most verified general correlations are those of Katto and Ohno and of Shah. In this study, these correlations were compared to an extensive database with 55 data sets from 30 sources, which included single tubes and multichannels with diameters from 0.13 mm to 3.0 mm, 15 diverse fluids (water, halocarbon refrigerants, hydrocarbons, and cryogens), reduced pressures from 0.009 to 0.887, and flow rates from 10 to 41810 kg/m2-s. The correlations of Shah and of Katto and Ohno gave mean absolute deviations of 18.9 and 33.2%, respectively. To put the performance of these general correlations in perspective, the same database was also compared to two of the best correlations specifically developed for minichannels.
Spaces of Maps into Eilenberg-Maclane Spaces

In this note we provide alternative and unified proofs for two theorems on the homotopy groups of spaces of (continuous) maps into Eilenberg-MacLane spaces. The first theorem is due to Thorn, and independently Federer, and deals with spaces of maps into Eilenberg- MacLane spaces of type (π, n) for n ≧ 1 with π abelian. The second theorem is due to Gottlieb and deals with spaces of maps into Eilenberg- MacLane spaces of type (π, 1) with π nonabelian. As a main tool we shall use the homotopy sequences for certain fibrations of spaces of maps. 2. Basic notation and some preliminary remarks. For any pair of connected CW-complexes X and Y with base points, we denote by M(X, Y), respectively F(X, Y), the space of free maps, respectively based maps, of X into Y.
Thyroarytenoid Oxidative Metabolism and Synaptic Signaling Dysregulation in the Female Pink1−/− Rat

Vocal dysfunction, including hypophonia, in Parkinson disease (PD) manifests in the prodromal period and significantly impacts an individual's quality of life. Data from human studies suggest that pathology leading to vocal deficits may be structurally related to the larynx and its function. The Pink1−/− rat is a translational model used to study pathogenesis in the context of early‐stage mitochondrial dysfunction. The primary objective of this work was to identify differentially expressed genes in the thyroarytenoid muscle and examine the dysregulated biological pathways in the female rat.
Nonparametric search algorithm for outliers and hubs using minimum spanning tree

Abstract In data mining detection of anomalous pattern in data is more interesting than detecting inliers. A novel breadth-first search based algorithm for detection of outliers and hubs is proposed. Minimum Spanning Tree based Clustering is an important task for the discovery of underlying structures in graph. Many algorithms find clusters by maximizing the number of intra-cluster edges. While such algorithms find useful and interesting structures, they tend to fail to identify and isolate two kinds of vertices that play special roles-vertices that bridge clusters (hubs) and vertices that are marginally connected to clusters (outliers). In this paper, we model hubs as high-degree nodes having marginal distance (sparseness) with neighboring nodes. Identifying hubs is useful for applications such as viral marketing and epidemiology since hubs are responsible for spreading ideas and disease. In contrast, outliers have little or no influence, and many may be isolated as noise in the data. In this paper we propose an algorithm called Minimum Spanning Tree based Search Algorithm for Outliers and Hubs (MSTSOH), which detect outliers and hubs in graph.
Nonlinear drift-wave and energetic particle long-time behaviour in stellarators: solution of the kinetic problem

We propose a theoretical scheme for the study of the nonlinear interaction of drift-wave-like turbulence and energetic particles in stellarators. The approach is based on gyrokinetics, and features a separation of time and scales, for electromagnetic fluctuations, inspired by linear ballooning theory. Two specific moments of the gyrokinetic equation constitute the main equations of the system, which requires a full kinetic nonlinear solution. This is found iteratively, expanding in the smallness of the bounce-average radial drift frequency, and nonlinear $\boldsymbol {E}\times \boldsymbol {B}$ drift frequency, compared with the inverse time scales of the resonantly interacting energetic particles. Our analysis is therefore valid for neoclassically optimised stellators. The resummation of all iterative and perturbative nonlinear kinetic solutions is discussed in terms of Feynman diagrams. Particular emphasis is put on the role of collisionlessly undamped large-scale structures in phase space, the kinetic equivalent of zonal flows, i.e. phase-space zonal structures, and on wave-like fluctuations generated by energetic particles.
Solvability And Bifurcations Of Nonlinear Equations

Thank you for downloading solvability and bifurcations of nonlinear equations. Maybe you have knowledge that, people have look numerous times for their chosen readings like this solvability and bifurcations of nonlinear equations, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some malicious virus inside their computer.
Effects of appearance and gender on pre-touch proxemics in virtual reality

Virtual reality (VR) environments are increasingly popular for various applications, and the appearance of virtual characters is a critical factor that influences user behaviors. In this study, we aimed to investigate the impact of avatar and agent appearances on pre-touch proxemics in VR. To achieve this goal, we designed experiments utilizing three user avatars (man/woman/robot) and three virtual agents (man/woman/robot). Specifically, we measured the pre-touch reaction distances to the face and body, which are the distances at which a person starts to feel uncomfortable before being touched. We examined how these distances varied based on the appearances of avatars, agents, and user gender. Our results revealed that the appearance of avatars and agents significantly impacted pre-touch reaction distances. Specifically, those using a female avatar tended to maintain larger distances before their face and body to be touched, and people also preferred greater distances before being touched by a robot agent. Interestingly, we observed no effects of user gender on pre-touch reaction distances. These findings have implications for the design and implementation of VR systems, as they suggest that avatar and agent appearances play a significant role in shaping users’ perceptions of pre-touch proxemics. Our study highlights the importance of considering these factors when creating immersive and socially acceptable VR experiences.
Editorial: Antimicrobial resistant pathogens in sustainable food systems: how to prevent, detect, or control contamination?

COPYRIGHT © 2023 Concha-Meyer, Toro and Eifert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Antimicrobial resistant pathogens in sustainable food systems: how to prevent, detect, or control contamination?
Species composition and habitat associations of birds around Jhilmila Lake at Western Chure Landscape, Nepal

Abstract Wetlands support around 27% of birds in Nepal, however, there is a paucity of information about bird diversity and the wetland habitat of Western Chure Landscape Nepal. The “point count” method along transects was carried out to evaluate the species composition and habitat associations of birds. A total of 2,532 individuals representing 152 species (winter: N = 140 and summer: N = 91) from 19 orders and 51 families were reported from Jhilmila Lake and its surrounding area. The number of birds was reported to be significantly higher during winter than in the summer season. The species diversity was also higher in winter (Shannon’s index (H) = 4.38, Fisher’s alpha = 30.67) than in summer (H = 4.21, Fisher’s alpha = 34.69) as this area is surrounded by old-growth forest that provides available habitats for forest, grassland- and wetland-dwelling birds. This lake is an example of a wetland present in the Chure area that plays an important role in the conservation of biodiversity along with birds. Hence, we recommend its detailed study in terms of biodiversity and water quality.
Autoencoders with deformable convolutions for latent representation of EEG spectrograms in classification tasks

Electroencephalogram (EEG) is a set of time series each of which can be represented as a 2D image (spectrogram), so that EEG recording can be mapped to the C-dimensional image (where C denotes the number of channels in the image and equals to the number of electrodes in EEG montage). In this paper, a novel approach for automated feature extraction from spectrogram representation is proposed. The method involves the usage of autoencoder models based on 3-dimensional convolution layers and 2-dimensional deformable convolution layers. Features, extracted by autoencoders, can be used to classify patients with Major Depressive Disorder (MDD) from healthy controls based on resting-state EEG. The proposed approach outperforms baseline ML models trained on spectral features extracted manually.
GAM-UNet: incorporating global attention mechanism into U-Net for improved medical image segmentation

Automatic segmentation of targets from medical images is a difficult task because of the high complexity of medical images and the lack of simple linear features. U-Net is widely used in the field of medical image segmentation for its state-of-the-art performance. However, there are still many problems, the most serious of which are the lack of accuracy of image segmentation and the large amount of information loss. To alleviate these deficiencies, we propose a UNetbased image segmentation model GAM-UNet, which has the same coder-decoder structure. The Global Attention Mechanism (GAM) is introduced and embedded into the encoding part of the proposed model to automatically adjust the weights of the feature maps. Evaluation results on the public datasets Data Science Bowl and CVC-ClinicDB show that our model performs better than other U-Net variants. After additional analysis, our proposed model is more hardware friendly and allows for faster medical diagnosis with a slight increase in accuracy. Our proposed approach can also be extended to other image processing tasks, such as object detection and tracking.
Research on optimization algorithm of abnormal driving behavior detection based on PFLD

Driver's fatigue driving and mood are two important factors that cause traffic accidents, and with the widespread use of mobile devices, it is of significant research value to detect driver's abnormal driving behavior on the mobile terminal. In this paper, the PFLD model is used on mobile to obtain face keypoints and improve the EAR by fusing binocular opening and closing degree to detect abnormal driving behavior such as fatigue and mood. Fatigue detection integrates the improved EAR, yawning duration, head pitch angle and other multiple features to determine, which mainly includes the process of extracting the driver's both eyes and mouth features through the PFLD model, monitoring the driver's heart rate and head distance with Internet of Things devices, constructing feature vectors and using SVM to determine driving fatigue. The threshold parameters are selected by calculating the five-dimensional features of the face, and the driver's mood is detected based on KNN. Through experiments, the driver fatigue detection accuracy is about 90% and the mood recognition accuracy is up to 96%.
Startup algorithm of pulse deinterleaving based on RNN model

Accurate and timely pulse sorting is the basis of passive signal processing and the common pursuit of many works in this field. This paper improves the previous method of deinterleaving pulses using trained RNN models and proposes a paragraph-startup algorithm, which effectively improves the success rate and timeliness of deinterleaving.
Update on follicular lymphoma

The past two decades have seen remarkable progress in both biological understanding and optimizing treatment of follicular lymphoma. Historically considered an incurable disease, long‐term follow‐up of several induction approaches demonstrates that up to 40% of patients enjoy remission durations of 10 or more years, and risk of dying of lymphoma continues to fall. This update will focus on progress in follicular lymphoma over the past 3 years, which has included refinements in staging and prognosis, novel immunotherapy treatment approaches for relapsed and refractory disease, and long‐term follow‐up of pivotal trials. Ongoing trials will define the optimal sequence for these novel treatments, including whether earlier incorporation of these approaches may result in definitive cure of this disease. Through ongoing and planned correlative studies, we are poised to ultimately achieve the goal of a precision management approach to follicular lymphoma.
Mantle cell lymphoma—Update on molecular biology, prognostication and treatment approaches

Mantle cell lymphoma (MCL) is clinically characterized by its heterogenous behavior with courses ranging from indolent cases that do not require therapy for years to highly aggressive MCL with very limited prognosis. The development and implementation of new targeted and immunotherapeutic approaches have already improved therapeutic options especially for refractory or relapsed disease. Nevertheless, to further optimize MCL treatment, early identification of individual risk profile and risk‐adapted, patient‐tailored choice of therapeutic strategy needs to be prospectively incorporated in clinical patient management. This review summarizes the current knowledge and standard of care regarding biology and clinical management of MCL, highlighting the implementation of new therapeutic approaches especially targeting the immune system.
Functional consequences of inhibition of Bruton's tyrosine kinase by ibrutinib in chronic lymphocytic leukemia

The leukemic B cells from patients with chronic lymphocytic leukemia (CLL) require interactions with non‐malignant cells and matrix in the tissue microenvironment to survive and grow. These interactions are mediated through the B‐cell antigen receptor (BCR), C‐X‐C chemokine receptor type 4 (CXCR4), and a variety of integrins, including VLA‐4. Exciting each receptor type leads to activation of Bruton's tyrosine kinase (BTK), which in turn helps initiate trophic signals that prevent cell death and promote cell activation and growth as well as allowing cells to return to anatomic sites for rescue signals. These represent the two major functional actions targeted by inhibitors of Btk. Here we relate some of the therapeutic actions of ibrutinib, a Btk inhibitor that is extremely helpful for patients with CLL, certain Diffuse Large B‐cell Lymphomas (ABC type), and other non‐Hodgkin's lymphomas, emphasizing that ibrutinib's value results from blocking beneficial signals, not by inducing lethal ones.
Introduction of a Method for Systematic Surface Defect Classification on Virtual Car Body Parts

This paper proposes a method to classify systematic surface defects on virtual car body parts through a CRNN. Systematic surface defects occur during the manufacturing process of outer surface parts. Those defects get classified by auditors according to their appearance. The proposed network classifies the defects in the same manner as the auditor and takes physiological-optical aspects into account. Therefore, the proposed method takes a virtual rendered video as input and generates a single label classification of each defect area.
Human-Centered Assembly Process Validation in Virtual Reality using Tool-, Part- and Auxiliary Geometry Tracking

Virtual reality has become a common tool in vehicle development for both product and process development. For product improvement, design reviews are carried out in VR, for process improvement, assembly feasibility analyses. The involvement of developers, factory and production planners, as well as those who will be directly affected in the future, such as customers, assembly workers or maintenance staff, allows good products and mature processes to be designed at an early stage of development. In this article, human-centered improvement of product assembly and maintainability is considered in VR. In order to represent assembly conditions in VR as close to reality as possible, the use of tracked real tools, parts and auxiliary geometries is tested in addition to virtual representations. More realistic assemblability analyses could be achieved with the use of these tracked real tools, parts and auxiliary geometries, which provide passive haptic feedback. In the daily VR application for design reviews and assemblability analyses, the use of purely virtual parts and tools has proven itself for reasons of simplicity, for assembly training this passive haptic feedback has potential.
State-feedback regulation for a class of higher-order nonlinear systems with integral input-to-state stability unmodeled dynamics

We investigate the state-feedback regulation for a class of higher-order nonlinear systems with integral input-to-state stability(iISS) unmodeled dynamics.Based on the backstepping design technique and the properties of iISS,the design procedures for a state-feedback controller are developed by appropriately choosing the designed functions and the update laws of parameters.The designed controller guarantees that all the signals in the closed-loop system are bounded,and the states can be regulated asymptotically to zero.A simulation example demonstrates the effectiveness of the control scheme.
A Derivation of the Soft-Thresholding Function

These notes show the derivation of non-linear soft-thresholding function for signal denoising. The softthresholding function can be used for denoising by applying it to the transform-domain representation, provided the transform yields a sparse representation of the signal. For example, wavelet transforms provide sparse representations of piece-wise smooth signals, and the short-time Fourier transform (STFT) provides sparse representations of oscillatory signals (like speech).
Engineering the Maize Root Microbiome: A Rapid MoClo Toolkit and Identification of Potential Bacterial Chassis for studying Plant-Microbe Interactions

Sustainably enhancing crop production is a necessity given the increasing demands for staple crops and their associated carbon/nitrogen inputs. Plant-associated microbiomes offer one avenue for addressing this demand; however, studying these communities and engineering them has remained a challenge due to limited genetic tools and methods. In this work, we detail the development of the Maize Root ToolKit (MRTK); a rapid Modular Cloning (MoClo) toolkit that only takes 2.5 hours to generate desired constructs (5400 potential plasmids) that replicate and express heterologous genes in Enterobacter ludwigii strain AA4 (Elu), Pseudomonas putida AA7 (Ppu), Herbaspirillum robiniae strain AA6 (Hro), Stenotrophomonas maltophilia strain AA1 (Sma) and Brucella pituitosa strain AA2 (Bpi) which comprise a model maize root synthetic community (SynCom). In addition to these genetic tools, we describe a highly efficient transformation protocol (10^7-10^9 transformants/µg of DNA) for each of these strains. Utilizing this highly efficient transformation protocol, we identified endogenous expression sequences for each strain (ES; promoter and ribosomal binding sites) via genomic promoter trapping. Overall, the MRTK is a scalable platform that expands the genetic engineering toolbox while providing a standardized, high efficiency transformation method that can be implemented across a diverse group of root commensals. These results unlock the ability to elucidate and engineer plant-microbe interactions promoting plant growth for each of the 5 bacterial strains in this study.
Study on minimum completion time multiple traveling salesman problem based on genetic algorithm

Previous research on multiple traveling salesman problems is mostly limited to the kind that employed total-path-shortest as the evaluating rule,but little notice is made on the kind that employed longest-time-shortest as the evaluating rule. In order to solve this problem,used genetic algorithm to optimize it and proposed decoding method with matrix. This paper simulated symmetric and asymmetric multiple traveling salesman problems and compared different crossover operators. It is fit for solving symmetric and asymmetric multiple traveling salesman problems.
The microbial derived bile acid lithocholate and its epimers inhibit Clostridioides difficile growth and pathogenicity while sparing members of the gut microbiota

C. difficile infection (CDI) is associated with antibiotic usage, which disrupts the indigenous gut microbiota and causes the loss of microbial derived secondary bile acids that normally provide protection against C. difficile colonization. Previous work has shown that the secondary bile acid lithocholate (LCA) and its epimer isolithocholate (iLCA) have potent inhibitory activity against clinically relevant C. difficile strains. To further characterize the mechanisms by which LCA and its epimers iLCA and isoallolithocholate (iaLCA) inhibit C. difficile, we tested their minimum inhibitory concentration (MIC) against C. difficile R20291, and a commensal gut microbiota panel. We also performed a series of experiments to determine the mechanism of action by which LCA and its epimers inhibit C. difficile through bacterial killing and effects on toxin expression and activity. Here we show that epimers iLCA and iaLCA strongly inhibit C. difficile growth in vitro while sparing most commensal Gram-negative gut microbes. We also show that iLCA and iaLCA have bactericidal activity against C. difficile, and these epimers cause significant bacterial membrane damage at subinhibitory concentrations. Finally, we observe that iLCA and iaLCA decrease the expression of the large cytotoxin tcdA while LCA significantly reduces toxin activity. Although iLCA and iaLCA are both epimers of LCA, they have distinct mechanisms for inhibiting C. difficile. LCA epimers, iLCA and iaLCA, represent promising compounds that target C. difficile with minimal effects on members of the gut microbiota that are important for colonization resistance. Importance In the search for a novel therapeutic that targets C. difficile, bile acids have become a viable solution. Epimers of bile acids are particularly attractive as they may provide protection against C. difficile while leaving the indigenous gut microbiota largely unaltered. This study shows that iLCA and iaLCA specifically are potent inhibitors of C. difficile, affecting key virulence factors including growth, toxin expression and activity. As we move toward the use of bile acids as therapeutics, further work will be required to determine how best to deliver these bile acids to a target site within the host intestinal tract.