Miracle-dz/UniFAR-FTUs huggingface dataset:
A Practical Solution for Reducing Critical Height in Drain Sink Problem during Ladle Teeming Process

A common problem that affects the continuous casting process is the formation of a sink funnel or a vortex funnel in the end of the teeming process of a steel ladle. This causes the slag to be carried over with the molten steel to the tundish, generating inclusions in the produced steel ingot. In this work we propose an innovative device designed to delay the sink-funnel formation and hence decrease the critical draining height. The apparatus consists of an air-suctioning spear that induces a reduced pressure gradient just above the slag/air interface, in a position vertically alignedwith the draining nozzle axis. The device operation was modeled with Computational Fluid Dynamics (CFD). The results showed that the introduction of the suctioningspear allowedfor an optimizedladle teeming process, resulting in a shorter critical draining height, thus, reducing the amount of leftover steel to be recycled.
Pathogenic TNF-α drives peripheral nerve inflammation in an Aire-deficient model of autoimmunity

Significance GBS and CIDP are autoimmune disorders of the PNS that can be debilitating and even life threatening. Current therapies, which include corticosteroids and intravenous gammaglobulin, have poorly defined mechanisms of action and are ineffective in a fraction of patients. To identify more specific therapeutic targets, we used single-cell RNA sequencing to analyze immune cells in nerves during autoimmune attack. This analysis revealed a previously unappreciated TNFα cell–cell communication pathway that recruits and activates multiple immune cell types. Moreover, we show that TNF-α signaling is an essential feature of PNS autoimmunity, since ablating TNF-α signaling protects against disease. These findings suggest that anti–TNF-α agents, which are already used to treat other inflammatory diseases, should be considered for inflammatory neuropathies. Immune cells infiltrate the peripheral nervous system (PNS) after injury and with autoimmunity, but their net effect is divergent. After injury, immune cells are reparative, while in inflammatory neuropathies (e.g., Guillain Barré Syndrome and chronic inflammatory demyelinating polyneuropathy), immune cells are proinflammatory and promote autoimmune demyelination. An understanding of immune cell phenotypes that distinguish these conditions may, therefore, reveal new therapeutic targets for switching immune cells from an inflammatory role to a reparative state. In an autoimmune regulator (Aire)-deficient mouse model of inflammatory neuropathy, we used single-cell RNA sequencing of sciatic nerves to discover a transcriptionally heterogeneous cellular landscape, including multiple myeloid, innate lymphoid, and lymphoid cell types. Analysis of cell–cell ligand–receptor interactions uncovered a macrophage-mediated tumor necrosis factor-α (TNF-α) signaling axis that is induced by interferon-γ and required for initiation of autoimmune demyelination. Developmental trajectory visualization suggested that TNF-α signaling is associated with metabolic reprogramming of macrophages and polarization of macrophages from a reparative state in injury to a pathogenic, inflammatory state in autoimmunity. Autocrine TNF-α signaling induced macrophage expression of multiple genes (Clec4e, Marcksl1, Cxcl1, and Cxcl10) important in immune cell activation and recruitment. Genetic and antibody-based blockade of TNF-α/TNF-α signaling ameliorated clinical neuropathy, peripheral nerve infiltration, and demyelination, which provides preclinical evidence that the TNF-α axis may be effectively targeted to resolve inflammatory neuropathies.
Placental Glucose Transporters and Response to Bisphenol A in Pregnancies from of Normal and Overweight Mothers

Bisphenol A (BPA) is a synthetic phenol extensively used in the manufacture of polycarbonate plastics and epoxy resins and a component of liquid and food storages. Among health disorders potentially attributed to BPA, the effects on metabolism have been especially studied. BPA represents a hazard in prenatal life because of its presence in tissues and fluids during pregnancy. Our recent study in rats fed with BPA showed a placental increase in glucose type 1 transporter (GLUT-1), suggesting a higher uptake of glucose. However, the role of BPA on GLUT transporters in pregnant women with metabolic dysfunction has not yet been investigated. In this study, placental tissue from 26 overweight (OW) women and 32 age-matched normal weight (NW) pregnant women were examined for expression of GLUT1 and GLUT4. Placental explants from OW and NW mothers were exposed to BPA 1 nM and 1 μM and tested for GLUTs expression. The data showed a different response of placental explants to BPA in GLUT1 expression with an increase in NW mothers and a decrease in OW ones. GLUT4 expression was lower in the explants from OW than NW mothers, while no difference was showed between OW and NW in placental biopsies for any of the transporters.
Core Temperature during Cold-Water Triathlon Swimming

Triathlon and other endurance races have grown in popularity. Although participants are generally fit and presumably healthy, there is measurable morbidity and mortality associated with participation. In triathlon, most deaths occur during the swim leg, and more insight into risk factors, such as hypothermia, is warranted. In this study, we measured the core temperature of 51 participants who ingested temperature sensor capsules before the swim leg of a full-distance triathlon. The water temperature was 14.4–16.4 °C, and the subjects wore wetsuits. One subject with a low body mass index and a long swim time experienced hypothermia (<35 °C). Among the remaining subjects, we found no association between core temperature and swim time, body mass index, or sex. To conclude, the present study indicates that during the swim leg of a full-distance triathlon in water temperatures ≈ 15–16 °C, subjects with a low body mass index and long swim times may be at risk of hypothermia even when wearing wetsuits.
Analysis of shogging motion vibration of guide bars on warp knitting machines

In the light of the fact that the control system malfunction will occur because of the damage by friction between needles or yarns resulting from the mechanical vibration of shogging motion of guide bars when the knitting machine is running at a high speed.This paper proposed to conduct a dynamic analysis of the guide bars using the modal analysis technology.Based on the built acoustic vibration test platform,the key dynamic parameters of the guide bar are tested,including damping ratio,natural frequencies,and natural vibration mode,so as to get the amplitude features reflecting the frequency response of vibration of the guide bars.And then,the paper puts forward the effective measures of anti-vibration for warp knitting guide bars,and the contrast test results show that the measures are valid,and the vibration analysis method is feasible.
Nonlinear stability analysis of long-span CFST arch bridge under construction

Located in Hubei Province of China, Jingyang River Bridge is a long-span concrete filled steel tube (CFST) arch bridge with a span of 260 m and width of 9rn. Due to the small ratio of width to span, the stability of the arch is crucial to the construction control of the bridge. In the paper, the stability analysis methods of elastic buckling, geometrical nonlinearity and material nonlinearity are introduced. Based on the Jingyang River Bridge and finite element method (FEM), the spatial model of the bridge is developed. Both the linear and nonlinear stability of long-span CFST arch bridge under construction are analyzed, and the result of which is used to provide the basis for the construction control. The results show that at every construction step, stability coefficients of the bridge are all larger than 10 and the instability possibility is small. The influence of geometrical nonlinearity is small. However, the influence of material nonlinearity is quite great and therefore should be wall taken into account.
Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.
Machine vision for road pavement applications: bitumen coverage and grain size estimation

In this thesis some research questions regarding durability and quality of roads has been investigated. The questions are analyzed from an image analysis point of view and aims to be a complement to existing methods for analyzing asphalt. One important factor for the durability of the asphalt layer on roads is the affinity between the stones in the asphalt and the binder that holds the stones together, called bitumen. One step in testing the affinity is to manually estimate the degree of bitumen coverage after the stones covered in bitumen has been washed for a while. The goal with the first two papers is to replace this manual estimation by image analysis methods. The first paper deals with the easier problem where there is a clear color difference between the stones and the bitumen. By using reference images to get information of the typical stone and bitumen color and a graph-cut algorithm we get result that seems to be close to the real degree of bitumen coverage. In the second paper we instead look at the problem with darker stones. In this case we cannot see a clear color difference between the stones and the bitumen. Instead we notice that bitumen and stones reflect light in different ways and take multiple images with lighting from different directions. The degree of bitumen coverage is then estimated by detecting specular reflections in the images. Another quality control of asphalt is to estimate the size distribution in an asphalt sample and see if it corresponds to the recipe for the asphalt. This is investigated in the third paper, where slices of the asphalt are analyzed. The analysis consists of segmenting the stones individually so that the size of all grains can later be estimated. (Less)
PREDICTING VISUAL PERCEPTION AND WORKING MEMORY DEFICITS AMONG PATIENTS WITH TYPE 1 DIABETES: THE IMPLICATION OF EATING ATTITUDE AND MENTAL HEALTH STATUS

Several recent studies designate that individuals with type-1-diabetes (T1D) are at higher risk for cognitive impairment than their peers without T1D. The aim of this study was to determine whether socio-demographic and clinical characteristics, eating attitude and psycho-emotional characteristics can predict visual perception (VP) and working memory (WM) impairment in the Moroccan population with T1D. A cross-sectional analysis was carried out in adult T1D patients (N=140). The Rey-Osterrieth Complex Figure test (electronical version) was used to evaluate the VP and WM. Moreover, patients also completed the 21-item Depression, Anxiety, and Stress Scale to assess their mental health state. In order to evaluate the risk of eating disorders the Eating attitudes Test-26 were used. Finally, anthropometric properties and clinical characteristics were evaluated. Predictors of cognitive dysfunction were determined using the hierarchical regression analysis. The major result was that age, long diabetes duration, glycemia, HbA1c, dieting and bulimia were the potential predictors of low scores in VP and WM. Other significant predictors were cholesterol, triglycerides for visual perception. Although psych-emotional symptoms including stress, anxiety, and depression were not a significant predictors of VP and WM impairment. Furthermore, our findings showed that the more the level of stress increases, the VP and WM scores significantly decrease. This study suggests that it can be helpful for medical practitioners to screen for disordered eating behaviors and emotional symptoms as a supplemental risk factor for cognitive impairment in T1D.
Diaphragm muscle fibrosis involves changes in collagen organization with mechanical implications in Duchenne Muscular Dystrophy

In Duchenne muscular dystrophy (DMD), diaphragm muscle dysfunction results in respiratory insufficiency, a leading cause of death in patients. Increased muscle stiffness occurs with buildup of fibrotic tissue, characterized by excessive accumulation of extracellular matrix (ECM) components such as collagen. However, changes in mechanical properties are not explained by collagen amount alone and we must consider the complex structure and mechanics of fibrotic tissue. The goals of our study were to (1) determine if and how collagen organization changes with the progression of DMD in diaphragm muscle tissue, and (2) predict how collagen organization influences the mechanical properties of ECM. We first visualized collagen structure with scanning electron microscopy (SEM) images and then developed an analysis framework to quantify collagen organization and generate image-based finite-element models. The image analysis revealed significant age- and disease-dependent increases in collagen fiber straightness and alignment, ranging from 4.7 to 13.4%, but collagen fibers retained a transverse orientation relative to muscle fibers. The mechanical models predicted significant age- and disease-dependent increases in transverse effective stiffness and average stress, ranging from 8.8 to 12.4%. Additionally, both healthy and diseased models revealed an increase in transverse stiffness relative to longitudinal stiffness, with significant age- and disease-dependent increases in the ratio of transverse to longitudinal stiffness, ranging from 19.7 to 24.5%. This study revealed changes in diaphragm ECM structure and mechanics during the progression of disease in the mdx muscular dystrophy mouse phenotype, highlighting the need to consider the role of collagen organization on diaphragm muscle function.
A novel thermal management system for EV batteries using phase-change material

A novel thermal management system for electric vehicle(EV) high-power lithium-ion pack that incorporates phase-change material(PCM) was proposed and investigated under the normal and stressful conditions. Based on ecomputational fluid dynamics(CFD),numerical simulations were perfomed under extreme conditions,such as high ambient temperature and high discharge rate,to analyze the thermal behavior of lithium-ion battery pack with a PCM thermal management system. Contrary to the lithium-ion pack without thermal management system for the same compact module and pack configuration,the temperature profile of the cells using PCM was obviously improved during abusive discharge,with a desired maximum temperature below 50℃ and temperature difference within 5℃ and full capacity utilized. In addition,the passive cooling system was able to meet the operating range requirements under these abusive conditions without additional external power.
Personnel and Vehicle Data Collection at Aberdeen Proving Ground (APG) and its Distribution for Research

Abstract : The US Army Research Laboratory s (ARL) Networked Sensing and Fusion Branch continually strives to develop next generation unattended ground sensors (UGSs) and other sensing platforms. In support of these goals, it is a requirement to have access to seismic, acoustic, magnetic, and other sensor signatures of vehicle and people. These signature databases allow researchers to develop cutting-edge detection algorithms that detect and classify 1 or more vehicles or persons. These algorithms will be geared toward current and new UGS applications. For this effort, a team was deployed to Spesutie Island at Aberdeen Proving Ground, Maryland, to collect signatures of personnel and vehicles from July 27 30, 2015.
The interaction protein of SORBS2 in myocardial tissue to find out the pathogenic mechanism of LVNC disease

Background: Left ventricular noncompaction cardiomyopathy (LVNC) is a cardiac disorder characterized by an excessive trabecular meshwork of deep intertrabecular recesses within the ventricular myocardium. Sorbin and SH3 domain-containing protein 2 (SORBS2) converges on the actin and microtubule cytoskeleton. Here, we investigated the proteins interacting with SORBS2 to elucidate the pathogenic mechanism of LVNC. As reported in previous studies, SORBS2 enhances the occurrence of LVNC by potentiating heart failure, but the specific mechanism remains unclear. Methods: Building from our previous finding of elevated SORBS2 levels in LVNC hearts, we screened for proteins interacting with SORBS2 by proteomics and conducting IP experiments. Co-IP and immunofluorescence were used to verify the effects. Results: We selected several proteins with high scores and high coverage that could be closely related to SORBS2 according to earlier reports showing a correlation with LVNC for verification. We finally obtained several proteins that were related to the pathogenesis of LVNC and also interacted with SORBS2, such as α-actinin, β-tubulin, MYH7, FLNA, MYBPC3, YWHAQ and DES, and YWHAQ was the most associated. Conclusions: We focused on the YWHAQ protein, and we identified a novel mechanism through which SORBS2 interacts with YWHAQ, having a negative effect on the cell cycle, potentially leading to LVNC.
Understanding Data Analytics Recommendation Execution: The Role of Recommendation Quality

ABSTRACT Although significantly more organizations have recently invested in Data Analytics (DA), most business users do not execute DA recommendations. Conceptualizing the novel concept of DA recommendation quality, shaped by tool, data and analyst quality, this study draws on the Stimulus-Organism-Response framework to investigate its effect on shaping users’ perceptions of concordance, actionability, and risk, ultimately influencing their DA recommendation execution. The theoretical model is empirically validated using a sample of senior managers across North America. Enriching DA literature, this study shows that DA recommendation quality is positively associated with recommendation execution, while actionability is the dominant factor in increasing it.
Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, $k$ -means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.
Improvement of Information Retrieval Systems by Using Hidden Vertical Search

The exponential growth of the number of documents in digital libraries and on the Web calls for very intensive development of retrieval systems. One possible architectural approach to IRS, an architecture with hidden verticals, is proposed in this paper. In IRS with hidden verticals, documents from the searched corpus are stored into a predefined set of classes. The user’s query is classified before the search, and searching is done only within the corresponding class. The performance of the proposed system is compared to the performance of standard IRS (that contains a unique inverted index) and IRS with cluster pruning (in which searching corpus is clustered and query is compared to the clusters’ centroids first, then search is done only in the most similar cluster). Search time in the proposed system is 7.9 times shorter than in the standard IRS and 1.7 times shorter than in the system with cluster pruning. The precision of the proposed system is 2.59 times higher than the precision of the standard IRS, and 1.68 times better compared to the IRS with cluster pruning. The recall of the proposed system is 1.09 times smaller than the recall of the standard IRS, but it is 1.28 times better than the recall of the IRS with cluster pruning. Based on the above results, we can say that proposed approach reduces search time and increases search precision with a minimal reduction in recall.
Development and Evaluation of Bluetooth Low-Energy Device for Electronic
 Encounter Metrics

The coronavirus disease 2019 (COVID-19) pandemic led to the need for tracking of physical contacts and potential exposure to disease. Traditional contact tracing can be augmented by electronic tools called “electronic contact tracing” or “exposure notification. ”. Some methods were built to work with smartphones; however, smartphones are not prevalent in some high-contact areas (e.g., schools and nursing homes). We present the design and initial testing of low-cost, highly privacy preserving wearable exposure notification devices. Several devices were constructed based on existing hardware and operated independently of a smartphone. The method (devices and analyses) was not able to reliably use the received signal strength indicator (RSSI) as a proxy for distance between pairs of devices; the accuracy of RSSI as a proxy for distance decreased dramatically outside of the idealized conditions. However, even an imperfect device could be useful for research on how people use and move through spaces. With some improvement, these devices could be used to understand disease spread and human or animal interaction in indoor environments.