Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Optimal Control of MR Damped Structures with Uncertain Parameters

Magneto-rheological (MR) damper has proved its value in vibration mitigation of engineering structures subjected to dynamic excitations such as seismic ground motion and strong wind. The accurate operation, however, of MR damper still remains a challenge due to incomplete knowledge on the randomness inherent in the dynamical behaviors of the damper. While the classical models of MR damper are most of phenomenal formulation lacking of the in-depth analysis of working mechanism at material scale. The stochastic modeling of MR damper is carried out in this paper of which the variability definition of critical parameters renders to the dynamic yield analysis of Magneto-rheological fluids. A randomly base-excited structure controlled by the MR damper is investigated. Numerical results indicate that the MR damping control can reduce the seismic response significantly, where the distribution range of probability density function becomes narrower comparing with that without control. It is thus remarked that the appropriately designed semi-active controller can achieve almost the same effect as the active controller in probabilistic sense. The randomness, meanwhile, of damper parameters could be neglected safely. Magneto-rheological (MR) damper is regarded as one of the most promising control devices due to its perfect dynamic damping behaviors. While the accurate operation of the damper still remains a challenge due to the statistical incompleteness on the classical phenomenal models. The authors explored the variability of dynamic yield behavior of MR fluids using a micro-scale method referring to molecular dynamics simulations (Peng and Li, 2011). The previous work provides a path for the stochastic modeling of MR damper through updating the fluctuation of dynamic yield at MR fluid level. This paper firstly addresses the stochastic modeling of MR dampers, and then investigates the optimal semi-active control of structures using the random-parameterized MR dampers in the context of physically based stochastic optimal control. The physically based stochastic optimal control of structures, hinged on the generalized density evolution equation (GDEE), is proved to be highly efficient for linear and nonlinear structural systems subjected to engineering excitations with non-stationary and non-Gaussian behaviors (Li et al, 2010). In order to reach a good agreement with the dynamic behavior of MR damper, a bounded Hrovat semi-active control strategy is addressed. For illustrative purpose, a randomly base-excited structure controlled by MR damper is investigated, of which viscous damping coefficient is viewed as random variable. Numerical results indicate that the MR damping control can reduce the seismic response
How job insecurity affects emotional exhaustion? A study of job insecurity rumination and psychological capital during COVID-19.

Abstract Drawing on conservation of resources theory (COR), this study takes a nuanced approach to understanding job insecurity by proposing rumination as a mediator in its relationship with well-being during the COVID-19 global pandemic. The moderating role of psychological capital as a resource to buffer this negative relationship is also explored. A cross sectional study of employees in Ireland during the early months of the COVID-19 pandemic found that job insecurity rumination mediated the relationship between affective job insecurity and emotional exhaustion. There was no support for the hypothesis that psychological capital could moderate the relationship between job insecurity and emotional exhaustion. Thus, this research advances the job insecurity literature by identifying a potential mediator and moderator in the process of how employees may experience job insecurity particularly during a global pandemic.
Numerical and experimental analysis of the cavitation and study of flow characteristics in ball valve

Abstract Cavitation in ball valve was numerically investigated using Computational Fluid Dynamics (CFD) modeling and then validated against results gained through experiments. The experiment was carried out in an assembly unit of the automotive industry to analyze flow patterns. The effect of bubbles on other thermophysical properties of the fluid was also examined using the multiphase k-ϵ viscous model in ANSYS FLUENT. The impact of changes in inlet pressure on vapor fraction was visualized through simulations and validated against experimental data, cavitation was calculated via cavitation index equation. It was observed that cavitation values ranged from 0.51 to 0.84 through computational fluid dynamics and from 0.46 to 0.80 in the experiment. Moreover, fluctuations in Turbulence Kinetic Energy (TKE) in the fluid through the boundary layers in the valve region, deformation in fluid particles in the form of Strain Rate (SR), and variations in the value of Wall Shear Stress (WSS) of the valve's internal walls were also studied through numerical simulations. The results show the pressure just before the valve drops and result in cavitation. Besides, turbulence kinetic energy, shear stress on the walls of a valve, strain rate, and fluid velocity were gradually increased at inlet pressure resulting erosion in the ball valve.
Retrospective analysis of crescent score in clinical prognosis of IgA nephropathy

Abstract The scoring of crescents (Cs) was recently added to the Oxford classification for IgA nephropathy (IgAN). Because of the short-term use of the C score in clinical practice, its validity and applicability need to be verified. We, retrospectively, analyzed the clinicopathological data of 144 primary IgAN patients diagnosed at our hospital from March 2017 to March 2019 and with complete ≥6-month follow-up data. We found that the C score was positively correlated with the Lee’s classification in the assessment of renal pathological changes and significantly correlated with increased proteinuria and decreased estimated glomerular filtration rate. Univariate Cox regression analysis showed an association of C formation with IgAN prognosis, and multivariate Cox regression indicated Cs as an independent prognosis factor. The optimal proportion of Cs for prognosis prediction by the receiver operating characteristic curve was 11%. Kaplan–Meier survival curve revealed a significantly decreased renal survival rate in patients with C proportions ≥11%. Further multivariate Cox regression analysis confirmed that the C proportion ≥11% is an independent risk factor for poor prognosis of IgAN patients. Our findings demonstrate that Cs are independently related to the prognosis of patients with IgAN, and the proportion of Cs ≥11% is an independent risk factor for poor outcomes.
A Unified Productivity-Performance Approach, with an Application to Secondary Schools in the Netherlands

We introduce a novel and simple diagnostic tool to improve the performance of public services (e.g. in health, education, utilities and transportation). We propose a method to compute performance/productivity ratios, which can be applied as soon as data on production units’ outcomes and resources are available. These ratios have an intuitive interpretation: values below unity indicate that better outcomes can be attained through weaker resource constraints (pointing at scarcity of resources) and, conversely, values above unity indicate that better outcomes can be achieved with the given resources (pointing at unexploited production capacity). We demonstrate the practical usefulness of our methodology through an application to secondary schools in the Netherlands. In this application, we also account for outlier behavior and environmental effects by using a robust nonparametric estimation method. Our empirical results indicate that in most cases schools’ performance improvement is a matter of unexploited production capacity, while scarcity of resources is a lesser issue.
Nurses' willingness to participate in public health emergency: A qualitative study in China

Along with the increasing of infections of COVID‐19, nurses are needed more in caring patients with COVID‐19. The aim of this study is to explore the real intention and influencing factors of the nurses' willingness to participate in public health emergency in facing the COVID‐19. A total of 10 nurses who volunteered to care patients with COVID‐19 were selected. Data were collected by semi‐structured interviews and analyzed by content analysis method based on the theory of planned behavior. Three main categories were attitude, subjective norms and perceived behavior control. Under the first category, two subcategories were included: nurses' personal and professional value expectation, patriotism. Two subcategories were yielded for the second category: support from family and friends, role model impact of important people. In addition, three subcategories were identified for the third category: physical condition, professional knowledge and skills, national measures and incentive policies. Nurses with high willingness have seven features: high value expectation, patriotism, generous family support, good physical condition, experienced professional knowledge and skills, awareness of national measures and incentive policies. Combined with our previous quantitative study, developing tailored training programs to improve nurses’ professional value expectation, knowledge and skills in coping with unwilling attitude and weak perceived behavior control are effective ways.
Organization of dermal glands and characteristic of secretion in the freshwater mite, Limnesia maculata (O. F. Müller, 1776) (Acariformes, Limnesiidae)

The presence of dermal glands is a synapomorphy in the freshwater mites—the large branch of the hyporder Parasitengonina. Dermal glands of the mite Limnesia maculata (O. F. Müller, 1776) (Acariformes, Limnesiidae) were studied using light and electron microscopy (TEM and SEM) for the first time. Two types of dermal glands were recognized in adult mites—14 pairs of the uniformly organized common dermal glands scattered throughout the entire body volume, and one pair of the characteristic so‐called idiosomal dermal glands centrally located and stretched along the ventral body wall, supposedly corresponding to the epimeroglandularia 4. The common dermal glands are composed of a single large alveolus with the basally located columnar epithelium. An intra‐alveolar lumen is positioned above the epithelium and packed with a secretion in the form of long curved electron‐dense bands. These bands are directed to the excretory opening and released to the outside in the form of strongly coiled bands. The cytoplasm of the common dermal glands is filled with short RER cisterns and shows few characteristic Golgi bodies with round dense secretory granules at the distal pole. In contrast, a large elongated sac represents the idiosomal dermal glands having a relatively thin secretory epithelium on the periphery. The cuboidal epithelial cells contain areas with tightly packed RER cisterns and weakly recognized Golgi bodies. The apical cell surface bears irregular short microvilli. The large intra‐alveolar lumen always contains an electron‐dense secretion with myriads of small lighter particles. This kind of secretion was never seen released through the opening. The excretory opening of the common and the idiosomal dermal glands shows differences in its organization but basically is represented by a cuticular “bell” narrowing to the opening and showing a particular kind of valves at the inner side of the flaps of the orifice.
Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines

Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbo-fan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by-prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods. © 2011 ASME
Data mining applications in university information management system development

Abstract Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.
Development of 3D breast cancer models with human T cells expressing engineered MAIT cell receptors

Immunotherapy has revolutionized cancer treatment with the advent of advanced cell engineering techniques aimed at targeted therapy with reduced systemic toxicity. However, understanding the underlying immune-cancer interactions require development of advanced three-dimensional (3D) models of human tissues. In this study, we developed proof-of-concept 3D tumor models with increasing complexity to study the cytotoxic responses of CD8+ T cells, genetically engineered to express mucosal-associated invariant T (MAIT) cell receptors, towards MDA-MB-231 breast cancer cells. Homotypic MDA-MB-231 and heterotypic MDA-MB-231/human dermal fibroblast (HDF) tumor spheroids were primed with precursor MAIT cell ligand 5-amino-6-D-ribitylaminouracil (5-ARU). Engineered T cells effectively eliminated tumors after a 3-day culture period, demonstrating that the engineered TCR recognized MR1 expressing tumor cells in the presence of 5-ARU. Tumor cell killing efficiency of engineered T cells were also assessed by encapsulating these cells in fibrin, mimicking a tumor extracellular matrix microenvironment. Expression of proinflammatory cytokines such as IFNγ, IL-13, CCL-3 indicated immune cell activation in all tumor models, post immunotherapy. Further, in corroborating the cytotoxic activity, we found that granzymes A and B were also upregulated, in homotypic as well as heterotypic tumors. Finally, a 3D bioprinted tumor model was employed to study the effect of localization of T cells with respect to tumors. T cells bioprinted proximal to the tumor had reduced invasion index and increased cytokine secretion, which indicated a paracrine mode of immune-cancer interaction. Development of 3D tumor-T cell platforms may enable studying the complex immune-cancer interactions and engineering MAIT cells for cell-based cancer immunotherapies.
ACCURATE DECODING OF THE SPINAL CORD OUTPUT IN HUMANS WITH IMPLANTED HIGH-DENSITY ELECTRODE ARRAYS

Invasive electromyography opened a new window to explore motoneuron behaviour in vivo. However, the technique is limited by the small fraction of active motoneurons that can be concurrently detected, precluding a population analysis in natural tasks. Here, we developed a high-density intramuscular electrode for in vivo human recordings along with a fully automatic methodology that could detect the discharges of action potentials of up to 67 concurrently active motoneurons with 99% accuracy. These data revealed that motoneurons of the same pool receive common synaptic input at frequencies up to 75 Hz and that late recruited motoneurons inhibit the discharges of those recruited earlier. These results constitute an important step in the population coding analysis of the human motor system in vivo.
SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data

The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who may be less cognitively and emotionally mature. In addition to academic performance, employers’ unconscious biases are a potential obstacle to graduating students in becoming employed. Thus, it is necessary to understand the nature of such unconscious biases to assist students at an early stage with personalized intervention. In this paper, we analyze the existing bias in college graduate employment through a large-scale education dataset and develop a framework called SUMMER (biaS-aware gradUate eMployMEnt pRediction) to predict students’ employment status and employment preference while considering biases. The framework consists of four major components. Firstly, we resolve the heterogeneity of student courses by embedding academic performance into a unified space. Next, we apply a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to overcome the label imbalance problem of employment data. Thirdly, we adopt a temporal convolutional network to comprehensively capture sequential information of academic performance across semesters. Finally, we design a bias-based regularization to smooth the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.
Regulation of Peripheral Inflammation by a Non-Viable, Non-Colonizing Strain of Commensal Bacteria

The gastrointestinal tract represents one of the largest body surfaces that is exposed to the outside world. It is the only mucosal surface that is required to simultaneously recognize and defend against pathogens, while allowing nutrients containing foreign antigens to be tolerated and absorbed. It differentiates between these foreign substances through a complex system of pattern recognition receptors expressed on the surface of the intestinal epithelial cells as well as the underlying immune cells. These immune cells actively sample and evaluate microbes and other particles that pass through the lumen of the gut. This local sensing system is part of a broader distributed signaling system that is connected to the rest of the body through the enteric nervous system, the immune system, and the metabolic system. While local tissue homeostasis is maintained by commensal bacteria that colonize the gut, colonization itself may not be required for the activation of distributed signaling networks that can result in modulation of peripheral inflammation. Herein, we describe the ability of a gut-restricted strain of commensal bacteria to drive systemic anti-inflammatory effects in a manner that does not rely upon its ability to colonize the gastrointestinal tract or alter the mucosal microbiome. Orally administered EDP1867, a gamma-irradiated strain of Veillonella parvula, rapidly transits through the murine gut without colonization or alteration of the background microbiome flora. In murine models of inflammatory disease including delayed-type hypersensitivity (DTH), atopic dermatitis, psoriasis, and experimental autoimmune encephalomyelitis (EAE), treatment with EDP1867 resulted in significant reduction in inflammation and immunopathology. Ex vivo cytokine analyses revealed that EDP1867 treatment diminished production of pro-inflammatory cytokines involved in inflammatory cascades. Furthermore, blockade of lymphocyte migration to the gut-associated lymphoid tissues impaired the ability of EDP1867 to resolve peripheral inflammation, supporting the hypothesis that circulating immune cells are responsible for promulgating the signals from the gut to peripheral tissues. Finally, we show that adoptively transferred T cells from EDP1867-treated mice inhibit inflammation induced in recipient mice. These results demonstrate that an orally-delivered, non-viable strain of commensal bacteria can mediate potent anti-inflammatory effects in peripheral tissues through transient occupancy of the gastrointestinal tract, and support the development of non-living bacterial strains for therapeutic applications.
The Remodeling Effects of High-Concentrate Diets on Microbial Composition and Function in the Hindgut of Dairy Cows

At present, research on high-concentrate (HC) diets mostly focused on the rumen, and there is a paucity of information on the hindgut microbiota of dairy cows. In the present study, a 2 × 2 crossover design with four healthy Holstein cows was used, and the metagenomics approach was adopted to reveal the remodeling effects of HC diets on hindgut microbiota and their metabolic functions. Results showed that, compared with the low-concentrate (LC) diets, HC diets have markedly decreased (p < 0.05) the abundance of cellulolytic bacteria (such as Fibrobacter, Ruminococcus, and Ruminiclostridium) and methanogens (such as Methanobrevibacter, Methanosarcina, and Methanosphaera); and correspondingly, HC diets have significantly reduced (p < 0.05) the abundance of carbohydrate-active enzymes (CAZy) related to hemicellulases (GH10, GH11, and GH54) and cellulases (GH1, GH44, and GH45) and increased the abundance of one oligosaccharide-degrading enzyme (GH32). Furthermore, 62 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of hindgut microbiota were affected (p < 0.05) by different dietary treatments, and the major pathways altered by HC diets were “Methane metabolism” (enriched in the LC group), “Lipid metabolism” (enriched in the HC group), and several sub-pathways in “Amino acid metabolism” (such as Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis). Also, the microbial genes involved in the pathways “Methane metabolism” (except 1 gene), “Tryptophan metabolism”, and “Phenylalanine metabolism” were all decreased (p < 0.05) in the present study. These findings suggested that HC diets caused the remodeling of hindgut microbiota and its potential functions, and these results may benefit in gaining a deeper understanding of the impact of HC diets on the hindgut microbiota of dairy cows.