Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved sensing properties of Zinc Oxide gas sensor to detect hydrogen gas by using ZIF-8

In this research, ZnO nanorods were utilized as gas sensor materials for detecting toluene vapour and hydrogen gas prior to and following the use of Zeolitic imidazolate frameworks-8 (ZIF-8) as gas filter. On porous Al 2 O 3 , ZIF-8 was deposited and the thickness was 200 nm. Scanning electron microscope (SEM), XRD, and gas sensing tests were used to examine the material characteristics regarding ZIF-8 and ZnO. Thin films' sensitivity and selectivity to different gases such as C 7 H 8 and H 2 were also investigated. Comparing with pristine ZnO nanorods sensor, ZnO sensor is highly sensitive to C 7 H 8 and H 2 , whereas the nanostructure regarding ZnO with ZIF8 as sensor has significantly selective response to the hydrogen gas. The small pore aperture of the ZIF-8 explains this behavior as H 2 molecules could rapidly spread across ZIF-8 network and reach the surface of the ZnO.
Molecular Targets of Pinocembrin Underlying Its Regenerative Activities in Human Keratinocytes

Pinocembrin is one of the well-known compounds in the group of flavonoids. The pharmacological activities of pinocembrin in association with wound-healing activities have been reported. However, its effects on the aspect of cellular interaction underlying growth and survival are still unidentified in human keratinocytes. Our previous study reported that Boesenbergia rotunda potently stimulated survival and proliferation of a human keratinocyte cell line (HaCaT). On the basis that pinocembrin is revealed to be one of the major constituents of this plant, we aimed to define the survival- and proliferation-enhancing effects of this compound at the cellular level. Results from the current study confirmed that pinocembrin induced an increase in HaCaT cell number. At the signaling perspective, we identified that pinocembrin significantly triggered ERK1/2 and Akt activation. The stimulating effects of pinocembrin were clearly inhibited by MEK and PI3K inhibitors authenticating that proliferation- and survival-promoting activities of pinocembrin were mainly acted on these two signaling cascades. Altogether, we successfully identified that pinocembrin functions to induce keratinocyte proliferation and survival, at least by provoking MAPK and PI3K pathways. Our study encourages the fact that pinocembrin is one of the interesting natural flavonoid compounds to be developed as a wound closure-promoting agent.
Quality and asset management: conceptual compatibility towards sustainable infrastructures management

Asset management has been gaining momentum in both academic and industry, but is it so distinct from other existing management philosophies? This research effort discusses the relation between asset and quality management, particularly for organizations managing (public) infrastructures, to highlight the similarities and differences between both and their role towards sustainable infrastructure development. The compatibility matrix developed demonstrates that, in conceptual terms, asset and quality management are highly similar in the scope of these organizations. As such, organizations implementing formal and certified asset management systems should assess the existing processes to avoid overlaps and make the best use of the experience accumulated. It also proposed the balance between performance and risk as the metric to measure the quality (or value, adopting the asset management concept) of management decisions. The growing recognition that infrastructures exist in highly complex uncertain contexts requires changing management decisions from choosing the options with the highest expected outcome to the options with the best balance between expected outcome and potential variability of the outcome. Finally, considering sustainability as the ultimate goal, the relation between quality and asset management is linked to the need for integrated infrastructure management by analysing sustainable mobility versus sustainable transport infrastructures.
Psychometrické vlastnosti dotazníku The Teacher Efficacy for Inclusive Practices (TEIP) : adaptace české verze škály u učitelů mateřských škol

Mezinárodně široce využívaný dotazník Teacher Efficacy for Inclusive Practices (TEIP, Sharma et al., 2012; do češtiny přeloženo jako "Zdatnost učitele v inkluzivních postupech") byl jedním z prvních dotazníků, který byl vyvinut pro účely sebeposouzení vlastní vnímané zdatnosti (self-efficacy) u učitelů v implementování inkluzivních postupů ve vzdělávání. Psychometrické vlastnosti dotazníku byly ověřeny v řadě validačních studií, v českém výzkumném kontextu však dosud nebyly blíže analyzovány. Cílem této studie je přinést informace o průběhu a výsledcích adaptace dotazníku TEIP za účelem jeho využití v českém prostředí a předložit empirické důkazy o konstruktové validitě (faktorové struktuře), reliabilitě a o invarianci měření nástroje, který byl administrován na dostupném vzorku 628 učitelů z celkem 160 běžných mateřských škol (MŠ). Výsledky získané konfirmační faktorovou analýzou prokázaly existenci hierarchické struktury české verze nástroje. Osmnáctipoložkový dotazník po vzoru originální anglické verze měří subjektivní vnímanou zdatnost učitele v oblasti inkluzivních postupů, zdatnost zvládat rušivé chování dětí ve výuce a zdatnost spolupráce. Tyto tři specifické faktory zároveň reprezentují jeden obecný dominantní faktor vyššího řádu. Prokázána byla rovněž vysoká reliabilita nástroje, plná striktní invariance měření pro srovnávané skupiny učitelů MŠ z hlediska jejich věku a nejvyššího dosaženého vzdělání a také relativně vysoká úroveň vnímané zdatnosti učitelů ve vztahu k implementaci inkluzivních postupů. Dotazník lze doporučit jak pro účely pedagogického výzkumu, tak pro účely pedagogické diagnostiky v praxi. Další využití nástroje, podněty pro budoucí výzkumy a limity studie jsou diskutovány.
D-branes and Non-Perturbative Quantum Field Theory: Stringy Instantons and Strongly Coupled Spintronics

The non-perturbative dynamics of quantum field theories is studied using theoretical tools inspired by string formalism. Two main lines are developed: the analysis of stringy instantons in a class of four-dimensional N=2 gauge theories and the holographic study of the minimal model for a strongly coupled unbalanced superconductor. The field theory instanton calculus admits a natural and efficient description in terms of D-brane models. In addition, the string viewpoint offers the possibility of generalizing the ordinary instanton configurations. Even though such generalized, or stringy, instantons would be absent in a purely field-theoretical, low-energy treatment, we demonstrate that they do alter the IR effective description of the brane dynamics by introducing contributions related to the string scale. In the first part of this thesis we compute explicitly the stringy instanton corrections to the effective prepotential in a class of quiver gauge theories. In the second part of the thesis, we present a detailed analysis of the minimal holographic setup yielding an effective description of a superconductor with two Abelian currents. The model contains a scalar field whose condensation produces a spontaneous symmetry breaking which describes the transition to a superfluid phase. This system has important applications in both QCD and condensed matter physics; moreover, it allows us to study mixed electric-spin transport properties (i.e. spintronics) at strong coupling.
Fluorine Production in Thermal Pulses on the Asymptotic Giant Branch

The evolution of a 3M ⊙ star has been computed from the T Tauri stage up to the asymptotic giant branch (AGB) phase using a one-dimensional hydrodynamical stellar evolution code that includes a self-consistent treatment of the concomitant nucleosynthesis. In this paper, we concentrate on the mechanisms of fluorine production that have been encountered during thermal pulses on the AGB.
A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction

Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.
Locating a narrative paradigm Nexus in the circuit of culture: articulating the anti-proposition 112 public relations campaign in Colorado

ABSTRACT In the fall of 2018, fracking interests in Colorado initiated a public relations campaign against Proposition 112 – a measure that these interests perceived as an emergent threat to their continued viability. This thematic analysis reviewed the messaging used by the industry and its supporters as it appeared across 1,515 text articles (e.g., news accounts, op-eds, etc.) and 38 Facebook posts. We found that pro-fracking messages, rather than concentrating on the quality of the ideas offered in support of fracking (e.g., facts and data) often chose to emphasize connections to the lived experiences of the audiences. As such, this work offers an exploratory model of this phenomena called the Circuit of Culture/Narrative Paradigm Nexus Model, which includes as components values, aesthetics, and resonance. This model offers both a theoretical and applied framework for how an organization may affirm alliance with key audiences, especially when detecting an emergent threat to its continued existence.
Zileuton Alleviates Radiation-Induced Cutaneous Ulcers via Inhibition of Senescence-Associated Secretory Phenotype in Rodents

Radiation-induced cutaneous ulcers are a challenging medical problem for patients receiving radiation therapy. The inhibition of cell senescence has been suggested as a prospective strategy to prevent radiation ulcers. However, there is no effective treatment for senescent cells in radiation ulcers. In this study, we investigated whether zileuton alleviated radiation-induced cutaneous ulcer by focusing on cell senescence. We demonstrate increased cell senescence and senescence-associated secretory phenotype (SASP) in irradiated dermal fibroblasts and skin tissue. The SASP secreted from senescent cells induces senescence in adjacent cells. In addition, 5-lipoxygenase (5-LO) expression increased in irradiated dermal fibroblasts and skin tissue, and SASP and cell senescence were regulated by 5-LO through p38 phosphorylation. Finally, the inhibition of 5-LO following treatment with zileuton inhibited SASP and mitigated radiation ulcers in animal models. Our results demonstrate that inhibition of SASP from senescent cells by zileuton can effectively mitigate radiation-induced cutaneous ulcers, indicating that inhibition of 5-LO might be a viable strategy for patients with this condition.
New frequency domain performance bounds for uncertain structural systems with actuator and sensor dynamics

A new majorant robustness analysis test that yields frequency dependent performance bounds for closed-loop uncertain vibrational systems with frequency, damping, and mode shape uncertainties is developed. Specifically, for closed-loop systems consisting of uncertain positive real plants in tandem with sensor and actuator dynamics and controlled by strictly positive real compensators, performance bounds are developed by decomposing the equivalent compensator (which includes the actuator and sensor dynamics) to a positive real part and a nonpositive real part and using concepts of M-matrices and majorant analysis. <<ETX>>
Nematicidal Activity of Organic Food Additives

Organic food additives are popular in various spheres of human activity: the food industry, agriculture, veterinary and human medicine, the chemical industry, and other sectors. Due to the relatively short time of their breakdown in the environment, their non-toxicity to various organisms in certain concentrations, and their large annual amounts of production, application, and influx into the environment, it is an important task to study their influence on ecology. To help reduce toxic pressure on the environment, we studied the influence of 32 organic food additives on larvae of the nematodes Strongyloides papillosus, Haemonchus contortus, Muellerius capillaris, which are parasites of the digestive tracts of ruminants. This article presents the results of in vitro studies of organic food additives (acetic, propionic, lactic, sorbic, adipic, fumaric, malic, citric, ascorbic, and dehydroacetic acids, sodium formate, calcium formate, potassium acetate, calcium acetate, sodium diacetate, calcium propanoate, sodium lactate, potassium sorbate, sodium sorbate, calcium sorbate, trisodium citrate, monosodium glutamate, sodium dehydroacetate, sodium benzoate, potassium benzoate, calcium benzoate, biphenyl, dibutylhydroxytoluene, hexamethylenetetramine, 2-phenylphenol, natamycin, and nisin) on nematode larvae at various stages of their development. Propionic acid and 2-phenylphenol, used in the food industry, exerted notable nematicidal effects on larvae of S. papillosus, M. capillaris, and H. contortus: 1% concentrations of the substances killed 85% of these larvae, except H. contortus, which were tolerant to 2-phenylphenol in 52% of cases. Other tested food additives at 1% concentrations could not kill more than 70% of invasive nematode larvae in 24 h. The research determined various degrees of toxicity of the studied substances to invasive (third age) and non-invasive (first and second age) larvae, which are related to a decrease in the feeding intensity of parasitic nematodes on the stage of third age larvae. Thus, some organic food additives, introduced into the environment with food wastes, are able to affect the vitality of certain nematode species to a varying extent.
Bayesian classification using noninformative Dirichlet priors

Abstract : In this dissertation, the Combined Bayes Test (CBT) and its average probability of error, P(s), are developed. The CBT combines training and test data to infer symbol probabilities where a Dirichlet (completely noninformative) prior is assumed for all classes. Using P(s), several results are shown based on the best quantization complexity, M*(which is related to the Hughes Phenomenon). For example, it is shown that M* increases with the training and test data. Also, it is demonstrated that the OST outperforms a more conventional Maximum Likelihood (ML) based test, and the Kolmogorov-Smimov Test (KST). With this, the Bayesian Data Reduction Algorithm (BDRA) is developed. The BDRA uses P(s) (conditioned on the training data) and a greedy approach for reducing irrelevant features from each class, and its performance is shown to be superior to that of a neural network From here, the CBT is extended to demonstrate performance when the training data of each class are mislabeled. Performance is shown to degrade when mislabeling exists in the training data, being dependent on the mislabeling probabilities. However, it is also shown that the BRDA can be used to diminish the effect of mislabeling. Further, the BDRA is modified, using two different approaches, to classify test observations when the training data of each class contain missing feature values. In the first approach, each missing feature is assumed to be uniformly distributed over its range of values; in the second approach, the number of discrete levels for each feature is increased by one. Both methods of modeling missing features are shown to perform similarly, and both also outperform a neural network.
Muscle Weakness and Walking Slowness for the Identification of Sarcopenia in the Older Adults from Northern Brazil: A Cross-Sectional Study

Background: This study aimed to analyze the prevalence of sarcopenia in elderly people from Northern Brazil according to muscle weakness or walking slowness. Methods: The sample consisted of 312 elderly people (72.6 ± 7.8 years). For walking slowness, a gait speed ≤ 0.8 m/s was used as a cut-off value, and for muscle weakness the following handgrip strength criteria were used for men and women, respectively: CI: <27.0/16.0 kg; CII: <35.5/20.0 kg; CIII: grip strength corrected for body mass index (BMI) < 1.05/0.79; CIV: grip strength corrected for total fat mass: <1.66/0.65; CV: grip strength corrected for body mass: <0.45/0.34. Results: Walking speed was reduced in 27.0% of women and 15.2% of men (p < 0.05). According to grip strength criteria, 28.5% of women and 30.4% of men (CI), 58.0% of women and 75.0% of men (CII), 66.0% of women and 39.3% of men (CIII), 28.8% of women and 19.6% of men (CIV), and 56.5% of women and 50.0% of men (CV) were identified as having sarcopenia. Conclusions: Walking slowness is more prevalent in women and muscle weakness is more prevalent in men in Northern Brazil. Walking slowness proved to be more concordant with muscle weakness in both sexes when the CI for handgrip strength was adopted.
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance of GNNs is usually unsatisfactory due to the highly sparse and irregular graph-based operations. To this end, we propose TC-GNN, the first GNN acceleration framework based on GPU Tensor Core Units (TCUs). The core idea is to reconcile the"Sparse"GNN computation with the high-performance"Dense"TCUs. Specifically, we conduct an in-depth analysis of the sparse operations in mainstream GNN computing frameworks. We introduce a novel sparse graph translation technique to facilitate TCU processing of the sparse GNN workload. We implement an effective CUDA core and TCU collaboration design to fully utilize GPU resources. We integrate TC-GNN with the PyTorch framework for high programmability. Rigorous experiments show an average of 1.70X speedup over the state-of-the-art DGL framework across various models and datasets.
Efficient feature descriptor selection for improved Arabic handwritten words recognition

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.
A Complexity Theory for VLSI

The established methodologies for studying computational complexity can be applied to the new problems posed by very large-scale integrated (VLSI) circuits. This thesis develops a "VLSI model of computation" and derives upper and lower bounds on the silicon area and time required to solve the problems of sorting and discrete Fourier transformation. In particular, the area A and time T taken by any VLSI chip using any algorithm to perform an N-point Fourier transform must satisfy AT('2) (GREATERTHEQ) cN('2)log('2)N, for some fixed c > 0. A more general result for both sorting and Fourier transformation is that AT('2x) = (OMEGA)(N('1+x)log('2x)N), for any x in the range 0 (LESSTHEQ) x (LESSTHEQ) 1. Also, the energy dissipated by a VLSI chip during the solution of either of these problems is at least (OMEGA)(N('3/2)log N). The tightness of these bounds is demonstrated by the existence of nearly optimal circuits for both sorting and Fourier transformation. The circuits based on the shuffle-exchange interconnection pattern are fast but large: T = O(log('2)N) for Fourier transformation, T = O(log('3)N) for sorting; both have area A of at most O(N('2)/log('1/2)N). The circuits based on the mesh interconnection pattern are slow but small: T = O(N('1/2)loglog N), A = O(N log('2)N).
Detecting clusters in disease incidence

This paper is concerned with searching for localized environmental risk factors. The approach taken here uses case-control data to search for clusters of disease cases. In this context, case-control data means a sample of locations associated with diseased subjects (cases) and healthy subjects (controls). A cluster of cases is a region where the number of cases appears to be larger than what would have been expected had the cases occurred randomly in the underlying population. Clusters indicate areas where localized risk factors are likely. The methodology developed here produces a random field over the region where the cases and controls are located. The field is large where there are clusters of cases. Asymptotically, as the number of cases and controls becomes large, the field tends in distribution to a smooth Gaussian field. The operating characteristics of inferential procedures based on the random field may be approximated by considering the random field's limiting distribution.