Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Parametrization of a Regional Groundwater Flow Model with the Aid of Modelling and GIS: Test and Application of Different Approaches

The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying out the automated pilot point (PP) method. The results indicate that automated PP is more flexible and robust as compared to other approaches. Different statistical indicators show that this method yields reliable calibration as values of coefficient of determination (R2) range from 0.98 to 0.99, Nash Sutcliffe efficiency (ME) range from 0.964 to 0.976, and root mean square errors (RMSE) range from 1.68 m to 1.23 m, for manual and automated approaches, respectively. Validation results of automated PP show ME as 0.969 and RMSE as 1.31 m. The results of output sensitivity suggest that hydraulic conductivity is a more influential parameter. Considering the limitations of the current study, it is recommended to perform global sensitivity and linear uncertainty analysis for the better estimation of the modelling results.
Robust Droop Controller for Accurate Proportional Load Sharing Among Inverters Operated in Parallel

In this paper, the inherent limitations of the conventional droop control scheme are revealed. It has been proven that parallel-operated inverters should have the same per-unit impedance in order for them to share the load accurately in proportion to their power ratings when the conventional droop control scheme is adopted. The droop controllers should also generate the same voltage set-point for the inverters. Both conditions are difficult to meet in practice, which results in errors in proportional load sharing. An improved droop controller is then proposed to achieve accurate proportional load sharing without meeting these two requirements and to reduce the load voltage drop due to the load effect and the droop effect. The load voltage can be maintained within the desired range around the rated value. The strategy is robust against numerical errors, disturbances, noises, feeder impedance, parameter drifts and component mismatches. The only sharing error, which is quantified in this paper, comes from the error in measuring the load voltage. When there are errors in the voltage measured, a fundamental tradeoff between the voltage drop and the sharing accuracy appears. It has also been explained that, in order to avoid errors in power sharing, the global settings of the rated voltage and frequency should be accurate. Experimental results are provided to verify the analysis and design.
Ethylene-methyl acrylate-glycidyl methacrylate toughened poly(lactic acid) nanocomposites

Poly (lactic acid) (PLA) was melt blended in a twin screw extruder using an ethylene-methyl acrylate-glycidyl methacrylate rubber as a toughener. PLA/rubber blends were immiscible as observed by scanning electron microscopy. Impact strength and ductility of PLA were improved by the addition of the rubber at the expense of strength and stiffness. An organo-montmorillonite (OMMT) was used at 2 wt % to counteract the negative effect of the rubber on modulus, and balanced properties were observed at 10 wt % rubber content. X-ray diffraction and transmission electron microscopy revealed the formation of intercalated/exfoliated structure in the ternary nanocomposites. Thermal behavior analysis indicated that the degree of crystallinity is slightly affected by the clay and the rubber. Both the clay and the rubber decreased the crystallization temperature of PLA and acted as nucleating agents for PLA. The viscosity of the mixtures as measured by melt flow index was highly influenced by the rubber and the OMMT. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
Preparation and characterisation of nanocellulose reinforced polyamide-6

A procedure is described for preparing polyamide-6 (PA-6) reinforced with cellulose nanofibres. The cellulose nanofibres were obtained from flax and microcrystalline cellulose (MCC) using combinations of acid hydrolysis, ball milling and ultrasound, then characterised by transmission electron microscopy (TEM) in order to determine their size and geometry. The nanofibres produced from the different feedstock sources were of a similar order with lengths ranging from 21 to 300 nm and diameters between 2 and 22 nm. PA-6 nanocomposite films were subsequently prepared from these nanofibres using a solution casting technique. Their chemical and physical structure was analysed using Fourier transform infrared analysis (FTIR) and TEM. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were also applied to compare their thermal properties with unfilled polymer. Dynamic mechanical thermal analysis and tensile measurements demonstrated a significant enhancement in mechanical properties was possible with a low addition of cellulose nanofibres to the polymer matrix.
Counting faces of randomly-projected polytopes when the projection radically lowers dimension

1.1. Three surprises of high dimensions. This paper develops asymptotic methods to count faces of random high-dimensional polytopes; a seemingly dry and unpromising pursuit. Yet our conclusions have surprising implications - in statistics, probability, information theory, and signal processing - with potential impacts in practical subjects like medical imaging and digital communications. Before involving the reader in our lengthy analysis of high-dimensional face counting, we describe three implications of our results. 1.1.1. Convex Hulls of Gaussian Point Clouds. Consider a random point cloud of n points xi, i = 1, . , n, sampled independently and identically from a Gaussian distribution in R d with nonsingular covariance. This is a standard model of multivariate data; its properties are increasingly important in a wide range of applications. At the same time, it is an attractive and in some sense timeless object for theoretical study. Properties of the convex hull of the random point cloud X = {xi} have attracted interest for several decades, increasingly so in recent years; there is a nowvoluminous literature on the subject. The results could be significant for understanding outlier detection, or classification problems in machine learning.
Peptide YY mediates the satiety effects of diets enriched with whey protein fractions in male rats

Dairy proteins—whey protein, in particular—are satiating and often recommended for weight control; however, little is known about the mechanisms by which whey protein and its components promote satiety and weight loss. We used diet‐induced obese rats to determine whether the hypophagic effects of diets that are enriched with whey and its fractions, lactalbumin and lactoferrin, are mediated by the gut hormone, peptide YY (PYY). We demonstrate that high protein diets that contain whey, lactalbumin, and lactoferrin decreased food intake and body weight with a concurrent increase in PYY mRNA abundance in the colon and/or plasma PYY concentrations. Of importance, blockade of PYY neuropeptide Y receptor subtype 2 (Y2) receptors with a peripherally restricted antagonist attenuated the hypophagic effects of diets that are enriched with whey protein fractions. Diets that are enriched with whey fractions were less preferred; however, in a modified conditioned taste preference test, PYY Y2 receptor blockade induced hyperphagia of a lactoferrin diet, but caused a reduction in preference for Y2 antagonist‐paired flavor, which suggested that PYY signaling is important for lactoferrin‐induced satiety, but not essential for preference for lactoferrin‐enriched diets. Taken together, these data provide evidence that the satiety of diets that are enriched with whey protein components is mediated, in part, via enhanced PYY secretion and action in obese male rats. —Zapata, R. C. , Singh, A. , Chelikani, P. K. Peptide YY mediates the satiety effects of diets enriched with whey protein fractions in male rats. FASEB J. 32, 850–861 (2018). www.fasebj. org
Stone-on-Stone Contact of Permeable Friction Course Mixtures

Stone-on-stone contact of the coarse-aggregate fraction is one of the main characteristics of permeable friction course (PFC) asphalt mixtures that is required to provide adequate resistance to both raveling and permanent deformation. Currently, stone-on-stone contact is determined by comparing the air voids content in the coarse aggregate (VCA), assessed in both the dry-rodded condition (VCA DRC ) and the compacted PFC mixture (VCA mix ). The underlying assumption is that the coarse aggregate of a compacted PFC mixture with VCA mix equal to VCA DRC would develop a stone-on-stone contact condition equivalent to that existing in the dry-rodded aggregate. This study focused on proposing enhancements for the quantitative determination of stone-on-stone contact of PFC mixtures. The assessment supported on both laboratory testing and application of the discrete element method and image analysis techniques, led to recommendation of a criterion to determine the breaking-sieve size. In addition, verification of stone-on-stone contact using a maximum VCA ratio of 0.9 was recommended to ensure the design and construction of PFC mixtures with fully developed stone-on-stone contact.
Pattern of Near Transfer Effects Following Working Memory Training With a Dual N-Back Task

In a randomized controlled trial, we investigated the pattern of near transfer effects of working memory (WM) training with an adaptive auditory-visuospatial dual n-back training task in healthy young adults. The results revealed significant task-specific transfer to an untrained single n-back task, and more general near transfer to a WM updating composite score plus a nearly significant effect on a composite score measuring interference control in WM. No transfer effects were seen on Active or Passive WM composites. The results are discussed in the light of cognitive versus strategy-related overlap between training and transfer tasks.
Effects of Heavier Truck Loadings and Super-Single Tires on Subgrades

Due to their efficiency and economy, super-single tires have gradually been replacing conventional dual tires in the trucking industry. According to recent studies, super-single tires generate much higher vertical contact stresses than do conventional dual tires, resulting in larger deformations and more severe damage to the subgrade. In order to better assess the higher stresses generated by super-single tires and their effect on the subgrade, analyses are done taking into account soil plasticity rather than simple elastic analysis. In this study, the effects of super-single tires on subgrades for typical road cross-sections are investigated using plane-strain (2D) and 3D static and dynamic finite element analyses. The analyses focus on the sand and clay subgrades rather than on asphalt and base layers. The subgrades are modeled as saturated in order to investigate the effects of porewater pressures under the most severe conditions. The analyses suggest that current flexible pavement design methods are unconservative for the increased loads imposed by super-single tires on the pavement system. Load Equivalent Factors (LEF) and damage factors for super-single tires are suggested for use of those values in design practice. The effects of overlay and subgrade improvement are investigated in order to mitigate the higher strains generated by super-single tire loadings in the subgrade. Subgrade improvement allows decreasing the adverse effects of super-single tires on the subgrades. Finite element analyses are also done in order to assess the performance of the Indiana typical pavements for the super-heavy loads (occurred in Texas during 1990s). This investigation is to evaluate how much plastic strain by super-heavy loads occurs in the subgrade and asphalt layers as compared with the super-single tire loadings.
Road Network Detection Using Probabilistic and Graph Theoretical Methods

Road network detection from very high resolution satellite and aerial images has diverse and important usage areas such as map generation and updating. Although an expert can label road pixels in a given image, this operation is prone to errors and quite time consuming. Therefore, an automated system is needed to detect the road network in a given satellite or aerial image in a robust manner. In this paper, we propose such a novel system. Our system has three main modules: probabilistic road center detection, road shape extraction, and graph-theory-based road network formation. These modules may be used sequentially or interchangeably depending on the application at hand. To show the strengths and weaknesses of our system, we tested it on several very high resolution satellite (Geoeye, Ikonos, and QuickBird) and aerial image sets. We compared our system with the ones existing in the literature. We also tested the sensitivity of our system to different parameter values. Obtained results indicate that our system can be used in detecting the road network on such images in a reliable and fast manner.
UNIFORM L p -BOUND OF THE ALLEN–CAHN EQUATION AND ITS NUMERICAL DISCRETIZATION

We study uniform bounds associated with the Allen–Cahn equation and its numerical discretization schemes. These uniform bounds are diﬀerent from, and weaker than, the conven-tional energy dissipation and the maximum principle, but they can be helpful in the analysis of numerical methods. In particular, we show that ﬁnite diﬀerence spatial discretization, like the original continuum model, shares the uniform L p -bound for all even p , which also leads to the maximum principle. In comparison, a couple of other spatial discretization schemes, namely the Fourier spectral Galerkin method and spectral collocation method preserve the L p -bound only for p = 2. Moreover, fully discretized schemes based on the Fourier collocation method for spa- tial discretization and Strang splitting method for time discretization also preserve the uniform L 2 -bound unconditionally.
Spreading of components of mood in adolescent social networks

Recent research has provided evidence that mood can spread over social networks via social contagion, but that, in seeming contradiction to this, depression does not. Here, we investigate whether there is evidence for the individual components of mood (such as appetite, tiredness and sleep) spreading through US adolescent friendship networks while adjusting for confounding by modelling the transition probabilities of changing mood state over time. We find that having more friends with worse mood is associated with a higher probability of an adolescent worsening in mood and a lower probability of improving, and vice versa for friends with better mood, for the overwhelming majority of mood components. We also show, however, that this effect is not strong enough in the negative direction to lead to a significant increase in depression incidence, helping to resolve the seeming contradictory nature of existing research. Our conclusions, therefore, link in to current policy discussions on the importance of subthreshold levels of depressive symptoms and could help inform interventions against depression in high schools.
Characteristics of Infection Immunity Regulated by Toxoplasma gondii to Maintain Chronic Infection in the Brain

To examine the immune environment of chronic Toxoplasma gondii infection in the brain, the characteristics of infection-immunity (premunition) in infection with T. gondii strain ME49 were investigated for 12 weeks postinfection (PI). The results showed that neuronal cell death, microglia infiltration and activation, inflammatory and anti-inflammatory cytokine expression, Stat1 phosphorylation, and microglia activation and inflammatory gene transcripts related to M1 polarization in the brain were increased during the acute infection (AI) stage (within 6 weeks PI), suggesting that innate and cellular inflammatory response activation and neurodegeneration contributed to excessive inflammatory responses. However, these immune responses decreased during the chronic infection (CI) stage (over 6 weeks PI) with reductions in phosphorylated STAT1 (pSTAT1) and eosinophilic neurons. Notably, increases were observed in transcripts of T-cell exhaustion markers (TIM3, LAG3, KLRG1, etc.), suppressor of cytokines signaling 1 protein (SOCS1), inhibitory checkpoint molecules (PD-1 and PD-L1), and Arg1 from the AI stage (3 weeks PI), implying active immune intervention under the immune environment of M1 polarization of microglia and increases in inflammatory cytokine levels. However, when BV-2 microglia were stimulated with T. gondii lysate antigens (strain RH or ME49) in vitro, nitrite production increased and urea production decreased. Furthermore, when BV-2 cells were infected by T. gondii tachyzoites (strain RH or ME49) in vitro, nitric oxide synthase and COX-2 levels decreased, whereas Arg1 levels significantly increased. Moreover, Arg1 expression was higher in ME49 infection than in RH infection, whereas nitrite production was lower in ME49 infection than in RH infection. Accordingly, these results strongly suggest that immune triggering of T. gondii antigens induces M1 polarization and activation of microglia as well as increase NO production, whereas T. gondii infection induces the inhibition of harmful inflammatory responses, even with M1 polarization and activation of microglia and Th1 inflammatory responses, suggesting a host–parasite relationship through immune regulation during CI. This is a characteristic of infection immunity in infection with T. gondii in the central nervous system, and SOCS1, a negative regulator of toxoplasmic encephalitis, may play a role in the increase in Arg1 levels to suppress NO production.
fuzzyPSM: A New Password Strength Meter Using Fuzzy Probabilistic Context-Free Grammars

To provide timely feedbacks to users, nearly every respectable Internet service now imposes a password strength meter (PSM) upon user registration or password change. It is a rare bit of good news in password research that well-designed PSMs do help improve the strength of user-chosen passwords. However, leading PSMs in the industrial world (e.g., Zxcvbn, KeePSM and NIST PSM) are mainly composed of simple heuristic rules and found to be highly inaccurate, while state-of-the-art PSMs from academia (e.g., probabilistic context-free grammar based ones and Markov-based ones) are still far from satisfactory, especially incompetent at gauging weak passwords. As preventing weak passwords is the primary goal of any PSM, this means that existing PSMs largely fail to serve their purpose. To fill this gap, in this paper we propose a novel PSM that is grounded on real user behavior. Our user survey reveals that when choosing passwords for a new web service, most users (77.38%) simply retrieve one of their existing passwords from memory and then reuse (or slightly modify) it. This is in vast contrast to the seemingly intuitive yet unrealistic assumption (often implicitly) made in most of the existing PSMs that, when user registers, a whole new password is constructed by mixing segments of letter, digit and/or symbol or by combining n-grams. To model users' realistic behaviors, we use passwords leaked from a less sensitiveservice as our base dictionary and another list of relatively strong passwords leaked from a sensitive service as our training dictionary, and determine how mangling rules are employed by users to construct passwords for new services. This process automatically creates a fuzzy probabilistic context-free grammar (PCFG) and gives rise to our fuzzy-PCFG-based meter, fuzzyPSM. It can react dynamically to changes in how users choose passwords and is evaluated by comparisons with five representative PSMs. Extensive experiments on 11 real-world password lists show that fuzzyPSM, in general, outperforms all its counterparts, especially accurate in telling apart weak passwords and suitable for services where online guessing attacks prevail.
TabVec: Table Vectors for Classification of Web Tables

There are hundreds of millions of tables in Web pages that contain useful information for many applications. Leveraging data within these tables is difficult because of the wide variety of structures, formats and data encoded in these tables. TabVec is an unsupervised method to embed tables into a vector space to support classification of tables into categories (entity, relational, matrix, list, and non-data) with minimal user intervention. TabVec deploys syntax and semantics of table cells, and embeds the structure of tables in a table vector space. This enables superior classification of tables even in the absence of domain annotations. Our evaluations in four real world domains show that TabVec improves classification accuracy by more than 20% compared to three state of the art systems, and that those systems require significant in domain training to achieve good results.
THE DIRECT TENSION TEST AND THE BEHAVIOR OF ASPHALT BINDERS AT LOW AND INTERMEDIATE TEMPERATURES

The direct tension test (DTT) is used to define the rheology of binders in the range of relaxation modulus values between 5 and 1000 MPa. Modifications have been made to the analysis of the stress strain information to improve the accuracy of the data obtained from the DTT test. In addition, energy, strain and stress ultimate property master curves are produced. These data can be used with time-temperature suppositions to assist with the definition of properties at other loading times and temperatures. Suggestions are made with respect to how data could be applied to specifications for fatigue and fracture properties. For the covering abstract see ITRD E121480.
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the {\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on. Additionally, a task-specific learner of an {\em MT-net} performs gradient descent with respect to a meta-learned distance metric, which warps the activation space to be more sensitive to task identity. We demonstrate that the dimension of this learned subspace reflects the complexity of the task-specific learner's adaptation task, and also that our model is less sensitive to the choice of initial learning rates than previous gradient-based meta-learning methods. Our method achieves state-of-the-art or comparable performance on few-shot classification and regression tasks.