Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Mechanical Fibrillation on Cellulose Reinforced Poly(Ethylene Oxide)

The objective of this work was to extract sugar bagasse cellulose nanofibres by using three different processes, namely: mechanical fibrillation, bleaching and mild acid hydrolysis. Cellulose nano-fibres with diameters in the nano range and estimated lengths of several micrometers were obtained from SB. Fourier transform-infrared (FTIR) spectroscopy analysis confirmed the removal of hemicellulose and lignin components by alkali/bleaching and acid hydrolysis. XRD results showed an increase in crystalline which resulted from the removal of lignin and hemicellulose, especially after mercerization and mild acid hydrolysis. Moreover, the extracted cellulose nanofibres were used to reinforce poly(ethylene oxide) (PEO). PEO was dissolved in water and mixing with the cellulose nanofibres suspension followed by casting. The nanocomposites were characterized by using FTIR analysis, thermogravimetric analysis, X-Ray diffractometry and tensile tester. The thermal stability of the nanocomposites was enhanced depending on the treatment of the SB fibres.
Asset pricing based on stochastic delay differential equations

This dissertation studies stochastic delay differential equations (SDDEs), applies them to real market data, and compares them with classic models. In Chapter 2, we study the mathematical properties of stochastic differential equations with or without delay, and introduce the linear SDDE for several specific financial market behaviors we are interested in. Since it is hard to find an explicit solution of an SDDE, we introduce a numerical technique and use it to analyze the SDDE. In Chapter 3, we use the Euler-Maruyama method to discretize a continues-time stochastic system and show the convergence in different senses of the numerical scheme to the true solution of the linear SDDE. Furthermore, it is crucial to understand the quantitative behavior of the parameters for the stochastic system and the impact of introducing the delay term, but these parameters are unknown and hard to estimate. In Chapter 4, we propose a blocking method to group the price points and use the Bayesian methods to estimate the parameters in the linear SDDE. We then apply the model to real stock price data, estimate and calibrate all the parameters in the stochastic system, and compare them with the parameters obtained from the classic geometric Brownian motion model.
Effect of nitrogen and zinc on maize ( zea mays l . ) Yield components and plant concentration 1

This field experiment was carried out to study the effect of different levels of nitrogen (N) and Zinc (Zn) on maize productivity. The experiment was performed at the Agricultural Research Centre, Swabi, during spring season, 2015. Three N levels (100, 150 and 200 kg ha ) were applied as urea, in combination with four Zn levels (0, 5, 10 and 15 kg ha) were applied as zinc sulphate (ZnSO4). Treated plots were replicated four times and were laid out in RCB Design. Size of the plot was 3m x 4m (12 m). Experimental data showed that growth, yield and quality parameters of maize were significantly affected by both N and ZnSO4. Highest ears weight (6020 kg ha ) was gained @ N 200 kg ha, plant height (231.9 cm) was recorded @ N 150 kg ha, maximum values of numbers of grains per cob (427), number of grain rows per cob (14), 1000 grain weight (277 gm) and total yield of grains (5012 kg ha) were gained @ N 200 kg ha. Highest values of most parameters were recorded in plots treated with N @ 200 kg ha along with Zn @ 15 kg ha which were parallel to that of N @ 150 kg ha and Zn @ 15 kg ha. Total plant N and Zn in plant were found superior in those plots which were treated by N @ 200 kg ha -1 in combination with Zn applied @ 15 kg ha. This field experimental study suggested that N should be applied @ 150 kg ha in combination with Zn @ 10 kg ha to get better yield of maize crop.
EngagedScholarship@CSU EngagedScholarship@CSU

Although earlier research on leader-member exchange (LMX) theory supported a negative linear relationship between LMX quality and role stressors, recent studies suggest that a more complex, nonlinear relationship may exist between LMX quality and variables traditionally associated with it. Based on communication research of LMX and social exchange theory, the aim of this article is to revisit the relationship between LMX quality and role stressors by reconceptualizing their associations and testing the hypotheses of an inverted U relationship. A survey study among immigrant employees revealed differential effects of LMX quality on role stressors. In particular, with role conflict and role overload LMX quality was found to have an inverted U relationship, but a negative linear relationship with role ambiguity. These findings challenge the prevailing assumptions and carry significant theoretical and practical implications.
The Entropy Function of an Invariant Measure

Given a countable relational language $L$, we consider probability measures on the space of $L$-structures with underlying set $\mathbb{N}$ that are invariant under the logic action. We study the growth rate of the entropy function of such a measure, defined to be the function sending $n \in \mathbb{N}$ to the entropy of the measure induced by restrictions to $L$-structures on $\{0, \ldots, n-1\}$. When $L$ has finitely many relation symbols, all of arity $k\ge 1$, and the measure has a property called non-redundance, we show that the entropy function is of the form $Cn^k+o(n^k)$, generalizing a result of Aldous and Janson. When $k\ge 2$, we show that there are invariant measures whose entropy functions grow arbitrarily fast in $o(n^k)$, extending a result of Hatami-Norine. For possibly infinite languages $L$, we give an explicit upper bound on the entropy functions of non-redundant invariant measures in terms of the number of relation symbols in $L$ of each arity; this implies that finite-valued entropy functions can grow arbitrarily fast.
DeepScores and Deep Watershed Detection: current state and open issues

This paper gives an overview of our current Optical Music Recognition (OMR) research. We recently released the OMR dataset \emph{DeepScores} as well as the object detection method \emph{Deep Watershed Detector}. We are currently taking some additional steps to improve both of them. Here we summarize current and future efforts, aimed at improving usefulness on real-world task and tackling extreme class imbalance.
Professionals' supervisor–subordinate relationships, autonomy and commitment in Australia: a leader–member exchange theory perspective

This article used leader–member exchange theory as a lens for comparing the impact of the supervisor–subordinate relationship on two types of professionals' perceptions of autonomy, and in turn upon their affective commitment. The reason for examining autonomy is because a characteristic of being a professional is having autonomy; however, we argued that such perceptions are affected by the quality of the supervisor–subordinate relationship. The findings confirmed this argument, although the trend was stronger for engineers than for nurses. Using the ordinary least square procedure, the goodness of fit of the model identified that supervision and autonomy accounted for approximately a third of the variance for engineers' levels of affective commitment and a fifth of the variance for nurses. That is, the impact of supervision practices was stronger on autonomy and commitment for engineers than for nurses in Australia. Moreover, statistically, the two groups of professionals were similar in their perceptions of the quality of their supervisor–subordinate relationship as well as in their perceptions of autonomy, and the qualitative findings supported similar factors impacting upon their perceptions. The only significant difference between the two groups was in their levels of affective commitment. The implications of these results include the need for those managing professionals to consider ways of improving workplace supervisor–subordinate relationships because of the impact upon perceived autonomy as well as commitment to their organisation, and hence the retention of such professionals.
Grounding-line basal melt rates determined using radar-derived internal stratigraphy

Abstract We use ice-penetrating radar data across grounding lines of Siple Dome and Roosevelt Island, Antarctica, to measure the spatial pattern, magnitude and duration of sub-ice-shelf melting at these locations. Stratigraphic layers across the grounding line show, in places, a large-amplitude downwarp at, or slightly downstream of, the grounding line due to sub-ice-shelf basal melting. Localized downwarping indicates that melting is transient; melt rates, or the grounding line position, have changed within a few hundred years in order to produce the observed stratigraphy. Elsewhere, no melt-related stratigraphic signature is preserved. In part, heterogeneity in the amount of sub-ice-shelf melt is due to regional circulation patterns in the sub-shelf cavity, but local (on the order of tens of kilometers) heterogeneity in the melt pattern may reflect small differences in the shape of the ice-shelf base at the grounding line. We find that all of the grounding lines crossed have been in place for at most ∼400 years.
Anti-diabetic effects of Allium cepa ( onions ) aqueous extracts on alloxan-induced diabetic Rattus

The hypoglycaemic and hypolipidaemic effects of the increasing dosages of Allium cepa aqueous extracts on alloxan induced diabetic Rattus novergicus for possible use in the management of diabetes mellitus was investigated. Diabetes mellitus was induced in 54 out of a total of 63 adult R. novergicus using 150 mg/kg of alloxan monohydrate. Increasing dosages (200, 250 and 300 mg/kg) of A. cepa aqueous extracts were given to the diabetic rats for six weeks while the control rats got either normal saline (1 ml) or increasing dosages of glibenclamide (2.5, 3.8 and 5.0 mg/kg) during the same period. Blood glucose level, total serum lipids and total serum cholesterol were assessed with routine methods. F-LSD was employed to test significant differences (P < 0.05) among treatment means. Increasing dosages of A. cepa aqueous extracts produced a dose-dependent significant (P < 0.05) reductions in the blood glucose levels, total serum lipid and total serum cholesterol when compared with that of the control rats. The most effective percentage reduction in blood glucose level, total serum lipids and cholesterol were observed at 300 mg/kg. From the experimental findings, it is possible to conclude that A. cepa studied exhibited promising hypoglycaemic and hypolipidaemic activity in alloxan-induced diabetic rats. It’s hypoglycaemic and hypolipidaemic effects could represent a protective mechanism against the development of hyperglycaemia and hyperlipidaemia characteristic of diabetes mellitus.
Maximal Coupling Procedure and Stability of Continuous-Time Markov Chains

In this study we first investigate the stability of subsampled discrete Markov chains through the use of the maximal coupling procedure. This is an extension of the available results on Markov chains and is realized through the analysis of the subsampled chain , where is an increasing sequence of random stopping times. Then the similar results are realized for the stability of countable-state Continuous-time Markov processes by employing the skeleton-chain method.
Forecasting GDP Over the Business Cycle in a Multi‐Frequency and Data‐Rich Environment

This paper merges two specifications recently developed in the forecasting literature: the MS-MIDAS model (Guerin and Marcellino, 2013) and the factor-MIDAS model (Marcellino and Schumacher, 2010). The MS-factor MIDAS model that we introduce incorporates the information provided by a large data set consisting of mixed frequency variables and captures regime-switching behaviours. Monte Carlo simulations show that this specification tracks the dynamics of the process and predicts the regime switches successfully, both in-sample and out-of-sample. We apply this model to US data from 1959 to 2010 and properly detect recessions by exploiting the link between GDP growth and higher frequency financial variables.
Evaluation of Thermal Discomfort in Outdoor Environments: A Cross Sectional Study throughout IRAN

Background: In terms of occupational health and the effects of global warming on worker's health, especially workers in outdoor environments has not been much attention unfortunately. Objective: One of the most important consequences of the temperature rise, especially in outdoor environments, is creating a thermal stress and discomfort. So, in this investigation using Discomfort Index (DI), thermal condition of occupational outdoor environments throughout the country Iran was evaluated. Methods: Environmental parameters including dry air temperature and wet temperature were measured for 9 climatic areas of Iran, at 3 time intervals in a shift work (early, middle and end of the shift work) and twice a year (spring and summer). Discomfort Index was calculated for the stations and the obtained results showed graphically in 6 separate geographic maps using Arc/GIS 10.2 software. Results: The obtained results indicated that the values of discomfort index (DI) are varied in different seasons and climatic regions (P< 0.001). Also according to recommended values of DI , 28.8 percent of measurements were in area without any discomfort in spring. Unlike, the largest percentage of discomfort index for summer was at area with severe heat load (61.2%). Regardless of seasons, 76.4 % of measurements have been discomfort values higher than 24 °C (Areas with moderately to severe heat load). Conclusion: According to the results in this study heat stress can be exist especially in central and southern areas of Iran, which similarly have been predicted as areas with more influences by climate changes in other performed studies. Therefore, it is necessary to take prevention measures for outdoor workers who spend a lot of times outdoor under unhealthy conditions.
Genetics of Castes and Tribes of India: Indian Population Milieu

Abstract South Asia, constituting mainly the Indian sub-continent, is a panorama of social diversities, racial differences and rich cultural heritage. However many facets of history, race and culture of this area are still relatively unexplored. Debates and doubts centering around the autochthonous status of the early settlers continue - it is very difficult to ascertain how human groups and settlements were formed in the pre-historic times, whether they were the original inhabitants or migrated from some other place and if they migrated, which route did they take. However, the infiltration and admixture of new racial and cultural elements, from time to time, have made the Indian population more diverse and heterogeneous.
EXPERIMENTS WITH IPV6 NETWORK MOBILITY USING NEMO PROTOCOL

The NEMO (NEtwork MObility) protocol, as normalized by IETF, allows a transparent movement of a whole IPv6 network. This mobile network can thus attach to different points in the Internet without losing TCP/IP connectivity of its mobile nodes. In this paper we briefly describe the NEMO protocol and present a simple architecture deployed to test its main functionalities. We also discuss the results obtained thus far with the use of popular TCP and UDP applications in a mobile environment: video streaming transfer, remote access using SSH and FTP file transfer.
Sparsity Oriented Importance Learning for High-Dimensional Linear Regression

ABSTRACT With now well-recognized nonnegligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection process, regardless of its sophistication. To improve reliability and reproducibility in model choice, one constructive approach is to make good use of a sound variable importance measure. Although interesting importance measures are available and increasingly used in data analysis, little theoretical justification has been done. In this article, we propose a new variable importance measure, sparsity oriented importance learning (SOIL), for high-dimensional regression from a sparse linear modeling perspective by taking into account the variable selection uncertainty via the use of a sensible model weighting. The SOIL method is theoretically shown to have the inclusion/exclusion property: When the model weights are properly around the true model, the SOIL importance can well separate the variables in the true model from the rest. In particular, even if the signal is weak, SOIL rarely gives variables not in the true model significantly higher important values than those in the true model. Extensive simulations in several illustrative settings and real-data examples with guided simulations show desirable properties of the SOIL importance in contrast to other importance measures. Supplementary materials for this article are available online.
Effect of ocean acidification on early life stages of Atlantic herring (Clupea harengus L.)

Abstract. Due to atmospheric accumulation of anthropogenic CO2 the partial pressure of carbon dioxide (pCO2) in surface seawater increases and the pH decreases. This process known as ocean acidification might have severe effects on marine organisms and ecosystems. The present study addresses the effect of ocean acidification on early developmental stages, the most sensitive stages in life history, of the Atlantic herring (Clupea harengus L.). Eggs of the Atlantic herring were fertilized and incubated in artificially acidified seawater (pCO2 1260, 1859, 2626, 2903, 4635 μatm) and a control treatment (pCO2 480 μatm) until the main hatch of herring larvae occurred. The development of the embryos was monitored daily and newly hatched larvae were sampled to analyze their morphometrics, and their condition by measuring the RNA/DNA ratios. Elevated pCO2 neither affected the embryogenesis nor the hatch rate. Furthermore the results showed no linear relationship between pCO2 and total length, dry weight, yolk sac area and otolith area of the newly hatched larvae. For pCO2 and RNA/DNA ratio, however, a significant negative linear relationship was found. The RNA concentration at hatching was reduced at higher pCO2 levels, which could lead to a decreased protein biosynthesis. The results indicate that an increased pCO2 can affect the metabolism of herring embryos negatively. Accordingly, further somatic growth of the larvae could be reduced. This can have consequences for the larval fish, since smaller and slow growing individuals have a lower survival potential due to lower feeding success and increased predation mortality. The regulatory mechanisms necessary to compensate for effects of hypercapnia could therefore lead to lower larval survival. Since the recruitment of fish seems to be determined during the early life stages, future research on the factors influencing these stages are of great importance in fisheries science.
Molecular MRI of Atherosclerosis

Atherosclerosis and its consequences contribute to more than 50% of mortality in Europe, the United States of America and many developed or developing countries (Schafers et al., 2010). The main pathologic substrate in atherosclerosis is the atherosclerotic lesion. The classical high-risk plaque is characterized by a large lipid core, a thin fibrous cap and many macrophages in the shoulder regions of the plaque (Falk et al., 1995; Stary et al., 1995). Rupture of the fibrous cap or, more subtle, erosion leads to exposure of the thrombogenic sub-endothelial matrix to blood leading to thrombus formation. Recurrent thrombosis or major atherothrombosis may lead to an acute cardiovascular event via sub(total) occlusion of the vessel of interest. Macrophages are the key mediators of plaque inflammation and progression (Lusis, 2000; Ross, 1999). Inflammatory activity can be evaluated at the cellular (macrophages and neutrophils) or sub-cellular (secreted enzymes, membrane-bound proteins) level. Sensitive and specific biomarkers of plaque inflammation and instability are highly desired for more accurate definition of high-risk plaques in high risk patient populations. At present, serologic markers do not accomplish this need sufficiently. Therefore, there is a need for noninvasive imaging techniques for identification and quantification of local plaque biomarkers, which can direct interventional cardiologists and vascular surgeons to diseased sites and can monitor therapeutic response.
Aggregation of magnetic microparticles in the context of targeted therapies actuated by a magnetic resonance imaging system

A study of magnetic aggregation in the context of magnetic resonance imaging (MRI) based actuated targeting is proposed. MRI systems can induce displacement forces on magnetized particles as they flow through the blood vessels. Magnetic aggregation of the particles happens when they are placed within the magnetic field of the MRI system and can greatly influence the MRI steering dynamics of magnetic particles. In this paper, a review of the different parameters that can be used to tailor the size, geometry, stiffness, and density of magnetic aggregates is proposed. Then, magnetic aggregation experiments on a suspension of Fe3O4 microparticles ranging from 0.1 to 100 μm in diameter are described. The effects of particle concentration, flow rate, and magnetic field amplitude were evaluated. Field amplitudes of 1.5 mT, 0.4 T, and 1.5 T fields were applied without any magnetic steering gradients and caused aggregates that could sometimes exceed 1 mm in length. Since magnetic aggregates can reach higher magnet.
SoMin.ai: Social Multimedia Influencer Discovery Marketplace

In this technical demonstration, we showcase the first ai-driven social multimedia influencer discovery marketplace, called SoMin. The platform combines advanced data analytics and behavioral science to help marketers find, understand their audience and engage the most relevant social media micro-influencers at a large scale. SoMin harvests brand-specific life social multimedia streams in a specified market domain, followed by rich analytics and semantic-based influencer search. The Individual User Profiling models extrapolate the key personal characteristics of the brand audience, while the influencer retrieval engine reveals the semantically-matching social media influencers to the platform users. The influencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By leveraging influencers at a large scale, marketers will be able to execute more effective marketing campaigns of higher trust and at a lower cost.