Miracle-dz/UniFAR-FTUs huggingface dataset:
Relevant Education in a Changing World: Expanding Value for the Motivation Sciences

The idea that education should be made relevant to students is long-standing and pervasive in American society. Recently, motivation scientists have clarified important characteristics of students’ relevance beliefs, ways to intervene, and individual characteristics moderating intervention effects. Yet, there has been little consideration of the role of situational constraints and sociocultural influences on students’ relevance appraisal processes. We describe how societal changes and broader educational purposes affect the issues that students consider to be relevant to their educational experiences and the values they subsequently attribute to their studies. After differentiating components of relevance and highlighting ways in which particular components may be influenced by changing sociocultural milieus, we consider the implications of these processes for the development of subjective task value beliefs. Specifically, we show how the proposed model of relevance helps to parse out aspects of relevance appraisals that can be used to differentiate between components of subjective task value and argue that there is need to expand current models proposed in expectancy-value theory (EVT). Finally, we explore how recent global events may impact the social construction of educational relevance and constrain students’ developing beliefs about the value of their educational opportunities and implications for future research and educators.
Predicting potential hybridization between native and non-native Rhodeus ocellatus subspecies: The implications for conservation of a pure native population in northern Kyushu, Japan

Hybridization between native and non-native fishes is a serious global problem. Thus, there is a need to develop monitoring methods for predicting potential hybridization to evaluate the risk of genetic introgression and to identify important areas for conservation of pure native populations. Here, we developed a prediction model for intersubspecific hybridization, based on distribution and genetic data. We selected Rhodeus ocellatus kurumeus and R. ocellatus ocellatus as the native and non-native subspecies, respectively. First, we developed generalized linear models (GLMs) for the species habitat requirements by using presence/absence data and environmental variables. The best-fit models showed river length gave conflicting effects for the two subspecies. Next, we developed a GLM using the ratio of non-native haplotypes in mitochondrial DNA as an objective variable, with the predicted probabilities of the occurrence of each fish and spatial information as explanatory variables. The best-fit model selected the distance from the center of native distribution and the non-native habitat requirement as key factors. Our findings indicate that hybridization occurs highly and/or initially near the margin of native distribution where non-native habitat requirements are available. Our model could identify sites in native habitats with very low potential risk for genetic invasion as important areas for conservation of pure native populations.
G R ] 1 0 A ug 2 01 8 On lexicographic representatives in braid monoids

The language of maximal lexicographic representatives of elements in the positive braid monoid An with n generators is a regular language. We describe with great detail the smallest Finite State Automaton accepting such language, and study the proportion of elements of length k whose maximal lexicographic representative finishes with the first generator. This proportion tends to some number Pn,1, as k tends to infinity, and we show that Pn,1 ≥ 1 8 for every n ≥ 1. We also provide an explicit formula, based on the Fibonacci numbers, for the number of states of the automaton.
Have the Special Economic Zones Succeeded in Attracting FDI?—Analysis for India

The introduction of Special Economic Zones (SEZs) in India has injected hope for augmented economic growth in recent future. The motive behind establishment of SEZs was mainly to fuel rapid economic growth, provide world class infrastructure and employment, promote exports, increase foreign exchange reserves and attract more Foreign Direct Investment (FDI). The main objective of the paper is to investigate whether the enactment of SEZ policies had any impact on inflow of FDI among Indian states. This is tested using panel data techniques on 16 groups of states over 14 years period from 2001 to 2014. The results indicate that enactment of SEZ policy (as well as operational SEZs) in a state has induced more FDI inflow. Based on the results, it can be concluded that the states, which want to benefit from FDI inflow, they need to enact the policies sooner.
Actuation, Sensing And Control For Micro Bio Robots

The continuing trend in miniaturization of technology, advancements in micro and nanofabrication and improvements in high-resolution imaging has enabled microand meso-scale robots that have many applications. They can be used for micro-assembly, directed drug delivery, microsurgery and high-resolution measurement. In order to create microrobots, microscopic sensors, actuators and controllers are needed. Unique challenges arise when building microscale robots. For inspiration, we look toward highly capable biological organisms, which excel at these length scales. In this dissertation we develop technologies that combine biological components and synthetic components to create actuation, sensing and assembly onboard microrobots. For actuation, we study the dynamics of synthetic micro structures that have been integrated with single-cell biological organisms to provide un-tethered onboard propulsion to the microrobot. For sensing, we integrate synthetically engineered sensor cells to enable a system capable of detecting a change in the local environment, then storing and reporting the information. Furthermore, we develop a bottom-up fabrication method using a macroscopic magnetic robot to direct the assembly of inorganic engineered micro structures. We showcase the capability of this assembly method by demonstrating highly-specified, predictable assembly of microscale building blocks in a semi-autonomous experiment. These magnetic robots can be used to program the assembly of passive building blocks, with the building blocks themselves having the potential to be arbitrarily complex. We extend the magnetic robot actuation work to consider control algorithms for multiple robots by exploiting spatial gradients of magnetic fields. This thesis makes contributions toward actuation, sensing and control of autonomous micro systems and provides technologies that will lead to the development of swarms of microrobots with a suite of manipulation and sensing capabilities working together to sense and modify the environment. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Mechanical Engineering & Applied Mechanics First Advisor Vijay Kumar
Deep Target Algorithms for Deep Learning

There are many algorithms for training shallow architectur es, such as peceptrons, SVMs, and shallow neural networks. Backpropagation (gradi ent descent) works well for a few layers but breaks down beyond a certain depth du e to the wellknown problem of vanishing or exploding gradients, and simi lar observations can be made for other shallow training algorithms. Here we intro duce a novel class of algorithms for training deep architectures. This class r educes the difficult problem of training a deep architecture to the easier problem of t raining many shallow architectures by providing suitable targets for each hidde n layer without backpropagating gradients, hence the name of deep target algorithms . This approach is very general, in that it works with both differentiable and non-d ifferentiable functions, and can be shown to be convergent under reasonable assumptio ns. It is demonstrated here by training a four-layer autoencoder of non-di fferentiable threshold gates and a a 21-layer neural network on the MNIST handwritte n digit dataset.
Avaliação do papel das células T reguladoras na imunomodulação da paracoccidiodomicose pulmonar estabelecida.

GALDINO, N. A. L. Immunomodulatory function of regulatory T cells during pulmonary paracoccidioidomycosis. 2017. 78 f. Master thesis (Immunology) Instituto de Ciências Biomédicas, Universidade de São Paulo, 2017. The importance of regulatory T cells (Treg) in maintaining tolerance to self antigens and controlling the immune response in various infections has been recently demonstrated. In many infectious diseases, Treg cells suppressive activity of the effector immune response has been associated with deleterious effects, however in other models this activity has been considered protective in order to control excessive inflammation and tissue pathology. In paracoccidioidomycosis (PCM), the most frequent systemic mycosis in Latin America caused by Paraccoccidioides brasiliensis (Pb), the role of Treg cells role in the infection has been recently studied. We used C57BL/6 transgenic mice for expression of the diphtheria toxin receptor (DTR) and GFP ("green fluorescent protein") under the influence of FoxP3 gene transcription factor, which is characteristic of Treg cells. Depletion was performed by treatment of C57BL/6 (DEREG) mice with diphtheria toxin after 3 weeks of infection with a million yeast by intratracheal surgery. Disease severity was assessed 6 weeks after the infection by characterizing the tissue fungal burden, CD8 immune development, CD4 (Th1/Th2/Th17) and histopathology. Over the infection period, DEREG mice treated with diphtheria toxin showed smaller number of Treg cells, which was associated with a lower fungal burden in the lung, liver and spleen, less tissue damage and reduced granulomas when compared to control mice (infected and treated saline). Additionally, we observed a greater influx of CD4 and CD8 effector cells in the lungs of DEREG mice followed by significant increase in cytokine production in Th1 and Th17 cell types when compared to control animals. Thus, this data indicates beneficial effects of depletion of regulatory T cells in PCM already established, for the first time. All results showed an improvement of the disease characterized by decreased in fungal burden and increased activation of CD4 immunity and CD8. More importantly, these findings highlight a immunotherapeutic treatment perspective for established PCM through immunomodulation of Treg cells, controlling the number and function of these population.
A study on growth parameters, blood factors and proximate composition of rainbow trout ( Oncorhynchus mykiss ) cultured in underground brackish and freshwater

In this research, growth rate, blood parameters and proximate composition of rainbow trout ( Oncorhynchus mykiss ) cultured in brackish and freshwater were evaluated. Two treatments (brackish and freshwater) in 3 replications were investigated. Six 1.5 m³ fiberglass tanks were used for this experiment, containing 180 rainbow trout specimens with average weight of 47.2±0.1g. Feeding rate in treatments was about 3% of body weight based on water temperature and fish biomass. Total length and weight of fish were measured in 15 days intervals. After 135 days of culture period, some fish samples were transferred to the Lab. Blood were directly collected from the heart. Muscle composition was analyzed for proximate composition. The results showed that the body weight of samples in brackish water was increased significantly after 45 days of culture period. Blood factors including WBC, RBC, Hb, Hct, MCV, MCH, MCHC, lymphocyte, thrombocyte and concentration of K + ions in fish cultured in brackish water showed a significant increase (P<0.05). Muscle composition analysis showed a significant increasing (p<0.05) in fat and dry matter in freshwater treatment, while moisture was significantly higher in brackish water (P<0.01)
Gaussian entanglement revisited

We present a novel approach to the separability problem for Gaussian quantum states of bosonic continuous variable systems. We derive a simplified necessary and sufficient separability criterion for arbitrary Gaussian states of m versus n modes, which relies on convex optimisation over marginal covariance matrices on one subsystem only. We further revisit the currently known results stating the equivalence between separability and positive partial transposition (PPT) for specific classes of Gaussian states. Using techniques based on matrix analysis, such as Schur complements and matrix means, we then provide a unified treatment and compact proofs of all these results. In particular, we recover the PPT-separability equivalence for: (i) Gaussian states of 1 versus n modes; and (ii) isotropic Gaussian states. In passing, we also retrieve (iii) the recently established equivalence between separability of a Gaussian state and and its complete Gaussian extendability. Our techniques are then applied to progress beyond the state of the art. We prove that: (iv) Gaussian states that are invariant under partial transposition are necessarily separable; (v) the PPT criterion is necessary and sufficient for separability for Gaussian states of m versus n modes that are symmetric under the exchange of any two modes belonging to one of the parties; and (vi) Gaussian states which remain PPT under passive optical operations can not be entangled by them either. This is not a foregone conclusion per se (since Gaussian bound entangled states do exist) and settles a question that had been left unanswered in the existing literature on the subject. This paper, enjoyable by both the quantum optics and the matrix analysis communities, overall delivers technical and conceptual advances which are likely to be useful for further applications in continuous variable quantum information theory, beyond the separability problem.
A HEXAGONAL NETWORK DIVISION APPROACH FOR REDUCING ENERGY HOLE ISSUE IN WSN

Energy hole issue in wireless sensor network is a critical issue due to the energy discharging of the sensor nodes in a rapid manner which are very close to the sink. This is because of nearer sensor nodes sending their own information to the sink as well as information getting from other regions. After sometime these sensor nodes die and incapable to transfer data to sink despite the fact energy still remained for other region’s nodes which further disturbs the network lifetime. In this article we propose Hexagonal Network Division Approach for solving energy hole issue.Initially network divided into concentric hexagon and each hexagon act as a different layer. Alsoeach hexagon divided into six equal portions. In the subsequentstagelarger layer will be selected among all other layers. Now to decrease additional energy discharging from this layer,numerous sensor nodes positioned. In the finalphase to prevent the energy holeissue, a suitable directing and ordering have been done which further improves network lifetime.
Accelerated Inference of Positive Selection on Whole Genomes

Positive selection is the tendency of beneficial traits to increase in prevalence in a population. Its detection carries theoretical significance and has practical applications, from shedding light on the forces that drive adaptive evolution to identifying drug-resistant mutations in pathogens. With next-generation sequencing producing a plethora of genomic data for population genetic analyses, the increased computational complexity of existing methods and/or inefficient memory management hinders the efficient analysis of large-scale datasets. To this end, we devise a system-level solution that couples a generic out-of-core algorithm for parsing genomic data with a decoupled access/execute accelerator architecture, thereby providing a method-independent infrastructure for the rapid and scalable inference of positive selection. We employ a novel detection mechanism that mostly relies on integer arithmetic operations, which fit well to FPGA fabric, while yielding qualitatively superior results than current state-of-the-art methods. We deploy a high-end system that pairs Hybrid Memory Cube with a mid-range FPGA, forming a high-throughput streaming accelerator that achieves 751x, 62x, and 20x faster analyses of simulated genomes than the widely used software tools SweepFinder2 (1 thread), OmegaPlus (40 threads), and SweeD (40 threads), respectively. Importantly, our solution can scan thousands of human genomes and millions of genetic polymorphisms (1000 Genomes dataset, 5,008 samples) in a matter of hours, requiring between 4 and 22 minutes per autosome, depending on the chromosomal length.
Impact of Dietary Carbohydrate and Protein Source and Content on Swine Manure Foaming Properties

Abstract. Diet ingredients are thought to contribute to foaming problems associated with swine manure stored in deep-pit systems. Two experiments explored the impact of protein and carbohydrate sources and levels in swine diets on the physicochemical properties, methane production potential, and foaming potential of swine manure. The first experiment was specific to protein and evaluated the impact of dietary protein level and source on manure properties, while the second experiment focused on evaluating the impact of different dietary carbohydrate sources on manure foaming properties. Manure from the animals was tested for total and volatile solids, methane production rate and biochemical methane potential, surface tension, foaming capacity and stability, and microbial community structure. No single diet yielded manure with all of the anticipated qualities associated with foaming manure. However, manure collected from pigs fed diets containing soy hulls and distillers dried grains with solubles (DDGS) exhibited higher methane production rates (0.95 ±0.20 and 0.96 ±0.20 L CH 4 kg -1 VS, respectively) and biochemical methane potential (322 ±25 and 269 ±22 mL CH 4 g -1 VS, respectively) when compared to manure obtained from pigs fed the other diets. Additionally, the results showed that both protein level and source exhibited greater influence over the microbial community than carbohydrate source, with manipulations in the protein diet leading to positive correlations with specific microbial community and higher methane production rates, foaming capacity, and foam stability. In this study, these parameters appeared to be tied to higher levels of corn, or corn protein, in the diet. Although some of the microbial community was explained by diet, this study also demonstrated that factors other than diet have significant influence on microbial community.
The effect of a laser guided cane on stride length in patients with Parkinson's Disease

Parkinson’s disease (PD) is a progressive neurological disease leading to multiple impairments, including disorders of gait. Past research has shown that visual cues may be beneficial in increasing stride length. The current study tests the effects of a laser beam cane on stride length in patients with PD using a single-subject, randomized alternating-treatments research design with two additional replications. The three subjects were males, aged 67, 62, and 83. A GaitRite sensory mat was used to record stride length . Each subject walked this mat 16 times, with equal numbers of randomly assigned trials in each condition (laser on and laser off). The laser cane seemed to enhance stride length in a person with relatively serious impairment, but it had little functional effect on two subjects with relatively less impairment. Although the laser cane did not appear to enhance gait in the two younger, more able subjects, there was evidence that the laser cane was used to guide gait, in that cane placement tended to precede the stepping foot in the laser-on condition. Additional research is needed that focuses particularly on persons with relatively severe gait deficiencies associated with advanced PD. Laser cane and stride length 3 The effect of a laser guided cane on stride length in patients with Parkinson’s disease Every year approximately 40,000 Americans are diagnosed with Parkinson’s disease (PD), typically with onset between 55 and 60 years old (Parkinson’s Disease Foundation, 2006). The prevalence of PD in America is one percent of the population over sixty years old (Kaminsky, Dudgeon & Billingsley, 2007). There is a broad array of conditions that patients with PD tend to experience, which includes impairments in range of motion, strength, coordination in fine and gross motor skills, oral motor skills, mood, and, in advanced stages, certain mental processes (Forwell, Copperman & Hugos, 2007). With regard to motor symptoms, PD patients typically exhibit tremors, rigidity, bradykinesia (slow movement), and postural instability. These symptoms are progressive in nature; however, timing and severity do vary from person to person. Hoehn and Yahr (Hoehn & Yahr, 1967) identified five stages of PD. In the first stage a patient experiences unilateral symptoms, with little of no functional implications. A resting tremor is the most common symptom in stage one. In stage two there is midline or bilateral symptom involvement; mild trunk mobility and postural reflex problems are also likely. Stage three is often characterized by postural instability and mild functional loss. Stage four is classified as increased postural and functional disability. Although patients are generally able to walk, occupations of daily living become more challenging, especially due to increased difficulty with hand manipulation and dexterity. The individual is confined to a bed or a wheelchair in the
Speaker Position Detection System Using Audio-visual Information

This paper describes a speaker position detection system that achieves a high degree of accuracy using a multimodal interface that integrates audio and visual information from a microphone array and a camera. First, the system detects the position and angle of the microphone array relative to the camera position. Next, audio processing detects sound source positions and visual processing detects the positions of human faces. Finally, by integrating the sound source positions and face positions, the system determines the speaker’s position. The system can integrate audio and visual information, even if the spatial relationship between the microphone array and camera is initially unknown. The system achieves a high detection rate for the speaker’s position in a noisy environment.
Antirheumatic drug therapy and its effec ts on cardiovascular health

Objective: Rheumatoid arthritis (RA) is a disabling disorder associated with an increased incidence of cardiovascular disease (CVD) and a decreased life expectancy. This review investigates why CVD is more abundant in this population and how antirheumatic drug therapy affects cardiovascular (CV) health. Method: This review was conducted using the MEDLINE database. Results: Evidence supports inflammation playing a major role in progression of CVD. Traditional antirheumatic drug therapy combats these effects on the CV system, as methotrexate and anti-TNF drugs improve CV risk. Glucocorticoids remain controversial as they have a poor side effect profile, but some propose the anti-inflammatory effects are cardioprotective. Some COX-2 selective inhibitors have been associated with increased risk of CV events. HMG- CoA reductase inhibitors may be a future option for treating RA since they correct dyslipidemia and exhibit anti-inflammatory effects. Conclusion: Treatment plans for individuals with RA must be tailored to reduce RA progression and prevent CVD.