LLukas22/scidocs huggingface dataset:
DEVELOPING A MODEL OF COOPERATING TEACHER EFFECTIVENESS

A trend in agricultural education has been a shortage of graduates from preservice agricultural education programs who choose to enter the teaching profession, thus causing a deficit in the number of qualified teachers to fill vacancies. When examining preservice agricultural education programs, student teaching is often the capstone experience, during which, the student teacher works closely with the cooperating teacher. Given that the student teaching experience is often the final semester of preservice agricultural education programs, it is reasonable to assume that satisfaction with that experience contributed to a student teacher’s decision to enter the teaching profession. Because the decision to enter teaching is made by the student teacher, insight into their perceptions of the student teaching experience, particularly the cooperating teacher, is invaluable. The purpose of this study was to develop a model of cooperating teacher effectiveness by replicating the work of Roberts and Dyer (2004). The Delphi method was utilized with an expert panel (N = 13) of all the student teachers from the University of Florida. Thirty characteristics were identified and grouped into the categories of Teaching/Instruction, Professionalism, Student Teacher/Cooperating Teacher Relationship, and Personal Characteristics.
Rhodiola rosea for physical and mental fatigue: a systematic review

BACKGROUND
Rhodiola rosea (R. rosea) is grown at high altitudes and northern latitudes. Due to its purported adaptogenic properties, it has been studied for its performance-enhancing capabilities in healthy populations and its therapeutic properties in a number of clinical populations. To systematically review evidence of efficacy and safety of R. rosea for physical and mental fatigue.


METHODS
Six electronic databases were searched to identify randomized controlled trials (RCTs) and controlled clinical trials (CCTs), evaluating efficacy and safety of R. rosea for physical and mental fatigue. Two reviewers independently screened the identified literature, extracted data and assessed risk of bias for included studies.


RESULTS
Of 206 articles identified in the search, 11 met inclusion criteria for this review. Ten were described as RCTs and one as a CCT. Two of six trials examining physical fatigue in healthy populations report R. rosea to be effective as did three of five RCTs evaluating R. rosea for mental fatigue. All of the included studies exhibit either a high risk of bias or have reporting flaws that hinder assessment of their true validity (unclear risk of bias).


CONCLUSION
Research regarding R. rosea efficacy is contradictory. While some evidence suggests that the herb may be helpful for enhancing physical performance and alleviating mental fatigue, methodological flaws limit accurate assessment of efficacy. A rigorously-designed well reported RCT that minimizes bias is needed to determine true efficacy of R. rosea for fatigue.
Enhanced lipid peroxidation and platelet activation in the early phase of type 1 diabetes mellitus: role of interleukin-6 and disease duration.

BACKGROUND
To investigate early events possibly related to the development of diabetic angiopathy, we examined whether 8-iso-prostaglandin F2alpha (8-iso-PGF2alpha) formation, a marker of in vivo oxidant stress, is altered in different stages of type 1 diabetes (T1DM) and whether it correlates with the rate of thromboxane (TX) A2 biosynthesis, a marker of in vivo platelet activation. We also investigated the relationship between inflammatory markers and F2-isoprostane formation in this setting.


METHODS AND RESULTS
A cross-sectional study was performed in 23 insulin-treated patients aged <18 years with new-onset T1DM (<or=6 weeks, group A), matched for age and gender with 23 patients with stable disease (>1 year, group B). Urinary 8-iso-PGF2alpha and 11-dehydro-TXB2 were measured in all patients and in age- and gender-matched controls. Circulating interleukin-6 (IL-6), tumor necrosis factor-alpha, and C-reactive protein were also determined as markers of the inflammatory response. Fifteen of the 23 children in group A were reexamined after 12 months. Compared with either controls or group B, diabetic children in group A showed significantly higher levels of 8-iso-PGF2alpha, 11-dehydro-TXB2, IL-6, tumor necrosis factor-alpha, and C-reactive protein. Statistically significant correlations between IL-6 and both 8-iso-PGF2alpha (r=0.63, P<0.001) and 11-dehydro-TXB2 (r=0.51, P<0.01) were observed. The 15 patients reexamined after 1 year showed a significant reduction in lipid peroxidation and platelet activation (P<0.02 and P<0.001, respectively), consistent with reduced levels of IL-6 and tumor necrosis factor-alpha.


CONCLUSIONS
These results demonstrate that enhanced lipid peroxidation and platelet activation represent early events in T1DM that are possibly related to an acute inflammatory response. These noninvasive indexes may help in further examining T1DM pathophysiology and monitoring pharmacological interventions to interfere with disease development and progression.
Web Service Composition Based on QoS Rules

For workflow-based service composition approach, the relations between the Web service QoS and environments are usually not considered, so that the information about QoS for composite service selection is inaccurate. It makes the selected composite service inefficient, or even unexecutable. To address this problem, a novel service composition approach based on production QoS rules is proposed in this paper. Generally, it is very difficult to directly analyze how different kinds of environment factors influence the Web service QoS. We adopt “black-box” analysis method of optimizing composite services, discovering the knowledge such as “the QoS of one Web service will be higher in specific environments”. In our approach, the execution information of the composite service is recorded into a log first, which will be taken as the basis of the subsequent statistical analysis and data mining. Then, the timely QoS values of the Web services are estimated and the production QoS rules being used to qualitatively express the different performances of the Web service QoS in different environments are mined. At last, we employ the mined QoS knowledge of the Web services to optimize the composite service selection. Extensive experimental results show that our approach can improve the performance of selected composite services on the premise of assuring the selecting computation cost.
Impact of Capital Structure on Firms’ Financial Performance: Evidence from Pakistan

This research examines the impact of capital structure on firms’ financial performance in Pakistan of top 100 consecutive companies in Karachi Stock Exchange for a period of four years from 2006 to 2009. Exponential generalized least square regression is used to test the relationship between capital structure and firms’ financial performance. The results show that all the three variables of capital structure, Current Liabilities to Total Asset, Long Term Liabilities to Total Asset, Total Liabilities to Total Assets, negatively impacts the Earning before Interest and Taxes, Return on Assets, Earning per Share and Net Profit Margin whereas Price Earning ratio shows negative relationship with Current Liabilities to Total Asset and positive relationship is found with Long Term Liabilities to Total Asset where the relationship is insignificant with , Total Liabilities to Total Assets. The results also indicate that Return on Equity has an insignificant impact on Current Liabilities to Total Asset and Total Liabilities to Total Assets but a positive relationship exists with Long Term Liabilities to Total Asset. These results, in general, lead to the conclusion that capital structure choice is an important determinant of financial performance of firms. This is the first study in Pakistan examining the relationship between firms’ performance and capital structure of top 100 consecutive companies in Karachi Stock Exchange for a period of four years.
Exponentiated Gradient Versus Gradient Descent for Linear Predictors

We consider two algorithms for on-line prediction based on a linear model. The algorithms are the well-known gradient descent (GD) algorithm and a new algorithm, which we call EG. They both maintain a weight vector using simple updates. For the GD algorithm, the update is based on subtracting the gradient of the squared error made on a prediction. The EG algorithm uses the components of the gradient in the exponents of factors that are used in updating the weight vector multiplicatively. We present worst-case loss bounds for EG and compare them to previously known bounds for the GD algorithm. The bounds suggest that the losses of the algorithms are in general incomparable, but EG has a much smaller loss if only few components of the input are relevant for the predictions. We have performed experiments which show that our worst-case upper bounds are quite tight already on simple artificial data. ] 1997 Academic Press
The relationship between addiction to smartphone usage and depression among adults: a cross sectional study

BACKGROUND
Addiction to smartphone usage is a common worldwide problem among adults, which might negatively affect their wellbeing. This study investigated the prevalence and factors associated with smartphone addiction and depression among a Middle Eastern population.


METHODS
This cross-sectional study was conducted in 2017 using a web-based questionnaire distributed via social media. Responses to the Smartphone Addiction Scale - Short version (10-items) were rated on a 6-point Likert scale, and their percentage mean score (PMS) was commuted. Responses to Beck's Depression Inventory (20-items) were summated (range 0-60); their mean score (MS) was commuted and categorized. Higher scores indicated higher levels of addiction and depression. Factors associated with these outcomes were identified using descriptive and regression analyses. Statistical significance was set at P < 0.05.


RESULTS
Complete questionnaires were 935/1120 (83.5%), of which 619 (66.2%) were females and 316 (33.8%) were males. The mean ± standard deviation of their age was 31.7 ± 11  years. Majority of participants obtained university education 766 (81.9%), while 169 (18.1%) had school education. The PMS of addiction was 50.2 ± 20.3, and MS of depression was 13.6 ± 10.0. A significant positive linear relationship was present between smart phone addiction and depression (y = 39.2 + 0.8×; P < 0.001). Significantly higher smartphone addiction scores were associated with younger age users, (β = - 0.203, adj. P = 0.004). Factors associated with higher depression scores were school educated users (β = - 2.03, adj. P = 0.01) compared to the university educated group and users with higher smart phone addiction scores (β =0.194, adj. P < 0.001).


CONCLUSIONS
The positive correlation between smartphone addiction and depression is alarming. Reasonable usage of smart phones is advised, especially among younger adults and less educated users who could be at higher risk of depression.
A new VHDL-AMS simulation framework in Matlab

In this paper we present SAMSA, a new tool for the simulation of VHDL-AMS systems in Matlab. The goal is the definition of a VHDL framework in which analog/digital systems can be designed and simulated and new simulation techniques can be studied, exploiting both the powerful Matlab functions and Toolboxes.
Design and simulation of 16 Bit UART Serial Communication Module Based on VHDL

This paper describes the design of UART (universal asynchronous receiver transmitter) based on VHDL. As UART is consider as a low speed, low cost data exchange between computer and peripherals.[1].To overcome the problem of low speed data transmission , a 16 bit UART is proposed in this paper. It works on 9600bps baud rate. This will result in increased the speed of UART.Whole design is simulated with Xilinx ISE8.2i software and results are completely consistent with UART protocol. Keywords— Baud rate generator, HDL, ISE8.2i, Receiver, Serial communication, Transmitter, Xilinx.
An efficient watershed algorithm based on connected components

In this paper, a formal de"nition and a new algorithmic technique for the watershed transformation is presented. The novelty of the approach is to adapt the connected component operator to solve the watershed segmentation problem. The resulting algorithm is independent of the number of grey-levels, employs simple data structures, requires less error prone memory management, and issues a lower complexity and a short running time. However, the algorithm does not modify the principle of the watershed segmentation; the output result is the same as that of using any traditional algorithm which does not build watershed lines. ! 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Geothermal power production from abandoned oil wells

A simulation for the determination of geothermal power production from abandoned oil wells by injecting and retrieving a secondary fluid was performed. The analysis takes into consideration local geothermal gradients and typical well depths and pipe diameters. Isobutane is chosen as the secondary fluid, which is injected in the well at moderate pressures and allowed to heat up and produce vapor. The computational model that was developed takes into account mass, energy, and momentum conservation equations for the well flow, and the simulation helps determine the state of the fluid from injection to retrieval. It is observed that the operation of such systems attains a maximum power that depends on the temperature of the well bottom and the injection pressure. In general, 2–3 MW of electric power may be produced from wells that are typical in the South Texas region. Published by Elsevier Ltd.
Applications of visual evoked potentials and Fourier-domain optical coherence tomography in Parkinson's disease: a controlled study.

PURPOSE
The goal of this cross-sectional observational study was to quantify the pattern-shift visual evoked potentials (VEP) and the thickness as well as the volume of retinal layers using optical coherence tomography (OCT) across a cohort of Parkinson's disease (PD) patients and age-matched controls.


METHODS
Forty-three PD patients and 38 controls were enrolled. All participants underwent a detailed neurological and ophthalmologic evaluation. Idiopathic PD cases were included. Cases with glaucoma or increased intra-ocular pressure were excluded. Patients were assessed by VEP and high-resolution Fourier-domain OCT, which quantified the inner and outer thicknesses of the retinal layers. VEP latencies and the thicknesses of the retinal layers were the main outcome measures.


RESULTS
The mean age, with standard deviation (SD), of the PD patients and controls were 63.1 (7.5) and 62.4 (7.2) years, respectively. The patients were predominantly in the initial Hoehn-Yahr (HY) disease stages (34.8% in stage 1 or 1.5, and 55.8 % in stage 2). The VEP latencies and the thicknesses as well as the volumes of the retinal inner and outer layers of the groups were similar. A negative correlation between the retinal thickness and the age was noted in both groups. The thickness of the retinal nerve fibre layer (RNFL) was 102.7 μm in PD patients vs. 104.2 μm in controls.


CONCLUSIONS
The thicknesses of retinal layers, VEP, and RNFL of PD patients were similar to those of the controls. Despite the use of a representative cohort of PD patients and high-resolution OCT in this study, further studies are required to establish the validity of using OCT and VEP measurements as the anatomic and functional biomarkers for the evaluation of retinal and visual pathways in PD patients.
Undergraduate Computational Science and Engineering Education

It is widely acknowledged that computational science and engineering (CSE) will play a critical role in the future of the scientific discovery process and engineering design. However, in recent years computational skills have been de-emphasized in the curricula of many undergraduate programs in science and engineering. There is a clear need to provide training in CSE fundamentals at the undergraduate level. An undergraduate CSE program can train students for careers in industry, education, and for graduate CSE study. The courses developed for such a program will have an impact throughout the science, technology, engineering and mathematics (STEM) undergraduate curriculum. This paper outlines the content of a CSE curriculum, the skills needed by successful graduates, the structure and experiences of some recently-developed CSE undergraduate programs, and the potential career paths following a CSE undergraduate
The role of mirror neurons in speech perception and action word semantics

The discovery of mirror neurons in the macaque monkey has ignited intense interest in motor theories of cognition, including speech and language. Here we examine two such claims, that the perception of speech sounds critically depends on motor representations of speech gestures (the motor theory of speech perception) and that the representation of action-related semantic knowledge critically depends on motor representations involved in performing actions. We conclude that there is strong evidence against the claim that speech perception critically depends on the motor system and that there is no conclusive evidence in support of the view that the motor system supports action semantics. We propose instead that motor-related activity during perceptual processes stem from spreading activation in sensory-motor networks that are critical for speech and language production.
Iterative shrinking method for clustering problems

Agglomerative clustering generates the partition hierarchically by a sequence of merge operations. We propose an alternative to the merge-based approach by removing the clusters iteratively one by one until the desired number of clusters is reached. We apply local optimization strategy by always removing the cluster that increases the distortion the least. Data structures and their update strategies are considered. The proposed algorithm is applied as a crossover method in a genetic algorithm, and compared against the best existing clustering algorithms. The proposed method provides best performance in terms of minimizing intra-cluster variance. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
The Dichotomy for Conservative Constraint Satisfaction is Polynomially Decidable

Given a fixed constraint language Γ , the conservative CSP over Γ (denoted by c-CSP( Γ )) is a variant of CSP( Γ ) where the domain of each variable can be restricted arbitrarily. In [5] a dichotomy has been prove n for conservative CSP: for every fixed language Γ , c-CSP( Γ ) is either in P or NP-complete. However, the characterization of conservatively tractable languages i s of algebraic nature and the recognition algorithm provided in [5] is super-exponen tial in the domain size. The main contribution of this paper is a polynomial-time alg orithm that, given a constraint language Γ as input, decides if c-CSP( Γ ) is tractable. In addition, if Γ is proven tractable the algorithm also outputs its coloured graph, which contains valuable information on the structure of Γ .
Effects of yeast trehalose-6-phosphate synthase 1 on gene expression and carbohydrate contents of potato leaves under drought stress conditions

The development of drought-tolerant, elite varieties of potato (Solanum tuberosum L.) is a challenging task, which might be achieved by introducing transgenic lines into breeding. We previously demonstrated that strains of the White Lady potato cultivar that express the yeast trehalose-6-phosphate synthase ( TPS1) gene exhibit improved drought tolerance. We investigated the responses of the drought-sensitive potato cultivar White Lady and the drought-tolerant TPS1 transgenic variant to prolonged drought stress at both the transcriptional and metabolic levels. Leaf mRNA expression profiles were compared using the POCI microarray, which contains 42,034 potato unigene probes. We identified 379 genes of known function that showed at least a 2-fold change in expression across genotypes, stress levels or the interaction between these factors. Wild-type leaves had twice as many genes with altered expression in response to stress than TPS1 transgenic leaves, but 112 genes were differentially expressed in both strains. We identified 42 transcription factor genes with altered expression, of which four were uniquely up-regulated in TPS1 transgenic leaves. The majority of the genes with altered expression that have been implicated in photosynthesis and carbohydrate metabolism were down-regulated in both the wild-type and TPS1 transgenic plants. In agreement with this finding, the starch concentration of the stressed leaves was very low. At the metabolic level, the contents of fructose, galactose and glucose were increased and decreased in the wild-type and TPS1 transgenic leaves, respectively, while the amounts of proline, inositol and raffinose were highly increased in both the wild-type and TPS1 transgenic leaves under drought conditions. To our knowledge, this study is the most extensive transcriptional and metabolic analysis of a transgenic, drought-tolerant potato line. We identified four genes that were previously reported as drought-responsive in non-transgenic Andean potato cultivars. The substantial increases in proline, inositol and raffinose contents detected in both the wild-type and TPS1 transgenic leaves appears to be a general response of potatoes to drought stress. The four transcription factors uniquely up-regulated in TPS1 transgenic leaves are good candidates for future functional analyses aimed at understanding the regulation of the 57 genes with differential expression in TPS1 transgenic leaves.
Prediction of tractor repair and maintenance costs using Artificial Neural Network

The prediction of repair and maintenance costs has significant impacts on proper economical decisions making of machinery managers, such as machine’s replacement and substitution. In this article the potential of Artificial Neural Network (ANN) technique has evaluated as an alternative method for the prediction of machinery (specifically tractor) repair and maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry in Iran. Optimal parameters for the network were selected via a trial and error procedure on the available data. In this paper, the performance of Basic Back-propagation (BB) training algorithm was also compared with Back-propagation with Declining Learning-Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of tractor’s costs. The prediction of repair and maintenance cost components of tractors with a single network produced a better result than using separate networks for prediction of each cost component. It has been concluded that ANN represents a promising tool for predicting repair and maintenance costs. 2011 Elsevier Ltd. All rights reserved.