Miracle-dz/UniFAR-FTUs huggingface dataset:
Rain Fed Barley Seed Cultivars Identification Using Neural Network and Different Neurons Number

3 Abstract: Artificial neural network (ANN) models have found wide applications, including prediction, classification, system modeling and image processing. In order to identify of rain fed barely seed cultivars using artificial neural network with different neuron numbers of hidden layers, this research done in Islamic Azad University, Shahr-e-Ray Branch, during 2010 on 3 main rain fed barley cultivars were grown in different environments of Iran. Data on 22 features used to train and test ANN, so 6 colors, 12 morphological features and 4 shape factors used. These features fed Multilayer Perceptron (MLP) neural network. The topological structure of this MLP model consisted of 22 input neurons in the input layer, 3 output neurons (Sahand, Bolbol and Abidar) in the output layer and two hidden layers (30-10, 30-20 and 30-30). Accuracy average for classification of rain fed barley seeds was 80.00% and after doing feature selection with UTA increased to 82.22% in 22-30-30-3 structure. The results showed that combination of ANN, image analysis and the optimum model architecture 22-30-30-3 has excellent potential for cultivars identification.
Classification and Retrieval from Mailing Lists and Forums

In this paper, we address the tasks of classifying the posts of mailing lists and forums, and retrieving relevant information from the same (describing our participation in the FIRE 2010 evaluation sub-tasks for the same). We approach the classification problem in two ways. In the first method, we pose it as a sequence labeling task using conditional random fields (CRF), where the entire thread information is used to find the labels for each of the posts in that thread. In the second approach, we model it as a document classification task considering each post as an independent document and using a support vector machine (SVM) like classifier. We discuss the performances of both of these approaches, and also, the effect of different features that we used for training the CRFs. For the retrieval task, the approach we propose is to combine the text match score with a goodness score of the thread. This score is a measure of the quality of the resolution text contained in the thread. A classifier is employed to identify posts of the thread that could be a solution, and the goodness score is computed based on its contents.
Natural infection with Fasciola hepatica (Linnaeus, 1758) in the European bison (Bison bonasus) in Białowieża National Park, Poland

SummaryThe aim of our study was to determine prevalence of appearance of Fasciola hepatica L. among European bison Bison bonasus selected in Białowieża National Park, Poland from 2001 to 2006. The analyzed ITS-1 and ITS-2 sequences of nuclear DNA of Fasciola hepatica have a length of 433bp and an AT content of 47.8 % and a length of 365 bp and an AT content of 51.2 % respectively. Among 178 bison liver fluke was found in liver of 63 (35.3 %) individuals including as follow: in 39 (21.9 %) adults, 11 (6.1 %) juveniles and 13 (7.3 %) calves. Results of researches are as follow: prevalence of infection of Fasciola hepatica among bison in Białowieża National Park between 2001–2006 oscillated about 35.3%. The most extensive invasion of fascioliasis was observed in adult animals (21.9 %). We pay attention that between infected animals were calves 13 (7.3 %). Conclusions: The obtained results referring to infection of bison within the Białowieża National Park, Poland of F. hepatica are very interesting. The epidemiological importance of these finding is evident, demonstrating the fascioliasis expansion in restricted or protected areas, as National Parks such as Białowieża National Park, habitat of this wild host, and where no domestic animals are present.
The self-management of chronic illnesses: Theories and technologies

A patient who is self-managing a chronic condition is actively involved in the prevention, understanding and regulation of his symptoms by means of his behavior and choices. We present the most acknowledged self-management theories and strategies, with an accent on those that can be relevant for supporting ICT solutions for Healthcare. Finally we review five projects about sensing technologies, mobile devices and ambient information displays for self-care and behavior change.
Multiple Visualizations of the Personal Promotion Histories of Court Nobles in Japan

This paper proposes methods of visualizing the personal promotion records of Japanese government officials in medieval times.Our research is aimed at developing multiple visualization tools that facilitate discovery of exceptional promotions as a way of highlighting interesting or important factors behind historical events, or that enable comparison of various promotion patterns to aid in understanding the political background the period. We have developed the following tools for visualizing personal promotion records:(i) the Personal Promotion Graph (PPG-I) enables visualization of the promotional transition of court nobles in medieval Japan, and (ii) the Promotion Pattern Graph (PPG-II) shows various promotion patterns that help reveal typical patterns of promotion for court nobles in relation to a particular period or family. There are many techniques for visualizing data in different ways. These tools provide variations in viewpoints for analyzing the political background of the era and help trigger consideration of different scenarios.
A multiresolution EM algorithm for unsupervised image classification

Using the causal Markov model defined on a quadtree we derive a multiresolution EM algorithm for unsupervised image classification. This algorithm is an efficient alternative to expensive or approximate EM algorithms associated with Markov random fields (MRFs). We show on synthetic and real images that our algorithm also provides good or even better results than those obtained by spatial MRF models.
Nested Monte Carlo EM algorithm for switching state-space models

Switching state-space models have been widely used in many applications arising from science, engineering, economic, and medical research. In this paper, we present a Monte Carlo Expectation Maximization (MCEM) algorithm for learning the parameters and classifying the states of a state-space model with a Markov switching. A stochastic implementation based on the Gibbs sampler is introduced in the expectation step of the MCEM algorithm. We study the asymptotic properties of the proposed algorithm, and we also describe how a nesting approach and the Rao-Blackwellized forms can be employed to accelerate the rate of convergence of the MCEM algorithm. Finally, the performance and the effectiveness of the proposed method are demonstrated by applications to simulated and physiological experimental data.
Microarray missing values imputation methods: Critical analysis review

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.
Bacteria in Ostreococcus tauri cultures – friends, foes or hitchhikers?

Marine phytoplankton produce half of the oxygen we breathe and their astounding diversity is just starting to be unraveled. Many microbial phytoplankton are thought to be phototrophic, depending solely on inorganic sources of carbon and minerals for growth rather than preying on other planktonic cells. However, there is increasing evidence that symbiotic associations, to a large extent with bacteria, are required for vitamin or nutrient uptake for many eukaryotic microalgae. Here, we use in silico approaches to look for putative symbiotic interactions by analysing the gene content of microbial communities associated with 13 different Ostreococcus tauri (Chlorophyta, Mamilleophyceae) cultures sampled from the Mediterranean Sea. While we find evidence for bacteria in all cultures, there is no ubiquitous bacterial group, and the most prevalent group, Flavobacteria, is present in 10 out of 13 cultures. Among seven of the microbiomes, we detected genes predicted to encode type 3 secretion systems (T3SS, in 6/7 microbiomes) and/or putative type 6 secretion systems (T6SS, in 4/7 microbiomes). Phylogenetic analyses show that the corresponding genes are closely related to genes of systems identified in bacterial-plant interactions, suggesting that these T3SS might be involved in cell-to-cell interactions with O. tauri.
Learning ordinal discriminative features for age estimation

In this paper, we present a new method for facial age estimation based on ordinal discriminative feature learning. Considering the temporally ordinal and continuous characteristic of aging process, the proposed method not only aims at preserving the local manifold structure of facial images, but also it wants to keep the ordinal information among aging faces. Moreover, we try to remove redundant information from both the locality information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation. Finally, we formulate these two issues into a unified optimization problem of feature selection and present an efficient solution. The experiments are conducted on the public available Images of Groups dataset and the FG-NET dataset, and the experimental results demonstrate the power of the proposed method against the state-of-the-art methods.
Develop an algorithm for image forensics using feature comparison and sharpness estimation

The paper presents a new method to detect the forgery in Copy-Move images using feature comparison to find the similar parts and sharpness estimation to collect the suspicious edges. Besides, one-level DWT decomposition, with the role of multiresolution, is used to limit the computational complexity and morphological operation is applied for presenting counterfeit objects. Both feature comparison and sharpness are done in overlapping 16×16 blocks of LL and HH sub-bands. Searching similar blocks in approximation part by modified Zernike Moments and calculating the sharpness of edges in HH sub-band confirm the copy-move objects in image. Faked regions have features similar to any other image's region and sharpness at boundaries higher than at other edges in the image. The combination of LL and HH sub-band can improve the reliability of the detection. The suspicious regions are then colored by opening and closing of morphological analysis and shown by logical operator "AND" to the tested image. The fact that similar regions can be detected and defined to be copy-move or same texture is the novelty of the proposed method. Results are simulated in Matlab environment with high accuracy and also compared to some related methods.
Radiographically occult lung cancer treated with fibreoptic bronchoscopic electrocautery: a pilot study of a simple and inexpensive technique.

The curative potential of bronchoscopic intervention, e.g. photodynamic therapy (PDT) and brachytherapy, for resectable radiographically occult lung cancer has been reported previously. Bronchoscopic electrocautery is currently feasible using an insulated flexible bronchoscope to coagulate and vaporize tumour tissue. Since the lesions are usually small, noninvasive bronchoscopic electrocautery may be able to eradicate radiographically occult lung cancer completely. In a prospective study, 13 patients with 15 radiographically occult lung cancer lesions were treated with bronchoscopic electrocautery. The duration of follow-up was > or = 16 months. The median age of the patients was 69 yrs (range 48-79 yrs). Fibreoptic bronchoscopy under local anaesthesia was used to coagulate the occult lung cancer. Approximately 30 W of energy was applied until visible necrosis of the tumour area became apparent. There were no immediate complications. In 10 patients with 12 lesions, a complete response (CR) was obtained (CR rate 80%; 95% confidence interval (95% CI) 52-96%). Median duration of follow-up was 21 months (range 16-43 months). Bronchoscopic electrocautery did not obtain a CR in the remaining three patients, but PDT also failed to achieve CR. Two patients underwent radical resection, and the tumours were histologically confirmed to be more invasive. One patient received external radiotherapy. Three patients with a CR died during follow-up, two as a result of myocardial infarction and apoplexy, and one because of metastasis from his previously resected T3N1 primary large cell cancer. Current data show bronchoscopic electrocautery to be equally effective and potentially as curative as photodynamic therapy for treating patients with radiographically occult lung cancer. Obvious advantages are that it is an inexpensive and simple procedure, which does not cause photosensitivity.
Effect of sampling protocol and volunteer bias when sampling for macroinvertebrates

Abstract We evaluated the efficacy of different field sampling approaches for volunteers sampling macroinvertebrates in low-gradient streams. We used a series of analytical metrics to compare results using the Environmental Protection Agency (EPA) multihabitat, the Minnesota Pollution Control Agency multihabitat, and EPA single-habitat sampling protocols. We also investigated the effect of 2 scenarios in which volunteers fail to follow (and potentially bias) the widely used EPA multihabitat protocol by including either more snag and vegetated banks or more run and riffle habitat than prescribed by the protocol. We collected jab samples from cobble, snags, vegetated banks, submerged macrophytes, and sand in 4 contiguous 125-m reaches in an Anoka sand-plain stream in Minnesota. We identified up to 100 macroinvertebrates in each jab sample to family. We subjected a parent population of 40 jab samples/reach to a bootstrap analysis to sample and create metric or index scores 100 times without replacement for each of the 3 volunteer sampling methods and 2 biased scenarios. The EPA multihabitat protocol and the biased scenario in which woody debris and bank vegetation were oversampled yielded the highest diversity of organisms, whereas the biased scenario in which riffle and run habitats were oversampled yielded the lowest diversity. The EPA multihabitat protocol used correctly was more likely to indicate “good” water quality (on the basis of the EPA muddy-bottom narrative assessment tool designed for volunteers) than either biased sampling scenario. This result illustrates that poor field methods could result in underestimation of water quality.
A real time algorithm based cascade multilevel inverter with step modulation integrated upon ANFIS based Solar MPPT

Building Integrated Photovoltaic cell has become the emerging Trend in the area of power electronics and energy conversion. This paper pay attention on the interfacing of MPPT based Photovoltaic cell with Cascade Multilevel Inverter. Integrated Solar panel has efficiency losses due to rapid variation in light intensity and temperature of the panel. This control issue on PV side is handled by an efficient Adaptive neuro fuzzy [ANFIS] based intelligent MPPT control algorithm. The system also comprises of a power conditioning unit (Inverter). The switching angle calculation for the inverter is determined by a novel algorithm. The optimization aim focuses on the reduction of total harmonic distortion of Multilevel Inverter.
Service-oriented storage resource architecture for cloud computing

Cloud computing, as an emerging computing model, is drawing much attention from academia and industry. Storage resource, computing resource and network resource are three basic resources in a cloud computing infrastructure. How to utilize these resources efficiently and conveniently is a big challenge for building a good cloud computing infrastructure. This paper presents a service-oriented storage resource architecture intended to enable efficient storage resource utilization and management in a cloud computing infrastructure. A Web Service-based hierarchical Storage Resource Management approach is introduced into the design and implementation of such a system, and a concept-proofing system built on LAN and Linux has been developed. A WAN-based prototyping system is under development. Another idea of this paper is that it hopes to bring in a new prospective for the research of cloud computing infrastructure.
Photobiostimulation effects caused by a high power IR laser with λ=850nm in wheat (Triticum aestivum L)

This paper has as principal objective to determine the value of some parameters of light that are determinants in the activation or inhibition of the growth stimulus of wheat seeds (Triticum aestivum L). In this case intensity and dose, as important parameters, were considered. GaAlAs laser with lambda=850 nm was used to irradiate the biological system. The growth of main shoot was considered. All data were processed using statistical methods. Results obtained indicate that when the appropriate light parameters are used activation can occur.
System Justification, Environmental Perceptions and Sustainable Behaviour in Sweden

Consumers’ knowledge that unsustainable consumption has a negative effect on the environment does not hinder their engagement in unsustainable consumption patterns. Applying system justification theory, it was of interest to investigate its impact on Swedish citizens’ attitudes and behaviours related to sustainable consumption. This study intended to discover why Swedish consumers do not always engage in pro-environmental behaviours. An online questionnaire was composed to measure levels of consumer habits, environmental concern, system justifying tendencies, competence and autonomy support, and identification with the state among Swedish consumers. To investigate the hypothesis that Swedish consumers hold high misconceptions of what sustainability is, participants’ open-ended answers were compared to the Brundtland definition of sustainability. All proposed associations were explored using ANOVA, correlation analysis and regression analysis. System justification tendencies was, as hypothesised, associated with higher levels of identification with the state. The hypothesized positive relationship between system justifying tendencies and consumer habits was not significant. Neither was the proposed impact of system justification tendencies on consumer behaviour. Findings showed that Swedish consumers hold relatively large misconceptions of what sustainable consumption is. Also, levels of environmental concern, competence support and gender were found to be good predictors of consumer behaviour in the current study. Implications with these findings and limitations with the study are discussed. (Less)
Results and Findings I. Theory of Linear-time Subset Scanning Ii. Univariate Linear-time Subset Scanning for Spatial Datasets

In Neill (2011a), we present formal proofs of the linear-time subset scanning property for a large class of score functions, using the " proof by inclusion " and " proof by substitution " methods described in the " Research Activities " section. First, we show (via proof by inclusion), that if F(S) can be written as a function of two additive sufficient statistics X(S) = ∑ i∈S x i and Y(S) = ∑ i∈S y i , and if F(X,Y) is monotonically increasing with X, quasi-convex, and restricted to positive Y, then F(S) satisfies the LTSS property. As a corollary, this set of sufficient conditions allows us to prove that Kulldorff's (1997) spatial scan statistic satisfies LTSS, as do the more recently proposed expectation-based Poisson (Neill et al., 2005) and Gaussian (Neill, 2006) statistics. We also present a counterexample which shows that these functions do not satisfy the strong LTSS property. Second, we show (via proof by substitution) that if F(S) can be written as F(X(S), |S|) for a single additive sufficient statistic X(S) = ∑ i∈S x i , and if F(X) is monotonically increasing with X, then F(S) satisfies the (strong) LTSS property. As a corollary, this set of sufficient conditions for LTSS allows us to prove that the nonparametric scan statistics (Neill and Lingwall, 2007) satisfy LTSS, as does a simplified version of the Bayesian scan statistic proposed by Neill et al. (2006). Finally, we show that LTSS holds for a large class of expectation-based scan statistics, likelihood ratio statistics for which the alternative hypothesis H 1 (S) assumes a constant multiplicative increase in the expected values in region S. In particular, we show that LTSS holds for any distribution in the separable exponential family, including the expectation-based Poisson, Gaussian, and exponential scan statistics, as well as a novel scan statistic used to detect changes in the variance of a Gaussian distribution with unknown mean. In Neill (2011a) we evaluate the run time, detection power, and spatial accuracy of our fast proximity-constrained subset scan approaches, using simulated disease outbreaks injected into real-world hospital Emergency Department data from 97 Allegheny County zip codes. With LTSS, run time increases linearly with neighborhood size, up to a maximum of 0.05 seconds per day of data for the parametric scan methods, and 0.07 seconds for the nonparametric scan. Without LTSS, run time increases exponentially with neighborhood size (k), requiring approximately 30 …
Early Literacy and Numeracy Skills in Bilingual Minority Children: Toward a Relative Independence of Linguistic and Numerical Processing

Many studies have suggested that the concept of “number” is relatively independent from linguistic skills, although an increasing number of studies suggest that language abilities may play a pivotal role in the development of arithmetic skills. The condition of bilingualism can offer a unique perspective into the role of linguistic competence in numerical development. The present study was aimed at evaluating the relationship between language skills and early numeracy through a multilevel investigation in monolingual and bilingual minority children attending preschool. The sample included 156 preschool children. Of these, 77 were bilingual minority children (mean age = 58.27 ± 5.90), and 79 were monolinguals (mean age = 58.45 ± 6.03). The study focused on three levels of analysis: group differences in language and number skills, concurrent linguistic predictors of early numeracy and, finally, profile analysis of linguistic skills in children with impaired vs. adequate numeracy skills. The results showed that, apart from the expected differences in linguistic measures, bilinguals differed from monolinguals in numerical skills with a verbal component, such as semantic knowledge of digits, but they did not differ in a pure non-verbal component such as quantity comparison. The multigroup structural equation model indicated that letter knowledge was a significant predictor of the verbal component of numeracy for both groups. Phonological awareness was a significant predictor of numeracy skills only in the monolingual group. Profile analysis showed that children with a selective weakness in the non-verbal component of numeracy had fully adequate verbal skills. Results from the present study suggest that only some specific components of language competence predict numerical processing, although linguistic proficiency may not be a prerequisite for developing adequate early numeracy skills.