Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between dietary fibre intake and asthma (symptoms and control): results from the French national e-cohort NutriNet-Santé

Abstract Despite growing evidence suggesting a potential health benefit of high fibre intake for the prevention of chronic inflammatory diseases, studies regarding the role of total dietary fibre intake, types and sources of fibre on asthma are lacking. The present study aimed to investigate the cross-sectional association of dietary fibre intakes and source of fibre with the asthma symptom score and asthma control. A total of 35 380 participants from the NutriNet-Santé cohort were included. Asthma was defined by the asthma symptom score and asthma control by the asthma control test. Fibre intake (g/d) was categorised according to sex-specific quintiles. Multi-adjusted negative binomial regressions were used to evaluate the association between dietary fibre with the asthma symptom score and logistic regressions with asthma control. Participants were aged on average 54 years. After adjustment for confounders, higher intake of total, soluble, insoluble fibres from cereals, fruit and seeds were significantly negatively associated with the asthma symptom score both among women and men; OR for the highest quintile of total dietary fibre compared with the lowest quintile were 0·73 (95 % CI 0·67, 0·79) in women and 0·63 (95 % CI 0·55, 0·73) in men. We also found inverse significant associations between total, soluble and insoluble fibre with uncontrolled asthma; OR for participants in the highest quintile of total dietary fibre was 0·72 (95 % CI 0·55, 0·95) in women and 0·45 (95 % CI 0·26, 0·79) in men. Our results suggested that higher intake of dietary fibre, mostly insoluble fibre and fibre from cereals, was associated with fewer asthma symptoms and greater asthma control.
Feature Selection based on Rough Sets and Minimal Attribute Reduction Algorithm

Numerous studies have focused on feature selection using many algorithms, but most of these algorithms encounter problems when the amount of data is large. In this paper, we propose an algorithm that handles a large amount of data by partitioning the data to process a reduction, and then selecting the intersection of all reducts as a stable reduct. This algorithm is successful but may suffer from loss of information if the samples are unsuitable. The proposed algorithm is based on discernibility matrix and function. Furthermore, the method can address the case in which the data consist of a significant amount of information. Our results show that the proposed algorithm is powerful and flexible enough to successfully target a range of different domains and can effectively reduce computational complexity as well as increase reduction efficiency. The efficiency of Proposed Algorithm is illustrated by experiments with UCI datasets further.
A Dynamic Boundary Ductile-Fracture-Propagation model for CO$_2$ pipelines

The development and testing of a dynamic boundary ductile fracture propagation model for pressurised CO 2 pipelines is presented. The model accounts for all the important fluid-structure interactions governing the fracture process. These include expansion wave propagation, real fluid behaviour, pipe/wall friction, heat transfer as well as the rapidly diminishing dynamic loading effects as the crack tip opens. The resistance to crack tip propagation is determined based on the Drop Weight Tear Test energy approach. The performance of the fracture model is tested by comparison of its predictions of the crack propagation velocity versus crack length against real data. The latter include the High-Strength Line Pipe Committee, ECSC X100 and Alliance full scale burst tests conducted for pipes containing either air or rich gas mixtures. In all cases good agreement is obtained between the model predictions and the real data. The validated model is used to test the propensity of a hypothetical but realistic pressurized CO 2 pipeline to ductile fracture propagation failure. The simulations indicate the remarkably significant role of the starting line temperature on fracture propagation in CO 2 pipelines.
Efficiency Evaluation of LP-MOLA, FLP-MOLA and GP-MOLA Mathematical-Spatial Optimization Models in Environmental Planning

Corresponding author. Tel. :+989131850756 E-mail address: heydari_moslem@yahoo.com Article info Abstract Article history: Received 06 Novemver 2016 Accepted 06 December 2016 Available online 1 Junuary 2017 In recent years, due to absence of integrated watershed management in most regions, natural resources have witnessed numerous damages with severe floods as one of its consequences which further leads to economic, social and environmental damages. Presently, there is no optimal utilization of land in most watersheds hence no optimal model matching with facilities and objectives is used in the watersheds. Thus, management and planning are essential for the proper utilization, protection and revival of these resources. This study aims to develop some mathematical-spatial optimum utilization environmental plans in watershed using LP, FLP and GP mathematical optimization approaches including environmental and economic objectives while considering social issues. About the LP model the results showed that the proposed model can decrease the amount of soil erosion to 9 percent and can increase benefit to 96% respectively. About the FLP model the results showed that this proposed model can decrease the amount of soil erosion to 12 percent and can increase benefit to 200% respectively. About the GP model the results showed that the land use optimization can increase benefit to 97% and decrease erosion to 12%, respectively. Furthermore the result of performing spatial optimization indicated that people are still making use of land that does not follow the optimized procedure in the current condition. It is obvious land use structure indicate some displacement of land uses because of optimal spatial allocation of them on the basis of FAO measurements. In addition to, the results demonstrated the efficiency of the proposed models.
Development of a Spatial Discount Task to Measure Impulsive Choices in Dogs

Simple Summary Impulsivity is believed to play a role in problematic behaviors in dogs. In this study, we developed a test to assess dogs’ tendency to make impulsive choices, that is their preference for smaller immediate reward instead of larger, but harder to obtain ones. Dogs were first trained that a bowl presented on a certain side always contained a large food amount, whereas the one presented on the opposite side (although at the same distance from the dog) contained less food. Then, the bowl with less food was progressively placed closer to the dog. As expected, dogs’ choices to feed from the bowl with less food increased as the distance of the latter decreased. Choices did not depend on factors that could interfere, such as dogs’ level of motivation for food, training experience, or learning ability. This indicates that the test is likely to be actually assessing impulsivity, not other traits. Also, female dogs were more likely to make impulsive choices than males, in accordance with what is known in humans and rodents, supporting the validity of the test. The test was completed in less than 1 h, making it a valid option to assess impulsivity in dogs in various contexts. Abstract Impulsive choices reflect an individual’s tendency to prefer a smaller immediate reward over a larger delayed one. Here, we have developed a behavioural test which can be easily applied to assess impulsive choices in dogs. Dogs were trained to associate one of two equidistant locations with a larger food amount when a smaller amount was presented in the other location, then the smaller amount was placed systematically closer to the dog. Choices of the smaller amount, as a function of distance, were considered a measure of the dog’s tendency to make impulsive choices. All dogs (N = 48) passed the learning phase and completed the entire assessment in under 1 h. Choice of the smaller food amount increased as this was placed closer to the dog. Choices were independent from food motivation, past training, and speed of learning the training phase; supporting the specificity of the procedure. Females showed a higher probability of making impulsive choices, in agreement with analogue sex differences found in human and rodent studies, and supporting the external validity of our assessment. Overall, the findings support the practical applicability and represent a first indication of the validity of this method, making it suitable for investigations into impulsivity in dogs.
A soluble FcγR homolog inhibits IgM antibody production in ayu spleen cells

Classical Fc receptors (FcRs) mediate the binding to and recognition of the Fc portion of antibodies and play an important role during immune responses in mammals. Although proteins similar to soluble FcRs have been identified in fish, little is known about the role of such proteins in fish immunity. Here, we cloned a cDNA sequence encoding a soluble Fc receptor for an immunoglobulin G (FcγR) homolog from ayu (Plecoglossus altivelis) (PaFcγRl). The predicted protein was composed of two immunoglobulin C2-like domains but lacked a transmembrane segment and a cytoplasmic tail. The PaFcγRl transcripts were distributed at low levels in all tested tissues, but significantly increased after Vibrio anguillarum infection. The PaFcγRl protein was expressed in the head kidney, trunk kidney, and neutrophils. Recombinant PaFcγRl (rPaFcγRl) was secreted when transfected into mammalian cells and the native protein was also detected in serum upon infection. rPaFcγRl was also demonstrated to bind to ayu IgM, as assessed by cell transfection. Suppressive activity of the recombinant mature protein of PaFcγRl (rPaFcγRlm) on in vitro anti-sheep red blood cell (SRBC) responses was detected by a modified hemolytic plaque forming cell assay. In conclusion, our study revealed that PaFcγRl is closely involved in the negative regulation of IgM production in the ayu spleen.
Skin color detection by fissive K-means clustering method

The complex image background and the varying lighting conditions lead to the low detection rate, this paper presents a novel color detection method to solve this problem. The Gray World color balance is carried out to compensating the light before detecting the skin area. After that choose to create the high detection efficiency ellipse model for skin color detection, then the paper detects skin color area on the K-means clustering using split(FKM)for the second sentence to make the detection more accurate. The experiment results show that this detection algorithm can detect skin area accurately and efficiently, and it has higher accuracy and robustness.
A novel hybrid deep learning scheme for four-class motor imagery classification

Objective. Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of classes and increased variability from different people. In this study, a four-class MI task is investigated. Approach. An end-to-end novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed algorithm consists of two parts: a. A one-versus-rest filter bank common spatial pattern is adopted to preprocess and pre-extract the features of the four-class MI signal. b. A hybrid deep network based on the convolutional neural network and long-term short-term memory network is proposed to extract and learn the spatial and temporal features of the MI signal simultaneously. Main results. The main contribution of this paper is to propose a hybrid deep network framework to improve the classification accuracy of the four-class MI-EEG signal. The hybrid deep network is a subject-independent shared neural network, which means it can be trained by using the training data from all subjects to form one model. Significance. The classification performance obtained by the proposed algorithm on brain–computer interface (BCI) competition IV dataset 2a in terms of accuracy is 83% and Cohen’s kappa value is 0.80. Finally, the shared hybrid deep network is evaluated by every subject respectively, and the experimental results illustrate that the shared neural network has satisfactory accuracy. Thus, the proposed algorithm could be of great interest for real-life BCIs.
The GRID superscalar project: current status

GRID superscalar is a Grid program- ming environment that allows to easily program ap- plications that will be eciently run on a computa- tional Grid. Is able to parallelise, at runtime and at task level, a sequential application and execute it in the Grid. The used approach is able to take benefit from those applications that are composed of coarse grained tasks. These tasks can be the size of a sim- ulation, a program, a solver. These kinds of appli- cations are very common in bioinformatics, computa- tional chemistry and other scientific fields. From the very initial prototype in Condor, GRID superscalar has evolved in a robust framework based in Globus and other middlewares. The eort of the GRID su- perscalar project goes beyond the Grid computing field, tackling now the newest field of programming multi-core chip's platforms. This paper describes the currently available versions of GRID superscalar. Keywords— Grid computing, Grid programming models, Grid workflows, Cell BE processor, multi- core programming models
The association between personal history and the outcomes of transobturator sling surgery.

OBJECTIVE This study aims to verify the association between risk factors for the onset of SUI and transobturator suburethral sling surgical treatment outcomes. PATIENTS AND METHODS A retrospective study was conducted with 57 patients operated by the Pelvic Floor Surgery Service. Demographic data were compiled from the sample, the body mass index (BMI) was calculated, and the patients were divided according to the response to the surgical treatment. RESULTS A total of 77.2% of the sample was cured or improved after surgical treatment. Out of the total sample, 75.4% of the women were postmenopausal, and 73.7% denied current or past smoking. The median age was 61 years, the median number of births was 4.0, the median BMI was 28.6 kg/m2, and 50.9% of the sample was classified as pre-obese. BMI, menopausal status, age, smoking, and sexual activity were not factors associated with the surgical outcome. However, parity equal to or greater than 5 was associated with worse postoperative results (p = 0.004). CONCLUSIONS among risk factors associated with the emergence of SUI, only parity greater than 4 showed a negative impact on transobturator sling surgery outcomes.
The Interaction with Disabled Persons Scale: translation and cross-cultural validation into Greek

Abstract Aim Cross-cultural adaptation of the Interaction with Disabled Persons Scale (IDPS) into Greek. Methods The IDPS was forward and back-translated by two bilingual physiotherapists and a Greek-English translator, respectively. Greek-speaking health professionals provided modifications in order to develop the final version of the Scale. Health professionals filled in the Greek version of the IDPS and the Caring Behaviors Inventory (for criterion validity). The factors of the Greek IDPS were extracted as well. After 8–10 days, the scale was re-distributed to the same health professionals (test–retest reliability) and to a general population sample (discriminant validity). Results Eighty-seven health professionals (36 ± 7.6 years) and 80 general population participants (44 ± 11.6 years) participated. There was a trend for the Greek IDPS variability to predict the variability of the Caring Behaviors Inventory (r 2 = 0.05; p = 0.054), but the coefficient of determination was low. An explanatory factor analysis extracted four factors explaining 66.66% of the total variance, confirmed by reliability analysis. The health care professionals had a significantly lower score than the general population in the Greek IDPS (mean difference: −11.0; confidence interval: −7.3 to −14.7), indicating familiarisation with the management of people with disability. The scale reliability and internal consistency were excellent; ICC(2,1) = 0.92 (confidence interval: 0.87–0.95) and Cronbach’s α = 0.96 respectively. No ceiling or floor effects were observed. Conclusions Substantial validity and reliability were observed for the Greek IDPS to assess Greek health professionals’ attitudes towards people with disabilities. Implications for rehabilitation The Greek IDPS version was shown to be comprehensible, and has demonstrated a sufficient amount of validity and reliability for assessing the perceptions and attitudes of Greek health professionals towards people with disabilities. Exploring attitudes towards people with disabilities in Greek-speaking populations, especially health professionals, with a scale such as the Greek IDPS is very important as it can help promote positive changes in approaches towards disability.
Hand Gesture Recognition Based on HOG Characters and SVM

Hand gesture recognition has abundant application fields,and the change of fields will result great influence to the result of recognition.In all gesture recognition methods,the one which based on computer vision is most sensitive to environment,such as too bright or too dark light,complex background,hand gesture rotation,etc.In order to overcome the impact of environment,histograms of oriented gradient which is being used in target detection research widely is used in hand gesture recognition.This method makes hand gesture recognition based on computer vision be sensitive to environment no longer,and a good recognition effect has been achieved.
INTERACTING MULTIPLE MODEL ALGORITHM WITH NEURAL NETWORKS

The interacting multiple model(IMM) algorithm has been shown to be one of the most effective estimation algorithms in the field of maneuvering target tracking. “Current”statistical model filtering algorithm is modified by neural networks and then adopted in IMM algorithm.The tracking performance for maneuvering target can be improved . The results of the modified IMM algorithm are evaluated through Monte Carlo simulation.
Fast Method for EEG Feather Extraction and Pattern Classification

In the study of brain-computer interfaces,a method of feature extraction and classification used for two kinds of imaginations was proposed. It considered Euclidean distance between mean traces recorded from channels of the two kinds as a feature,and determined imagination classes using threshold value. The background of experiment and theoretical foundation were analyzed referring to data sets of BCI 2003,and the classification error was compared with the best result of the competition. It is showed that the method has a better effectivity and advantage for applying to practical systems.
Design and Study of Web Application Framework Based on Struts

Using system framework in the Web application development, developers can simplify the process of developing software and improve the quality and the maintain ability of software. To realize the development of a Web application based on MVC, the paper develops a framework, which is based on Struts framework and J2EE core design patterns. A Web application based on this framework is designed in a short period.
Feeding Value of Western Canadian Oilseed and Biodiesel Co-products

Who can afford to feed fat(s) now-a-days? With fat prizes at record high, oilseed and biodiesel coproducts offer the only cost-effective alternative to supplementing dietary fats in monogastric animal diets. Cost per Mcal NE of residual oil content has changed our paradigm from considering canola as a traditional supplemental protein source to a novel dietary energy source. Expeller-pressed (~12% oil), extruded-pressed (~17% oil), and screw-pressed (> 20% oil) canola meal or cake, the latter two processed locally, offer opportunities to improve feed mill margins and reduce producers‟ feed cost.
Retracted: New collaborative platform for managing users in using the spectrum bands on cognitive radio networks

Cognitive (or smart) Radio Networks (CRNs) are an innovative approach to wireless engineering in which radios are designed with an unprecedented level of intelligence and agility. The CRNs have imperative component as called spectrum sensing. These networks can enable the secondary users (SUs) for reusing or detecting of idle spectrum bands through this component. This paper proposes a new collaborative platform of this spectrum to employ in resource‐constrained CRNs. In this case, each SU has a certain transmission cost that it is subtracted from a limited resource budget. Deciding and managing of licensed SUs that can transmit without exceeding the resource budget is the paper goal. To handle of a wide variety of resources the proposed schema creates a general platform. These resources can be limited data rate, limited tolerable interference, etc. This platform is based on modeling the CRN as a normal graph. Our simulation results show that the proposed platform has many advantageous features such as achieving fairness amongst SUs, reasonable amount of message exchange, favoring SUs that have reliable connectivity with the Primary User (PU), in addition to high detection rate.
Survey on Review Analysis of Sentence

the opinion is a view formed about something not necessarily based on fact or knowledge. The opinion mining is use for tracking the mood of the public about a particular thing. The opinions that are given on various online shopping websites, portals and media (e.g. e-paper, blogs) are the important benchmark in product or website success. Bag of words is the most accepted approach to model text in statistical machine learning approach but it has polarity shift problem. For that purpose we can propose a model which called as three class sentiment analysis. In this we can propose a data expansion technique for making dual sentiment review for each training and text review. In this we use dual training and dual prediction algorithm for sentiment classification by considering both side of review or sentiment. Keywords— Natural Language Processing, Dual Sentiment Analysis, Bag of Word, opinion mining.
Image Fusion Using DWT & PCA

Image fusion is of great importance in defence and information from multiple images of same scene. The result of fusion is a new image which is more suitable for human and machine perception. Pixel level image fusion using wavelets and principal component analysis has been implemented and demonstrated. different performance metrics with and without reference image are implemented to evaluate the performance of mage fusion algorithms. It has been concluded that image fusion using wavelets with higher level of decomposition showed better performance in some metrics and in other metrics PCA showed better performance.
Neobit@ -High Reliable Logic Non-Volatile Memory (NVM)

In this paper, a new embedded QTP bit-cell (Neobit) is presented for one-/multiple-time programming (OTPIMTP) application, which is fabricated by using generic logic process. Without additional processing or extra fabrication cost, this single-poly NVM can be implemented in various CMOS technologies such as logic, mixed-mode, analog, RF, HV and etc. Due to its easy incorporation with any IC design, this single-poly NVM can be comprehensively used for program storage in micro controllers, device identification in IC cards and network lC, code storage in RFlD tag, fuse device in memory redundancy, and RC trimming in analogiRF circuits.