Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence and Distribution of some Aerobic Bacterial Agents Associated with High Chick Mortality in some Broiler Flocks in Egypt

A monitoring study was carried out on one hundred two broiler flocks of different breeds with housing capacity ranged from 1000 to 24000 birds located in nine Egyptian governorates (Sharkia, Giza, Gharbia, Fayoum, Beni suef, Menia, Dakahlia, Beheira and Alexandria) in a trial for isolation of responsible aerobic bacterial agents causing high chicks mortality in such flocks. The diagnosis based on history of each examined flock, the observed clinical signs at time of examination, post-mortem (PM) lesions as well as isolation and identification of responsible bacterial agents incriminated in this problem. Two hundred six moribund chicks and one hundred forty one freshly dead chicks were collected for PM examination and samples collection. One hundred two pooled tissue samples include heart, liver, bile, spleen and yolk sac as well as one hundred two cecal content were collected under complete aseptic conditions for bacterial isolation and identification by cultural, biochemical and serological methods. The result of history revealed that examined breeds were Cobb, Hubbard, Saso, Ross, Avian and Balady with age ranged from 1 to 28 days and mortality rate ranged from 0.07 to 6.8%. Observed clinical signs were respiratory signs, omphalitis, lamness, pasty vent and diarrhea. PM lesions were septecaemic picture, fibrinous polyserositis, nodules on heart muscle, pneumonia, distended gall bladder, enteritis, unabsorbed yolk sac and cecal core. The incidence of bacteriologically positive pooled tissue samples were (99%) 101 out of 102 farms and the negative samples were (1%) 1 out of 102 farms, while culturing of pooled cecal content samples for suspected Salmonella infections revealed that 8 out of 102 (7.8%) positive samples and 94 out of 102 (92.2%) negative samples. 146 bacterial isolates were recovered from all bacteriologically cultured samples. Escherichia coli isolates were the predominant strains (74\146) 50.7% followed by Salmonella (29\146)19.8%, Klebsiella pneumonia (20\146)13.7%, Proteus mirabilis (16\146)10.9% and Pseudomonas aeruginosa (7\146) 4.8%. Serological identification of E. coli isolates revealed that both O158 and O78 serotypes were the most predominant isolates (14\74) 18.9%, each, followed by serotype O27 (8\74) 10.8%, serotype O114 (5\74) 6.76% , serotypes O18 and O55 (3\74) 4%, each, serotypes O6, O111 and O159 (2\74) 2.7%, each finally serotypes O8, O15, O26, O44, O119, O142, O153, O166 and O169 (1\74) 1.35%, each. Testing the pathogenicity of E. coli isolates using Congo red test revealed that all isolated E. coli serotypes were pathogenic. Serological identification of Salmonella isolates revealed that S. infantis, S.Virshow, S Entereditis, S, Gallinarum, S. Kentuky and S. Typhimurium were the only isolated Salmonella species with a percentage of (8\29)27.6%, (7\29)24.1%, (6\29) 20.8%, (4\29)13.8%, (3\29)10.3% and (1\29)3.4% respectively.
Elementary school classroom emotional climate as a moderator of anxious solitary children’s risk for peer exclusion: a child × environment model.

The current study examined classroom emotional climate as a moderator of anxious solitary children’s risk for peer exclusion over a three-year period from third through fifth grade. A sample of 688 children completed peer nominations for anxious solitude and peer exclusion in the Fall and Spring semesters of each grade, and observations of classroom emotional climate were conducted at the same timepoints. Cross-classified growth curves with Poisson distributions were computed using hierarchical linear modeling. Results supported the child × environment model and provide evidence that elementary school classroom emotional climate has concurrent by not long-term effects on anxious solitary children’s peer adjustment in later middle childhood. Current results suggest that supportive classroom emotional climates have protective effects on anxious solitary children by Spring semesters across all grades. Results suggest that teachers who are able to provide supportive classroom environments can decrease levels of peer exclusion in their classrooms.
A RDBMS based Efficient Method for Shortest Path Searching over Large Graphs considering Constraint Conditions

Nowadays, graphs are used in many applications and the graph size is rapidly increasing. The shortest path discovery considering constraint conditions is very important. For example, when a road is temporarily not available, paths not including the invalid road are required. In this paper, we propose an efficient RDBMS based method that searches a shortest path considering constraint conditions in a large graph. The proposed method finds the shortest path not containing the constraint path by avoiding expanding the paths including the given constraint path. For improving the performance, we propose an efficient expanding strategy that uses the index table proposed by the FEM framework. Experiments show that the proposed method can achieves high space efficiency in a large graph.
RUNNING HEAD : HOW NOISY IS LEXICAL DECISION ? How Noisy is Lexical Decision ?

Lexical decision is one of the most frequently used tasks in word recognition research. Theoretical conclusions are typically derived from a linear model on the reaction times (RTs) of correct word-trials only (e.g., linear regression and ANOVA). Although these models estimate random measurement error for RTs, considering only correct trials implicitly assumes that word/nonword categorizations are without noise: Words receive a yes-response because they have been recognized, and they receive a no-response when they are not known. Hence, when participants are presented with the same stimuli on two separate occasions, they are expected to give the same response. We demonstrate that this not true and that responses in a lexical decision task suffer from inconsistency in participants’ response choice, meaning that RTs of “correct” word responses include RTs of trials on which participants did not recognize the stimulus . We obtained estimates of this internal noise using established methods from sensory psychophysics (Burgess & Colborne, 1988). The results show similar noise values as in typical psychophysical signal-detection experiments when sensitivity and response bias are taken into account (Neri, 2010). These estimates imply that, with an optimal choice model, only 83-91% of the response choices can be explained (i.e., can be used to derive theoretical conclusions). For word responses, word frequencies below 10 per million yield alarmingly low percentages of consistent responses (near 50%). The same analysis can be applied to RTs, yielding noise estimates about three times higher. Correspondingly, the estimated amount of consistent trial-level variance in RTs is only 8%. These figures are especially relevant given the recent popularity of trial-level lexical decision models using the linear mixed-effects approach (e.g., Baayen et al., 2008). How Noisy is Lexical Decision? 3
Synthesis and Characterization of Polyarylacetylene for Use in the Monolithic Vitreous Carbon Processing

In this work, polyarylacetylene (PAA) prepolymer obtained from 1,4-diethynylbenzene was evaluated as polymeric precursor of monolithic vitreous carbon (MVC). The monomer 1,4-diethynylbenzene was synthesized and the PAA prepolymer was prepared with 13, 16 and 19 wt % of nickel catalyst. The best percentage of nickel catalyst considered was 13 wt %, due to the lower molecular weight and polydispersity index and higher carbon yield. The traditional production of MVC is obtained from phenolic and poly(furfuryl alcohol) resins, which require many hours to pyrolyze due to the large quantity of gases generated. Furthermore, the porosity is hard to control using these conventional resins. Polyarylacetylene releases little volatiles due to the polyaddition reaction and the aromatic rings in PAA chain also provide a high thermal stability, therefore the heat treatment could be faster without cracking the carbon material. This would also lower the cost of fabrication of MVC, in addition to the low porosity observed, making this polymer a good matrix for MVC.
Experience-driven Predictive Control

—This work presents Experience-driven Predictive Control (EPC) as a fast technique for solving nonlinear model predictive control problems with uncertain system dynamics. EPC leverages a linear dynamics model that is updated online via Locally Weighted Project Regression (LWPR) to capture nonlinearities, uncertainty, and changes in the system dynamics. This allows the NMPC problem to be re-cast as a quadratic program. The QP can then be solved via multi-parametric techniques to generate a mapping from state, reference, and dynamics model to a locally optimal, afﬁne feedback control law. These mappings, in conjunction with the basis functions learned in LWPR, deﬁne a notion of experience for the controller as they capture the full input-output relationship for previous actions the controller has taken. The resulting experience database allows EPC to avoid solving redundant optimization problems, and as it is constructed online, enables the system to operate more efﬁciently over time. We demonstrate the performance of EPC through a set of simulation studies with a quadrotor micro aerial vehicle that is subjected to unmodeled exogenous perturbations.
Effect of trawling on bird and seal distributions in the southern Benguela region

Attendance patterns of seabirds and Cape fur-seals Arctocephalus pusillus pusillus at research trawls in the southern Benguela region during the austral winter are analysed to infer the importance of trawl fisheries to different seabird and seal species. Three specific responses to trawlers are recognized: ( 1 ) species that avoid feeding assemblages at trawlers (jackass penguin Spheniscus demersus, soft-plumaged petrel Pterodroma mollis, Cape cormorant Phalacrocorax capensis); (2) species that cons~stently are attracted to trawlers and whose distributions are influenced by trawlers (black-browed albatross Diomedea melanophris, shy albatross D. cauta, pintado petrel Daption capense, white-chinned petrel Procellaria aequinoctialis); (3) species that only forage at trawlers in deep, offshore waters (Cape gannet Sula capensis, kelp gull Larus dominicanus, Cape fur-seal). Other species lie between these extremes. These variations in response indicate the importance of trawler discards in each species' diet. The divisions do not follow the guild classification used for the region to date. Reported increases in seabird populations as a result of trawling activity should be interpreted with caution.
Parallel Genetic Algorithms for Financial Pattern Matching using NVIDIA GPU ’ s

Pattern recognition or matching algorithms process large datasets of information that can be either images for recognition or time series in pattern matching. To gather accurate results, these algorithms are trained using advanced techniques of machine learning such as Neural Networks (NN) and Genetic Algorithms (GA) that require extensive periods of time and computational power. This paper presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implementation of SAX/GA and genetic operators work to optimize possible solutions. This study is later used as the baseline for the development of parallel techniques capable of exploring the identified points of parallelism that simply focus on accelerating the heavy duty fitness function to a full Graphical Processing Unit (GPU) accelerated GA. The implemented solutions accelerated the sequential SAX/GA solution in around 30× with a maximum of nearly 180×. Keywords—Parallel Genetic Algorithms; Technical Analysis; Financial Computation; Pattern Matching; GPU; CUDA
The emergence of obesity among Australian Aboriginal children

Abstract Obesity is of significant and growing concern among Australian Aboriginal children, and is linked to patterns of child growth. The aim of this paper is to show diverse patterns of growth and obesity emergence among Australian Aboriginal children using historical anthropometric data. Child growth in height, weight and body mass index (BMI) is reanalysed for children aged 2 to 19 years in Australian Aboriginal communities spanning two distinct time periods (the 1950s and 1960s; and the 1990s and 2000s) and six different geographical locations: Yuendumu, Haast’s Bluff, Beswick, Kalumburu, Gerard, and Raukkan. Comparisons of stature and BMI between the earlier and later years of measurement were made, and the proportion of children classified as overweight or obese by the International Obesity Task Force criteria estimated, to allow international comparison. Aboriginal children in the 1990s and 2000s were heavier, with higher BMI than those in the 1950s and 1960s, differences in height being less marked. While no children were classified as overweight or obese in the earlier period, 15% of males and 3% of females were classified so in the later period. The data suggests that the period of onset of the epidemic of overweight and obesity among rural Australian Aboriginal children was likely to have been between the 1960s and 1980s.
Coupling between molecular rotations and OH⋯O motions in liquid water: Theory and experiment

A new theory is proposed to describe spectral effects of the coupling between molecular rotations and OH⋯O motions in liquid water. The correlation function approach is employed together with a special type of development in which the coupling energy of these two motions is the expansion parameter. The isotropy of the liquid medium plays an essential role in this study. Based on this theory, a new infrared pump–probe experiment is described permitting a visualization of molecular rotations at subpicosecond time scales. Full curves relating the mean squared rotational angle and time, and not only the rotational relaxation time, are measured by this experiment. However, very short times where the incident pulses overlap must be avoided in this analysis. The lifetime of OH⋯O bonds in water is rotation–limited.
Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences

Imitation Learning is the task of mimicking the behavior of an expert player in a Reinforcement Learning(RL) Environment to enhance the training of a fresh agent (called novice) beginning from scratch. Most of the Reinforcement Learning environments are stochastic in nature, i.e., the state sequences that an agent may encounter usually follow a Markov Decision Process (MDP). This makes the task of mimicking difficult as it is very unlikely that a new agent may encounter same or similar state sequences as an expert. Prior research in Imitation Learning proposes various ways to learn a mapping between the states encountered and the respective actions taken by the expert while mostly being agnostic to the order in which these were performed. Most of these methods need considerable number of states-action pairs to achieve good results. We propose a simple alternative to Imitation Learning by appending the novice’s action space with the frequent short action sequences that the expert has taken. This simple modification, surprisingly improves the exploration and significantly outperforms alternative approaches like Dataset Aggregation. We experiment with several popular Atari games and show significant and consistent growth in the score that the new agents achieve using just a few expert action sequences.
A Neural Framework for Analyzing the Tweets of dril

Natural Language Processing is a major field within computer science research, but by definition it limits itself to common human language. Many interesting linguistic phenomena can be found in unnatural, absurd language, especially of the sort common in the twitterverse. In this work, we present the first scholarly study of the tweets of Twitter user dril. We gather a new dataset containing the text of dril tweets and associated absurdity rankings collected from mechanical turkers. Then, we suggest state-of-the-art neural techniques to perform the given classification task on these tweets. We report promising experimental results that suggest wider value for this specific task.
Covalent attachment of functional side-groups to polyaniline nanofibres

Polyaniline (PAni) is an example of a conducting polymer that can be switched between an insulating and a conductive state. This switching is accompanied by a colour change. Recently, interest has developed in the nanofibre form of PAni as these low dimensional structures have a very high surface area, thus enabling a faster response time. We investigate how the surface chemistry of these nanofibres can be modified by covalently attaching functional side-groups. In particular, we demonstrate the attachment of both amide and carboxylic acid groups. This can be achieved using a simple reflux technique. The modified material retains its nanomorphology and the intrinsic electrochemical, spectroscopic and redox properties of PAni are also preserved. Both acid and amine side-groups are interesting in that they provide a template, which could be further altered to enhance the selectivity of PAni. Acid terminated chains can also be used to introduce self-doping behaviour to PAni.
The flow dynamics of the garden-hose instability

We present fully resolved simulations of the flow–structure interaction in a flexible pipe conveying incompressible fluid. It is shown that the Reynolds number plays a significant role in the onset of flutter for a fluid-conveying pipe modelled through the classic garden-hose problem. We investigate the complex interaction between structural and internal flow dynamics and obtain a phase diagram of the transition between states as function of three non-dimensional quantities: the fluid-tension parameter, the dimensionless fluid velocity and the Reynolds number. We find that the flow patterns inside the pipe strongly affect the type of induced motion. For unsteady flow, if there is symmetry along a direction, this leads to in-plane motion whereas breaking of the flow symmetry results in both in-plane and out-of-plane motions. Hence, above a critical Reynolds number, complex flow patterns result for the vibrating pipe as there is continuous generation of new vorticity due to the pipe wall acceleration, which is subsequently shed in the confined space of the interior of the pipe.
Técnicas de correção do teste qui-quadrado para amostras não normais

Techniques for Correction the Chi-Square Test for Non-Normal Samples This paper aims to evaluate techniques for correcting the chi-square test (χ2) as applied to Confirmatory Factor Analysis (CFA) models in non-normal data. In a simulated and exploratory approach, distinct distributions were analyzed in terms of multivariate kurtosis. In most situations, it was observed a tendency of the analyzed tests to produce differing corrections on the χ2 values, as well as for the CFI and RMSEA values. Among other tests evaluated, this study suggested the use of the Elliptical Test with Least Squares (Elliptical Theory), Heterogeneous Kurtosis Test with Reweighted Least Squares (Heterogeneous Kurtosis Theory) and Satorra-Bentler Scaled Test with Maximum Likelihood estimation (for distributions with excessive univariate asymmetry and/or kurtosis). However, due to the correction factor, the Satorra-Bentler Scaled test can accept moderately poorly specified models in the presence of extreme kurtosis.
Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral–spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral–spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral–spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods
Suzaku Discovery of Twin Thermal Plasma from the Tornado Nebula

The Tornado nebula (G357.7-0.1) is a mysterious radio source with bright "head" and faint "tail" located in the direction of the Galactic center (GC) region. We here report the discovery of two diffuse X-ray sources at the head and tail of the Tornado with the Suzaku satellite. We found emission lines from highly ionized atoms in the two sources. The spectra are reproduced by an optically thin thermal plasma with a common temperature of 0.6-0.7 keV. The interstellar absorption (NH) of these sources are the same and are slightly larger than that of the GC distance. Since the estimated distance using the NH value is consistent with the radio observation of the Tornado, these X-ray sources are likely associated with the Tornado nebula. The twin-plasma morphology at the both ends of the Tornado suggests that the system is a bipolar/outflow source.
Understanding Large Persons ’ Networks through Semantic Categorization

In this work, we describe a methodology to interpret large persons’ networks extracted from text by classifying cliques using the DBpedia ontology. The approach and the challenges faced when building networks based on persons’ co-occurrence are discussed in detail, especially the problem of mention normalisation and coreference resolution. The classification methodology that first starts from single nodes and then generalises to cliques is effective in terms of performance and is able to deal also with nodes that are not linked to Wikipedia. The gold standard manually developed for evaluation shows that groups of co-occurring entities share in most of the cases a category that can be automatically assigned. The outcome of this work may be of interest in a Big Data scenario to enhance the visualisation of large networks and to provide an additional semantic layer on top of cliques, so as to ease the comprehension of the network from a distance.