Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence, risk factors and multidrug resistance profile of Staphylococcus aureus isolated from bovine mastitis in selected dairy farms in and around Asella town, Arsi Zone, South Eastern Ethiopia

A cross-sectional study was carried out from November 2012 - May 2013 to estimate the prevalence of multi drug resistance Staphylococcus aureus from bovine mastitis and to assess its associated risk factors in selected dairy farms in and around Asella town, Arsi Zone, Ethiopia. A total of 384 fresh raw milk samples from dairy cows were examined for mastitis. Milk samples collected from 251 mastitic lactating cows were subjected to bacteriological examinations for isolation and identification of S. aureus. S. aureus isolates were identified from 44.62% (112/251) mastitic milk samples. Higher prevalence rates of S. aureus isolates were recorded in subclinical mastitis (45.78%, 103/225) compared to clinical cases (34.62%, 9/26), however, no statistically significant difference (p > 0.05) in prevalence of S. aureus between subclinical mastitis and clinical cases. Multivariate logistic regression analysis of the effect of different risk factors on the prevalence of S. aureus revealed that; cross breed (OR = 2.501, 95%CI: 1.173 - 5.334), late stage of lactation (OR = 4.260, 95%CI: 1.9309.402), previous mastitis record (OR = 2.553, 95%CI: 1.3324.894), large sized herd (OR = 15.824, 95%CI: 6.36839.320) and poor udder hygiene (OR = 2.040, 95%CI: 1.184,3.514) were more likely to be infected with antimicrobial resistance S. aureus. All the isolates of S. aureus were subjected to antimicrobial susceptibility testing. The highest rate of susceptibility was to chloramphenicol (97.5%) followed by gentamycin (95.3%), vancomycin (92.7%), and clindamycin (90.9%). Whereas, the highest rate of resistance among the isolates was against penicillin G (87.3%) followed by tetracycline (82.2%), trimethoprim-sulfamethoxazole (69.1%), oxacillin (56.4%), ampicillin (55.1%) and cefoxitin (58.1%). The results of the present study reveal that 65.18% of the isolates were found to be multiple antibiotic resistance phenotypes. Regular antimicrobial sensitivity testing and best practices for achieving hygienic milking should be established. Key words: Milk, bovine mastitis, multidrug resistance, prevalence, risk factors, Staphylococcus aureus, Assella.
Utilizing the HTM algorithms for weather forecasting and anomaly detection

Various studies have utilized different artificial neural networks (ANN) for weather forecasting. This thesis examines how well the official implementation of a novel online ANN called the Hierarchical Temporal Memory (HTM) can forecast the weather and detect anomalies in the weather data. Created by Numenta1, the HTM emulates the brain’s neocortical structures and processes to mimic its capabilities of memory retention. By using sparse distributed representations instead of binary representations as its foundation for information storage and representation, it is able to learn complex patterns in noisy data sets that can be used to make predictions and detect anomalies in streamed data. Numenta has officially implemented the theory of HTM in an open-source Python platform called NuPIC. Although there are slight differences between the theory of HTM and its implementation, the most important factor about NuPIC is the addition of several purely engineered algorithms. Two of the most notable additions, are an algorithm that enables NuPIC to make the final decisions in cases when more than one possible prediction is possible, and an algorithm that makes it possible to simultaneously input multiple metrics to NuPIC. The weather data that was to be predicted consisted of several weather factors, wind direction, wind speed, atmospheric pressure, precipitation, temperature, and relative humidity measurements spanning over a period of 12 years. Originally, the goal was to input the data sets simultaneously. However, because the functionality responsible for enabling this feature was malfunctioning at the time of the thesis work, every weather data set had to be input separately. The results showed that NuPIC was able to make decent forecasts, but was for the most part outperformed by a simple technique that made predictions by calculating the average of the last few days. The main reasons for this was due to the weather’s lack of similarity between past and current conditions, and NuPIC’s inability to generalize its knowledge in order to factor weather trends in its predictions. Although there is also a minor issue with the current engineered prediction algorithm, the results indicate that prediction is not NuPIC’s strongest suit. NuPIC was completely unable to detect any noteworthy anomalies in the weather data, which again is most likely due to the weather data’s chaotic nature.
Cloacal mycobiota in wild females of Caiman latirostris (Crocodylia: Alligatoridae)

There are few reports of cloacal mycobiota on wild reptiles, and in particular, fungal presence and function in Caiman latirostris remains unknown. Our objective was to describe the fungal community present in the cloaca of wild female broad-snouted caimans during their reproductive season determine whether the number of fungi has some relationship with the female’s corporeal condition. Fungi were found in 9 out of 13 cloacal samples and 14 species of fungi were isolated and identified. Three of the species isolated had the highest occurrence values, and 2 of them are pathogenic. In this case, body condition index had no relationship with fungal frequency; the fungi found in this study may have originated from soil habitat and nest substrate that are in constant contact with the cloaca of the C. latirostris females. The findings in this work support the theory that reptiles are facultative carriers of fungi or their spores.
Comparative Susceptibility of Tomato Leafminer, Tuta absoluta (Lepidoptera: Gelechiidae) to Common Used Chemical Synthetic Pesticides and Biopesticides in Egypt.

The journal of Toxicology and pest control is one of the series issued twice by the Egyptian Academic Journal of Biological Sciences, and is devoted to publication of original papers related to the interaction between insects and their environment. The goal of the journal is to advance the scientific understanding of mechanisms of toxicity. Emphasis will be placed on toxic effects observed at relevant exposures, which have direct impact on safety evaluation and risk assessment. The journal therefore welcomes papers on biology ranging from molecular and cell biology, biochemistry and physiology to ecology and environment, also systematics, microbiology, toxicology, hydrobiology, radiobiology and biotechnology. www.eajbs. eg.net Provided for non-commercial research and education use. Not for reproduction, distribution or commercial use.
Isolation and Purification of Isoaloeresin D and Aloin from Aloe vera by High-speed Counter-current Chromatography

Objective To develop an efficient method to isolate and purify the main components isoaloeresin D and aloin from Aloe vera for its industrial production. Methods High-speed counter-current chromatography was used to isolate isoaloeresin D and aloin in a one-step separation from dried crude extract of A. vera. The biphasic solvent system composed of hexane-ethyl acetate-acetone-water (0.2︰5︰1.5︰5) was used at a flow rate of 1.0 mL/min, while the lipophilic phase was selected as the mobile phase and the apparatus was rotated at 840 r/min. The effluent was detected at 254 nm. Results Isoaloeresin D (53.1 mg) and aloin (106.9 mg) were separated from the crude extract (384.7 mg) with the purities of 98.6% and 99.5%, respectively. Conclusion HSCCC is a powerful technique for isolation and separation of chemical composition from aloe.
An ANN Based Pattern Classification Algorithm for Diagnosis of Swine Flu

Embedding Machine Learning technology into Medical Diagnosis Systems adds a new potential to such systems. This paper presents an algorithm using multilayer perceptron based Artificial Neural Network (ANN) for detecting swine flu accurately and efficiently. In this approach once an ANN model is trained using appropriate data patterns of different patients some having swine flu, the model becomes intelligent and is ready for detecting correctly whether the case is a case of swine flu or not. Firstly feature selection is performed, which is driven by the goal of confirming a target class by a feature s discriminating power. A k-NN based algorithm is used to reduce the size of data to be used for training the ANN model with an objective of making the training more efficient and accurate. Then the structure of a pattern is represented by these important features along with a class label. Once the ANN model is trained using patterns of training set related to Swine flu, its performance is evaluated on test pattern set of Swine flu. The results achieved with the proposed approach demonstrate the ability of the algorithm to provide high level of accuracy for the Swine Flu classification problem. When the assessment (classification) ability of the ANN model is compared with that of CBR approaches, the superiority of the neural network approach is established. Swine Flu is a type of flu which is highly contagious and has potential for rapid spread. The disease generally spreads through the air and water. The swine flu, more properly called the H1N1, is a new strain of the common influenza virus. The Influenza virus is a very significant zoonotic pathogen and it causes recurrent outbreaks at the local or global scale, resulting in potentially severe consequences for human health as well as for the global economy. The 2009 flu pandemic is a global outbreak of a new strain, which was first identified in April 2009. The outbreak began in Mexico, with the evidence that Mexico was already in the midst of an epidemic for months before the outbreak was recognized. Swine influenza virus infections in humans have been reported in the United States, Canada, Europe and Asia as in (1). Swine Flu is on the rise at present. There are thousands of people dying of it and the most common cause of death from the virus is respiratory failure. Therefore in this paper, an artificial neural network (ANN) based model is proposed in order to solve the classification and prediction problem and to develop a Swine flu prediction system that decreases the cost of authentic tests and also time.
A bioassay approach to seasonal variation in the nutritional value of sediment

Smaller surface deposit feeding macroinvertebrates probably rely on both rnicroblal and non-living sources of organic matter, but only scant evidence supports the hypothesis that food is limiting to natural populations. Summer declines in deposit feeding populations have been ascribed to food limitation and to the seasonal influx of predators. We used a bioassay approach to test the following hypotheses: ( l ) Sediment supports more population growth in the spring and early summer, relative to the late summer and fall. (2) The nutritive value of sediment declines from spring to early fall, and absorption therefore should decrease. Laboratory tests using the common deposit feeding oligochaete Paranais litoralis partially support both hypotheses. Population growth studies of spring and summer sediment show a relatively nutritionally enriched surface sediment layer, suggesting that the sediment is recharged in the late fall and winter, either by particulate organic matter deposition or microbial transformation of previously unabsorbable material. High summer temperature contributes to the negative effect of poor sediment quality. In Flax Pond. Long Island, New York, field studies show summer declines of several species of deposit feeding invertebrates, but caging studies demonstrate that predation is not a likely cause of the crashes, which proceed at similar rates inside and outside of cages.
THE USE OF COMPUTER VISION ALGORITHMS FOR AUTOMATIC ORIENTATION OF TERRESTRIAL LASER SCANNING DATA

The paper presents analysis of the orientation of terrestrial laser scanning (TLS) data. In the proposed data processing methodology, point clouds are considered as panoramic images enriched by the depth map. Computer vision (CV) algorithms are used for orientation, which are applied for testing the correctness of the detection of tie points and time of computations, and for assessing difficulties in their implementation. The BRISK, FASRT, MSER, SIFT, SURF, ASIFT and CenSurE algorithms are used to search for key-points. The source data are point clouds acquired using a Z+F 5006h terrestrial laser scanner on the ruins of Ilza Castle, Poland. Algorithms allowing combination of the photogrammetric and CV approaches are also presented.
Corruption and Country Size: Evidence Using Firm-Level Survey Data

What sorts of conditions make some countries more prone to corruption than the others? This is an important question for understanding how corruption arises and how to combat it. The present paper attempts to answer this question by exploring the link between the size of the country and corruption. Economic theory suggests advantages and disadvantages of being a large country. Fixed costs in monitoring and punishing corrupt politicians and bureaucrats implies lower corruption in larger countries. However, congestion or administrative costs may escalate with country size. Further, greater diversity in the larger countries implies that such countries may find it harder to reach a consensus on growth-enhancing anti-corruption reforms. Thus, the corruption and country size relationship is an empirical issue. Using firm-level survey data for 135 countries, this paper finds that the level corruption experienced by the firms is positively correlated with country size. This holds for a measure of overall corruption and petty corruption that arises in availing specific government services. According to a conservative estimate, moving from a country the size of Namibia (25th percentile level in size) to a country the size of Morocco (75th percentile level) is associated with an increase in the level of overall corruption by 0.28 percentage point or about 23 percent of its mean value. The results are robust to several controls, alternative corruption measures, sample alternations, and different country size measures.
NUTRITIONAL POTENTIAL FOR IMPROVING MEAT QUALITY IN POULTRY

This review presents current opinions on health-related effects of meat consumption, emphasizing associations between meat (red meat and processed meat) and colo-rectal cancers. It indicates that consumption of poultry and fish has not been found to be associated with increased risk of cancers. The idea of functional foods in Europe is described and poultry meat is shown as a target for modification by means of nutritional strategies. These strategies include enrichment with polyunsatutated fatty acids (PUFAs), notably n-3 acids, conjugated linoleic acid (CLA) and antioxidant vitamines. It is shown that feeding fish oils or fish meals (n-3 fatty acid sources) to growing poultry leads to their subsequent deposition in adipose and muscle tissue (mainly C20:5, C22:5, and C22:6) at the expense of decreasing incorporation of n-6 fatty acids. At the same line, dietary CLA isomers are deposited in poultry tissues. Moreover, since the increasing amout of polyunsaturates in meat increases their oxidation, efforts aiming at improving the oxidative stability of modified poultry meat (notably nutritional enrichmemnt with vitamin E) are described. Finally, the potentially detrimental effects of dietary PUFAs and CLA on broiler productive performance (feed intake, growth rate, feed conversion) and sensory meat quality (off-flavours) are analysed. Also, available means are discussed to reduce or to prevent these effects.
Dependence Structure of Korean Financial Markets Using Copula-GARCH Model

This paper investigates the dependence structure of Korean financial markets (stock, foreign exchange (FX) rates and bond) using copula-GARCH and dynamic conditional correlation (DCC) models. We examine GJRGARCH with skewed elliptical distributions and four copulas (Gaussian, Student’s t, Clayton and Gumbel) to model dependence among returns, and then employ DCC model to describe system-wide correlation dynamics. We analyze the daily returns of KOSPI, FX (WON/USD) and KRX bond index (Gross Price Index) from 2nd May 2006 to 30th June 2014 with 2,063 observations. Empirical result shows that there is significant asymmetry and fat-tail of individual return, and strong tail-dependence among returns, especially between KOSPI and FX returns, during the 2008 Global Financial Crisis period. Focused only on recent 30 months, we find that the correlation between stock and bond markets shows dramatic increase, and system-wide correlation wanders around zero, which possibly indicates market tranquility from a systemic perspective.
Shifting Strategies: the Pursuit of Closure and the ‘Association of German Auditors’

Abstract Drawing on Weber’s [(1972). Wirtschaft und Gesellschaft: Grundriss der verstehenden Soziologie [Economy and society. An outline of interpretive sociology]. (5th ed.). Tübingen: Mohr Siebeck] theories of social stratification and closure, and on Parkin’s [(1979). Marxism and class theory. London: Tavistock] development thereof, this paper contributes a case study of Germany to the literature on closure processes (rather than outcomes), and outcomes falling short of professionalization. It explores the early history of the first German association of auditors, the Verband Deutscher Bücherrevisoren (VDB), founded in 1896. The paper traces how closure was pursued, at various times, by different means and to different degrees, depending on the changing social, economic and political contexts and on changing perception of what would best serve the association’s interest. By thus focusing on the shifting aims and strategies of the VDB, the paper contributes new insights into the dynamics within professions and between professions and their environments, and the dynamics and conditions underlying failed professionalization projects. The paper shows that, while the VDB had a degree of success in creating many of the structure and processes of a modern professional body, its ambition to achieve market and professional closure was not fulfilled.
An Extraction Method of Acoustic Features for Speech Recognition

This study presents a novel method that deals with extracting acoustic features for recognition of isolated speech words. This extraction method is based on the use of a bank of 41 Gabor filers, which aim to select the specific modulation frequencies and bring a limitation of information redundancy on feature level. The robustness and performance of proposed features, named as Gabor Mel Spectrum features (GMS features) are validated on isolated speech words in both clean and noisy environment case and compared to those of two classic methods such as PLP-features and MFCC-features. The recognition results obtained using HMM, show that our extraction method is more robust and achieve better recognition rates than the two latter methods.
Making Sense of Play in Video Games: Ludus, Paidia, and Possibility Spaces

This article synthesizes the work of video game theorists such as Gonzalo Frasca (2003), Katie Salen and Eric Zimmerman (2003), Ian Bogost (2008), and Steven E. Jones (2008) in order to build on academic and interdisciplinary discussions of play and its effect on PC- or console-based video games. Examples are used to elucidate the potentially problematic categories of “paidic” and “ludic” games, as well as to explore further the ways in which uninhibited play, “metagaming,” and the inevitable influence of socio-cultural factors gradually transform—and become codified within—the landscape of digital games. Finally, the article offers Mikhail Bakhtin’s notions of opposing “centripetal” and “centrifugal” forces as analytical tools according to which the connections between in-game activities and out-of-game social and cultural contexts can be usefully examined by future critics.
Spectroscopic and Thermal Degradation Study of Cow-Dung during Combustion and Pyrolysis

This study reports the analysis of gases evolved during controlled pyrolysis and combustion of cowdung. The combined thermogravimetric (TG)/ Fourtier transform infrared (FTIR)/ mass spectrometry (MS) were employed to analyze the evolved gaseous products. The results revealed that the thermal decomposition of cow-dung occurs in three steps assigned to drying of the sample, fast thermal decomposition and further cracking. The complete combustion of cow-dung occurs at 500°C, while during pyrolysis the total weight loss of 58.91 wt. % was found at 950°C. The release of gaseous product during pyrolysis was CO2, CO, methanol, formaldehyde, formic acid, acetic acid, whereas the products during combustion were CO2, CO. Among them, CO2 was the dominant gaseous product in the whole combustion process.
RELEVANT ASPECTS OF MINING PROJECT EVALUATION

Investment projects in mining industry are considered as being risky. The main difficulty of the project evalaution process is how to deal with the uncertainty involved in capital investment. This paper presents four methods used for evaluating mining projects: Discounted Cash-Flow analysis, Decision Trees, Monte Carlo Simulation and Real Options Also, pros and cons of them are discussed in the final section.
Oktober 2012 , hal 113-118 ANALYTICAL MODELING OF NON PLANAR MOSFET

The Non Planar MOSFET structure with curved-channel is one alternative MOSFET structure for enhance the electrical performance. The study was focused on the non planar devices which has curved-channel including grooved-gate, recessed-channel, V-shaped and sidewall vertical MOSFET. The presence of corner region can effective in reducing the electric field at the drain, thus improving reliability of short channel effects (SCEs). The corner effect can reduce surface potential. It can improve the characteristic of the device electrical performance, especially the reduction of short channel effect and hot carrier effects. Therefore, the curved-channel MOSFET has a very great application prospect in deep submicron device architecture.
Electronic structure and transport properties of sulfur-passivated graphene nanoribbons

Electronic structure of newly synthesized sulfur-terminated graphene nanoribbons (S-GNRs) has been presented from the calculations based on ab initio density functional theory and non-equilibrium Green's function (NEGF) method. The calculations reveal that zigzag-edged S-GNRs (Z-S-GNRs) are thermodynamically more stable than armchair edged S-GNRs (A-S-GNRs). It has been observed that the band gap of S-GNRs depends both on ribbon width and edge symmetry. The calculated band gap, in case of A-S-GNRs, is also supported by the presence of threshold bias in the I-V characteristics obtained from NEGF formalism. It is shown that all A-S-GNRs having width up to 1.50 nm are semiconducting but the Z-S-GNRs of similar widths are metallic. For A-S-GNRs, the width dependent band-gap hierarchy follows three different trends which seem to be different from that of H-passivated GNRs. The band-gaps for A-S-GNRs arise from both quantum confinement as well as crucial effect of edge, where the passivating S atoms play an imp.
MapReduce Scheduler Using Classifiers for Heterogeneous Workloads

Summary Hadoop is a large-scale distributed processing infrastructure, designed to efficiently distribute large amounts of work across a set of machines. Scheduling of jobs or work is very important in order to achieve efficiency. The proposed work incorporates the design of a new scheduler that will not overload any TaskTracker at any time and thus preventing the unnecessary re-launch of tasks. The scheduler also classifies the job into CPU bound and IO bound. So, a balance is maintained between either of them.