Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF OZONATED WATER TREATMENT ON CLINICAL SIGNS , SURVIVAL RATE AND HISTOPATHOLOGICAL ALTERATIONS IN COMMON CARP , CYPRINUS CARPIO L . INFECTED WITH SAPROLEGNIA SPP

Present study was undertaken to assess the efficacy of Ozone and formaldehyde (as a reference treatment) on controlling Saprolegniasis in common carp, Cyprinus carpio L. Saprolegnia spp. were isolated on special culture media for fungi from 50 infected specimens of fishes were identified as Saprolegnia spp. Viable fungal suspension of Saprolegnia was determined and adjusted at a concentration of 2×10 zoospores l . To control this fungus, a total of 120 common carp weighing 80±10g were randomly distributed into six replicated groups (10 fish/replicate) and were treated as follows; C-: control healthy without treatment; C+: control infected with Saprolegnia spp. without treatment; T1, T2 and T3: fish were infected with Saprolegnia spp. and treated with Ozone 0.25, 0.50 and 0.75 mg/l per hour respectively; T4: fish were infected with Saprolegnia spp. and treated with formalin 0.15 ml/ l for 30 min for 3 successive days. Clinical signs and survival rate were studied. After 14 days of treatment with Ozone, samples were collected from fish for histopathological studies. Among the Ozone treatment 0.50 mg/l showed highest survival rate (90%), survival rate of the control group (without disinfectant) was 20%. Histopathological studies revealed significantly increased (p<0.05) percentage of gill epithelial proliferation and epithelial lifting, also fusion of the secondary lamellae, in fish from ozonated groups relative to C+ and Cgroups. However, there were no significant differences in histopathology frequency/severity among the ozonated groups (T1, T2, T3 and T4). Skin of C+ group exhibited severe histopathological alterations including sloughing, erosion and ulcerative of epidermis penetrating up to dermal tissue. While Ozone treatment groups showed increase number of mucous cells and MNCs infiltration. Ozone appears to be a valuable disinfectant against Saprolegnia infection; at the dose of 0.50 mg/l. In conclusion, the results indicated the efficacy of Ozone as antifungal in controlling Saprolegnia infection. Thus, Ozone could be used as potential and promising alternatives to chemotherapeutics compounds in aquaculture, to achieve sustainable, economic, and safer fish production.
Integrated recycle system concept for low cost 3 D-printer sustainability

Plastic prototypes that are produced by a low cost 3d printing process may contribute to the increases of plastic waste due to its post-process or from the waste from prototype building error. In this paper, with the goal to sustain and reuse the plastic waste, a recycle system which integrated to the low cost 3Dprinter was proposed. This system concept which consisted of 13 parts was designed and analyzed by computer modelling and computer aided engineering software. Afterwards, most parts were built by 3D printer before they were assembled together and attached to the low cost 3D printer in order to prove the working design concept. From the result of the analysis, it shows that all parts were safe to produce where the factor safeties were in the range of 3 to 10 and the working concept of the system was proven.
On Radon Measure

In this paper we define and discuss Radon measures, which are measures on certain topological spaces which interact nicely with the underlying topology. Radon measures allow for approximation by open sets or compact sets. They also allow for a unique representation of linear functionals as integrals so that measure and integration can be handled with the tools of functional analysis [9]. We will also address Radon spaces, on which every finite measure is Radon, and metrics on a space of Radon measures. In particular, the Wasserstein distance defines a metric on Radon probability measures which has close ties to transport optimization and other applied problems.
ARA simulator PARADE [ ICCAD ' 15 ] is open source Accel TLB Shared TLB Accel TLB Accel TLB Accel

Since its establishment in 2009, the Center for Domain-Specific Computing (CDSC) has focused on customizable computing. We believe that future computing systems will be customizable with extensive use of accelerators, as custom-designed accelerators often provide 10-100X performance/energy efficiency over the general-purpose processors. Such an accelerator-rich architecture presents a fundamental departure from the classical von Neumann architecture, which emphasizes efficient sharing of the executions of different instructions on a common pipeline, providing an elegant solution when the computing resource is scarce. In contrast, the accelerator-rich architecture features heterogeneity and customization for energy efficiency; this is better suited for energy-constrained designs where the silicon resource is abundant and spatial computing is favored—which has been the case with the end of Dennard scaling. Currently, customizable computing has garnered great interest; e.g. this is evident by Intel’s $17B acquisition of Altera in 2015 and Amazon’s introduction of FPGAs in its AWS public cloud. In this paper we present an overview of the research programs and accomplishments of CDSC on customizable computing, from single-chip to server node and to data centers, with extensive use of composable accelerators and field-programmable gatearrays (FPGAs). We highlight our successes in several application domains, such as medical imaging, machine learning, and computational genomics. In addition to architecture innovations, an equally important research dimension enables automation for customized computing. This includes automated compilation for combining source-code-level transformation for highlevel synthesis with efficient parameterized architecture template generations, and efficient runtime support for scheduling and transparent resource management for integration of FPGAs for datacenter-scale acceleration with support to the existing programming interfaces, such as MapReduce, Hadoop, and Spark, for large-scale distributed computation. We shall present the latest progress in these areas, and also discuss the challenges and opportunities ahead.
Performance Analysis of NoSQL Databases with Hadoop Integration

According to the recent study by IBM in 2012 “the 90% of the internet data is produced in last 2 years and every day 2.5 exabytes of data is being created”. All of these was reason for the development of new databases called NoSQL. NoSQL databases have been designed to cater the requirements of handling the very large data also called “Big Data”. Unlike relational databases which are inefficient to process the big data the NoSQL databases have been designed to address the specific needs of current big data era like storing unstructured data, scalability and read/write efficiency. Relational databases have fixed schema due to which unstructured information cannot be stored in it and internet is exploding with such kind of information which comes from numerous sources. Since the data comes from variety of sources so there storage format cannot be controlled which has given rise to unstructured storage and to handle all these task through relational database has become inefficient. Along with the fulfilling storage requirements NoSQL databases have made their entry in real time analytics. Real time analytics is now being used in e-commerce, social networking websites to predict the customer needs and make business decisions accordingly. In this thesis different types of NoSQL databases are explained and integration of NoSQL databases “MongoDB” and “Cassandra” with analytics tool “Hadoop” is done. Hadoop is not a database but it is a framework to handle large amount of data by distributing the data among different cluster nodes and applies parallel processing on them. We have evaluated both integrated technologies on different parameters like read and write, scalability and fault tolerance.
USING OF NEURAL NETWORKS FOR CLASSIFICATION AND DATA ANALYSIS IN EDDY – CURRENT TESTING

At present a very prospective solution of indications classification in defectoscopy is neural network application. One of the fields is classification of indications into classes that are characterized by the signal shape or by the signatures related to the signal shape. Nondestructive defectoscopy of steam generator tubes of nuclear power plants by multifrequency eddy current method is the field in which the use of classifiers based on neural network is very prospective. The contribution concentrates on the choice of suitable neural network structures and of a suitable representation of indications. Selected structures are compared using real records of steam generator tubes and also using artificial defects and imitations of construction elements.
Generating Word Representations with Convolutional Neural Networks

Various architectures have been used for the task of language modelling. In this paper we look at a previously introduced architecture that combines CNNs, highway layers and LSTMs in and end to end trainable model. By introducing a small modification to this model we were able to achieve the same perplexity levels with less parameters.
SCHOOL LOCATION , SCHOOL TYPE AND STUDENTS ’ ACADEMIC ASPIRATION : THE CASE OF PUBLIC SECONDARY SCHOOLS IN LAIKIPIA COUNTY-KENYA

Formal education is one of the key forms of investment in relation to the development process in a nation. The role of education in a nation’s endevour to realize its development agenda, however is dependent on the rate of positive learning gains among learners which is largely influenced by their level of academic aspiration. Evidence adduced from data on students’ performance in the Kenya Certificate of Secondary Education (KCSE) exit examination from 2009 to 2013 in Laikipia County shows that only 28% of candidates attained the minimum grade for university admission in the county. Although many variables are linked to students’ academic performance, studies outside Laikipia County have consistently shown that students’ academic is one of the key determinants of academic achievement. In this regard, the study sought to determine level of students’ academic aspiration in public secondary schools in Laikipia County and the extent to which level of academic aspiration could be linked to school location (urban vis a vis rural) and school type (in terms of whether the school is day, boarding or partly day and boarding). Using expost facto research design, data were collected from 375 randomly selected students in 106 public secondary schools in the County. The data was analysed using χ statistic at 0.05 alpha level of confidence. Evidence adduced from the analysed data showed that level of academic aspiration was higher in urban schools compared with rural schools. The noted differential levels of academic aspiration was also statistically significant (χ =13.42; df=2; p<.05). The association between academic aspiration and school location was also strong (Cramer’s V=.338). The data analysis further revealed that level of students’ academic aspiration increased towards school in which all students were boarders. This finding suggested that boarding factor had a positive impact on academic aspiration. However, the association between school type was weak (Cramer’s V=.085) and statistically insignificant (χ =4.79; df=6; p<.05). The study has offered insights into the relationship between students’ academic aspiration, and school characteristics including ways in which schools can enhance students’ desire to achieve.
icrostructures and mechanical properties of polylactic acid repared by a cold rolling process

A cold rolling process was performed with extruded polylactic acid (PLA) as the crystalline polymer at different rolling ratios. The variations of the crystalline morphology, crystallinity, density, molecular orientation, and the microhardness were investigated during the process. Moreover, the dynamic mechanical properties and mechanical properties were evaluated by dynamic mechanical analysis and tensile test, respectively. The results showed that plastic deformation more easily occurred on the surface than on the interior. In addition, with the increase of the rolling ratio, the crystallinity decreased; however, the molecular orientation increased. Dynamic mechanical analysis revealed that the rolled PLA
Results Competition of 5 A peptide and known CD 36 ligands with Alexa-Fluor 488 oxLDL in CD 36-overexpressing HeLa cells

Scavenger receptor CD36 participates in lipid metabolism and inflammatory pathways important for cardiovascular disease and chronic kidney disease (CKD). Few pharmacological agents are available to slow the progression of CKD. However, apolipoprotein AI-mimetic peptide 5A antagonizes CD36 in vitro. To test the efficacy of 5A, and to test the role of CD36 during CKD, we compared wild type to CD36 knockout mice and wild type mice treated with 5A, in a progressive CKD model that resembles human disease. Knockout and 5A-treated wild type mice were protected from CKD progression without changes in blood pressure and had reductions in cardiovascular risk surrogate markers that are associated with CKD. Treatment with 5A did not further protect CD36 knockout mice from CKD progression, implicating CD36 as its main site of action. In a separate model of kidney fibrosis, 5A-treated wild type mice had less macrophage infiltration and interstitial fibrosis. Peptide 5A exerted anti-inflammatory effects in the kidney and decreases renal expression of inflammasome genes. Thus, CD36 is a new therapeutic target for To whom correspondence should be addressed: Robert A. Star, M.D., Division of Kidney, Urologic, and Hematologic Diseases, National Institutes of Health, NIDDK 6707 Democracy Blvd., Room 625; Bethesda, MD 20892; Phone: (301) 496-6325; starr@niddk.nih. gov. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The authors declare that there are no conflicts of interest. HHS Public Access Author manuscript Kidney Int. Author manuscript; available in PMC 2017 April 01. Published in final edited form as: Kidney Int. 2016 April ; 89(4): 809–822. doi:10.1016/j.kint. 2015.12. 043. A uhor M anscript
DIRECT NUMERICAL SIMULATION OF FLOWS OVER A CAVITY WITH FLOW CONTROL USING A MOVING BOTTOM WALL

Abstract. In this study, we investigate the control of self-sustained oscillating flows over an open cavity using a moving bottom wall. The three-dimensional incompressible NavierStokes equations are solved by finite difference method. A series of direct numerical simulations is performed for a variety of the bottom wall velocity from 0.00 to 1.00 − with a cavity length-to-depth ratio of 2.0. The results show that velocity fluctuations and pressure fluctuations are suppressed due to the shear stress on the bottom wall of the cavity by the moving bottom wall. The most effective condition of the bottom wall velocity is 0.34 − . The rms value of the vertical velocity fluctuation is reduced by 68.9% when the moving bottom wall velocity is -0.34. The control method using the moving bottom wall is proved to be useful to suppress self-sustained cavity oscillations.
alcium signals induced by FGF-2 in parasympathetic neurons : Role of second essenger pathways

Basic Fibroblast Growth Factor, or FGF-2, has been shown to promote neuronal survival and neurite outgrowth in dissociated neurons from the embryonic chick ciliary ganglion; in these effects the three main signal transduction pathways downstream the activated FGFR receptor, i.e. the MAPK, the PI3-K and the PLC ones, are differentially involved. While it has been shown that FGF-2 can elicit long lasting elevations in intracellular calcium concentration, [Ca]i, the role of the three pathways in this process has
Analysis of Readiness for Change and Self-Efficacy Perceptions of IT Teachers and Pre-Service Teachers

In the present study, the readiness for change, programming self-efficacy and teaching selfefficacy of Information Technology (IT) teachers and pre-service teachers in Information Technologies and Software course were investigated based on various variables. The study was conducted with 209 females and 252 males, a total of 461 individuals. In the present study that was designed with the relational survey model, the Readiness for Change Scale developed by Kondakçı, Zayim and Çalışkan (2010), the Programming Self-Efficacy Perception Scale developed by Ramalingam and Wiedenbeck (1998) and adapted by Altun and Mazman (2012) and Teacher Self-Efficacy Scale developed by Tschannen-Moran and Woolfolk Hoy (2001) and adapted by Çapa, Çakıroğlu and Sarıkaya (2005) were utilized in conjunction to analyze the results based on gender, the type of the school of graduation, experience status, active employment status, and the school level of employment variables. Study findings suggested that although experience did not affect participants’ perceptions of teachings selfefficacy, it was an effective factor on their readiness for change. furthermore, gender or school of graduation type were not effective on their readiness for change. Study results also suggested that gender had an impact on programming self-efficacy perception, and the difference between male and female participants in simple programming skills was lower, while the male participants had significantly higher level of self-efficacy in complex programming skills when compared to female participants.
Analyzing Systems

• When presented with a system, how do you know it's secure? • Often, you're called upon to analyze a system you didn't design — application architects and programmers build it; security people get to pick up the pieces. • It's better to build security in from the start, but that doesn't happen nearly as often as it should
Sparsifying Deep Learning Layers for Constrained Resource Inference on Wearables

Deep learning has revolutionized the way sensor data are analyzed and interpreted. The accuracy gains these approaches offer make them attractive for the next generation of mobile, wearable and embedded sensory applications. However, state-of-the-art deep learning algorithms typically require a significant amount of device and processor resources, even just for the inference stages that are used to discriminate high-level classes from low-level data. The limited availability of memory, computation, and energy on mobile and embedded platforms thus pose a significant challenge to the adoption of these powerful learning techniques. In this paper, we propose SparseSep, a new approach that leverages the sparsification of fully connected layers and separation of convolutional kernels to reduce the resource requirements of popular deep learning algorithms. As a result, SparseSep allows large-scale DNNs and CNNs to run efficiently on mobile and embedded hardware with only minimal impact on inference accuracy. We experiment using SparseSep across a variety of common processors such as the Qualcomm Snapdragon 400, ARM Cortex M0 and M3, and Nvidia Tegra K1, and show that it allows inference for various deep models to execute more efficiently; for example, on average requiring 11.3 times less memory and running 13.3 times faster on these representative platforms.
Deep learning for face recognition at a distance

Face recognition is a research area that has been widely studied by the computer vision community in the past years. Most of the work deals with close frontal images of the face where facial structures can be easily distinguished. Little work deals with recognizing faces at a distance, where faces are at a very low resolution and barely distinguishable. In this work, we present a deep learning architecture that can be used to enhance lower resolution facial images captured at a distance. The proposed framework uses Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed architecture works well even in the presence of a small number of images for learning. The new enhanced images are then sent to a face recognition algorithm for classification. The proposed framework outperforms classical enhancement techniques and leads to an increase in the face recognition performance.
Treatment of cellulite deformities of the thighs with topical aminophylline gel

Cellulite deformities of the lateral thighs and buttocks are a poorly understood phenomenon, an embarrassment for even the fittest of women, and a frustration for the cosmetic surgeon. Multiple treatments have been attempted for this particular malady, and none have been universally successful. Early reports of the use of topical aminophylline/theophylline gel have shown pleasing improvements in the texture of the skin as early as three weeks after application.
Friend or Foe? A Case Study of iPad Usage during Small Group Reading Instruction

The purpose of this case study is to examine how two early childhood teachers in one university laboratory school utilize the iPad in their reading practices. Data collection involved: (a) observations, (b) audio recordings, and (c) researcher journal. Findings indicate that the teachers had a continuum of purposeful uses for the iPad and the associated applications during their small group reading instruction. In addition, the teachers had mixed overall perceptions toward using the iPad as an effective literacy tool. Implications for practice are presented.
Vliv tepelného stárnutí na pevnost vazby ortodontického adhezivního systému bez přebytků Shear bond strength of a flash-free orthodontic adhesive system after thermal aging procedure

Abstrakt Cíl práce: Cílem této studie bylo porovnat pevnost vazby ke sklovině (SBS) bezpřebytkového (flash-free) předem naneseného ortodontického adheziva a ortodontického adheziva s kompomerem před a po termocyklování. Pro oba materiály byl také stanoven Index zbytků adheziva (ARI). Materiál a metodika: Celkem 120 lidských premolárů bylo náhodně rozděleno do dvou skupin (n = 60) podle použitého ortodontického ahdeziva: APC Flash-Free Adhesive Coated Appliance System (APC FF) a Transbond PLUS Color Change Adhesive (TP) jako kontrola. Poté byly vytvořeny tři podskupiny podle použité metody stárnutí: skladování ve vodě (37°C, 24h), termocyklování 10 000 nebo 20 000 cyklů. Následně byla testována SBS a vyhodnocen ARI pro každý vzorek. Výsledky byly analyzovány obousměrným ANOVA a Tukeyho Chi-square testy (p < 0,05).
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEY

— Data mining environment produces a large amount of data, that need to be analyses, pattern have to be extracted from that to gain knowledge. In this new period with rumble of data both ordered and unordered, by using traditional databases and architectures, it has become difficult to process, manage and analyses patterns. To gain knowledge about the Big Data a proper architecture should be understood. Classification is an important data mining technique with broad applications to classify the various kinds of data used in nearly every field of our life. Classification is used to classify the item according to the features of the item with respect to the predefined set of classes. This paper provides an inclusive survey of different classification algorithms and put a light on various classification algorithms including j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.