Miracle-dz/UniFAR-FTUs huggingface dataset:
In vitro culture of ovine primordial follicles in media supplemented with coconut water

We investigated the effect of minimal essential medium (MEM), coconut water solution (CWS), and MEM supplemented with CWS on viability, activation, and growth of ovine primordial follicles. Ovarian cortical slices were cultured in vitro for 1, 3, or 5 days in MEM, CWS, or MEM supplemented with 5, 10, 20, 50, 80, 90, or 95% CWS. Both MEM and CWS were supplemented with FCS, ITS, glutamine, pyruvate, and hypoxanthine. Before (Day 0) and after 1, 3, or 5 days of in vitro culture, follicles were isolated from the ovarian tissue, and their diameters were measured as well as evaluated by using trypan blue staining. Based on the uptake or not of trypan blue, follicles were classified as viable or nonviable, respectively. Follicles were also classified as primordial or developing based on their morphology. The results showed a reduction (P < 0.05) in viable follicles after 1 day of culture compared to uncultured controls with the exception of MEM supplemented with up to 20% CWS. After 5 days of culture, significantly higher percentages of viable follicles were observed in MEM and MEM plus 5% CWS. A percentages of developing follicles increased significantly after culturing cortical tissue for 5 days in MEM or MEM plus 5 or 10% CWS. In all media tested, a significant increase in follicle diameter was observed after 5 days of culture with MEM only or MEM with 10% CWS. In conclusion, sheep primordial follicles are successfully activated in vitro after culture in MEM alone or with 5 or 10% CWS.
Distributed , Concurrent , and Independent Access to Encrypted Cloud Databases B

Since data in cloud will be placed anywhere, because of the critical nature of the applications, it is important that clouds be secure. The major security challenge with clouds is that the owner of the data may not have control of where the data is placed. This is because if one wants to exploit the benefits of using cloud computing. This requirement imposes clear data management choices: original plain data must be accessible only by trusted parties that do not include cloud providers, intermediaries, and Internet; in any untrusted context, data must be encrypted. Satisfying these goals has different levels of complexity depending on the type of cloud service. We propose SecureDBaaS as the first solution that allows cloud tenants to take full advantage of DBaaS qualities, such as availability, reliability, and elastic scalability, without exposing unencrypted data to the cloud provider. The architecture design was motivated by goal: to allow multiple, independent, and geographically distributed clients to execute concurrent operations on encrypted data, including SQL statements that modify the database structure.
AN OPTIMIZATION BASED APPROACH TO MULTI-BLOCK STRUCTURED GRID GENERATION

Grid generation is a necessary step for all computational fluid dynamics (CFD) calculations. It has a big impact on the convergence speed and quality of the results. Especially within a design optimization process (e.g. [1]) grid generation is crucial. The geometries may vary greatly which often leads to failures in the grid generation step or results in poor quality grids which lead to non-convergence or to doubtful results. Multi-block structured meshes, compared with unstructured grids, need to follow a complex topology and are more difficult to create but provide faster CFD calculations and more accurate gradient estimations.
3D Reconstruction and Visualization : a comparison between 2D, 3D CT Images

In this paper, three-dimensional (3D) reconstruction and visualization of several 2D CT images based on MATLAB were investigated. The 3D visualization of CT images will provide the realistic 3D environment; which increases the efficiency of diagnosis and treatment in medicine. Here, the marching cubes algorithm was used for surface rendering and volume rendering. The splitting-box algorithm presented here which reduces the number of polygonal chains by adapting their size to the shape of the surface. The resulting polygonal chains offer a wide spectrum for representing the contour surface. An exact representation is achieved by a new type of generic patches calculated from the polygonal chains. Approximations of different quality may be obtained by combining the algorithm generating the patches with simple triangulations. Finally, 3D reconstruction of 2D CT images was done by using MATLAB programme.
Educational simulator app and web page for exploring Nuclear and Compass Magnetic Resonance

A graphical app and browser-based simulator, CompassMR , was developed for initial Magnetic Resonance (MR) education. It is available at http://drcmr.dk/CompassMR/ and executes directly in most browsers with no further need for software. Easy access and a simple user interface invite student experimentation that improves understanding of basic MR phenomena. The simulator is used to introduce and explore electromagnetism, magnetic dipoles, static and radiofrequency fields, Compass MR, the free induction decay (FID), relaxation, the Fourier transform (FFT), the resonance condition, spin, precession, the Larmor equation, Nuclear MR, resonant excitation (linear and quadrature), and off-resonance effects.
The impact of technological innovation on SME's profitability in Nigeria

There is no doubt that new or improved product or process of production continues to create firm's competitive advantage over others in the market. This study examined the impact of research and development (R&D) expenditure, product and process innovations on small and medium enterprises (SMEs) performance in the manufacturing industry in Nigeria using a survey of 1,000 SMEs with a response rate of 52.1% in year 2009. The results with least squares method showed that R&D spending by the firms as well as product and process innovation has significant impacts on the firm's performance with the probability value of 0.0529, 0.0624 and 0.0086 respectively at 10% level of significant. Also, training of workforce constitutes the major innovation activities in the Nigerian manufacturing SMEs as against in-house and outsourced R&D activities. This study suggests improvement in R&D spending and other technological activities which are expected to increase SMEs' profitability and thus generate more employment in the country.
NeuroImage: Clinical White matter hyperintensity and stroke lesion segmentation and di ﬀ erentiation using convolutional neural networks

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the e ﬀ ects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been speci ﬁ cally developed to segment WMH in MRI but cannot di ﬀ erentiate between WMHs and strokes. Other methods, capable of distinguishing between di ﬀ erent pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task speci ﬁ c, reliable, fully automated method that can segment and di ﬀ erentiate between these two pathological manifestations on MRI has not yet been fully identi ﬁ ed. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and di ﬀ erentiate between WMHs and stroke lesions. Speci ﬁ cally, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up- samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.
Interface Reconfiguration Techniques in Interactive Data Mining and Processing

Data mining tasks are often correlated with complex interaction processes. In the context of data mining software, two perspectives are of particular interest. The first is concerned with the representation of data for visual or aural cues. The second meets the interactive data mining process via an appropriate user interface that offers all necessary operations in a usable and effective way. Data mining processes are often contextand user-dependent processes that differ in the type and complexity of task and the amount of data as well as in the nature of the user’s involvement. This paper describes a new approach to interactive data mining based on Concept Keyboards (CK) and user interface reconfiguration methods.
The Noise Cluster Model A Greedy Solution to the Network Community Extraction Problem

RÉSUMÉ. Cet article présente un algorithme permettant d’extraire une communauté de nœuds densément connectés dans un graphe. La solution proposée à ce problème s’appuie sur une approche semi-supervisé au sens où un ensemble de graines (nœuds appartenant à la communauté à extraire) doit être fourni. En partant de ces nœuds l’algorithme explore le graphe en largeur et décide d’ajouter ou non les nouveaux nœuds rencontrés à la communauté en utilisant deux tests construit à l’aide d’un modèle génératif simple relié au modèle de mélange de graphe de type Erdös-Rényi [DAU 08]. Ce modèle simple sera appelé "noise cluster model". Une méthode d’estimation en ligne est utilisée pour mettre à jour les paramètres du modèle tout au long de la procédure d’extraction de la communauté. Cette approche est donc locale au sens ou elle ne nécessite pas une connaissance exhaustive du graphe ce qui permet d’utiliser celle-ci sur des graphes de tailles quelquonques. Finalement, des expériences sur des communautés réelles de blogs seront présentées pour juger de la pertinence de l’approche proposée.
Numerical Simulation of 3-D One-Way Fluid-Structure Interaction in a Tube with Twisted Tape under Laminar and Turbulent Flow Regime

Fluid-structure interaction (FSI) analysis is an example of a multi-physics problem where the interaction between two different analyses is taken into account. The FSI analysis involves performing a structure analysis taking into account the interaction with the corresponding fluid analysis. The interaction between the two analysis typically takes place at the boundary of the model solution (the fluidstructure interface), where the results of one analysis is passed to the other analysis as a load. The Fluid-Structure Interaction has gained great interest of scholars recently, meanwhile, extensive studies have been conducted by the virtue of numerical methods which have been implemented on heat exchanger models. This paper presents numerical results of the fluid-structure investigation in a circular finned-tube heat exchanger. Results are obtained with the use of ANSYS-15 commercial code. Simulation is conducted in the Reynolds number range of (1009–10172) with tape of twist ratio (H/D = 1.85). In this study, the effect of total deformation of structure, strain and stress for the tube and twisted tape are observed. We performed the one-way FSI analysis on a finned-tube, using the Finite Volume Method with ANSYSfluent solver and the RNG k-ε turbulence model for turbulent flow, to achieve a comprehensive cognition of it. The simulation results show that the maximum deformation occurs in the area before the end of the tape due to high temperature with a maximum stress of (1 MPa and 1.3 MPa) for laminar and turbulent flow respectively. This study highlights that a one-way fluid structure interaction simulation of a real engineering configuration is still a challenging task for today’s commercially available simulation tools.
Comparison and Analysis of the Pixel-based and Object-Oriented Methods for Land Cover Classification with ETM + Data

Automated features extraction techniques have gone a long way to ameliorate pains on image processing and information extraction from remotely sensed image; this has opened many doors for many applications. However, pixel based image classification and feature extraction of Nigerian urban areas are still very difficult due to the spectral similarity of many of the urban features such as roads, bare ground, rock outcrop, built-up areas and many more. This is as a result of the nature of the roads (untarred) and other features like built-up areas with large area coverage of bare ground rather than well landscaped with grass gardens. This makes high classification accuracy of urban feature extraction very difficult to achieve. The performance of urban feature extraction from LANDSAT imagery of Abuja, Nigeria with eCognition is overwhelming. This research compared the result of object oriented image classification (i.e. eCognition) and other traditional (i.e. Erdas Imagine) methods of image classification vis-à-vis urban feature extraction from medium resolution remotely sensed image of Abuja Nigeria which has a very highly similar spectral reflectance value. The result shows overall accuracy of 94.9% as against the overall accuracy of 89% in using the traditional method in Erdas Imagine. While the ever difficult and very confused class differentiation in image classification in Nigerian urban areas between built-up and bare ground was clearly separated with high level of accuracy Keyword: Pixel Based Classification, Object-Oriented Classification, Automatic Feature Extraction, e Cognition.
MAPPING OF OBJECT AWARENESS USING MOBILE VISION SERVICES

In the context of mapping and mobile vision services, the recognition of objects of the urban infrastructure plays an important role. E.g., the recognition of buildings can foster pointers to relevant information spaces, such as, annotation services, it can provide a semantic index for scene understanding, and can enable more efficient navigation by direct reference to landmark buildings. The presented work provides a generic technology for the recognition of urban objects, i.e., buildings, in terms of a reliable mobile vision service. The presented detection system grounds recognition on a Maximum A Posteriori based decision making on weak object hypotheses from local descriptor responses in the mobile imagery. We present an improvement over standard image descriptors by selecting only informative keys for descriptor matching. Selection is applied first to reduce the complexity of the object model and second to accelerate detection by selective attention. We trained a decision tree to rapidly and efficiently estimate an image descriptor’s posterior entropy value, retaining only those keys for thorough analysis and voting with high information content. The experiments were performed on typical, low quality mobile phone imagery on urban tourist sites under varying environment conditions (changes in scale, viewpoint, illumination, varying degrees of partial occlusion). We demonstrate in this challenging outdoor object detection task the superiority in using informative keys to standard descriptors using a publicly available mobile phone image database, reporting increased reliability in object/background separation, accurate object identification, and providing a confidence quality measure that enables a highly stable mobile vision service. We show further results on imagery captured from a mobile mapping van that demonstrate the capability to localize complex objects of interest in un-calibrated imagery within urban environments. * Corresponding author
A Unified Approach to Proximal Algorithms using Bregman Distance

We show that proximal gradient algorithm (PGA) with Bregman distance for minimizing the sum of two convex functions can be viewed as proximal point algorithm (PPA) incorporating with another Bregman distance. Consequently, the convergence result of the PGA follows directly from that of PPA, and this leads to a simpler convergence analysis with a tighter convergence rate than existing ones. We further propose and analyze the backtracking line search variant of PGA with Bregman distance.
Traffic and Energy Aware Routing for Heterogeneous Wireless Sensor Networks

The energy-efficiency of routing algorithm is crucial for improving the lifetime of battery constrained Wireless Sensor Networks (WSNs). The consideration of nodes heterogeneity in routing is essential for achieving optimal resource utilization. This letter considers sensor nodes with random initial energies and random disparities in data generation rate (traffic) to model a realistic clustering based WSN suited for heterogeneous sensing applications. The letter presents an energy model for the scenario and proposes a Traffic and Energy Aware Routing (TEAR) scheme to improve the stability period. The simulation results indicate that TEAR outperforms other clustering based routing algorithms under the scenario.
ssessing the monitorability of CO 2 saturation in subsurface saline aquifers rash

We propose a method to assess the geophysical monitorability of petrophysical parameters (in this case CO2 saturation) in subsurface geological reservoirs. The approach is based on measuring and inverting up to six geophysical parameters: Pand S-wave impedances and quality factors (1/attenuation), density and electrical resistivity. We use Shannon’s information measure to quantify the information obtained from the inversion of different combinations of geophysical parameters. We thus develop a generic approach to assess the joint monitorability of supercritical CO2 saturation when CO2 is stored in subsurface reservoirs. Uncertainty analysis shows that expected information is in general nonlinearly related to the level of a priori or measurement uncertainty in the geophysical and petrophysical parameters, to the true CO2 saturation, and also to the measurement frequency in the case of seismic measurements. Prior uncertainties in petrophysical parameters such as porosity have a significant effect on the monitorability, highlighting the need for accurate benchmark (pre-injection) measurements and reservoir characterization in the case urvey design of time-lapse (4D) monitoring. We show that estimates of P-wave seismic attenuation contribute most to the overall information obtained, and that in principle the joint application of different geophysical methods can significantly improve monitorability. We applied the approach to assess the monitorability of the CO2 saturation after 10 years of simulated injection into a saline aquifer reservoir in the nearshore UK North Sea. Application of the approach to design optimal data combinations for hydrocarbon d mo saturation assessment an
An Analysis on Metaphoricity of Design Competition Winners

Metaphor underlies many creative designs by juxtaposing target domain and source domain to generate a novel mapping between them. Among metaphor theories, Andrew Ortony’s salience imbalance theory [4] is useful for determining the metaphoricitythe aesthetic pleasingness of a metaphor depending on maximizing the distance between the different domains involved and minimizing the difference between the features within each domain. This paper is to apply the imbalance model to the metaphor within design competition winners in the field of industrial design. Twenty-eight metaphorical designs chosen from winners of several international competitions were used as stimuli. Given the target and source of each stimulus in a questionnaire, nine subjects were asked to determine the degree of similarity between a target and its source, and the salience of the features of the target and source, respectively. Results reveal the metaphoricity of the stimuli largely depends on the salience imbalance of features, and the salience of common features in the source domain tends to be higher than that in the target domain. The empirical evidence of this study supports the application of imbalance model to product design.
Correlation of BODE Index with Diffusing Capacity of Lung for Carbon Monoxide in Assessing Disease Severity in Chronic Obstructive Pulmonary Disease

Background: Chronic Obstructive Pulmonary Disease(COPD) is the third leading cause of death globally but about 90% of COPD mortality occurs in lowand middle-income nations. There are several studies in literature on role of BODE Index and DLCO (Diffusion capacity of carbon monoxide) as independent factors in predicting prognosis and disease severity in COPD. But there is paucity of data pertaining to relationship between BODE Index and DLCO in establishing disease severity in COPD. In this study, we intend to analyze correlation between BODE index and DLCO in patients with various stages of COPD. Methods: The study was conducted as an observational, cross-sectional, study involving stable COPD patients( diagnosed and grouped according to GOLD guidelines ) of all age groups attending respiratory medicine OPD of a tertiary care hospital . The 73 patients selected were subjected to spirometery,DLCO and Six minute walk Test( 6MWT) after taking proper history and detailed physical examination.Subsequently BODE index was calculated and patients were categorized amongfour quartiles of BODE index, similarly patients were also categorized based on DLCO(Mild, Moderate, Severe,Very severe). Uncategorized and categorized data on BODE index and DLCO were correlated independently various groups of GOLD classification (Group A,B,C& D). Association between various study variables was doneby chisquare test. Spearman correlation coefficient was used to correlate BODE Index and DLCO. Results:Majority of patients in our study were belonging to quartile -2 of BODE score, Medium grade according to CAT score and category “D” according to GOLD. In our study BODE score was much better in predicting the prognosis in COPD than FEV1.BODE index was correlated well with GOLD scoring. Conclusion: Overall BODE index is accurate in categorizing COPD severity. Cross tabulation with DLCO categories and GOLD categories showed better correlation compared to BODE index.