Miracle-dz/UniFAR-FTUs huggingface dataset:
Electroseismic waves from point sources in layered media

In a porous material saturated by a fluid electrolyte, mechanical and electromagnetic (EM) disturbances are coupled. The coupling is electrokinetic in nature. The seismic waves generate relative fluid-solid motion that induces an electrical streaming current. When a seismic pulse traverses contrasts in elastic and/or fluid-chemistry properties, the streaming-current imbalance creates dipolar and multipolar charge separations across the interface that, in turn, produce EM disturbances that are measurable at the earth's surface. This paper numerically determines a full-waveform electroseismic point-source response in a stratified porous medium. It is shown that the macroscopic governing equations controlling the coupled electromagnetics and acoustics of porous media decouple into two sets corresponding to vertical or horizontal polarization of the transverse wave fields. The frequency content of the converted EM field has the same frequency content (at the generating interface) as the incident seismic pulse. Snapshots in time and converted EM amplitudes versus source to antenna offset are calculated for contrasts in mechanical and/or electrical medium properties. The converted EM radiation pattern away from the interface is similar to having an effective vertical-electric dipole centered right beneath the source on the contrast. The transverse magnetic mode amplitudes fall off rapidly with distance, from the generating interface thus suggesting the importance of a vertical electroseismic profiling geometry to record the converted EM signal at antennas close to an interface of interest.
Comparing recurrent networks for time-series forecasting

This paper provides a comparison between two methods for time series forecasting. The first method is based on traditional recurrent neural networks (RNNs) while the second method is based in Reservoir Computing (RC). Reservoir Computing is a new paradigm that offers an intuitive methodology for using the temporal processing power of RNNs without the inconvenience of training them. So we decided to compare the advantages / disadvantages of using Reservoir Computing and RNNs in the problem of time series forecasting. The first method uses a Nonlinear Autoregressive Network with exogenous inputs (NARX). Optimization was carried out on the NARX architecture through an optimization procedure focused on the best mean squared error (MSE) metrics in the training set. The second method, called RCDESIGN, combines an evolutionary algorithm with Reservoir Computing and simultaneously looks for the best values of parameters, topology and weight matrices without rescaling the reservoir by the spectral radius. Nevertheless RCDESIGN has yielded fast tracking and excellent performance in some benchmark problems including the Narma and Mackey-Glass time-series.
PROTEINS, SUGARS AND ION LEAKAGE AS A SELECTION CRITERION FOR THE SALT TOLERANCE OF THREE SORGHUM CULTIVARS AT SEEDLING STAGE GROWN UNDER NACL AND NICOTINAMIDE

The interactive effects of salinity stress (0.90 MPa NaCl) and seeds presoaking in 100 ppm. nicotinamide (vitamin PP) in three sorghum cultivars (Dorado, Hagen Shandawil and Giza 113) were studied to examine their response to salinity stress and the possible role played by vitamin PP in regulating saltinduced changes in seedling growth, protein patterns and some relevant metabolic activities of these cultivars. According to the dry mass production and tissue water content, the cv. Dorado seemed to be more or less unaffected by salinity, whereas the cv. Giza 113, was severely reduced, the cv. Hagen Shandawil was almost intermediated. Consequently, carbohydrate and protein contents were increased in cv. Dorado and Hagen Shandawil, while the opposite occurred in cv. Giza 113. On the other hand, while proline content decreased in cv. Hagen Shandawil and Giza 113, it markedly increased in cv. Dorado as a result of salinity stress. Na / K ratio was higher in cv. Dorado than in cv. Giza 113. cv. Hagen Shandawil was intermediate. Treatment with vitamin PP was accompanied with higher values of saccharids and protein compared with the untreated seedlings, which partially alleviated the adverse effect of salinity on growth. This was linked with a marked variation in protein patterns among these cvs. Salinity stress led to the appearance of 67 and 26 kD (in cv. Dorado) and 45 kD (in cv. Hagen Shandawil). While in cv. Giza 113, no new band appeared. Seeds treated with vitamin PP lead to appearance of bands with 67 and 46 kD in cv. Dorado and 38, 26 and 18 kD in cv. Hagen Shandawil, while in cv. Giza 113, the 67 and 26 kD which had disappeared by salinity, they reappeared under the interactive effect of salinity and vitamin PP. This confirms the metabolic importance of the 67 and 26 kD proteins in salt tolerance.
Linear snow accumulation models and applications

Noise reduction is an important pre-processing step in image processing. The undesirable side effect of many noise reduction methods is that they always blur the characteristic information of the images, the boundaries, which are supposed to be enhanced in image processing. Understanding the digital image as an uniform sample of a spatial curve and the de-noising process as the evolution of the spatial curve, we find that natural snow accumulation process is very similar as the evolution of the ground surface, which also can be viewed as a spatial curve. Based on this observation, one-dimensional and two-dimensional linear snow accumulation models are established in this paper. With two-dimensional linear snow accumulation model, we de-noised a 256-by-256 image with random noise. De-noising results are compared with that by the discrete 2-D wavelet transform, the reduced 2-D dual-tree wavelet transform, and the complex 2D dual-tree wavelet transform. These results show that our two-dimensional linear snow accumulation model works well in image noise reduction.
Gastrointestinal Infection with Mexican TcI Trypanosoma cruzi strains: Different Degrees of Colonization and Diverse Immune Responses

Mexican Ninoa and Queretaro (Qro) TcI strains of Trypanosoma cruzi have shown different degrees of virulence, and the two strains produce heterogeneous immune responses in the hearts of infected mice. This work shows that the same strains can invade the intestine by an intraperitoneal route and establish an infection, mainly in the colon. The three segments of the small intestine (duodenum, jejunum and ileum) were infected to a lesser degree than the colon. Despite the fact that parasites were predominantly found in the colon, an obvious inflammatory reaction was observed in the submucosal layer along the entire intestinal tract, with the virulent Qro strain causing significantly more areas of higher immune infiltration. A clear recruitment of CD4+ and CD8+ T lymphocytes to the mesenteric ganglia was observed during infection with the virulent strain. Macrophages were also differentially distributed in the gastrointestinal tract. These later cells infiltrated fewer amastigote nests in the mice infected with the Qro strain than in the mice infected with the Ninoa strain. When IFN-γ, TNF-α, and IL-4 levels were measured, an increase in these cytokines was observed compared with the uninfected mice. The role of these inflammatory reactions in the pathogenesis of Chagas enteropathy is also discussed in this paper.
Noise Resilient Recovery Algorithm for Compressed Sensing

In this article, we discuss a novel greedy algorithm for the recovery of compressive sampled signals under noisy conditions. Most of the greedy recovery algorithms proposed in the literature require sparsity of the signal to be known or they estimate sparsity, for a known representation basis, from the number of measurements. These algorithms recover signals when noise level is significantly low. We propose Entropy minimization based Matching Pursuit (EMP) which has the capability to reject noise even when noise level is comparable to that of signal level. The proposed algorithm can cater to compressible signals and signals for which sparsity is not known in advance. Simulation study of the proposed scheme shows improved robustness to white Gaussian noise in comparison with the conventional greedy recovery algorithms.
DeepInterface: Protein-protein interface validation using 3D Convolutional Neural Networks

Motivation Protein–protein interactions are crucial in almost all biological processes. Proteins interact through their interfaces. It is important to determine how proteins interact through interfaces to understand protein binding mechanisms and to predict new protein-protein interactions. Results We present DeepInterface, a deep learning based method which predicts, for a given protein complex, if the interface between the proteins of a complex is a true interface or not. The model is a 3-dimensional convolutional neural networks model and the positive datasets are obtained from all complexes in the Protein Data Bank, the negative datasets are the incorrect solutions of the docking decoys. The model analyzes a given interface structure and outputs the probability of the given structure being an interface. The accuracy of the model for several interface data sets, including PIFACE, PPI4DOCK, DOCKGROUND is approximately 88% in the validation dataset and 75% in the test dataset. The method can be used to improve the accuracy of template based PPI predictions.
Participation without a voice? Rural women’s experiences and participation in local governance activities in the Abura–Asebu–Kwamankese district of Ghana

Abstract This paper reports on a study conducted to explore rural women’s experiences and participation in local governance activities in the Abura–Asebu–Kwamankese district of Ghana. Using a qualitative case study design, the study collected data from 53 women with different socio-economic backgrounds in focus group discussions and interview sessions. The study results show that while women are as likely as men to be engaged in local governance activities, they are less likely to be recruited into key decision-making roles. Women’s participation is linked more to physical presence during local governance activities without much power to influence and control decisions. The study found that women want to be more empowered through their formal and “political” education in order to increase their levels of assertiveness and confidence to enable them assume key decision-making roles during their participation in local governance activities. Also, government must enforce strictly the implementation of the quota system to increase women’s appointments into the district assemblies. Finally, the local government elections should be regulated to adhere to its non-partisan nature in order to stimulate more women’s interest and participation.
Partitioned ROBDDs-a compact, canonical and efficiently manipulable representation for Boolean functions

We present a new representation for Boolean functions called Partitioned ROBDDs. In this representation we divide the Boolean space into 'k' partitions and represent a function over each partition as a separate ROBDD. We show that partitioned-ROBDDs are canonical and can be efficiently manipulated. Further they can be exponentially more compact than monolithic ROBDDs and even free BDDs. Moreover, at any given time, only one partition needs to be manipulated which further increases the space efficiency. In addition to showing the utility of partitioned-ROBDDs on special classes of functions, we provide automatic techniques for their construction. We show that for large circuits our techniques are more efficient in space as well as time over monolithic ROBDDs. Using these techniques, some complex industrial circuits could be verified for the first time.
A novel approach to ensemble learning in distributed data mining

Several data mining techniques have been proposed to take out hidden information from databases. Data mining and knowledge extraction becomes challenging when data is massive, distributed and heterogeneous. Classification is an extensively applied task in data mining for prediction. Huge numbers of machine learning techniques have been developed for the purpose. Ensemble learning merges multiple base classifiers to improve the performance of individual classification algorithms. In particular, ensemble learning plays a significant role in distributed data mining. So, study of ensemble learning is crucial in order to apply it in real-world data mining problems. We propose a technique to construct ensemble of classifiers and study its performance using popular learning techniques on a range of publicly available datasets from biomedical domain.
Towards Syntax-aware Compositional Distributional Semantic Models

Compositional Distributional Semantics Models (CDSMs) are traditionally seen as an entire different world with respect to Tree Kernels (TKs). In this paper, we show that under a suitable regime these two approaches can be regarded as the same and, thus, structural information and distributional semantics can successfully cooperate in CSDMs for NLP tasks. Leveraging on distributed trees, we present a novel class of CDSMs that encode both structure and distributional meaning: the distributed smoothed trees (DSTs). By using DSTs to compute the similarity among sentences, we implicitly define the distributed smoothed tree kernels (DSTKs). Experiment with our DSTs show that DSTKs approximate the corresponding smoothed tree kernels (STKs). Thus, DSTs encode both structural and distributional semantics of text fragments as STKs do. Experiments on RTE and STS show that distributional semantics encoded in DSTKs increase performance over structure-only kernels.
The new generation of PowerPC VMEbus front end computers for the CERN SPS and LEP accelerators control system

The CERN SPS and LEP PowerPC project is aimed at introducing a new generation of PowerPC VMEbus processor modules running the LynxOS real-time operating system. This new generation of front-end computers using the state-of-the-art microprocessor technology will first replace the obsolete XENIX PC based systems (about 140 installations) successfully used since 1988 to control the LEP accelerator. The major issues addressed in the scope of this large scale project are the technical specification for the new PowerPC technology, the re-engineering aspects, the interfaces with other CERN-wide projects, and the set up of a development environment. This project also offers support for other major SPS and LEP projects interested in the PowerPC microprocessor technology.
Using a video app as a tool for reflective practice

ABSTRACT Background: Videos have been shown to be beneficial in helping teachers reflect on their teaching practice and in contributing to their professional development, as they allow teachers and their mentors and colleagues to analyse teaching from different perspectives. Purpose: This study investigated the perceptions and experiences of some student teachers and their supervisors who used a video application during one practicum period. Student teachers recorded each other’s lessons with the video app, and supervisors oversaw student teachers online, via a portal. The study aimed to explore how far the app constituted a useful tool for student teachers’ professional development and for the supervision of student teachers’ practice. Sample, design and method: The data were collected by gathering the experiences of 12 student teachers and 9 supervisors. Data included focus groups, interviews and video diaries. Analysis was undertaken using principles of content analysis and phenomenographic analysis such as thematising, coding and categorising. Results: Findings suggested that student teachers in general felt that the video app supported their self-reflection. The influence on their professional development may have been limited by factors including lack of guidance for individual and collaborative reflection. Using the video app as a supervisory tool was regarded as challenging in some ways, particularly because video clips taken out of context were unable to capture the classroom’s atmosphere, environment and culture. Conclusions: The study highlights that video excerpts in themselves do not provide a sufficient basis for lesson observation. However, the study’s results draw attention to the app’s potential for guiding individual and collaborative video-based reflection. Consideration should be given to the changes that video-elicited reflection encourages with respect to supervision and its effects on the roles of supervisor and student teacher.
on Circuit , Power and Computing Technologies [ ICCPCT ] Secure Magnetic Resonance Image transmission and tumor detection techniques

The transmission of important medical diagnostic, MRI (Magnetic Resonance Imaging) images are vulnerable to third party hackers who does spoofing and they are able to introduce faulty and noisy data that damage the transmission data, which hinders the proper medical diagnostics, research and credibility of labs and doctors, there is a clear lack of awareness and lack of proper security measures taken in transmission of MRI images in the present labs, hospitals and research centers. This project is helpful to reduce the problem of secure transmission of medical images. There are many algorithms which can be applied to these medical images. This project is helpful to provide good security to medical images during transmission. Tumor detection or prediction in medical science is a very complex and expensive job, which is not yet been addressed properly and no proper graphical user interface exists in an open source environment. This project is dedicated to analyze the best tumor detection from an MRI brain image after several segmentation methods such as K-means Clustering and Watershed segmentation. Security is realized considering various techniques for encryption and decryption of the image. The encryption technique finally selected after the survey was based on Rivest, Shamir & Adleman [RSA] algorithm. Keywords—Digital Image Processing, Cryptography, Tumor, Magnetic Resonance Imaging, Encryption, Decryption, RivestShamir-Adleman Algorithm, K-Means Clusteing, Marker Controlled Watershed Segmentation.
The Dark Side of Leadership: Does Boldness Predict Successful Interpersonal Behaviors?

“Successful psychopaths“ display psychopathic personality traits (i.e. lying, manipulating) in business environments but do not display deviant lifestyles. The Triarchic Psychopathy Model proposed that psychopathy encompasses three phenotypic constructs: boldness, meanness and disinhibition. In line with theoretical assumptions, boldness is adaptive component of psychopathy and can therefore be useful for identifying “successful“ expressions of psychopathy. The aim of this research was to investigate whether boldness added incrementally in predicting emotional intelligence. Data was collected on 495 students (252 male) using the Triarchic Psychopathy Measure, and Wong and Law Emotional Intelligence Scale. Hierarchical regression analysis showed that boldness did add incrementally to meanness and disinhibition in accounting for variance in total WLEIS score (12%), Selfemotional appraisal (5%), and Others' emotional appraisal (2%), Use of emotion (14%) and Regulation of emotion (6%). Results of this study indicate that boldness has adaptive potential and represent a helpful factor for successful interpersonal behaviors such as EI.
How Drosophila males make eggs: it is elemental

Oogenesis in Drosophila requires a significant amount of phosphorus. Oocytes mature in follicles, each of which contains 15 highly polyploid, transcriptionally active chromosomes. We show that the demand for phosphorus is met in part from the male's ejaculate following mating. Females incorporate phosphorus–32 from radiolabelled males into their ovaries, specifically into their nucleic acids. Male–derived phosphorus is also present in significant amounts in mature oocytes. The mechanism by which phosphorus uptake from the female reproductive tract occurs must differ from that previously reported for radiolabelled carbon and hydrogen derived from ejaculatory proteins, as phosphorus uptake is observed in species not showing female incorporation of radiolabel derived from ejaculate proteins.
Building geospatial mashups to visualize information for crisis management

In time-sensitive environments such as disaster management, decision-making often requires rapidly gathering the information from diverse data sources and then visualizing the collected information to understand it. Thus, it is critical to reduce the overhead in data integration and visualization for efficient decision-making. Geospatial mashups can be an effective solution in such environments by providing an integrated approach to extract, integrate and view diverse information. Currently, mashup building tools exist for creating mashups, but none of them deal with the issue of data visualization. An improper visualization of the data could result in users wasting precious time to understand the data. In this paper, we introduce a programming-by-demonstration approach to data visualization in geospatial mashups that allows the users to customize the data visualization.
A secure and delay-efficient scheme for NetLMM

Network-based Localized Mobility Management (NetLMM) is a fairly recent mobility scheme in IETF because the Mobile Node (MN) does not need to have a mobility stack. However, almost previous mobility solutions for the enhanced NetLMM assume good channel quality for direct link between Access Point (AP) and Mobile Node (MN) and secure under attack. However, in real wireless communications, the channel quality of the direct link between AP and MN can be bad and cause a long handover delay. In fact packets between AP and MN can be delivered faster through a relay coding node. In special, network coding has been shown to improve the throughputs of wireless networks and reduce the communication delay. Therefore, in this paper, we propose a Secure and Channel-aware Relay Coding (SCRC) scheme for Network-based Localized Mobility Management (LetLMM). In the proposed scheme, both AP and MN select the proper relay coding node through the RTS-CTS negotiation and encrypt the message using Modified Cryptographic Generated Address (MCGA) to support secure and delay-efficient communication in NetLMM.
Process Development Through Simulation

This paper will focus on the strategic value of process simulation of titanium parts. By using process simulation concurrently with the design process, we are seeing significant benefits in the product development cycle such as, Faster product flow through optimized tool selection and machine parameters (speed, feed, etc) Fewer design changes due to process issues An example of process development through simulation will be presented. The example will focus on the S-92 yoke, a titanium rotorhead part whose existing manufacturing process is based on traditional methods including many different set-ups and long machining times. To improve productivity and reduce manufacturing costs, Sikorsky Aircraft has partnered with Third Wave Systems to implement a simulation-based approach. Using Third Wave’s AdvantEdge modeling environment, optimal machining parameters were determined using physics based models. Results will demonstrate productivity improvements in the areas of part quality, tool life, and labor costs.