Miracle-dz/UniFAR-FTUs huggingface dataset:
The experience of female patients seeking elective rhinoplasty surgery : a narrative inquiry

The aim of this research is to understand what motivates patients to have an elective rhinoplasty, taking into consideration any societal, familial, cultural and intrapersonal influences. The research question explored how, if at all, surgery impacts women’s embodied sense of themselves and if there is a way of providing psychological support to this patient group. Four female patients were interviewed preoperatively and postoperatively using a narrative inquiry approach. The research offers a detailed qualitative contribution in a field that is predominantly quantitatively studied. The research explored the nuances of why female patients want to have rhinoplasty surgery. The narratives of the patients showed that the motivation to have surgery is based on external and internal factors. The external factors revealed the following: society’s acceptance of cosmetic surgery, the influence of the media, the experience of the consultation and how risk is understood. The internal factors were influenced by the patients’ experience of the death of significant family members, a need to separate from patriarchal and matriarchal family members, feelings of body shame, a fear of negative evaluation from others, and objectification of their body in preparation for surgery. Following surgery, a psychological shift was discussed by each patient; this indicates that cosmetic surgery does indeed have a psychological impact. The implication for practice is that clinicians need to have a better understanding of the motivation of this patient group to be able to offer the appropriate psychological support. Awareness also needs to be raised with surgeons, to help them better understand how the surgery they perform can have a psychological impact. This research showed that patients proceeding with cosmetic surgery could benefit from having specialised psychological support preoperatively and postoperatively. This would contribute to more realistic expectations for surgery and, hopefully, a better outcome for both patient and surgeon.
Classification model of network users based on optimized LDA and entropy

The classification of Network users is very important in user behavior analysis. The algorithm which was based entropy and latent Dirichlet allocation (LDA) was used in this paper. It is important but difficult to select an appropriate number of topics for a specific dataset. Entropy was first used to solve the problem. A concept named difference-entropy was built to determine the number of topics. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.
A Declarative Characterization of Disjunctive Paraconsistent Answer Sets

In this work, paraconsistent answer sets for extended disjunctive logic programs are presented as a fully declarative approach. In order to do so, we introduce a frame-based semantics. Frames are a powerful and elegant tool which have been used to characterise and study substructural logics. Unlike the original definition, no kind of syntactic transformation is employed. Indeed, paraconsistent answer sets are defined by minimising models satisfying some conditions. Considering that paraconsistent answer sets embed both answer sets and stable models, these semantics are also captured via frames.
Analysis and implementation of the Large Scale Video-on-Demand System

Next Generation Network (NGN) provides multimedia services over broadband based networks, which supports high definition TV (HDTV), and DVD quality video-on-demand content. The video services are thus seen as merging mainly three areas such as computing, communication, and broadcasting. It has numerous advantages and more exploration for the large-scale deployment of video-on-demand system is still needed. This is due to its economic and design constraints. It's need significant initial investments for full service provision. This paper presents different estimation for the different topologies and it require efficient planning for a VOD system network. The methodology investigates the network bandwidth requirements of a VOD system based on centralized servers, and distributed local proxies. Network traffic models are developed to evaluate the VOD system's operational bandwidth requirements for these two network architectures. This paper present an efficient estimation of the of the bandwidth requirement for the different architectures.
Study of Gene Trafficking between Acanthamoeba and Giant Viruses Suggests an Undiscovered Family of Amoeba-Infecting Viruses

The nucleocytoplasmic large DNA viruses (NCLDV) are a group of extremely complex double-stranded DNA viruses, which are major parasites of a variety of eukaryotes. Recent studies showed that certain unicellular eukaryotes contain fragments of NCLDV DNA integrated in their genome, when surprisingly many of these organisms were not previously shown to be infected by NCLDVs. These findings prompted us to search the genome of Acanthamoeba castellanii strain Neff (Neff), one of the most prolific hosts in the discovery of giant NCLDVs, for possible DNA inserts of viral origin. We report the identification of 267 markers of lateral gene transfer with viruses, approximately half of which are clustered in Neff genome regions of viral origins, transcriptionally inactive or exhibit nucleotide-composition signatures suggestive of a foreign origin. The integrated viral genes had diverse origin among relatives of viruses that infect Neff, including Mollivirus, Pandoravirus, Marseillevirus, Pithovirus, and Mimivirus. However, phylogenetic analysis suggests the existence of a yet-undiscovered family of amoeba-infecting NCLDV in addition to the five already characterized. The active transcription of some apparently anciently integrated virus-like genes suggests that some viral genes might have been domesticated during the amoeba evolution. These insights confirm that genomic insertion of NCLDV DNA is a common theme in eukaryotes. This gene flow contributed fertilizing the eukaryotic gene repertoire and participated in the occurrence of orphan genes, a long standing issue in genomics. Search for viral inserts in eukaryotic genomes followed by environmental screening of the original viruses should be used to isolate radically new NCLDVs.
Living Flows: Enhanced Exploration of Edge-Bundled Graphs Based on GPU-Intensive Edge Rendering

This paper describes an approach exploiting the full capabilities of GPU's to enhance the usability of edge bundling in real applications. Edge bundling, as well as other edge clustering approaches relying on the use of high quality edge rerouting. Typical approach for drawing edge-bundled graph is to render edges as curves. But curves generation can have a relatively high computational costs and do not easily comply with real-time interaction. Furthermore, while edge bundling provides a much better overall readability of a graph, the bundles make it more difficult to recover local information. Our goal was thus to provide fluid interaction allowing the recovery of local information through specific interaction techniques. The system we built offers folklore or classical interaction such as zoom & pan, fish-eye and magnifying lens. We also implemented the Bring & Go technique by Tominski et al. We proposed an approach exploiting the full computing power of GPU's when rendering graph edges as parametric splines. The gain in efficiency when running all curves computations on the GPU turns bundling techniques into techniques that can be embedded in interactive systems concerned with graphs of several thousands of nodes and edges.
A Galerkin/Neural-Network-Based Design of Guaranteed Cost Control for Nonlinear Distributed Parameter Systems

This paper presents a Galerkin/neural-network- based guaranteed cost control (GCC) design for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities. A parabolic PDE system typically involves a spatial differential operator with eigenspectrum that can be partitioned into a finite-dimensional slow one and an infinite-dimensional stable fast complement. Motivated by this, in the proposed control scheme, Galerkin method is initially applied to the PDE system to derive an ordinary differential equation (ODE) system with unknown nonlinearities, which accurately describes the dynamics of the dominant (slow) modes of the PDE system. The resulting nonlinear ODE system is subsequently parameterized by a multilayer neural network (MNN) with one-hidden layer and zero bias terms. Then, based on the neural model and a Lure-type Lyapunov function, a linear modal feedback controller is developed to stabilize the closed-loop PDE system and provide an upper bound for the quadratic cost function associated with the finite-dimensional slow system for all admissible approximation errors of the network. The outcome of the GCC problem is formulated as a linear matrix inequality (LMI) problem. Moreover, by using the existing LMI optimization technique, a suboptimal guaranteed cost controller in the sense of minimizing the cost bound is obtained. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.
A Sparsity Preserving Stochastic Gradient Method for Composite Optimization

We propose new stochastic gradient algorithms for solving convex composite optimization problems. In each iteration, our algorithms utilize a stochastic oracle of the gradient of the smooth component in the objective function. Our algorithms are based on a stochastic version of the estimate sequence technique introduced by Nesterov (Introductory Lectures on Convex Optimization: A Basic Course, Kluwer, 2003). We establish convergence results for the expectation and variance as well as large deviation properties of the objective value of the iterates generated by our algorithm. When applied to sparse regression problems, our algorithms have the advantage of readily enforcing sparsity at all iterations. We present some numerical experiments on simulated data sets.
Molecular detection of p16 promoter methylation in the serum of patients with esophageal squamous cell carcinoma.

PURPOSE AND EXPERIMENTAL DESIGN Recent evidence shows that the presence of promoter hypermethylation of tumor suppressor genes has been demonstrated in the serum DNA of patients with various cancers such as lung, liver, and head and neck cancer. We have examined promoter hypermethylation of the p16 gene in esophageal squamous cell carcinoma (SCC) using methylation-specific PCR to detect tumor DNA in the serum. RESULTS Aberrant promoter methylation of the p16 gene was detected in 31 of 38 (82%) esophageal SCCs. Subsequently, we tested for promoter methylation in the paired serum DNA of 31 patients with a p16 alteration in the primary tumor. We found that 7 of these 31 (23%) patients had the same methylation changes in the serum DNA. CONCLUSIONS This result indicates that promoter methylation present in the tumors of esophageal SCC patients can be detected in the serum of the same patient and that this approach can potentially be used for the screening and monitoring of the disease.
Surface noise cancellation for acoustic downhole communication systems

This article investigates the usefulness of using two sensors and a blind-separation algorithm in reducing the effect of surface noise in downhole communication systems. The acoustic channel provides a challenging environment for the acoustic waves that propagate from the downhole to the surface of an oil or gas well. As a result, acoustic waves experience a noticeable attenuation before reaching the surface of the well. Consequently, surface noise, which is generated by the surface tools, dominates the performance of acoustic downhole communication systems that have the receiver unit close to the well surface. The application of a two-receiver noise cancellation algorithm is investigated. The article also describes a testbed that was designed to study the effectiveness of the proposed algorithm in reducing the impact of the surface noise. The communication system was built using two speakers, five connected segments of 7 inch production pipes, and two microphones. One of the speakers was used to transmit a noise-like signal in order to simulate the surface noise. The noise cancellation algorithm was applied to the outputs of the two microphones, and the quality of the acoustic signals is investigated after applying the noise cancellation solution. Results of this work emphasize the usefulness of the proposed solution in enhancing the performance of the acoustic downhole communication systems.
Measurement of Multitasking with Focus Shift Analysis

Perhaps one of the most prevalent, yet least understood, forms of modern behavior is human multitasking aided by technology. Multitasking is both difficult to define and to measure. Based on Activity Theory and the concept of focus shifts, this study defines multitasking as shifts that occur when the flow of work is interrupted and tasks are interchanged during a session. The analysis of focus shift diversity, with respect to the tasks that receive attention, enables researchers to measure multitasking. With this approach, several indices of diversity are examined in order to provide recommendations about alternative measurement choices. This methodological study advances our understanding of the possibilities and limitations of using diversity indices for measuring multitasking.
A Robust Indoor Pedestrian Tracking System with Sparse Infrastructure Support

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.
Coordinated SLNR Based Precoding in Large-Scale Heterogeneous Networks

This paper focuses on the downlink of large-scale two-tier heterogeneous networks composed of a macrocell overlaid by microcell networks. Our interest is on the design of coordinated beamforming techniques that allow to mitigate the intercell interference. Particularly, we consider the case in which the coordinating base stations have imperfect knowledge of the channel state information. Under this setting, we propose a regularized signal-to-leakage-and-noise ratio (SLNR) based precoding design in which the regularization factor is used to allow better resilience with respect to the channel estimation errors. Based on tools from the random matrix theory, we provide an analytical analysis of the signal-to-noise ratio and SLNR performances. These results are then exploited to propose a proper setting of the regularization factor. Simulation results are finally provided in order to validate our findings and to confirm the performance of the proposed precoding scheme.
Antioxidant and antiapoptotic effects of darbepoetin-α against traumatic brain injury in rats

Introduction In this study, we tried to determine whether darbepoetin-α would protect the brain from oxidative stress and apoptosis in a rat traumatic brain injury model. Material and methods The animals were randomized into four groups; group 1 (sham), group 2 (trauma), group 3 (darbepoetin α), group 4 (methylprednisolone). In the sham group only the skin incision was performed. In all the other groups, a moderate traumatic brain injury modelwas applied. Results Following trauma both glutathione peroxidase, superoxide dismutase levels decreased (p < 0.001 for both); darbepoetin-α increased the activity of both antioxidant enzymes (p = 0.001 and p < 0.001 respectively). Trauma caused significant elevation in the nitric oxide synthetase and xanthine oxidase levels (p < 0.001 for both). Administration of darbepoetin-α significantly decreased the levels of nitric oxide synthetase and xanthine oxidase (p < 0.001 for both). Also, trauma caused significant elevation in the nitric oxide levels (p < 0.001); darbepoetin-α administration caused statistically significant reduction in the nitric oxide levels (p < 0.001). On the other hand, malondialdehyde levels were increased following trauma (p < 0.001), and darbepoetin α significantly reduced the malondialdehyde levels (p < 0.001). Due to the elevated apoptotic activity following the injury, caspase-3 activity increased significantly. Darbepoetin-α treatment significantly inhibited apoptosis by lowering the caspase-3 activity (p < 0.001). In the darbepoetin group, histopathological score was lower than the trauma group (p = 0.016). Conclusions In this study, darbepoetin-α was shown to be at least as effective as methylprednisolone in protecting brain from oxidative stress, lipid peroxidation and apoptosis.
XML Document Clustering Using Common XPath

XML is becoming a common way of storing data. The elements and their arrangement in the document’s hierarchy not only describe the document structure but also imply the data’s semantic meaning, and hence provide valuable information to develop tools for manipulating XML documents. In this paper, we pursue a data mining approach to the problem of XML document clustering. We introduce a novel XML structural representation called common XPath (CXP), which encodes the frequently occurring elements with the hierarchical information, and propose to take the CXPs mined to form the feature vectors for XML document clustering. In other words, data mining acts as a feature extractor in the clustering process. Based on this idea, we devise a path-based XML document clustering algorithm called PBClustering which groups the documents according to their CXPs, i.e. their frequent structures. Encouraging simulation results are observed and reported.
Effect of pre-heating on the mechanical properties of silorane-based and methacrylate-based composites

Background The use of composites in dental restoration has been commonly criticized, due to their underwhelming mechanical properties. This problem may be solved partially by preheating. The present research aims to determine the effect of preheating on the mechanical properties of two different classes of composites. Material and Methods A Silorane-based (Silorane) and a Methacrylate-based (Z250) composite were preheated to different temperatures (25, 37, and 68 °C) and afterwards were tested with the appropriate devices for each testing protocol. The material’s flexural strength, elastic modulus, and Vickers microhardness were evaluated. Two-way ANOVA, and Tukey’s post hoc were used to analyze the data. Results Microhardness and elastic modulus increased with preheating, while flexural strength values did not increase significantly with preheating. Furthermore the methacrylate-based composite (Z250) showed higher values compared to the Silorane-based composite (Silorane) in all the tested properties. Conclusions Preheating Silorane enhances the composite’s microhardness and elastic modulus but does not affect its flexural strength. On the other hand, preheating Z250 increases its microhardness but does not change its flexural strength or elastic modulus. In addition, the Z250 composite shows higher microhardness and flexural strength than Silorane, but the elastic modulus values with preheating are similar. Therefore Z250 seems to have better mechanical properties making it the better choice in a clinical situation. Key words:Composite, elastic modulus, flexural strength, microhardness, preheating.
LOW PHASE NOISE CMOS RING OSCILLATOR VCOs FOR FREQUENCY SYNTHESIS

We propose a methodology that uses a new phase noise model to trade-off phase noise and power dissipation in the design of ring oscillators suitable for frequency synthesis. We compare the theoretical phase noise performance of three buffer stages using clamped, symmetric and cross-coupled loads, respectively. We propose a cross-coupled buffer topology that achieves lower phase noise by exploiting symmetry. This achieved a 95% reduction in the 1/f 3 corner frequency of the phase noise characteristic.
SENTIMENT ANALYSIS USING REPRESENTATIVE TERMS - A GROUPING APPROACH FOR BINARY CLASSIFICATION OF DOCUMENTS

Automatic classification of customer reviews as eit her positive or negative has been of great interest among the academic and business community in the recent t imes. In this paper, an attempt has been made to represent the text documents using just eight repre sentative terms (RT) viz. good, very good, excellen t, recommended, bad, very bad, disgusting, and never r ecommended. Thus a new way of representing text documents as a structured data matrix has been crea ted. A consistent classification accuracy of near 8 0% and above was achieved for datasets of various size s ranging from 403 to 25000. The precision (P), recall(R) and F-Measure were also very consistent a nd comparable to the previously reported results. A comparative analysis of classification performance has been carried out using machine learning algorit hms like Naive Bayes (NB), Bayesian logistic regression (BLR), multi layer perceptron (MLP) etc., revealed that the proposed way of representing the text docu ments results in consistently superior performance.
Rectangular microstrip patch antenna based on resonant circuit approach

This paper presents the investigation based upon the resonant circuit approach to characterize the rectangular microstrip patch antenna from the low-pass prototype lumped element. The physical layout of the rectangular microstrip patch antenna based on single-mode and dual-mode will be established. An improvement on bandwidth of the antenna can be achieved by increasing the number of modes. In the paper, the understanding of microwave filter synthesis technique is applied in order to obtain the resonance at 2 GHz. A notch technique is used in the design to produce dual-mode frequencies on the microstrip patch antenna. The prototype circuit and proposed physical layout of the single and dual-mode microstrip patch antennas are demonstrated through the analysis of circuit and EM simulations in order to proof the proposed concept. This study would be useful to realize antenna for broadband applications as well as to investigate the appropriate technique for integrating antenna and microwave filter.