Miracle-dz/UniFAR-FTUs huggingface dataset:
Double-walled corrugated structure for bending-stiff anisotropic morphing skins

Morphing promises to enhance the performance of wing-like structures by allowing for operating optimally in a wide range of flying conditions. Yet, a key unresolved problem is the realization of a skin capable of concurrently carrying bending and shear loads, as well as allowing for significant levels of in-plane stretching. In this article, a novel concept exhibiting these desirable characteristics is introduced by means of a double walled structure hereafter called double corrugation. Numerical results show that the double corrugation is capable of offering a high bending stiffness while achieving, with low applied forces, a 20% in-plane stretching. The numerically obtained results are validated with experimental tests, showing the feasibility of the concept. Furthermore, the structural characteristics of the double corrugation are optimized for different material constructions. Nonlinear optimization results concurrently considering strength, bending stiffness, axial compliance and weight show the capabilities of this concept to potentially address the conflicting requirements of morphing skins.
Expanded graphite/Novolac phenolic resin composite as single layer electromagnetic wave absorber for x-band applications

Expanded graphite/novolac phenolic resin (EG/NPR) composites are developed as dielectric absorbers with 4mm thickness and its microwave absorption ability studied in the frequency range 8.4 to 12.4 GHz. A high reflection loss ~ -43 dB is observed at 12.4 GHz for 5 wt. % EG/NPR composites. With the increase in EG concentration in the composite the reflection loss decreases and the absorption peak shifts towards lower frequency. 7 wt. %, 8 wt. % and 10 wt. % composites shows a 10dB absorption bandwidth of order of 1GHz. Light weight EG/NPR composite shows potential to be used as cost-effective broadband microwave absorber over the X-band.
A Novel Two-Axis Load Sensor Designed for in Situ Scratch Testing inside Scanning Electron Microscopes

Because of a lack of available miniaturized multiaxial load sensors to measure the normal load and the lateral load simultaneously, quantitative in situ scratch devices inside scanning electron microscopes and the transmission electron microscopes have barely been developed up to now. A novel two-axis load sensor was designed in this paper. With an I-shaped structure, the sensor has the function of measuring the lateral load and the normal load simultaneously, and at the same time it has compact dimensions. Finite element simulations were carried out to evaluate stiffness and modal characteristics. A decoupling algorithm was proposed to resolve the cross-coupling between the two-axis loads. Natural frequency of the sensor was tested. Linearity and decoupling parameters were obtained from the calibration experiments, which indicate that the sensor has good linearity and the cross-coupling between the two axes is not strong. Via the decoupling algorithm and the corresponding decoupling parameters, simultaneous measurement of the lateral load and the normal load can be realized via the developed two-axis load sensor. Preliminary applications of the load sensor for scratch testing indicate that the load sensor can work well during the scratch testing. Taking advantage of the compact structure, it has the potential ability for applications in quantitative in situ scratch testing inside SEMs.
Cystic fibrosis program directors’ attitudes toward sexual and reproductive health in young women with CF

Advancements in therapy have led to dramatic increases in the life expectancy of patients with cystic fibrosis (CF). As survival improves, young women with CF will have expectations for their sexual and reproductive health (SRH) futures similar to their counterparts without CF. As they face unique CF‐specific SRH concerns, they may rely on CF care providers for disease‐specific SRH care provision. The purpose of this study was to investigate the attitudes of CF providers toward female SRH and perceived barriers in the current CF care model.
Learning discriminative models from structured multi-sensor data for human context recognition

In this work, statistical machine learning and pattern recognition methods were developed and applied to sensor-based human context recognition. More precisely, we concentrated on an effective discriminative learning framework, where input-output mapping is learned directly from a labeled dataset. Non-parametric discriminative classification and regression models based on kernel methods were applied. They include support vector machines (SVM) and Gaussian processes (GP), which play a central role in modern statistical machine learning. Based on these established models, we propose various extensions for handling structured data that usually arise from real-life applications, for example, in a field of context-aware computing. We applied both SVM and GP techniques to handle data with multiple classes in a structured multi-sensor domain. Moreover, a framework for combining data from several sources in this setting was developed using multiple classifiers and fusion rules, where kernel methods are used as base classifiers. We developed two novel methods for handling sequential input and output data. For sequential time-series data, a novel kernel based on graphical presentation, called a weighted walk-based graph kernel (WWGK), is introduced. For sequential output labels, discriminative temporal smoothing (DTS) is proposed. Again, the proposed algorithms are modular, so different kernel classifiers can be used as base models. Finally, we propose a group of techniques based on Gaussian process regression (GPR) and particle filtering (PF) to learn to track multiple targets. We applied the proposed methodology to three different human-motion-based context recognition applications: person identification, person tracking, and activity recognition, where floor (pressure-sensitive and binary switch) and wearable acceleration sensors are used to measure human motion and gait during walking and other activities. Furthermore, we extracted a useful set of specific high-level features from raw sensor measurements based on time, frequency, and spatial domains for each application. As a result, we developed practical extensions to kernelbased discriminative learning to handle many kinds of structured data applied to human context recognition.
Effects of Hydrostatic Pressure on the Mechanical Behavior of Polymers: Polyurethane, Polyoxymethylene, and Branched Polyethylene

Abstract The mechanical behavior of polyurethane, polyoxymethylene and branched low density polyethylene is evaluated at various levels of hydrostatic pressure from atmospheric up to 6.9 Kb. Polyurethane undergoes a “ductile-brittle-ductile” transition accompanied by decreased strain hardening and also exhibits intrinsic yield behavior in compressive tests at higher pressures. A study was made of polyoxymethylene with emphasis below 1.4 Kb and in the vicinity of 5.5 Kb. The effect on the mechanical behavior in the 5.5 Kb region is attributed to a possible pressure-induced shift of a β-transition. Additional data not reported by Sardar et al. are discussed. Tests on branched low density polyethylene show that it behaves under pressure in a way similar to that of medium density polyethylene. With increased pressure, failure of the specimens tested, occurs by necking to a fine point. The elastic modulus versus pressure data indicates a possible pressure-induced shift of a β-transition up to room temperature.
An Image Consulting Framework for document analysis of Internet graphics

A new system approach for image understanding, called the Image Consulting Framework, is proposed. It allows for the validation of image properties. The kinds of image properties considered are textual, textural, hierarchical, color and symbolic. Its main application field is information filtering from images used in World Wide Web documents. The Image Consulting Framework consists of four stages: the color separation stage, the information granulation-verification modules (GVMs), the task stage and the recognition stage. At the base of the framework are the GVMs, which are designed to solve very special tasks. They consists of three parts: a method maintainer, a parameter chooser and a tester (verifier). The parameter chooser uses a given set of parameter settings for different runs of the maintained method on the input images of the GVM. The resulting images are tested for the occurrence of the property for which the GVM is designed. All successful images are put into a queue. The task stage calls new GVMs due to the filling of the queue, and it also assigns input images to the GVMs. All fully-treated images are passed to the recognition stage, where the information extraction is performed.
Injectable hydrogel carriers for the delivery of exo- and endogenous growth factors for bone regeneration

Bone regeneration may benefit from the local delivery of bone-promoting growth factors. In this view, we developed, optimized and evaluated in vivo a novel syringeable hydrogel based on chitosan, intended as a drug carrier. The effect of the carrier pH and nature on recombinant human Bone morphogenetic protein-2 (rhBMP-2) bioactivity was demonstrated. As for carrier pH, rhBMP-2 bone-forming ability decreased when shifting hydrogel pH from acidic to neutral conditions. Observed aggregation and conformational changes of rhBMP-2 in solution might explain the decrease of growth factor bioactivity. Although combining rhBMP-2 with a cross-linked hyaluronan hydrogel induced formation of higher amounts of bone, a more mature bone was observed with the chitosan hydrogel carrier. The addition of β-tricalcium phosphate to chitosan hydrogel increased rhBMP-2-induced bone formation. Finally, the sustained release of a platelet growth factor from chitosan hydrogel formulations demonstrated the potential of this approach as an alternative to exogenous growth factors.
Low-Active Male Adolescents: A Dose Response to High-Intensity Interval Training.

PURPOSE High-intensity interval training (HIIT) is a potential alternative to traditionally recommended steady state exercise for providing health benefits in adolescents, yet its dose-response relationship in this cohort remains unclear, as does its translatability to real-world, nonclinical settings. The present study adopts a novel dose-response design to investigate the effects of undertaking 8 wk of HIIT on the cardiometabolic health of low-active male adolescents. METHODS Twenty-six male adolescents (age 16 ± 1 yr), identified as low active by nonparticipation in structured sport and physical education classes, were randomly assigned to one of five treatment groups. Corresponding with their group numbers (1-5), participants completed a number of HIIT "sets," which consisted of 4 repeated bouts of 20-s near-maximal exertion interspersed with 10-s passive recovery. Participants performed two HIIT sessions and one resistance training session each week for 8 wk. Baseline and follow-up health measures consisted of peak oxygen uptake (V˙O2peak) with an incremental ramp test to volitional exhaustion; body composition (including visceral fat mass, body fat, and lean tissue mass) with dual-energy x-ray absorptiometry; and lipid profile, glucose, insulin, and interleukin-6 from blood analysis. All health outcomes were analyzed as percentage changes, and data were modeled using a quadratic function to explore dose-response relationships. RESULTS Significant improvements were observed for V˙O2peak (∼6%), body fat percentage (∼4%), visceral fat mass (∼10%), and waist circumference-to-height ratio (∼3%), but there was no clear effect of dose across groups. CONCLUSIONS Low-active adolescent males performing a single HIIT set twice weekly, in addition to one resistance training session, gained meaningful improvements in fitness and body composition. Performing additional HIIT sets provided no additional improvements to those of the lowest dose in this study.
Finite mixture models for exponential repeated data

Nous nous interessons a un modele de melange pour des donnees repetees de loi exponentielle. Les composants du melange traduisent differents etats possibles des individus. Pour chacun de ces composants, on modelise la dependance et l'extra-variabilite dues a la repetition des donnees par l'introduction d'effets aleatoires. Dans ce modele de melange exponentiel mixte, la distribution marginale n'etant pas accessible, l'utilisation de l'algorithme EM n'est pas directement envisageable. Nous proposons alors une premiere methode d'estimation des parametres basee sur une linearisation specifique a la loi exponentielle. Nous proposons ensuite une methode plus generale puisque s'appuyant sur une etape de Metropolis Hastings pour construire un algorithme de type MCEM. Cet algorithme est applicable pour un melange de modele lineaires generalises mixtes quelconques.
An Intrusion-Tolerant and Self-Recoverable Network Service System Using A Security Enhanced Chip Multiprocessor

This paper proposes a novel system design using a chip multiprocessor (CMP) to provide intrusion tolerance and self-recovery for server applications. Our platform provides three major advantages over previously proposed approaches, 1) security insulation from remote exploits and attacks; 2) close coupling between processor cores in a CMP to ensure immediate logging, fine-grained inspection and fast recovery; 3) concurrent and fine-grained inspection, logging and recovery techniques that are off of the critical path. We have designed a multi-point defense and recovery system to defeat remote exploits. We used a checkpoint based approach to recover server applications under attack. It takes a snapshot of the application's context and memory state before it handles the next request. If the request turns out to be malicious, the system can discard the malicious request and rollback the application's state to a known good one through checkpointing. We have also designed an rapid recovery system for kernel space rootkit attacks. Our intrusion survivable and self-recovery design provides reliable production services that System Administrators are seeking
Causal Learning of Mixtures of Trees

Mixtures of trees can be used to model any multivariate distributions. In this work the possibility to learn these models from data by causal learning is explored. The algorithm developed aims at approximating all first order relationships between pairs of variables by a mixture of a given size. This approach is evaluated based on synthetic data, and seems promising.
Resource Allocation of noncooperative multi-cell for cognitive radio networks

Cognitive Radio (CR) is proposed to improve frequency efficiency while avoiding interference with licensed users. In this paper, we adopt a Resource Allocation (RA) algorithm to perform uplink frequency allocation and power control between noncooperative multi-cell for CR network. The proposed RA algorithm is an extension of Iterative Water-Filling Algorithm (IWFA) applied to single-cell. The maximization of total information rates of multiple users in one cell is considered for Gaussian channel with pathloss, subject to power constraint on each user. By using Multiple Access Channel (MAC) capacity region, the optimization problem is formulated as a decentralized game, in which each base station, trying to maximize the sum-rate of its own users, is a player, and Nash Equilibrium (NE) can be achieved to guarantee global convergence. Our algorithm shows higher sum-rate than the traditional FDMA by sharing one and more subcarriers for different users. Final simulation results show that our algorithm can achieve more information rate than fixed channel allocation algorithm.
JDAS: a software development framework for multidatabases

Modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture and framework design. Most current popular frameworks are not designed for multidatabases, and many practical problems in development arise. This study designs and implements a software development framework called Java data access service (JDAS) for multidatabases using the object‐oriented programming language Java. The JDAS framework solves related problems that arise when other frameworks are employed in practical software development with multidatabases by presenting and introducing design methods. JDAS consists of the modules of the database abstract, object relational mapping, connection pools management, configuration management, data access service, and inversion of control. Results and case study reveal that the JDAS framework effectively reduces development complexity and improves development efficiency of the software systems with multidatabases. Copyright © 2017 John Wiley & Sons, Ltd.
Overview of Root End Filling Materials

Root end filling is one of the most important aspects of the periradicular surgery. The purpose of root end filling material is to establish a impermeable seal of all the apical avenues of the root canal system and prevent the percolation of bacteria or their products between the root canal systems and periradicular tissues. The purpose of this article is to provide with a comprehensive review of studies on sealing ability, biocompatibility and clinical outcomes of Amalgam, Zinc Oxide Eugenol cements, Intermediate Restorative Material, Glass Ionomer Cement, Retroplast. Mineral Trioxide Aggregate and Biodentine when used as root end filling materials
International Journal of Soft Computing and Engineering

The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining, Internet technology and World Wide Web, and for the more recent Semantic Web. Semantic Web Mining is the outcome of two new and fast developing domains: Semantic Web and Data Mining. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Data Mining is the nontrivial process of identifying valid, previously unknown, potentially useful patterns in data. Semantic Web Mining refers to the application of data mining techniques to extract knowledge from World Wide Web or the area of data mining that refers to the use of algorithms for extracting patterns from resources distributed over in the web. The aim of Semantic Web Mining is to discover and retrieve useful and interesting patterns from a huge set of web data. This web data consists of different kind of information, including web structure data, web log data and user profiles data. Semantic Web Mining is a relatively new area, broadly interdisciplinary, attracting researchers from: computer science, information retrieval specialists and experts from business studies fields. Web data mining includes web content mining, web structure mining and web usage mining. All of these approaches attempt to extract knowledge from the web, produce some useful results from the knowledge extracted and apply these results to the real world problems. This paper gives an overview of how the semantic web is used for mining the World Wide Web.
The role of Epac proteins, novel cAMP mediators, in the regulation of immune, lung and neuronal function

Chronic degenerative inflammatory diseases, such as chronic obstructive pulmonary disease and Alzheimer's dementia, afflict millions of people around the world, causing death and debilitation. Despite the global impact of these diseases, there have been few innovative breakthroughs into their cause, treatment or cure. As with many debilitating disorders, chronic degenerative inflammatory diseases may be associated with defective or dysfunctional responses to second messengers, such as cyclic adenosinemonophosphate (cAMP). The identification of the cAMP‐activated guanine nucleotide exchange factors for Ras‐like GTPases, Epac1 (also known as cAMP‐GEF‐I) and Epac2 (also known as cAMP‐GEF‐II), profoundly altered the prevailing assumptions concerning cAMP signalling, which until then had been solely associated with protein kinase A (PKA). Studies of the molecular mechanisms of Epac‐related signalling have demonstrated that these novel cAMP sensors regulate many physiological processes either alone and/or in concert with PKA. These include calcium handling, cardiac and smooth muscle contraction, learning and memory, cell proliferation and differentiation, apoptosis, and inflammation. The diverse signalling properties of cAMP might be explained by spatio‐temporal compartmentalization, as well as A‐kinase anchoring proteins, which seem to coordinate Epac signalling networks. Future research should focus on the Epac‐regulated dynamics of cAMP, and, hopefully, the development of compounds that specifically interfere with the Epac signalling system in order to determine the precise significance of Epac proteins in chronic degenerative inflammatory disorders.
A Study of Feedback Strategies in Foreign Language Classrooms and Tutorials with Implications for Intelligent Computer-Assisted Language Learning Systems

This paper presents two new corpus-based studies of feedback in the domain of teaching Spanish as a foreign language, concentrating on the type and frequency of different feedback moves, as well as what happens in the moves that follow the feedback. In particular, as well as looking at positive feedback, it concentrates on two general kinds of negative feedback strategies: (1) Giving-Answer Strategies (GAS), where the teacher directly gives the desired target form or indicates the location of the error, and (2) Prompting-Answer Strategies (PAS), where the teacher pushes the student less directly to notice and repair their own error. Investigating the GAS/PAS distinction sheds light on the relative importance for Intelligent Computer-Assisted Language Learning (ICALL) systems of knowledge construction from interaction, which many believe is crucial for effective learning from ITS. The main finding here is that, although GAS occur more frequently than PAS in both corpora, it is the PAS that are more effective, in terms of eliciting explicit repairs by the students. The first study takes place in a classroom context, whereas the second, smaller, study looks at tutorial interactions. This makes it possible to investigate the extent to which the mode of interaction influences the frequency and effectiveness of feedback moves, as well as to look at how concepts such as "wait time" are relevant to explain moves that are ineffective. The paper concludes by using these results to make recommendations about how to choose appropriate feedback moves in ICALL systems.
Grafting in the mammalian central nervous system.

The technique of CNS grafting has provided valuable insights into the development, function, and plasticity of the CNS. Grafting studies have not only clearly demonstrated that damaged CNS tissue will regrow under certain conditions but have also identified and explored factors influencing that growth. In developmental work, grafts have revealed the prominent role of the cellular environment in directing the differentiation and organization of CNS tissues. Finally, in some of the most dramatic work of the past two decades, grafts were used to successfully reconstruct damaged neural circuitries and were found to restore a variety of cognitive, sensory, and motor abilities in animal models of neurological disease