Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale analysis of composite material reinforced by randomly-dispersed particles

A multiscale analysis method is presented in which detailed information on the microscopic level is incorporated into macroscopic models capable of simulating damage evolution and ultimate failure. The composite considered is reinforced by randomly-dispersed particles, which reflects the statistical characteristics of real materials, such as cement-based materials. Specifically, a three-dimensional material body is decomposed into many unit cells. Each unit cell is reinforced by a cylindrical particle, the orientation of which is characterized by three Euler angles generated by the random number generator. Based on a detailed finite element analysis, the material properties of the representative volume element are obtained. As verification, the properties of the cylindrical particles are set equal to those of the matrix and the computed ‘composite’ properties reduce exactly to those of the ‘isotropic’ material, as expected. Through coordinate transformation, the effective material properties of each unit cell are calculated. The assembly of stiffness matrices of all unit cells leads to the stiffness matrix of the whole specimen. Under the simple tension loading condition, the initial damaged unit cell can be identified according to the von Mises yield criterion. The stiffness of the damaged unit cell will then be reduced to zero and it will cause stress redistribution and trigger further damage. It was found that the reinforcement is effective to mitigate and arrest the damage propagation, and therefore prolongs the material's lifetime. These results suggest that the hierarchical coupling approaches used here may be useful for material design and failure protection in composites.
Polymicrobial interactions stimulate resistance to host innate immunity through metabolite perception

Bacteria in the human oral cavity often grow in an attached multispecies biofilm community. Members of this community display defined interactions that have an impact on the physiology of the individual and the group. Here, we show that during coculture growth with streptococci, the oral pathogen Aggregatibacter actinomycetemcomitans displays enhanced resistance to killing by host innate immunity. The mechanism of resistance involves sensing of the streptococcal metabolite hydrogen peroxide by A. actinomycetemcomitans, which stimulates a genetic program resulting in enhanced expression of the complement resistance protein ApiA. The oxidative stress response regulator OxyR mediates induction of apiA transcription, and this induction is required for coculture resistance to killing by human serum. These findings provide evidence that interaction between community members mediates prokaryotic resistance to host innate immunity and reinforce the need to understand how polymicrobial growth affects interaction with the host immune system.
Mechanistic insights into dinuclear zinc enzymes from density functional theory studies

RNase Z is a dinuclear zinc enzyme that catalyzes the removal of the tRNA 3'-end trailer. Density functional theory is used to investigate the phosphodiester hydrolysis mechanism of this enzyme with a model of the active site constructed on the basis of the crystal structure. The calculations imply that the reaction proceeds through two steps. The first step is a nucleophihc attack by a bridging hydroxide coupled with protonation of the leaving group by a Glu-His diad. Subsequently, a water molecule activated by the same Glu-His diad makes a reverse attack, regenerating the bridging hydroxide. The second step is calculated to be the rate-limiting step with a barrier of 18 kcal/mol, in good agreement with experimental kinetic studies. Both zinc ions participate in substrate binding and orientation, facilitating nucleophilic attack. In addition, they act as electrophilic catalysts to stabilize the pentacoordinate trigonal-bipyramidal transition states.
Homotypic fetal transplants into an experimental model of spinal cord neurodegeneration

Many neurotransplantation studies have dealt with the ability of solid fetal spinal grafts to develop in the previously traumatized spinal cord of a host. In neurodegenerative spinal diseases, however, motoneuronal death occurs in the absence of a trauma, i.e., in the absence of axotomy of afferent fibers. Lesioning the spinal cord with an excitotoxic agent may provide a useful neurodegenerative model. The present study has been undertaken to determine whether homotypic fetal neurons transplanted as a cell suspension are able to rebuild a neural circuitry. Emphasis is given here to the analysis of the development of transplanted motoneurons and host‐graft connectivity.
Classification of Bioacoustic Time Series by Training a Fusion Layer with Decision Templates

The classification of time series is the topic of this paper. In particular we discuss the combination of local classifier decisions from several feature spaces with static and adaptable fusion schemes, e.g. averaging and decision templates. The decision templates are calculated over a set of feature vectors which are extracted in local time windows. We present algorithms to calculate decision templates, and demonstrate the behaviour of this approach on a real world data set from the field of bioacoustics.
Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.
Multiwalled carbon nanotubes impregnated rubber nanocomposites: thermal transport/decomposition and differential scanning calorimetric study

In order to analyze the effect of multiwalled carbon nanotubes on the thermal transport/stability characteristics of styrene butadiene rubber, five diverse loadings of multiwalled carbon nanotubes were impregnated in the rubber matrix using dispersion kneader and two roller mixing mill. Thermal conductivity and thermal impedance of the nanocomposite specimens were evaluated according to ASTM E1225-99 and D5470-03. It was observed that the thermal conductivity was reduced up to 79% while thermal impedance of the polymer nanocomposite was improved up to 390% at 523 K with 1 wt% impregnation of nanotubes into the base composite formulation. Thermal stability of the polymer nanocomposite was augmented up to 6%, with the utmost incorporation of the nanotubes. Glass transition and crystallization temperatures were diminished, while the onset and peak melting temperatures were increased with increasing filler to matrix ratio. The percent crystallinity of the nanocomposites has been augmented up to 5%, with 1 wt% incorporation of multiwalled carbon nanotubes into the host matrix. Scanning electron microscopy along with energy dispersive X-ray spectroscopy was used to examine the multiwalled carbon nanotubes dispersion in the rubber matrix, for surface analysis of the post-thermally tested composite specimens and for their compositional analysis.
Exploiting Symmetries in Logistics Distribution Planning

Logistics distribution and movements planning involves developing an efficient plan to move all of the required supplies, equipment and personnel to desired locations in a timely manner. It is an inherently difficult problem to solve and given the complexity of the problem, scenarios of a realistic size can take a significant amount of time to compute. A greedy distribution algorithm has been created for the task of developing transport schedules to satisfy the movement requirements. At each phase of the simulation the algorithm assigns the most capable transport to deliver the highest priority resources. Although it is observed that the distribution algorithm performs faster than exact algorithms based on integer programming or brute force, even greedy based approaches can have performance issues as the scenario size increases. In this paper, a technique is examined which exploits redundancies garnered due to symmetries in the distribution model to improve performance of the distribution algorithm. A key redundancy in the model occurs when there are multiple similar transports at the same location. By performing one calculation for all transports of a similar type at the same location, it is suspected that computational savings could be made. Given that many of the distribution scenarios are complex, many of the transports are not always at the exact location as each other. One way to increase the effectiveness of the symmetry exploitation is to perform only one calculation for all transports of a similar type within a certain range. However, there is a side effect to increasing the range value, and that is it introduces an error in the planning calculation. Part of the analysis to be explored through simulation is what an effective range is, given that the larger the range the greater the computational savings but also the greater the error is introduced into the distribution plan. Simulations were run testing the performance of the distribution algorithm with and without utilising the symmetries with varying range values to minimize the distribution model. It is shown empirically that by exploiting the symmetries in the distribution model, for the scenarios tested computation time could be reduced on average by approximately 24-45%, while the efficiency of the distribution plan is only reduced by approximately 0.3-18% due to introduced error.
Capacity Characterization of UAV-Enabled Two-User Broadcast Channel

Unmanned aerial vehicles (UAVs) have recently gained growing popularity in wireless communications owing to their many advantages such as swift and cost-effective deployment, line-of-sight (LoS) aerial-to-ground link, and controllable mobility in three-dimensional (3D) space. Although prior works have exploited the UAV’s mobility to enhance the wireless communication performance under different setups, the fundamental capacity limits of UAV-enabled/aided multiuser communication systems have not yet been characterized. To fill this gap, we consider, in this paper, a UAV-enabled two-user broadcast channel (BC), where a UAV flying at a constant altitude is deployed to send independent information to two users at different fixed locations on the ground. We aim to characterize the capacity region of this new type of BC over a given UAV flight duration, by jointly optimizing the UAV’s trajectory and transmit power/rate allocations over time, subject to the UAV’s maximum speed and maximum transmit power constraints. First, to draw essential insights, we consider two special cases with asymptotically large/low UAV flight duration/speed, respectively. For the former case, it is shown that a simple hover-fly-hover (HFH) UAV trajectory with time division multiple access (TDMA)-based orthogonal multiuser transmission is capacity-achieving; while in the latter case, the UAV should hover at a fixed location that is nearer to the user with larger achievable rate and in general superposition coding (SC)-based non-orthogonal transmission with interference cancellation at the receiver of the nearer user is required. Next, we consider the general case with finite UAV speed and flight duration. We show that the optimal UAV trajectory should follow a general HFH structure, i.e., the UAV successively hovers at a pair of optimal initial and final locations above the line segment connecting the two users each with a certain amount of time and flies unidirectionally between them at the maximum speed, and SC is generally needed. Furthermore, when TDMA-based transmission is considered for low-complexity implementation, we show that the optimal UAV trajectory still follows an HFH structure, but the hovering locations can only be those above the two users. Extensive simulation results are provided to verify our analysis, which also reveal useful guidelines to the practical design of UAV trajectory and communication jointly.
Oblivious Query Processing

Motivated by cloud security concerns, there is an increasing interest in database systems that can store and support queries over encrypted data. A common architecture for such systems is to use a trusted component such as a cryptographic co-processor for query processing that is used to securely decrypt data and perform computations in plaintext. The trusted component has limited memory, so most of the (input and intermediate) data is kept encrypted in an untrusted storage and moved to the trusted component on ``demand. '' In this setting, even with strong encryption, the data access pattern from untrusted storage has the potential to reveal sensitive information; indeed, all existing systems that use a trusted component for query processing over encrypted data have this vulnerability. In this paper, we undertake the first formal study of secure query processing, where an adversary having full knowledge of the query (text) and observing the query execution learns nothing about the underlying database other than the result size of the query on the database. We introduce a simpler notion, oblivious query processing, and show formally that a query admits secure query processing iff it admits oblivious query processing. We present oblivious query processing algorithms for a rich class of database queries involving selections, joins, grouping and aggregation. For queries not handled by our algorithms, we provide some initial evidence that designing oblivious (and therefore secure) algorithms would be hard via reductions from two simple, well-studied problems that are generally believed to be hard. Our study of oblivious query processing also reveals interesting connections to database join theory.
Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible) has been gaining popularity in order to minimize this labor-intensive task. However, bottlenecks remain that hinder the immediate adoption of supervised learning methods. With supervised classification, training samples are required for learning the parameters that govern the performance of a classifier, yet the selection of training data is often subjective and the quality of such samples is critically important. For LiDAR scanning in forest environments, the quantification of data quality is somewhat abstract, normally referring to some metric related to the completeness of individual tree crowns; however, this is not an issue that has received much attention in the literature. Intuitively the choice of training samples having varying quality will affect classification accuracy. In this paper a Diversity Index (DI) is proposed that characterizes the diversity of data quality (Qi) among selected training samples required for constructing a classification model of tree genera. The training sample is diversified in terms of data quality as opposed to the number of samples per class. The diversified training sample allows the classifier to better learn the positive and negative instances and; therefore; has a higher classification accuracy in discriminating the “unknown” class samples from the “known” samples. Our algorithm is implemented within the Random Forests base classifiers with six derived geometric features from LiDAR data. The training sample contains three tree genera (pine; poplar; and maple) and the validation samples contains four labels (pine; poplar; maple; and “unknown”). Classification accuracy improved from 72.8%; when training samples were selected randomly (with stratified sample size); to 93.8%; when samples were selected with additional criteria; and from 88.4% to 93.8% when an ensemble method was used.
Minimum information entropy based q-matrix learning in DINA model

Cognitive diagnosis models (CDMs) are of growing interest in test development and measurement of learners' performance. The DINA (deterministic input, noisy, and gate) model is one of the most widely used models in CDM. In this paper, we propose a new method and present an alternating recursive algorithm to learn Q-matrix and uncertainty variables, slip and guessing parameters, based on Boolean Matrix Factorization (BMF) and Minimized Information Entropy (MIE) respectively for the DINA model. Simulation results show that our algorithm for Q-matrix learning has fast convergence to the local optimal solutions for Q-matrix and students' knowledge states A matrix. This is especially important and applicable when the method is extended to big data.
Primary Sjögren's Syndrome Is Characterized by Distinct Phenotypic and Transcriptional Profiles of IgD+ Unswitched Memory B Cells

The significance of distinct B cell abnormalities in primary Sjögren's syndrome (SS) remains to be established. We undertook this study to analyze the phenotype and messenger RNA (mRNA) transcript profiles of B cell subsets in patients with primary SS and to compare them with those in sicca syndrome patients and healthy controls.
Symmetric image encryption scheme based on multiple chaotic maps

In this paper, a new asymmetric encryption algorithm based on multiple chaotic maps with invertible matrices to encrypt the image is proposed. In order to increase the security of the image, a new shuffling algorithm is proposed based on Arnold Cat map. The combination of invertible matrices and shuffling algorithm in the image encryption provides dual layer protection. The 120 bit long secret key is used to generate the initial conditions of all chaotic maps and also the key length can be increased further to improve the security. To achieve higher security with high complexity to break the cipher image, the size of the invertible matrices can be adaptive. The results of several experimental, statistical analysis, differential analysis and key sensitivity tests show that the proposed asymmetric image encryption algorithm provides an effective way for real-time applications and transmission.
Evolución de la textura durante el recocido continuo de un acero laminado en frío

En el presente trabajo, se ha realizado un analisis de la evolucion de la textura de recristalizacion desarrollada en un acero deformado en frio, con un contenido ultrabajo en carbono, 0.0033%, al ser recocido a diferentes temperaturas. Los cambios observados se han contrastado con los dos mecanismos propuestos en bibliografia para interpretar la formacion de las texturas de recristalizacion: nucleacion orientada y crecimiento selectivo.
Scalable Conjunction Processing using Spatiotemporally Indexed Ephemeris Data

Abstract : The collision warnings produced by the Joint Space Operations Center JSpOC) are of critical importance in protecting U.S. and allied spacecraft against destructive collisions and protecting the lives of astronauts during space flight. As the Space Surveillance Network (SSN) improves its sensor capabilities for tracking small and dim space objects, the number of tracked objects increases from thousands to hundreds of thousands of objects, while the number of potential conjunctions increases with the square of the number of tracked objects. Classical filtering techniques such as apogee and perigee filters have proven insufficient. Novel and orders of magnitude faster conjunction analysis algorithms are required to find conjunctions in a timely manner. Stellar Science has developed innovative filtering techniques for satellite conjunction processing using spatiotemporally indexed ephemeris data that efficiently and accurately reduces the number of objects requiring high-fidelity and computationally-intensive conjunction analysis. Two such algorithms, one based on the k-d Tree pioneered in robotics applications and the other based on Spatial Hashing used in computer gaming and animation, use, at worst, an initial O(N log N) preprocessing pass (where N is the number of tracked objects) to build large O(N) spatial data structures that substantially reduce the required number of O(N2) computations, substituting linear memory usage for quadratic processing time. The filters have been implemented as Open Services Gateway initiative (OSGi) plug-ins for the Continuous Anomalous Orbital Situation Discriminator (CAOS-D) conjunction analysis architecture. We have demonstrated the effectiveness, efficiency, and scalability of the techniques using a catalog of 100,000 objects, an analysis window of one day, on a 64-core computer with 1TB shared memory.
Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.
Bidding efficiently in repeated auctions with entry and observation costs

This paper introduces an efficient bidding strategy for budget-constrained buyers in repeated auctions with entry fees. We present a general algorithm that is applicable to distributed resource allocation. The game is modeled on an economically reasonable assumption [1] according to which any player can participate in an auction after paying for information about the value of the auctioned item, and for the preparation of his bid. We address learning by each bidder of an optimal participation strategy for spending his budget profitably, based on the history of his successes and failures in past transactions. Players' transient and long-term attitudes are illustrated in a symmetric Bayesian equilibrium of a market-based network resource allocation problem.
Common SNPs of AmelogeninX (AMELX) and Dental Caries Susceptibility

Genetic approaches have shown that several genes could modify caries susceptibility; AmelogeninX (AMELX) has been repeatedly designated. Here, we hypothesized that AMELX mutations resulting in discrete changes of enamel microstructure may be found in children with a severe caries phenotype. In parallel, possible AMELX mutations that could explain resistance to caries may be found in caries-free patients. In this study, coding exons of AMELX and exon-intron boundaries were sequenced in 399 individuals with extensive caries (250) or caries-free (149) individuals from nine French hospital groups. No mutation responsible for a direct change of amelogenin function was identified. Seven single-nucleotide polymorphisms (SNPs) were found, 3 presenting a high allele frequency, and 1 being detected for the first time. Three SNPs were located in coding regions, 2 of them being non-synonymous. Both evolutionary and statistical analyses showed that none of these SNPs was associated with caries susceptibility, suggesting that AMELX is not a gene candidate in our studied population.