Atomistic fracture: QFM vs. MD

In this work we discuss the paradigmatic case of brittle fracture in defective elastic-plastic cubic silicon carbide, by a combination of Quantized Fracture Mechanics and Molecular Dynamics atomistic simulations. Different defect sizes and shapes, or crack-inclusion interactions are considered. Our results show that Quantized Fracture Mechanics is able to effectively incorporate the main lattice-related features, while Molecular Dynamics atomistic simulations do provide the most basic level of understanding of mechanical behaviour of brittle materials.     

Physiological basis of sensitivity to enzymatic browning in ‘lettuce’, ‘escarole’ and ‘rocket salad’ when stored as fresh-cut products

In fresh-cut leafy vegetables, the operation of cutting may stimulate enzymatic browning, with important commercial consequences. In this work, a number of physiological and biochemical parameters, including the activities of key enzymes involved in the metabolism of phenols (such as PAL, PPO, and PODs) and ascorbic acid (ASA), were measured in three species: lettuce (Lactuca sativa var. capitata L.), escarole (Cichorium indivia var. latifolium) and rocket salad (Eruca sativa), upon cold storage as fresh cuts. The first two species are quite sensitive to leaf browning, which does not affect rocket salad.     

Luminescence of heat-treated silicon-based polymers: promising materials for LED applications

A new strategy to obtain transparent, thermally stable, and formable photoluminescent materials for LED applications is presented. Starting from commercially available silicon-based polymers, luminescence properties are developed by means of simple heat treatment. Solid polymethylsilsesquioxane MK (Wacker-Besil^®PMS MK) and liquid poly(ureamethylvinyl)silazane Ceraset (Kion Ceraset^® PUVMS) were thermally treated between 200 and 700 °C for 2 h under Ar atmosphere. Photoluminescence properties were observed in all the samples. The structural rearrangements during thermal annealing were effective in order to red-shift the emission spectra of the untreated polymers to the visible range. The formation of dangling bonds and carbon sp², associated with the annealing procedure and confirmed by means of Electron Paramagnetic Resonance (EPR) spectroscopy and solid state Magic Angle Spinning NMR (MAS-NMR) contribute to the red-shift of the photoluminescence emissions of the polymers. After heat treatment at low temperatures (200, 300, and 400 °C), both the polymers show fluorescence in the UV range. While the polysiloxane reveals white luminescence after annealing at 500 and 600 °C, the polysilazane heat-treated at 500 °C exhibits emission in the blue-green range and is transparent. At higher temperatures the presence of free carbon counteracts the luminescence properties.     

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30 min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab^® implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn.     

Executable structural operational semantics in Maude

This paper describes in detail how to bridge the gap between theory and practice when implementing in Maude structural operational semantics described in rewriting logic, where transitions become rewrites and inference rules become conditional rewrite rules with rewrites in the conditions, as made possible by the new features in Maude 2. We validate this technique using it in several case studies: a functional language Fpl (evaluation and computation semantics), an imperative language WhileL (evaluation and computation semantics), Kahn’s functional language Mini-ML (evaluation or natural semantics), Milner’s CCS (with strong and weak transitions), and Full LOTOS (including ACT ONE data type specifications). In addition, on top of CCS we develop an implementation of the Hennessy–Milner modal logic for describing local capabilities of processes, and for LOTOS we build an entire tool where Full LOTOS specifications can be entered and executed (without user knowledge of the underlying implementation of the semantics). We also compare this method based on transitions as rewrites with another one based on transitions as judgements.     

Vitamin D-Related Single Nucleotide Polymorphisms as Risk Biomarker of Cardiovascular Disease

Cardiovascular diseases (CVDs) are a group of disorders of the heart and blood vessels. In addition to environmental risk factors, genetic predisposition increases the risk; this includes alterations in the vitamin D receptor gene (VDR). These alterations play a key role in modifying vitamin D uptake, being able to modify its function and increasing susceptibility to cardiovascular disorders. The aim of this study was to evaluate the association of polymorphisms in the VDR gene and risk of CVD in a Caucasian population. A retrospective case-control study was conducted comprising 246 CVD patients and 246 controls of Caucasian origin from Southern Spain. The genetic polymorphisms BsmI (rs1544410), TaqI (rs731236), ApaI (rs7975232), FokI (rs2228570) and Cdx2 (rs11568820) were determined by means of real-time polymerase chain reaction (PCR) for allelic discrimination using TaqMan^® probes. The logistic regression analysis adjusted for body mass index and diabetes revealed that the TT genotype was associated with a higher risk of CVD in both the genotypic model (p = 0.0430; OR = 2.30; 95% CI = 1.06–5.37; TT vs. CC) and the recessive model (p = 0.0099; OR = 2.71; 95% CI = 1.31–6.07; TT vs. C). Haplotype analysis revealed that the haplotype GAC (p = 0.047; OR = 0.34; 95% CI = 0.12–0.98) was associated with increased risk of CVD. The VDR polymorphisms FokI (rs2228570) was significantly associated with the development of CVD. No influence was observed of the VDR polymorphisms BsmI (rs1544410), TaqI (rs731236), ApaI (rs7975232) and Cdx2 (rs11568820) on the risk of developing CVD in the patients studied.     

Analyzing the Spanish strip cipher by combining combinatorial and statistical methods

According to historical reports, many telegrams that date from the Spanish Civil War (1936–1939) still remain undisclosed. It is believed that these telegrams were encrypted with a cryptosystem called the “Spanish Strip Cipher” (SSC).

During this civil war, SSC was the most used cryptographic algorithm. This method corresponds to a homophonic substitution cipher in which a plaintext letter can map to between three and five ciphertext symbols.

By means of cryptanalysis, the authors detect a weakness in the encryption process of the SSC. In this article, they describe how this vulnerability is exploited to efficiently reconstruct a plaintext from a relatively short ciphertext. The attack is based on combinatorial and statistical methods, and it is divided into three phases: homophones-table analysis, letter-frequency analysis, and dictionary search.

The attack was implemented in Java and tested on a laptop with an i7 processor and 4 GB of RAM. The tests were carried out with several real telegrams from the Spanish Civil War. In this article, the authors provide the results of one test that was successfully performed only using the first 201 ciphertext symbols of a Spanish telegram.     

Visual analysis of a cold rolling process using a dimensionality reduction approach

The rolling process is a strategical industrial and economical activity that has a large impact among world-wide commercial markets. Typical operating conditions during the rolling process involve extreme mechanical situations, including large values of forces and tensions. In some cases, these scenarios can lead to several kinds of faults, which might result in large economic losses. Thereby, a proper assessment of the process condition is a key aspect, not only as a fault detection mechanism, but also as an economic saving system. In the rolling process, a remarkable kind of fault is the so-called chatter, a sudden powerful vibration that affects the quality of the rolled material. In this paper, we propose a visual approach for the analysis of the rolling process. According to physical principles, we characterize the exit thickness and the rolling forces by means of a large dimensional feature vector, that contains the energies at specific frequency bands. Afterwards, we use a dimensionality reduction technique, called t-SNE, to project all feature vectors on a visual 2D map that describes the vibrational states of the process. The proposed methodology provides a way for an exploratory analysis of the dynamic behaviors in the rolling process and allows to find relationships between these behaviors and the chatter fault. Experimental results from real data of a cold rolling mill are described, showing the application of the proposed approach.     

Prototype Platforms for Distributed Agreements

We present a prototype application for coordinating distributed agreements in multi-parties negotiations, where participants can dynamically join ongoing negotiations and where participants know only those parties they have interacted with. Our prototype is tailored to Ad-Hoc network scenarios involving the assignment of tasks for a rescue team operating over disaster areas. Our application is based on asynchronous communication and it exploits the d2pc protocol for committing or aborting a negotiation. Parties have been developed both in Jocaml+Perl and Polyphonic C. The implementation of the commit protocol allows components of both types to participate within the same negotiation.     

Early dropout prediction using data mining: a case study with high school students

Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.