Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: chymotrypsin inhibitor 2 mutants

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.     

Model for small superconducting variable-thickness bridge constrictions

We present a simple model to simulate small three-dimensional superconducting constrictions of variable thickness (VTBs) for which the cross-sectional dimensions are of the same order as the length, and both are less than (T). We study the behavior of the modulus of the order parameterf and the supercurrent densityJ _s as a function of the various dimensions. We find that the extent to whichf is depressed in the banks depends on the ratio of width to length of the constriction. We show that even for very abrupt geometries,J _s is not zero andf does not reach its equilibrium value immediately outside the constriction. On the basis of our results, we propose a more general expression for the effective length valid also when all dimensions are of the same order. The calculated values ofdI _c /dT are consistent with experimental data from well-characterized VTBs. We suggest that the discrepancy between the calculated and experimental values ofI _c R _n may be due to the nonuniformity of the normal current density in the constriction.     

A flexible thin-film for powering stand alone electronic devices

This paper presents a flexible thin-film (FTF) composed by a photovoltaic (PV) cell, a flexible solid-state rechargeable battery and power management electronics. A flexible printed circuit board (fPCB) was used for providing physical support to the electronics and to the battery, whereas a PDMS (polydimethylsiloxane) layer was used for providing mechanical adhesion between the PV cell and the fPCB. The high elasticity of the PDMS makes this material an excellent candidate for adapting two materials with different thermal expansions (fPCB and PV). The physical measurements showed a prototype with a thickness in the range 1.52 ± 0.10 mm, 10 g of weight and 37 × 114 mm of size. The electrical measurements showed ability to provide an output voltage of 3.8 V in a continuously form under these conditions: the PV providing DC power for solar irradiations higher than 6 W m⁻², and the battery providing DC power for solar irradiations above bellow 6 W m⁻² (value that has emerged during the twilight between sunset and dusk).     

Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems.     

Influence of photoperiod on carbon dioxide and methane emissions from two pilot-scale stabilization ponds

Greenhouse gas (GHG) emissions (CO(2), CH(4)) from pilot-scale algal and duckweed-based ponds (ABP and DBP) were measured using the static chamber methodology. Daylight and nocturnal variations of GHG and wastewater characteristics (e.g. chemical oxygen demand (COD), pH) were determined via sampling campaigns during midday (12:30-15:30) and midnight (00:30-03:30) periods. The results showed that under daylight conditions in ABP median emissions were -232 mg CO(2) m(-2) d(-1) and 9.9 mg CH(4) m(-2) d(-1), and in DBP median emissions were -1,654.5 mg CO(2) m(-2) d(-1) and 71.4 mg CH(4) m(-2) d(-1), respectively. During nocturnal conditions ABP median emissions were 3,949.9 mg CO(2) m(-2) d(-1), 12.7 mg CH(4) m(-2) d(-1), and DBP median emissions were 5,116 mg CO(2) m(-2) d(-1), 195.2 mg CH(4) m(-2) d(-1), respectively. Once data measured during daylight were averaged together with nocturnal data the median emissions for ABP were 1,566.8 mg CO(2) m(-2) d(-1) and 72.1 mg CH(4) m(-2) d(-1), whilst for DBP they were 3,016.9 mg CO(2) m(-2) d(-) and 178.9 mg CH(4) m(-2) d(-1), respectively. These figures suggest that there were significant differences between CO(2) emissions measured during daylight and nocturnal periods (p < 0.05). This shows a sink-like behaviour for both ABP and DBP in the presence of solar light, which indicates the influence of photosynthesis in CO(2) emissions. On the other hand, the fluxes of CH(4) indicated that DBP and ABP behave as net sources of CH(4) during day and night, although higher emissions were observed from DBP. Overall, according to the compound average (daylight and nocturnal emissions) both ABP and DBP systems might be considered as net sources of GHG.     

Exploiting eye–hand coordination to detect grasping movements

Human beings are very skillful at reaching for and grasping objects under multiple conditions, even when faced with an object's wide variety of positions, locations, structures and orientations. This natural ability, controlled by the human brain, is called eye–hand coordination. To understand this behavior it is necessary to study both eye and hand movements simultaneously. This paper proposes a novel approach to detect grasping movements by means of computer vision techniques. This solution fuses two viewpoints, one viewpoint which is obtained from an eye-tracker capturing the user's perspective and a second viewpoint which is captured by a wearable camera attached to a user's wrist. Utilizing information from these two viewpoints it is possible to characterize multiple hand movements in conjunction with eye-gaze movements through a Hidden–Markov Model framework. This paper shows that combining these two sources makes it possible to detect hand gestures using only the objects contained in the scene even without markers on the surface of the objects. In addition, it is possible to detect which is the desired object before the user can actually grasp said object.     

Acoustic scattering solver based on single level FMM for multi-GPU systems

In this paper, we present a heterogeneous parallel solver of a high frequency single level Fast Multipole Method (FMM) for the Helmholtz equation applied to acoustic scattering. The developed solution uses multiple GPUs to tackle the compute bound steps of the FMM (aggregation, disaggregation, and near interactions) while the CPU handles a memory bound step (translation) using OpenMP. The proposed solver performance is measured on a workstation with two GPUs (NVIDIA GTX 480) and is compared with that of a distributed memory solver run on a cluster of 32 nodes (HP BL465c) with an Infiniband network. Some energy efficiency results are also presented in this work.     

Exploring different technological platforms for supporting co-located collaborative games in the classroom

Computer Supported Collaborative Learning is a pedagogical approach that can be used for deploying educational games in the classroom. However, there is no clear understanding as to which technological platforms are better suited for deploying co-located collaborative games, nor the general affordances that are required. In this work we explore two different technological platforms for developing collaborative games in the classroom: one based on augmented reality technology and the other based on multiple-mice technology. In both cases, the same game was introduced to teach electrostatics and the results were compared experimentally using a real class.     

GPGPU implementation of growing neural gas: Application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6×$6\times$ compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.