Design, analysis, and test verification of advanced encapsulation systems

Design sensitivities are established for the development of photovoltaic module criteria and the definition of needed research tasks. The program consists of three phases. In Phase I, analytical models were developed to perform optical, thermal, electrical, and structural analyses on candidate encapsulation systems. From these analyses several candidate systems will be selected for qualification testing during Phase II. Additionally, during Phase II, test specimens of various types will be constructed and tested to determine the validity of the analysis methodology developed in Phase I. In Phse III, a finalized optimum design based on knowledge gained in Phase I and II will be developed. All verification testing was completed during this period. Preliminary results and observations are discussed. Descriptions of the thermal, thermal structural, and structural deflection test setups are included

Design, analysis and test verification of advanced encapsulation systems

Analytical models were developed to perform optical, thermal, electrical and structural analyses on candidate encapsulation systems. Qualification testing, specimens of various types, and a finalized optimum design are projected

From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice

High quality protein sequence alignment by combining structural profile prediction and profile alignment using SABERTOOTH

<p>Abstract</p> <p>Background</p> <p>Protein alignments are an essential tool for many bioinformatics analyses. While sequence alignments are accurate for proteins of high sequence similarity, they become unreliable as they approach the so-called 'twilight zone' where sequence similarity gets indistinguishable from random. For such distant pairs, structure alignment is of much better quality. Nevertheless, sequence alignment is the only choice in the majority of cases where structural data is not available. This situation demands development of methods that extend the applicability of accurate sequence alignment to distantly related proteins.</p> <p>Results</p> <p>We develop a sequence alignment method that combines the prediction of a structural profile based on the protein's sequence with the alignment of that profile using our recently published alignment tool SABERTOOTH. In particular, we predict the contact vector of protein structures using an artificial neural network based on position-specific scoring matrices generated by PSI-BLAST and align these predicted contact vectors. The resulting sequence alignments are assessed using two different tests: First, we assess the alignment quality by measuring the derived structural similarity for cases in which structures are available. In a second test, we quantify the ability of the significance score of the alignments to recognize structural and evolutionary relationships. As a benchmark we use a representative set of the SCOP (structural classification of proteins) database, with similarities ranging from closely related proteins at SCOP family level, to very distantly related proteins at SCOP fold level. Comparing these results with some prominent sequence alignment tools, we find that SABERTOOTH produces sequence alignments of better quality than those of Clustal W, T-Coffee, MUSCLE, and PSI-BLAST. HHpred, one of the most sophisticated and computationally expensive tools available, outperforms our alignment algorithm at family and superfamily levels, while the use of SABERTOOTH is advantageous for alignments at fold level. Our alignment scheme will profit from future improvements of structural profiles prediction.</p> <p>Conclusions</p> <p>We present the automatic sequence alignment tool SABERTOOTH that computes pairwise sequence alignments of very high quality. SABERTOOTH is especially advantageous when applied to alignments of remotely related proteins. The source code is available at <url>http://www.fkp.tu-darmstadt.de/sabertooth_project/</url>, free for academic users upon request.</p

A Reconfigurable Linear RF Analog Processor for Realizing Microwave Artificial Neural Network

Owing to the data explosion and rapid development of artificial intelligence (AI), particularly deep neural networks (DNNs), the ever-increasing demand for large-scale matrix-vector multiplication has become one of the major issues in machine learning (ML). Training and evaluating such neural networks rely on heavy computational resources, resulting in significant system latency and power consumption. To overcome these issues, analog computing using optical interferometric-based linear processors have recently appeared as promising candidates in accelerating matrix-vector multiplication and lowering power consumption. On the other hand, radio frequency (RF) electromagnetic waves can also exhibit similar advantages as the optical counterpart by performing analog computation at light speed with lower power. Furthermore, RF devices have extra benefits such as lower cost, mature fabrication, and analog-digital mixed design simplicity, which has great potential in realizing affordable, scalable, low latency, low power, near-sensor radio frequency neural network (RFNN) that may greatly enrich RF signal processing capability. In this work, we propose a 2X2 reconfigurable linear RF analog processor in theory and experiment, which can be applied as a matrix multiplier in an artificial neural network (ANN). The proposed device can be utilized to realize a 2X2 simple RFNN for data classification. An 8X8 linear analog processor formed by 28 RFNN devices are also applied in a 4-layer ANN for Modified National Institute of Standards and Technology (MNIST) dataset classification.Comment: 11 pages, 16 figure

N-dimensional static and evolving Lorentzian wormholes with cosmological constant

We present a family of static and evolving spherically symmetric Lorentzian wormhole solutions in N+1 dimensional Einstein gravity. In general, for static wormholes, we require that at least the radial pressure has a barotropic equation of state of the form $p_r=\omega_r \rho$, where the state parameter $\omega_r$ is constant. On the other hand, it is shown that in any dimension $N \geq 3$, with $\phi(r)=\Lambda=0$ and anisotropic barotropic pressure with constant state parameters, static wormhole configurations are always asymptotically flat spacetimes, while in 2+1 gravity there are not only asymptotically flat static wormholes and also more general ones. In this case, the matter sustaining the three-dimensional wormhole may be only a pressureless fluid. In the case of evolving wormholes with $N \geq 3$, the presence of a cosmological constant leads to an expansion or contraction of the wormhole configurations: for positive cosmological constant we have wormholes which expand forever and, for negative cosmological constant we have wormholes which expand to a maximum value and then recollapse. In the absence of a cosmological constant the wormhole expands with constant velocity, i.e without acceleration or deceleration. In 2+1 dimensions the expanding wormholes always have an isotropic and homogeneous pressure, depending only on the time coordinate.Comment: 10 pages, accepted for publication in Phys. Rev.