Effect of electron irradiation in vacuum on FEP-A silicon solar cell covers

Fluorinated ethylene-propylene-A (FEP-A) covers on silicon solar cells were irradiated with 1-MeV electrons, in vacuum, to an accumulated fluence equivalent to approximately 28 years in synchronous orbit. The effect of irradiation on the light transmittance of FEP-A was checked by measuring the short-circuit current of the cells after each dose increment. The results indicate no apparent overall loss in transmission due to irradiation of FEP-A. Filter wheel measurements revealed some darkening of the FEP-A at the blue end of the spectrum. Although no delamination from the cell surface was observed while in vacuum, embrittlement of FEP-A occurred at the accumulated dose

Irradiation and measurements of fluorinated ethylene-propylene-A on silicon solar cells in vacuum

Silicon monoxide (SiO) coated silicon solar cells covered with fluorinated ethylene-propylene-A (FEP-A) were irradiated by 1-MeV electrons in vacuum. The effect of irradiation on the light transmittance of FEP-A was checked by measuring the short-circuit current of the cells while in vacuum after each dose increment, immediately after the irradiation, and again after a minimum elapsed time of 16 hr. The results indicated no apparent loss in transmission due to irradiation of FEP-A and no delamination from the SiO surface while the cells were in vacuum, but embrittlement of FEP-A occurred at the accumulated dose

Ultraviolet irradiation at elevated temperatures and thermal cycling in vacuum of FEP-A covered silicon solar cells

Experiments were designed and performed on silicon solar cells covered with heat-bonded FEP-A in an effort to explain the rapid degeneration of open-circuit voltage and maximum power observered on cells of this type included in an experiment on the ATS-6 spacecraft. Solar cells were exposed to ultraviolet light in vacuum at temperatures ranging from 30 to 105 C. The samples were then subjected to thermal cycling from 130 to -130 C. Inspection following irradiation indicated that all the covers remained physically intact. However, during the temperature cycling heat-bonded covers showed cracking. The test showed that heat-bonded FEP-A covers embrittle during UV exposure and the embrittlement is dependent upon sample temperature during irradiation. The results of the experiment suggest a probable mechanism for the degradation of the FEP-A cells on ATS-6

Accelerated growth in outgoing links in evolving networks: deterministic vs. stochastic picture

In several real-world networks like the Internet, WWW etc., the number of links grow in time in a non-linear fashion. We consider growing networks in which the number of outgoing links is a non-linear function of time but new links between older nodes are forbidden. The attachments are made using a preferential attachment scheme. In the deterministic picture, the number of outgoing links $m(t)$ at any time $t$ is taken as $N(t)^\theta$ where $N(t)$ is the number of nodes present at that time. The continuum theory predicts a power law decay of the degree distribution: $P(k) \propto k^{-1-\frac{2} {1-\theta}}$, while the degree of the node introduced at time $t_i$ is given by $k(t_i,t) = t_i^{\theta}[ \frac {t}{t_i}]^{\frac {1+\theta}{2}}$ when the network is evolved till time $t$. Numerical results show a growth in the degree distribution for small $k$ values at any non-zero $\theta$. In the stochastic picture, $m(t)$ is a random variable. As long as $$is independent of time, the network shows a behaviour similar to the Barab\'asi-Albert (BA) model. Different results are obtained when$$ is time-dependent, e.g., when $m(t)$ follows a distribution $P(m) \propto m^{-\lambda}$. The behaviour of $P(k)$ changes significantly as $\lambda$ is varied: for $\lambda > 3$, the network has a scale-free distribution belonging to the BA class as predicted by the mean field theory, for smaller values of $\lambda$ it shows different behaviour. Characteristic features of the clustering coefficients in both models have also been discussed.Comment: Revised text, references added, to be published in PR

Social Skills Training For Aggressive Children In School Counseling: Implications Of Current Understanding Of Subtypes Of Aggressive Children

Social skills intervention (SSI) is one of the most popular choices for many school counselors when working with children who exhibit a wide range of behavior problems. However, a review of research findings indicates that social skills training has limited treatment efficacy in improving the social competence of children with behavior problems. Heterogeneous characteristics of these children may offer one explanation for the limited success of social skills training. This article reviews empirical research findings on the two forms of aggression (reactive aggression and proactive aggression) and proposes more individually tailored SSI as a way to improve its efficacy. Implications for social skills intervention are discussed

Modeling Dynamics of Information Networks

We propose an information-based model for network dynamics in which imperfect information leads to networks where the different vertices have widely different number of edges to other vertices, and where the topology has hierarchical features. The possibility to observe scale free networks is linked to a minimally connected system where hubs remain dynamic.Comment: 4 pages, 5 figures; changed content and new fig

Random Unitaries Give Quantum Expanders

We show that randomly choosing the matrices in a completely positive map from the unitary group gives a quantum expander. We consider Hermitian and non-Hermitian cases, and we provide asymptotically tight bounds in the Hermitian case on the typical value of the second largest eigenvalue. The key idea is the use of Schwinger-Dyson equations from lattice gauge theory to efficiently compute averages over the unitary group.Comment: 14 pages, 1 figur

Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data

Recent developments in extracting and processing biological and clinical data are allowing quantitative approaches to studying living systems. High-throughput sequencing, expression profiles, proteomics, and electronic health records are some examples of such technologies. Extracting meaningful information from those technologies requires careful analysis of the large volumes of data they produce. In this note, we present a set of distributions that commonly appear in the analysis of such data. These distributions present some interesting features: they are discontinuous in the rational numbers, but continuous in the irrational numbers, and possess a certain self-similar (fractal-like) structure. The first set of examples which we present here are drawn from a high-throughput sequencing experiment. Here, the self-similar distributions appear as part of the evaluation of the error rate of the sequencing technology and the identification of tumorogenic genomic alterations. The other examples are obtained from risk factor evaluation and analysis of relative disease prevalence and co-mordbidity as these appear in electronic clinical data. The distributions are also relevant to identification of subclonal populations in tumors and the study of the evolution of infectious diseases, and more precisely the study of quasi-species and intrahost diversity of viral populations

Optimization of Robustness of Complex Networks

Networks with a given degree distribution may be very resilient to one type of failure or attack but not to another. The goal of this work is to determine network design guidelines which maximize the robustness of networks to both random failure and intentional attack while keeping the cost of the network (which we take to be the average number of links per node) constant. We find optimal parameters for: (i) scale free networks having degree distributions with a single power-law regime, (ii) networks having degree distributions with two power-law regimes, and (iii) networks described by degree distributions containing two peaks. Of these various kinds of distributions we find that the optimal network design is one in which all but one of the nodes have the same degree, $k_1$ (close to the average number of links per node), and one node is of very large degree, $k_2 \sim N^{2/3}$, where $N$ is the number of nodes in the network.Comment: Accepted for publication in European Physical Journal