A new conventional regression model to estimate hourly photosynthetic photon flux density under all sky conditions

"This is the pre-peer reviewed version of the following article: Foyo-Moreno, I., Alados, I. and Alados-Arboledas, L. (2017), A new conventional regression model to estimate hourly photosynthetic photon flux density under all sky conditions. Int. J. Climatol.. doi:10.1002/joc.5063, which has been published in final form at hppt://dx.doi.org/10.1002/joc.5063 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."In this work we propose a new and simple empirical model to estimate photosynthetic photon flux density under all sky conditions, developed using experimental measurements carried out at Granada, an urban site in Southeastern Spain during two recent years (2014-2015). The model uses the solar zenith angle and clearness index as input parameters, and thus needs only global irradiance measurements usually registered in most radiometric networks. Five stations located in the northern and southern hemisphere with different climatological characteristics at Europe, Asia and America (Spain, Japan and Argentina) were used to validate the model. The model provides satisfactory results, giving low mean bias error for all stations, particularly Mean Bias Error, MBE, being less than 1% in absolute values in three stations and Root Mean Square Error, RMSE, below 6% for all stations except one with 6.1%. These results show better accuracy in comparison to other earlier empirical models and suggest the effectiveness of the model by its general applicability.Andalusia Regional Government. Grant Numbers: P11-RNM-7186, P12-RNM-2409Spanish Ministry of Economy and Competitiveness. Grant Numbers: CGL2016-81092-R, CGL2013-45410-R, CGL2014-52838-C2-1-REuropean Union's Horizon 2020 Research And Innovation Programme. Grant Number: 65410

Porto-systemic shunt using adrenal vein as a conduit; an alternative procedure for spleno – renal shunt

PubMed ID: 17555599Background. Currently, portal hypertension is still big problem for the patients with serious liver diseases. Variceal bleeding is one of the most important complications of portal hypertension. In case of failure of endoscopic and combined medical treatments, surgical decompressive shunts are required. We emphasized an alternative splenorenal shunt procedure using adrenal vein as a conduit. Case presentation. A 26-year-old male suffered from recurrent variceal bleeding was considered for surgical therapy. Although we planned to perform a distal splenorenal shunt procedure, it was observed to be difficult. Therefore left adrenal vein was used as a conduit between left renal vein and splenic vein after splenic artery was ligated. He did well and was discharged from the hospital on the postoperative day 6. In the follow up period for nine months, endoscopic and ultrasonographic examinations were normal. Conclusion. We concluded that, in case of failure to perform distal splenorenal shunt due to technical problems, alternative porto-systemic shunt procedure using the adrenal vein as a vascular conduit can be safely employed. © 2007 Aydin et al; licensee BioMed Central Ltd

Dynamic Analysis of Double Wishbone Front Suspension Systems for Sport Motorcycles

In this paper, two alternative front suspension systems and their influence on motorcycle dynamics are investigated. Based on an existing motorcycle mathematical model, the front end is modified to accommodate both Girder and Hossack suspension systems. Both of them have in common a double wishbone design that varies the front end geometry on certain manoeuvrings and, consequently, the machine’s behaviour. The kinematics of the two systems and their impact on the motorcycle performance is analysed and compared to the well known telescopic fork suspension system. Stability study for both systems is carried out by means of root-loci methods, in which the main oscillation modes, weave and wobble, are studied and compared to the baseline model

Simulation of the CMS Resistive Plate Chambers

The Resistive Plate Chamber (RPC) muon subsystem contributes significantly to the formation of the trigger decision and reconstruction of the muon trajectory parameters. Simulation of the RPC response is a crucial part of the entire CMS Monte Carlo software and directly influences the final physical results. An algorithm based on the parametrization of RPC efficiency, noise, cluster size and timing for every strip has been developed. Experimental data obtained from cosmic and proton-proton collisions at $\sqrt{s}=7$ TeV have been used for determination of the parameters. A dedicated validation procedure has been developed. A good agreement between the simulated and experimental data has been achieved.Comment: to be published in JINS

Improving SNR and reducing training time of classifiers in large datasets via kernel averaging

Kernel methods are of growing importance in neuroscience research. As an elegant extension of linear methods, they are able to model complex non-linear relationships. However, since the kernel matrix grows with data size, the training of classifiers is computationally demanding in large datasets. Here, a technique developed for linear classifiers is extended to kernel methods: In linearly separable data, replacing sets of instances by their averages improves signal-to-noise ratio (SNR) and reduces data size. In kernel methods, data is linearly non-separable in input space, but linearly separable in the high-dimensional feature space that kernel methods implicitly operate in. It is shown that a classifier can be efficiently trained on instances averaged in feature space by averaging entries in the kernel matrix. Using artificial and publicly available data, it is shown that kernel averaging improves classification performance substantially and reduces training time, even in non-linearly separable data

Markovian Dynamics on Complex Reaction Networks

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm

Detection of Pathogenic Mycobacteria Based on Functionalized Quantum Dots Coupled with Immunomagnetic Separation

Mycobacteria have always proven difficult to identify due to their low growth rate and fastidious nature. Therefore molecular biology and more recently nanotechnology, have been exploited from early on for the detection of these pathogens. Here we present the first stage of development of an assay incorporating cadmium selenide quantum dots (QDs) for the detection of mycobacterial surface antigens. The principle of the assay is the separation of bacterial cells using magnetic beads coupled with genus-specific polyclonal antibodies and monoclonal antibodies for heparin-binding hemagglutinin. These complexes are then tagged with anti-mouse biotinylated antibody and finally streptavidin-conjugated QDs which leads to the detection of a fluorescent signal. For the evaluation of performance, the method under study was applied on Mycobacterium bovis BCG and Mycobacterium tuberculosis (positive controls), as well as E. coli and Salmonella spp. that constituted the negative controls. The direct observation of the latter category of samples did not reveal fluorescence as opposed to the mycobacteria mentioned above. The minimum detection limit of the assay was defined to 104 bacteria/ml, which could be further decreased by a 1 log when fluorescence was measured with a spectrofluorometer. The method described here can be easily adjusted for any other protein target of either the pathogen or the host, and once fully developed it will be directly applicable on clinical samples

Learner control in animated multimedia instructions

The interactivity principle in multimedia learning states that giving learners control over pace and order of instructions decreases cognitive load and increases transfer performance. We tested this guideline by comparing a learner-paced instruction with a system-paced instruction. Time-on-task and interactive behavior were logged, and were also related to interest, prior knowledge, and cognitive involvement. We successfully replicated the interactivity principle in terms of better transfer. However, this coincided with a large increase in time-on-task. Also, large individual differences existed in the use of learner control options, which were mostly unrelated to the other variables. Thus, the benefits of introducing learner control in multimedia learning are at the expense of learning efficiency, and it remains unclear for whom the interactivity principle works best