Effect of the variable porosity on the heat transfer process in solar air receiver

Solar air receiver is the core component of central receiver system (CRS) in solar thermal power plants due to the unique feature of some porous medium like silicon carbide foam ceramic and so on. In the air receiver, the porous material receives the concentrated sunlight from the heliostat field and heats up the pumped inlet air by convection and radiation. The incident radiation is distributed in the inner space of the porous medium rather than located on the boundary of the heated face in the front of the receiver. Aiming at this phenomenon which called volumetric effect, we propose a novel solar air receiver using the porous medium with variable porosity along the incident direction to optimize its heat transfer process and increase the thermal efficiency of the receiver. Please download the full abstract below

The Expression and Roles of Nde1 and Ndel1 in the Adult Mammalian Central Nervous System

Open Access funded by Wellcome Trust Under a Creative Commons license Acknowledgments We thank Prof Angelo Sementilli, Department of Pathology, Universidade Metropolitana de Santos, SP, Brazil, for the human sample collection. This study is funded by Scottish Universities Life Sciences Alliance (HR07019 to S. Shen and C.D. McCaig), Medical Research Scotland (384 FRG to B. Lang, United Kingdom), Tenovus Scotland (G12/25 to B. Lang), Sino-UK Higher Education Research Partnership for PhD Studies (C.D. McCaig and Y.Q. Ding) and Wellcome Trust (WT081633MA-NCE to P.J.A. McCaffery, United Kingdom).Peer reviewedPublisher PD

Abstraction of analytical models from cognitive models of human control of robotic swarms

In order to formally validate cyber-physical systems, analytically tractable models of human control are desirable. While those models can be abstracted directly from human data, limitations on the amount and reliability of data can lead to over-fitting and lack of generalization. We introduce a methodology for deriving formal models of human control of cyberphysical systems based on the use of cognitive models. Analytical models such as Markov models can be derived from an instance-based learning model of the task built using the ACT-R cognitive architecture. The approach is illustrated in the context of a robotic control task involving the choice of two options to control a robotic swarm. The cognitive model and various forms of the analytical model are validated against each other and against human performance data. The current limitations of the approach are discussed as well as its implications for the automated validation of cyber-physical systems

A unified approach to combinatorial key predistribution schemes for sensor networks

There have been numerous recent proposals for key predistribution schemes for wireless sensor networks based on various types of combinatorial structures such as designs and codes. Many of these schemes have very similar properties and are analysed in a similar manner. We seek to provide a unified framework to study these kinds of schemes. To do so, we define a new, general class of designs, termed “partially balanced t-designs”, that is sufficiently general that it encompasses almost all of the designs that have been proposed for combinatorial key predistribution schemes. However, this new class of designs still has sufficient structure that we are able to derive general formulas for the metrics of the resulting key predistribution schemes. These metrics can be evaluated for a particular scheme simply by substituting appropriate parameters of the underlying combinatorial structure into our general formulas. We also compare various classes of schemes based on different designs, and point out that some existing proposed schemes are in fact identical, even though their descriptions may seem different. We believe that our general framework should facilitate the analysis of proposals for combinatorial key predistribution schemes and their comparison with existing schemes, and also allow researchers to easily evaluate which scheme or schemes present the best combination of performance metrics for a given application scenario

Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process.

To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual

Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement

Cloning and expression of a tomato glutathione S- transferase (GST) in Escherichia coli

Glutathione S- transferases (GSTs) fulfill a diverse range of functions in an organism. In a previous study, a putative glutathione S-transferase gene (ShGSTU1) from a wild-type tomato, Solanum habrochaites G1.1560, was identified to be a key gene in pathogen resistant response against powdery mildew in tomato. In this study, ShGSTU1 was cloned into plasmid pET-28a, efficiently expressed in Escherichia coli upon isopropyl-β-D-1-thiogalactopyronoside (IPTG) induction, purified with Ni2+ affinity chromatography and biochemically characterized. The results show that the optimal conditions for the expression of recombinant ShGSTU1 in E. coli were growth under 37°C, and 4-h IPTG induction with 1 mM concentration. About 18.93 mg ShGSTU1 was recovered from 1 g wet bacteria. The recombinant ShGSTU1 exhibited enzymatic activity with specific activity 0.625 U/mg. These results might provide a significant foundation for the later research on the mechanism of ShGSTU1 in tomato resistance to powdery mildew.Key words: Tomato, glutathione S-transferase, expression, purification, enzyme activity

Flow regime identification for air valves failure evaluation in water pipelines using pressure data

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordAir valve failure can cause air accumulation and result in a loss of carrying capacity, pipe vibration and even in some situations a catastrophic failure of water transmission pipelines. Air is most likely to accumulate in downward sloping pipes, leading to flow regime transition in these pipes. The flow regime identification can be used for fault diagnosis of air valves, but has received little attention in previous research. This paper develops a flow regime identification method that is based on support vector machines (SVMs) to evaluate the operational state of air valves in freshwater/potable pipelines using pressure signals. The laboratory experiments are set up to collect pressure data with respect to the four common flow regimes: bubbly flow, plug flow, blow-back flow and stratified flow. Two SVMs are constructed to identify bubbly and plug flows and validated based on the collected pressure data. The results demonstrate that pressure signals can be used for identifying flow regimes that represent the operational state (functioning or malfunctioning) of air valves. Among several signal features, Power Spectral Density and Short-Zero Crossing Rate are found to be the best indictors to classify flow regimes by SVMs. The sampling rate and time of pressure signals have significant influence on the performance of SVM classification. With optimal SVM features and pressure sampling parameters the identification accuracies exceeded 93% in the test cases. The findings of this study show that the SVM flow regime identification is a promising methodology for fault diagnosis of air valve failure in water pipelines.National Natural Science Foundation of Chin

Ion energy distribution measurements in rf and pulsed dc plasma discharges

A commercial retarding field analyzer is used to measure the time-averaged ion energy distributions of impacting ions at the powered electrode in a 13.56 MHz driven, capacitively coupled, parallel plate discharge operated at low pressure. The study is carried out in argon discharges at 10 mTorr where the sheaths are assumed to be collisionless. The analyzer is mounted flush with the powered electrode surface where the impacting ion and electron energy distributions are measured for a range of discharge powers. A circuit model of the discharge, in combination with analytical solutions for the ion energy distribution in radio-frequency sheaths, is used to calculate other important plasma parameters from the measured energy distributions. Radio-frequency compensated Langmuir probe measurements provide a comparison with the retarding field analyzer data. The time-resolved capability of the retarding field analyzer is also demonstrated in a separate pulsed dc magnetron reactor. The analyzer is mounted on the floating substrate holder and ion energy distributions of the impinging ions on a growing film, with 100 ns time resolution, are measured through a pulse period of applied magnetron power, which are crucial for the control of the microstructure and properties of the deposited films