A comprehensive review on bioinspired solar photovoltaic cells

Researchers have derived inspiration from the biophotosynthetic structures in nature and have started to synthesize the modified bioinspired solar cells copying the evolved organic and inorganic material properties. One of the highlighted examples of bioinspired photo voltaic (PV) cells is the astonishing achievement of an increase in the absorption of integrated sunlight waves in unpatterned solar cells simulated from the wings of the butterfly. Further, deployment possibilities of incorporating flexible cells on flat or curved surfaces for optimizing performance are also under progress. This article mainly discusses the recent concepts of bioinspired solar cells at the research and development level with the prospects and challenges that lie ahead in the upcoming field of photovoltaic renewable energy cell technology. Different potential materials found suitable for bioinspired solar cells construction are reviewed with their particular challenges.     

The Legendre wavelet method for solving initial value problems of Bratu-type

The aim of this work is to study the Legendre wavelets for the solution of initial value problems of Bratu-type, which is widely applicable in fuel ignition of the combustion theory and heat transfer. The properties of Legendre wavelets together with the Gaussian integration method are used to reduce the problem to the solution of nonlinear algebraic equations. Also a reliable approach for convergence of the Legendre wavelet method when applied to a class of nonlinear Volterra equations is discussed and an error estimation for the proposed method is also introduced. Illustrative examples have been discussed to demonstrate the validity and applicability of the technique and the results have been compared with the exact solution. We finally show the high accuracy and efficiency of the proposed method.     

Solid state synthesis of nano-mineral particles

Many researchers in academia and industries are interested in reducing particle sizes from few sub-micrometers to nano-meter levels. These nano-particles find application in several areas including ceramics, paints, cosmetics, microelectronics, sensors, textiles and biomedical, etc. This article reviews the present state of the art for solid state synthesis of mineral nano-particles by wet milling, including their operating variables such as ball size, solid mass fraction and suspension stability. This article concludes and recommends with a critical discussion of nano-particles synthesis and a few common strategies to overcome stability issues.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.     

Implementations of Sorted-QR Decomposition for MIMO Receivers: Complexity,Reusability and Efficiency Analysis

Matrix decomposition of the channel matrix in the form of QR decomposition (QRD) is needed for advanced multiple input and multiple output (MIMO) demapping algorithms like sphere decoder. Due to the computation-intensive nature of the QRD, its implementation has to be highly efficient. Flexibility in several forms, e.g. support for different algorithms, reusability of wireless implementations, portability, etc. is highly sought in wireless devices. The contradictory nature of flexibility and efficiency requires tradeoffs to be made between them in system development. In this paper, we have analyzed such tradeoffs by implementing two minimum mean squared error-sorted QRD algorithms. The algorithms have been implemented in four different methods with varying degree of reusability and in five different forms of portability. The performance of the implementations is evaluated by using the real-time constraints from the LTE standard. For all the implementations, modular equations for accurately estimating the execution time are derived.     

Ultrathin PFPE Film Systems Fabricated by Covalent Assembly: An Application to Tribology

In this study, we have employed covalent molecular assembly to fabricate robust thin film structures comprising molecular layers and have demonstrated its application in tribology. An anhydride-functionalized polymer (gantrez) was deposited over an amine-functionalized silicon surface through covalent binding and employed as an intermediate layer between derivatized silicon and perfluoropolyether (PFPE). X-ray photoelectron spectroscopy, atomic force microscopy, and ellipsometry were employed to study the interfacial chemistry, morphology, and thickness of the assembled films. The films show excellent stability and strength against sonication, which can be attributed to the covalent interlayer linkage. Such films showed wear life of >100,000 cycles in ball-on-disk sliding tests at a normal load of 0.5 N and a sliding rotation of 200 rpm at a track radius of 3.2 mm. The performance was superior compared to that of PFPE-coated self assembled monolayers used as the lubricating layer. The film systems and assembly technique can be employed as nano-lubrication in several technological applications, such as information storage devices and micro-electro-mechanical systems.     

An empirical study of on-line models for relational data streams

To date, Inductive Logic Programming (ILP) systems have largely assumed that all data needed for learning have been provided at the onset of model construction. Increasingly, for application areas like telecommunications, astronomy, text processing, financial markets and biology, machine-generated data are being generated continuously and on a vast scale. We see at least four kinds of problems that this presents for ILP: (1) it may not be possible to store all of the data, even in secondary memory; (2) even if it were possible to store the data, it may be impractical to construct an acceptable model using partitioning techniques that repeatedly perform expensive coverage or subsumption-tests on the data; (3) models constructed at some point may become less effective, or even invalid, as more data become available (exemplified by the “drift” problem when identifying concepts); and (4) the representation of the data instances may need to change as more data become available (a kind of “language drift” problem). In this paper, we investigate the adoption of a stream-based on-line learning approach to relational data. Specifically, we examine the representation of relational data in both an infinite-attribute setting, and in the usual fixed-attribute setting, and develop implementations that use ILP engines in combination with on-line model-constructors. The behaviour of each program is investigated using a set of controlled experiments, and performance in practical settings is demonstrated by constructing complete theories for some of the largest biochemical datasets examined by ILP systems to date, including one with a million examples; to the best of our knowledge, the first time this has been empirically demonstrated with ILP on a real-world data set.     

Minimising total cost with regular and emergency outsourcing sources: a neuro-dynamic programming approach

This study models and explains the business situation of an organisation which has regular and emergency outsourcing sources and where decisions have to be made at the beginning of every period regarding how much to order from these sources, so as to balance between different cost components of current and future periods. Previous works in this area have tried to attack this problem with dynamic programming. In this project, neuro-dynamic programming has been applied, and the reasons for doing so have been clearly stated. This model not only derives policies in order to minimise the expected total discounted cost over a period of time, but also enables the system to learn to make such decisions, and to improve its actions by using reinforcement learning. The performance of the present work has been measured quantitatively and has been compared with the models stated in literature. This study will be very useful for the organisations where such business problems exist or are likely to exist. This study will also be of great use to researchers, who are keen to understand and model the given business situation with distribution independent demand based models.     

Correlation between central receiver size and solar field using flat heliostats

In Central Receiver Systems (CRSs), thousands of heliostats track the sunrays and reflect beam radiation on to a receiver surface. The size of the reflected image and the extent of reflection from the heliostats are one of the important criteria that need to be taken into account while designing a receiver, since spillage losses may vary from 2 to 16% of the total losses. The present study aims to determine the size of an external cylindrical receiver, such that the rays reflected from all the heliostats in the field are intercepted. A dimensionless correlation with respect to tower height and receiver size (diameter and height) as a function of heliostat size and its position is discussed in the paper. This correlation could be used as a first-order approximation to estimate the receiver dimensions. When applied to the Ivanpah Solar Electricity Generating Station (ISEGS) plant, the correlation yields satisfactory estimation of receiver dimensions.     

Computational Modelling on Heat Transfer Study of Molten Silicon During Multi-crystalline Silicon Growth Process for PV Applications

Multi-crystalline silicon is an important material with advantages of low-production cost and high conversion efficiency for photovoltaic solar cells. Directional solidifi cation has become the main technique for producing mc-Si ingots for solar cell applications. The study is performed in the framework of the incompressible Navier-Stokes equation in the Boussinesq approximation with convection-conduction equations. The computations are carried out in a two dimensional (2D) axisymmetric model by the finite- element method. The influence of the Reynolds numbers, total heat flux and velocity streamline pattern on the silicon melt was simulated and analyzed for various Rayleigh numbers between 10 to 10 ⁶ with the help of a numerical technique. The following key findings are presented in this paper. The velocity field value is increased above 0.02(m/s), heat flux value is increased to 10 ⁴(W/m ²), when the Rayleigh number is increased above 1000. Reynolds numbers are also studied in five parallel horizontal cross-sections of the melt silicon region for various Prandtl numbers at a critical Rayleigh number of 1000. Reynolds numbers are varied between 100 and 10 ⁵ for the Rayleigh numbers between 10 to 10 ⁶. Meanwhile, the melt has high fluctuation when the Prandtl number is increased above 0.01. The flow is converted from laminar to turbulence at a critical Rayleigh number 1000 and Prandtl number 0.01. These results provide important information for controlling the melt fluctuations during the solidification process which are used to increase the average grain size in growing silicon multicrystals and reduce the dislocation density.