The implicit approximate factorization scheme known asaf2 is investigated here for the purpose of application to the solution of two-and three-dimensional transonic full potential equations in conservative form. The artificial viscosity used by different authors has been deduced, and is discussed in detail. A second-order correction to the implicit artificial viscosity is tested for transonic flow past a Korn aerofoil at both design and off-design conditions. The inviscid transonic flow past different aerofoils, wings and wing-body configurations has been computed using theaf2 scheme and the solutions are compared with experimental and other numerical results. It is shown that theaf2 scheme is fast, and is not sensitive to grid stretching.

An ion cyclotron resonance heating (ICRH) system has been designed, fabricated indigenously and commissioned on Tokamak Aditya. The system has been commissioned to operate between 20.0 and 47.0 MHz at a maximum power of 200 kW continuous wave (CW). Duration of 500 ms is sufficient for operation on Aditya, however, the same system feeds the final stage of the 1.5 MW ICRH system being prepared for the steady-state superconducting tokamak (SST-1) for a duration of 1000 s. Radio frequency (RF) power (225 kW) has been generated and successfully tested on a dummy load for 100 s at 30.0 MHz. Lower powers have been coupled to Aditya in a breakdown experiment. We describe the system in detail in this work.

One of the main objectives of a farmer is to sell his final agricultural produce so as to maximize his profits. While he has several options, in terms of the markets where he can sell his produce, he is faced with a dilemma of identifying a market where he should sell his produce. There are several factors, like

that determine and influence the choice of market to sell. The main contributions of this paper include

Genetic algorithms (GAs) are perhaps the oldest and most frequently used search techniques for dealing with complex and intricate real-life problems that are otherwise difficult to solve by the traditional methods. The present article provides an extensive literature review of the application of GA on supply chain management (SCM). SCM consists of several intricate processes and each process is equally important formaintaining a successful supply chain. In this paper, eight processes (where each process has a set of subprocesses) as given by Council of SCM Professionals (CSCMF) are considered. The idea is to review the application of GA on these aspects and to provide the readers a detailed study in this area. The authors have considered more than 220 papers covering a span of nearly two decades for this study. The analysis is shown in detail with the help of graphs and tables. It is expected that such an extensive study will encourage and motivate the fellow researchers working in related area; to identify the gaps and to come up with innovative ideas.

Spoken language is one of the distinctive characteristics of the human race. Spoken language processing is a branch of computer science that plays an important role in human–computer interaction (HCI), which has made remarkable advancement in the last two decades. This paper reviews and summarizes theacoustic, phonetic and prosody features that have been used for spoken language identification specifically for Indian languages. In addition, we also review the speech databases, which are already available for Indian languages and can be used for the purposes of spoken language identification.

Binary class imbalance problem refers to the scenario where the number of training samples in one class is much lower compared with the number of samples in the other class. This imbalance hinders the applicability of conventional machine learning algorithms to classify accurately. Moreover, many real world training datasets often fall in the category where data is not only imbalanced but also low-resourced. In this paper we introduce a novel technique to handle the class imbalance problem, even in low-resource scenarios. Inour approach, instead of, as is common, learning using one sample at a time, two samples are simultaneously considered to train the classifier. The simultaneous two-sample learning seems to help the classifier learn both intra- and inter-class properties. Experiments conducted on a large number of benchmarked datasets demonstrate the enhanced performance of our technique over the existing state of the art techniques