Intermetallic phases in Al-Mn alloys

The intermetallic phases present in high-purity and commercial purity Al-Mn alloys (up to 2% Mn) in as-cast and heat-treated conditions were extracted electrolytically in 10% HCl in methanol solution and identified by X-ray diffraction. As iron is known to increase the coarse cast-intermetallic particles and to cause refinement of the grain size after recrystallization, different amounts of iron (up to 0.5%) were added and the resulting intermetallic phases were extracted and identified with and without heat treatment. The unidentified phase -Al (Mn, Fe)Si, reported by Sperry and others, was recognized as corresponding for ASTM card number 6-0669 and the conditions favourable for its formation were determined. This phase may be responsible for refining the grain size after recrystallization in commercial purity alloys as compared to high-purity Al-Mn alloys and may therefore be tried as an inoculant (by adding powdered compound to liquid alloys) for grain refining.     

Assessing Mobile Technology Usage for Knowledge Dissemination among Farmers in Punjab

Agriculture-based precise and accurate information needs to be disseminated promptly to farmers so that better decisions such as managing farm fields, making continuous and scientific changes in their production systems and grabbing advantage of market opportunities can be made. In this paper, mobile technology is assessed for the agriculture information dissemination system. A survey has been conducted to find out potential technology, related to the use of the Internet and mobile among farmers in the state of Punjab in India, to deliver agriculture-related information to them. Results show that agricultural information system needs to be developed based on the mass communication technology such as mobile systems. It is also noted that localization and native language of farmers are the concerns to be incorporated into the systems. It has been focused that the use of soft-computing techniques in conjunction with communication networks, for inferring the decision regarding best practices for agricultural activities, is helpful in the development of these systems.     

Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system

The main objective of this paper is to solve the bi-objective reliability redundancy allocation problem for series-parallel system where reliability of the system and the corresponding designing cost are considered as two different objectives. In their formulation, reliability of each component is considered as a triangular fuzzy number. In order to solve the problem, developed fuzzy model is converted to a crisp model by using expected values of fuzzy numbers and taking into account the preference of decision maker regarding cost and reliability goals. Finally the obtained crisp optimization problem has been solved with particle swarm optimization (PSO) and compared their results with genetic algorithm (GA). Examples are shown to illustrate the method. Finally statistical simulation has been performed for supremacy the approach.     

A parametrization for dissipative behaviors

The behavioral equivalent of single input single output (SISO) systems are behaviors with two manifest variables. Passive SISO systems can, therefore, be viewed as J-dissipative behaviors with two manifest variables. Here the special matrix J defines a QDF that captures the passivity property of SISO systems. In this paper, we investigate more general QDFs Q_Φs induced by some operator Φ. These QDFs define some relation between the input, the output and their derivatives of a SISO system. We characterize all behaviors that are dissipative with respect to the prescribed QDF Q_Φ. In fact, we parametrize all the behaviors dissipative with respect to Q_Φ in terms of those dissipative with respect to the special QDF Q_J induced by the matrix J. Similar results can also be given for lossless systems.     

Garuda: a scalable tiled display wall using commodity PCs

Cluster-based tiled display walls can provide cost-effective and scalable displays with high resolution and a large display area. The software to drive them needs to scale too if arbitrarily large displays are to be built. Chromium is a popular software API used to construct such displays. Chromium transparently renders any OpenGL application to a tiled display by partitioning and sending individual OpenGL primitives to each client per frame. Visualization applications often deal with massive geometric data with millions of primitives. Transmitting them every frame results in huge network requirements that adversely affect the scalability of the system. In this paper, we present Garuda, a client-server-based display wall framework that uses off-the-shelf hardware and a standard network. Garuda is scalable to large tile configurations and massive environments. It can transparently render any application built using the Open Scene Graph (OSG) API to a tiled display without any modification by the user. The Garuda server uses an object-based scene structure represented using a scene graph. The server determines the objects visible to each display tile using a novel adaptive algorithm that culls the scene graph to a hierarchy of frustums. Required parts of the scene graph are transmitted to the clients, which cache them to exploit the interframe redundancy. A multicast-based protocol is used to transmit the geometry to exploit the spatial redundancy present in tiled display systems. A geometry push philosophy from the server helps keep the clients in sync with one another. Neither the server nor a client needs to render the entire scene, making the system suitable for interactive rendering of massive models. Transparent rendering is achieved by intercepting the cull, draw, and swap functions of OSG and replacing them with our own. We demonstrate the performance and scalability of the Garuda system for different configurations of display wall. We also show that the server and network loads grow sublinearly with the increase in the number of tiles, which makes our scheme suitable to construct very large displays.     

An efficient two phase approach for solving reliability–redundancy allocation problem using artificial bee colony technique

The main goal of the present paper is to present a two phase approach for solving the reliability–redundancy allocation problems (RRAP) with nonlinear resource constraints. In the first phase of the proposed approach, an algorithm based on artificial bee colony (ABC) is developed to solve the allocation problem while in the second phase an improvement of the solution as obtained by this algorithm is made. Four benchmark problems in the reliability–redundancy allocation and two reliability optimization problems have been taken to demonstrate the approach and it is shown by comparison that the solutions by the new proposed approach are better than the solutions available in the literature.     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.