Host Plant Constancy in Ovipositing Manduca sexta

Journal of Chemical Ecology - Many pollinating insects exhibit flower constancy, i.e. they target flower species they have already experienced and fed from. While the insects might profit from...     

High Performance MPI over the Slingshot Interconnect

Journal of Computer Science and Technology - The Slingshot interconnect designed by HPE/Cray is becoming more relevant in high-performance computing with its deployment on the upcoming exascale...     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

Nitrogen utilization by lowland rice as affected by fertilization with urea and green manure

Field studies were conducted during two consecutive wet seasons in flooded rice (Oryza sativa L.) to determine the effect of green manure on urea utilization in a rice-fallow-rice cropping sequence. Replicated plots were fertilized with 60 to 120 kg of urea N ha^–1 in three split applications (50, 25 and 25%) with or without incorporation of dhaincha (Sesbania aculeata L.) (100 kg N ha^–1). During the first crop only 31 to 44% of the urea added was used by the rice. Incorporatingin situ grown dhaincha (GM) into the soil at transplanting had little effect on urea utilization. Forty-four to 54% of the N added was not recovered in the soil, rice crop, or as nitrate leachate during the first cropping season. Incorporation of GM had no effect on fertilizer N recovery. Only about 2% of the urea N added to the first rice crop was taken up by the second rice crop and, as in the first crop, the GM had little effect on residual N, either in amount or utilization.     

Design of a power efficient,high slew rate and gain boosted improved recycling folded cascode amplifier with adaptive biasing technique

This paper presents an adaptive Improved Recycling Folded Cascode (IRFC) amplifier with improved gain, high slew rate, high phase margin and reduced power consumption. The proposed design is implemented using 180 nm technology with a supply voltage of 1.8 V and a capacitive load of 1 pF. The proposed design is compared with basic two stage op-amp, cascode amplifier and conventional recycling folded cascode amplifier (RFC). Analysis demonstrates that the flexible structure of IRFC with adaptive biasing shows an improvement in gain to 87.74 dB, approximately three times enhancement in slew rate to 53.8 V/µs when compared with the design specifications. The phase margin was observed to be 64.86°. The design also reports an increase in output swing. The gain increases to 109 dB when a cascode stage is added to the IRFC structure.

     

Improving the transport performance in delay tolerant networks by random linear network coding and global acknowledgments

We propose and study a new set of enhancement features to improve the performance of reliable transport in Delay Tolerant Networks (DTNs) consisting of both unicast and multicast flows. The improvement in reliability is brought in by a novel Global Selective ACKnowledgment (G-SACK) scheme and random linear network coding. The motivation for using network coding and G-SACKs comes from the observation that one should take the maximum advantage of the contact opportunities which occur quite infrequently in DTNs. Network coding and G-SACKs perform “mixing” of packet and acknowledgment information, respectively, at the contact opportunities and essentially solve the randomness and finite capacity limitations of DTNs. In contrast to earlier work on network coding in DTNs, we observe and explain the gains due to network coding even under an inter-session setting. Our results from extensive simulations of appropriately chosen “minimal” topologies quantify the gains due to each enhancement feature. We show that substantial gains can be achieved by our proposed enhancements that are very simple to implement.     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Abstract

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

Experimental study on mix proportion and fresh properties of fly ash based geopolymer for 3D concrete printing

This paper presents the material design and fresh properties of geopolymer mortar developed for 3D concrete printing application. Unlike traditional casting, in 3D printing, extruded materials are deposited layer-by-layer to build complex architectural and structural components without the need of any formwork and human intervention. Extrudability, shape retention, buildability and thixotropic open time (TOT) are identified as critical early-age properties to characterize the 3D printable geopolymer material. Five different mix designs of geopolymer are tested in a systematic experimental approach to obtain a best printable mix and later it is used to print a 60-centimeter-tall freeform structure using a concrete gantry printer to validate the formulation.     

CO2 emissions structure of Indian economy

This paper analyses carbon dioxide (CO₂) emissions of the Indian economy by producing sectors and due to household final consumption. The analysis is based on an Input–Output (IO) table and Social Accounting Matrix (SAM) for the year 2003–04 that distinguishes 25 sectors and 10 household classes. Total emissions of the Indian economy in 2003–04 are estimated to be 1217 million tons (MT) of CO₂, of which 57% is due to the use of coal and lignite. The per capita emissions turn out to be about 1.14 tons. The highest direct emissions are due to electricity sector followed by manufacturing, steel and road transportation. Final demands for construction and manufacturing sectors account for the highest emissions considering both direct and indirect emissions as the outputs from almost all the energy-intensive sectors go into the production process of these two sectors. In terms of life style differences across income classes, the urban top 10% accounts for emissions of 3416 kg per year while rural bottom 10% class accounts for only 141 kg per year. The CO₂ emission embodied in the consumption basket of top 10% of the population in urban India is one-sixth of the per capita emission generated in the US.     

Role of nanoscale AlN and InN for the microwave characteristics of AlGaN/(Al,In)N/GaN-based HEMT

A new AlGaN/GaN-based high electron mobility transistor (HEMT) is proposed and its micro-wave characteristics are discussed by introducing a nanoscale AlN or InN layer to study the potential improvement in their high frequency performance. The 2DEG transport mechanism including various sub-band calculations for both (Al,In) N-based HEMTs are also discussed in the paper. Apart from direct current characteristics of the proposed HEMT, various microwave parameters such as transconductance, unit current gain (h ₂₁ = 1) cut-off frequency (f _t ), high power-gain frequency (f _max). Masons available/stable gain and masons unilateral gain are also discussed for both devices to understand its suitable deployment in microwave frequency range.