Understanding e‐Government portal use in rural India: role of demographic and personality characteristics

Electronic government (e‐Government) is one of the most important ways to bridge the digital divide in developing countries. We develop a model of e‐Government portal use. We use various individual characteristics, namely demographics and personality, as predictors of e‐Government portal use. Specifically, our predictors were (1) gender, age, income and education; (2) the Big Five personality characteristics, i.e. extraversion, neuroticism, conscientiousness, agreeableness and openness to experience; and (3) personal innovativeness with information technology. We conducted a field study in a village in India. We collected data from over 300 heads of household. We found support for our model, with most variables being significant and explaining 40% of the variance in e‐Government portal use.     

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.     

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.     

Incremental cell formation considering alternative machines

Many algorithms for cell formation have been developed for past three decades in cellular manufacturing. Some use binary data for cell formation and others use production data such as operation sequence, processing times, production volumes, etc. for cell formation. All these algorithms assume that the conversion of job shop to cellular manufacturing is performed comprehensively. (In other words, they assume that all the cells are formed at a time.) However, this is far from reality. In practice, cell formation is done incrementally, one after the other, rather than comprehensively. None of the algorithms developed so far addresses the issue of incremental cell formation. In this paper, the incremental cell formation problem is defined and various categories of problems are mentioned. One type of those categories is selected for solving. Two methods, namely the branch and bound technique and a heuristic based on a multistage programming approach, have been applied to solve the chosen problem. Data sets have been generated to compare these two methods in terms of quality of solution and demand on computational time. It has been found that the branch and bound technique gives a superior quality solution, but is computationally more demanding, where as heuristic based on a multistage programming approach is computationally far superior.     

Plug-load densities for energy analysis: K-12 schools

Benchmarking plug-load densities is essential to bypass arbitrary and/or incorrect inputs used in building energy analysis. As more building simulationists play a decision-making role for the design team, they tend to lean on building energy standards and guidelines for preliminary inputs such as plug-load densities. It is necessary for building energy standards and rating systems to implement plug-load density benchmarks to reward design teams in their efforts to reduce plug-load energy use. Using case study buildings, this paper establishes benchmark plug-load densities for K-12 schools under two new categories – classrooms with computers and classrooms without computers. Eighteen K-12 schools including 9 elementary, 2 middle, and 7 high schools are assessed for actual plug-load densities. For the same case study buildings, four existing approaches – NREL, COMNET, ASHRAE 90.1-1989, and Title-24 are evaluated for plug-load densities. Results show under- and over-estimation of plug-load densities over actual densities. The development of benchmark for K-12 schools will pave way for instituting targets for trimming plug-load densities in new and retrofit building projects.     

Mechanical characterization of a bituminous mix under quasi-static and high-strain rate loading

There are various methods to determine the compressive and tensile strength of asphalt concrete under static loading conditions and most studies on asphalt strength and fracture have been conducted under such loading conditions. However, pavement materials also have to withstand a wide variety of loading and temperature conditions which may vary from quasi-static to high-strain rate impact, and pavement breakdown may occur due to fracture and/or fatigue failure. In the present study, a bituminous mix with 30% RAP has been characterized under quasi-static (10^?3–10^?4 strain/s) and high-strain rate (200–700 strain/s) regimes. The experimental studies have been performed to better understand the compressive, tensile and fracture response of bituminous mixes. Split Hopkinson pressure bar (SHPB) and its modifications were used for high-strain rate characterization of this bituminous mixture. It was observed that the mechanical properties of the hot mix asphalt (HMA) changed significantly under high-strain rate testing. Also, the failure mechanisms observed under the high-strain rate loading were found to be considerably different from those obtained in static testing, where failure of binder was a predominant mechanism. It was observed that high-strain rate loading caused trans-aggregate failures in the specimens; in addition to failure of the binder.     

Effect of underlap and soft error performance in 30 nm FinFET-based 6T-SRAM cells with simultaneous and independent driven gates

The effect of Gate-Source/Drain underlap (L _un) on soft error performance in 30 nm common double gate-FinFET (simultaneously driven gates) and independent double gate-FinFET (independently driven gates) have been examined through extensive mixed mode-device and circuit simulations using Sentaurus TCAD. Four different 6T-SRAM topologies, one simultaneously driven double gate-FinFET and three independently driven double gate-FinFETs-based topologies namely Flex-V _TH, Flex-PG, and PG-SN are chosen to study the geometrical parameter L _un and also to calculate their soft error performance. When L _un increases, current decreases due to increase in parasitic series resistance. The simulation results reveal that L _un increase in independently driven double gate-FinFETs in place of access devices in 6T-SRAM does not degrade the soft error performance significantly whereas the L _un increase inside the cell, in the inverters, degrade the performance significantly.