Adoption of Rainwater Harvesting: a Dual-factor Approach by Integrating Theory of Planned Behaviour and Norm Activation Model

The objective of the study is to identify the factors that lead to the adoption of rainwater harvesting in enabling sustainable ground water. The Theory of Planned Behavior and Norm Activation Model has been adopted. The sample consists of 400 participants who were either constructing and likely to construct houses have been considered for the study. Structural Equation Modelling was used to analyze the data. The study results exhibited the adoption of rainwater harvesting, and the moderation effect of intention to acquire rainwater harvesting knowledge on the relationship between environmental concern; environmental responsibility and rainwater harvesting. Based on the results, significant theoretical and practical implications have been made.

     

Uniform shell patterning using rubber‐assisted hot embossing process. II. Process analysis

This two‐article sequence on rubber‐assisted embossing was aimed to understand the basic mechanisms affecting the pattern uniformity and replicability and to determine a process window for achieving uniform patterning and faithful replication. In Part I, the effects of major process and material parameters were identified and studied, and strategies for successful hot embossing with rubber as a pressure medium were proposed. In Part II, the rubber‐assisted embossing process was analyzed considering the unique rheological behavior of the materials involved to develop useful predictive capabilities for this new process. Specifically, a finite‐strain hyperelastic formulation was used for simulating the isothermal embossing stage, and a generalized Maxwell model was used to study the stress relaxation during the holding stage and to predict the elastic recovery after demolding. The rheological properties obtained in Part I were fitted to the constitutive models and implemented in the simulation procedures. The simulation results agreed with the major findings in the experimental work and provided a more quantitative insight into the process. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers     

A 2‐D surface‐potential‐based threshold voltage model for short channel asymmetric heavily doped DG MOSFETs

In recent times, transistors with heavily doped body have generated much interest because of junctionless channel. In addition, proper threshold voltage regulation requires adjustment of the channel doping, as a result of which most of the compact models become invalid as they consider an intrinsic body. In this paper, a compact surface‐potential‐based threshold voltage model is developed for short channel asymmetric double‐gate metal–oxide–semiconductor field‐effect transistors with heavily/lightly doped channel. The 2‐D surface potential is computed and compared with Technology Computer Aided Design, and a relative error of 2–4 % was obtained. The threshold voltage is solved from 2‐D Poisson's equation using ‘virtual cathode’ method, and a good agreement is observed with the numerical simulations. Also, the model is compared with a reference model and a better result is obtained for heavily doped channel. Copyright © 2014 John Wiley & Sons, Ltd.     

DFT Investigation of Formaldehyde Adsorption Characteristics on MgO Nanotube

The adsorption characteristics of formaldehyde on to MgO nanotube along inner surface, outer surface and terminating end are studied using DFT method with B3LYP/LanL2DZ basis set. The favorable adsorption site is discussed in terms of adsorbed energy which is found to be adsorption of C atom in HCHO with O atom in MgO along inner surface, outer surface and terminating end. The average energy gap variations for all the possible adsorption sites in MgO nanotube are reported. Mulliken population analysis confirms the transfers of electrons from MgO nanotube to HCHO. The conductivity of MgO base material is influenced by the energy gap variation when HCHO is adsorbed on to MgO nanotube. The result of the present study reveals that synthesizing MgO in nanotube form will enhance HCHO sensing characteristics.     

Experimental study and hydrodynamic modeling of countercurrent liquid–solid system with batch liquid

It is known that a transient effluent outlet concentration is obtained with a batch of adsorbent solids in any operation. A preferred steady state outlet concentration can be achieved with a continuous flow of solids. In the present work, information on pressure profiles, the total pressure drop across the column and holdup of solids are experimentally obtained for various solid flow rates, particle sizes and densities in a countercurrent liquid–solid system. These experimental results are compared with the prediction obtained using a phenomenological model containing continuity and momentum balance equations. The dominant drag force term was expressed in terms of various drag equations. The drag expression developed by Foscolo et al. (1983 Foscolo, P. U., Gibilaro, L. G., and Waldram, S. P. (1983). A unified model for particulate expansion of fluidized beds and flow in fixed porous media, Chem. Eng. Sci., 38(8), 1251–1260.[Crossref], [Web of Science ®] , [Google Scholar]) could predict the axial profiles of pressure drop and holdup, and the effect of various parameters on total pressure drop and solid holdup most satisfactorily.     

A system for debugging via online tracing and dynamic slicing

Dynamic slicing is a promising trace based technique that helps programmers in the process of debugging. In order to debug a failed run, dynamic slicing requires the dynamic dependence graph (DDG) information for that particular run. The two major challenges involved in utilizing dynamic slicing as a debugging technique are the efficient computation of the DDG and the efficient computation of the dynamic slice, given the DDG. In this paper, we present an efficient debugger, which first computes the DDG efficiently while the program is executing; dynamic slicing is later performed efficiently on the computed DDG, on demand. To minimize program slowdown during the online computation of DDG, we make the design decision of not outputting the computed dependencies to a file, instead, storing them in memory in a specially allocated fixed size circular buffer. The size of the buffer limits the length of the execution history that can be stored. To maximize the execution history that can be maintained, we introduce optimizations to eliminate the storage of most of the generated dependencies, at the same time ensuring that those that are stored are sufficient to capture the bug. Experiments conducted on CPU‐intensive programs show that our optimizations are able to reduce the trace rate from 16 to 0.8 bytes per executed instruction. This enables us to store the dependence trace history for a window of 20 million executed instructions in a 16‐MB buffer. Our debugger is also very efficient, yielding slicing times of around a second, and only slowing down the execution of the program by a factor of 19 during the online tracing step. Using recently proposed architectural support for monitoring, we are also able to handle multithreaded programs running on multicore processors. Copyright © 2011 John Wiley & Sons, Ltd.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

Automated dynamic detection of busy–wait synchronizations

With the advent of multicores, multithreaded programming has acquired increased importance. In order to obtain good performance, the synchronization constructs in multithreaded programs need to be carefully implemented. These implementations can be broadly classified into two categories: busy–wait and schedule‐based. For shared memory architectures, busy–wait synchronizations are preferred over schedule‐based synchronizations because they can achieve lower wakeup latency, especially when the expected wait time is much shorter than the scheduling time. While busy–wait synchronizations can improve the performance of multithreaded programs running on multicore machines, they create a challenge in program debugging, especially in detecting and identifying the causes of data races. Although significant research has been done on data race detection, prior works rely on one important assumption—the debuggers are aware of all the synchronization operations performed during a program run. This assumption is a significant limitation as multithreaded programs, including the popular SPLASH‐2 benchmark have busy–wait synchronizations such as barriers and flag synchronizations implemented in the user code. We show that the lack of knowledge of these synchronization operations leads to unnecessary reporting of numerous races. To tackle this problem, we propose a dynamic technique for identifying user‐defined synchronizations that are performed during a program run. Both software and hardware implementations are presented. Furthermore, our technique can be easily exploited by a record/replay system to significantly speedup the replay. It can also be leveraged by a transactional memory system to effectively resolve a livelock situation. Our evaluation confirms that our synchronization detector is highly accurate with no false negatives and very few false positives. We further observe that the knowledge of synchronization operations results in 23% reduction in replay time. Finally, we show that using synchronization knowledge livelocks can be efficiently avoided during runtime monitoring of programs. Copyright © 2009 John Wiley & Sons, Ltd.     

Improving the performance of speaker and language identification tasks using unique characteristics of a class

In classification tasks, the error rate is proportional to the commonality among classes. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. This paper proposes to use unique characteristics of a class at the feature-level and at the phoneme-level, separately, to improve the classification accuracy. At the feature-level, the performance of a classifier has been analyzed by capturing the unique features while modeling, and removing common feature vectors during classification. Experiments were conducted on speaker identification task, using speech data of 40 female speakers from NTIMIT corpus, and on a language identification task, using speech data of two languages (English and French) from OGI_MLTS corpus. At the phoneme-level, performance of a classifier has been analyzed by identifying a subset of phonemes, which are unique to a speaker with respect to his/her closely resembling speaker, in the acoustic sense, on a speaker identification task. In both the cases (feature-level and phoneme-level) considerable improvement in classification accuracy is observed over conventional GMM-based classifiers in the above mentioned tasks. Among the three experimental setup, speaker identification task using unique phonemes shows as high as 9.56 % performance improvement over conventional GMM-based classifier.