An experimental investigation into soft-pad grinding of wire-sawn silicon wafers

Silicon is the primary semiconductor material used to fabricate microchips. A series of processes are required to manufacture high-quality silicon wafers. Surface grinding is one of the processes used to flatten wire-sawn wafers. A major issue in grinding of wire-sawn wafers is reduction and elimination of wire-sawing induced waviness. Results of finite element analysis have shown that soft-pad grinding is very effective in reducing the waviness. This paper presents an experimental investigation into soft-pad grinding of wire-sawn silicon wafers. Wire-sawn wafers from a same silicon ingot were used for the study to ensure that these wafers have similar waviness. These wafers were ground using two different soft pads. As a comparison, some wafers were also ground on a rigid chuck. Effectiveness of soft-pad grinding in removing waviness has been clearly demonstrated.     

Empirical evaluation of computational fear contagion models in crowd dispersions

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) and Durupinar, From audiences to mobs: Crowd simulation with psychological factors, PhD dissertation, Bilkent University, 2010) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.     

K–La–MgO as heterogeneous catalyst for synthesis of 3-(2-hydroxyethyl)-1,3-oxazolidin-2-one from diethanol amine and carbon dioxide

Cost-effective valorization of carbon dioxide into bulk and specialty chemicals using catalysis will be attractive in the foreseeable future. 1,3-Oxazolidin-2-one derivatives are one of the important classes of heterocyclic compounds which have wide applications in pharmaceutical industries due to their biological activities such as antibacterial, antimicrobial, antiseptic. Various synthetic routes are employed to prepare these compounds which include phosgenation, oxidative carbonylation, etc., which make use of polluting chemicals and homogeneous catalysts. The heterogeneous catalytic processes to synthesize these derivatives are quite limited. Thus, developing a green route which is environmental friendly is highly desirable. The current work deals with development of a heterogeneous reusable catalyst and its application to synthesize 1,3-oxazolidin-2-one derivatives using carbon dioxide as a C1 source. The fact that no use of promoter or organic co-catalyst is made in the current process makes the synthesis route more favorable. Pure La–MgO and K–La–MgO with different K loading (1, 3, 5, and 7 wt%) synthesized by combustion route were screened for carbonylation of diethanol amine. 5% K–La–MgO was found to be the best catalyst. The catalyst was well characterized in virgin form and after use by various analytical techniques like TEM, SEM, XRD, CO₂ and NH₃-TPD, BET surface area analysis. With 5% K–La/MgO, 72% conversion of diethanol amine was achieved with 100% selectivity of the desired product at optimum conditions, i.e., 150 °C, 5 wt% K–La/MgO catalyst loading of 0.02 g/cm³ and 2.0 MPa CO₂ pressure. Reaction mechanism was proposed and kinetic model developed. The apparent activation energy was calculated as 18.76 kcal/mol. The catalyst was robust and recyclable. The process is clean and green.     

Robust solutions to Stackelberg games: Addressing bounded rationality and limited observations in human cognition

How do we build algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human interaction, such as oligopolistic markets and security domains. In Stackelberg games, one player, the leader, commits to a strategy and the follower makes her decision with knowledge of the leader's commitment. Existing algorithms for Stackelberg games efficiently find optimal solutions (leader strategy), but they critically assume that the follower plays optimally. Unfortunately, in many applications, agents face human followers (adversaries) who — because of their bounded rationality and limited observation of the leader strategy — may deviate from their expected optimal response. In other words, human adversaries' decisions are biased due to their bounded rationality and limited observations. Not taking into account these likely deviations when dealing with human adversaries may cause an unacceptable degradation in the leader's reward, particularly in security applications where these algorithms have seen deployment. The objective of this paper therefore is to investigate how to build algorithms for agent interactions with human adversaries.To address this crucial problem, this paper introduces a new mixed-integer linear program (MILP) for Stackelberg games to consider human adversaries, incorporating: (i) novel anchoring theories on human perception of probability distributions and (ii) robustness approaches for MILPs to address human imprecision. Since this new approach considers human adversaries, traditional proofs of correctness or optimality are insufficient; instead, it is necessary to rely on empirical validation. To that end, this paper considers four settings based on real deployed security systems at Los Angeles International Airport (Pita et al., 2008 [35]), and compares 6 different approaches (three based on our new approach and three previous approaches), in 4 different observability conditions, involving 218 human subjects playing 2960 games in total. The final conclusion is that a model which incorporates both the ideas of robustness and anchoring achieves statistically significant higher rewards and also maintains equivalent or faster solution speeds compared to existing approaches.     

Analysis of fractal inter frame video coding using parallel approach

Digital video has many applications varying from telecommunications to broadcasting and so on. Video compression techniques have evolved over the past two decades with prominent technique being developed using fractal. However, this technique was not so popular because of its computationally intensive nature. This paper proposes an inter frame video compression technique, which consists of a combination of a block matching using fractal compression. The proposed algorithm is implemented on CUDA-enabled GPU which significantly reduces the encoding time of the video and provides a very high compression ratio with reasonable quality of the decoded video. Extensive simulations were carried out to analyze the performance of the proposed algorithm.     

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.     

Experimental study of the burner for FAA fire test: NexGen burner

The NexGen (Sonic) burner is the new burner developed by the Federal Aviation Administration, FAA, to replace old oil burners used for the required fire certification tests on power plant‐related materials, as it provides the capability to control both air and fuel flow rates. During a fire test, the burner is supposed to simulate a certain fire condition, so the flame properties should be robust and repeatable. The NexGen burner can achieve this due to the precise fuel and air controls. However, the current calibration criterion (ISO2685:1998 and AC20‐135) may not be good enough to ensure consistent flame properties. In the presented work, the sensitivity of the burner performance to air and fuel flow rate, as measured by the temperature and heat flux for calibration purposes, was studied. Additionally, the influence of the turbulator and the thermocouple size used for flame calibration was also studied. The impact of varying fuel/air ratio and thermocouple sizes was studied by conducting fire tests on aluminum samples, to show the inadequacies in the current calibration standards.     

TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA’s assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110,000 meetings held at nine campus buildings during an 8-month period in 2011–2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.     

The propagation of pressure in a gelled waxy oil pipeline as studied by particle imaging velocimetry

Paraffinic crude oils in pipelines may form waxy gels during flow shutdowns. These gels can be dislodged by applying pressure if the wall shear stress, proportional to the local pressure gradient, exceeds the gel yield stress. The simplest models assume that the axial pressure profile becomes linear immediately after a jump in upstream pressure, but this fails to account for gel time‐dependent rheology or the effect of gel voids on pressure wave propagation. To investigate the former factor, pressure profile and particle imaging velocimetry (PIV) measurements were performed on a model oil gelled under pressure to reduce void formation. After a jump in upstream pressure to a value insufficient to restart flow, the axial pressure profile becomes linear in a two‐step process, with an immediate small rise in downstream pressure followed by a time‐delayed jump. The local downstream gel deformation measured by PIV exhibits similar two‐step time dependence. © 2011 American Institute of Chemical Engineers AIChE J, 2012