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.     

Security Driven Scheduling Model for Computational Grid Using NSGA-II

Number of software applications demands various levels of security at the time of scheduling in Computational Grid. Grid may offer these securities but may result in the performance degradation due to overhead in offering the desired security. Scheduling performance in a Grid is affected by the heterogeneities of security and computational power of resources. Customized Genetic Algorithms have been effectively used for solving complex optimization problems (NP Hard) and various heuristics have been suggested for solving Multi-objective optimization problems. In this paper a security driven, elitist non-dominated sorting genetic algorithm, Optimal Security with Optimal Overhead Scheduling (OSO₂S), based on NSGA-II, is proposed. The model considers dual objectives of minimizing the security overhead and maximizing the total security achieved. Simulation results exhibit that the proposed algorithm delivers improved makespan and lesser security overhead in comparison to other such algorithms viz. MinMin, MaxMin, SPMinMin, SPMaxMin and SDSG.     

Validity as a Measure of Data Quality in Internet of Things Systems

Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis. On analyzing the data quality of the two data sets on the basis of above-mentioned parameters. We show that validity for data set 1 was found to be 75% while it was found to be 67% only for data set 2. Availability and data freshness metrics performance were analyzed graphically. It was found that for data set 1, data freshness was better while availability metric was found better for data set 2. Usability obtained for data set 2 was 86% which was higher as compared to data set 1 whose usability metric was 69%. Thus, this work presents methods that can be leveraged for estimating data quality that can be beneficial in various IoT based industries which are essentially data centric and the decisions made by them depends upon the validity of data.

     

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.     

Competitiveness and an Emerging Sector: The Russian Software Industry and its Global Linkages

This paper analyzes the Russian software industry in the context of trade in information technology services. We assess Russia's underlying sources of competitive edge in software, such as its scientific establishments, education system, diaspora and low costs, and identify the institutional impediments to growth. A survey reveals that foreign outsourcing contracts, a high value-added niche and high education levels characterize Russia's small, privately held software firms. A comparison with the Indian software industry underscores the structural differences in outsourcing relationships developed by the two countries and emphasizes that national advantages are complex amalgamations of many factors and need to be overtly marketed. Beyond the resources that give comparative and competitive advantage to a transitioning economy, a critical role is played by powerful industry organizations and by non-market state institutions that can level the economic field and inject credibility into market structures.     

Stokes flow past a swarm of porous circular cylinders with Happel and Kuwabara boundary conditions

The problem of creeping flow past a swarm of porous circular cylinders with Happel and Kuwabara boundary conditions is investigated. The Brinkman equation for the flow inside the porous cylinder and the Stokes equation outside the porous cylinder in their stream function formulations are used. The force experienced by each porous circular cylinder in a cell is evaluated. Explicit expressions of stream functions are obtained for both the inside and outside flow fields. The earlier results reported by Happel and Kuwabara for flow past a solid cylinder in Happel’s and Kuwabara’s cell model, have been deduced. Analytical expressions for the velocity components, pressure, vorticity and stresstensor are also obtained     

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     

Identification of Species Origin of Meat and Meat Products on the DNA Basis: A Review

The adulteration/substitution of meat has always been a concern for various reasons such as public health, religious factors, wholesomeness, and unhealthy competition in meat market. Consumer should be protected from these malicious practices of meat adulterations by quick, precise, and specific identification of meat animal species. Several analytical methodologies have been employed for meat speciation based on anatomical, histological, microscopic, organoleptic, chemical, electrophoretic, chromatographic, or immunological principles. However, by virtue of their inherent limitations, most of these techniques have been replaced by the recent DNA-based molecular techniques. In the last decades, several methods based on polymerase chain reaction have been proposed as useful means for identifying the species origin in meat and meat products, due to their high specificity and sensitivity, as well as rapid processing time and low cost. This review intends to provide an updated and extensive overview on the DNA-based methods for species identification in meat and meat products.