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Deoxyribonucleic Acid (DNA) evidence has been shown to have a strong effect on juror decision-making when presented in court. While DNA evidence has been shown to be extremely reliable, fingerprint evidence, and the way it is presented in court, has come under much scrutiny. Forensic fingerprint experts have been working

Deoxyribonucleic Acid (DNA) evidence has been shown to have a strong effect on juror decision-making when presented in court. While DNA evidence has been shown to be extremely reliable, fingerprint evidence, and the way it is presented in court, has come under much scrutiny. Forensic fingerprint experts have been working on a uniformed way to present fingerprint evidence in court. The most promising has been the Probabilistic Based Fingerprint Evidence (PBFE) created by Forensic Science Services (FSS) (G. Langenburg, personal communication, April 16, 2011). The current study examined how the presence and strength of DNA evidence influenced jurors' interpretation of probabilistic fingerprint evidence. Mock jurors read a summary of a murder case that included fingerprint evidence and testimony from a fingerprint expert and, in some conditions, DNA evidence and testimony from a DNA expert. Results showed that when DNA evidence was found at the crime scene and matched the defendant other evidence and the overall case was rated as stronger than when no DNA was present. Fingerprint evidence did not cause a stronger rating of other evidence and the overall case. Fingerprint evidence was underrated in some cases, and jurors generally weighed all the different strengths of fingerprint testimony to the same degree.
ContributorsArthurs, Shavonne (Author) / McQuiston, Dawn (Thesis advisor) / Hall, Deborah (Committee member) / Schweitzer, Nicholas (Committee member) / Arizona State University (Publisher)
Created2012
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A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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This chapter integrates from cognitive neuroscience, cognitive psychology, and social psychology the basic science of bias in human judgment as relevant to judgments and decisions by forensic mental health professionals. Forensic mental health professionals help courts make decisions in cases when some question of psychology pertains to the legal issue,

This chapter integrates from cognitive neuroscience, cognitive psychology, and social psychology the basic science of bias in human judgment as relevant to judgments and decisions by forensic mental health professionals. Forensic mental health professionals help courts make decisions in cases when some question of psychology pertains to the legal issue, such as in insanity cases, child custody hearings, and psychological injuries in civil suits. The legal system itself and many people involved, such as jurors, assume mental health experts are “objective” and untainted by bias. However, basic psychological science from several branches of the discipline suggest the law’s assumption about experts’ protection from bias is wrong. Indeed, several empirical studies now show clear evidence of (unintentional) bias in forensic mental health experts’ judgments and decisions. In this chapter, we explain the science of how and why human judgments are susceptible to various kinds of bias. We describe dual-process theories from cognitive neuroscience, cognitive psychology, and social psychology that can help explain these biases. We review the empirical evidence to date specifically about cognitive and social psychological biases in forensic mental health judgments, weaving in related literature about biases in other types of expert judgment, with hypotheses about how forensic experts are likely affected by these biases. We close with a discussion of directions for future research and practice.

ContributorsNeal, Tess M.S. (Author) / Hight, Morgan (Author) / Howatt, Brian C. (Author) / Hamza, Cassandra (Author)
Created2017-04-30