University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

Design of Space Trusses Using Ant Colony Optimization

Abstract: A design procedure utilizing an ant colony optimization (ACO) technique is developed for discrete optimization of space trusses. The objective function considered is the total weight (or cost) of the structure subjected to material and performance constraints in the form of stress and deflection limits. The design of space trusses using discrete variables is transformed into a modified traveling salesman problem (TSP) where the network of the TSP reflects the structural topology and the length of the TSP tour is the weight of the structure. The resulting truss, mapped into a TSP, is minimized using an ACO algorithm. The ACO design procedure uses discrete design variables, an open format for prescribing constraints, a penalty function to enforce design constraints, and allows for multiple loading cases. A comparison is presented between the ACO truss design procedure and designs developed using a genetic algorithm and classical continuous optimization methods.

Video Self-Modeling Applications With Students With Autism Spectrum Disorder in a Small Private School Setting:

Abstract: Videotaped self-modeling (VSM) Was developed as a means to alloW participants to vieW themselves in situations Where they are performing at a more advanced level than they typically function. VSM has been used effectively to train positive behaviors and reduce unWanted behaviors across a range of ages and behaviors; hoWever, feW studies of VSM have been conducted With students With autism. The present study Was designed to analyze the effects that VSM had on children With autism spectrum disorders across a variety of behaviors, including language, social initiations, tantrums, and aggression. Multiple-baseline designs across students and behaviors Were used to evaluate performance in several substudies. The results indicated that all of the 5 participants exhibited immediate and significant gains and that those gains Were maintained after cessation of treatment. The findings suggest that VSM may constitute a positive behavior change intervention Worthy of consideration for persons With autism.

Family characteristics and long-term consequences associated with sexual abuse.

Abstract: As opposed to father—daughter incest, little attention has been paid to the long-term consequences and family dynamics associated with child sexual abuse of females perpetrated by extended family members or extrafamilial contacts. Female undergraduates (n = 586) completed questionnaires on family history, sexual experiences, and current functioning. Results confirmed other research suggesting that father—daughter incest is associated with a traditional patriarchal family structure. However, sexual abuse overall, regardless of perpetrator, was associated with certain uniform family characteristics. Moreover, sexual abuse had certain long-term consequences, regardless of perpetrator. These results suggest the importance of attention to family characteristics in all cases of child sexual abuse.

A Comparison of Naive and Experienced Bidders in Common Value Offer Auctions: A Laboratory Analysis

Abstract: Laboratory economics experiments typically use financially motivated students as subjects. An ongoing issue is whether this is an appropriate subject pool since the students are typically inexperienced in the types of decision-making required of them in the lab. This paper addresses this issue in the context of common value offer auctions as we compare the -behaviour of experienced business executives in the construction contract industry ('experts') with that of ('naive') student subjects. Results of previous research of this sort have been equivocal; in some cases experts make the same errors as novices, in other cases they do not (Hogarth and Reder, I987). A series of sealed-bid, common value offer auctions in which bidders compete for the right to supply an item of unknown cost were conducted. Inherent to common value auctions (CVAs) is an adverse selection problem which may result in below normal or negative profits (the winner's curse). Experimental studies have documented the presence of the winner's curse with financially motivated student subjects in high price demand-side auctions (Kagel et al., I986; Kagel and Levin, I986). The experiments reported here generalise these earlier studies from bid to offer auctions. Also, in employing offer auctions we establish a setting with which our 'experts' are familiar, thus allowing their experience the best chance to manifest itself.'

From hype to reality: data science enabling personalized medicine

Abstract: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.