San Diego State University

About: San Diego State University is a education organization based out in San Diego, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 12418 authors who have published 27950 publications receiving 1192375 citations. The organization is also known as: SDSU & San Diego State College.

Grooming future hospitality leaders: A competencies model

Abstract: Competency models can be useful tools for identifying and grooming future leaders. Rather than base leadership assessment on personality traits or other unrelated characteristics, competency models specify the actions and behavior needed for successful leaders. While some hotel companies have begun to identify leadership compentencies, the hotel industry does not have an overall competency model. Starting with competency models from other industries and the assessments from a pilot study, the authors complied a list of 99 competencies or skills (grouped into eight overarching factors comprising 28 dimensions) that might contribute to leadership success in the hospitality industry. Those compentencies were rated on a five-point scale, ranging from not at all important to extremely important, in a survey of 137 industry leaders. The competency labeled “self management” was the top dimension (of the 28)—composed of ethics and integrity, time management, flexibility and adaptability, and self development. Second in importance was competency in strategic positioning, comprising awareness of customer needs, commitment to quality, managing stakeholders, and concern for the community. (However, concern for the community was rated least important compared to the other three dimensions in that category). Industry knowledge, leadership, and interpersonal skill were factors that, while important, were ranked lower by the respondents.

A History of the Concept of Spatial Autocorrelation: A Geographer's Perspective

Abstract: Spatial autocorrelation is a concept that helps to define the field of spatial analysis. It is central to studies using spatial statistics and spatial econometrics. In this paper, we trace the early development of the concept and explain the academic links that brought the concept to the fore in the late 1960s. In geography, the importance of the work of Michael F. Dacey, Andrew D. Cliff, and J. Keith Ord is emphasized. Later, with the publication of a volume on spatial econometrics by Luc Anselin, spatial research and the use of the concept of spatial autocorrelation received a considerable boost. These developments are outlined together with comments about recent and possible future trends in spatial autocorrelation-based research.

The Sociopolitical Nature of Counseling

Abstract: Counseling is a sociopolitical act. However, many counselors are unaware of the fact that the profession has at its core a set of cultural values and norms by which clients are judged. In order to make the counseling psychology field more responsive to the needs of multicultural populations, the profession must be willing to engage in self-examination. This article explicates how White culture serves as the foundation for counseling theory, research, and practice. The use of this theory base with multicultural populations is explored and specific recommendations for the field of counseling psychology are presented.

An Intelligent Home Energy Management System to Improve Demand Response

Abstract: Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs which offer incentives to customers who curtail their energy use during times of peak demand. In this paper, we propose an integrated solution to predict and re-engineer the electricity demand (e.g., peak load reduction and shift) in a locality at a given day/time. The system presented in this paper expands DR to residential loads by dynamically scheduling and controlling appliances in each dwelling unit. A decision-support system is developed to forecast electricity demand in the home and enable the user to save energy by recommending optimal run time schedules for appliances, given user constraints and TOU pricing from the utility company. The schedule is communicated to the smart appliances over a self-organizing home energy network and executed by the appliance control interfaces developed in this study. A predictor is developed to predict, based on the user's life style and other social/environmental factors, the potential schedules for appliance run times. An aggregator is used to accumulate predicted demand from residential customers.

Resolution of Phylogenetic Conflict in Large Data Sets by Increased Taxon Sampling

Abstract: The debate about whether phylogenetic accuracy is most efficiently increased by sampling more charac ters or more taxa is certainly not new (e.g., Kim, 1996; Graybeal, 1998; Poe, 1998a,b; Rannala et al., 1998; Poe and Swofford, 1999; Pollock and Bruno, 2000; Rosenburg and Kumar, 2001; Pollock et al., 2002; Zwickl and Hillis, 2002; Rosenberg and Kumar, 2003; Hillis et al., 2003). However, the recent increase of whole genomic sequences available from an assortment of distantly related taxa makes this debate highly relevant to researchers across fields of bi ology. Recently, Rokas et al. (2003) argued that the true species tree can be recovered despite conflicting phylo genetic signal between genes if enough genes are used in the analysis. Using the bootstrap proportion (BP) as a measure of phylogenetic accuracy, they concluded that approximately 20 genes are needed to ensure a robustly supported tree (>95% BP) for their study group of eight yeast taxa. From these empirical results, they generalized that most molecular phylogenetic studies have probably included insufficient numbers of genes to confidently re solve relationships within their respective focal groups. This approach to measuring accuracy can be sensitive to method inconsistency, or the failure to converge on the correct tree as the data set becomes infinitely large. When a method is inconsistent, measures of support such as nonparametric bootstrapping can increase as more se quence data are added?but in support of the wrong phy logeny (Phillips et al., 2004; Collins et al., 2005; Delsuc et al., 2005). Although most methods perform well over most of tree space (Huelsenbeck, 1995; Poe, 2003), regions of inconsistency have been identified in the literature for all of the most commonly used phylogenetic meth ods. For example, compositional bias can affect the accu racy of minimum evolution (Phillips et al., 2004), model misspecification may affect parametric methods such as maximum likelihood (ML) (Poe, 2003; Philippe et al, 2005; Collins et al., 2005), and branch-length asymme try can lead to inconsistency in maximum parsimony (Felsenstein, 1978; Hendy and Penny, 1989). Parsimony is particularly prone to long-branch attraction (LBA), an analytical artifact in which two taxa on long branches are incorrectly placed as sister taxa (Felsenstein, 1978; Hendy and Penny, 1989; Huelsenbeck and Hillis, 1993). Although there are many reasons for conflicting phylo genetic signal between genes, one relevant reason could be related to method inconsistency: differing rates of evo lution between genes could cause a particular method to be inconsistent for some genes and not for others. We argue that by addressing this source of conflict between genes, fewer genes may be needed to return an accu rate phylogeny. One source of conflict in the Rokas et al. (2003) data set may be nonstationarity: taxa that differ from the others in their base compositional bias may be erroneously drawn together as sister taxa (Collins et al., 2005). Here, we show that an additional source of conflict between the 106 genes in the Rokas et al. data set may be branch-length asymmetry. Using simulations of 106 genes from the Rokas et al. data set on a 79-taxon yeast phylogeny, we additionally show that when genes are added to a data set, support for the wrong reconstruc tion can increase when there is LBA. However, when taxa are added to the analysis, support for the correct reconstruction increases, and fewer genes are needed to achieve accuracy.