10 resultados para collection problems

em Digital Commons at Florida International University


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The job of a principal is becoming more demanding and more critical each year. Principals are asked to undertake huge challenges and to succeed regardless of what obstacles lie ahead. The purpose of this study was to identify which Administrative Task Areas and Specific Task Areas caused the most difficulties for first- and second-year principals.^ A survey was taken of first- and second-year principals in Dade County, Florida. These beginning principals rated their level of proficiency for each administrative task area and each specific task within those areas. Participants rated their perceptions on a scale from one to four. The data were analyzed based on frequency distributions, percentages, means, and standard deviations.^ Beginning principals perceived themselves as least proficient in the administrative task areas of management and personnel duties. They believed their strongest areas were curriculum and instruction and school-community relations. Within these areas, the specific administrative task areas identified as most problematic were identifying proper procedures for construction in the schools, visiting classrooms to help teachers improve instruction, awareness of issues related to school law, establishing accounting procedures for the school's internal funds, and procedures for dismissing incompetent staff members.^ Many beginning principals surveyed volunteered to make recommendations for future beginning principals. Of these recommendations, the most popular responses addressed obtaining more experience with the budget and internal funds prior to becoming a principal. In addition, there was a strong need for more training dealing with school personnel and the importance of networking with a veteran principal.^ The principal training programs for five of the largest school districts in Florida were reviewed. These programs were found to incorporate a vast amount of the recommendations included in the literature. Florida is moving in the right direction toward excellence in the public schools. ^

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The goal of this study was to examine the longitudinal effects of five family factors on alcohol use among adolescent males. The family factors included familism (family pride, loyalty, and cohesion), parent derogation (being put down by parents), parent/child communication, family alcohol problems and family drug problems. The study focused on the effects of the family factors reported by a sample of 451 White-non-Hispanic and African American males during early and mid-adolescence on (1) the intensity of alcohol use in mid-adolescence, and (2) the number of problems associated with alcohol use during the transition to young adulthood. The study also explored racial differences in the effects of the family factors. The data for this study were derived from a two-phase longitudinal epidemiologic cohort study of male and female adolescents enrolled in middle schools in Miami, FL. Data were collected at four points between 1990 and 2001. Linear and logistical regressions were used to analyze the effects of the family variables on the dependent variables. ^ The results of the analyses indicated that all of the family variables except family drug problems were statistically significant predictors of the level of alcohol use and alcohol-related problems. Familism had a moderate influence on both of the dependent variables at all data points, while parent derogation, parent/child communication and family alcohol problems were weak predictors. While the family factors varied by race, their impact on the dependent variables did not vary substantially. ^ This study had methodological shortcomings related to measurement and design that may have contributed to the weak influence of the variables. Future studies should explore possible mediating effects of these variables, and should employ more sensitive measures that are culturally appropriate. The results suggest that, since early family factors have long-term effects on children's substance-using behaviors, the family environment should be addressed in prevention and intervention efforts. ^

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The main objective of the study was to investigate the relationship between parent-related, acculturation-related, and substance use-related variables found within individual, familial/parental, peer and school adolescent ecological domains, in a clinical sample (i.e. adolescents who met criteria for a Diagnostic Statistical Manual-IV [DSMIV] clinical diagnosis of substance abuse/dependence) of Hispanic adolescents from Miami, Florida. ^ The sample for this study consisted of 94 adolescent-mother pairs. The adolescent sample was 65% male, and 35% female, with a mean age of 15 years. More than half of the adolescents were born in the United States (60%) and had resided in the U.S. for an average of 12 years; 80% of the caregivers (primarily mothers) were foreign-born and lived in the U.S. for an average of 21 years. ^ Correlation and hierarchical regression were used to answer the research questions. The findings indicate that the hypothesized model and corresponding anticipated effect of the relationship between parental school and peer involvement on adolescents’ frequency of alcohol, marijuana and cocaine use was not supported by the data. Parental “acculturation-related” variables did not explain any of the variance in adolescent substance use frequency in this sample. Mediation and moderation models were not supported either. However, some interesting relationships were found: ^ The larger the acculturation gap, the lower the parental involvement in school tended to be (r = -.21, p < .05). Adolescents who experienced a greater acculturation gap with their parents (-.81, p >.01) had an earlier onset of marijuana (-.33, p < .01) and cocaine use (r = -.24, p <.01). The less acculturated parents experienced more parenting stress (r = -.31, p = < .01). Attachment was positively associated with parental peer involvement (r = .24, p < .05) and inversely associated with parenting acculturative stress (r = -.24, p < .05). Attachment was also positively associated with marijuana (r = .39, p < .01) and cocaine use (r = .33, p < .01). Adolescent males reported being more attached to their mothers when compared to adolescent females (r = .22, p >.05), they also reported using marijuana more frequently than females (.21, p >.05). ^

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Groundwater systems of different densities are often mathematically modeled to understand and predict environmental behavior such as seawater intrusion or submarine groundwater discharge. Additional data collection may be justified if it will cost-effectively aid in reducing the uncertainty of a model's prediction. The collection of salinity, as well as, temperature data could aid in reducing predictive uncertainty in a variable-density model. However, before numerical models can be created, rigorous testing of the modeling code needs to be completed. This research documents the benchmark testing of a new modeling code, SEAWAT Version 4. The benchmark problems include various combinations of density-dependent flow resulting from variations in concentration and temperature. The verified code, SEAWAT, was then applied to two different hydrological analyses to explore the capacity of a variable-density model to guide data collection. ^ The first analysis tested a linear method to guide data collection by quantifying the contribution of different data types and locations toward reducing predictive uncertainty in a nonlinear variable-density flow and transport model. The relative contributions of temperature and concentration measurements, at different locations within a simulated carbonate platform, for predicting movement of the saltwater interface were assessed. Results from the method showed that concentration data had greater worth than temperature data in reducing predictive uncertainty in this case. Results also indicated that a linear method could be used to quantify data worth in a nonlinear model. ^ The second hydrological analysis utilized a model to identify the transient response of the salinity, temperature, age, and amount of submarine groundwater discharge to changes in tidal ocean stage, seasonal temperature variations, and different types of geology. The model was compared to multiple kinds of data to (1) calibrate and verify the model, and (2) explore the potential for the model to be used to guide the collection of data using techniques such as electromagnetic resistivity, thermal imagery, and seepage meters. Results indicated that the model can be used to give insight to submarine groundwater discharge and be used to guide data collection. ^

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Multi-problem youth undergoing treatment for substance use problems are at high behavioral risk for exposure to sexually transmitted infections (STIs), including human immunodeficiency virus (HIV). Specific risk factors include childhood adversities such as maltreatment experiences and subsequent forms of psychopathology. The current study used a person-centered analytical approach to examine how childhood maltreatment experiences were related to patterns of psychiatric symptoms and HIV/STI risk behaviors in a sample of adolescents (N = 408) receiving treatment services. Data were collected in face-to-face interviews at two community-based facilities. Descriptive statistics and Latent Profile Analysis (LPA) were used to (a) classify adolescents into groups based on past year psychiatric symptoms, and (b) examine relations between class membership and forms of childhood maltreatment experiences, as well as past year sexual risk behavior (SRB). ^ LPA results indicated significant heterogeneity in psychiatric symptoms among the participants. The three classes generated via the optimal LPA solution included: (a) a low psychiatric symptoms class, (b) a high alcohol symptoms class and (c) a high internalizing symptoms class. Class membership was associated significantly with adolescents’ self-reported scores for childhood sexual abuse and emotional neglect. ANOVAs documented significant differences in mean scores for multiple indices of SRB indices by class membership, demonstrating differential risk for HIV/STI exposure across classes. The two classes characterized by elevated psychiatric symptom profiles and more severe maltreatment histories were at increased behavioral risk for HIV/STI exposure, compared to the low psychiatric symptoms class. The high internalizing symptoms class reported the highest scores for most of the indices of SRB assessed. The heterogeneity of psychiatric symptom patterns documented in the current study has important implications for HIV/STI prevention programs implemented with multi-problem youth. The results highlight complex relations between childhood maltreatment experiences, psychopathology and multiple forms of health risk behavior among adolescents. The results underscore the importance of further integration between substance abuse treatment and HIV/STI risk reduction efforts to improve morbidity and mortality among vulnerable youth. ^

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This dissertation delivers a framework to diagnose the Bull-Whip Effect (BWE) in supply chains and then identify methods to minimize it. Such a framework is needed because in spite of the significant amount of literature discussing the bull-whip effect, many companies continue to experience the wide variations in demand that are indicative of the bull-whip effect. While the theory and knowledge of the bull-whip effect is well established, there still is the lack of an engineering framework and method to systematically identify the problem, diagnose its causes, and identify remedies. ^ The present work seeks to fill this gap by providing a holistic, systems perspective to bull-whip identification and diagnosis. The framework employs the SCOR reference model to examine the supply chain processes with a baseline measure of demand amplification. Then, research of the supply chain structural and behavioral features is conducted by means of the system dynamics modeling method. ^ The contribution of the diagnostic framework, is called Demand Amplification Protocol (DAMP), relies not only on the improvement of existent methods but also contributes with original developments introduced to accomplish successful diagnosis. DAMP contributes a comprehensive methodology that captures the dynamic complexities of supply chain processes. The method also contributes a BWE measurement method that is suitable for actual supply chains because of its low data requirements, and introduces a BWE scorecard for relating established causes to a central BWE metric. In addition, the dissertation makes a methodological contribution to the analysis of system dynamic models with a technique for statistical screening called SS-Opt, which determines the inputs with the greatest impact on the bull-whip effect by means of perturbation analysis and subsequent multivariate optimization. The dissertation describes the implementation of the DAMP framework in an actual case study that exposes the approach, analysis, results and conclusions. The case study suggests a balanced solution between costs and demand amplification can better serve both firms and supply chain interests. Insights pinpoint to supplier network redesign, postponement in manufacturing operations and collaborative forecasting agreements with main distributors.^

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Satisfiability, implication and equivalence problems are important and widely-encountered database problems that need to be efficiently and effectively solved. We provide a comprehensive and systematic study of these problems. We consider three popular types of arithmetic inequalities, (X op C), (X op Y), and (X op Y + C), where X and Y are attributes, C is a constant of the domain of X, and op $\in\ \{{<},\ {\le},\ {=},\ {\not=},\ {>},\ {\ge}\}.$ These inequalities are most frequently used in a database system, since the first type of inequalities represents $\theta$-join, the second type represents selection, and the third type is popular in deductive databases. We study the problems under the integer domain and the real domain, as well as under two different operator sets.^ Our results show that solutions under different domains and/or different operator sets are quite different. In this dissertation, we either report the first necessary and sufficient conditions as well as their efficient algorithms with complexity analysis, or provide improved algorithms. ^

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The present study has the primary aim of examining the predictors of treatment attrition among racial/ethnic minority adolescents with substance use problems. This study explores the potential differential influence of specific individual, social, cultural, and treatment factors on treatment attrition within three racial/ethnic subgroups of adolescents. Participants: A unique feature of the study is the use of a racial/ethnic minority sample (N=453), [U.S.-born Hispanics (n = 262), Foreign-born Hispanics (n = 117), and African-Americans (n = 74)]. Multivariate logit analyses were used to examine the influence of specific factors on treatment attrition among the full sample of adolescents, as well as within each racial/ethnic subgroup. Consistent with expectations, multivariate logit analyses reveal that, the specific factors associated with attrition varied across the three racial/ethnic subgroups. Having parents with problem substance use, being on the waitlist, and being court mandated to treatment emerged as predictors of attrition among the US-born Hispanics, while only Conduct Disorder was significantly associated with greater attrition among foreign-born Hispanics. Finally, among African-Americans, parental crack/cocaine use, parental abstinence from alcohol, and being on the waitlist were predictive of attrition. Multiple factors were associated with treatment attrition among racial/ethnic minority adolescents with specific factors differentially predicting attrition within each racial/ethnic subgroup. African-American youth were more than twice as likely as their Hispanic counterparts to leave treatment prematurely. It is critically important to understand the predictors of attrition among racial/ethnic minority youth in order to better meet the needs of youth most at risk of dropping out. ^

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Maltreatment experienced in childhood or adolescence is a known risk factor for later problem alcohol and/or other drug (AOD) use (Bailey & McCloskey, 2005; Shin, Edwards, Heeren, 2009). A growing body of empirical work has found significant associations between adolescent girls’ AOD use and maltreatment experiences. However, questions remain as to how this relation unfolds with African-American and Hispanic adolescent girls. Guided by four relational models that have been proposed in the literature, this study examined the links between maltreatment, trauma symptoms, and alcohol and/or other drug (AOD) problems in a sample of 170 African-American and Hispanic adolescent girls who were participants in a school-based AOD use intervention. Results of this study revealed that maltreatment experiences (physical and emotional abuse) were positively related to trauma symptoms, which were positively related to AOD problem severity, alcohol abuse, alcohol dependency, drug abuse, and drug dependency. Perceived discrimination moderated this relation between sexual abuse and trauma symptoms, such that more perceived discrimination resulted in a stronger effect of sexual abuse on trauma symptoms. Ethnic identity moderated the relation between sexual abuse and AOD problem severity, such that ethnic identity demonstrated protective properties in the relation between sexual abuse and AOD problem severity. My research adds to extant knowledge on the relation between maltreatment and AOD use in adolescent girls and suggests the importance of developing interventions targeting maltreatment and AOD use concurrently.

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With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.