Collaboration as a Foundation for the Project Approach in Family Child Care

Supporting children's curiosity was considered important at my family child care home. How could we best achieve this? As my assistant caregiver Deb and 1 attended professional development workshops, we began to wonder if the project approach (Helm & Katz 2001) would be an effective means of supporting inquiry and collaborative learning. Before we would commit ourselves, we wanted to learn more. We had many questions. Just what is the project approach? What does it look like? How will it support children's learning? What do we need to be successful with it? The literature suggested many examples of successful projects at child care centers and preschools (Breig-Allen et al. 1998; Harkem: 1999; Beneke 2000; Glassman & Whaley 2000). Our challenge was how to adapt the project approach to our home child care situations.

Minimizing the Number of Optical Amplifiers Needed to Support a Multi-Wavelength Optical LAN/MAN

Optical networks based on passive star couplers and employing wavelength-division multiplexing (WDhf) have been proposed for deployment in local and metropolitan areas. Amplifiers are required in such networks to compensate for the power losses due to splitting and attenuation. However, an optical amplifier has constraints on the maximum gain and the maximum output power it can supply; thus optical amplifier placement becomes a challenging problem. The general problem of minimizing the total amplifier count, subject to the device constraints, is a mixed-integer non-linear problem. Previous studies have attacked the amplifier placement problem by adding the “artificial” constraint that all wavelengths, which are present at a particular point in a fiber, be at the same power level. In this paper, we present a method to solve the minimum amplifier- placement problem while avoiding the equally powered- wavelength constraint. We demonstrate that, by allowing signals to operate at different power levels, our method can reduce the number of amplifiers required in several small to medium-sized networks.

The Quality of Spousal Social Support as a Moderator of the Associations Between Child Maltreatment Severity and Adult Trauma Symptoms

Child maltreatment has been linked to a myriad of long-term difficulties, including trauma symptomatology. However, not all victims experience long-term distress. Thus, a burgeoning area of research focuses on factors that may impede or facilitate resiliency to the psychological correlates of child maltreatment. Specifically, the severity of the abusive acts may be associated with greater long-term difficulties. To date, however, with the exception of child sexual abuse, few studies have examined the severity of maltreatment as a risk factor in the development of trauma symptoms. In contrast, social support has been theorized to contribute to resiliency following abuse. However, to date, the majority of studies examining positive social support as a protective factor have relied on self-report measures of perceived social support, rather than observational measures of received social support. Moreover, no study to date has examined the role that negative social support (i.e, blaming, criticizing) may play in potentiating trauma symptoms among victims of child maltreatment. Because child maltreatment involves serious boundary violations by a trusted person, a marital relationship is an important domain in which to examine these constructs. That is, it may serve as an arena for the manifestation of psychological disturbances related to maltreatment. Thus, the present study examined whether observationally measured positive and negative spousal social support moderated the relationship between child maltreatment severity (i.e., sexual, physical, psychological abuse; neglect) and trauma symptomatology in women and men. Results indicated that the severity of each type of child maltreatment significantly predicted increased adult trauma symptomatology. Contrary to hypothesized outcomes, positive spousal social support did not predict decreased trauma symptomatology. However, negative spousal social support generally did predict increased trauma symptomatology. There were no consistent patterns of interactions between child maltreatment severity and either type of social support. Future directions for research will be discussed and clinical implications with regard to the intrapersonal and interpersonal functioning of child maltreatment victims will be highlighted.

Psychological and Support Characteristics of Parents of Child Sexual Abuse Victims: Relationship with Child Functioning and Treatment

Child sexual abuse continues to be a prevalent and complex problem in today’s society as it poses serious and pervasive mental health risks to child victims and their non-offending parents. The main objectives of this study were (a) to elucidate the psychological symptoms and support needs of parents of child sexual abuse victims as they present to group treatment, (b) to examine changes in psychological symptoms and support needs and their relationship with child functioning over the course of a parallel group treatment, and (c) to examine the impact of these factors on completion of group treatment. Participants included 104 sexually abused youth and their non-offending parent presenting to Project SAFE Group Intervention, a 12-session cognitive-behavioral group treatment for sexually abused children and their non-offending parents. This project had a unique advantage of utilizing a variety of demographic, parent-, and child-report measures, allowing for a more comprehensive examination of change in symptomatology and needs over the course of treatment. Several significant findings were noted, including the identification of four clusters of youth at pre-treatment, which were maintained at post-treatment; elevations on the CTQ Sexual Abuse scale; parents of youth sexually abused by a non-family member had significantly higher PSI-Restriction of Role subscale scores; parental expectations of a negative impact on their child were worse for older children; several parent characteristics predicted client treatment retention (e.g., older parents, lower SCL-90-R GSI scores); and an early age of onset of abuse also increased treatment retention. Future directions, recommendations, and limitations were discussed.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.