Survey of International Field Education Placements of Accredited Social Work Education Programs

Abstract

During the last 2 decades, social work education programs have sought to incorporate cross-cultural and international content into their curriculum, including the establishment of international field placements. This study reports the results of a full survey (N=446) of all accredited social work education programs. Findings include the number of programs placing students internationally, the number of students placed, the countries into which they are placed, whether these students are graduates or undergraduates, and whether these international placements represent an ongoing commitment by the programs. Several factors are examined which are hypothesized to affect a program's willingness to place students internationally.     

Social Workers' Postgraduate Use of the IFD Interpersonal Communication Model

Interpersonal communication assessment models have become very popular as a means of quickly establishing rapport in many settings across the United States. Despite their popularity and possible utility, there has been minimal research into the impact of these models, especially over time and in educational settings (more than 6 months after the training is over). In the master's of social work (MSW) program at Brigham Young University (BYU), the interpersonal family dynamics (IFD) model is used as a tool to better understand human behavior and strengthen relationships in the social work practice environment. This training is provided to first-year students in the foundation human behavior in the social environment course and to field supervisors in their field training conferences. BYU's MSW program graduates were surveyed to find out their perceptions of the utility of IFD. With a relatively high level of accuracy, the graduates reported IFD was a useful approach to better understanding human behavior in their daily practice. Graduates used the model as much in their personal lives as in their professional lives.     

Optimal solutions to minimum total energy broadcasting problem in wireless ad hoc networks

In this paper, we present three schemes to solve minimum total energy broadcasting problem in wireless ad hoc networks based on an efficient IP (integer programming) subproblem technique. Due to its NP-hardness, many heuristics have been studied. However, the heuristics in the literature suffer from coarse performance ratio. It is important to have knowledge of the optimal solution structure in order to develop more efficient heuristics and algorithms. We present one IP formulation and two iterative algorithms which make use of relaxed IP's to solve subproblems. The computational results show that our approaches outperform other techniques in the literature. Research is partially supported by NSF and Air Force grants.     

Heuristics for the network design problem with connectivity requirements

We consider the NP-complete problem of finding a spanning \(k\)-tree of minimum weight in a complete weighted graph. This problem has a number of applications in designing reliable backbone telecommunication networks. We propose effective algorithms based on a greedy strategy and several variable neighborhood search metaheuristics. We also develop an integer linear programming model for calculating a lower bound. Preliminary numerical experiments using random and real-word data sets are reported to show the effectiveness of our approach. In addition, we compare our approach with known metaheuristics.     

GRASP with a New Local Search Scheme for Vehicle Routing Problems with Time Windows

The NP-Hard Vehicle Routing Problem with Time Windows (VRPTW) is one of the major transportation problems. In this paper, a Greedy Randomized Adaptive Search Procedure (GRASP) for VRPTW is discussed for minimizing the fleet size and the travel distance. There are two phases within each GRASP iteration: construction phase and local search phase. In the construction phase, the initial solution is computed by applying an adaptive randomized greedy function. In the local search phase, the search procedure is applied to the constructed initial solution obtained by the construction phase for an improvement. In this paper, we propose an improved solution technique by using new local search ideas and new lower bounding procedures. In addition, we report computational results and address some practical issues in this area.     

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG’s dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.     

Closed-loop supply chain inventory management with recovery information of reusable containers

This paper considers a closed-loop supply chain consisting of one-manufacturer and one-retailer. This supply chain provides single-kind products with reusable containers. The main purpose of this study is to explore and evaluate the value of recovery information captured by embedded sensors in the environment of internet of things. The recovery information of containers dynamically monitors recovery status and provides a reliable estimation of return quantity. The value of information is measured by the cost saving performances with full, partial or no recovery information. When the full or partial recovery information is available, the decisions are made based on the known quantities of the usable or total return flow. When no recovery information is available, the decisions are made based on the stationary distribution of the return flow. A periodic inventory model is built with uncertainties of forward and reverse flows. Then, a myopic order policy is proposed for the different levels of information utilization. Through the optimality analysis, we introduce a farsighted inventory control policy. Using the general result of Markov decision processes, the performance of heuristic policies is displayed. The farsighted policy performs better than the myopic policy. In addition, the farsighted policy helps to lessen the convex impact of utilization rate on the expected cost. Afterwards, we extend the model with the selective disposal behavior. A simulation study is used to depict sensitivity and robustness of the farsighted policy. Finally, we extend the simulation experiment with uniformly distributed in-use time for a more general applicability.     

Selective support vector machines

In this study we introduce a generalized support vector classification problem: Let X _i , i=1,…,n be mutually exclusive sets of pattern vectors such that all pattern vectors x _i,k , k=1,…,|X _i | have the same class label y _i . Select only one pattern vector $x_{i,k^{*}}In this study we introduce a generalized support vector classification problem: Let X _i , i=1,…,n be mutually exclusive sets of pattern vectors such that all pattern vectors x _i,k , k=1,…,|X _i | have the same class label y _i . Select only one pattern vector from each set X _i such that the margin between the set of selected positive and negative pattern vectors are maximized. This problem is formulated as a quadratic mixed 0-1 programming problem, which is a generalization of the standard support vector classifiers. The quadratic mixed 0-1 formulation is shown to be -hard. An alternative approach is proposed with the free slack concept. Primal and dual formulations are introduced for linear and nonlinear classification. These formulations provide flexibility to the separating hyperplane to identify the pattern vectors with large margin. Iterative elimination and direct selection methods are developed to select such pattern vectors using the alternative formulations. These methods are compared with a na?ve method on simulated data. The iterative elimination method is also applied to neural data from a visuomotor categorical discrimination task to classify highly cognitive brain activities.     

Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL _max ), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion’s sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL _max , a measure of phase (φ _max ) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α=0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL _max synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy.     

Cross‐Cultural Measurement Invariance of Adolescent Self‐Report on the Pediatric Quality of Life Inventory™ 4.0

This study evaluated the cross‐cultural measurement invariance of the Pediatric Quality of Life Inventory version 4.0 (PedsQL^?) among adolescents sampled from Bulgaria, Croatia, India, Indonesia, Nigeria, Serbia, and Turkey. The multiple‐indicator multiple‐cause (MIMIC) model was used, which allowed controlling of demographic variables (i.e., age, gender, and socioeconomic status). Significant effects of country on scores within the PedsQL^? domains were observed, with up to 17 items showing differential item functioning (DIF) across the countries. We did not find support for cross‐cultural measurement invariance hypotheses for scores on the PedsQL^? adolescent self‐report in this study. Researchers should use caution in making cross‐cultural quality of life comparisons while using the PedsQL.