PCB congener analysis with Hall electrolytic conductivity detection

This work reports the development of an analytical methodology for the analysis of PCB congeners based on integrating relative retention data provided by other researchers. The retention data were transposed into a multiple retention marker system which provided good precision in the calculation of relative retention indices for PCB congener analysis. Analytical run times for the developed methodology were approximately one hour using a commercially available GC capillary column. A Tracor Model 700A Hall Electrolytic Conductivity Detector (HECD) was employed in the GC detection of Aroclor standards and environmental samples. Responses by the HECD provided good sensitivity and were reasonably predictable. Ten response factors were calculated based on the molar chlorine content of each homolog group. Homolog distributions were determined for Aroclors 1016, 1221, 1232, 1242, 1248, 1254, 1260, 1262 along with binary and ternary mixtures of the same. These distributions were compared with distributions reported by other researchers using electron capture detection as well as chemical ionization mass spectrometric methodologies. Homolog distributions acquired by the HECD methodology showed good correlation with the previously mentioned methodologies. The developed analytical methodology was used in the analysis of bluefish (Pomatomas saltatrix) and weakfish (Cynoscion regalis) collected from the York River, lower James River and lower Chesapeake Bay in Virginia. Total PCB concentrations were calculated and homolog distributions were constructed from the acquired data. Increases in total PCB concentrations were found in the analyzed fish samples during the fall of 1985 collected from the lower James River and lower Chesapeake Bay. Comparisons between the homolog distribution patterns in the fish samples with the previously mentioned Aroclor distribution patterns suggests a different source of PCBs for different areas. Sediments, oysters (Crassostrea virginica) and brackish water clams (Rangia cuneata) collected from the tidal James River in 1986 were also analyzed. Total PCB concentrations and homolog distributions were calculated for all samples. Sediment total PCB concentrations were relatively constant over the sampling range except in the region of the turbidity maximum which were significantly higher. Total PCB concentrations in the Rangia from the region of the turbidity maximum were the highest of all the biota samples. Rangia homolog distribution patterns from this area were distinctly different from the sediment distribution patterns or the other Rangia distribution patterns in this segment of the river. Alteration of the endemic distribution pattern may be due to physical-chemical processes occurring within the turbidity maximum

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Refining victims’ self-reports on bullying:Assessing frequency, intensity, power imbalance, and goal-directedness

Bullying can be differentiated from other types of peer aggression by four key characteristics: frequency, intensity, power imbalance, and goal‐directedness. Existing instruments, however, usually assess the presence of these characteristics implicitly. Can self‐report instruments be refined using additional questions that assess each characteristic? We examined (a) what proportion of children classified as victims by the commonly used Revised Olweus’ bully/victim questionnaire (BVQ) also experienced the characteristics of bullying, and (b) the extent to which the presence of the characteristics was associated with emotional (affect, school, and classroom well‐being), relational (friendship, defending), and social status (popularity, rejection) adjustment correlates among victims. Using data from 1,738 students (Mage = 10.6; grades 5–8), including 138 victims according to the BVQ, the results showed that 43.1% of the children who were classified as victims by BVQ experienced all the four characteristics of bullying. Frequency ratings of victimization did not capture experiences that involved a power imbalance. Victims who reported all four key characteristics had greater emotional, relational, and social status problems than victims who did not report all characteristics. Thus, researchers who focus on victimization for diagnostic and prevention purposes can enrich self‐report measurements of bullying victimization by adding questions that assess the characteristics explicitly