Description of NRA tracking studies: tracking techniques and fish counter technology

The study began on the 7th January 1991 and was completed in June 1991. Two reports have been produced. This report published as R&D Note 33 describes NRA tracking studies, tracking techniques and fish counter technology. The second report published as R&D Note 34 evaluates NRA tracking studies and recommends future research. The latter will be used solely for NRA management purposes. This report briefly outlines the programme of the NRA, placing the Fisheries programme in the context of the work of the NRA as a whole, and viewing the tracking work against the broader requirements of the NRA Fisheries research programme. Two techniques currently exist for studying the detailed timing and extent of movements of adult salmon: tracking of individually identifiable fish, and counting the numbers of fish moving past a fixed point in the river. Further details of these techniques and their development are given in Sections 2 and 3. Section 4 summarises and assesses completed and current NRA tracking Studies. Complete project descriptions for the studies are contained in Appendix A. Section 5 discusses the scientific content of these studies in relation to similar work carried out elsewhere in the UK. Section 6 details the future development of tracking techniques. Tracking work on migratory salmonids has tended to concentrate largely upon the movements of adult salmon. Much of this report will therefore be concerned with salmon tracking studies. NRA studies involving sea trout are referred to where appropriate. The methodological problems of sea trout tracking studies are summarised in Section 2.1.3.

Exploring Game Performance in the National Basketball Association Using Player Tracking Data

Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.

Detection of particles with a cloud chamber

Cloud chambers were essential devices in early nuclear and particle physics research. Superseded by more modern detectors in actual research, they still remain very interesting pedagogical apparatus. This thesis attempts to give a global view on this topic. To do so, a review of the physical foundations of the diffusion cloud chamber, in which an alcohol is supersaturated by cooling it with a thermal reservoir, is carried out. Its main results are then applied to analyse the working conditions inside the chamber. The analysis remarks the importance of using an appropriate alcohol, such as isopropanol, as well as a strong cooling system, which for isopropanol needs to reach −40ºC. That theoretical study is complemented with experimental tests that were performed with what is the usual design of a home-made cloud chamber. An effective setup is established, which highlights details such as a grazing illumination, a direct contact with the cooling reservoir through a wide metal plate, or the importance of avoiding vapour removal. Apart from that, video results of different phenomena that cloud chamber allow to observe are also presented. Overall, it is aimed to present a physical insight that pedagogical papers usually lack.

A low-level geographic information system for coastal zone management with applications to Brunei Darussalam: Part III:: simulation and tracking oil spills

B:RUN is a low-level GIS software designed to help formulate options for the management of the coastal zone of Brunei Darussalam. This contribution presents the oil spill simulation module of B:RUN. This simple module, based largely on wind and sea surface current vector parameters, may be helpful in formulating relevant oil spill contingency plans. It can be easily adapted to other areas, as can the B:RUN software itself.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed