Interactive Teaching Tools for Spatial Sampling

The statistical analysis of data which is measured over a spatial region is well established as a scientific tool which makes considerable contributions to a wide variety of application areas. Further development of these tools also remains a central part of the research scene in statistics. However, understanding of the concepts involved often benefits from an intuitive and experimental approach, as well as a formal description of models and methods. This paper describes software which is intended to assist in this understanding. The role of simulation is advocated, in order to explain the meaning of spatial correlation and to interpret the parameters involved in standard models. Realistic scenarios where decisions on the locations of sampling points in a spatial setting are required are also described. Students are provided with a variety of sampling strategies and invited to select the most appropriate one in two different settings. One involves water sampling in the lagoon of the Mururoa Atoll while the other involves sea bed sampling in a Scottish firth. Once a student has decided on a sampling strategy, simulated data are provided for further analysis. This extends the range of teaching activity from the analysis of data collected by others to involvement in data collection and the need to grapple with issues of design. It is argued that this approach has significant benefits in learning.

Interactive Teaching Tools for Spatial Sampling

The statistical analysis of data which is measured over a spatial region is well established as a scientific tool which makes considerable contributions to a wide variety of application areas. Further development of these tools also remains a central part of the research scene in statistics. However, understanding of the concepts involved often benefits from an intuitive and experimental approach, as well as a formal description of models and methods. This paper describes software which is intended to assist in this understanding. The role of simulation is advocated, in order to explain the meaning of spatial correlation and to interpret the parameters involved in standard models. Realistic scenarios where decisions on the locations of sampling points in a spatial setting are required are also described. Students are provided with a variety of sampling strategies and invited to select the most appropriate one in two different settings. One involves water sampling in the lagoon of the Mururoa Atoll while the other involves sea bed sampling in a Scottish firth. Once a student has decided on a sampling strategy, simulated data are provided for further analysis. This extends the range of teaching activity from the analysis of data collected by others to involvement in data collection and the need to grapple with issues of design. It is argued that this approach has significant benefits in learning

Effects of carbon dioxide on the searching behaviour of the root-feeding clover weevil <i>Sitona lepidus</i> (Coleoptera: Curculionidae)

The respiratory emission of CO2 from roots is frequently proposed as an attractant that allows soil-dwelling insects to locate host plant roots, but this role has recently become less certain. CO2 is emitted from many sources other than roots, so does not necessarily indicate the presence of host plants, and because of the high density of roots in the upper soil layers, spatial gradients may not always be perceptible by soil-dwelling insects. The role of CO2 in host location was investigated using the clover root weevil Sitona lepidus Gyllenhall and its host plant white clover (Trifolium repens L.) as a model system. Rhizochamber experiments showed that CO2 concentrations were approximately 1000 ppm around the roots of white clover, but significantly decreased with increasing distance from roots. In behavioural experiments, no evidence was found for any attraction by S. lepidus larvae to point emissions of CO2, regardless of emission rates. Fewer than 15% of larvae were attracted to point emissions of CO2, compared with a control response of 17%. However, fractal analysis of movement paths in constant CO2 concentrations demonstrated that searching by S. lepidus larvae significantly intensified when they experienced CO2 concentrations similar to those found around the roots of white clover (i.e. 1000 ppm). It is suggested that respiratory emissions of CO2 may act as a ‘search trigger’ for S. lepidus, whereby it induces larvae to search a smaller area more intensively, in order to detect location cues that are more specific to their host plant.<br/

A low-cost desktop software defined radio design environment using MATLAB, simulink, and the RTL-SDR

In the last 5 years, the availability of powerful DSP and Communications design software, and the emergence of relatively affordable devices that receive and digitize RF signals, has brought Software Defined Radio (SDR) to the desktops of many communications engineers. However, the more recent availability of very low cost SDR devices such as the RTL-SDR, costing less than $20, brings SDR to the home desktop of undergraduate and graduate students, as well as both professional engineers and the maker communities. Since the release of the various open source drivers for the RTL-SDR, many in the digital communications community have used this device to scan the RF spectrum and digitise I/Q signals that are being transmitted in the range 25MHz to 1.75GHz. This wide bandwidth enables the sampling of frequency bands containing signals such as FM radio, ISM signals, GSM, 3G and LTE mobile radio, GPS and so on. In this paper we will describe the opportunity and operation of the RTL-SDR, and the development of a hands-on, open-course for SDR. These educational materials can be integrated into core curriculum undergraduate and graduate courses, and will greatly enhance the teaching of DSP and communications theory, principles and applications. The lab and teaching materials have recently been used in Senior (4th year Undergraduate) courses and are available as open course materials for all to access, use and evolve

The impact of symptom clusters on endocrine therapy adherence in patients with breast cancer

Background: When taken as prescribed, endocrine therapy is effective in reducing risk of recurrence and mortality in the treatment of patients with breast cancer. However, treatment side effects can act as a barrier to medication adherence. Existing research has not identified any specific side effects as consistent predictors of nonadherence. Our aim was to explore the influence of symptom clusters on self-reported adherence in patients with breast cancer. Methods: A cross-sectional online survey was conducted, including patients with breast cancer currently or previously prescribed endocrine therapy (N=1051). This included measures of self-reported endocrine therapy adherence and common symptoms among this population (insomnia, depression, anxiety, fatigue, musculoskeletal, and vasomotor symptoms). Results: Unintentional nonadherence was higher than intentional nonadherence (50.8% vs 31.01%). The most troublesome symptom was insomnia (73.83% displayed probable insomnia disorder). K-means cluster analysis identified 2 symptom clusters: overall High symptoms, and overall Low symptoms. Participants in the Low symptoms cluster were significantly more likely to be classed as adherent based on unintentional and intentional items. Conclusions: Nonadherence was high in the current sample, and significantly more likely in participants reporting overall severe symptoms. Clinicians should be aware of the scale of common side effects and facilitate open conversation about potential barriers to adherence. Follow-up care should include assessment of common symptoms and signpost patients to appropriate support or treatment when required. Future research should explore potential for a central symptom to act as a target for intervention, to relieve overall side effect burden and facilitate better medication adherence

The impact of symptom clusters on endocrine therapy adherence in patients with breast cancer

Background: When taken as prescribed, endocrine therapy is effective in reducing risk of recurrence and mortality in the treatment of patients with breast cancer. However, treatment side effects can act as a barrier to medication adherence. Existing research has not identified any specific side effects as consistent predictors of nonadherence. Our aim was to explore the influence of symptom clusters on self-reported adherence in patients with breast cancer. Methods: A cross-sectional online survey was conducted, including patients with breast cancer currently or previously prescribed endocrine therapy (N=1051). This included measures of self-reported endocrine therapy adherence and common symptoms among this population (insomnia, depression, anxiety, fatigue, musculoskeletal, and vasomotor symptoms). Results: Unintentional nonadherence was higher than intentional nonadherence (50.8% vs 31.01%). The most troublesome symptom was insomnia (73.83% displayed probable insomnia disorder). K-means cluster analysis identified 2 symptom clusters: overall High symptoms, and overall Low symptoms. Participants in the Low symptoms cluster were significantly more likely to be classed as adherent based on unintentional and intentional items. Conclusions: Nonadherence was high in the current sample, and significantly more likely in participants reporting overall severe symptoms. Clinicians should be aware of the scale of common side effects and facilitate open conversation about potential barriers to adherence. Follow-up care should include assessment of common symptoms and signpost patients to appropriate support or treatment when required. Future research should explore potential for a central symptom to act as a target for intervention, to relieve overall side effect burden and facilitate better medication adherence

The Scottish economy [November 1984]

With the inauguration of the quarterly Scottish Business Survey (SBS) in September there are now two regular and up-to-date indicators of trends in the Scottish industrial sector. The combination of the new survey and the long-standing CBI Industrial Trends Survey provides a comprehensive and up to date assessment of trends in Scottish industry