Capturing affective experiences using the SMS experience sampling (SMS-ES) method

This paper reports the feasibility and methodological considerations of using the Short Message System Experience Sampling (SMS-ES) Method, which is an experience sampling research method developed to assist researchers to collect repeat measures of consumers’ affective experiences. The method combines SMS with web-based technology in a simple yet effective way. It is described using a practical implementation study that collected consumers’ emotions in response to using mobile phones in everyday situations. The method is further evaluated in terms of the quality of data collected in the study, as well as against the methodological considerations for experience sampling studies. These two evaluations suggest that the SMS-ES Method is both a valid and reliable approach for collecting consumers’ affective experiences. Moreover, the method can be applied across a range of for-profit and not-for-profit contexts where researchers want to capture repeated measures of consumers’ affective experiences occurring over a period of time. The benefits of the method are discussed to assist researchers who wish to apply the SMS-ES Method in their own research designs.

Use of precision time protocol to synchronize sampled value process buses

Transmission smart grids will use a digital platform for the automation of high voltage substations. The IEC 61850 series of standards, released in parts over the last ten years, provide a specification for substation communications networks and systems. These standards, along with IEEE Std 1588-2008 Precision Time Protocol version 2 (PTPv2) for precision timing, are recommended by the both IEC Smart Grid Strategy Group and the NIST Framework and Roadmap for Smart Grid Interoperability Standards for substation automation. IEC 61850, PTPv2 and Ethernet are three complementary protocol families that together define the future of sampled value digital process connections for smart substation automation. A time synchronisation system is required for a sampled value process bus, however the details are not defined in IEC 61850-9-2. PTPv2 provides the greatest accuracy of network based time transfer systems, with timing errors of less than 100 ns achievable. The suitability of PTPv2 to synchronise sampling in a digital process bus is evaluated, with preliminary results indicating that steady state performance of low cost clocks is an acceptable ±300 ns, but that corrections issued by grandmaster clocks can introduce significant transients. Extremely stable grandmaster oscillators are required to ensure any corrections are sufficiently small that time synchronising performance is not degraded.

Development of an autonomous unmanned aerial system to collect time-stamped samples from the atmosphere and localize potential pathogen sources

This paper presents the hardware development and testing of a new concept for air sampling via the integration of a prototype spore trap onboard an unmanned aerial system (UAS).We propose the integration of a prototype spore trap onboard a UAS to allow multiple capture of spores of pathogens in single remote locations at high or low altitude, otherwise not possible with stationary sampling devices.We also demonstrate the capability of this system for the capture of multiple time-stamped samples during a single mission.Wind tunnel testing was followed by simulation, and flight testing was conducted to measure and quantify the spread during simulated airborne air sampling operations. During autonomous operations, the onboard autopilot commands the servo to rotate the sampling device to a new indexed location once the UAS vehicle reaches the predefined waypoint or set of waypoints (which represents the region of interest). Time-stamped UAS data are continuously logged during the flight to assist with analysis of the particles collected. Testing and validation of the autopilot and spore trap integration, functionality, and performance is described. These tools may enhance the ability to detect new incursions of spores

Sampling environmental acoustic recordings to determine species richness : I

Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.

Nitrous oxide emissions from subtropical horticultural soils : a time series analysis

Time series regression models were used to examine the influence of environmental factors (soil water content and soil temperature) on the emissions of nitrous oxide (N2O) from subtropical soils, by taking into account temporal lagged environmental factors, autoregressive processes, and seasonality for three horticultural crops in a subtropical region of Australia. Fluxes of N2O, soil water content, and soil temperature were determined simultaneously on a weekly basis over a 12-month period in South East Queensland. Annual N2O emissions for soils under mango, pineapple, and custard apple were 1590, 1156, and 2038 g N2O-N/ha, respectively, with most emissions attributed to nitrification. The N2O-N emitted from the pineapple and custard apple crops was equivalent to 0.26 and 2.22%, respectively, of the applied mineral N. The change in soil water content was the key variable for describing N2O emissions at the weekly time-scale, with soil temperature at a lag of 1 month having a significant influence on average N2O emissions (averaged) at the monthly time-scale across the three crops. After accounting for soil temperature and soil water content, both the weekly and monthly time series regression models exhibited significant autocorrelation at lags of 1–2 weeks and 1–2 months, and significant seasonality for weekly N2O emissions for mango crop and for monthly N2O emissions for mango and custard apple crops in this location over this time-frame. Time series regression models can explain a higher percentage of the temporal variation of N2O emission compared with simple regression models using soil temperature and soil water content as drivers. Taking into account seasonal variability and temporal persistence in N2O emissions associated with soil water content and soil temperature may lead to a reduction in the uncertainty surrounding estimates of N2O emissions based on limited sampling effort.

Collective sampling and analysis of high order tensors for chatroom communications

This work investigates the accuracy and efficiency tradeoffs between centralized and collective (distributed) algorithms for (i) sampling, and (ii) n-way data analysis techniques in multidimensional stream data, such as Internet chatroom communications. Its contributions are threefold. First, we use the Kolmogorov-Smirnov goodness-of-fit test to show that statistical differences between real data obtained by collective sampling in time dimension from multiple servers and that of obtained from a single server are insignificant. Second, we show using the real data that collective data analysis of 3-way data arrays (users x keywords x time) known as high order tensors is more efficient than centralized algorithms with respect to both space and computational cost. Furthermore, we show that this gain is obtained without loss of accuracy. Third, we examine the sensitivity of collective constructions and analysis of high order data tensors to the choice of server selection and sampling window size. We construct 4-way tensors (users x keywords x time x servers) and analyze them to show the impact of server and window size selections on the results.

Sampling environmental acoustic recordings to determine bird species richness

Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced surveyors can produce accurate results; however the time and effort required to process even small volumes of data can make manual analysis prohibitive. This study examined the use of sampling methods to reduce the cost of analyzing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilizing five days of manually analyzed acoustic sensor data from four sites, we examined a range of sampling frequencies and methods including random, stratified, and biologically informed. We found that randomly selecting 120 one-minute samples from the three hours immediately following dawn over five days of recordings, detected the highest number of species. On average, this method detected 62% of total species from 120 one-minute samples, compared to 34% of total species detected from traditional area search methods. Our results demonstrate that targeted sampling methods can provide an effective means for analyzing large volumes of acoustic sensor data efficiently and accurately. Development of automated and semi-automated techniques is required to assist in analyzing large volumes of acoustic sensor data. Read More: http://www.esajournals.org/doi/abs/10.1890/12-2088.1

Sampling designs on stream networks using the pseudo-Bayesian approach

Monitoring stream networks through time provides important ecological information. The sampling design problem is to choose locations where measurements are taken so as to maximise information gathered about physicochemical and biological variables on the stream network. This paper uses a pseudo-Bayesian approach, averaging a utility function over a prior distribution, in finding a design which maximizes the average utility. We use models for correlations of observations on the stream network that are based on stream network distances and described by moving average error models. Utility functions used reflect the needs of the experimenter, such as prediction of location values or estimation of parameters. We propose an algorithmic approach to design with the mean utility of a design estimated using Monte Carlo techniques and an exchange algorithm to search for optimal sampling designs. In particular we focus on the problem of finding an optimal design from a set of fixed designs and finding an optimal subset of a given set of sampling locations. As there are many different variables to measure, such as chemical, physical and biological measurements at each location, designs are derived from models based on different types of response variables: continuous, counts and proportions. We apply the methodology to a synthetic example and the Lake Eacham stream network on the Atherton Tablelands in Queensland, Australia. We show that the optimal designs depend very much on the choice of utility function, varying from space filling to clustered designs and mixtures of these, but given the utility function, designs are relatively robust to the type of response variable.

Determining gas sampling timelines for estimating emissions in small chamber incubation experiments

A laboratory experiment was set up in small chambers for monitoring greenhouse gas emissions and determining the most suitable time for sampling. A six-treatment experiment was conducted, including a one week pre-incubation and a week for incubation. Timelines for sampling were 1, 2, 3, 6 and 24 hours after closing the lid of the incubation chambers. Variation in greenhouse gas fluxes was high due to the time of sampling. The rates of gas emissions increased in first three hours and decreased afterward. The rates of greenhouse gas emissions at 3 hours after closing lids was close to the mean for the 24-h period.

Genetic bottlenecks in time and space: reconstructing invasions from contemporary and historical collections

Herbarium accession data offer a useful historical botanical perspective and have been used to track the spread of plant invasions through time and space. Nevertheless, few studies have utilised this resource for genetic analysis to reconstruct a more complete picture of historical invasion dynamics, including the occurrence of separate introduction events. In this study, we combined nuclear and chloroplast microsatellite analyses of contemporary and historical collections of Senecio madagascariensis, a globally invasive weed first introduced to Australia c. 1918 from its native South Africa. Analysis of nuclear microsatellites, together with temporal spread data and simulations of herbarium voucher sampling, revealed distinct introductions to south-eastern Australia and mid-eastern Australia. Genetic diversity of the south-eastern invasive population was lower than in the native range, but higher than in the mid-eastern invasion. In the invasive range, despite its low resolution, our chloroplast microsatellite data revealed the occurrence of new haplotypes over time, probably as the result of subsequent introduction(s) to Australia from the native range during the latter half of the 20th century. Our work demonstrates how molecular studies of contemporary and historical field collections can be combined to reconstruct a more complete picture of the invasion history of introduced taxa. Further, our study indicates that a survey of contemporary samples only (as undertaken for the majority of invasive species studies) would be insufficient to identify potential source populations and occurrence of multiple introductions.

Combining passive sampling and toxicity testing for evaluation of mixtures of polar organic chemicals in sewage treatment plant effluent

Effluent from sewage treatment plants has been associated with a range of pollutant effects. Depending on the influent composition and treatment processes the effluent may contain a myriad of different chemicals which makes monitoring very complex. In this study we aimed to monitor relatively polar organic pollutant mixtures using a combination of passive sampling techniques and a set of biochemistry based assays covering acute bacterial toxicity (Microtox™), phytotoxicity (Max-I-PAM assay) and genotoxicity (umuC assay). The study showed that all of the assays were able to detect effects in the samples and allowed a comparison of the two plants as well as a comparison between the two sampling periods. Distinct improvements in water quality were observed in one of the plants as result of an upgrade to a UV disinfection system, which improved from 24× sample enrichment required to induce a 50% response in the Microtox™ assay to 84×, from 30× sample enrichment to induce a 50% reduction in photosynthetic yield to 125×, and the genotoxicity observed in the first sampling period was eliminated. Thus we propose that biochemical assay techniques in combination with time integrated passive sampling can substantially contribute to the monitoring of polar organic toxicants in STP effluents.

Calibration, estimation, and sampling issues of car-following parameters

Drivers behave in different ways, and these different behaviors are a cause of traffic disturbances. A key objective for simulation tools is to correctly reproduce this variability, in particular for car-following models. From data collection to the sampling of realistic behaviors, a chain of key issues must be addressed. This paper discusses data filtering, robustness of calibration, correlation between parameters, and sampling techniques of acceleration-time continuous car-following models. The robustness of calibration is systematically investigated with an objective function that allows confidence regions around the minimum to be obtained. Then, the correlation between sets of calibrated parameters and the validity of the joint distributions sampling techniques are discussed. This paper confirms the need for adapted calibration and sampling techniques to obtain realistic sets of car-following parameters, which can be used later for simulation purposes.

Determination of dimethyl sulfide in gas samples by single photon ionization time of flight mass spectrometry

It is difficult to determine sulfur-containing volatile organic compounds in the atmosphere because of their reactivity. Primary off-line techniques may suffer losses of analytes during the transportation from field to laboratory and sample preparation. In this study, a novel method was developed to directly measure dimethyl sulfide at parts-per-billion concentration levels in the atmosphere using vacuum ultraviolet single photon ionization time-of-flight mass spectrometry. This technique offers continuous sampling at a response rate of one measurement per second, or cumulative measurements over longer time periods. Laboratory prepared samples of different concentrations of dimethyl sulfide in pure nitrogen gas were analyzed at several sampling frequencies. Good precision was achieved using sampling periods of at least 60 seconds with a relative standard deviation of less than 25%. The detection limit for dimethyl sulfide was below the 3 ppb olfactory threshold. These results demonstrate that single photon ionization time-of-flight mass spectrometry is a valuable tool for rapid, real-time measurements of sulfur-containing organic compounds in the air.

Applications of passive sampling techniques in monitoring organic pollutants in the environment

As there are a myriad of micro organic pollutants that can affect the well-being of human and other organisms in the environment the need for an effective monitoring tool is eminent. Passive sampling techniques, which have been developed over the last decades, could provide several advantages to the conventional sampling methods including simpler sampling devices, more cost-effective sampling campaign, providing time-integrated load as well as representative average of concentrations of pollutants in the environment. Those techniques have been applied to monitor many pollutants caused by agricultural activities, i.e. residues of pesticides, veterinary drugs and so on. Several types of passive samplers are commercially available and their uses are widely accepted. However, not many applications of those techniques have been found in Japan, especially in the field of agricultural environment. This paper aims to introduce the field of passive sampling and then to describe some applications of passive sampling techniques in environmental monitoring studies related to the agriculture industry.

Estimating the efficiency of a small beam trawl for sampling tiger prawns Penaeus esculentus and P. semisulcatus in seagrass by removal experiments

The efficiency with which a small beam trawl (1 x 0.5 m mouth) sampled postlarvae and juveniles of tiger prawns Penaeus esculentus and P, semisulcatus at night was estimated in 3 tropical seagrass communities (dominated by Thalassia hemprichii, Syringodium isoetifolium and Enhalus acoroides, respectively) in the shallow waters of the Gulf of Carpentaria in northern Australia. An area of seagrass (40 x 3 m) was enclosed by a net and the beam trawl was repeatedly hand-hauled over the substrate. Net efficiency (q) was calculated using 4 methods: the unweighted Leslie, weighted Leslie, DeLury and Maximum-likelihood (ML) methods. The Maximum-likelihood is the preferred method for estimating efficiency because it makes the fewest assumptions and is not affected by zero catches. The major difference in net efficiencies was between postlarvae (mean ML q +/- 95% confidence limits = 0.66 +/- 0.16) and juveniles of both species (mean q for juveniles in water less than or equal to 1.0 m deep = 0.47 +/- 0.05), i.e. the beam trawl was more efficient at capturing postlarvae than juveniles. There was little difference in net efficiency for P, esculentus between seagrass types (T, hemprichii versus S. isoetifolium), even though the biomass and morphologies of seagrass in these communities differed greatly (biomasses were 54 and 204 g m(-2), respectively). The efficiency of the net appeared to be the same for juveniles of the 2 species in shallow water, but was lower for juvenile P, semisulcatus at high tide when the water was deeper (1.6 to 1.9 m) (0.35 +/- 0.08). The lower efficiency near the time of high tide is possibly because the prawns are more active at high than low tide, and can also escape above the net. Factors affecting net efficiency and alternative methods of estimating net efficiency are discussed.