Postglacial formation and dynamics of North Patagonian Rainforest in the Chonos Archipelago, Southern Chile

Pollen analysis of continuous sediment cores from two lakes in the northern Chonos Archipelago (44S) in southern Chile shows a complete postglacial record of vegetation change. The fossil records indicate that deglaciation was complete in the northern Chonos by at least 13,600 14Cyr BP. Ericaceous heath and grassland persisted for more than 600 years after deglaciation under the influence of dry/cold climates and frequent burning. Nothofagus-Pilgerodendron-Podocarpus forest, with modern analogues in the southern Chonos Archipelago, was established across the northern islands by 12,400 14Cyr BP under increasingly warm and wet climates. There is no evidence for a return to cooler climates during the Younger Dryas chronozone. The rise of Tepualia stipularis and Weinmannia trichosperma as important forest components between 10,600 and 6000 14Cyr BP may be associated withclimates that were warmer than present. The collapse of Pilgerodendron communities during this time may have been triggered by a combination of factors related to disturbance frequency including tephra deposition events, fire and climate change. After 6000 14Cyr BP Pilgerodendron recovers and Nothofagus-Pilgerodendron-Tepualia forest persists until the present. European logging and burning activity may have increased the susceptibility of North Patagonian Rainforest to invasion by introduced species and to future collapse of the long-lived Pilgerodendron communities.

Emotional intelligence, personality and decoding non-verbal expressions of emotion

Previous research has highlighted theoretical and empirical links between measures of both personality and trait emotional intelligence (EI), and the ability to decode facial expressions of emotion. Research has also found that the posed, static characteristics of the photographic stimuli used to explore these links affects the decoding process and differentiates them from the natural expressions they represent. This undermines the ecological validity of established trait-emotion decoding relationships. This study addresses these methodological shortcomings by testing relationships between the reliability of participant ratings of dynamic, spontaneously elicited expressions of emotion with personality and trait EI. Fifty participants completed personality and self-report EI questionnaires, and used a computer-logging program to continuously rate change in emotional intensity expressed in video clips. Each clip was rated twice to obtain an intra-rater reliability score. The results provide limited support for links between both trait EI and personality variables and how reliably we decode natural expressions of emotion. Limitations and future directions are discussed.

How Porosity and Permeability Vary Spatially With Grain Size, Sorting, Cement Volume, and Mineral Dissolution In Fluvial Triassic Sandstones: The Value of Geostatistics and Local Regression

Although it is well known that sandstone porosity and permeability are controlled by a range of parameters such as grain size and sorting, amount, type, and location of diagenetic cements, extent and type of compaction, and the generation of intergranular and intragranular secondary porosity, it is less constrained how these controlling parameters link up in rock volumes (within and between beds) and how they spatially interact to determine porosity and permeability. To address these unknowns, this study examined Triassic fluvial sandstone outcrops from the UK using field logging, probe permeametry of 200 points, and sampling at 100 points on a gridded rock surface. These field observations were supplemented by laser particle-size analysis, thin-section point-count analysis of primary and diagenetic mineralogy, quantitiative XRD mineral analysis, and SEM/EDAX analysis of all 100 samples. These data were analyzed using global regression, variography, kriging, conditional simulation, and geographically weighted regression to examine the spatial relationships between porosity and permeability and their potential controls. The results of bivariate analysis (global regression) of the entire outcrop dataset indicate only a weak correlation between both permeability porosity and their diagenetic and depositional controls and provide very limited information on the role of primary textural structures such as grain size and sorting. Subdividing the dataset further by bedding unit revealed details of more local controls on porosity and permeability. An alternative geostatistical approach combined with a local modelling technique (geographically weighted regression; GWR) subsequently was used to examine the spatial variability of porosity and permeability and their controls. The use of GWR does not require prior knowledge of divisions between bedding units, but the results from GWR broadly concur with results of regression analysis by bedding unit and provide much greater clarity of how porosity and permeability and their controls vary laterally and vertically. The close relationship between depositional lithofacies in each bed, diagenesis, and permeability, porosity demonstrates that each influences the other, and in turn how understanding of reservoir properties is enhanced by integration of paleoenvironmental reconstruction, stratigraphy, mineralogy, and geostatistics.

Investigating Hydraulically Active Fractures in A Shallow Meta-Sedimentary Bedrock Aquifer Using Single Well Tracer Testing, Mount Stewart, Northern Ireland

Knowledge of groundwater flow/mass transport, in poorly productive aquifers which underlie over 65% of the island of Ireland, is necessary for effective management of catchment water quality and aquatic ecology. This research focuses on a fractured low-grade Ordovician/Silurian greywacke sequence which underlies approximately 25% of the northern half of Ireland. Knowledge of the unit’s hydrogeological properties remain largely restricted to localised single well open hole “transmissivity” values. Current hydrogeological conceptual models of the Greywacke view the bulk of groundwater flowing through fractures in an otherwise impermeable bedrock mass.
Core analysis permits fracture characterisation, although not all identified fractures may be involved in groundwater flow. Traditional in-situ hydraulic characterisation relies on cumbersome techniques such as packer testing or geophysical borehole logging (e.g. flowmeters). Queen’s University Belfast is currently carrying out hydraulic characterization of 16 boreholes at its Greywacke Hydrogeological Research Site at Mount Stewart, Northern Ireland.
Development of dye dilution methods, using a recently-developed downhole fluorometer, provided a portable, user-friendly, and inexpensive means of detecting hydraulically active intervals in open boreholes. Measurements in a 55m deep hole, three days following fluorescent dye injection, demonstrated the ability of the technique to detect two discrete hydraulically active intervals corresponding to zones identified by caliper and heat-pulse flowmeter logs. High resolution acoustic televiewer logs revealed the zones to correspond to two steeply dipping fractured intervals. Results suggest the rock can have effective porosities of the order of 0.1%.
Study findings demonstrate dye dilution’s utility in characterizing groundwater flow in fractured aquifers. Tests on remaining holes will be completed at different times following injection to identify less permeable fractures and develop an improved understanding of the structural controls on groundwater flow in the uppermost metres of competent bedrock.

Development of a Temperature-Recording Vaginal Ring for Monitoring User Adherence

Background: Vaginal ring devices are being actively developed for controlled delivery of HIV microbicides and as multi-purpose prevention technology (MPT) products combining hormonal contraception with prevention of HIV and other sexually transmitted diseases. Presently, there is no reliable method for monitoring user adherence in HIV vaginal ring trials; previous acceptability studies have included some type of participant self-reporting mechanism, which have often been unreliable. More objective, quantitative and accurate methods for assessing adherence are needed.
Methods: A silicone elastomer vaginal ring containing an encapsulated miniature temperature recording device has been developed that can capture and store real-time temperature data during the period of designated use. Devices were tested in both simulated vaginal environments and following vaginal placement in cynomolgus macaques. Various use protocols and data sampling rates were tested to simulate typical patient usage scenarios. Results: The temperature logging devices accurately recorded vaginal temperature in macaques, clearly showing the regular diurnal temperature cycle. When environmental temperature and vaginal temperature was significantly different, the device was able to accurately pinpoint the insertion and removal times. Based on the data collected it was possible to infer removal periods as short as 5 min when the external environmental temperature was 25 °C. Accuracy increased with data sampling rate. Conclusions: This work provides proof-of-concept for monitoring adherence using a vaginal ring device containing an encapsulated temperature logger. The addition of one or more active agents into the ring body is not anticipated to affect the temperature monitoring function. A clinical study to compare self- reported user adherence data with that obtained by the device would be highly informative.

Sprinkler irrigation of rice fields reduces grain arsenic but enhances cadmium

Previous studies have demonstrated that rice cultivated under flooded conditions has higher concentrations of arsenic (As) but lower cadmium (Cd) compared to rice grown in unsaturated soils. To validate such effects over long terms under Mediterranean conditions a field experiment, conducted over 7 successive years was established in SW Spain. The impact of water management on rice production and grain arsenic (As) and cadmium (Cd) was measured, and As speciation was determined to inform toxicity evaluation. Sprinkler irrigation was compared to traditional flooding.

Both irrigation techniques resulted in similar grain yields (similar to 3000 kg grain ha(-1)). Successive sprinkler irrigation over 7 years decreased grain total As to one-sixth its initial concentration in the flooded system (0.55 to 0.09 mg As kg(-1)), while one cycle of sprinkler irrigation also reduced grain total As by one-third (0.20 mg kg(-1)). Grain inorganic As concentration increased up to 2 folds under flooded conditions compared to sprinkler irrigated fields while organic As was also lower in sprinkler system treatments, but to a lesser extent. This suggests that methylation is favored under water logging. However, sprinkler irrigation increased Cd transfer to grain by a factor of 10, reaching 0.05 mg Cd kg(-1) in 7 years. Sprinlder systems in paddy fields seem particularly suited for Mediterranean climates and are able to mitigate against excessive As accumulation, but our evidence shows that an increased Cd load in rice grain may result.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.