SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.

Improved Dynamic Headspace Sampling and Detection using Capillary Microextraction of Volatiles Coupled to Gas Chromatography Mass Spectrometry

Sampling and preconcentration techniques play a critical role in headspace analysis in analytical chemistry. My dissertation presents a novel sampling design, capillary microextraction of volatiles (CMV), that improves the preconcentration of volatiles and semivolatiles in a headspace with high throughput, near quantitative analysis, high recovery and unambiguous identification of compounds when coupled to mass spectrometry. The CMV devices use sol-gel polydimethylsiloxane (PDMS) coated microglass fibers as the sampling/preconcentration sorbent when these fibers are stacked into open-ended capillary tubes. The design allows for dynamic headspace sampling by connecting the device to a hand-held vacuum pump. The inexpensive device can be fitted into a thermal desorption probe for thermal desorption of the extracted volatile compounds into a gas chromatography-mass spectrometer (GC-MS). The performance of the CMV devices was compared with two other existing preconcentration techniques, solid phase microextraction (SPME) and planar solid phase microextraction (PSPME). Compared to SPME fibers, the CMV devices have an improved surface area and phase volume of 5000 times and 80 times, respectively. One (1) minute dynamic CMV air sampling resulted in similar performance as a 30 min static extraction using a SPME fiber. The PSPME devices have been fashioned to easily interface with ion mobility spectrometers (IMS) for explosives or drugs detection. The CMV devices are shown to offer dynamic sampling and can now be coupled to COTS GC-MS instruments. Several compound classes representing explosives have been analyzed with minimum breakthrough even after a 60 min. sampling time. The extracted volatile compounds were retained in the CMV devices when preserved in aluminum foils after sampling. Finally, the CMV sampling device were used for several different headspace profiling applications which involved sampling a shipping facility, six illicit drugs, seven military explosives and eighteen different bacteria strains. Successful detection of the target analytes at ng levels of the target signature volatile compounds in these applications suggests that the CMV devices can provide high throughput qualitative and quantitative analysis with high recovery and unambiguous identification of analytes.

Positive regional species-people correlations: A sampling artefact or a key issue for sustainable development?

Many studies are documenting positive large-scale species– people correlations (Luck, 2007; Schuldt & Assmann, 2010). The issue is scale dependent: the local association of species richness and people is in many cases a negative one (Pautasso, 2007; Pecher et al., 2010). This biogeographical pattern is thus important for conservation. If species-rich regions are also densely populated, preserving biodiversity becomes more difficult, ceteris paribus, than if species-rich regions were sparsely populated. At the same time, positive, regional species–people correlations are an opportunity for the biodiversity education of the majority of the human population and underline the importance of conservation in human-modified landscapes (e.g. Sheil & Meijaard, 2010; Ward, 2010).

Geographical patterns of the species richness of helminth parasites of moles (Talpa spp.) in Spain: Separating the effects of sampling effort from those of other conditioning factors

We analysed the viscera of 534 moles (Ta l p a spp.) from 30 of the 47 provinces of peninsular Spain, including 255 individuals of T. europaea from eight provinces, 154 individuals of T. occidentalis from 20 provinces, and 125 unidentified Ta l p a individuals from two provinces. We identified their helminth parasites and determined parasite species richness. We related parasite species richness with sampling effort using both a linear and a logarithmic function. We then performed stepwise linear regressions to predict mole parasite species richness from a small set of selected predictor variables that included sampling effort. We applied the resulting models to forecast T. euro p a e a, T. occidentalis, and Ta l p a spp. parasite species richness in all provinces with recorded host presence, assuming different levels of sampling eff o r t . F i n a l l y, we used partial regression analysis to partition the variation explained by each of the selected variables in the models. We found that mole parasite species richness is strongly conditioned by sampling effort, but that other factors such as cropland area and environmental disturbance have significant independent effects.

Spatial scale effects of sampling on the interpolation of species distribution models in the southwestern Amazon.

Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

Smart sampling approaches for Decision-Focused Learning

Many real-word decision- making problems are defined based on forecast parameters: for example, one may plan an urban route by relying on traffic predictions. In these cases, the conventional approach consists in training a predictor and then solving an optimization problem. This may be problematic since mistakes made by the predictor may trick the optimizer into taking dramatically wrong decisions. Recently, the field of Decision-Focused Learning overcomes this limitation by merging the two stages at training time, so that predictions are rewarded and penalized based on their outcome in the optimization problem. There are however still significant challenges toward a widespread adoption of the method, mostly related to the limitation in terms of generality and scalability. One possible solution for dealing with the second problem is introducing a caching-based approach, to speed up the training process. This project aims to investigate these techniques, in order to reduce even more, the solver calls. For each considered method, we designed a particular smart sampling approach, based on their characteristics. In the case of the SPO method, we ended up discovering that it is only necessary to initialize the cache with only several solutions; those needed to filter the elements that we still need to properly learn. For the Blackbox method, we designed a smart sampling approach, based on inferred solutions.

Impact Of Pharmacist Interventions On Drug-related Problems And Laboratory Markers In Outpatients With Human Immunodeficiency Virus Infection.

Substantial complexity has been introduced into treatment regimens for patients with human immunodeficiency virus (HIV) infection. Many drug-related problems (DRPs) are detected in these patients, such as low adherence, therapeutic inefficacy, and safety issues. We evaluated the impact of pharmacist interventions on CD4+ T-lymphocyte count, HIV viral load, and DRPs in patients with HIV infection. In this 18-month prospective controlled study, 90 outpatients were selected by convenience sampling from the Hospital Dia-University of Campinas Teaching Hospital (Brazil). Forty-five patients comprised the pharmacist intervention group and 45 the control group; all patients had HIV infection with or without acquired immunodeficiency syndrome. Pharmaceutical appointments were conducted based on the Pharmacotherapy Workup method, although DRPs and pharmacist intervention classifications were modified for applicability to institutional service limitations and research requirements. Pharmacist interventions were performed immediately after detection of DRPs. The main outcome measures were DRPs, CD4+ T-lymphocyte count, and HIV viral load. After pharmacist intervention, DRPs decreased from 5.2 (95% confidence interval [CI] =4.1-6.2) to 4.2 (95% CI =3.3-5.1) per patient (P=0.043). A total of 122 pharmacist interventions were proposed, with an average of 2.7 interventions per patient. All the pharmacist interventions were accepted by physicians, and among patients, the interventions were well accepted during the appointments, but compliance with the interventions was not measured. A statistically significant increase in CD4+ T-lymphocyte count in the intervention group was found (260.7 cells/mm(3) [95% CI =175.8-345.6] to 312.0 cells/mm(3) [95% CI =23.5-40.6], P=0.015), which was not observed in the control group. There was no statistical difference between the groups regarding HIV viral load. This study suggests that pharmacist interventions in patients with HIV infection can cause an increase in CD4+ T-lymphocyte counts and a decrease in DRPs, demonstrating the importance of an optimal pharmaceutical care plan.