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Journal of the South African Institution of Civil Engineering
On-line version ISSN 2309-8775
Print version ISSN 1021-2019
J. S. Afr. Inst. Civ. Eng. vol.55 n.1 Midrand Apr. 2013
Pile design practice in southern Africa Part I: Resistance statistics
M Dithinde; J V Retief
The paper presents resistance statistics required for reliability assessment and calibration of limit state design procedures for pile design reflecting southern African practice. The first step of such a development is to determine the levels of reliability implicitly provided for in present design procedures based on working stress design. Such an assessment is presented in an accompanying paper (please turn to page 72). The statistics are presented in terms of a model factor M representing the ratio of pile resistance interpreted from pile load tests to its prediction based on the static pile formula. A dataset of 174 cases serves as sample set for the statistical analysis. The statistical characterisation comprises outliers detection and correction of erroneous values, using the corrected data to compute the sample moments (mean, standard deviation, skewness and kurtosis) needed in reliability analysis. The analyses demonstrate that driven piles depict higher variability compared to bored piles, irrespective of materials type. In addition to the above statistics, reliability analysis requires the theoretical probability distribution for the random variable under consideration. Accordingly it is demonstrated that the lognormal distribution is a valid theoretical model for the model factor. Another key basis for reliability theory is the notion of randomness of the basic variables. To verify that the variation in the model factor is not explainable by deterministic variations in the database, an investigation of correlation of the model factor with underlying pile design parameters is carried out. It is shown that such correlation is generally weak.
Key words: pile foundation design, southern African practice, pile load tests, model factor, statistical characterisation
Geotechnical design is performed under a considerable degree of uncertainty. The two main sources of this uncertainty include: (i) Soil parameter uncertainty and (ii) calculation model uncertainty. Soil parameter uncertainty arises from the variability exhibited by properties of geotechnical materials from one location to the other, even within seemingly homogeneous profiles. Geotechnical parameter prediction uncertainties are attributed to inherent spatial variability, measurement noise/random errors, systematic measurements errors, and statistical uncertainties. Conversely, model uncertainty emanates from imperfections of analytical models for predicting engineering behaviour. Mathematical modelling of any physical process generally requires simplifications to create a useable model. Inevitably, the resulting models are simplifications of complex real-world phenomena. Consequently there is uncertainty in the model prediction even if the model inputs are known with certainty.
For pile foundations, previous studies (e.g. Ronold & Bjerager1992; Phoon & Kulhawy 2005) have demonstrated that calculation model uncertainty is the predominant component. One of the fundamental objectives of reliability-based design is to quantify and systematically incorporate the uncertainties in the design process. The current state of the art in the quantification of model uncertainty associated with a given pile design model entails determining the ratio of the measured capacity to theoretical capacity. Accordingly, in this paper a series of pile performance predictions by the static formula are compared with measured performances. To capture the distinct soil types for the geologic region of southern Africa, as well as the local pile design and construction experience base, pile load tests and associated geotechnical data from the southern African geologic environment are used.
In reliability analysis and modelling, both materials properties and calculation model uncertainties are incorporated into a performance function representing the limit state design function in terms of basic variables which express design variables (loads, material properties, geometry) as probabilistic variables. The objective of this paper is to present detailed statistical characterisation of model uncertainty for pile foundations. The analysis is an extension of the model uncertainty characterisation reported by Dithinde et al (2011). The purpose of the characterisation is to relate southern African pile foundation design practice to reliability-based design as it has been developed and standardised for geotechnical and structural design. The derived statistics constitute the backbone of all subsequent pile foundations limit state design initiatives in southern Africa. Specific usage of the derived statistics include: assessment of reliability indices embodied in the current southern African pile design practice, as presented in the accompanying paper (Retief & Dithinde 2013 - please turn to page 72); derivation of the characteristic model factor for pile foundations design in conjunction with SANS 10160-5 (2011); and reliability calibration of resistance factors. The following topics are presented subsequently:
■ The geotechnical background to the dataset is briefly reviewed, including the basis and application for classification into homogeneous datasets and the formal definition of the model factor M to represent model uncertainty.
■ An assessment and detection of outliers and correction of erroneous samples, considering the sensitivity of reliability analysis to even a limited number of such values in a dataset.
■ Using the corrected data and conventional statistical methods to compute the sample moments: mean, standard deviation, skewness and kurtosis for the respective datasets.
■ Verification of randomness of the dataset through investigation of any systematic dependence on the relevant design variables.
■ Determination of the appropriate probability distribution to represent model uncertainty provides the final step in characterising model factor statistics.
Although this paper primarily considers the statistical characteristics of southern African pile model uncertainty, as based on the database of model factors reported by Dithinde et al (2011), with additional background provided by Dithinde (2007), it is also necessary to appreciate the geotechnical basis and integrity of the dataset. This section presents an extract of the way in which the dataset has been compiled and a formal definition of a model factor (M).
The database of static pile load tests reported by Dithinde et al (2011) include information on the associated geotechnical data, such as soil profiles, field and laboratory test results. A comprehensive range of soil conditions, pile geometry and resistance is incorporated in the dataset, to provide extensive representation of southern African pile construction practice. Although the pile load test reports were collected from various piling companies in South Africa, a significant number of pile tests were performed in countries such as Botswana, Lesotho, Mozambique, Zambia, Swaziland and Tanzania. The main pile types in the database include Franki (expanded base) piles, Auger piles, and Continuous Flight Auger (CFA) piles. In addition, there are a few cases of steel piles and slump cast piles. The steel piles are mainly H-piles, with one case where a steel tubular pile was used.
The collected pile load test data was carefully studied in order to evaluate its suitability for inclusion in the current study. For each load test, emphasis was placed on the completeness of the required information, including test pile size (length and diameter), proper record of the load-deflection data, and availability of subsurface exploration data for the site. Only cases where sufficient soil data was available for the prediction of pile resistance were included in the database.
The pile load tests were used to determine the measured pile resistance, while the geotechnical data was used to compute the predicted resistance. The measured resistances from the respective load-settlement curves were interpreted on the basis of Davisson's offset criterion (Davison 1972). However, for working piles, Chin's extrapolation (Chin 1970) was carried out prior to the application of the Davisson's offset criterion. The predicted resistance was based on the classic static formula which is essentially the generic theoretical pile design model based on the principles of soil mechanics. The soil data that was obtained from the survey, and used for the predicted resistance, was mainly in the form of borehole log descriptions and standard penetration (SPT) results. Soil design parameters were selected on the basis of common southern African practice (Dithinde 2007).
Model factor statistics
The primary output of the database of pile load tests reported by Dithinde et al (2011) consists of the interpreted pile resistance (Qi) and the predicted pile resistance (Qp) from which a set of observations of the Model Factor (M) as given by Equation [1] can be obtained:
Qi = pile capacity interpreted from a load test, to represent the measured capacity;
Qp = pile capacity generally predicted using limit equilibrium models, and M = model factor.
Each case of pile test included in the dataset is consequently treated as a sample of the set of n cases under consideration. In Dithinde et al (2011) the complete set of 174 cases was further classified in terms of four theoretical principal pile design classes based on both soil type and installation method. These fundamental sets of classes include: (i) driven piles in non-cohesive soil (D-NC) with 29 cases, (ii) bored piles in non-cohesive soil (B-NC) with 33 cases, (iii) driven piles in cohesive soils (D-C) with 59 cases, and (iv) bored piles in cohesive soils (B-C) with 53 cases. In this paper, the principle four data sets are now combined into various practical pile design classes considered in design codes such as SANS 10169-5 (2011) and EN 1997-1 (2004). The additional classification schemes include:
■ Classification based on pile installation method irrespective of soil type. This is the classification adopted in EN 1997-1 (2004) and it yields: 87 cases of driven piles (D) and 83 cases of bored piles (B).
■ Classification based on soil type. This classification system is supported by the general practice where a higher factor of safety is applied to pile capacity in clay as compared to sand. This combination results in 58 cases in non-cohesive soil (NC) and 112 cases in cohesive soil (C).
■ All pile cases as a single data set irrespective of pile installation method and soil type. This is the practical consideration presented in SANS 10160-5 (2011) where a single partial factor is given for all compressive piles. The scheme yields 174 pile cases (ALL).
The presence of outliers may greatly influence any calculated statistics, leading to biased results. For instance, they may increase the variability of a sample and decrease the sensitivity of subsequent statistical tests (McBean & Rovers 1998). Therefore prior to further numerical treatment of samples and application of statistical techniques for assessing the parameters of the population, it is absolutely imperative to identify extreme values and correct erroneous ones.
The statistical detection and treatment of outliers in the principal four sets were reported by Dithinde et al (2011). The methods used include (i) load-settlement curves, (ii) sample z-scores, (iii) box plots, and (iv) scatter plots. The results for cases with outliers are reproduced in Figure 1. Inspection of Figure 1(a) reveals two potential outliers (i.e. cases 27 and 54). The curves for these two cases depict different behaviour from the rest of the curves (case 27 with a soft initial response and case 54 with a large normalised capacity). Visual inspection of Figure 1(b) for outliers shows one data point marked as outlier for B-NC and B-C data sets. The tagged data points correspond to pile cases number 53 and 156. However, it should be noted that the box plot method for identifying outliers has shortcomings, particularly for small sample sizes as is the case here. Accordingly the identified cases will have to be corroborated by other methods. Examination of Figure 1(c) shows two data points with z-score values at a considerable distance from the rest of the data points. These data points belong to B-NC (case 53) and B-C (case 156) with z-scores of 3.13 and 2.95 respectively. Although the z-score for case 156 is less than the criterion limit value of 3, and therefore technically is not an outlier, it is sufficiently close to the limit to require further scrutiny. The results of the scatter plots of pile capacity (Qi) versus the predicted capacity (Qp) revealed the same outliers detected by the other methods.
Aggregate of outliers
A total of five observations were detected as potential outliers, namely cases 27, 53, 54, 55 and 156. However, it is not proper to automatically delete a data point once it has been identified as an outlier through statistical methods (Robinson et al 2005). Since an outlier may still represent a true observation, it should only be rejected on the basis of evidence of improper sampling or error. Accordingly the five data points identified as outliers were carefully examined by double-checking the processes of determination of interpreted capacities and computation of predicted capacities. This entailed going back to the original data (pile testing records and derivation of soil design parameters) and checking for recording and computational errors. Following this procedure the corrections were as follows:
■ Cases 53, 54 and 55: Examination of records for these cases showed that an uncommon pile installation practice was employed. The steel piles were installed in predrilled holes and then grouted. The strength of the grout surrounding the piles contributed to the high resistance and hence the higher interpreted capacities. Since the installation procedure for these piles deviates from the normal practice, they represent a different population. These were the only piles in the database constructed in this rather unusual method. These data points were therefore regarded as genuine outliers and were deleted from the data set.
■ Case 27: There was no obvious physical explanation for the behaviour of pile case 27. The depicted behaviour is attributed to extreme values of the hyperbolic parameters representing the non-linear behaviour of the test results. Since piles in terms of pile type, size and soils conditions (i.e. cases 28 and 29) did not show similar characteristics, it was concluded that an error was made during the execution of the pile test. Accordingly this pile case was regarded as having incomplete information, and was therefore deleted.
■ Case 156: Again there was no obvious physical explanation for the behaviour of this pile case. Furthermore, the location of this data point on the scatter plot of Qi versus Qp fits the general trend for the dataset. Therefore no correction was justified for this pile case.
In summary, four outliers were removed and one retained, bringing the dataset to n = 170 cases in total and for the respective subsets nD-NC = 28; nB-NC = 30; nD-C = 59; nB-C = 53.
Scatter plots of Qi versus Qp can serve as a multivariate approach to outlier detection. However they are presented here to provide an indication of whether the variance of the data set is constant or varies with the dependent variable (i.e. homoscedasticity). The ensuing scatter plots are presented in Figure 2. Visual inspection of the scatter plots seems to suggest variation in the degree of scatter increases with values of Qp. In this regard, it is evident that there is reduced scatter at smaller values of Qp. However, due to the small sample size, the case for large values of Qp is not sufficiently clear to make any firm conclusion. Furthermore, Figure 2 gives the impression that the variance of the points around the fitted line increases linearly, thereby suggesting that the standard deviation increases with the square root of the values of Qp. This explains why the scatter tends to flatten off for large values of Qp. The foregoing assumption implies that weighted regression analysis must be used to establish the relationship between Qi and Qp. Such regression analysis was applied in this study (Figure 2) with the regression line forced to pass through the origin. In this case, the slope of the regression line is an estimate of the model factor M.
Following the outlier detection and removal process, the descriptive statistics for M consisting of mean (mM), standard deviation (sM), skewness and kurtosis are presented in Table 1. The sample descriptive statistics were computed using conventional statistical analysis approach. These are quantities used to describe the salient features of the sample and are required for calculations, statistical testing, and inferring the population parameters.
The sample mean mM indicates the average ratio of Qi to Qp, with mM > 1 indicating a conservative bias of Qi exceeding Qp. This is generally the case, with a positive bias of between 1.04 and 1.17 shown in Table 1, except for the B-NC case where Qi is on average slightly less than Qp with mM = 0.98, which is slightly un-conservative. The general conservative bias reflected by mM is, however, small in comparison to the dispersion of M as reflected by the sample standard deviation Sm for which values range from 0.23 to 0.36; the dispersion is also presented in normalised form as the coefficient of variation VM = sM/mM.
The combined effect of values of mm close to 1 and the relatively large values of Sm or Vm indicate large probabilities of realisations of M in the un-conservative range M < 1. The lower tail of the distribution of M derived from the dataset and M statistics is therefore of specific interest for application of the results in reliability assessment.
A comparison of the standard deviations or coefficient of variations for the respective cases indicates small differences. However, there seems to be a distinct trend that is influenced by the pile installation method (i.e. driven or bored). In this regard, driven piles depict higher variability compared to bored piles, irrespective of soil type. This implies that the densification of the soil surrounding the pile emanating from the pile driving process is not well captured in the selection of the soil design parameters. Even the bias for the driven piles dataset is relatively higher, thereby reiterating the notion that current practice is conservative in selecting design parameters for driven piles. Furthermore, the variability in non-cohesive materials is higher than that in cohesive materials. This is attributed to the fact that in cohesive materials the un-drained shear strength derived from the SPT measurement is directly used in the computation of pile capacity, while in non-cohesive materials, the angle of friction obtained from the SPT measurement is not directly used. Instead, the key pile design parameters in the form of bearing capacity factor (Nq), earth pressure coefficient (ks) and pile-soil interface friction (δ) are obtained from the derived angle of friction on the basis of empirical correlation, thus introducing some additional uncertainties.
Skewness provides an indication of the symmetry of the dataset. The skewness represented in Table 1 is generally positive, indicating a shift towards the upper tail (conservative) of the values for M. There is, however, no consistent trend amongst the values for the respective datasets. The value of 0.24 for the combined dataset (ALL) could therefore be taken as indicative of the general trend. As a guideline it should be noted that the skewness of the symmetrical normal distribution is 0; for a lognormal distribution it is dependent on the distribution parameters, with a value of 0.83 based on the parameter values for the combined dataset.
Values of kurtosis indicate the peakedness of the data, with a positive value indicating a high peak, and a negative value indicating a flat distribution of the data. Negative values generally listed in Table 1 indicate flat distribution of the data, particularly for driven piles. Since these characteristics can only be captured by advanced probability distributions not generally considered in reliability modelling, kurtosis is not further considered.
In order to provide for uncertainties in parameter estimation, Table 1 also presents the confidence limits of the mean and standard deviation at a confidence level of 0.75; this is the confidence level recommended by EN1990:2002 for parameter estimation for reliability models with vague information on prior distributions. The lower confidence limit of the mean (mm; -0.75) and the upper confidence limit of the standard deviation (sm- +0 75) is used to present conservative estimates of the range of parameter estimates.
Although the mean and standard deviation values presented in Table 1 provide a useful data summary, they combine data in ways that mask information on trends in the data. If there is a strong correlation between M and some pile design parameters (pile length, pile diameter and soil properties), then part of its total variability presented in Table 1 is explained by these design parameters. The presence of correlation between M and deterministic variations in the database would indicate that:
■ The classical static formula method does not fully take the effects of the parameter into account.
■ The assumption that M is a random variable is not valid.
Reliability-based design is based on the assumption of randomness of the basic variables. Since the model factor is among the variables that serve as input into reliability analysis of pile foundations, it is critical to verify that it is indeed a random variable. This was partially verified by investigating the presence or absence of correlation with various pile design parameters. The measure of the degree of association between variables is the correlation coefficient. The basic and most widely used type of correlation coefficient is Pearson r, also known as linear or product-moment correlations. The correlation can be negative or positive. When it is positive, the dependent variable tends to increase as the independent variable increases; when it is negative, the dependent variable tends to decrease as the independent variable increases. The numerical value of r lies between the limits -1 and +1. A high absolute value of r indicates a high degree of association, whereas a small absolute value indicates a small degree of association. When the absolute value is 1, the relationship is said to be perfect and when it is zero, the variables are independent. For the numerical correlation values in-between the limits a critical question is, "When is the numerical value of the correlation coefficient considered significant?" Several authors in various fields have suggested guidelines for the interpretation of the correlation coefficient. For the purposes of this study an interpretation by Franzblau (1958) is adopted as follows:
■ Range of r: 0 to ±0.2 - indicates no or negligible correlation
■ Range r: ±0.2 to ±0.4 - indicates a low degree of correlation
■ Range r: ±0.4 to ±0.6 - indicates a moderate degree of correlation
■ Range r: ±0.6 to ±0.8 - indicates a marked degree of correlation
■ Range r: ±0.8 to ±1 - indicates a high correlation
The statistical significance of the correlation is determined through hypothesis testing and presented in terms of a p-value. In this regard, the null hypothesis is that there is no correlation between M and the given design parameter (indicative of statistical independence). A small p-value (p < 0.05) indicates that the null hypothesis is not valid and should be rejected. Values for the correlation between M and the respective pile design parameters with the associated p-values are listed in Table 2. The results indicate that R < 0.4 for all the pile design parameters and therefore the degree of correlation is low. The associated p-values are generally much greater than 0.05, confirming that the correlation between the model factor and the various pile design parameters is statistically insignificant. Therefore, variations in the model factor are at least not explainable by systematic variations in the key pile design parameters, and a random variable model appears reasonable.
For visual appreciation of the correlation results in Table 2, some of scatter plots of M versus pile design parameters are shown in Figure 3.
The theory of reliability is based on the general principle that the basic variables (actions, material properties and geometric data) are considered as random variables having appropriate types of distribution. One of the key objectives of the statistical data analysis is to determine the most appropriate theoretical distribution function for the basic variable. This is the governing probability distribution for the random process under consideration and therefore extends beyond the available sample (i.e. the distribution of the entire population). Once the probability distribution function is known, inferences based on the known statistical properties of the distribution can be made.
For reliability calibration and associated studies, the most commonly applied distributions to describe actions, materials properties and geometric data are the normal and log-normal distributions (Holický 2009; Allen et al 2005). Accordingly, for the current analysis only the normal and lognormal distribution fit to the data are considered. The fit is investigated through (i) a cumulative distribution function (CDF) plotted using a standard normal variate with z as the vertical axis, and (ii) direct distribution fitting to the data.
The cumulative distribution function is the most common tool for statistical characterisation of random variables used in reliability calibration (e.g. Allen et al 2005). In the context of the current analysis, the CDF is a function that represents the probability that a value of M less than or equal to a specified value will occur. This probability can be transformed to the standard normal variable (or variate), z, and plotted against M values (on x-axis) for each data point. This plotting approach is essentially the equivalent of plotting the bias values and their associated probability values on normal probability paper. An important property of a CDF plotted in this manner is that normally distributed data plot as a straight line, while lognormally distributed data on the other hand will plot as a curve. The following steps were used to create the standard normal variate plot of the CDF: ■ The capacity model factor values in a given data set were sorted in a descending order, then the probability associated with each value in the cumulative distribution was calculated as i/(n +1).
■ For the probability value calculated in Step 1 associated with each ranked capacity model factor value, z was computed in Excel as: z = NORMSINV(i/(n +1)) where i is the rank of each data point as sorted, and n is the total number of points in the data set.
■ Once the values of z have been calculated, z versus model factor (X) was plotted for each data set.
The ensuing plots are presented in Figure 4(a) from which it can be seen that the CDF for the five data sets plot more as curves than straight lines, thereby implying that the data follow a lognormal distribution. A further characterisation entailing fitting predicted normal and lognormal distributions to the CDF of the data sets is carried out. These theoretical distributions are also shown in Figure 4(a). Both distributions seem to fit the data reasonably well. However, with the exception of the bored piles data set, the lognormal distribution has a better fit to the lower tail of the data, which is important for reliability analysis and design.
To further confirm that the data best fits a lognormal distribution, z-scores are plotted as a function of Ln (M). The plots would follow a straight line if the data in fact follows the lognormal distribution. The results are presented in Figure 4(b) from which it is apparent that all the data sets plot as a straight line. This therefore confirms the strong case for a lognormal distribution assumption for the data.
In Figure 5 the histogram of M for the respective datasets are compared to normal, lognormal and general lognormal (also three-parameter 3P) probability density function distributions based on the sample moments listed in Table 1 as distribution parameters. The graphic comparison indicates the degree to which the alternative distributions provide a reasonably smoothed representation of the M data. At the same time the approximate nature of the M data is indicated by the uneven nature of the histogram. The quantitative assessment of the difference between the empirical data frequencies and the assumed distributions is provided by the chisquare goodness-of-fit test. In this regard, the p-value is a measure of the goodness-of-fit, with larger values indicating a better fit.
In testing the hypothesis that the distribution of the data is similar to the selected probability distribution (normal or lognormal), the hypothesis is rejected if p < 0.05. The p-values for chi-square testing are presented in Table 3 from which it is apparent that such values for all the data sets are greater than 0.05 and therefore there is no evidence to reject the null hypothesis of either normal or lognormal distributions. However, on the basis of the magnitude of the p-values, the lognormal distribution seems to show a better fit compared to the other two distributions. The general lognormal distribution provides distributions which are generally intermediate between the normal and lognormal distributions (Figure 5), with similar results for the p-values (Table 3).
On the basis of the results of the two standard distribution fitting approaches studied, it can be concluded that the data fits both the normal and lognormal distributions, although the ordinary lognormal distribution has a slight edge, particularly towards the lower tail (Figure 5). However, theoretically M ranges from zero to infinity, resulting in an asymmetric distribution with a zero lower bound and an infinite upper bound. The lognormal probability density function is often the most suitable theoretical model for such data, as it is a continuous distribution with a zero lower bound and an infinite upper bound. On the basis of this practical consideration, past studies (e.g. Phoon 2005; Briaud & Tucker 1988; Ronold & Bjerager 1992; Titi & Abu-Farsakh 1999; FHWA-H1-98-032 2001; Rahman et al 2002) have recommended the lognormal distribution as the most suitable theoretical model for model uncertainty. Furthermore, in the Probabilistic Model Code by the Joint Committee on Structural Safety (JCSS) (2001), model uncertainty is modelled by the lognormal distribution. Therefore the lognormal distribution is considered a valid probability model for M. Nonetheless, it should be acknowledged that there could be some other distributions that can provide a better fit to the tails. Generally such advanced and complex distributions require a large sample size. For a small sample size, as is the case in this study, such distributions may only lead to a refinement of the results, but not a significant improvement.
Pile foundation design uncertainties are captured by the M statistics. The M statistics constitute the main input into reliability calibration and associated studies. Since the M statistics are derived from raw data, statistical characterisation of such data is of paramount importance. Accordingly characterisation of the data collected for pile foundation reliability studies have been presented in this paper. The key conclusions reached are as follows:
■ Based on the mean values for M, the static formula yields a positive bias of between 1.04 and 1.17, except for the B-NC data set where Qi is on average slightly less than Qp with mm = 0.98, which is slightly un-conservative.
■ There is a distinct trend that driven piles depict higher variability compared to bored piles, irrespective of materials type. This suggests that the densification induced by pile driving is not fully captured by existing procedures for selecting design parameters.
■ The variability in non-cohesive materials is higher than that in cohesive materials. This is attributed to the high degree of empiricism associated with the selection of pile design parameters (Nq, ks and δ) in non-cohesive soils.
■ The values of mM close to 1 and the relatively large values of Sm or Vm indicate large probabilities of realisations of M in the un-conservative range M < 1. Therefore the lower tail of the distribution of M is of specific interest for application of the results in reliability assessment.
■ At the customary 5% confidence level, the chi-square goodness-of-fit test results indicate that both the normal and log-normal distributions are valid theoretical distributions for M. However, when taking into account other practical considerations, the lognormal distribution is considered to be the most appropriate distribution for M.
■ None of the pile design parameters is significantly correlated with the model factor. From the probabilistic perspective, this implies that the variation in the model factor is not caused by the variations in the key pile design parameters. Therefore it is correct to model the model factor as a random variable.
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Contact details:
Dr Mahongo Dithinde
Departmert of Civil Ergireerirg
Stellerbosch University
Private Bag XI
South Africa
Departmert of Civil Ergireerirg
University of Botswana
Private Bag UB 0061
E: email@example.com
Contact details:
Prof Johan Retief
Departmert of Civil Ergireerirg
Stellerbosch University
Private Bag X1
E: email@example.com
DR MAHONGO DITHINDE (Visitor) holds a PhD in Civil Engineering from the Stellenbosch University, an MSc in Foundation Engineering from the University of Birmingham (UK), and BEng in Civil Engineering from the University of Botswana. He works as a Senior Lecturer at the University of Botswana. His specialisation and research interests are in the broad area of geotechnical reliability-based design. In addition to academic work, he is also a geotechnical partner for Mattra International where he is active in consultancy work in the field of geotechnical engineering.
PROF JOHAN RETIEF (Fellow of SAICE) has, since his retirement as Professor in Structural Engineering, maintained involvement at the Stellenbosch University, supervising graduate students in the fi eld of risk and reliability in civil engineering. He is involved in various standards committees, serving as the South African representative to ISO TC98 (basis of structural design and actions on structures). He holds a BSc (cum laude) and a DSc from the University of Pretoria, a DIC from Imperial College London, and an MPhil from London University. Following a career at the Atomic Energy Corporation, he joined Stellenbosch University in 1990.


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Registry court: Iserlohn Local Court
and are available as a portal where Dornbracht products are presented to users.
1. General information1.1 Dornbracht takes the protection of your personal data very seriously and adheres strictly to the rules of the Data Protection Acts of the Federal Republic of Germany, the German Telemedia Act and the data protection regulations of the European Union (hereafter: "Data Protection Regulations").
1.2 Dornbracht has accordingly obliged its employees to comply with the Data Protection Regulations.
1.3 The following statement gives you an overview of how Dornbracht ensures this protection, what kind of data is collected and for which purpose.
1.4 For all questions and queries regarding data protection, please contact the data protection officer of Aloys F. Dornbracht GmbH & Co. KG by email at firstname.lastname@example.com or by post at the above mentioned address of Aloys F. Dornbracht GmbH & Co. KG.
2. Personal dataTo be able to contact you, we shall collect and store the following information about you:
Compulsory fields: Title
User group
Post code, City
Email address
Your agreement to the privacy clause
Optional information: Complete telephone number
(hereafter referred to as "personal data").
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• To process respective enquiries/messages
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3.3 The personal data is stored and processed exclusively on servers located in the European Union.
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4.2 You also have the rights of enquiry and deletion, as laid down by the Federal Data Protection Act. These rights are exercised by sending a message by post or via electronic mail to email@example.com.
5. Duration of data useYour personal data will be deleted immediately if you revoke your consent, or if the purpose of data use has expired.
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8. Crazy EggThis website uses Crazy Egg, a web analytics service provided by Crazy Egg, Inc., 6220 E. Ridgeview Lane, La Mirada, CA , 90638, USA.. Crazy Egg uses cookies that enable us to analyze your use of our website. The data generated by cookies about your use of our website is transferred to a Crazy Egg server in the USA and stored there. You can configure your browser settings at any time, should you not wish to receive cookies or in order to delete them from your terminal. You may opt-out of the collection and storage of your information (including your IP address) by Crazy Egg at any time by following the instructions under the following link: http://www.crazyegg.com/opt-out
For more information about the software used and Crazy Egg's Privacy Policy, click here: http://www.crazyegg.com/privacy
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First, Sitecore uses "cookies" that are stored on your computer and allow your use of the Website to be analysed. The information generated by the cookie on your use of this Website (e.g. browser settings, language settings, content you have clicked on) is transmitted to, and stored by, a Sitecore server in the US. You can use a corresponding setting in your browser software to prevent cookies from being saved; we would, however, like to draw your attention to the fact that, if you do so, you may not be able to use all of the functions offered by the Website in full.
For information on data processing by Sitecore and the corresponding data protection provisions, please visit: http://www.sitecore.net/Company/Contact/Privacy-Policy.aspx.
10. AddthisThis Website contains AddThis plugins that allow you to bookmark or share interesting website content. The use of Addthis involves the use of cookies. The Data generated during this process (e.g. time of use, browser language) is transmitted to, and processed by, Add This LLC in the US. We do not process this Data. By using the Addthis field, you declare your consent to data processing by Add This LLC. You can object to your Data being used at any time by using an opt-out cookie. For further information on data processing by Add This LLC and the data protection provisions of Add This LLC, please visit: www.addthis.com/privacy.
11. FacebookOur Website uses social plugins ("Plugins") of the social network facebook.com, which is operated by Facebook Inc., 1601 S. California Ave, Palo Alto, CA 94304, USA ("Facebook"). You can view the list of Facebook social plugins and see what they look like here: http://www.developers.facebook.com/plugins
If you click on a page of our Website that contains this sort of Plugin, your browser establishes a direct connection with the Facebook servers. Facebook directly transfers the Plugin content to your browser which embeds the latter into the Website. We cannot influence the scope of Data that Facebook collects with the help of this Plugin, meaning that the information we provide is based on our current knowledge.
The embedding of the Plugin informs Facebook that you accessed the respective page of our Website. If you are logged into Facebook, Facebook can assign the visit to your Facebook account. If you interact with the Plugins, e.g. by clicking on the "Like" button or entering a comment, the corresponding information is transmitted from your browser directly to Facebook, where it will be saved. If you are not a member of Facebook, it is still possible for Facebook to identify and save your IP address.
For information on the purpose and scope of the data collection and the further processing and utilisation of the Data by Facebook, as well as your rights in this respect and the settings that you can adjust to protect your privacy, please consult Facebook's data protection information: http://www.facebook.com/policy.php.
If you are a member of Facebook and want to prevent Facebook from collecting Data on you via our Website and linking this Data to your member data stored with Facebook, you must log out of Facebook before you visit our Website.
12. YouTubeOur Website also features the Plugin of the YouTube platform. This Plugin is distinguished by a logo of the cooperation partner and additional written information. If you click on a page of our Website that contains this sort of Plugin, your browser establishes a direct, brief connection with the Plugin's servers. This primarily serves to display the content of the Plugin. In such cases, the cooperation partner learns of your IP address. In practice, it is not possible to readily identify you on the basis of this IP address. Under certain circumstances, the platform provider can save a cookie on your computer in the process. You decide whether you want to allow these cookies or not using the settings in your Internet browser.
For information on the purpose and scope of the data collection and the further processing and utilisation of the Data by YouTube, as well as your rights in this respect and the settings that you can adjust to protect your privacy, please consult YouTube's data protection information: http://www.youtube.com/t/privacy.
13. Data securityDornbracht uses appropriate technical and organisational methods to prevent unauthorised access or disclosure and to ensure the accuracy and legitimate use of the data. Nevertheless, no electronic communication can be regarded as completely secure. This means that any data and information that you voluntarily submit to Dornbracht may be retrieved by third parties through unlawful means. Dornbracht shall not assume any responsibility or liability for the disclosure of information due to errors and/or unauthorised access by third parties during data transfer.
14. References and links14.1 When accessing other web pages suggested by this website, you may again be asked for information such as name, address, e-mail address, browser properties, etc. This privacy policy does not govern the collection, transfer or handling of personal data by third parties.
14.2 Third party service providers may have different and separate terms on how they deal with the collection, processing and use of personal data. It is therefore advised to refer to the websites of third parties and to check on how they handle personal data before submitting your personal data.
Version: 10/12/2018

subset: commoncrawl


text: Men's Lacrosse 3/18/2016 11:15:59 AM Rabil, Schwartzman and Wolf named Players of the Week BOSTON (June 23, 2014)- Major League Lacrosse, the premier professional outdoor lacrosse league, announced Week 8's Warrior Offensive and Defensive Players of the Week and the Cascade Rookie of the Week today. This week, Paul Rabil was awarded honors for his offensive presence in helping the Boston Cannons defeat the Ohio Machine in overtime, and Jesse Schwartzman was recognized for the second time this season for serving as an impenetrable backstop for the Denver Outlaws in their victory over the Chesapeake Bayhawks. Rookie attackman Jordan Wolf continues to prove his worth as he has now collected 12 points in two games played, this week helping the Rochester Rattlers defeat the Florida Launch. All awarded players earned Coca-Cola Player of the Game honors in their respective matches. Rabil led the Cannons to their second 17-16 victory over the Ohio Machine this season, this time in sudden victory overtime. Having already collected a hat trick complemented by a two-point goal, Rabil entered the extra time with a total of eight points. Coming off of a time-out, Rabil found attackman Will Manny and set him up to fire a shot past Ohio goaltender Brian Phipps for Rabil's fifth assist of the game and Manny's fifth goal. Rabil now leads the team and the League with 40 points, light years ahead of teammates Manny and Kevin Buchanan, who are tied for second place on the Cannons' roster with 29 points apiece. Schwartzman and the Outlaws shut down the Bayhawks in one of the lowest scoring games of the season, defeating their visitors 9-6. Schwartzman had a save percentage of 0.82, stopping all five shots faced in the first, nine out of ten in the second, three out of five in the third, and five out of eight in the fourth. Not only did Schwartzman post career numbers in the game, but he also took the lead in every net-minder category on the League leaderboard thus far this season, most impressively with with an 8.50 goals against average and a .633 save percentage. Wolf was awarded his second Rookie of the Week honors as he and the Rochester Rattlers downed the Florida Launch 18-13. The first year attackman netted two goals and accrued five assists, upping his season total to six goals and six assists in merely two games played. Follow Wolf into his third game in his exciting inaugural season as he and the Rattlers host Schwartzman and the Outlaws next Sunday at 1:00 p.m. (ET), featured on CBS Sports Network. Rabil and the Cannons are back in action Saturday, visiting the Charlotte Hounds for a 7:00 p.m. (ET) game on ESPN3.com and syndicated channels. Don't forget the tune into the All-Star Game this Thursday, June 26 at 7:30 p.m. (ET) on CBS Sports Network, as Rabil and Schwartzman don the red, white and blue for Team USA and faceoff against Team MLL. If you're in the Boston area, grab tickets to the event at Harvard Stadium here! Warrior Offensive Players of the Week Week 1 – Kevin Buchanan (Boston) Week 2 – Garrett Thul (Florida) Week 3 – Casey Powell (Florida) Week 4 – Casey Powell (Florida) Week 5 – Marcus Holman (Ohio) Week 6 – Kevin Leveille (Rochester) Week 7 – Ben Hunt (Chesapeake) Week 8 – Paul Rabil (Boston) Warrior Defensive Players of the Week Week 1 – Chad Wiedmaier (Ohio) Week 2 – Mike Manley (Rochester) Week 3 – Jesse Schwartzman (Denver) Week 4 – Joe Fletcher (New York) Week 5 – Mike Manley (Rochester) Week 6 – Joe Cinosky (Florida) Week 7 – Lee Zink (Denver) Week 8 – Jesse Schwartzman (Denver) Previous Cascade Rookies of the Week Week 3 – John LoCascio (Rochester) Week 4 – Kieran McArdle (Florida) Week 5 – Tom Schreiber (Ohio) Week 6 – Justin Ward (Charlotte) Week 7 – Jordan Wolf (Rochester) Week 8 – Jordan Wolf (Rochester)


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Fellowship Baptist Church
Wednesday, January 29, 2020
Fellowship Baptist Church is nestled in the mountains of southwestern Virginia just outside the town of Meadowview. Our desire is to lead people to become devoted followers of Jesus Christ. We seek to do so by LINKing them with Christ and a community of faith. We want every person in this fellowship to learn to LOVE Jesus with all they are in every aspect of their lives. Finally we want to LIVE more and more for Christ everyday as we remember that we are Jesus, LIVE to our world everyday.
Thank you for taking time to visit our website. If you have any questions, please contact our church office at (276) 944-5832. We would love to see you for one of our services. You may just find the church where you belong in the process.
Sunday School 9:45am
Morning Worship 11:00am
Juniors Church 11:00am
Evening Worship 6:00pm
Youth Bible Study 6:00pm
Choir Practice 7:00pm
Bible Study for all ages 7:00pm
Church Website by E-zekiel


text:
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Banco do Brasil


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Ivey, 3, wanted a Barbie for Christmas. I knew how this would end. We'd buy a new Barbie, she'd take all the clothes off and get the hair snarled into one disgusting dreadlock, the Barbie would fall into the bowels of a toy bin, and we'd repeat this process in a few months. I don't judge her for wanting a Barbie, and I don't mind having Barbies. I had a ball playing with them when I was a little girl. I do mind spending money on a brand-new Barbie just as we place our old, tangled, naked ones into the Goodwill donation box.
I hate that Barbies and many other toys have become disposable. I hate the waste. I hate knowing that thousands of Barbies are trickling from Wal-Mart to homes to Goodwill to landfills while we continue to buy more shiny new Barbies. They don't stay shiny and new for five minutes! I wanted to stop the cycle.
So here is what happened. The other day I went to a local holiday consignment sale. I knew Ivey wanted a Barbie, so that was one of the things I planned to look at. There were a few expensive collector's Barbies new in the box. Then I saw this unusual-looking big Barbie. I thought Ivey would like her. It looked like her hair could probably be brushed out and restyled, and she was only $2. She was also a jointed, posable ballerina, and Ivey had requested a "dancing doll." So I bought her.
Upon closer inspection after I got home, I realized the Barbie's cheap, flimsy clothes were ripped and stained. I knew they wouldn't survive being washed. Her hair was really tangled, and she smelled a bit smoky. Yuck. After a bit of research, I learned that she was one of these fancy dancers, separated from all her high-tech accessories. (Please note the crappy reviews. Less is more, toy designers!) I thought about throwing her in the trash and ordering the plain, inexpensive ballerina Barbie I'd seen on Amazon, but it'd been a hard day and I was in need of a fun project.
I asked Jordan to watch the kids for "a few minutes" and got to work. First I had to remove all the tiny rubber bands--you know, the ones they use to style the hair so it looks impeccable on the store shelf but then, after your child touches it, never ever looks good again? I tried brushing her hair with an oversized Barbie brush, but it was too tangled. To help things along, I wet it and smoothed some rich peppermint lotion I happened to have into it. I brushed and brushed, starting at the ends and working my way up. It took a long time. Then I washed it with a small amount of gentle bar soap and conditioned it again with lotion before rinsing it out. I brushed it several more times between these steps.
Barbie had a few scuff marks and dings on her face and body, and I thought they might be permanent, but they came right off with a Norwex cloth and water! I love Norwex.
I really wish I'd taken a before shot of this 1998 Barbie in all her tangled-up, dingy, raggedy glory, but the makeover was honestly a last-ditch effort and I didn't know it would work! So here she is after her day at the spa, leaning against my serger and telling me her troubles while I sew her a new outfit. If you look closely, you can probably see that her old dress just wouldn't do.
"I mean, seriously! After all that tireless dancing and smiling I did, they put a $2 tag on me and threw me in a box! Can you believe that? Really hurt my feelings. Glad I met you, though. And by the way--pink is my favorite color!"
Well, anyway, pink is one of Ivey's favorite colors and was the primary color worn by the Barbie she pointed out and asked for. It was a picture in a National Geographic! I give my kids Nat Geo to look at, and they pinpoint the one plastic toy pictured in a story and ask for it. Funny, huh? Anyway, I had this delightful hot pink sparkly remnant that just spoke to me in the fabric store about four years ago. It was stretchy and perfect! I certainly didn't have a pattern, but all I needed was a simple leotard, so I laid Barbie on the wrong side of the fabric and traced around her body with a pencil. Leaving a bit of room for seam allowance and to hem the top edge, I came up with this:
I sewed right sides together, except for the crotch, which I left open,
And I tried it on her. She really liked how it was coming along.
I decided that I didn't really need to trim the crotch and sewed it closed. Because this was a stretchy, non-raveling fabric, I didn't have to hem the leg holes. I knew they would be hidden under the skirt I was about to make, anyway.
I did hem the top edge, though, to give it a more finished look. This is how it came out. Easier than I thought!
I thought it needed some sort of strap(s) to secure it at the shoulder. To make things simpler, I decided on one halter-style strap made out of a tube of some of the plain hot pink fabric on the selvage of that same remnant. It was a little aggravating to turn that tube right side out because I have misplaced my bodkin, but I got it done.
I sewed it to one side just to the front of the side seam, and briefly considered sewing her into the leotard by doing the same thing on the other side, but ended up putting in a snap instead. Now Ivey can undress her. And probably will.
Then I cut some strips of tulle and made the tutu while she wore it! So easy. In this picture you can see the rectangular hole where she would've plugged into her dancing stand thingy.
After a few minutes of tying strips on and a quick trim to make it all the same length, her fancy new outfit was done and Barbie was starting to feel fabulous again!
She just needed a few finishing touches:
1) Arm warmers (to cover those joints). I just made two little tubes out of rectangular scraps and turned them right side out, no hemming necessary.
2) A new hairdo. I was a little nervous over this. It's not like I have any Barbie-sized bobby pins, and I'm terrible at hair! It worked out okay, though. I pulled back the top and sides and braided it, then parted the rest into two pieces and twisted it around the braid, pulling it into an updo and securing it with one of those tiny plastic bands (same ones I used on my girls' hair). Then I cut a strip of the hot pink fabric, tied it around the whole thing, and trimmed it.
I am sure Ivey will dismantle her 'do, but I can fix this one again! Ha!
3) I took some hot pink metallic acrylic paint and a tiny paintbrush and painted her ballet slippers to match.
Yeah, I know it all seems a little obsessive, but once I get started on a project it's hard to stop. This is how I do arts and crafts. Some people knit, some people quilt, I do random stuff like this! And I do love the way she turned out. The best part is that the entire project only cost me the $2 I paid for the Barbie, because all my materials and tools were things I had on hand or were left over from other projects!
The moral of this project: If you want your child to have a lovingly handmade toy but you're not quite that crafty and don't have enough money to buy handmade, why not make over a toy instead? It's a fun option that comes in many forms and at varying degrees of difficulty. Have you ever tried this and have some ideas to share? I think I'll post again later with some of my ideas for pre-loved toy makeovers!


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We want to encourage kids to form opinions about what they watch - to react to what they see on the screen. In this lesson, children begin to think about basic concepts - such as how audiences interpret meaning, and the constructed world of television and film.
In this lesson, students identify stereotypical images of girls and women as represented by female action heroes.
This lesson looks at the increasing prominence of gambling in the media, particularly movies and television.
In this lesson, students examine the visual codes used on television and in movies through an exploration of various camera techniques. Students begin with a discussion about camera-subject distance, and review various film techniques that are used to create visual meaning.
This is the first of three lessons that address gender stereotypes. The objective of this lesson is to encourage students to develop their own critical intelligence with regard to culturally inherited stereotypes, and to the images presented in the media - film and television, rock music, newspapers and magazines.
This lesson focuses on put-down mentality in the media.
This lesson introduces students to the phenomenon of the"blockbuster" movie – its history,characteristics and influences. Students will also explore the role of audience in the creation of a "blockbuster" and analyze their own responses to current blockbuster films. Students will learn about the process involved in turning a film into a blockbuster by devising promotional campaigns for an imaginary movie.
This teaching unit helps students to become more aware of the language and techniques used in print advertising, as well as the impact of advertising on their daily lives.
This lesson encourages students to analyze the forensic science crime drama as a television show genre.
The place of the hero in our modern lives is a site of struggle. On the one hand, the hero's quest can have meanings for individuals who seek to understand their own journey through life.On the other hand, the hero can be seen as a repository of those values esteemed by the society.


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You've just returned from courageously honoring your commitment to your country, and now you're ready to concentrate on what life has to offer next. The Department of Industrial Relations' Division of Apprenticeship Standards (DAS) has programs that can help you look forward to a future of employment and possibilities. The apprenticeship programs and the on-the-job-training (OJT) program allow you to learn the skills for a lifelong career, while earning money and collecting your Montgomery GI Bill benefit. There are a wide variety of career opportunities that include not only construction related trades but healthcare, manufacturing, firefighting, culinary and auto mechanics, among others.
Any occupation that requires at least one year of training and related and supplemental instruction classes can be made apprenticeable. This enables you to receive pay as you learn and collect your GI Benefit. In the beginning when your apprenticeship paycheck is the smallest, you receive the largest GI benefit to which you are eligible. The payments decline as your apprenticeship salary increases. If the apprenticeship program is long enough, you can receive all the money in the GI fund to which you are eligible. You may find work with one of the existing apprenticeship programs, or you may seek out a job that qualifies and ask the employer to contact DAS and set up an apprenticeship program.
In distinction to apprenticeships, if a program requires two years or less of training and does not require related and supplemental education, the job may qualify as an OJT program therefore qualifying you to receive your GI Bill benefit. If you find a job that requires OJT but they do not have an official program set up, have the employer can contact DAS for information on how to do so and what the benefits to the employer are.
The DAS programs offer flexibility, honor you as a veteran by enabling you to collect your earned GI Bill benefit and set you on a lifelong career path.
- For more information on the variety of existing apprenticeship programs visit www.dir.ca.gov/das/ibuiltit/opportunities.asp.
- For more information on your Montgomery GI Bill and eligibility visit www.gibill.va.gov.
- For more information on job opportunities visit the Employment Development Department at www.edd.ca.gov.
Also keep in mind that the DAS programs offer many opportunities for your spouse, as well. A spouse, however, does not collect GI Bill benefits unless he/she is a veteran.


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You know, computers aren't just for getting things done. They're also for getting your game on. With that in mind, the latest Cult of Mac Deals offer has packed 10 fun games that are compatible with both Mac and PC systems into one bundle known as The Entertainment Games Bundle.
And for a limited time we're offering it to you for just $29!


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Clouds I can handle, but it's the rain that drives me crazy when I am supposed to be shooting outside. Thankfully I was photographing weather experts for this assignment, and it turns out they know a thing or two about predicting the weather.
As I was packing up to head out for what I knew would be a long day at "the office" I began to feel a little nervous about the dark clouds hanging over Seattle. After checking weather.com, my nervousness began to change into a good case of anxiety because they were predicting rain by 9:30am, and that was just two hours away. Maybe I could beat the rain and get in at least one outside portrait. The problem was I had to photograph one subject in the morning, and the second one in the afternoon. At the very best it looked like I would only get one of the subjects outside, but the show must go on. I headed over to the University of Washington where I would meet up with my first subject, Cliff Mass. My assignment was to photograph Cliff and Brad Coleman in studio for the opener, and get an environmental portrait of each of them individually. The thing that made this all really interesting was I could not get them both in studio at the same time or place. I had to set up a studio at UW in the morning, then tear it down, and set it up again, exactly the same at NOAA to photograph Brad. I had been planning this shoot for a couple of weeks including some minor styling and location scouting. This was an assignment I was really looking forward to, and I would have been really bummed if the rain forced us inside for the environmental shots.
To give you a little background on the men in the photographs. Cliff Mass was mentored by Carl Sagan while in undergrad at Cornell University. He is the author of "The Weather of the Pacific Northwest", he runs a very popular blog, Cliff Mass Weather Blog, he is a weekly guest on KUOW radio, and he is a professor of atmospheric sciences at the University of Washington. To quote a UW press release, "He has published dozens of articles on Northwest weather and leads the regional development of advanced weather prediction tools." Many people in the Pacific Northwest plan their weekends around Cliff's predictions.
Brad Colman is the meteorologist-in-charge of the Seattle Weather Forecast Office. "A meteorologist-in-charge is the front line officer carrying out the National Weather Service's mission of serving the American public by helping protect lives and property," said Brig. Gen. David L. Johnson, U.S. Air Force (Ret.), director of NOAA's National Weather Service. "NOAA's National Weather Service is the official source of weather data, forecasts and warnings for the United States and its territories. The National Weather Service operates the most advanced weather and flood warning and forecast systems in the world, helping to protect lives and property and enhance the national economy."
Back at UW, it seemed like it would start raining any minute, so I asked Cliff if we could change the schedule a little and shoot the environmental portrait right away because I was nervous about the rain. Embarrassed as I am to admit, it never occurred to me that I was working with one of the top experts in weather. When I told Cliff we should try to shoot outside while it is still dry, he promptly responded, "It's not going to rain." "It's not?" I asked. "No. The cloud cover will begin to burn off at 10:30, and by 11:30 we will have clear skies." And he was right. Take that weather.com! Once we finished at UW with Cliff, it was off to NOAA to photograph Brad. By the time we got there, the skies were opening up, and it looked quite different than just a few hours before, as you can tell in the pictures.
From this point on, I will include a weatherman on all of my bids. First assistant, second assistant, makeup artist, stylist, and a weatherman. Oh, and a falconer. I recently learned about the benefits of having a falconer on set. If nothing else it can be very entertaining, but that's a story for another time.
A fun fact I learned on this assignment is Houston, New York, Miami and Boston all get more average annual rainfall than Seattle. We win the prize for most cloudy days though… Bummer.
Special thanks goes out to Robyn and Seattle North Face for the clothes. And also to Mandy for painting some amazing clouds which were not used in the final.
This is a video of Cliff explaining some weather basics. I like how he explains things in terms anyone can understand. Plus he has soothing voice.


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Integrate Secure File Transfer Protocol (SFTP) with Mention
About Secure File Transfer Protocol (SFTP)
SFTP (or Secure File Transfer Protocol) is a network protocol that provides file access, file transfer, and file management functionalities over any reliable data stream.
About Mention
Mention monitors conversations about a brand by tracking any time that the brand is mentioned on a range of online platforms, including social media, blogs, or news outlets. Mention can then analyze the sentiment of those conversations and send that information to users in the form of alerts. This data also allows Mention to gauge overall public sentiment and reputation of a brand. If those metrics seem particularly unusual - i.e. if there is a spike in negative public sentiment - they can send notifications immediately so that changes can be made.
Popular Use Cases
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Mention's End Points
Mention Accounts
Create or modify a Mention user account, including the user's contact info, how often they receive alerts and what kind of access they have to the data on Mention. This allows you to ensure that users can operate as efficiently as possible and interact with Mention in a way that creates the best workflow for them.
Retrieve a list of mentions that have been tracked by your alerts, which can be filtered by a number of parameters including source, date range and tone. This query then returns details - like description, source and author's influence score - about the relevant mentions, allowing you to gauge if the mention warrants a response.
Mention Alerts
Define the parameters of an alert, such as the alert's name, query terms and tracked sources. Then, retrieve data about the mentions that have triggered the alert, including how many mentions there are and how important it is to respond to them.
Mention Tags
Design tags, which can be used to filter responses generated by your alerts. Once a tag is created, you can either fetch all the Mentions with that tag in them or use the tag as one of many parameters to filter queries about the mentions in an alert.


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This is the article I wrote on Health and Wellness for the Autism Community Magazine.
I recently heard Rebecca Onie speak on TedTalk about the current healthcare system in the US. She poses the questions: "What if our health care system kept us healthy? What if waiting rooms were a place to improve daily healthcare? What if doctors could/would prescribe/facilitate diet and lifestyle change and improve health?" The work the patient needs to do is not done after the prescription is Read More »


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|USD 8.69 $8.49 750ml
|USA Wine Traders Club
|USD 5.99 $5.99 750ml
Blackstone Winery Pinot Grigio 2014
Region: USA » California
Winery: Blackstone
Color: White
Varietal: Pinot Grigio
Sonoma County has always been Blackstone's home and inspiration. Since our beginning we've established a reputation for consistently delivering balanced, reliable wines of uncompromising quality. For nearly 20 years, we've cultivated close ties with grape growers and viticulturists throughout Sonoma County. Both our inspiration and our home, Sonoma remains the heart of Blackstone. You'll find ... Read more
Sonoma County has always been Blackstone's home and inspiration. Since our beginning we've established a reputation for consistently delivering balanced, reliable wines of uncompromising quality. For nearly 20 years, we've cultivated close ties with grape growers and viticulturists throughout Sonoma County. Both our inspiration and our home, Sonoma remains the heart of Blackstone. You'll find the region's signature style blended into every bottle. As a grape-growing region, Sonoma County is like no other place on earth. With redwood forests, benchlands, hillsides, and nearly 80 miles of majestic coastline, Sonoma offers a wide variety of microclimates, soils, and terrain. This uniquely diverse region supports several different grape varieties but is best known for its lush Merlots and full-bodied Cabernets. Sonoma County is a designated American Viticultural Area (AVA) made up of thirteen distinctly different World-Class wine growing appellations. Each appellation and each vineyard within that appellation contributes something special - it is from this vibrant flavor palate that complex, expressive wines emerge. At Blackstone, we find Sonoma County inspiring. Each different AVA contributes something unique and special to the final blend. Our Sonoma Reserve wines, in particular, exemplify Sonoma's signature style, and help set the standard carried through to our Winemaker's Select wines. "Sonoma County is one of the most amazing places in the world to live and make wine," observes Winemaker Gary Sitton. "There's no place like it. The immense diversity—from the coastline to the mountains, valleys, and rivers—still leaves me in awe. For me, tapping into this range of growing conditions is fundamental to making beautifully expressive wines from Sonoma County." From the start, our goal has been to produce smooth, flavorful, harmonious wines with rich fruit, supple texture, and balanced oak. Early on, this approach found its ideal expression in our Merlot, which soon became — and still remains — America's favorite Merlot. Over the years, the same passion for consistent, balanced wines has expanded to new varieties bearing Blackstone's trademark label. Our Winemaker's Select wines are sourced from premier growing regions throughout California, each meticulously selected to contribute certain nuances to the final blend. Drawing from the best parcels in the same areas year after year ensures Winemaker's Select wines maintain the consistency that is Blackstone's heritage. Our signature style and quality is elevated in our Sonoma Reserve wines, which are sourced from Sonoma's world-class vineyards. Each vineyard site contributes distinct characteristics that uniquely define the region. Through it all, Blackstone has stayed true to the approach inspired by our Sonoma roots - to craft wines that complement food and any occasion in which you celebrate the things in life you can count on. Tasting Room Hours: Every day: 10:00 a.m. - 4:30 p.m. Read less
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Tony Chan
Wisconsin LawyerVol. 80, No. 4, April 2007
In September 2006, Hewlett-Packard (HP) chair Patricia Dunn landed herself in hot water and triggered a congressional hearing when she hired a security consultant who uses a questionable tactic called "pretexting" to obtain personal information.1 According to the Federal Trade Commission, pretexting is the practice of getting an individual's personal information under false pretenses, and it is against the law.2 Unfortunately, pretexting creates a misconception about what competitive intelligence is.
Tony Chan is an information specialist at Quarles & Brady LLP, Milwaukee. He has taught Internet research and technology classes for the State Bar of Wisconsin and the National Business Institute. He serves on the boards of the Law Librarian Association of Wisconsin and the Wisconsin Chapter of the Special Libraries Association.
So what exactly is competitive intelligence (CI)? Simply put, CI is collecting information about others. The Society of Competitive Intelligence Professionals (SCIP) states that CI professionals should "comply with all applicable laws, domestic and international" and "accurately disclose all relevant information, including one's identity and organization, prior to all [research] interviews."3
Just a few months before the HP debacle, the U.S. Government Accountability Office issued a report finding that the federal and data security laws have limited applicability to information sellers.4 In response to the U.S. GAO finding, the federal government passed additional privacy legislation in an attempt to alleviate the existing problem.5
The goals of CI research run the gamut from verifying an individual's credentials to gaining a competitive edge in the marketplace. This investigative effort may include skip tracing; finding assets; identifying ownership, affiliations, or relationships; or discovering who gets the most business from certain clients. Oftentimes public and private information on people and companies is selectively available via government databases as well as via commercial and noncommercial databanks.
Bankruptcies, liens and judgments, professional licenses, intellectual property filings, real estate records, business filings, and public company and Uniform Commercial Code filings fall into the broad category of public records. Each state also has its own public record laws defining the format of public records (paper versus electronic) and retention and disposition policies.
People and Assets Finder. Preliminary searches for information about individuals may involve simple address and phone number lookups using services such as switchboard.com. A good starting point to begin CI research is BRB Publications Inc. It offers a pathfinder to identify searchable lists of free public record sites that provide information such as current address, voter registration, and real property data that is extracted from local, state, and federal government entities' databases.
Accurint.com, LexisNexis, and Westlaw all provide sizeable public record databases to supplement the BRB searches. The advantage of using these commercial vendors is that their search engines allow for multi-state queries and keyword searching. In addition to extracting past and recent addresses and phone numbers, a key feature of Accurint reporting is that it attempts to identify the subject's neighbors, relatives, and associates or business partners.
For property assets research, BRB provides links to government Web sites in all 50 states, and Accurint, LexisNexis, and Westlaw have excellent sources for locating other types of assets such as motor vehicles, watercraft, and aircraft.
Individual stock holdings are reported in many public company filings. These company filings are generally available via the U.S. Securities and Exchange Commission's EDGAR system. Westlaw's STOCK database provides current information on stock transactions and holdings of shareholders owning at least 10 percent of stock in public companies and the directors and executive officers of those companies.
Before engaging in a business relationship with a potential client, some firms opt to perform a background check on individuals and their affiliates. Information obtained from background checks may include civil and criminal records, credit history, educational history, medical history, and military records.
In Wisconsin, the Wisconsin Circuit Court Access (WCCA) system provides coverage of family, forfeiture, and traffic cases, as well as information about civil and criminal cases and judgments in most counties that have docket entries.
While many states provide case information from their local court systems, PACER allows authorized users to obtain civil and criminal case docket information and selected pleadings from federal district, appellate, and bankruptcy courts. In addition, Legal Dockets Online provides a subscription pathfinder service identifying local, state, and federal Web sites that contain court case and other public records information.
Criminal records for individuals fall into two categories: arrest records and conviction records. In general, most state law enforcement databases provide conviction records rather than arrest records. An exception is the Wisconsin Online Criminal History Record Check System, which contains records for arrests as well as convictions occurring since 1971.
Not all state systems provide information on minor infractions such as vehicle accidents. However, the Police Department of the City of Madison, Wisconsin Accident Information Query provides free PDF images of police reports for accidents occurring since January 2002.
Although PACER and many state systems provide certain coverage on criminal records, the FBI's National Crime Information Center provides the most comprehensive source of state and federal criminal records in existence. This resource, however, is available only to law enforcement and government agencies.
Choicepoint's National Criminal File contains criminal conviction records from all 50 states. However, it does not cover misdemeanor cases.
Incarceration Checks. To determine whether an individual currently is incarcerated or has served time in a federal prison, try the Federal Bureau of Prisons Inmate Locator. In addition, the U.S. Department of Justice National Sex Offender Registry provides information on the location (residential address) of registered sex offenders.
In addition, the U.S. Department of Defense Manpower Data Center provides a database to verify current active military status. Military service information also is accessible to the public without authorization of the veteran or the next-of-kin of deceased veterans if the information is requested pursuant to the Freedom of Information Act and the Privacy Act.
Most of the civilian military employees personnel records held at the National Personnel Records Center are the legal responsibility of the agency under which the records were created. Go to the NPRC Web site for more information on obtaining civilian personnel records.
For securities information, both AMEX.com and NYSE.com provide current stock quotes, and finance.yahoo.com serves up historical stock data going back to 1987.
While the LexisNexis Nelson's Public Company Profiles database contains brief descriptions of 22,000 publicly traded corporations worldwide, the Standard & Poor's Register of Corporations provides vital business facts on more than 400,000 executives, 55,000 leading companies including 45,000 private, subsidiary, and affiliate companies, and more than 10,000 publicly-held corporations. Relevant news and trends on particular companies and industries can be obtained from services such as Factiva.com.
To determine whether a firm or company is blacklisted by the federal government, EPLS.gov provides the electronic version of the Lists of Parties Excluded from Federal Procurement and Nonprocurement Programs. The Better Business Bureau also provides free reports on complaints and other claims filed against registered companies.
Another business marketing tool, Firm360 is capable of generating reports that reveal legal activity and litigation trends by company, industry, and law firm. Company litigation profiles can help law firms to identify cross-selling opportunities within existing client accounts.
Firm360 also features a company monitor that aggregates information from public records, Westlaw, Thomson Financial, and numerous third-party providers to create online "Company Watch Lists." The information provides an up-to-date view of key client developments.
The LexisNexis Legal Industry Monitor offers a daily electronic newsletter, which is provided free to LexisNexis subscribers. This is another tool to keep lawyers abreast of what is happening in the legal market.
TrackEngine.com is another useful service to add to the competitive intelligence toolbox. It allows users to specify the Web pages they want to monitor and can be used to track corporate press releases, executive appointments, new partnerships and alliances, forums, and discussion groups.
To market their legal services, some major legal firms send out direct mail to particular markets and regions. Whosmailingwhat.com provides detailed information on direct mail in nearly 200 categories, including financial services, insurance, publishing, and legal services.
Securities arbitrator decisions are provided for free via the SAC Library of NASD Arbitration Awards Online database, which covers decisions from 1980. NASD arbitration awards also are covered in Westlaw's NASD-ARB and FSEC-ARB databases. Another service that provides labor arbitrator information is R.C. Simpson's Arbitrator Qualification Reports (http://rcsimpson.com).
Disciplinary actions are found in the LexisNexis National Association of Securities Dealers Disciplinary Actions and the National Financial Institutions Abstracted Disciplinary Actions and Sanctions databases. In Westlaw, disciplinary actions are in the NASD-DISP database.
Westlaw covers arbitrator information in its LRR-DIR database and the "Arbitrator Directory" of the Labor Arbitration Information System (LAIS). In addition, the LAIS Arbitrator Directory provides statistics on the number of cases the arbitrator decided in favor of management, the number in favor of the union, and the number that were split decisions.
There are two ways to find out the types of cases a lawyer has handled. In Wisconsin, the Wisconsin Circuit Court Access system allows you to search for cases handled by particular lawyers by using the lawyer's State Bar member number. Member numbers can be found on the State Bar's Web site, www.WisBar.org, using the Lawyer Directory function.
For federal cases, Courtlink allows attorney name searches in conjunction with other criteria such as the type of suit. The site identifies lawyers who have argued the case type in front of a particular judge. The report also provides a sampling of a lawyer's or law firm's experience in a specific type of suit or in front of a particular judge.
Both LexisNexis and Westlaw can pinpoint published and some unpublished cases handled by particular lawyers and judges. To determine whether a lawyer handled a patent and trademark registration, the United States Patent and Trademark Office provides lawyer name searches.
Free services such as newspaperlinks.com provide a gateway to U.S. and international newspapers that have a listed Web site. For archival newspaper research, NewspaperARCHIVE.com offers a database to search more than 2,100 historical newspapers, with coverage from 1759 to the present.
To minimize research costs, any attempt to locate or verify information should begin with many of the known free resources. The objective is to collect readily available information and use it as a building block for further in-depth research.
In light of recent incidents of data theft,6 both government and commercial information brokers are closely scrutinizing access to their databases. Many states have begun to propose legislation to change open access laws so that personal and corporate information can be redacted or removed from public records.7
Researchers should gather competitive intelligence legally and for legitimate business purposes. In the case of outsourcing, the individual who outsources the research also bears the responsibility to make certain that third party investigators and data brokers obtain information in a legal manner and in compliance with the privacy laws.
1David A. Kaplan, "Suspicions and Spies in Silicon Valley," Newsweek, Sept. 18, 2006, at 40.
2"Pretexting: Your Personal Information Revealed," FTC Facts for Consumers Feb. 2006.
3Society of Competitive Intelligence Professionals Code of Ethics.
4"Personal Information: key federal privacy laws do not require information resellers to safeguard all sensitive data," GAO Highlights GAO-06-674, a report to the Committee on Banking, Housing and Urban Affairs, U.S. Senate, June 2006.
5See Telephone Records and Privacy Protection Act of 2006, Pub. L. No. 109-476 (2007).
6Stephen H. Wildstrom, "Personal Data Theft: It's Outrageous," BusinessWeekOnline, April 15, 2005.
7Tamara Thompson, "Why the judgments and liens databases will become obsolete," PI Buzz, Feb. 3, 2007.
Wisconsin Lawyer


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Ke$ha has released another teaser video for her upcoming single "Die Young." In one released earlier in the month she was whistling the chorus to the new single. This time she Tweeted a video of herself belting out a full a cappella version of the chorus, singing:
"I hear your heart beat to the beat of the drums
Oh, what a shame that you came here with someone
While you're here in my arms
Let's make the most of the night 'cause we're gonna die young
We're gonna die young
We're gonna die young."
The singer followed her latest teaser clip with the release of her single album art.
From what we gather, Ke$ha is going to be reinventing herself a bit. She looks to be trading in the glitter and neon, for much more basic, and a little bit undressed, look. She talked about exploring the genre of rock in an interview with MTV, saying, "I went on a spirit journey, came back, worked with the Flaming Lips, worked with Iggy Pop — really explored my rock roots."
Ke$ha and her executive producer Dr. Luke confirm her new album will feature more of her voice than the album Animals did. During an interview with Rolling Stone, when asked about her ability to sing, she said,"Some people weren't sure if I could sing. On this record, I said 'Let's leave the tricks to a minimum and let me wail.'"
The single "Die Young" is set for release on September 25th.
animals. die young. it's my first single. coming this month. weeeee ! xx
— ke$ha(@keshasuxx) September 7, 2012
-AJ Gordon, AMP Radio/Los Angeles
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