This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates.
The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence.
All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist.
The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence.
The Companion Animal Parasite Council (CAPC) has compiled a data set of over nine million heartworm antigen tests performed on dogs in the United States during 2011 and 2012[ 1]. These data are test results taken from dogs that visited veterinary clinics throughout the United States. From this data, our goal is to quantify the environmental, socio-economic, and vector factors that influence canine heartworm prevalence rates. Brown et al.[ 2] describe the data and list factors posited to influence canine heartworm prevalence rates (these are discussed fully below). This paper quantitatively assesses which of the factors are important (or unimportant) and in what direction the factors impact (increase or decrease) heartworm prevalence rates.
The data will be used to estimate the probability that a dog entering a veterinary clinic will test positive for heartworm. Although prevalence rates increase if a dog is from an area with a high heartworm transmission rate, the raw data do not describe transmission, but rather the risk that a dog’s infection is detected if it enters a clinic within the United States and is tested for heartworm for any reason whatsoever. The most common reasons for testing a dog are: assessing the negative status of a dog before it begins heartworm prevention, annual testing of dogs on preventive prophylaxis to verify that the dog has been protected, and assessing whether or not a dog with clinical signs suggestive of heartworm disease is indeed infected. In many areas of the United States, dogs are kept on prophylaxis year round; however, in some areas, veterinarians utilize heartworm prevention seasonally. Here, annual testing verifies whether or not infection occurred during the period when preventives were not taken. To assess true transmission rates, it would be more appropriate to follow dogs or other canines, specifically coyotes, that are not receiving any form of prophylaxis [ 3, 4]— these are not the canines studied here. According to the unpublished abstract of Pulaski et al. for the 58th Annual Meeting of the American Association of Veterinary Parasitologists and the unpublished presentation of Blagburn et al. at The Triennial Symposium of the American Heartworm Society in 2013, in parts of the United States, resistance to heartworm preventatives has been recognized. Thus, future data may help detect preventive failure.
A spatial logistic regression model will be fitted to the CAPC county-by-county heartworm test results and related to factor measurements. Logistic regression methods are used in lieu of ordinary regression techniques because prevalence probabilities, which must lie in the interval [0,1], are being modeled. A significant technical challenge involves the large number of counties reporting a small number of tests (often this count is zero). Small sample sizes from isolated counties can adversely impact results if not properly handled. Therefore, methods are developed that account for sample size issues. The head-banging algorithm, a method for smoothing the county-by-county prevalence rates, will be used to extract general spatial structure in the prevalence estimates; this procedure is adept at dealing with outlying observations and boundary (edge) features.
Our results are useful in a variety of contexts. First and foremost, predicting heartworm prevalence rates alerts the pet owner to high-risk areas. This will be evident from the baseline risk maps constructed in Section "Construction of the baseline heartworm prevalence map". Second, pinpointing the factors accompanying high heartworm prevalence rates provides an opportunity to target those factors in mosquito and heartworm control programs. Third, our results provide a quantitative analysis of canine heartworm across the entire country, allowing us to confirm that cases do occur in the Western United States. Finally, the fitted regression model can be used to forecast future prevalence levels of heartworm or its response to climate change.
This section describes the data provided by CAPC and lists the factors considered by our analysis.
The raw test results were supplied by the Antech and IDEXX corporations [ 5, 6] and report whether each performed test was positive or negative — no uncertainty margin is supplied for the results. While individual tests are reported by zip-code of the testing clinic, the raw data were aggregated into the number of positive tests and the total number of tests conducted over each calendar year in each county of the conterminous United States. Since only two calendar years of records are available, no attempt is made to include seasonal structure. The IDEXX samples represent both the results of pet-side heartworm antigen test kit and results from an IDEXX capture system [Heartworm RT and the 4Dx Plus (and originally 4Dx tests)] along with tests run by the IDEXX diagnostic laboratories [Heartworm Antigen by ELISA-Canine*]. Antech tests were performed at Antech Laboratories and utilized the Dirochek Assay and the AccuPlex4 heartworm antigen detection assay. Over 2011 and 2012, there were 9,580,719 total tests performed by either method, of which 111,259 were positive. Rudimentary statistical checks do not show vast differences between Antech and IDEXX samples.In most of the southeastern United States, veterinarians assume that outdoor dogs are at risk of heartworm infection, and thus, recommend that clients place their dogs on preventive protection. This may not be practical for all pet owners due to costs. In the CAPC data, many counties in the eastern United States report a small number (say less than 20) of tests. This is likely because tests are not being reported or are incommensurate with CAPC protocols, not because they are uncommonly performed. Tests from such counties are likely performed for the same reasons as other southeastern counties reporting a greater number of tests. In other areas of the United States, such as Montana or Idaho, testing is likely only performed if dogs have signs suggestive of heartworm disease and the veterinarian requires a confirmative test. Sometimes, veterinarians are aware that dogs travel to heartworm endemic areas for part of the year. These dogs may be tested annually when they return to their home state. Also, as is evident from Figures 1 and 2, it is now fairly obvious that heartworm occurs in much of the Western United States. In some of these areas, heartworm testing is probably conducted for the same reasons as more prevalent areas. Overall, while it is understood that there may be some sampling biases in certain areas of the United States, the CAPC data seems fairly reflective of a true random sample for many counties in the United States.
Raw reported heartworm prevalence rates for 2011 and 2012.
Head-banging smoothed heartworm prevalence rates for 2011 and 2012.
Other data aspects are worth illuminating. First, heartworm infections are not detectable by almost all testing methods until about 6 or 7 months after the dogs have become infected; this is how long it takes for either microfilariae or antigen to appear in the blood after infection. Thus, many of the detected infections likely commenced the year prior to a positive test result. Because no travel information exists, it cannot be known where a dog acquires infection. However, it is suspected that the majority of infected dogs were infected close to home. Second, it is not known if a dog has been tested more than once. Dogs may be tested more than once annually to verify the need for treatment, to verify successful treatment, or annually tested after the infection is first identified.
The factors chosen for inclusion in this study are those envisioned to impact whether a dog is likely to have heartworm. These factors are a subset of those listed in Brown et al.[ 2] and contain climate variables (annual temperature, precipitation, and relative humidity; Figures 3, 4, 5); geographic factors (elevation, forest coverage, surface water coverage; Figures 6, 7, 8); societal factors (human population density and household income; Figures 9 and 10); and the presence or absence of Aedes aegypti, Aedes albopictus and six other mosquito species (Figures 11, 12, 13, 14, 15, 16, 17, 18). Presence or absence of mosquito species was used because abundance data are not available. Table 1 lists all considered factors. Many (but not all) of the factors are available on a county-by-county basis across the United States. Our methods do not a priori assume that all factors significantly influence heartworm prevalence rates, but rather seek to determine which factors significantly influence prevalence.
2011 U.S. annual average temperature (Degrees F). The temperature data included in this study were annual in nature and were aggregated by the National Climatic Data Center (NCDC) [ 7] by climate region [ 8]. These data are not county-by-county — all counties within a climate region are assigned the same annual temperature. For example, the state of Alabama has 67 counties and 8 climate regions. Annual temperatures for 2011 were used to generate this graphic. Temperature dependence on latitude is clear.
2011 U.S. annual total precipitation (Inches). The precipitation data were also obtained from the NCDC and has the same spatial resolution as the temperature data. Data used in this figure are for 2011. One sees a relatively dry Southwestern United States and higher precipitation in the southeastern United States.
2011 U.S. annual relative humidities (Percent). Our humidity data were relative (and not absolute) humidities. Relative humidity is not measured directly, but can be estimated from temperature and dew point (dew point is measured) via
where T is annual temperature, D is annual dew point, and exp is the exponential function [ 9]. As expected, the Southeast is the most humid region in the United States.
U.S. county elevations (Feet). Elevation data were obtained on a county-by-county [ 10] basis, with height of the highest point in each county used to produce this figure. Data containing average county elevations would be preferable to use but were not readily available. Of course, any biases should be minor in the eastern United States where most counties are homogeneous in elevation. However, the western States are less homogeneous. For example, Inyo County in California contains both the highest (Mount Whitney, 14,505 ft.) and lowest points (Death Valley, -282 ft.) in the conterminous United States. These limitations aside, elevation is a potentially important factor for heartworm prevalence as higher elevations are often associated with drier conditions.
2007 U.S. county forestion coverage (Percent). County forest coverage was obtained from the United States Department of Agriculture (USDA) [ 11]. Total county area was obtained from the Census Bureau [ 12]. Percentages of forest coverage were calculated by dividing the forest coverage area by county area (times 100%). The definition of forest coverage here is restricted to agriculture woodland, which means land supporting trees capable of producing timber or other wood products, including but not limited to logs, lumber, posts, and firewood. This data are updated by the USDA every five years. However, the 2012 data is not available yet; hence, the 2007 data were used to generate the graph.
2007 U.S. county water coverage (Percent). County surface water coverage was obtained from the Census Bureau [ 12] and was calculated by dividing the surface water area by total county area reported in the Census Bureau [ 12]. The surface water coverage data were last updated in 2011, which were used in our analysis.
2010 U.S. population density (100 people per square mile). Population densities were calculated by dividing the number of 100 people in each county by the county area. The county populations and areas were taken from the most recent (2010) census data (Census Bureau [ 13]).
2011 U.S. median household income (Dollars). Median household incomes were obtained from the Census Bureau (2010 census). These data were adjusted for inflation based on 2010 dollars; the Census Bureau adjusts by multiplying 2011 median household income by the ratio of the Consumer Price Index of 2010 and 2011 [ 14].
Presence of Aedes aegypti .
Presence of Aedes albopictus .
Presence of Aedes canadensis .
Presence of Aedes sierrensis .
Presence of Aedes trivittatus .
Presence of Anopheles punctipennis .
Presence of Anopheles quadrimaculatus .
Presence of Culex quinquefasciatus .
Heartworm factors considered for inclusion in the study
| 3leftbottomClimate factors | centerbottomAnnual temperature | centerbottom2011 and 2012 | centerbottomDivision | centerbottomNational Climate Data Center (NCDC) |
| centerbottomAnnual precipitation | centerbottom2011 and 2012 | centerbottomDivision | centerbottomNCDC | |
| centerbottomAnnual relative humidity | centerbottom2011 and 2012 | centerbottomStation | centerbottomNCDC | |
| 3leftbottomGeographic factors | centerbottomElevation | centerbottom2012 | centerbottomCounty | centerbottom
http://www.cohp.org/
|
| centerbottomPercentage forest coverage | centerbottom2007 | centerbottomCounty | centerbottomUnited States Department of Agriculture (USDA) | |
| centerbottomPercentage surface water coverage | centerbottom2010 | centerbottomCounty | centerbottomU.S. Census Bureau | |
| 2leftbottomSocietal factors | centerbottomPopulation density | centerbottom2010 | centerbottomCounty | centerbottomU.S. Census Bureau |
| centerbottomMedian household income | centerbottom2011 | centerbottomCounty | centerbottomU.S. Census Bureau | |
| 8leftMosquito species | centerbottom Aedes aegypti | centerbottom2008 | centerbottomCounty | centerbottomMoore, CG. [ 15] |
| centerbottom Aedes albopictus | centerbottom2012 | centerbottomCounty | centerbottomHynes NA [ 16] | |
| centerbottom Aedes canadensis | centerbottom2004 | centerbottomCounty | centerbottomRF Darsie, Jr. and RA Ward [ 17] | |
| centerbottom Aedes sierrensis | centerbottom2004 | centerbottomCounty | centerbottomRF Darsie, Jr. and RA Ward | |
| centerbottom Aedes trivittatus | centerbottom2004 | centerbottomCounty | centerbottomRF Darsie, Jr. and RA Ward | |
| centerbottom Anopheles punctipennis | centerbottom2004 | centerbottomCounty | centerbottomRF Darsie, Jr. and RA Ward | |
| centerbottom Anopheles quadrimaculatus | centerbottom2004 | centerbottomCounty | centerbottomRF Darsie, Jr. and RA Ward | |
| center Culex quinquefasciatus | center2004 | centerCounty | centerRF Darsie, Jr. and RA Ward |
We first construct a baseline heartworm prevalence map. This is done on an annual basis as there is too little data to consider seasonal effects. As will become apparent, this analysis is a necessary precursor to assess factor importance — informative factors should be able to reproduce the structure of our baseline prevalence map. For the years 2011 and 2012, all data were combined into a single sample.
For a county s, let p( s) denote the probability that a single dog tests heartworm positive. For notation, n( s) is the number of tests in county s and k( s) is the number of positive tests at county s. For example, if county s has 3 positive tests out of 100 during 2011 and eight positive tests out of 200 during 2012, then k( s) = 11, n( s) = 300, and
Since the number of dogs tested in distinct counties greatly varies, the raw values of
The estimated standard error of
Technically, counties where all tests are positive or negative have
This estimator has desirable sampling properties and cannot be zero or unity [ 18]. Of course, for large k( s) and n( s),
The raw values of
While we will not delve into the details of weighted head-banging procedures, the technique is a median-based algorithm proposed by Tukey and Tukey [ 19] for smoothing spatial data. One needs to input the longitude and latitude of the centroid of each county, the county value to be smoothed, and the corresponding weights. Mungiole et al.[ 20] discuss the algorithm in detail. Head-banging takes its name from a child’s game, where the child presses their face against pins protruding from a board that are of various lengths. The result leaves an impression of the child’s face, smoothing the lengths of adjacent nails but leaving the general structure of the face’s impression. Head-banging techniques are very effective for down-weighting or removing noisy ‘spikes’ while preserving edge structures. A spike is an isolated observation that lacks confirmation from nearby data. Because of different testing practices from county to county, many spikes exist in the heartworm prevalence estimates. An edge occurs where data changes significantly in pattern — perhaps due to a mountain range. Edges are informative as they often demarcate distinct data regions.
To run the weighted head-banging algorithm, a parameter called the number of triples must be selected and the weights need to be specified. At each county where data is present, a set of triples (a triple for a county is represented by the county itself and two nearby counties) were selected based on the criteria proposed by Hansen [ 21]. The weight of county s, denoted by w( s), comes from the inverse of the standard error of
Figure 2 shows our smoothed prevalence rates based on the weighted head-banging procedure. The larger the triple parameter is, the smoother (less rough) the resulting map will be. We have intentionally left the graphic slightly under-smoothed. This is because it is easy to visually smooth variabilities away with the eye, but impossible to recover true fluctuations that are erroneously smoothed away. Thirty triples were used to produce this graphic.
Figure 2 has interesting implications. First and foremost, heartworm is most prevalent in the Lower Mississippi Valley. While the northern latitudes show less activity, places where the prevalence rates were relatively higher do exist. Michigan, Vermont, and Northwest Washington, for example, show greater heartworm disease prevalence than some of the other states at the same latitude. The Northern Rockies perhaps show the least heartworm disease prevalence. While many inferences can be made from Figure 2, we caution the reader not to over-interpret minutia. The map is constructed from only two years of data and there are variations in the results that may be spurious. For example, two very close locations — say Baton Rouge and New Orleans, Louisiana — might be shaded different colors on the map, but should not be expected to have radically different prevalence rates. As additional years of data are collected, we expect our baseline to become more accurate. Another issue involves dispersal of the disease: there is no a priori reason to think that prevalence rates are static in time. With only two years of observations, time trends will be difficult to discern and are not explored herein.
This section examines the significance of the individual factors presented in Table 1 in predicting heartworm prevalence. A logistic regression model was created using data from 2011 and 2012. Logistic regression methods (as opposed to ordinary regression methods) [ 22] are specifically designed for cases involving a binary outcome that can be summarized by a probability, and hence limited to take values in the interval [0,1]. Our goal is to reproduce the structure in Figure 2.
Let X( s) = ( f
where g( X( s)) has form
Clarifying terms, ln denotes natural logarithm and logit( x) = ln( x)- ln(1 - x). Notice that e g( X( s)) /(1 + e g( X( s)) ) ∈ [0,1] for any value of g( X( s)). This guarantees that all predicted prevalence rates lie between zero and unity. The overall location parameter, β
To estimate the parameters β
where quantities in (4) are estimated by
Figure 19 shows the results from a fitted logistic regression model after smoothing the
Predicted heartworm prevalence from all factors.
Clarifying details, in the head-banging procedure, the inverse of the standard deviation of
The individual factors and their predictive significance are worth discussing. Table 2 lists estimates of each logistic model parameter, standard errors, odds ratios, and 95 % confidence intervals for the odds ratios. Elaborating, the standard error is the square root of the estimated variability of the regression coefficient. Smaller standard errors imply greater precision for the parameter estimate. All p-values for the hypothesis test that the parameter is zero are less than 0.0001. If the parameter for a factor is in truth zero, it is not a good predictor of prevalence rates. A positive/negative parameter estimator means that the corresponding factor is judged to increase/decrease heartworm prevalance as the factor increases. Since the mosquito factors are only presence/absence indicators, a positive regression coefficient estimate means that prevalence is higher when the mosquito species is present, and lower when absent. Exp ( β) is interpreted as the odds of a dog being heartworm positive relative to the odds of the dog being negative when the corresponding predictor increases by one unit. For mosquito factors, Exp ( β) is interpreted directly as the odds of a dog testing heartworm positive when the corresponding mosquito species is present relative to the odds of the dog testing negative when this mosquito species is absent. Hence, if Exp ( β) is larger than unity, then heartworm prevalence is increased by the corresponding factor, and vice versa. The units for the elevation regression coefficient estimator are in thousands of feet; the estimated coefficient for median household income is in thousands of dollars.
Significance of factors**
| leftbottomIntercept | centerbottom-8.0610 | centerbottom0.0819 | centerbottom | centerbottom |
| leftbottomTemperature | centerbottom0.0720 | centerbottom0.0011 | centerbottom1.075 | centerbottom(1.072, 1.077) |
| leftbottomMedian household income | centerbottom-0.0227 | centerbottom0.0003 | centerbottom0.978 | centerbottom(0.977, 0.978) |
| leftbottomPopulation density | centerbottom-0.0175 | centerbottom0.0004 | centerbottom0.983 | centerbottom(0.982, 0.984) |
| leftbottomPrecipitation | centerbottom0.0982 | centerbottom0.0049 | centerbottom1.103 | centerbottom(1.093, 1.114) |
| leftbottomElevation | centerbottom-0.0605 | centerbottom0.0030 | centerbottom0.941 | centerbottom(0.936, 0.947) |
| leftbottomRelative humidity | centerbottom-0.0147 | centerbottom0.0008 | centerbottom0.985 | centerbottom(0.984, 0.987) |
| leftbottomForest coverage | centerbottom1.2493 | centerbottom0.1156 | centerbottom3.448 | centerbottom(2.781, 4.375) |
| leftbottomSurface water coverage | centerbottom0.1054 | centerbottom0.0265 | centerbottom1.111 | centerbottom(1.055, 1.170) |
| leftbottom Aedes trivittatus | centerbottom0.9987 | centerbottom0.0153 | centerbottom2.715 | centerbottom(2.634, 2.797) |
| leftbottom Culex quinquefasciatus | centerbottom0.4063 | centerbottom0.0152 | centerbottom1.501 | centerbottom(1.457, 1.547) |
| leftbottom Aedes sierrensis | centerbottom0.6947 | centerbottom0.0293 | centerbottom2.003 | centerbottom(1.892, 2.121) |
| leftbottom Anopheles punctipennis | centerbottom0.4132 | centerbottom0.0182 | centerbottom1.512 | centerbottom(1.459, 1.567) |
| leftbottom Anopheles quadrimaculatus | centerbottom-0.1663 | centerbottom0.0185 | centerbottom0.847 | centerbottom(0.817, 0.878) |
| leftbottom Aedes aegypti | centerbottom-0.1232 | centerbottom0.0112 | centerbottom0.884 | centerbottom(0.865, 0.904) |
| leftbottom Aedes canadensis | centerbottom0.1481 | centerbottom0.0143 | centerbottom1.160 | centerbottom(1.128, 1.192) |
| left Aedes albopictus | center-0.0894 | center0.0112 | center0.915 | center(0.895, 0.935) |
**All factors are significant at level 0.01.
The model results in a fairly good fit: McFadden’s pseudo R 2 = 0.62 [ 24]. All predictors were significant predictors of heartworm prevalence rates at level 0.0001. Higher temperatures are associated with higher prevalence – heartworm is generally more prevalent in warmer temperatures. Higher median household incomes are associated with lower prevalence rates. Logically, higher income pet owners can more easily afford heartworm preventives. Not surprisingly, heartworm prevalence decreases with increasing population density and elevation. It is surprising to see that prevalence declines with higher humidities. One explanation for this may lie with the high correlations among elevation, relative humidity, and temperature. This will be explored further below when a model that allows the factors to interact is examined.
Presence of Culex quinquefasciatus, Aedes sierrensis, Anopheles punctipennis, Anopheles quadrimaculatus, and Aedes canadensis are associated with higher heartworm prevalence. The other three mosquito species are estimated to decrease prevalence rates. The reader should not a priori believe that presence of any mosquito species acts to increase prevalences in the model: the 16 factors are being "judged" in tandem.
It is perhaps remarkable that every factor considered is judged to influence prevalence rates with a significance level of 0.0001. It leaves an unsettling feeling that other important factors may have been omitted. We reiterate that this study is exploratory and will hopefully be improved in the future. We should also mention the usual regression caveat: (3) presupposes a linear relationship on the factors that may not be realistic at all factor levels. This drawback is not germane to this study; linear regression models, of course, serve as rudimentary guidance.
The above results, often called a main effects analysis, can be improved by adding interaction terms into the regression. An interaction term between factors i and j adds an additional regression term of form ξ
Significance factors and interactions*
| leftbottomIntercept | rightbottom-8.4312 | rightbottom0.0993 |
| leftbottomMedian household income | rightbottom-0.0267 | rightbottom0.0004 |
| leftbottomTemperature | rightbottom0.0655 | rightbottom0.0015 |
| leftbottomElevation | rightbottom0.5609 | rightbottom0.0171 |
| leftbottomPopulation density | rightbottom-0.0444 | rightbottom0.0015 |
| leftbottomTemperature*Elevation | rightbottom-0.0087 | rightbottom0.0003 |
| leftbottomTemperature*Forest coverage | rightbottom0.4840 | rightbottom0.0195 |
| leftbottomTemperature*Surface water coverage | rightbottom-0.0345 | rightbottom0.0034 |
| leftbottomElevation*Relative humidity | rightbottom-0.0041 | rightbottom0.0002 |
| leftbottomForest coverage | rightbottom-36.5371 | rightbottom2.1127 |
| leftbottomPopulation density*Median household | rightbottom0.0005 | rightbottom<0.0001 |
| leftbottomincome | rightbottom | rightbottom |
| leftbottomForest coverage*Surface water coverage | rightbottom-18.1276 | rightbottom1.5896 |
| leftbottomPrecipitation | rightbottom0.3049 | rightbottom0.0278 |
| leftbottomPrecipitation*Surface water coverage | rightbottom-0.3247 | rightbottom0.0299 |
| leftbottomPrecipitation*Elevation | rightbottom0.0196 | rightbottom0.0020 |
| leftbottomPrecipitation*Relative humidity | rightbottom-0.0032 | rightbottom0.0004 |
| leftbottomElevation*Surface water coverage | rightbottom0.2472 | rightbottom0.0342 |
| leftbottomElevation*Woodland coverage | rightbottom0.7325 | rightbottom0.1019 |
| leftbottomForest coverage*Relative humidity | rightbottom0.0903 | rightbottom0.0234 |
| leftbottomSurface water coverage*Relative humidity | rightbottom0.0281 | rightbottom0.0075 |
| leftbottomSurface water coverage | rightbottom1.1773 | rightbottom0.5507 |
| leftbottom Aedes trivittatus | rightbottom1.0682 | rightbottom0.0158 |
| leftbottom Aedes sierrensis | rightbottom1.1311 | rightbottom0.0313 |
| leftbottom Culex quinquefasciatus | rightbottom0.5483 | rightbottom0.0163 |
| leftbottom Anopheles punctipennis | rightbottom0.3948 | rightbottom0.0191 |
| leftbottom Aedes canadensis | rightbottom0.1567 | rightbottom0.0151 |
| leftbottom Aedes albopictus | rightbottom-0.0976 | rightbottom0.0116 |
| leftbottom Aedes aegypti | rightbottom-0.0879 | rightbottom0.0126 |
| left Anopheles quadrimaculatus | right-0.1084 | right0.0163 |
*All factors and their interactions are significant at level 0.01, except for that surface water coverage is significant at level 0.05.
Including interactions increased McFadden‘s pseudo R 2 to 0.65. Table 3 summarizes the results of this procedure. In this table, Exp( β) and their confidence intervals were not included. This is because when factor interactions are included, the value of Exp( β) depends not only on the coefficient of this factor, but also on all the other factors that interact with the factor. All listed factors are significant with a significance level of 0.0001, except the surface water coverage*relative humidity interaction ( p-value of 0.0002) and surface water coverage ( p-value of 0.0327). Differences from the Table 2 results exist, but are not radical. Relative humidity, for example, is not itself significant but interacts with several other factors. All other individual factors remain significant predictors. The parameter estimates of the main effects are slightly different from those in Table 2 since interactions are now included. For example, the parameter estimates of both median household income and population density are smaller than those in Table 2 and their interaction is significant. The significant interaction indicates that the changes of heartworm prevalence as median household income changes are conditional on the value of population density, and vice versa.
Figure 20 presents an analogous graphic to Figure 19 when interactions were included in the logistic regression model and predictions are made from the fitted model in Table 3. Results allow for interactions and a backward selection was used to eliminate any insignificant factors and their interactions. The results are comparable to those in Figure 19.
Predicted heartworm prevalence from significant factors and interactions.
Our factor list in Table 1 is likely incomplete; however, it should serve as a starting point. Several important factors that were discussed in Brown et al.[ 2] could not be included in the analysis due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. Additionally, mosquito vector abundance data were unavailable. Should information regarding coyote counts and vector abundance be consistently collected in the future and incorporated in the methods discussed herein, the model would improve in power and practical implications.
Some of the factors could be refined. For example, it is frequently posited that heartworm degree units for temperature likely influence prevalence rates [ 26]. Such units are typically measured from 14 degree Celsius (57 degrees Fahrenheit), below which it is difficult to have high heartworm transmission. We have not looked at temperature departures above 14 degree Celsius because our data are annual. Also, subtracting 14 from the annual temperatures would reparametrize the value of β
The fitted regression model is currently being explored to forecast future values of heartworm prevalence. Indeed, many of the predictive factors vary with time (temperature and precipitation, for example). From a forecast of these factors — say a year in advance — and our fitted logistic regression model, predictions of prevalence rates can be made. This can inform the pet owner and practitioner in advance of a potentially bad heartworm season. The results can also be used to assess prevalence rate changes due to climate change. For example, if annual temperatures are expected to increase by one degree F, one could add one degree to the temperature in the fitted logistic regression model to predict the change.
The authors have no competing interests relative to the work presented in this report.
All authors were responsible for identifying candidate factors. DW, JS, and RL were responsible for the statistical analysis and the production of figures and tables. RL and DDB wrote the draft document. All authors revised the paper and agreed with its final version.
The authors would like to thank the Companion Animal Parasite Council for providing the data and supporting this study financially.


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- child has not been drinking fluids
- child has lost a lot of fluid due to vomiting or diarrhea
- child has high blood pressure, heart disease, liver cirrhosis, kidney disease, thyroid disease, diabetes or asthma
- child is taking a diuretic
Ask a doctor or pharmacist before use if the child is
- under a doctor’s care for any serious condition
- taking any other product that contains pseudoephedrine or any other nasal decongestant
- taking any other drug
When using this product
- take with food or milk if stomach upset occurs
- the risk of heart attack or stroke may increase if you use more than directed or for longer than directed
Stop use and ask a doctor if
- the child experiences any of the following signs of stomach bleeding:
- feels faint
- vomits blood
- has bloody or black stools
- has stomach pain that does not get better
- the child does not get any relief within first day (24 hours) of treatment
- symptoms continue or get worse
- fever or pain or nasal congestion gets worse or lasts more than 3 days
- redness or swelling is present in the painful area
- the child gets nervous, dizzy, or sleepless or sleepy
- any new symptoms appear
Keep out of reach of children.
In case of overdose, get medical help or contact a Poison Control Center right away.
DIRECTIONS
- this product does not contain directions or complete warnings for adult use
- do not give more than directed
- shake well before using
- find right dose on chart. If possible, use weight to dose; otherwise use age.
- if needed, repeat dose every 6 hours
- do not use more than 4 times a day
- replace original bottle cap to maintain child resistance
- measure only with dosing cup provided. Dosing cup to be used with Children’s Advil Cold Suspension only. Do not use with other products. Dose lines account for product remaining in cup due to thickness of suspension.
| Weight (lb) |
Age (yrs) |
Dose (teaspoons) |
| under 24 |
under 2 yrs |
do not use |
| 24-47 |
2-5 yrs |
1 teaspoon |
| 48-95 |
6-11 yrs |
2 teaspoons |
OTHER INFORMATION
-
each teaspoon contains: sodium 3 mg
- store at room temperature 20-25°C (68-77°F).
- alcohol free
- read all warnings and directions before use. Keep carton.
INACTIVE INGREDIENTS
artificial flavor, carboxymethylcellulose sodium, citric acid monohydrate, edetate disodium, FD&C blue no. 1, FD&C red no. 40, glycerin, microcrystalline cellulose, polysorbate 80, purified water, sodium benzoate, sorbitol solution, sucrose, xanthan gum
QUESTIONS OR COMMENTS?
Call weekdays from 9 AM to 5 PM EST at 1-800-88-ADVIL or ask your pharmacist, doctor or health care professional.
PRODUCT PACKAGING
The product packaging shown below represents a sample of that currently in use. Additional packaging may also be available.
See new warnings information
Children’s Advil Cold
Ibuprofen 100 mg Fever Reducer/Pain Reliever (NSAID) per 5 mL
Pseudoephedrine HCI 15 mg Nasal Decongestant per 5 mL
Cold
IBUPROFEN
Pain Relief
Fever
Sore Throat
PSEUDOEPHEDRINE HCl
Stuffy Nose
Liquid Oral Suspension
Alcohol-Free
Grape Flavor
for ages 2-11 years
4 FL OZ (120 mL)
Important: Read all product information before using. Keep this box for important information. This product is intended for use in children ages 2 years – 11 years.
Wyeth
Wyeth Consumer Healthcare, Madison, NJ 07940 USA
©2010 Wyeth
For most recent product information, visit www.advil.com
Packaged with Tamper-Evident bottle cap. Do Not Use if breakable ring is separated or missing.


Summary
I have a friend who has a kid who like Tony Tony Chopper from OnePiece. I must admit I share that inclination for the fluffy トナカイ ... I could make something nice for this kiddo, making a circuit that can play chopper sound and
turn on the light when you clap hands .
Links


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determine
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determine


As the world becomes more complex and governments everywhere struggle, trust in the internet is more important today...
Posted by Mark Zuckerberg on Thursday, 13 March 2014
How to Share With Just FriendsHow to share with just friends.
Posted by Facebook on Friday, 5 December 2014
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Posted by Mark Zuckerberg on Wednesday, September 14, 2011


| Total net profit | -4.51 | Gross profit | 0.81 | Gross loss | -5.32 |
| Profit factor | 0.15 | Expected payoff | -0.1 | ||
| Absolute drawdown | -4.51 | Maximal drawdown (%) | 4.5 (-45000.0%) | ||
| Total trades | 47 | Profit trades (% of total) | 8 (17.02%) | Loss trades (% of total) | 39 (82.98%) |
| Long positions (won %) | 22 (31.82%) | Short positions (won %) | 25 (4.0%) | ||
| Largest | profit trade | 0.26 | loss trade | -0.22 | |
| Average | profit trade | 0.1 | loss trade | -0.14 | |
| Minimum | profit trade | 0.01 | loss trade | -0.01 | |
| Maximum | take profit in pips | 161.1 | stop loss in pips | -18.8 | |
| Average | take profit in pips | 87.4 | stop loss in pips | -11.6 | |
| Minimum | take profit in pips | -1.2 | stop loss in pips | -4.0 | |
| Maximum | Reward-Risk | 32.5 | Duration in minutes | 16747 | |
| Average | Reward-Risk | 8.7 | Duration in minutes | 1395.0 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 1 | |
| Maximum | consecutive wins (profit in money) | 2.0 (0.15) | consecutive losses (loss in money) | 12.0 (-1.64) | |
| Average | consecutive wins | 1.0 | consecutive losses | 3.0 |
| Total net profit | -1.8 | Gross profit | 0.55 | Gross loss | -2.35 |
| Profit factor | 0.23 | Expected payoff | -0.08 | ||
| Absolute drawdown | -1.8 | Maximal drawdown (%) | 1.97 (1158.82%) | ||
| Total trades | 22 | Profit trades (% of total) | 7 (31.82%) | Loss trades (% of total) | 15 (68.18%) |
| Largest | profit trade | 0.18 | loss trade | -0.22 | |
| Average | profit trade | 0.08 | loss trade | -0.16 | |
| Minimum | profit trade | 0.01 | loss trade | -0.01 | |
| Maximum | take profit in pips | 159.1 | stop loss in pips | -18.8 | |
| Average | take profit in pips | 64.7 | stop loss in pips | -14.0 | |
| Minimum | take profit in pips | -1.2 | stop loss in pips | -9.5 | |
| Maximum | Reward-Risk | 14.0 | Duration in minutes | 16747.0 | |
| Average | Reward-Risk | 4.8 | Duration in minutes | 2240.4 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 8.0 | |
| Maximum | consecutive wins (profit in money) | 2.0 (0.15) | consecutive losses (loss in money) | 4.0 (-0.62) | |
| Average | consecutive wins | 1.0 | consecutive losses | 1.0 |
| Total net profit | -2.71 | Gross profit | 0.26 | Gross loss | -2.97 |
| Profit factor | 0.09 | Expected payoff | -0.11 | ||
| Absolute drawdown | -2.71 | Maximal drawdown (%) | 2.55 (-1593.75%) | ||
| Total trades | 25 | Profit trades (% of total) | 1 (4.0%) | Loss trades (% of total) | 24 (96.0%) |
| Largest | profit trade | 0.26 | loss trade | -0.19 | |
| Average | profit trade | 0.26 | loss trade | -0.12 | |
| Minimum | profit trade | 0.26 | loss trade | -0.05 | |
| Maximum | take profit in pips | 161.1 | stop loss in pips | -17.9 | |
| Average | take profit in pips | 107.4 | stop loss in pips | -9.5 | |
| Minimum | take profit in pips | 3.2 | stop loss in pips | -4.0 | |
| Maximum | Reward-Risk | 32.5 | Duration in minutes | 5392.0 | |
| Average | Reward-Risk | 12.1 | Duration in minutes | 651.2 | |
| Minimum | Reward-Risk | Duration in minutes | 1.0 | ||
| Maximum | consecutive wins (profit in money) | 1.0 (0.26) | consecutive losses (loss in money) | 17.0 (-2.01) | |
| Average | consecutive wins | 1.0 | consecutive losses | 7.0 |


Ready for a Job Boost? If you want to boost your web developer's career to the next level (or if you are a newly hatched web developer looking for a job), then my private network might be the best thing to help you. Send me an empty email with "network" in the subject to<EMAIL_ADDRESS>Click this button to pause video:
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@form(action=routes.HomeController.addItemSubmit(), 'class -> "form-horizontal", 'role -> "form") {
@* CSRF attack prevention *@
@* This token, sent by the controller, will be used to authenticate the form submission *@
@CSRF.formField
@inputText(itemForm("name"), '_label -> "Name", 'class -> "form-control")
@inputText(itemForm("description"), '_label -> "Description", 'class -> "form-control")
@inputText(itemForm("stock"), '_label -> "Stock", 'class -> "form-control")
@inputText(itemForm("price"), '_label -> "Price", 'class -> "form-control")
@inputText(itemForm("id"), '_label -> "", 'hidden -> "hidden")
} @* end of form *@


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Welcome to VirusTotal Graph Python API’s documentation!¶
vt_graph_api is the official Python client library for the VirusTotal Graph that implements the VirusTotal Graph REST API. This library requires Python 3.2+ or Python 2.7.
vt_graph_api is the official Python client library for the VirusTotal Graph that implements the VirusTotal Graph REST API. This library requires Python 3.2+ or Python 2.7.


---
layout: post
title: UI tip for Jamaican Banks
date: 2010-12-07 10:42:00.000000000 -05:00
comments: true
categories: []
published: true
---
As a consumer of services offered by both of Jamaica's largest banks, it pains me to deal with the little things that the banks take for granted.
For instance, look at how they quote the currency exchange rates.
NCB
Bank of Nova Scotia Jamaica (BNS JA)
This always confuses me. What do these rates mean? Who is doing the buying and selling?
A simple tip, that will get rid of ambiguity adding the word "You", or "We". So it reads "You Buy (Cash)", "You Buy (CHQ)", "You Sell".
 You guys get these quotes in real-time and update them many times throughout the day. To you, it is abundantly clear what these rates mean.
To 'John Public', that does an FX transaction once a month or once a few weeks or once a few months, we don't have time to memorize how you are portraying this. Every time I come to one of these charts I say to myself "Which vantage point are they giving me this information from again? Is it mine, or theirs?". Then I remember that it is yours, and that just seems selfish.
At the end of the day, it's not about you...it's about your customer.
Who cares what you buy and sell FX for? What we (your customers) care about is how much WE will pay, and how much WE will get if we sell to you.
You are welcome :)
Marc Gayle
www.marcgayle.com
www.twitter.com/marcgayle


DOG ABANDONED
Puppy dumped and left for dead in a box in Hamilton car park on coldest night of the year
by Justin BowieA PUPPY was dumped and left for dead in a Hamilton car park on the coldest night of the year.
The Scottish SPCA was alerted after the vulnerable Jack Russell terrier cross, believed to be around eight-weeks-old, was discovered alone on November 18.
The dog, who has been named Beans, was found in a cardboard box lined with a blanket near Quarry Street in the Lanarkshire town.
Rescuers say he would have almost certainly died had it not been for a member of the public finding him.
And the Scottish SPCA are now making an appeal to the public for information.
Animal rescue officer Sarah Parker said: “The dog was found in a cardboard box, lined with a blanket, at a local park near to Quarry Street.
“The young dog, which we believe is a Jack Russell terrier cross, is male and is around eight weeks old.
“We are grateful to the member of the public who took him in and kept him safe overnight.
“He is friendly so we suspect he is used to human contact.
“One of our animal rescue and rehoming centres is providing the care he needs.
“The team have affectionately named him Beans.
“The night in question was the coldest so far this year and temperatures were below freezing.
“Had he not been found, Beans would have almost certainly died so we are keen to find out the circumstances surrounding how he came to be in the park.
“If anyone has any information, we would urge them to call our confidential animal helpline on 03000 999 999.”


A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
Hollowman900026-Oct-18 02:56 PM
Yeah it’ll take someone more fluent on the subject to address the question
crumb26-Oct-18 02:59 PM
btw i like most of his blog posts even if i don't always 100% agree with them.
benohanlon26-Oct-18 03:02 PM
thanks @jorian i have rt'd
FishyGuts26-Oct-18 04:03 PM
@crumb Do you have to be online to transact on MimbleWimble? I thought I read this recently.
Is zero knowledge appliaed to all transactions?
hastrup26-Oct-18 04:18 PM
WTB JUMBLR Offer Dex 4 for 1(edited)
@Deleted User sry
:D
crumb26-Oct-18 04:32 PM
i'm not very knowledgeable with that stuff. it just seemed a good opportunity to point out the positives of zcash tech vs grin. the problem is i don't know what those positives are exactly
need a person with deeper knowledge like @jl777c
@FishyGuts
FishyGuts26-Oct-18 04:38 PM
Assuming zcash is used like Pirate is.. mandatory privacy, You would want to find out if Grin is A)Mandatory Private, and B) if you really do need to be online to transact on MimbleWimble, which I have read.
If the answer to A is no or B is yes, then there's no argument.
If the answer to A is yes AND B is no, then you'll need to dig deeper.
In My opinion based on my limited knowledge
We are excited to introduce the world's first full #zerocoin wallet to wallet deposit system: $NIX Commitment Key Packs.
Address-less transactions.
Powerful.
Fully #private.
Check our demo video at: https://t.co/mKgKnnLkka
FishyGuts26-Oct-18 05:03 PM
Thank you Crumb.
!kmdtip @crumb 0.999
KMD TipBotBOT26-Oct-18 05:04 PM
TipBot
@FishyGuts (FishyGuts#5085)
You tipped @crumb0.999 KMD
NIXfra26-Oct-18 05:25 PM
Zerocoin payments from wallet to wallet and complete address-less transactions. the world's first full zerocoin wallet to wallet deposit system.
there is literally no way the history can be compromised even tracing ip etc
The blockchain privacy is so powerful that there are no other factors that can expose the user. Revolutionizing privacy in all of crypto!
share address?
where you saw the addresses?
you can see the addresses in the blockchain or that showing Anonymous?
sorry but maybe you must watching better the video…..
so where you saw "you need to share the address every time"?
??? where you want i send you the coin??
you must tell me your address of course...if not how i can send you private transaction (blockchain )
NIXfra26-Oct-18 05:47 PM
wait less days and you will know everything when the technical paper come out
blackjok3r26-Oct-18 05:56 PM
as far as I know you need to be online for mimblewimble ... also privacy is forced as there are not UTXO... the blockchain is not a chain as such.
There is no address's
so you cant have a balance
NIXfra26-Oct-18 05:59 PM
If it "already exists" will you be kind enough to show me a similar transaction on blockchain? @Deleted User(edited)
NIXfra26-Oct-18 06:12 PM
you never know what the receiver does with those coins and nether does the network
Privacy set of zerocoin is so powerful, enabling deposit system like this makes it arguably the most powerful privacy transaction protocol(edited)
You aren’t actually sending coins through addressses, you are sending them using zero knowledge proofs
Which allows you to spend them later without compromising where they came from
jl777c26-Oct-18 06:13 PM
but the amounts are not hidden with zerocoin protocol (predecessor to zcash protocol)
and if amounts are not hidden, the amount data can be analyzed
so if amounts are visible it is not the most private blockchain, that goes to PIRATE
3
NIXfra26-Oct-18 06:22 PM
not if the privacy set of each denomination is large
good luck analyzing 1000's of similar tx's that cannot be distinguised from each other
and with NIX, each privacy set for each denomination is already large, and will get even bigger as the network adopts the simple privacy metrics
jl777c26-Oct-18 06:24 PM
well something to analyze is more than nothing to analyze. users could do silly things and allow themselves to be deanonymized via large amounts transferred with external information leaked
crumb26-Oct-18 06:25 PM
screenshot from the video::
https://imgur.com/a/HnyNXm1
There is following sentence: These are your private coins from Ghosting NIX. You can convert ghosted NIX to public coins.
Does that mean that privacy is optional?
Kitchen Table26-Oct-18 06:25 PM
He's copying and pasting someone elses response. Get them in the channel to talk. Other wise it's just a shill post with buzz words.
crumb26-Oct-18 06:25 PM
you can have transparent coins also
?
jl777c26-Oct-18 06:26 PM
oh, then it is leak city
1
NIXfra26-Oct-18 06:27 PM
yes sure. optional
crumb26-Oct-18 06:27 PM
oh man. then it's not even in the same league as PIRATE
if you give users option to use transparent addresses when they want, then most of the privacy flies right out of the window
sad, but true
NIXfra26-Oct-18 06:30 PM
but give both option i though is better
crumb26-Oct-18 06:31 PM
no, it's not. because there are too many ways you can correlate amounts, timing etc
too many ways to screw it up
privacy has to be 100% forced all the time or it doesn't work effectively
jl777c26-Oct-18 06:33 PM
@NIXfra not admitting to basic facts, you are looking foolish and probably that is not your goal
Markus26-Oct-18 06:37 PM
is it possible to buy KMD with fiat somehow that doesnt take long time to process?
jl777c26-Oct-18 06:48 PM
i think bitpanda in EU
Vertigo26-Oct-18 06:52 PM
Hey Nix is the shit ok don't pop his bubble.
Cryptoon26-Oct-18 07:15 PM
Bitpanda is pretty fast indeed @Markus
Markus26-Oct-18 07:17 PM
thanks
Llama26-Oct-18 07:22 PM
Selling KMDICE at multiple rates:
0-5k: 0.4 KMD each
5k-15k: 0.35 KMD each
15k+: 0.325 KMD each


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[continued]
to engrave on silver & copper with aqua fortis. With this wax too, one fills the cavity of a relief, & then casts in this cavity, with moistened sand, which immediately takes the relief very neatly. And then you can cast its cavity on it in copper, gold, and silver, and make really singular seals.
at left top margin
Seals
To cast in sulfur
To cast neatly in sulfur, arrange the pith of bread under the brazier, as you know. Mold whatever you want into it & let it dry & you will have very neat work.
at left top margin
Try sulfur passed through melted wax, because it will no longer ignite & and make eyelets.
Molding and shrinking a large shape
Mold it with the pith of bread just out of the oven, or like that aforementioned, & in drying out it will shrink & consequently so will the medal that you will cast. By these means — lengthening out or enlarging the imprinted bread — you can vary the shape & from one face make several different ones. The bread straight from the oven is best. And the one which has been heated twice contracts more. You can cast sulfur without letting the imprint on the bread dry, if you want to cast it as large as it is. But, if you want to let it shrink, let it dry to a greater or lesser extent.
Casting of lead and tin in plaster
If you want to cast any flat medal or any animal that is not very heavy, and that must not be burnt inside the mold, you can indeed cast in mixed plaster & brique, as above; and not in plaster alone, because it contracts too much feeling an ardent fire if it is not so mixed. But, with the brick, it holds well. However, take heed to dry out your mold at length & on a slow fire & with patience, because there is no need to reheat it. But when your work is of flowers or other things that want their mold to be reheated & set ablaze, mix the plaster with stone alum & even with crocum. I have molded in very neat plaster & brick & it has sustained several castings.
at left middle margin
I tried both plaster & brick alone and molded en noyau with like the others. My mold was very clean, having lightly oiled & rubbed my medal with spirits. I made my casting wide at the entrance, narrowing it as it goes along until it reaches the medal which is very thin. I notched the casting which embraced the medal well. I dried the mold out well on a slow fire & finally, I heated it well without turning it red. I let it cool in such a way that I could hold my finger to it without burning myself. I made a line of 4 ℥ of tin, & ix deniers of lead. I cast it red, and it was quite good and beautiful. Afterwards I put xii deniers of leadon top of 4 ℥ of tin. It is very good & beautiful.
at left bottom margin
When there is nothing to burn in the mold, it is not necessary to reheat it for lead and tin. But for flowers and other things that must be burned, it is.


There have been a few threads of late discussing noisy signals, especially on Arduino based home-built OEM meters. It often shows up as larger than expected Irms signals when measuring very small primary loads. I built up the standard mid-rail used by most OEMers.... 470K dividers, 10uF (I used tantulum) cap all on a big noisy breadboard with long noisy leads. I then ran the attached sketch to measure nothing other than the mid-rail.... no CTs in sight. In a perfect world, with perfect resistors and no noise, you'd expect a constant value of 511/512.
I ran it all on an EtherMega; using ethernet to broadcast the results means the board is totally isolated apart from its power feed (transformer-isolated ethernet is as close to RF as I could get). The other advantage of the EtherMega is it gives me precise control over the power source via jumpers. I then set about running the sketch using various power sources. In all cases, the power supplies were a long way away from the board.
If I power it all via DC-in jack, letting the onboard regulator provide the 5V Vcc rail, the sketch spits out:
Min: 518, Max: 519, Diff: 1
I get the same result if I power the Vcc rail directly from a 5V isolated lab supply. Then I grabbed a spare 5V USB wall-wart and powered from that and I get:
Min: 516, Max: 522, Diff: 6
Enabling INSPECT_SIGNAL in the sketch let me capture 1000 samples and plot them in a spreadsheet, which produced:
The sketch reads 2 analog pins each time around to somewhat mimic a real OEM meter, so the 1000 samples take roughly 110 * 2 * 1000 usecs, or 220 msecs. And there's pretty close to exactly 11 cycles in there, so it looks like I'm getting a 50 Hz 3 A/D units (~15 mV) peak-to-peak ripple right through the readings.
I can't see it on my scope (isolated and battery powered) attached to A1 so it may be getting in via AREF or AVCC, but all the traces look much noisier when it's powered by 5V USB than via the DC-in jack. One poster recently confirmed he got rid of his low-level noise by changing how he powered his Arduino (from 5V to DC-IN) so I suspect it should be the first (and easiest) thing to try. Of course, whether it works may well depend on how the regulator used in your particular Arduino design compares with your 5V USB wall-wart. I think a lot of those 5V USB wall-warts are designed for charging batteries, so a bit of ripple doesn't matter as much as when you're feeding it straight onto your Vcc rail.
Re: Noise in Arduino based builds.
"so it may be getting in via AREF or AVCC,"
That's been my gut feeling for a long time. It's also worth dusting off Trystan's post from a long time ago: "Not all USB power adapters are created the same - highlighting importance of selecting good quality power adapters"
Re: Noise in Arduino based builds.
Yes, this one I'm experimenting with has some nasty ripple at 50Hz (and again at 150Hz) that shows up quite clearly on Vcc, AVcc, and AREF. It doesn't show up with the scope on the mid-rail because it's designed to shunt 50 Hz stuff to GND. Hence it does show up in the sketch... the signal it's measuring is stable (albeit noisy), but the reference it's using to measure it is jumping around every mains cycle.
Re: Noise in Arduino based builds.
Even for supplying Vcc to 'normal' 5V circuitry the ripple (and poor stability in general) will not be a problem, but these units are certainly not made to be used as a reference source for high-resolution AD conversions. I would always use a good linear regulator for this. But this is not a real surprise, or is it?
Re: Noise in Arduino based builds.
"But this is not a real surprise, or is it?"
Jörg, to those of us who haven't played this game before, probably YES!
I'd suggest that possibly, the real reason is the designers of the Arduino and/or its clones have neglected the analogue performance, and that will be because it is perceived as a minority interest, or on cost grounds.
Re: Noise in Arduino based builds.
Ah ok, looking at the typical schematic of an Arduino (UNO), I understand what you mean. It is possible to power these units directly from the USB port. And then Vcc and AVcc is connected to the USB voltage via a diode or MOSFET. So at least when powered from the PC during development the problem of unstable voltage supply will be there.
Sorry for my hasty comment, but I never thought that you would use the USB connector for powering the Arduino in its final application. There is always the separate power supply jack with a linear regulator behind it which would be a much better choice (but then it is not possible to use one of the super-cheap 5V USB wall warts..... everything comes at a price!)
Re: Noise in Arduino based builds.
Actually, I'm with Jörg on this one. I've got a box full of various cheap-n-nasty Arduinos here, and I can't find a single one that doesn't have brilliant low level AtoD performance. Provided the end-user and the board designer follow a few basic rules it's really pretty easy to get right. The end-user needs to provide a nice low impedance source to measure, and a clean Vcc. The board designer needs to keep their Vcc and GND planes as low impedance as possible.
I suspect the main reason why people report more noise on Arduino based builds is that they have more options on how to power them, and make poor choices. I'll bet good money that if you were to marry the USB wall-wart above with an emonTx, it would impact it exactly the same as it does any Arduino.
Clearly breadboard and jumper wires are noisier than veriboard, which is noisier than a shield, which is noisier than soldering components onto a two layer board, which is noisier than soldering components onto a four layer board with dedicated Vcc and GND planes. But wherever you live in that build spectrum, you can get surprisingly good results if you take just a little bit of care.
Re: Noise in Arduino based builds.
By "Yes, you should be surprised", I meant that there must be many to whom a power supply is a power supply. End of story. They would not appreciate the difference between a switcher and linear - unless maybe they've read that linear are lossy and waste energy and therefore switchers must clearly be "better".
As you say, it's not hard to get right, or close, if you know what you're doing and follow the basic rules, but many don't know - and we really shouldn't expect them to know - what those rules are. But we have to recognise that we're special in demanding the absolute maximum performance out of the analogue side. That's no reason to take away the flexibility that different power sources offer for the sake of a feature (the analogue inputs) that a good proportion of users probably will never want.
Maybe one day someone should write up a fully engineered Arduino-based monitor project, mentioning all these things, because a lot of people appear to take the "demo" breadboard design illustrations as the start and the end of their design and are then disappointed with the performance.
[Acknowledging here Robin's early diverters, in which I think he's always used a linear supply regulator.]
Re: Noise in Arduino based builds.
I meant that there must be many to whom a power supply is a power supply. End of story.
Yes, and it's for those people that I posted this thread. When someone does prefer to build their own monitor using an Arduino, and experiences noise problems, we can point them at this thread as a starter, rather than just tell them to give up and buy an emonTx.
They would not appreciate the difference between a switcher and linear
I don't think we should write off all SMPS. You can certainly build a clean, extremely low ripple SMPS, but you're unlikely to find it in cheap USB wall-wart. But for the purpose of this discussion, I agree, it's far simpler to just tell them to use the DC-in jack. Most will be unequipped to investigate the suitability of a 5V SMPS.
I assume the hobbyists that want to build these monitors with an Arduino are doing so partly for the learning experience, and learning about noise and power supply options are part of that learning experience. I for one am happy to help them where I can, rather than just redirect them to the shop. Even if that help is no more sophisticated than "Use the DC-in jack, and lower the mid-rail divider Rs". Hopefully Gerardo will be able to report back how well that works (http://openenergymonitor.org/emon/node/10097). His Leonardo has great potential, as it even includes the Atmel recommended LC network on AVCC. I haven't looked at the latest OEM schematics, but I think I first pointed out that was missing over a year ago.
Re: Noise in Arduino based builds.
Very good idea!
I was not aware of the fact that many people will use the USB as main power supply and others might no be aware of the possible problems arising from this. So this thread was very helpful to sort this out!
Re: Noise in Arduino based builds.
Here are some pictures of the Atmel recommended LC network doing its stuff on AVcc. The red trace is the Vcc rail being driven directly by a SMPS (albeit one with much lower ripple than the USB wall-wart above). The yellow trace is AVcc.
PtoP ripple has dropped from 65.6mV to 36mV and RMS ripple has dropped from 4.3mV to 1.8mV, and the duration of those disturbances are way way shorter. AVcc is the voltage it's going to be using as a reference while it's trying to measure your voltage, and without that filter, you'll be using the red.
You can see in the FFTs it's really given the higher frequency stuff a haircut:
Re: Noise in Arduino based builds.
[Acknowledging here Robin's early diverters, in which I think he's always used a linear supply regulator.]
Indeed, he wouldn't know how to build a power supply in any other way!
Sometimes, the amount of background power that I see is nil; at other times, it can be up to a Watt. It all seems to depend on where the quiescent level of the current sensor is sitting within the ADC's range. Maybe there can be an element of noise on the PSU too.
Unlike the emonTx, my own PCBs don't have any connection to the power that appears via the programming lead.
Re: Noise in Arduino based builds.
Actually, Power is a bit easier to get right. If the noise isn't correlated to V (unlike that USB wall-wart noise above) then it won't show up in Power. Irms is a better test of how noise-free your input stage is.
Of all my circuits, I think the only two that are truly open circuit when they're not being used are the Hot Water and Hot Plates. The Pool, Lights and Oven all have some small background electronics running. I've not been able to get the open-circuit ones below 6mA (just 100uV at the CT terminals), and that does seem to be coming in from the CTs themselves. Clamping all 9 circuits in a confined space means the CTs can't help but be fairly close to other conductors, so I supsect there's some pick-up there.
And remember, even if your power supply delivers a perfect DC Vcc to your board, all that gate switching is going to introduce noise to the Vcc rail. The lower the source impedance of your Vcc, and the lower the impedance of your Vcc and GND planes, the less likely you are to benefit from various hardware filters to keep that stuff out of the critical analog circuitry.
Re: Noise in Arduino based builds.
I've been doing some tests with a regular arduino on my bench. I use an ethernet shield and tought it could generate noise.
So i measure working conditions with CT connected but no voltage passing on cable, in some schenarios.
1 - Powered only from PC USB i get up to 7W noise on no power. ADC jumps all arround.
2 - Tested without ethernet shield, no difference.
3 - Powered with a smps power supply 9V on VIN, it does reduce noise a bit but still some values read 509, 514.
4 - Powered with a cheap unregulated ferromagnetic transformer 9VDC, still ethernet and USB connected and i get a constant 511 value.
Noise comes from the power supply!
Arduino program used to test raw adc values:
Re: Noise in Arduino based builds.
It would be interesting if you could repeat step 3 but with nothing connected to the USB (you can use snippets out of my sketch above to transmit your results over ethernet rather than USB).
It sounds like you could be getting some ground currents from your SMPS, through your Arduino, out the USB port into your PC to earth. If you take a typical regulated SMPS, plug it into an outlet in your kitchen, set your multimeter to AC Volts and then measure between either output of the SMPS (-ve or +ve) and your earthed kitchen sink, you'll see about 90VAC. There's only uA of current behind it, so it's not as scarey as it sounds.
 Once you connect your Arduino to your PC via USB, you're grounding it, and giving that AC a path to earth.
P.S. The other thing you can do is fetch the readings you're getting and plot them in a spreadsheet, assuming your Arduino has enough RAM (again feel free to use snippets from the sketch above). Your Arduino is a pretty sensitive, albeit low-bandwidth, oscilloscope. A good starting point for determining the source of noise is seeing what it looks like.
P.P.S Do you have a schematic for your particular Arduino? You really need to look at how it behaves when you offer it two simultaneous power supplies (DC-in and USB). Power is one area where Arduinos vary a lot from model to model.
Re: Noise in Arduino based builds.
After long term test with the iron core transformer i do get some noise. not much, <0.1W noise average that occurs ocasional. I can live with that.
dBC what are your noise figures with no load?
Arduino is a duemilanove: http://arduino.cc/en/uploads/Main/arduino-duemilanove-schematic.pdf
Long term log of W with no load connected, line is powered and using same arduino, posting by ethernet, still attached to usb and powered by cheap iron core transformer.
Noise could come from the CT picking up high frequency noise on the line, see the strange change at 19:28-19:40.
Edit: Confirmed, comes from CT.
Edit: This is VA, not Watt. Its measured current * 220V (fixed constant). Can get current dividing by 220. So the spike of 0.5W / 220 = 2.27mA measured.
I will do other test with line off.
Edit: Done. One hour test, same conditions, but this time CT is not plugged on arduino. No noise this time.
Re: Noise in Arduino based builds.
dBC what are your noise figures with no load?
It depends on the stimulus:
No CT connected: 0W (real), 4.4mA
CT (lab) connected, CT clamped around a zero-current carrying lead: 0W (real), 3.91mA
CT (fusebox) connected, CT clamped around a zero-current carrying lead: 1.2W (real), 7.92mA
That last one varies quite a bit depending on what all the other nearby circuits are doing, and is coming in via the CT. The fact that it shows up as Real Power tells me it's not random noise, but rather is sync'd up with V.
I find it helpful to distinguish between "monitor noise", i.e. what does my monitor report when the inputs are missing (or shorted) and "sensor noise", i.e. by how much does it increase when I connect the CT. In my case my monitor noise is quite low, my sensor noise is quite low in the lab, but relatively high in the fusebox jammed in beside all those other current carrying conductors. By distinguishing like that, you can focus your efforts in the right location. My 9 CTs are all very close together, and their leads (unshielded twisted pairs) are all jammed into a conduit together, and run 2.2m to my monitor, so there's opportunity for pick-up there too.
Arduino is a duemilanove: http://arduino.cc/en/uploads/Main/arduino-duemilanove-schematic.pdf
It looks like they've attempted (somewhat unsuccessfully) to auto-select who drives the Vcc rail by the presence of any voltage on the DC-in jack. There's an interesting thread on that design here: http://forum.pololu.com/viewtopic.php?f=3&t=3083 that you should probably experiment with. See if yours too spits 5V back out the USB while being powered by DC-IN. You can see why, when working at this level, you really need to consider every Arduino model as unique... they all like to do power differently. I tend to look out for Arduinos that use physical jumpers for power-selection, then I know exactly how I'm powering the board.
Noise could come from the CT picking up high frequency noise on the line
Or even 50Hz noise on the line (and nearby lines), which is what's happening on mine.
[EDIT] If for some reason you believe yours is high frequency noise, then you may be able to combat that with a hardware filter on your inputs. Because my cross-talk arrives at the CT screw terminals looking exactly like a genuine low-level signal, it's impossible to do much about it at the monitor end without taking out the ability to pick up real low-level signals. For channels that I know will only have big loads on them (hot water and hot plates) I've just added an anti-creep feature in software.... "if(real_power < 5W) real_power = 0". To do better than that would require me to get back there and try to move CTs around, or change how I run their leads, neither of which I'm particularly enthused about.
Re: Noise in Arduino based builds.
Yeap, it's quite expected to have some noise in your conditions. But still acceptable.
I remember reading some appnote that below a small threshold (few watts) some digital company meters don't increment.
I always used shielded audio cable on my CT's so it must be a litle better.
New test: same conditions but CT on the air with no wire passing through.
Can induce noise by passing metalic objects near by or cell phone, but most of the time it measures zero with one rare sporadic small spike here and there >20 minutes appart.
Repeated the same test as before, line connected, no load. Difference is time of test, late night 1 o'clock. it still had noise but much spread in time. My conclusion is that it's something picked up from noisy lines.
So to conclude, excluding pcb design (that is not the case o arduino diecimila), major sources of noise can be :
1 - Power supply
2 - Picked up by connecting wiring
3 - Picked up by CT itself from line noise
Re: Noise in Arduino based builds.
Yeh, in my case I think it's just a natural hazard of doing per-circuit monitoring. If you're doing whole-house monitoring, you're never likely to be clamped around zero anyway (except in a much more controlled lab environment, where I do read 0W). With per-breaker monitoring, zero currents are more common and that CT is likely to be quite close to a conductor/CT that is conducting/measuring high currents on an adjacent circuit.
Re: Noise in Arduino based builds.
Currently I am powering the emonTX via the USB port on the board, but via a wall plug and not a PC USB port. Given that the USB port on board the emonTX goes through the regulator does that mean I have the least noise in the system?
Will the emonTX benefit from being powered by one of those ultra-low noise psu? How will the onboard regulator affect an already ultra-low noise input?
Re: Noise in Arduino based builds.
I would imagine the on-board regulator would clean up any ripple from your wall-wart. Most of the discussion above was comparing using an Arduino's DC-in jack (good) Vs using the USB (bad) but the real reason the former is good is because it uses the on-board regulator while the latter jams the often ripply USB's 5V signal straight onto the Vcc rail (on most Arduinos). If you've got an on-board regulator after your USB 5V input, then it too becomes a good option.
Re: Noise in Arduino based builds.
Hi, i registered on this forum to get some help but found this thread that is exaclty what i am looking for! Thank you all for the detailed explanation.
I'm struggling to get the least noise on my custom build emonTX and am at a point where i need your help:
Thuis is my build:
Arduino MEGA 1280
1x Voltage sensor (ADC 0)
8X CT Analog channels (ADC 1-8)
Burden resistor of the CT at 68 OHM to increase resolution at 0-50AMP
Calibration values on the sketch calculated accordingly (not yet measured to elimenate tolerances)
Ethernet with ENC28J60
The SW code is the basic one with discrete sampling and posting direcly to emoncms.
So to keep history short this is what i did and where i am, regarding supplying power to my custom emonTX:
From the three tests i can confirm that USB is no good ( should have searched this thread earlier, though!)
The channel above is not connected to any CT. Im am very close to ZERO Watt reading and would be happy if all the power readings were positive. Why do i have negative power readings and how can i eliminate them?
BR
cab123
Re: Noise in Arduino based builds.
I'm only just able to read the y-axis scale on the zoomed in look at your 3rd and least noisy solution, but it looks like it's about 0.02W per division? If that's true, then I think you can congratulate yourself on a noise-free build and move on. That's about as good as I've seen anywhere.
Why would you expect the noise to only produce positive power readings? It looks like your noise is genuine white noise with no particular correlation to your V sine wave which is one reason the readings are so small. The tiny part of the broad-spectrum noise that is at 50Hz is as much in-phase with V as it is out of phase with V, hence it swings either side of 0, and gives you an excellent average of 0.
Unless I'm mis-reading your graphic, I think your system is working brilliantly.
Re: Noise in Arduino based builds.
Hi dBC, sorry for the poor quality image, it's 0,025W/div. As you explain it i sounds absolutely fine, somehow i thought i was making some silly mistake. Thank you for that! I notice that powering the system through the DC input the board gets hotter than USB because of the on-board voltage regulators. The minimum DC in that produces a stable 5V on the arduino pins is about 7,5V. Anything higher than that just makes the regulators work harder and generate more heat.
I have been reading this and other threads with more attention and i guess that it would be just fine if i could set a threshold on the code to get rid of the noise and the negative readings of "-0 Watt". Something around -1W and 1W would be defaulted to 0.
I think it would be a nice addon to the code to be able to set a noise threshold per CT.
BR
Re: Noise in Arduino based builds.
What option to log data you used in emoncms to gather this graph and what data you were feeding from you rig (amps or watts)?
please advise,
Ramunas
Re: Noise in Arduino based builds.
Hi!
the emoncms graphics were created from the simplest form, a modified emontx is reporting power samples periodically and emoncms is logging the feed data that receives.
hope it helps
br


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FF11 -Slime (many varieties) |
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FF12 -Flan (boss) -Slime -Jelly -Foobar -White Mousse (mark) -Orthros (mark)
CP: Being an amorph, coated in slime, its eyes and mouth having a most evil aspect. They know no distinction of sex, instead absorbing prey until enough bulk has been gleaned, whereupon they divide their mass, creating offspring in this manner. Recent research has shown striking similarities in the magickal ordering of these creatures with the ordering seen in naturally occurring Mist, leading some to believe that their generation is tied to Mist at a fundamental level. |
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FF12RW Red/Blue/Yellow/Green Flan |
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Tactics A2 -Ice Flan -Red Marshmallow -White Pudding -Yellow Jelly |
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Crystal Chronicles -Flan -Dark Flan -Water Flan |
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What did you learn from some of your first jobs?
User Experience Designer
Did you know what you wanted to do in high school?
Engineering Program Manager
What personality trait helps you most at work?
What would surprise most people about your job?
College Student & Intern
Why did you choose your college major?
What is your favorite part of your job?
How does your college major relate to your current work?


Let’s unlock some stuff for the wife!
Epic: patrickulrich
PSN: asdr24
My live stream is at DLive
This page is synchronized from the post: ‘Playing Fortnite On The Wife’’s Account’
Let’s unlock some stuff for the wife!
Epic: patrickulrich
PSN: asdr24
My live stream is at DLive
This page is synchronized from the post: ‘Playing Fortnite On The Wife’’s Account’
These two are some of my favorite things in this world.
This page is synchronized from the post: ‘Always Keeping Watch’
What’s up yall? Eric Wilson nominated me last week for his #mydlivers challenge. With it I had to nominate 2 streamers who I felt embodied what a great DLive streamer should be. Watch the video to find out who I nominated!
@ericwilson’s Original #MyDLiver Challenge - https://dlive.io/video/ericwilson/3f2ff1a0-7f09-11e8-bbe8-435b8d3c86e8
DLive Profiles:
@ericwilson - https://dlive.io/@ericwilson
@chansetheguy - https://dlive.io/@chansetheguy
@clasicktv - https://dlive.io/@clasicktv
@gattisstatus - https://dlive.io/@gattisstatus
My video is at DLive
This page is synchronized from the post: ‘@patrickulrich’’s #MyDLivers Submission’
Playing some Fortnite and maybe more. Come hang out!
PSN: asdr24
Epic: patrickulrich
My live stream is at DLive
This page is synchronized from the post: ‘Monday Night Games’
Playing some Fortnite with the crew!
Epic: patrickulrich
PSN: asdr24
My live stream is at DLive
This page is synchronized from the post: ‘Fortnite w/ DLive Crew’
Two days in a row!
@patrickulrich tracked today a total of 10072 steps via Actifit Fitness Tracker
This page is synchronized from the post: ‘Actifit Daily Results’
Update your browser to view this website correctly. Update my browser now


{# Nunjucks Templating! Homepage: https://mozilla.github.io/nunjucks/ Docs: https://mozilla.github.io/nunjucks/api.html #} {% extends 'layouts/application.html' %} {% block content %}
| Cow ID | Health Status | Temperature | Activity | Ranch |
| 1 399 | Sick | 38.6 | Low | East Upper |
| Average Daily Milk Yield | 10l | 8000 cows in East Upper Ranch | Immunization Records |
| YTD Milk Yiled | 3090l | Related Cows | 1 / 07 / 2017 |
| 2017 Average Milk Yiled | 8l | 1 568 Mother 1 399 Calf | 1 / 06 / 2016 1 / 06 / 2015 |
| # of Calves Produced | 10 | 1 214 Calf 1 840 Calf | 1 / 06 / 2014 1 / 06 / 2013 |


About Pamela
Grew up in Albq.
Graduated Brown Mackie 2015 AAS info Tech.
Met her Husband taking ballet lessons.
She loves singing and playing hermonica.
She once received a phone call from Dolly Parton
Fun Fact
Attended the Red Wedding.


Meet and Greet
Dwindle Hall
Opening Ceremony
Dwindle Hall
Workshops
Dwindle Hall
Lunch, Work Session, and Dinner
Dwindle Hall
10:00 AM - 11:00 AMMeet and Greet
Dwindle Hall
11:00 AM - 11:30 AM Opening Ceremony
Dwindle Hall
11:30 AM - 12:00 PM Workshops
Dwindle Hall
12:00 PM - 9:00 PM Lunch, Work Session, and Dinner
Dwindle Hall


POSTS
Demo
To embed an imgur image such as this one, notice that the URL ends with vRbDiHJ
. Now, simply insert {{< imgur id=“vRbDiHJ” >}} to embed this in your blog. The result will look something like this:
Please note that this page was generated via Hugo using the quickstart tutorial. Only this page is being hosted to demonstrate the shortcode, and the entire quickstart site has not been made available here.


Select a person:
Peter
Lois
Joseph
Glenn
Select a hometown:
Quahog
Newport
Submit Query
Person info will be listed here...


This is what a post looks like. Straight forward. Date goes above the header. Content goes below the header.
In this section you can introduce a presentation to the template.
--- title: Exemplar template nav: Template subnav: exemplar-home layout: default ---
This is an example of the default theme I use for my jekyll sites. This theme is used for my blog and my project sites.
This site uses a combination of the following:
20 Aug, 2019
This is what a post looks like. Straight forward. Date goes above the header. Content goes below the header.
In this section you can introduce a presentation to the template.
{{ page.summary }}


{{msg.message}}
{{msg.name}} ({{msg.email}}) has joined the chat
{{msg.name}} ({{msg.email}}) has left the chat


The atom
Check whether the node connection is ready for requests
true if ready
Opens connection
a promise that resolves once the connection is ready, or rejects on error or timeout
Submit an atom to the ledger
A stream of the status of the atom submission
Subscribe for all existing and future atoms for a given address
base58 formatted address
a stream of atoms
Generated using TypeDoc
NOT IMPLEMENTED Query the ledger for an atom by its id