Chapter 12 Spatial Interpolation
Spatial interpolation is the activity of estimating values spatially continuous variables for spatial locations where they have not been observed, based on observations. The statistical methodology for spatial interpolation, called geostatistics, is concerned with the modelling, prediction and simulation of spatially continuous phenomena. The typical problem is a missing value problem: we observe a property of a phenomenon \(Z(s)\) at a limited number of sample locations \(s_i, i = 1,...,n\), and are interested in the property value at all locations \(s_0\) covering an area of interest, so we have to predict it for unobserved locations. This is also called kriging, or Gaussian Process prediction. In case \(Z(s)\) contains a white noise component \(\epsilon\), as in \(Z(s)=S(s)+\epsilon(s)\) (possibly reflecting measurement error) an alternative but similar goal is to predict \(S(s)\), which may be called spatial filtering or smoothing.
In this chapter we will show simple approaches for handling
geostatistical data, will demonstrate simple interpolation methods,
explore modelling spatial correlation, spatial prediction and
simulation. We will use package gstat
(E. Pebesma and Graeler 2021; Pebesma 2004),
which offers a fairly wide palette of models and options for
non-Bayesian geostatistical analysis. Bayesian methods with
R implementations are found in e.g. Diggle, Tawn, and Moyeed (1998),
Diggle and Ribeiro Jr. (2007), Blangiardo and Cameletti (2015), and Wikle, Zammit-Mangion, and Cressie (2019). An overview
and comparisons of methods for large datasets is given in
Heaton et al. (2018).
12.1 A first dataset
We can read NO\(_2\) data, which is prepared in chapter 13, from
package gstat
using
and convert it into an sf
object using
library(sf)
crs = st_crs("EPSG:32632")
no2.sf = st_as_sf(no2, coords = c("station_longitude_deg", "station_latitude_deg"), crs = "OGC:CRS84") %>%
st_transform(crs)
Next, we can load country boundaries and plot these data using ggplot
, shown
in figure 12.1.
data(air, package = "spacetime") # this loads German boundaries into DE_NUTS1
de <- st_transform(st_as_sf(DE_NUTS1), crs)
If we want to interpolate, we first need to decide where. This
is typically done on a regular grid covering the area of
interest. Starting with the country outline de
we can create a
regular grid with 10 km grid cells (pixels) over Germany by
# stars object with 2 dimensions and 1 attribute
# attribute(s):
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# values 0 0 0 0 0 0 1989
# dimension(s):
# from to offset delta refsys point values x/y
# x 1 64<PHONE_NUMBER>0 WGS 84 / UTM z... NA NULL [x]
# y 1 87 6101239 -10000 WGS 84 / UTM z... NA NULL [y]
Here, we chose grid cells to be not too fine, so that we still see them in plots.
Perhaps the simplest interpolation method is inverse distance weighted interpolation, which is a weighted average, using weights inverse proportional to distances from the interpolation location:
\[ \hat{z}(s_0) = \frac{\sum_{i=1}^{n} w_i z(s_i)}{\sum_{i=1}^n w_i} \]
with \(w_i = |s_0-s_i|^p\), and the inverse distance power typically
taken as 2, or optimized using cross validation. We can compute
inverse distance interpolated values using gstat::idw
,
# [inverse distance weighted interpolation]
and plot them in figure 12.2.
ggplot() + geom_stars(data = i, aes(fill = var1.pred, x = x, y = y)) +
geom_sf(data = st_cast(de, "MULTILINESTRING")) +
geom_sf(data = no2.sf)
12.2 Sample variogram
In order to make spatial predictions using geostatistical methods, we first need to identify a model for the mean and for the spatial correlation. In the simplest model, \(Z(s) = m + e(s)\), the mean is an unknown constant \(m\), and in this case the spatial correlation can be modelled using the variogram, \(\gamma(h) = 0.5 E (Z(s)-Z(s+h))^2\). For processes with a finite variance \(C(0)\), the variogram is related to the covariogram or covariance function through \(\gamma(h) = C(0)-C(h)\).
The sample variogram is obtained by computing estimates of \(\gamma(h)\) for distance intervals, \(h_i = [h_{i,0},h_{i,1}]\): \[ \hat{\gamma}(h_i) = \frac{1}{2N(h_i)}\sum_{j=1}^{N(h_i)}(z(s_i)-z(s_i+h'))^2, \ \ h_{i,0} \le h' < h_{i,1} \]
with \(N(h_i)\) the number of sample pairs available for distance interval \(h_i\).
Function gstat::variogram
computes sample variograms,
and the result of plotting this is shown in figure 12.3.
Function variogram
chooses default for maximum distance (cutoff
: one third of the length of the bounding box diagonal) and (constant) interval widths (width
: cutoff divided by 15). These defaults can be changed, e.g. by
shown in figure 12.4.
Note that the formula NO2~1
is used to select the variable of interest from the data file (NO2
), and to specify the mean model: ~1
refers to an intercept-only (unknown, constant mean) model.
12.3 Fitting variogram models
In order to progress toward spatial predictions, we need a variogram model \(\gamma(h)\) for (potentially) all distances \(h\), rather than the set of estimates derived above: in case we would for instance connect these estimates with straight lines, or assume they reflect constant values over their respective distance intervals, this would lead to statistical models with non-positive covariance matrices, which is a complicated way of expressing that you are in a lot of trouble.
To avoid these troubles we fit parametric models \(\gamma(h)\) to the estimates \(\hat{\gamma}(h_i)\), where we take \(h_i\) as the mean value of all the \(h'\) values involved in estimating \(\hat{\gamma}(h_i)\). For this, when we fit a model like the exponential variogram, fitted by
and shown in figure 12.5.
The fitting is done by weighted least squares, minimizing
\(\sum_{i=1}^{n}w_i(\gamma(h_i)-\hat{\gamma}(h_i))^2\), with \(w_i\) by
default equal to \(N(h_i)/h^2\), other fitting schemes are available
through argument fit.method
.
12.4 Kriging interpolation
Typically, when we interpolate a variable, we do that on
points on a regular grid covering the target area. We first create
a stars
object with a raster covering the target area, and NA’s
outside it.
Kriging involves the prediction of \(Z(s_0)\) at arbitrary locations
\(s_0\). We can krige NO\(_2\) by using gstat::krige
, with the model for the
trend, the data, the prediction grid, and the variogram model (figure 12.6):
# [using ordinary kriging]
ggplot() + geom_stars(data = k, aes(fill = var1.pred, x = x, y = y)) +
geom_sf(data = st_cast(de, "MULTILINESTRING")) +
geom_sf(data = no2.sf) +
coord_sf(lims_method = "geometry_bbox")
12.5 Areal means: block kriging
Computing areal means can be done in several ways. The simples is to take the average of point samples falling inside the target polygons:
A more complicated way is to use block kriging (Journel and Huijbregts 1978), which
uses all the data to estimate the mean of the variable over the
target area. With krige
, this can be done by giving the target
areas (polygons) as the newdata
argument:
# [using ordinary kriging]
we can now merge the two maps into a single object to create a single plot (figure 12.7):
b %>% select(sample, kriging) %>%
pivot_longer(1:2, names_to = "var", values_to = "NO2") -> b2
b2$var = factor(b2$var, levels = c("sample", "kriging"))
ggplot() + geom_sf(data = b2, mapping = aes(fill = NO2)) + facet_wrap(~var) +
scale_fill_gradientn(colors = sf.colors(20))
We see that the signal is similar, but that the simple means are more variable than the block kriging values; this may be due to the smoothing effect of kriging: data points outside the target area are weighted, too.
To compare the standard errors of means, for the sample mean we can get a rough guess of the standard error by \(\sqrt{(\sigma^2/n)}\):
which would have been the actual estimate in design-based inference if the sample was obtained by spatially random sampling. The block kriging variance is the model-based estimate, and is a by-product of kriging. We combine and rename the two:
b %>% select(sample, kriging) %>%
pivot_longer(1:2, names_to = "var", values_to = "Standard_error") -> b2
b2$var = factor(b2$var, levels = c("sample", "kriging"))
ggplot() + geom_sf(data = b2, mapping = aes(fill = Standard_error)) + facet_wrap(~var, as.table = FALSE) +
scale_fill_gradientn(colors = sf.colors(20))
where we see that the simple approach gives clearly more variability and mostly larger values for prediction errors of areal means, compared to block kriging.
12.6 Conditional simulation
In case one or more conditional realisation of the field \(Z(s)\) are needed rather than their conditional mean, we can obtain this by conditional simulation. A reason for wanting this may be the need to estimate areal mean values of \(g(Z(s))\) with \(g(\cdot)\) a non-linear function; a simple example is the areal fraction where \(Z(s)\) exceeds a threshold.
The standard approach used by gstat
is to use the sequential
simulation algorithm for this. This is a simple algorithm that randomly
steps through the prediction locations and at each location:
- carries out a kriging prediction
- draws a random variable from the normal distribution with mean and variance equal to the kriging variance
- adds this value to the conditioning dataset
- finds a new random simulation location
until all locations have been visited.
This is carried out by gstat::krige
when nsim
is set to a positive
value. In addition, it is good to constrain nmax
, the (maximum)
number of nearest neigbours to include in kriging estimation,
because the dataset grows each step, leading otherwise quickly to
very long computing times and large memory requirements (figure
12.9):
# drawing 6 GLS realisations of beta...
# [using conditional Gaussian simulation]
library(viridis)
g = ggplot() + coord_equal() +
scale_fill_viridis() +
theme_void() +
scale_x_discrete(expand=c(0,0)) +
scale_y_discrete(expand=c(0,0))
g + geom_stars(data = s[,,,1:6]) + facet_wrap(~sample)
Alternative methods for conditional simulation have recently been
added to gstat
, and include krigeSimCE
implementing the circular
embedding method (Davies and Bryant 2013), and krigeSTSimTB
implementing
the turning bands method (Schlather 2011). These are of particular
of interest for larger datasets or conditional simulations of
spatiotemporal data.
12.7 Trend models
Kriging and conditional simulation, as used so far in this chapter, assume that all spatial variability is a random process, characterized by a spatial covariance model. In case we have other variables that are meaningfully correlated with the target variable, we can use them in a linear regression model for the trend, \[ Z(s) = \sum_{j=0}^p \beta_j X_p(s) + e(s) \] with \(X_0(s) = 1\) and \(\beta_0\) an intercept, but with the other \(\beta_j\) regression coefficients. This typically reduces both the spatial correlation in the residual \(e(s)\), as well as its variance, and leads to more accurate predictions and more similar conditional simulations.
12.7.1 A population grid
As a potential predictor for NO2 in the air, we use population density. NO2 is mostly caused by traffic, and traffic is stronger in more densely populated areas. Population density is obtained from the 2011 census, and is downloaded as a csv file with the number of inhabitants per 100 m grid cell. We can aggregate these data to the target grid cells by summing the inhabitants:
v = vroom::vroom("aq/pop/Zensus_Bevoelkerung_100m-Gitter.csv")
v %>% filter(Einwohner > 0) %>%
select(-Gitter_ID_100m) %>%
st_as_sf(coords = c("x_mp_100m", "y_mp_100m"), crs = 3035) %>%
st_transform(st_crs(grd)) -> b
a = aggregate(b, st_as_sf(grd, na.rm = FALSE), sum)
Now we have the population counts per grid cell in a
. To get to
population density, we need to find the area of each cell; for cells
crossing the country border, this will be less than 10 x 10 km:
grd$ID = 1:prod(dim(grd)) # so we can find out later which grid cell we have
ii = st_intersects(grd["ID"], st_cast(st_union(de), "MULTILINESTRING"))
# Warning in st_intersects.stars(grd["ID"], st_cast(st_union(de),
# "MULTILINESTRING")): as_points is NA: assuming here that raster cells are small
# polygons, not points
grd_sf = st_as_sf(grd["ID"], na.rm = FALSE)[lengths(ii) > 0,]
iii = st_intersection(grd_sf, st_union(de))
# Warning: attribute variables are assumed to be spatially constant throughout all
# geometries
grd$area = st_area(grd)[[1]] + units::set_units(grd$values, m^2) # NA's
grd$area[iii$ID] = st_area(iii)
Instead of doing the two-stage procedure above: first finding cells that
have a border crossing it, then computing its area, we could also directly
use st_intersection
on all cells, but that takes considerably longer.
From the counts and areas we can compute densities, and verify totals (figure 12.10):
# 80323301 [1]
# [1] 80324282
We need to divide the number of inhabitants by the number of 100 m grid points contributing to it, in order to convert population counts into population density.
To obtain population density values at monitoring network stations, we can use
# stars object with 1 dimensions and 1 attribute
# attribute(s):
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# pop_dens [1/m^2] 3.37e-06 4.98e-05 8.93e-05 0.000195 0.000237 0.00224 1
# dimension(s):
# from to offset delta refsys point
# geometry 1 74 NA NA WGS 84 / UTM z... TRUE
# values
# geometry POINT<PHONE_NUMBER>802),...,POINT<PHONE_NUMBER>738)
#
# Call:
# lm(formula = NO2 ~ sqrt(pop_dens), data = no2.sf)
#
# Residuals:
# Min 1Q Median 3Q Max
# -7.96 -2.15 -0.50 1.60 8.10
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 4.561 0.697 6.54 8.0e-09 ***
# sqrt(pop_dens) 325.006 49.927 6.51 9.2e-09 ***
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# Residual standard error: 3.15 on 71 degrees of freedom
# (1 observation deleted due to missingness)
# Multiple R-squared: 0.374, Adjusted R-squared: 0.365
# F-statistic: 42.4 on 1 and 71 DF, p-value: 9.19e-09
and the corresponding scatterplot is shown in 12.11.
Prediction under this new model involves first modelling a residual variogram (figure 12.12):
no2.sf = no2.sf[!is.na(no2.sf$pop_dens),]
vr = variogram(NO2~sqrt(pop_dens), no2.sf)
vr.m = fit.variogram(vr, vgm(1, "Exp", 50000, 1))
and subsequently, kriging prediction is done by (figure 12.13)
# [using universal kriging]
k$kr1 = k$var1.pred
k$kr2 = kr$var1.pred
st_redimension(k[c("kr1", "kr2")],
along = list(what = c("kriging", "residual kriging"))) %>%
setNames("NO2") -> km
g + geom_stars(data = km, aes(fill = NO2, x = x, y = y)) +
geom_sf(data = st_cast(de, "MULTILINESTRING")) +
geom_sf(data = no2.sf) + facet_wrap(~what) +
coord_sf(lims_method = "geometry_bbox")
# Coordinate system already present. Adding new coordinate system, which will replace the existing one.
where, critically, the pop_dens
values are now available for
prediction locations in object grd
. We see some clear differences:
the map with population density in the trend follows the extremes of
the population density rather than those of the measurement stations,
and has a range that extends that of the former. It should be taken
with a large grain of salt however, since the stations used were
filtered for the category “rural background”, indicating that they
represent conditions of lower populations density. The scatter plot
of Figure 12.11 reveals that the the population density
at the locations of stations is much more limited than that in the
population density map, and hence the right-hand side map is based on
strongly extrapolating the relationship shown in 12.11.
12.8 Exercises
- Create a plot like the one in figure 12.13 that has the inverse distance interpolated map of figure 12.2 added on left side.
- Create a scatter plot of the map values of the idw and kriging map, and a scatter plot of map values of idw and residual kriging.
- Carry out a block kriging, predicting block averages for blocks
centered over grid cells, by setting the
block
argument inkrige()
, and do this for block sizes of 10 km (the grid cell size), 50 km and 200 km. Compare the resulting maps of estimates for these three blocks sizes with those obtained by point kriging, and do the same thing for all associated kriging standard errors. - Based on the residual kriging results obtained above, compute maps of the lower and upper boundary of a 95% confidence interval, when assuming that the kriging error is normally distributed, and show them in a plot with a single (joint) legend
- Compute and show the map with the probabilities that NO2 point values exceed the level of 15 ppm, assuming normally distributed kriging errors.
References
Blangiardo, Marta, and Michela Cameletti. 2015. Spatial and Spatio-Temporal Bayesian Models with R-Inla. John Wiley & Sons.
Davies, Tilman, and David Bryant. 2013. “On Circulant Embedding for Gaussian Random Fields in R.” Journal of Statistical Software, Articles 55 (9): 1–21. https://doi.org/10.18637/jss.v055.i09.
Diggle, P. J., and P. J. Ribeiro Jr. 2007. Model-Based Geostatistics. New York: Springer.
Diggle, P. J., J. A. Tawn, and R. A. Moyeed. 1998. “Model-Based Geostatistics.” Applied Statistics, no. 3: 299–350.
Heaton, Matthew J., Abhirup Datta, Andrew O. Finley, Reinhard Furrer, Joseph Guinness, Rajarshi Guhaniyogi, Florian Gerber, et al. 2018. “A Case Study Competition Among Methods for Analyzing Large Spatial Data.” Journal of Agricultural, Biological and Environmental Statistics, December. https://doi.org/10.1007/s13253-018-00348-w.
Journel, Andre G, and Charles J Huijbregts. 1978. Mining Geostatistics. Academic press London.
Pebesma, Edzer, and Benedikt Graeler. 2021. Gstat: Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation. https://github.com/r-spatial/gstat/.
Pebesma, Edzer J. 2004. “Multivariable Geostatistics in S: The Gstat Package.” Computers & Geosciences 30: 683–91.
Schlather, Martin. 2011. “Construction of Covariance Functions and Unconditional Simulation of Random Fields.” In Porcu, E., Montero, J.m. And Schlather, M., Space-Time Processes and Challenges Related to Environmental Problems. New York: Springer.
Wikle, Christopher K, Andrew Zammit-Mangion, and Noel Cressie. 2019. Spatio-Temporal Statistics with R. CRC Press.


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| 1 | ||
| 2 | package paavohuh.sourcream.emulation.instructions; | |
| 3 | ||
| 4 | import org.jooq.lambda.Seq; | |
| 5 | import org.joou.UByte; | |
| 6 | import org.joou.UShort; | |
| 7 | import paavohuh.sourcream.emulation.Instruction; | |
| 8 | import paavohuh.sourcream.emulation.Register; | |
| 9 | import paavohuh.sourcream.emulation.State; | |
| 10 | import static paavohuh.sourcream.emulation.InstructionFactory.*; | |
| 11 | ||
| 12 | /** | |
| 13 | * Contains control instructions. | |
| 14 | * This class shouldn't be instantiated. | |
| 15 | */ | |
| 16 | public final class Control { | |
| 17 | private Control() { } | |
| 18 | | |
| 19 | /** | |
| 20 | * Jumps to address. | |
| 21 | */ | |
| 22 | public static class JumpTo extends Instruction.WithAddress { | |
| 23 | ||
| 24 | /** | |
| 25 | * Jumps to address. | |
| 26 | * @param address The address. | |
| 27 | */ | |
| 28 | public JumpTo(UShort address) { | |
| 29 | super(address); | |
| 30 | } | |
| 31 | ||
| 32 | @Override | |
| 33 | protected UShort getBaseCode() { | |
| 34 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$JumpTo::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x1000); |
| 35 | } | |
| 36 | ||
| 37 | @Override | |
| 38 | public State execute(State state) { | |
| 39 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$JumpTo::execute to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return state.withProgamCounter(address); |
| 40 | } | |
| 41 | } | |
| 42 | | |
| 43 | /** | |
| 44 | * Jumps to address + V0. | |
| 45 | */ | |
| 46 | public static class JumpToWithOffset extends Instruction.WithAddress { | |
| 47 | ||
| 48 | /** | |
| 49 | * Jumps to address + V0. | |
| 50 | * @param address The address. | |
| 51 | */ | |
| 52 | public JumpToWithOffset(UShort address) { | |
| 53 | super(address); | |
| 54 | } | |
| 55 | | |
| 56 | @Override | |
| 57 | protected UShort getBaseCode() { | |
| 58 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$JumpToWithOffset::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0xB000); |
| 59 | } | |
| 60 | ||
| 61 | @Override | |
| 62 | public State execute(State state) { | |
| 63 |
1
1. execute : Replaced integer addition with subtraction → NO_COVERAGE |
int newAddress = address.intValue() + state.getRegister(Register.V0).intValue(); |
| 64 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$JumpToWithOffset::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return state.withProgamCounter(UShort.valueOf(newAddress)); |
| 65 | } | |
| 66 | } | |
| 67 | | |
| 68 | /** | |
| 69 | * Skips the next instruction if value at register equals constant. | |
| 70 | */ | |
| 71 | public static class SkipIfEquals extends Instruction.WithRegisterAnd8BitConstant { | |
| 72 | ||
| 73 | /** | |
| 74 | * Skips the next instruction if value at register equals constant. | |
| 75 | * @param register The register. | |
| 76 | * @param constant The constant. | |
| 77 | */ | |
| 78 | public SkipIfEquals(Register register, UByte constant) { | |
| 79 | super(register, constant); | |
| 80 | } | |
| 81 | ||
| 82 | @Override | |
| 83 | protected int getRegisterOffset() { | |
| 84 |
1
1. getRegisterOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return 2; |
| 85 | } | |
| 86 | ||
| 87 | @Override | |
| 88 | protected int getConstantOffset() { | |
| 89 |
1
1. getConstantOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return 0; |
| 90 | } | |
| 91 | ||
| 92 | @Override | |
| 93 | protected UShort getBaseCode() { | |
| 94 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfEquals::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x3000); |
| 95 | } | |
| 96 | ||
| 97 | @Override | |
| 98 | public State execute(State state) { | |
| 99 | UByte registerValue = state.getRegister(register); | |
| 100 | | |
| 101 |
1
1. execute : negated conditional → NO_COVERAGE |
if (registerValue.equals(constant)) { |
| 102 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfEquals::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return state.withIncrementedPc(); |
| 103 | } else { | |
| 104 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfEquals::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return new State(state); |
| 105 | } | |
| 106 | } | |
| 107 | } | |
| 108 | | |
| 109 | /** | |
| 110 | * Skips the next instruction if value at register doesn't equal constant. | |
| 111 | */ | |
| 112 | public static class SkipIfNotEquals extends Instruction.WithRegisterAnd8BitConstant { | |
| 113 | ||
| 114 | /** | |
| 115 | * Skips the next instruction if value at register doesn't equal constant. | |
| 116 | * @param register The register. | |
| 117 | * @param constant The constant. | |
| 118 | */ | |
| 119 | public SkipIfNotEquals(Register register, UByte constant) { | |
| 120 | super(register, constant); | |
| 121 | } | |
| 122 | ||
| 123 | @Override | |
| 124 | protected int getRegisterOffset() { | |
| 125 |
1
1. getRegisterOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return 2; |
| 126 | } | |
| 127 | ||
| 128 | @Override | |
| 129 | protected int getConstantOffset() { | |
| 130 |
1
1. getConstantOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → KILLED |
return 0; |
| 131 | } | |
| 132 | ||
| 133 | @Override | |
| 134 | protected UShort getBaseCode() { | |
| 135 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfNotEquals::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x4000); |
| 136 | } | |
| 137 | ||
| 138 | @Override | |
| 139 | public State execute(State state) { | |
| 140 | UByte registerValue = state.getRegister(register); | |
| 141 | | |
| 142 |
1
1. execute : negated conditional → NO_COVERAGE |
if (!registerValue.equals(constant)) { |
| 143 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfNotEquals::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return state.withIncrementedPc(); |
| 144 | } else { | |
| 145 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfNotEquals::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return new State(state); |
| 146 | } | |
| 147 | } | |
| 148 | } | |
| 149 | | |
| 150 | /** | |
| 151 | * Skips the next instruction if the values of registers X and Y are equal. | |
| 152 | */ | |
| 153 | public static class SkipIfEqualsRegister extends Instruction.WithTwoRegisters { | |
| 154 | ||
| 155 | | |
| 156 | /** | |
| 157 | * Skips the next instruction if the values of registers X and Y are equal. | |
| 158 | * @param x Register x. | |
| 159 | * @param y Register y. | |
| 160 | */ | |
| 161 | public SkipIfEqualsRegister(Register x, Register y) { | |
| 162 | super(x, y); | |
| 163 | } | |
| 164 | ||
| 165 | @Override | |
| 166 | protected int getRegXOffset() { | |
| 167 |
1
1. getRegXOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → SURVIVED |
return 2; |
| 168 | } | |
| 169 | ||
| 170 | @Override | |
| 171 | protected int getRegYOffset() { | |
| 172 |
1
1. getRegYOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → SURVIVED |
return 1; |
| 173 | } | |
| 174 | ||
| 175 | @Override | |
| 176 | protected UShort getBaseCode() { | |
| 177 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfEqualsRegister::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x5000); |
| 178 | } | |
| 179 | ||
| 180 | @Override | |
| 181 | public State execute(State state) { | |
| 182 |
1
1. execute : negated conditional → NO_COVERAGE |
if (getRegisterX(state).intValue() == getRegisterY(state).intValue()) { |
| 183 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfEqualsRegister::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return state.withIncrementedPc(); |
| 184 | } else { | |
| 185 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfEqualsRegister::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return new State(state); |
| 186 | } | |
| 187 | } | |
| 188 | } | |
| 189 | | |
| 190 | /** | |
| 191 | * Skips the next instruction if the values of register X and Y are not equal. | |
| 192 | */ | |
| 193 | public static class SkipIfNotEqualsRegister extends Instruction.WithTwoRegisters { | |
| 194 | ||
| 195 | /** | |
| 196 | * Skips the next instruction if the values of register X and Y are not equal. | |
| 197 | * @param x Register X. | |
| 198 | * @param y Register Y. | |
| 199 | */ | |
| 200 | public SkipIfNotEqualsRegister(Register x, Register y) { | |
| 201 | super(x, y); | |
| 202 | } | |
| 203 | | |
| 204 | @Override | |
| 205 | protected int getRegXOffset() { | |
| 206 |
1
1. getRegXOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → SURVIVED |
return 2; |
| 207 | } | |
| 208 | ||
| 209 | @Override | |
| 210 | protected int getRegYOffset() { | |
| 211 |
1
1. getRegYOffset : replaced return of integer sized value with (x == 0 ? 1 : 0) → SURVIVED |
return 1; |
| 212 | } | |
| 213 | ||
| 214 | @Override | |
| 215 | protected UShort getBaseCode() { | |
| 216 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfNotEqualsRegister::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x9000); |
| 217 | } | |
| 218 | ||
| 219 | @Override | |
| 220 | public State execute(State state) { | |
| 221 |
1
1. execute : negated conditional → NO_COVERAGE |
if (getRegisterX(state) != getRegisterY(state)) { |
| 222 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfNotEqualsRegister::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return state.withIncrementedPc(); |
| 223 | } else { | |
| 224 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$SkipIfNotEqualsRegister::execute to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return new State(state); |
| 225 | } | |
| 226 | } | |
| 227 | } | |
| 228 | | |
| 229 | /** | |
| 230 | * Calls the subroutine at address. Pushes the current program counter to | |
| 231 | * the call stack and increment the stack pointer. | |
| 232 | */ | |
| 233 | public static class CallSubroutine extends Instruction.WithAddress { | |
| 234 | ||
| 235 | /** | |
| 236 | * Calls the subroutine at address. | |
| 237 | * @param address The address. | |
| 238 | */ | |
| 239 | public CallSubroutine(UShort address) { | |
| 240 | super(address); | |
| 241 | } | |
| 242 | | |
| 243 | @Override | |
| 244 | protected UShort getBaseCode() { | |
| 245 |
1
1. getBaseCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$CallSubroutine::getBaseCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x2000); |
| 246 | } | |
| 247 | ||
| 248 | @Override | |
| 249 | public State execute(State state) { | |
| 250 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$CallSubroutine::execute to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return state.withCallTo(address); |
| 251 | } | |
| 252 | } | |
| 253 | | |
| 254 | /** | |
| 255 | * Returns from a subroutine. Replaces the program counter with the | |
| 256 | * topmost item of the call stack and decrements the stack pointer. | |
| 257 | */ | |
| 258 | public static class Return implements Instruction { | |
| 259 | ||
| 260 | @Override | |
| 261 | public State execute(State state) { | |
| 262 |
1
1. execute : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$Return::execute to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return state.withReturn(); |
| 263 | } | |
| 264 | ||
| 265 | @Override | |
| 266 | public UShort getCode() { | |
| 267 |
1
1. getCode : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control$Return::getCode to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return UShort.valueOf(0x00EE); |
| 268 | } | |
| 269 | | |
| 270 | } | |
| 271 | | |
| 272 | /** | |
| 273 | * Gets all Control instructions. | |
| 274 | * @return Sequence of instructions. | |
| 275 | */ | |
| 276 | public static Seq<Instruction> getAll() { | |
| 277 |
1
1. getAll : mutated return of Object value for paavohuh/sourcream/emulation/instructions/Control::getAll to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return Seq.concat( |
| 278 | Seq.of(new Return()), | |
| 279 | getAllInstances(JumpTo::new), | |
| 280 | getAllInstances(JumpToWithOffset::new), | |
| 281 | getAllInstances(SkipIfEquals::new), | |
| 282 | getAllInstances(SkipIfNotEquals::new), | |
| 283 | getAllInstances(SkipIfEqualsRegister::new), | |
| 284 | getAllInstances(SkipIfNotEqualsRegister::new), | |
| 285 | getAllInstances(CallSubroutine::new)); | |
| 286 | } | |
| 287 | } | |
Mutations | ||
| 34 |
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| 39 |
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| 63 |
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| 262 |
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| 267 |
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| 277 |
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The schedule makes it easy for you to provide a steady flow of posts to your subscribers.
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I've been giving some thought as to whether it's possible to be completely anonymous when connected to the 'net. Whilst I can think of obscure cases where it might be possible to track this setup, I reckon using the following list it would be next to impossible to track the user.
* Laptop with Wireless card
* bootable Linux Distro (preferrably from something like a Magazine cover disc purchased with cash). Use a different distro each time you boot.
* No persistant storage in the laptop at all (remove the hard disk)
* Change the MAC address of the Wireless and Wired Network cards on boot. Use a different one each time you boot.
* Find free wireless access, for this use something like the backnet nodes..
* use a decent gain antenna, so you can be further from the Wireless AP.
* Connect to the TOR network and direct all traffic through it.
* Don't put ANY personal information into any site
Now some of the steps there, eg, changing distro and using only magazine cover discs are only for the extremely extremely paranoid, but a basic version where you boot off the CD change the mac address connect over wireless and use TOR, would make it very difficult to tie up Internet activity to any real-world person....


Install the Xcode Tools package
Install the Xcode Tools package from http://connect.apple.com. Xcode 2.3 or later is required to build.
Install a Subversion Client
Subversion (svn) is the source code management tool used by the WebKit Open Source Project. A Subversion client for Mac OS X 10.4 and later is available.
If you own Visual Studio 2005 (newer versions of Visual Studio are currently unsupported):
Install Microsoft Visual Studio 2005 Team Suite Service Pack 1.
If you are building from Vista, install Service Pack 1 Update for Windows Vista.
Install Visual Studio 2005 Service Pack 1 ATL Security Update.
Install the following hotfixes to improve Visual Studio's performance and responsiveness:
Use the default options for these installations.
If not, you can use Visual C++ Express 2005 (newer versions of Visual C++ Express Edition are currently unsupported):
Install Visual C++ 2005 Express.
Install Microsoft Visual C++ Express 2005 Service Pack 1.
If you are building from Vista, install Service Pack 1 Update for Windows Vista.
Install Visual Studio 2005 Service Pack 1 ATL Security Update.
Install the Windows Server 2003 R2 Platform SDK, then follow steps 2 and 3 of “How to: Use Visual C++ Express Edition with the Microsoft Platform SDK.”
Use the default options for all installations.
In addition to the paths specified in step 3 of the Platform SDK installation instructions, you must also add the following include path. Update the Visual C++ directories in the Projects and Solutions section in the Options dialog box:
C:\Program Files\Microsoft Platform SDK for Windows Server 2003 R2\Include\mfc
Install Cygwin
Cygwin is a collection of utilities for Windows that includes not only a Subversion client, but also additional tools that are required to build the WebKit source. We have made a downloader available that automatically collects all of the required packages.
Download cygwin-downloader.zip.
Right-click cygwin-downloader.zip and choose Extract All.... Keep all the default options and click Next until the file is extracted and the cygwin-downloader folder opens.
Double-click cygwin-downloader.exe. This will download all the Cygwin packages you need.
When all the packages have finished downloading, the Cygwin installer will launch. Choose Install from Local Directory, then click Next until the install is complete. If you are running Vista, the installer won't be able to launch automatically, so you will have to manually launch Cygwin's Setup.exe.
Vista may warn you that Cygwin did not install correctly. Ignore this warning and tell Vista that the install was successful.
If you are running Vista, click on the Start menu, enter the following command, and press Enter:
C:\cygwin\bin\ash -c /bin/rebaseall
Install QuickTime SDK
Download QuickTime SDK for Windows from http://developer.apple.com/quicktime/download/ and install it to the default location (\Program Files\QuickTime SDK). This is needed for media support.
Once you have installed your developer tools, the next step is getting the code.


{% extends "base.html" %}
{% load entities %}
{% load stories %}
{% block title %}Stories groups you can edit - {% endblock title %}
{% block content %}
models
model groups
stories
protocols
datasets
fitting specifications
Your stories
{% can_create_entity 'model' as permission %}
{% if permission %}
Create a new story
{% else %}
Your account doesn't have the authority to create new stories; please contact us to request permission.
{% endif %}
Other stories
{% endblock content %}


Top Down City Official Server
Counter Strike
1.6
Server Name: Top Down City (CS 1.6)
Map: de_dust2
Server Status: 24/7
Ports: 27015
Check Server Ip
Admin & Vip member-ship
Youtube


Installing/Configuring Android Studio
1.
Please verify the JDK version is 1.8 or
above. By typing "javac
-version
" on your command prompt line.
1.1.
2. Verify or create environment variable “JAVA_HOME” is set to your JDK installed directory
2.1.
3. Review the system requirements for Android Studio/SDK at http://developer.android.com/sdk/index.html#Requirements
4. Download and install Android Studio
4.1. Go to https://developer.android.com/studio/index.html
4.2. Download Android Studio
4.2.1.
4.3. Follow the instructions
4.3.1.
4.3.2.
4.3.3.
4.3.4.
5. Once Installation is done, open Android Studio for the first time configuration
5.1. Follow the initial setup
5.1.1.
5.1.2.
5.1.3.
5.1.4.
5.2. Go to “Configure”
5.2.1.
5.3. Then “SDK Manager”
5.3.1.
5.4. Under the “SDK Platform” select the following:
5.4.1. Android 6.0
5.4.2. Android 5.1.1
5.4.3.
Android 5.0.1
5.4.4. And any other versions of android you may need
5.5. Under the “SDK Tools” select the following if not already selected:
5.5.1. Android SDK Build Tools
5.5.2. Android SDK Tools 24.4.1
5.5.3. Android SDK Platform- Tools 23.1
5.5.4. Documentation for Android SDK
5.5.5. GPU debugging tools
5.5.6. Local Maven repository for Support Libraries
5.5.7. Android Support Library
5.5.8. Android Auto Desktop Head Unit emulator
5.5.9. Google Play Services
5.5.10. Google Repository
5.5.11. Google Play APK Expansion Library
5.5.12. Google Play Billing Library
5.5.13. Google Play Licensing Library
5.5.14. Android Auto API Simulators
5.5.15. Google Web Driver
5.5.16. Intel x86 Emulator Accelerator (HAXM installer)
6. You are now configured to create/open Android projects. If you have any questions please refer to the following site: https://developer.android.com/studio/intro/index.html


Here is our pick for you: {{ random_business }}
{{number_review}} foodies have ranked this as 4 stars out of 5! Plus it's not expensive ;-)
Where is it: {{ where_is_it_0 }}, {{ where_is_it_1}}
Here is a peek into what you are getting here:


BitcoinX will continue to evolve. By embracing all existing platforms and networks, this allows various scenarios to be achieved on chain. Payment, clearing and settlement, supply chain, bills, evidence storage, copyright, IoT and gaming will be able to be integrated on the BitcoinX blockchain.
View source code Join us on TelegramSmart Contract, Timely transaction and other scenarios made possible with the features of high efficiency and low fees.
Providing unparalleled stability based on the Bitcoin network and Zero-knowledge proof to protect your Privacy.
Crosschain Technology - Data communication between chains are the foundation of application eco-system.
New: The new version adds Smart Contract features that require the miners to vote to activate the contract function.
The all new features will allow BCX become a future society proof Cryptocurrency. The combination of zero-knowledge proof, smart contract and DPOS consensus will enable the BitcoinX network to become more reliable, environmental-friendly and easier to use with greater efficiency. Therefore, BCX can build the ideal Bitcoin network that truly fits the future society.
Read MoreBitcoinX will continue to evolve. By embracing all existing platforms and networks, this allows various scenarios to be achieved on chain. Payment, clearing and settlement, supply chain, bills, evidence storage, copyright, IoT and gaming will be able to be integrated on the BitcoinX blockchain.
Ranging from Assets Digitization to Smart Contract - BCX will be everywhere.
Build the ideal Bitcoin system for the future and release the full potential of The Bitcoin Network
BitcoinX will continue to evolve. By embracing all existing platforms and networks, this allows various scenarios to be achieved on chain. Payment, clearing and settlement, supply chain, bills, evidence storage, copyright, IoT and gaming will be able to be integrated on the BitcoinX blockchain.
Smart Contract, Timely transaction and other scenarios made possible with the features of high efficiency and low fees
Providing unparalleled stability based on the Bitcoin network and Zero-knowledge proof to protect your Privacy.
Data communication between chains are the foundation of application eco-system.
In the network of BitcoinX, USERS can issue assets by simply drag & drop. Timely exchange is also supported. BCX will create a complete, effective blockchain contract system for assets circulation.
BitcoinX will continue to evolve. By embracing all existing platforms and networks, this allows various scenarios to be achieved on chain. Payment, clearing and settlement, supply chain, bills, evidence storage, copyright, IoT and gaming will be able to be integrated on the BitcoinX blockchain.
Supply: 210 Billion
Block Interval: 2 Minutes
Difficulty Adjustment: DAA
Block Size: 2M
Consensus: POW → DPOS
Fees: Low
Segwit: Supported
Replay Protection: Supported
Unique Address Format: Supported
Lightning Network: Supported
Privacy Protection: Zero Knowledge Proof
Smart Contract: Supported
This version adds smart contract features that require the miners to vote to activate the contract function.
The BCX Network is in high speed development. During this process, we have established long-term partnership with many excellent blockchain companies.
Capacity increases for the BitcoinX system
BCX Contributors From around the globe.
Designer / Webdeveloper
Marketing
Translator
Marketing
The new BitcoinX website has just been released! If you have any questions, please let us know on Telegram.
Join Telegram


In The Secret
1
In the secret, in the quiet place.
In the stillness You are there.
In the secret, in the quiet hour
I wait, only for You.

I want to know You more.
I want to touch You.
I want to see Your face.
I want to know You more.
3
I am reaching, for the highest goal,
That I might receive the prize.
Pressing onward,
Pushing every hindrance aside
Out of my way.
Cause I want to know You more.


Everyone has problems to deal with but sometimes life can be very difficult. When you feel like your problems are building up and overwhelming you, it may be a good idea to receive professional help. Here are six good reasons to seek professional counseling.
Positive Action – If you decide to see a counselor about specific problems, you take positive action towards getting something important done. Sometimes it can make you feel better just knowing that you are making an honest effort to improve. This provides hope and hope is a good reason to get up in the morning. You may never solve all of your problems and live a life of ease, but at least you are trying.
A Better Relationship with Your Significant Other – Perhaps your main issue has nothing to do with your spouse or partner. This may not matter much in the long run. When you have emotional issues they are bound to eventually spill over into your private life, and this includes your love life. Before you have to see a divorce lawyer, now is the time to talk about your problems with a professional.
Improve Job Performance – One cannot function well at work if all is not right. The more upset you become, the more distracted you are. This can make you much less efficient at your job and your work may suffer a great deal. Things like this could jeopardize your employment and if you lose your job, an entire Pandora's Box of issues may suddenly appear. If you get professional help soon, you may avoid a huge disaster.
Fewer Distractions in Life – If you think about your problems while you drive down the road, you are not a very good driver. It only takes a split second to enter a scenario where you need all of your reflexes. You need to deal with major problems now so you will not be distracted during important times.
 That is why it is always best to be a positive role model for your children. Kids need someone to look up to and to emulate, and you have a responsibility to your children to give them the best possible life.
Confidentiality – You can talk to a friend or a relative about your troubles and this can be a big help. However, many people feel they have to tell others about things and your problems could be public knowledge before you know it. One of the best reasons to seek counseling is the confidentiality. No one has to know what you are doing. Professionals have high standards and do not reveal information disclosed in a confidential conversation or meeting.
If you feel like you have lost your ability to cope, now is a great time to contact a professional for counseling. These are affordable services that can make a big difference in your life. Many of these services are covered by health insurance.


Fecha_Creacion: 2017-11-10T13:09:29.566Z
Dimension: Replacement Dimension
Id_Locacion: 9
Nombre: Purge Planet
Residentes: ['https://rickandmortyapi.com/api/character/26', 'https://rickandmortyapi.com/api/character/139', 'https://rickandmortyapi.com/api/character/202', 'https://rickandmortyapi.com/api/character/273']
Tipo: Planet
URL: https://rickandmortyapi.com/api/location/9


The status
field within the Streetview Metadata response object
contains the status of the request. The status
field may contain
the following values:
OK
indicates that no errors occurred; a panorama is found and
metadata is returned.
INVALID_REQUEST
indicates that the request was malformed.
NOT_FOUND
indicates that the address string provided in the
location
parameter could not be found. This may occur if a
non-existent address is given.
ZERO_RESULTS
indicates that no panorama could be found near the
provided location. This may occur if a non-existent or invalid
pano
id is given.
OVER_QUERY_LIMIT
indicates the requestor has exceeded quota.
REQUEST_DENIED
indicates that your request was denied. This may
occur if you did not
authorize
your request, or if the Street View Static API is not activated in the
Google Cloud Console project containing your API key.
UNKNOWN_ERROR
indicates that the request could not be processed
due to a server error. This is often a temporary status. The request may
succeed if you try again
Generated from the OpenAPI specification. Edit Report bug


Specify the location of where the regression results will be stored. By default, VRM creates a directory called VRMDATA which will be located in the CWD where the regression is invoked. Alternatively this can be overridden by specifying a directory upon invocation as follows:
vrun -vrmdata <my_directory> ...


The main component code in is licensed under the following terms and conditions:
Copyright (C) 2014-2015 by Institute of Mathematics and Computer Science, University of Latvia Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The code makes calls to the PapaParse library under the MIT License:
Copyright (c) 2015 Matthew Holt Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The code makes calls to the Dojo library. We have chosen to use the dojo libraries subject to the BSD 3-clause license.
Notice that some third-party modules may contain other explicit declarations of copyright.
Copyright (c) 2005-2014, The Dojo Foundation All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the Dojo Foundation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.


Select your state: California Texas Washington
Submit
Select your Town: Oceanside Houston Settale
Heated Seats
Tinted Windows
Cruise Control


Here, you can search the name of a driver and see all the cars they drive.
When you click search after entering some text in the driver field, this script will make a call to a PHP script, looking in the database for cars with the driver you type.
The contents will then be returned to you on this page, formatted in a table


My First Circuit
Welcome to electronics 101: My First Circuit. I had to write this post because I have brain glitches and sometimes forget how a breadboard works…
When you learn to code, the first thing you learn is how to output the string Hello World!
. Allow me to introduce the electronics equivalent of the hello world: a circuit which lights up a LED.
For this circuit, you’ll need:
- a breadboard
- a power source
- a LED
- a resistor
- jumper wires
I recommend getting a “electronics starter kit”, they should all have these basic components.

- Power rail: composed of two horizontal lines of metal
- the red line for the
+
- the blue (or black) line for
-
- note: the top and bottom power rails are not connected, you’ll need to connect them with jumper wires if you want the same power source on both sides.
- the red line for the
- Terminal strips: Pins of a vertical row are connected
- DIP support: used to plug integrated circuit with a DIP package. Legs of the chip are on each side of the ravine and prevent them from being connected.
If you’re interested by the anatomy of a breadboard, check out “how breadboard works” on Engineers Garage.
To connect components on the breadboard, we use jumper wires. Jumper wires are named from their end points:
- If the end has a pin and can be plugged into something, it’s a male end.
- If the end has a slot and things are plugged into it, it’s a female end.
This gives us 3 types of jumper wires:
To connect 2 points on the breadboard, you’ll need a M/M jumper.
Powering up the breadboard
There are many ways to supply power to the breadboard! I’ll present two of them that I can actually show, check out Seven Ways To Supply Power To Your Breadboard for more ideas.
With A Microcontroller
If you have a microcontroller, like an Arduino, you can connect the power and ground pins of the Arduino to the breadboard:
The Arduino itself can be powered from a USB cable either plugged to a computer or a battery pack.
With A Power Supply Module
These tiny modules can be plugged directly in the breadboard! Here’s is the “MB102 breadboard power supply” module:
It can output 3.3V or 5V, and you can choose the value for the top power rail and the bottom power rail. It also has a power on/off switch with a LED indicating the state.
The power supply board gets its power from a barrel jack power supply. What’s going on with the USB port: honestly, I have no idea. Some sources say you can also power it from the USB, but I’m not entirely sure what’s going on with the USB female socket. If it was an input USB, I would expect a micro USB rather than a USB female. Anyway, I don’t have male-male USB cable to test, so I just don’t use the USB on that board.
/!\ Avoid mixing up the +
and -
of the module with those of the power rail when you plug it in, this will reduce the risk of having reverse polarity in your circuit.
Building The Circuit
A LED has 2 pins:
- a long pin (anode) as the positive part
- a short pin (cathode) as the negative part
A LED will only light up if current is applied in the right direction: from positive to negative.
Your first instinct might be to build something like this:
A LED might be fine with a 3V powered circuit, but the LED will be permanently damaged if the circuit is powered with 9V for example. Kirchoff’s voltage law (or KVL) states that in any closed loop of the circuit, the sum of voltages is 0. This means that any current injected in the circuit must be consumed. We need to add an element which will consume the excess current: that’s exactly what resistors are for!
LEDs typically work with 20mA, so a 220 Ohm resistor is fine here. We’ll see later how to calculate and pick a resistor for a given LED.
The circuit works as follows:
- A red wire goes from the Arduino 5V pin to the positive power rail
- An orange wire connects the positive power tail to the positive pin of the LED (the anode)
- The negative pin of the LED (the cathode) is connected to one end of the resistor
- A blue wire connects the other end of the resistor to the negative power rail
- The negative power rail is connected to the GND pin of the Arduino
And when you actually apply power (the Arduino wasn’t powered up before):
Huzzah, the LED lights up! Impressive, I know, I know.
Links
That’s it for tonight gentlefolks, stay awesome.


| Total net profit | -1.12 | Gross profit | 69.74 | Gross loss | -70.86 |
| Profit factor | 0.98 | Expected payoff | -0.02 | ||
| Absolute drawdown | -14.26 | Maximal drawdown (%) | 25.38 (212.56%) | ||
| Total trades | 48 | Profit trades (% of total) | 19 (39.58%) | Loss trades (% of total) | 29 (60.42%) |
| Long positions (won %) | 22 (45.45%) | Short positions (won %) | 26 (34.62%) | ||
| Largest | profit trade | 3.74 | loss trade | -9.2 | |
| Average | profit trade | 3.67 | loss trade | -2.44 | |
| Minimum | profit trade | 3.58 | loss trade | -0.36 | |
| Maximum | take profit in pips | 37.3 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 36.6 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 35.7 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 6283 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 1030.2 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 51 | |
| Maximum | consecutive wins (profit in money) | 5.0 (18.28) | consecutive losses (loss in money) | 7.0 (-20.57) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | 13.02 | Gross profit | 36.53 | Gross loss | -23.51 |
| Profit factor | 1.55 | Expected payoff | 0.59 | ||
| Absolute drawdown | -4.33 | Maximal drawdown (%) | 9.74 (43.97%) | ||
| Total trades | 22 | Profit trades (% of total) | 10 (45.45%) | Loss trades (% of total) | 12 (54.55%) |
| Largest | profit trade | 3.7 | loss trade | -5.8 | |
| Average | profit trade | 3.65 | loss trade | -1.96 | |
| Minimum | profit trade | 3.58 | loss trade | -0.7 | |
| Maximum | take profit in pips | 36.9 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 36.5 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 35.7 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 6283.0 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 1406.5 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 146.0 | |
| Maximum | consecutive wins (profit in money) | 5.0 (18.28) | consecutive losses (loss in money) | 4.0 (-9.74) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | -14.14 | Gross profit | 33.21 | Gross loss | -47.35 |
| Profit factor | 0.7 | Expected payoff | -0.54 | ||
| Absolute drawdown | -25.85 | Maximal drawdown (%) | 29.53 (802.45%) | ||
| Total trades | 26 | Profit trades (% of total) | 9 (34.62%) | Loss trades (% of total) | 17 (65.38%) |
| Largest | profit trade | 3.74 | loss trade | -9.2 | |
| Average | profit trade | 3.69 | loss trade | -2.79 | |
| Minimum | profit trade | 3.66 | loss trade | -0.36 | |
| Maximum | take profit in pips | 37.3 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 36.7 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 35.7 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 2344.0 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 711.8 | |
| Minimum | Reward-Risk | Duration in minutes | 51.0 | ||
| Maximum | consecutive wins (profit in money) | 2.0 (7.34) | consecutive losses (loss in money) | 6.0 (-15.23) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |


Credit Card
Name on card:
Card number:
Expiry Date:
Security Code:
Customer name:
Telephone:
Buzzer code:
E-mail address:
Small
Medium
Large
Bacon
Extra Cheese
Onion
Mushroom
Preferred delivery time:
Delivery instructions:
Submit order
Password:
Confirm password:
Enter a breed: or select one from the list: (none selected) Abyssinian Alpaca
Name:
Address:
Description:
Which course would you like to watch today?
Course: Lecture 01: Powers of Ten Lecture 02: 1D Kinematics Lecture 03: Vectors Lecture 01: What holds our world together? Lecture 02: Electric Field Lecture 03: Electric Flux Lecture 01: Periodic Phenomenon Lecture 02: Beats Lecture 03: Forced Oscillations with Damping
Comment:
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Gets a list of roles available on a specified site.
Returns a list of site role objects, with the below fields:
id
- the role id in the database (integer)name
- the user friendly name, to be used as label (string)Accepts and returns JSON as Content-Type.
Request:
GET /api/v2/admin/sites/current/roles HTTPS/1.1
Connection: keep-alive
Content-Type: application/json
Authorization: {site_authorization_token}
Response:
HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8
Content-Length: length
{
"CurrentPage":1,
"Items":[
"Role":
{
"Id": 10000,
"Name": "Admin"
},
"Role":
{
"Id": 10013,
"Name": "Users"
}
],
"ItemsPerPageCount":100,
"Links":[
{
"Rel":"previous",
"Uri":null
},
{
"Rel":"next",
"Uri":null
}
],
"TotalItemsCount":2
}
This method will return the following error codes:
200
- success401
- when authentication token is incorrect403
- when the user attempting the call does not have the proper permissions


RESTful API (Models, Serializers and Views)¶
Introduction¶
An API is an application program interface - a set of functions and procedures that allow the creation of applications or scripts that can access the information, features or data of a separate operating system, application or service.
A RESTful (REpresentational State Transfer) API is an API written in a way that complies to a number of constraints to allow it to be used as a web service. These services are written so that different services across the internet are interoperable. RESTful web services use stateless operations - no session information is retained by the receiver – to allow requesting systems to manipulate text-based representations of web resources (information identifiable by URL).
RESTful APIs use HTTP methods to allow a number of different operations to be performed on a web resource over the web. The most common operations are:
GET: retrieve information
POST: send information to the server to create or update a resource
PUT: send information to the server to create or update a resource
DELETE: delete a specified resource
A more complex overview of these methods (and others) is available here: https://www.w3schools.com/tags/ref_httpmethods.asp
A more thorough explaination of RESTful APIs is available here: https://searchapparchitecture.techtarget.com/definition/RESTful-API
Django REST framework (DRF)¶
In fragalysis, our RESTFul API is built using Django and Django REST framework (DRF).
DRF uses standard classes to define what information can be accessed by users, how they can access that data, and how it is returned to the user when they make a request.
 Models
are the django representation of database
tables (for an example, see [link]).
Once we have a model containing data that we want to allow a user to access, we need to be able to convert that data to
a format that can be rendered in various appropriate forms (e.g. JSON or XML). To do this, we use Serializers.
These Serializers
simply inherit a ModelSerializer
class, and all we have to tell the Serializer is what
Model
we want to operate on, and which fields
from the model we want to use (make accessible to the
user).
Once we’ve serialized our data, we next need to write Views
. Views define what types of requests (e.g. GET) can
be performed against a resource, and what we do with that request to return information.  We can define, using standard django methods,
what filter we want to perform on the relevant Model
, which fields
from the model we want to allow the
user to filter by (filter_fields
) and what Response
we want to post back to them.
Finally, we need to specify an endpoint
(i.e. URL) that the View
is served at, so the user can make
requests against the web service.
EXAMPLE - Model, Serializer, View and URL for Target model¶
Model
The Target model contains information about a protein target. In django, we define this as an inhereted Model instance:
from django.db import models
class Target(models.Model):
"""Django model to define a Target - a protein.
Parameters
----------
title: CharField
The name of the target
init_date: DateTimeField
The date the target was initiated (autofield)
project_id: ManyToManyField
Links targets to projects for authentication
uniprot_id: Charfield
Optional field where a uniprot id can be stored
metadata: FileField
Optional file upload defining metadata about the target - can be used to add custom site labels
zip_archive: FileField
Link to zip file created from targets uploaded with the loader
"""
# The title of the project_id -> userdefined
title = models.CharField(unique=True, max_length=200)
# The date it was made
init_date = models.DateTimeField(auto_now_add=True)
# A field to link projects and targets together
project_id = models.ManyToManyField(Project)
# Indicates the uniprot_id id for the target. Is a unique key
uniprot_id = models.CharField(max_length=100, null=True)
# metadatafile containing sites info for download
metadata = models.FileField(upload_to="metadata/", null=True, max_length=255)
# zip archive to download uploaded data from
zip_archive = models.FileField(upload_to="archive/", null=True, max_length=255)
This model tells us which fields we want in our database table, what data type each field can contain, and some other optional parameters, such as wether the field has to have data in it, or the maximum length of data that can be added to the field.
Serializer
The Target serializer tells us what information from the Target model we want to pass back to the user when they make a request.
from rest_framework import serializers
from viewer.models import Target
class TargetSerializer(serializers.ModelSerializer):
template_protein = serializers.SerializerMethodField()
def get_template_protein(self, obj):
proteins = obj.protein_set.filter()
for protein in proteins:
if protein.pdb_info:
return protein.pdb_info.url
return "NOT AVAILABLE"
class Meta:
model = Target
fields = ("id", "title", "project_id", "protein_set", "template_protein", "metadata", "zip_archive")
The serializer uses the DRF serializers.ModelSerializer
class. We define the model
and fields
in
a Meta
subclass, where the model
is an instance of the Model
we want to operate on, and the
fields
parameter is a tuple containing the names of the fields we want to return as strings. Additionally, we
can add extra fields, and add a method to define how we get the value of the field. For example, in this
Serializer
we have added the template_protein
field, and defined how we get its value with
get_template_protein
.
View
This View
returns a list of information about a specific target, if you pass the title
parameter to the
request, or a list of information about all targets if you make a request against the URL.
The View
is written as a class inheriting the DRF ReadOnlyModelViewSet
, which is a standard View
class that is read-only. That means that only GET requests can be made against this view. There are other ways to define
this for different types of view, but we won’t go into detail here - this is the method we have chosen to use with most
of our standard views.
Additionally, in the actual code, you will notice that TargetView(viewsets.ReadOnlyModelViewSet)
is replaced by
TargetView(ISpyBSafeQuerySet)
. ISpyBSafeQuerySet
is a version of viewsets.ReadOnlyModelViewSet
that includes an authentication method specific for the deployment of fragalysis at https://fragalysis.diamond.ac.uk
from rest_framework import viewsets
from viewer.serializers import TargetSerializer
from viewer.models import Target
class TargetView(viewsets.ReadOnlyModelViewSet):
""" DjagnoRF view to retrieve info about targets
Methods
-------
url:
api/targets
queryset:
`viewer.models.Target.objects.filter()`
filter fields:
- `viewer.models.Target.title` - ?title=<str>
returns: JSON
- id: id of the target object
- title: name of the target
- project_id: list of the ids of the projects the target is linked to
- protein_set: list of the ids of the protein sets the target is linked to
- template_protein: the template protein displayed in fragalysis front-end for this target
- metadata: link to the metadata file for the target if it was uploaded
- zip_archive: link to the zip archive of the uploaded data
example output:
.. code-block:: javascript
"results": [
{
"id": 62,
"title": "Mpro",
"project_id": [
2
],
"protein_set": [
29281,
29274,
29259,
29305,
...,
],
"template_protein": "/media/pdbs/Mpro-x10417_0_apo.pdb",
"metadata": "http://fragalysis.diamond.ac.uk/media/metadata/metadata_2FdP5OJ.csv",
"zip_archive": "http://fragalysis.diamond.ac.uk/media/targets/Mpro.zip"
}
]
"""
queryset = Target.objects.filter()
serializer_class = TargetSerializer
filter_permissions = "project_id"
filter_fields = ("title",)
The docstring for this class is formatted in a way to allow a user or developer to easily read the docstring, and understand the URL to query, how the information is queried by django, what fields can be queried against, and what information is returned from a request against the views URL. All of the views in this documentation are written in the same way.
URL
Finally, we need to define where the view is served from, in context of the root (e.g. https://fragalysis.diamond.ac.uk)
URL. The target view is served at <root>/api/targets
. In api/urls.py
we use the following lines to add
the TargetView
to that endpoint:
from rest_framework.routers import DefaultRouter
from viewer import views as viewer_views
router = DefaultRouter()
router.register(r"targets", viewer_views.TargetView, "targets")
The DRF DefaultRouter
provides a simple, quick and consistent way of wiring ViewSet logic to a set of URLs.
Router automatically maps the incoming request to proper viewset action based on the request method type.
To make sure that we serve the URLS from api/urls.py
, we include the URLs from there in
fragalysis/urls.py
:
...
url(r"^api/", include("api.urls")),
...
and specify this file as the URL_ROOTCONF
in fragalysis/settings.py
- the django settings file:
ROOT_URLCONF = "fragalysis.urls"
If we navigate to the URL <root>/api/targets/?title=<target_name>
we are presented with the following page:
This is a page automatically generated by DRF, and includes options to see what kinds of requests you can make against this endpoint.


ID
|
FirstName
|
MiddleInitial
|
LastName
|
Prefix
|
Suffix
|
BSUNumber
|
CISNumber
|
SSN
|
DOB
|
Gender
|
Race01
|
Race02
|
Race03
|
Race04
|
Race05
|
Ethnicity
|
MaritalStatus
|
PrimaryLanguage
|
Religion
|
Citizen
|
CitizenDate
|
Type
|
SpecialPopulation01
|
SpecialPopulation02
|
SpecialPopulation03
|
SpecialPopulation04
|
CreatedBy
|
CreatedDate
|
CreatedFrom
|
UpdatedBy
|
UpdatedDate
|
UpdatedFrom
|
AssumedDOB
|
MR
|
MH
|
CY
|
EI
|
Crisis
|
DA
|
SO
|
Pharmacy
|
MHCM
|
MRCM
|
EICM
|
SOCM
|
CYCM
|
DACM
|
CRISISCM
|
PRIORMHCM
|
PRIORMRCM
|
PRIOREICM
|
PRIORSOCM
|
PRIORCYCM
|
PRIORDACM
|
PRIORCRISISCM
|
MAIDNUM
|
MCINUM
|
MHXNUM
|
MRXNUM
|
EIXNUM
|
ClosureDate
|
ClosureReason
|
ReceivingOrganization
|
TransferReason
|
TransferEffectiveDate
|
DrugOfChoice
|
DrugOfChoice2
|
CBHNP
|
MA
|
BillingEligible
|
veteran
|
MergedMemberID
|
CYMenu
|
AbuseRids
|
InquiryRids
|
ScreeningLog
|
CIS
|
Prothonotary
|
ClerkOfCourts
|
Other
|
OtherDescription
|
CD
|
PAGENUMBER
|
LegalCustody
|
PhysicalCustody
|
PowerOfAttorney
|
LegalGuardian
|
FinancialGuardian
|
Release
|
EmergencyContact
|
Agency
|
Notes
|
Grade
|
PreviousMemberID
|
MAPPRCase
|
Caseworker
|
EverAdopted
|
AdoptedAge
|
DeceasedDate
|
JCMS
|
SchoolName
|
WorkplaceName
|
UnableToDetermine
|
HasChildEverBeenAdopted
|
CWISParticipantID
|
CWISAnonymous
|
CWISConfidential
|
CWISPersonNotExist
|
CWISUnknownDOB
|
CWISUnknownSSN
|
CWISEstimatedDOD
|
CWISMilitaryCode
|
CWISEducationLevelCode
|
CWISEmploymentLevelCode
|
CWISChildLivingArrangementCode
|
CWISGenderNotReported
|
GuardianPrimaryLanguage
|
requiresCo
|
NameChangeDate
|
EmploymentStatus
|
MilitaryService
|
Orientation
|
SubstanceAbuse
|
MHEpisode
|


QUESTION PAPER
Semester 1
THEORY
English Language & Technical Communication-I [HU101]
Physics-I [PH101]
Mathematics-I [M101]
Basic Electrical & Electronic Engineering-I [ES101]
Engineering Mechanics [ME101]
Basic Computation & Principles of Computer Programming-I [CS101]
PRACTICAL
Physics-I [PH191]
Basic Electrical & Electronic Engineering I [ES191]
Engineering Drawing & Computer Graphics [ME191]
Workshop Practice [ME192]
Basic Computation & Principles of Computer Programming-I [CS191]
SESSIONAL
Language Laboratory-I [HU181]
Extra Curricular Activities(NSS/NCC/NSO etc)


@main("") {
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item.[1][2] Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services,[3] life insurance, persons (online dating), and Twitter followers.
definition from wikipedia


Ping Pong
Here's how to play!
Enter a number and it will be counted up to by 1, with the following exceptions:
Any number divisible by 3 will become "Ping"
Any number divisible by 5 will become "Pong"
Any number divisible by 15 will become "PingPong"
Enter your number here:
Start Pinging and Ponging
Reset


|
Mobiles
6,69,973 Ads
|
Electronics & Appliances
5,31,214 Ads
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Cars
2,94,782 Ads
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Bikes
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Pets
2,49,545 Ads
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Books, Sports & Hobbies
99,081 Ads
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Fashion
73,340 Ads
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Kids
22,916 Ads
|
Services
2,26,679 Ads
|
Jobs
16,17,724 Ads
|
Real Estate
14,94,136 Ads
|
Trending Ads


Provides functions to fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; and evaluate variation in linear variables with time (or other circular variables).
| Version: | 1.1 |
| Depends: | methods |
| Imports: | circular, overlap, pbapply |
| Published: | 2016-09-26 |
| Author: | Marcus Rowcliffe |
| Maintainer: | Marcus Rowcliffe <marcus.rowcliffe at ioz.ac.uk> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | activity results |
| Reference manual: | activity.pdf |
| Package source: | activity_1.1.tar.gz |
| Windows binaries: | r-devel: activity_1.1.zip, r-release: activity_1.1.zip, r-oldrel: activity_1.1.zip |
| OS X binaries: | r-release: activity_1.1.tgz, r-oldrel: activity_1.1.tgz |
| Old sources: | activity archive |
Please use the canonical form https://CRAN.R-project.org/package=activity to link to this page.


Prominent NPA advocate dies after court shooting accident
by Kaveel SinghSenior state advocate Addelaid Ferreira-Watt has died after she was shot at the regional court in Ixopo, KwaZulu-Natal, in what appears to be an accident on Monday.
A case of culpable homicide was being probed after the apparent freak shooting, KZN police confirmed.
The weapon - believed to be a loaded shotgun - was brought to court as evidence.
"It is alleged that the weapon was brought to court to be entered as evidence in a house robbery case when it accidentally discharged in court," said Brigadier Jay Naicker.
UPDATE | Shattered and heartbroken: Tributes pour in for top state advocate Ferreira-Watt
He said that the 51-year-old later died in hospital. "She was taken to hospital after she was struck by a bullet on the left of the hip."
"Unfortunately, we cannot divulge further information as the investigations are at a sensitive stage," he said.
Several people, including former Hawks KZN boss Johan Booysen, have expressed their shock at Ferreira-Watt's death.
- Additional reporting by Sheldon Morais


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User/CompanyDetails
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--- title: Pioneer Day Plants 2015 date: 2015-07-24 00:00:00 -06:00 categories: - whats-blooming layout: post blog-banner: whats-blooming-now-summer.jpg post-date: July 24, 2015 post-time: 9:39 AM blog-image: wbn-default.jpg ---
Desert Willow Chilopsis linearis
This water-wise native tree can be seen in our Children's Garden and Parking Lot beds.
Globe Thistle Echinops ritro
Found on our Floral Walk, this bee magnet comes in various shades from dark purple to white.
Red Yucca Hesperaloe parviflora
The red flowers of this desert succulent attract hummingbirds.
Oakleaf Hydrangea Hydrangea quercifolia 'Snow Queen'
This hardy Hydrangea can grow in full or part sun and can be found in the Rose Garden and Floral Walk.
Flower-of-an-hour Hibiscus trionum
Although beautiful, this large-flowered annual is a non-native garden weed that is found in almost every state. Its common name was given because of a very short flower life.
Lesser Goldfinch Spinus psaltria
This pair of birds was seen drinking from our wayside fountain. This is a great place to get out of the sun and take a break, but we don't recommend you drink out of the fountain.
The Garden changes with every passing day, it's a different experience each time you visit.


--- layout: page title: 'Office of Emergency Management' ---
phone:<PHONE_NUMBER>
Hours
8:30–4:30
3 Year Term:
Mike Tarantino, Coordinator
Matt Ziemkiewicz, Deputy Coordinator
1 Year Term:
Mayor Frank Nunziato
Jennifer Galarza, Administrative Officer of Health
Thomas Riser, Fire Chief
Mike Tarantino, Rutherford EMS
John Hurley, School Superintendent
Frank Recanati, Construction Official
Mike Tarantino, Rutherford EMS
Police Chief John Russo
Genevieve Kacmarcik, Social Service Director
Kim Bogosian, Public Information Officer
Robert Kakoleski, Borough Administrator
The Office of Emergency Management (OEM) is responsible for the planning and coordination of activities during natural disasters and other emergencies. Operating under an emergency operations plan, the OEM coordinates the activities of emergency service units during these incidents.
Auxiliary police officers serve as a adjunct to the OEM in emergencies and also assist the police department during parades, festivals and other community events. As volunteers who hold other jobs, these individuals are a valuable resource to the community whenever they are needed.


--- layout: default ---
I have expertise in different fields of renewable energy and mechanical engineering. The projects I worked with included heatpumps, CHP, storage solutions, cooling systems, solar thermal, wave and wind energy systems.
Renewable Energies are necessary to ensure a sustainable future. As an engineer I believe in innovative energy management and concepts to integrate energy efficient technology in our daily life.
I'm an open-minded person who loves to study and implement new technologies and approaches. The balance between scientific research and applied technology is important to me.
I advocate for equal opportunities for all genders. As a member of "hypatia - Frauennetzwerk Erneuerbare Energien und Cleantech e.V." and "Women of New Energies e.V." I am part of a growing network of amazing women supporting each other in the professional context.


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I IN THE HIGH COURT OF KARNATAKA AT BANGALORE DATED THIS THE 10'?" DAY OF SEPTEMBER 2009. PRESENT THE HONBLE MR. P.D. DINAKARAN, CHIEE 1. AND THE HONELE M1-2.JUST1CA_E = WRIT PETITION No.23646'1/2002;. . T O' BETWEEN: A O' h 1 THEKARNATA1<A:«*STATE.«""' - POLLUTION CONTRCL E-OAPD " BYITS MEMVER SE,'CY'., ' "PARISARA 13HAVANA"" ' NO. 49, CHLIRCHZ-ST.';' V. . = EANGALc'EE.'560Eo1g' PETITIONER (By Sri/Sniit : D«--NAGARAJ;ADV_.V';. AND 1 Mrs BAGALKOT CEMENT & INDUSTRIES LIMITED. HAVING OFFICE AT AIR 1ND1A ' 'BUILDING, '14TH'FLOOR, NARIMAN POINT, » MUMBAI. AN.D__1T_-3 PRODUCTION FACILITY A1>JD'"w0RKS AT BAGALKOT--587 1 1 1 _ .R:EP.»EY.  THIS WRIT PETITION COMING ON FOR HEARING ON THIS DAY, SABHAHIT J ., MADE THE FOLLOWII\IG:-- ORDER
This writ petition is ﬁled urider Articles 227 of the Constitution of India being order dated l6/ 4/ 2009 passedfoy "the Karnatal;a'Stat'e Appellate Authority at Bangalore:(he_reinafter"ca'lled"the~--,;L 'Appellate Authority') wherein,'-lthe "Appellatea"'Authorityit has allowed the Appeal N_o,'5'/éQtlV$i'-._andé 'set aside the order impugned in remitted the matter to the' of: the --:.=Karrilataka,V.fflgollution Control Board and to reconsider the "'a:ft.er appellant a reasonable opportunitypof"beir:ig.:h'eard;'as provided under law. Itis av'erre_d.»in the petition that the respondent .._Cement and industries Limited, is manufacture of cement of capacity 1000 Tones. day. The petitioner ~ Board had issued A to the respondent -- Industry both under the Water (Prevention 8: Control of Pollution] Acts, 1974 (hereinafter referred to as the 'Water Act') and Air (Prevention 8: Control of Pollution) Acts, 1981 \.53 3 (hereinafter referred to as the 'Air Act') which is valid up to 30/6/2008. Thereafter, the consent has npot/"been Validated and the industry had filed an _ 2/5/2008, the petitioner -- Board has refn.=:.e4d:*«». both under the Water Act and A._.ctLb}uf'aits...or<ierjd_a'ted 26/2 /2009. The Regional sﬁicer inspected:'the..i_ridnstry.it = on 16/5/2008 and 20/6_/4_2'0.08..__and certain observations regarding.' J u"in'ad'eq_uaCy in the resp0ndent--facto:_y an'd"'accord_i'n_g1ij', cause notice to the on 19/7/2008, whic1j.'dV\ifas' the representatives of theVV"re's'ponderitjV' appeared before the State Level ﬂonsent 'Ccrninittee and in the meeting it was 0 dec'ide3d' that theH"8enior Environmental Officer of the the piant and decision will be taken on"4«con._sent3'sought by the industry upon his report and byg_an"~o:rder dated 26/2/2009, the petitioner - Board 8' "refused consent under both Water Act and Air Act. The order was chalienged by the respondent in Appeal No.5 and though the order refusing consent was set U1 aside by the Appellate Authority, the fact remains that respondent - Industry has no counsel and the operation of industry is in violation of mandatory p:'fo'Vl--s1en's:.-'of _ Section 21 of the Air Act and Section \7l[at;er"= Act and therefore, it is illegal. yt"o,€':;t officials from the Central":_P_ollution ('ford short 'CPCB'), Zonal Ofﬁce_.._:lTI3'3I1é'5f=I10r§§,«.hiiclyyvviinspected the respondent on 8"'/"1al--/ .ri1a.de the following observationsvzf' V' V V . z t """ ope-rating without valid 'ill/ater (Prevention and Control lofVPoA'llu:tion}V'l. Act, 1974 and the Air an.dﬁConirol of Pollution) Act, .....
2) As per the consent dated 2007 given by the Karnataka Board, n 'Lenten expired on 30/6/2008 the unit is not supposed to operate the rotary kiln and the raw mill without the prior consent from the Board. But, subsequently an addendum ws issued on 12/3/2008 in which permission was granted to operate both kiln and raw mill \/'* with suitable air pollution control measures. At the time of inspection, both the raw mill and the rotary kiln were found in operation.
 The particulate from cement mill and clinker'»_co'o_ler we_re'A'quite*:.4 it high i.e., 2099 trig/N'rri3'~.t_and.V'3OO6VA respectively and exceeded _presc'ribed_ll'n1it of 100
4) was the unit is operating 5u2itho:iﬁ..i'15rop;ér lair pollution control condition laid down in theVV_adde:idu«.h:-- mgr dated 1 2/3/2008.
5) '"Fug.iiive emission control system it been provided, as per CREP rccofmﬁewiidations.
V 6) Source emission monitoring facilities not provided as per Emission Regulation Part -- III.
7) Overall house keeping of the unit is not satisfactory."
\,)~ 6
3. In exercise of the powers conferred under Section l8(1)(b] of the Air Act, 1981, the Chairman of CPCB issued directions to the respondent -- Industry and the directions read as fo1lows:-- V dd " 1) To stop operation of the indii's;trg. A the required air pollution control:sys-tem'"*to''_ '4 meet the emission standa»rds».,atjj.ce1Tient. 'm:i1__' f and clinker cooler section are'-provided: "and'=-4 consent issued by the State Board.' it 2} To subnfiit aciioiijalan for adhering to A " 3) on line emission n1oni_torinfj the stacks attached to :' -«,»5;w 'and cement mill with logging arrangerrientllat control room. * - provide suitable dust suppression it to control the fugitive emission from x the raw material storage and coal yards.
5) To improve the house keeping in such a manner so that the dust arising due to \/Q movernent of vehicles and conveyer belt operations are minimized.
6) To establish an environme_nt._'__i' management system within the indusﬁy V" l monitoring all the environmental.coriditjtons',_ it
7) To furnish a banlcgiiarantee an appropriate amount State Board." ' '
4. The 'issued speciﬁc-
direction to pe'tiiioner to stop the operatiionlvofxtiie ilndustry till the required air pollutionl is provided to meet the emission stanldards "at 'cement mill and clinker cooler
- v it.,5ec'tion»--."and._to time bound action plant from the Industry for adhering to CREP rec"o__mrnend'ations and etc. Pursuant to the said- Vhciiijeciionl, petitioner -- Board by its order dated >1_l1';'3'l/2009 issued directions to the respondent -
-~-Industry as directed by the CPCB under the Act. Since the direction issued by CPCB are binding on the State U Pollution Control Board and the petitioner -- Board in order to implement the decision taken by the CPCB. issued directions to the respondent -- Industry. an order passed by the petitioner -- No.5/ 2009 was challenged before» :A¢§pe1late--«., Authority, contending that direction 'beeri«-igitieri as required under Rule 204A of the Control of Pollution) Rules, The appeal was contested by filing Vtiael' 'objections by the petitioner -- Board. Ti'ie'*A:ppellate has passed the appeal and the matter was re1riitteVd'bacl};._to--.'petitioner -- Board for passing fresh. orders accordance with law and being aggrieved by their said orderlvdated 16/5/2009 passed by the in Appeal No.5 / 2009, petitioner has p1"eferredvi'.3' this writ petition contending that the .. petitioner -- Board is bound by the directions issued by it '~_Vthe':CPCB under Section 18(1) (b) of the Air Act and the
-"said Board cannot take any other decision and has to implement the direction issued by the CPCB and the Q TO Authority ought to have dismissed the Appeal No.5/2009.
7. Learned counsel appearing for submitted that the order passed' pbyfiiii Authority is justified in View 20--A of the Karnataka Air~_[ geventio__r1' and Coiiitrol of Pollution] Rules, 1983 and the has beengremitted to the petitioner -- isbax-d 'cannot have any grievance V' careful consideration to the contentions u1'ge'dfj hy'~..t:heV"'1earned counsel for parties and A.scrutini2:ed'the {material on record. * 9,V"'Th.e""m_ateria1 on record would clearly show that _ even to the writ petitioner -- Board, the direction has been issued by the CPCB under Section V' C_" E'): (b) of the Act. Section 20-A of the Air Act provides for procedure to be followed by implementing the direction issued and the same requires that a person, Officer or an authority to whom any direction is sought \,9 11 to be issued, shall be reserved with a copy of the proposed direction, and shall be given an oppo1ftuni_ty,,of not less than 15 days from the date notice of ﬁle with an officer designated the it ' objections, if any, to the issue of Thereafter, after consideringlpitiae olojections_, necessary direction has to Suing-'uleV46 of Rule 20-A provides that is of the opinion thatginiew a grave injury to the eigpedient to provide an against the proposed directi'on!,"itl to be recorded in Writing, issue. providing such an opportunity.
it the material on record would the petitioner - Board has simply issiied"direlctions to the respondent as per the directions A..iss4ued""by CPCB. There is no order dispensing with °..providir1g of an opportunity under Rule 20--A of the it ""Ruies and in the absence of any such order, the procedure contemplated under Rule 20--A of the Rules \P


COME SEE THE PARADISE (1992) A film review by Vince Deehan Copyright 1996 Vince Deehan
Written and Directed by Alan Parker Cast: Dennis Quaid, Tamlyn Tomita, Sab Shimono, Shizuko Hoshi, Stan Egi, Ronald Yamamoto.
I only recently discovered the existence of this great Alan Parker film, having somehow missed all mention of this film when it was released six years ago in 1990. I can't think how I haven't heard about this film before, and I can only guess that it must have met with little commercial and, possibly, little critical success.
COME SEE THE PARADISE begins with a young woman, Lily Kawamura (Tamlyn Tomita), walking with her daughter to an imminent reunion with her newly demobbed husband, Jack McGurn (Dennis Quaid). It is just after the end of World War II, and as they walk Lily tells her daughter how she met her husband and how they became separated by circumstances beyond their control.
We see Jack in New York in 1936, where he is working as a sweat shop lawyer, an unqualified one as he explains, for a film projectionist union. Our first sight of Jack is in a cinema where he and his friend Augie Farrell (Pruitt Taylor Vince) set light to a smoke bomb as part of a tactic to get some attention for their union grievances, but another pair of union men set light to a fire bomb which Jack wasn't warned about.  In the end his boss pays him off and tells him to leave as he has become too much of a liability to the union who have become concerned with Jack's superior knowledge of the law and his hot-headedness.
Jack moves out to Los Angeles and gets a job as a projectionist at a Japanese cinema in Little Tokyo. There he meets and falls in love with the beautiful Lily, who happens to be the daughter of the cinema boss. Lily's father (Sab Shimono) strongly objects when Jack, a white Irish-American, starts seeing Lily, his Japanese daughter. Lily and Jack eventually escape to Seattle where they get married on their own, and without Lily's parents blessing.
As world War II begins and the Japanese bomb Pearl Harbour, a wave of anti-Japanese feeling sweeps America which results in the Presidential order for all Japanese people to be evacuated and sent out into a huge camp in the desert. Thus, Jack and Lily and their daughter become separated. Jack gets drafted but manages to visit Lily and her family, who are also imprisoned with her.
When we first meet Lily's brother Charlie (Stan Egi), he is working in the cinema with Jack, and is clearly thrilled to be an American. He loves baseball and is full of life and always smiling and joking. Although he isn't by law allowed to become a US citizen, he seems to love America while obviously having great affection and pride in his Japanese roots. He reacts bitterly to his imprisonment and becomes very cynical and tough, losing his friendly demeanour. Stan Egi gives an outstanding performance as Charlie, who changes so much under the duress of his situation that he becomes almost unrecognisable from the light-hearted man we see earlier in the film.
In the part of Lily's husband Jack, Dennis Quaid shines in a hugely charismatic performance. The only slight misgiving I have about the film, is the frequent references to Jack being Irish. When Jack meets his brother, played by Colm Meaney, a reference is made to the fact that both brothers hail from Donegal, Ireland. So why do neither actors play their parts with Irish accents ? Strange. But this hardly detracts from the magnificence of the film.
The film is as much a love story as it is a vivid historical account of the appalling treatment of many innocent people, whose only crime was being Japanese, during World War II. This was a part of American history that I knew nothing about, and the film certainly proved very educational from that point, as well as being a great piece of cinema.
Review written on Mon 4 March 1996 by Vince Deehan<EMAIL_ADDRESS>The review above was posted to the
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The Golden Section Search 1 algorithm finds minima of functions which are expensive to compute or for which derivatives are not available. Although optimum for the general case, convergence is slow, requiring nearly 100 iterations for the example (x^3-2x-5).
If the derivative is available, Newton-Raphson is probably a better choice. If the function is inexpensive to compute, consider approximating the derivative.
x_0 are x_1 real numbers. The (single argument) procedure f is unimodal over the open interval (x_0, x_1). That is, there is exactly one point in the interval for which the derivative of f is zero.
golden-section-search
returns a pair (x . f(x)) where f(x) is the minimum. The prec parameter is the stop criterion. If prec is a positive number, then the iteration continues until x is within prec from the true value. If prec is a negative integer, then the procedure will iterate -prec times or until convergence. If prec is a procedure of seven arguments, x0, x1, a, b, fa, fb, and count, then the iterations will stop when the procedure returns#t
.Analytically, the minimum of x^3-2x-5 is 0.816497.
(define func (lambda (x) (+ (* x (+ (* x x) -2)) -5))) (golden-section-search func 0 1 (/ 10000)) ==><PHONE_NUMBER>245578e-3 . -6.0886621077391165) (golden-section-search func 0 1 -5) ==><PHONE_NUMBER>010515e-3 . -6.088637561916407) (golden-section-search func 0 1 (lambda (a b c d e f g ) (= g 500))) ==><PHONE_NUMBER>140557e-3 . -6.088662107903635)
[1] David Kahaner, Cleve Moler, and Stephen Nash Numerical Methods and Software Prentice-Hall, 1989, ISBN 0-13-627258-4


The Datical DB Drivers Directory is the full qualified path to your database drivers. These are JDBC Drivers for Oracle, MySQL, DB2, and SQL Server.
If you downloaded them using the Datical DB Software Updater, they will be located in the plugins directory in your Datical DB Installation Directory.