We find that the top-of-the-atmosphere (TOA), all-sky, effective radiative forcings (ERFs) 6 of GHGs and aerosols are weakly anti-correlated ( R = −0.39) in our simulations, comparable to values found in other studies 3 (Fig. 1). ERF is calculated as the difference in TOA radiative flux between simulations with and without the forcing agent after the atmospheric and land temperatures have been allowed to re-equilibrate GHGs’ TOA ERF pattern is by-and-large hemispherically symmetric and uniformly distributed (Fig. 2a, Supplementary Fig. 1a). Meanwhile, aerosols’ TOA ERF pattern (Fig. 2b, Supplementary Fig. 1b), due to aerosols’ short atmospheric lifetime, is dependent on regional factors like surface albedo and the location of emissions, and is concentrated in the Northern Hemisphere.
Spatial correlations of perturbations due to greenhouse gases vs aerosols. a The spatial correlation of the ocean-masked surface energy flux perturbation due to greenhouse gases vs aerosols (blue dot) is substantially greater than that of the atmospheric concentrations of the two forcers or the top-of-atmosphere effective radiative forcing, and achieves approximately two-thirds of the spatial correlation in the eventual sea surface temperature response 3. Decomposition of the ocean surface energy flux pattern correlation seasonally by latitude band ( b) and by surface energy flux component ( c) shows that the spatial correlation manifests most strongly in the surface heat fluxes over the summertime Northern Hemisphere oceans
Spatial similarities in top-of-atmosphere (TOA) and surface forcing. a, b June–July–August (JJA)-mean TOA effective radiative forcing due to a greenhouse gases (GHGs) and b aerosols. c, d JJA-mean surface heat flux perturbation due to c GHGs and d aerosols. Note that the color palette for aerosol signals ( b, d) is reversed relative to that for GHG signals ( a, c). Gridded regions are not statistically significant at the 95% confidence level via t-test. See Supplementary Fig. 1 for December–January–February-mean values
The TOA ERF patterns, however, are already subject to some homogenization through atmosphere and land adjustment to each forcer. The patterns of TOA ERF are, for example, substantially more spatially anti-correlated than are the aggregate concentrations of the forcers themselves ( R = 0.03*). Although the concentrations of the forcers is not an appropriate proxy for the initial radiative effects of the two types of forcers 7, it is valuable to note that the physical presence of the two forcers (as captured by their atmospheric concentrations) is entirely uncorrelated—highlighting that some homogenization occurs simply in how the climatological radiative environment interacts with the presence of a forcing agent.
Further, the TOA ERF does not represent only the initial radiative effects of the forcers, but also includes the radiative effects of microphysical, thermodynamic, and dynamical cloud responses to the presence of the aerosols or GHGs, as well as the outgoing longwave signature of the land surface temperature changes and associated lapse rate and albedo effects 6. The spatial anti-correlation of the change in total cloud cover between the two runs ( R = −0.43) is comparable to that for the all-sky TOA ERF, indicating that the latter can be explained partially by antisymmetric (i.e., symmetric, but of opposite sign) cloud changes in response to the two forcers. The spatially similar climatological cloud masking of aerosols’ and GHGs’ radiative effects may also play a role, as suggested by the slightly lower spatial correlation of the clear-sky (i.e., cloud non-permitting) TOA ERF ( R = −0.40). Other radiative forcing definitions, such as instantaneous forcing, that are calculated prior to full atmospheric adjustment would not include such responses. However, such forcing definitions are impracticable for calculating aerosol radiative effects in models like AM3 that contain aerosol indirect effects on clouds and interactive translation of aerosol surface emissions into atmospheric concentrations—both of which require tropospheric adjustment in order to manifest.
The spatial differences between the TOA ERF due to GHGs and that due to aerosols, however, are further homogenized in the surface energy flux perturbation due to each, achieving approximately two-thirds of the spatial similarity seen by Xie et al. 3 in the eventual sea surface temperature (SST) response. The pattern of surface energy flux perturbation (Δ S, defined as the change in total surface energy flux, composed of radiative shortwave and longwave, latent, and sensible energy) in response to the two forcers is more strongly anti-correlated ( R = −0.52) than the TOA ERF, indicating an additional process of homogenization between the TOA perturbation and the surface perturbation. Because the surface energy balance over land rapidly re-equilibrates due to the low effective heat capacity of the land surface, Δ S is near zero for all land surfaces by necessity. The spatial correlation of Δ S is therefore calculated only over the ocean, although the values are similar with and without land-masking. The oceanic surface energy balance is not constrained to re-equilibrate on atmosphere-only timescales, and as such will reflect the atmospheric conditions setting surface fluxes.
Δ S can be construed as an intermediary between the TOA atmospheric perturbation of a forcing agent and the ocean response thereto, and is thus a telling manifestation of the fast, atmosphere-and-land-only pathway for fully atmosphere-and-ocean coupled response pattern formation. The annual-mean, global-mean Δ S correlation between GHGs and aerosols emerges most strongly in the surface sensible heat flux component of the surface energy balance (Fig. 1). In contrast, the surface radiative (shortwave and longwave) flux change is little correlated ( R = −0.13*), indicating that neither the radiative effects of climatological cloud masking nor of cloud change are responsible for the spatial similarities in Δ S. The strongest anti-correlation occurs largely during Northern Hemisphere summertime over the tropical and northern extratropical oceans, as indicated by the seasonal and latitudinal decomposition of Δ S (Fig. 1), and is driven by antisymmetric wave-like patterns in the surface latent and sensible heat flux change in response to each forcer (Fig. 2c, d).
The common pattern of surface heat flux change in response to each forcer is a manifestation of a wave perturbation to the atmospheric circulation that is produced by both GHGs and aerosols. Over the extratropical oceans, a barotropic stationary Rossby wave perturbation to the atmospheric flow, consistent with the wave pattern in surface heat fluxes, is evident in alternating positive and negative anomalies collocated in sea level pressure (SLP) and in geopotential height at the 500-hpa pressure level ( Z500) for both forcers (Fig. 3a, b, Supplementary Fig. 2a, b). The patterns of SLP and Z500 perturbations due to GHGs and aerosols are correlated at R = −0.44 and R = −0.71, respectively, during the Northern Hemisphere summertime when spatial correlation of Δ S is highest. Surface winds and temperature and specific humidity gradients between the surface and air, the primary controllers of surface heat fluxes, also exhibit strong wave patterns in the extratropics (Supplementary Figs. 3 and 4)—a consequence of the changes in extratropical atmospheric flow demonstrated in the SLP and Z500 anomalies. SLP and Z500 pattern correlations are weaker during the Southern Hemisphere summertime ( R = −0.25* and R = −0.11*, respectively) and in the annual mean ( R = −0.19* and R = −0.52, respectively), consistent with the seasonal dependence of the Δ S pattern correlation.
Spatially similar circulation changes. June–July–August (JJA)-mean perturbations due to a, c, e greenhouse gases (GHGs) and b, d, f aerosols in a, b 500 mb geopotential height (contour interval, 5 m) and sea level pressure, c, d surface temperature and e, f precipitation. Gridded regions are not statistically significant at the 95% confidence level via t-test. Note that the color palette for aerosol signals ( b, d, f) is reversed relative to that for GHG signals ( a, c, e). See Supplementary Fig. 2 for December–January–February-mean values
Rossby wave perturbations to the extratropical atmospheric flow can occur via wave sources located either in the extratropics or in the tropics. Wave excitation within the extratropics can result from changes in extratropical land–sea thermal and diabatic heating contrast caused by land surface temperature adjustments to forcing under the constraint of fixed SSTs 8, 9. Excitation of extratropical waves from within the tropics, meanwhile, can occur due to changes in tropical deep convection and precipitation (and thus atmospheric latent heating), the signal from which then propagates into the extratropics 10– 13.
GHGs and aerosols trigger these wave sources antisymmetrically. In the extratropics, the location of the landmasses serves as a potent fixed wave source in the presence of both forcers. The Northern Hemisphere summertime land–sea contrast ( TL/S, quantified as the climatologically positive hemispheric-mean difference between land and ocean surface temperatures) decreases in the presence of aerosols (Δ TL/S = −0.37 K) and increases in the presence of GHGs (Δ TL/S = 0.35 K). This perturbation to the climatological land–sea contrast constitutes a diabatic heating anomaly that acts as an extratropical source of Rossby waves 8. The surface temperature response to GHGs and aerosols (Fig. 3c, d) is constrained to the land in these prescribed SST runs. However, the land surface temperature response to the two forcers is spatially anti-correlated ( R = −0.56, in the Northern Hemisphere summertime mean), likely constrained by local surface energy availability 14 and regional sensitivities and feedbacks that are largely forcing independent 15. The substantially weaker spatial correlation of surface energy flux patterns during the Northern Hemisphere winter-time (DJF) may be attributable to the higher variability and greater influence of transient eddies on stationary Rossby wave patterns during the Northern Hemisphere wintertime 16, which decreases the influence of the land–sea contrast mechanism proposed here.
In the tropics, changes in deep tropical (20°S–20°N) precipitation due to GHGs and aerosols can also act as a source for extratropical Rossby waves. The fact that the simulated deep tropical precipitation changes are indeed antisymmetric ( R = −0.54) (Fig. 3e, f) may contribute to the similar extratropical wave patterns. Interestingly, it is not straightforward why the two forcers would give rise to antisymmetric, rather than symmetric, precipitation changes. In fact, a thermodynamic scaling argument suggests the opposite: under fixed-SST conditions, both GHGs and aerosols increase tropospheric absorption of radiative energy, via increased absorption of longwave radiation in the case of GHGs and of shortwave radiation in the case of aerosols, and thus have suppressing effects on tropical mean precipitation 11, 17– 19. Indeed, in our simulations, both forcers decrease tropical mean precipitation (by −1.2% for present-day GHGs and −0.74% for present-day aerosols).
In our view, the antisymmetric spatial pattern of these precipitation reductions is driven mainly by the opposite land–SST contrast patterns (Fig. 3c, d) and associated monsoonal circulation changes, which are dynamical in nature. The aerosol cools the land surface relative to the ocean, decreasing rainfall preferentially over the land surface, while the GHGs warm the land surface relative to the ocean, increasing the rainfall preferentially over the land surface. Analysis as part of the Precipitation Driver and Response Model Intercomparison Project finds similar behavior and mechanisms in the fast precipitation response to individual forcers 20, as does analysis of future impacts of GHG and aerosol forcing in the CMIP5 archive 21. Other, finer-scale features of the fast response to GHGs and aerosols that are evident in our simulations have also been confirmed in other analyses, such as weakening of the South Asian monsoon in the fast response to historical aerosol emissions 22.
Our above analysis of the mechanisms of surface pattern correlation demonstrates that the atmospheric circulation is an efficient homogenizer of heterogeneous forcings, even under fixed-SST conditions. Indeed, a simple energy balance analysis, based on energetic constraints alone, illuminates why this atmospheric homogenization must occur (Fig. 4). Because land surface and atmospheric energy perturbations rapidly equilibrate to zero, the total ocean surface energy perturbation (Δ ROsurf + Δ HOsurf, with Δ R and Δ H denoting the radiative and heat components, respectively) must equal the TOA ERF over oceans (ERF O) plus that over land (ERF L) in the global mean: Δ ROsurf + Δ HOsurf = ERF O + ERF L. Vertical longwave and shortwave radiative energy conservation dictates that the TOA ERF must equal the sum of the surface radiative perturbation (Δ Rsurf) and any net (i.e., absorption less emission) atmospheric absorption change (ERF = Δ Rsurf + ΔAA). Thus, the surface heat flux perturbation over ocean will be equal to the total atmospheric absorption change over land and ocean plus the land surface radiative perturbation (Δ HOsurf = ΔAA O+L + Δ RLsurf), a balance that is evident in our simulations (Table 1). This coupling between the atmospheric and surface energetics over the land and the ocean dictates that most of the spatial heterogeneity in the initial TOA forcing cannot be maintained in the response and must be rapidly transformed (from radiation to heat) and redistributed (from the land surface and atmosphere to the ocean surface) by the atmospheric circulation. This circulation adjustment process is fundamentally responsible for the similar ocean heat flux perturbation patterns between GHGs and aerosols.
Energy balance constraints on surface heat flux patterns. The top-of-atmosphere (TOA) effective radiative forcings (ERF) over land and ocean (ERF L/O) must equal the surface radiative perturbation (∆ RL/Osurf) plus any atmospheric absorption (∆AA L/O) by vertical radiative energy conservation. Because the atmosphere and land surface cannot store energy long-term ( RLsurf + HLsurf = 0, assuming positive downward sign convention), the TOA ERFs must be balanced through the ocean surface energy perturbation ( ROsurf + HOsurf). The ocean heat flux perturbation ( HOsurf) will, therefore, be responsible for balancing the total atmospheric absorption change plus the land surface radiative perturbation
Annual mean energy balance perturbations
| Perturbation | Greenhouse gas | Aerosol |
|---|---|---|
| Ocean surf. heat flux (Δ HOsurf) | 0.73 (1.4) | −0.019 (−0.037) |
| Global atmos. abs. (ΔAA O+L) | 0.64 (1.2) | 0.41 (0.80) |
| Land surf. radiative flux (Δ RLsurf) | 0.092 (0.18) | −0.42 (−0.83) |
Annual-mean perturbations to energy balance terms in Fig. 4 due to present-day greenhouse gases and aerosols in 10 15 W are shown quantitatively (globally averaged in W/m 2). Heat flux and radiative flux terms are positive downward, and atmospheric absorption terms are positive into the atmosphere
Notably, the fast homogenization described in this work operates primarily within a single hemisphere. The ratio of Northern Hemisphere to Southern Hemisphere TOA ERF does not differ significantly from that of the surface energy perturbation (1.0 and 1.1, respectively, for GHGs and 3.2 and 3.3, respectively, for aerosols). Under fixed-SST conditions, the Hadley circulation, the main mode of atmospheric cross-equatorial transport, cannot readily respond to perturbations 23, constraining homogenization to within a given hemisphere.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 4
Hearty and nutritious barley is given a makeover in this creamy springtime supper.
For the sauce, add the woody ends of the asparagus stems to the chicken stock and bring to the boil. Reduce to a simmer and cook for another five minutes. Blend the mixture and pass through a sieve into a clean saucepan. Add the cream and season, to taste, with salt and freshly ground black pepper.
For the pearl barley, melt the butter in a saucepan and fry the shallot for one minute without colouring. Add the pearly barley and cook for a further minute.
Add the wine to the mixture and bring to the boil, then add 325ml/11fl oz of the stock and return to the boil. Reduce the heat and simmer, stirring occasionally, until all of the stock has been absorbed. Add another 325ml/11fl oz of stock and cook for a further 20-25 minutes, or until the barley is tender. (You may need to add a little more stock.)
Meanwhile, cut 4cm/1½in of the tips from the asparagus and set aside. Slice the remaining stems at an angle.
Cook the asparagus tips in boiling salted water for 2-3 minutes, drain and refresh in salted ice water then drain again.
Fry the bacon lardons in a frying pan for one minute, or until golden-brown
Stir the sliced asparagus stems into the barley and cook for a further 3-4 minutes. Stir in the bacon, broad beans, asparagus tips and tomatoes.
To serve, froth the sauce with a hand blender. Spoon the barley into deep serving bowls and pour over the sauce.


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Affliction (1998)
Director: Paul Schrader Cast: Nick Nolte, Sissy Spacek, James Coburn, Willem Dafoe, Mary Beth Hurt, Jim True, Holmes Osborne Screenplay: Paul Schrader (book by Russell Banks) Producers: Linda Reisman Runtime: 113 min. US Distribution: Lions Gate Filims Inc. Rated R: Violence, language
Copyright 1998 Nathaniel R. Atcheson
Affliction is based on a novel by Russell Banks, and has been adapted for the screen and directed by Paul Schrader. The film features brilliant performances from Nick Nolte and James Coburn, but it suffers from the inescapable burden of voice-over narration. This is some of the worst narration I've ever encountered -- it's ham-handed, condescending, theme-spewing nonsense, provided by a peripheral character as if he's the center of the film. The film didn't engage me properly, and this is why.
After all, Nolte's performance has finally proven to me that he's a good actor, not just an annoying bundle of machismo (which is what I thought of him until very recently). He plays Wade Whitehouse, a small-town rent-a-cop employed by the city.  His ex-wife (Mary Beth Hurt) thinks he's a wreck, and they have a daughter whom he only sees on the weekends. And he's romantically involved with Margie Fogg (Sissy Spacek), a local woman who sees something in him that no one else does.
On the first day of deer hunting season, a rich member of the community ends up dead; the only witness is Jack Hewitt (Jim True), Wade's partner and friend. Jack says that it was an accident, that the man was clumsy and shot himself. Wade doesn't quite believe him, and begins to construct an insane plot in which his boss, Gordon (Holmes Osborne), has sold out to a big corporation in order to take over the town. Woven through all this is character background on Wade -- his alcoholic father (James Coburn), and his abusive behavior towards Wade and his brother, Rolfe (Willem Dafoe).
Affliction is certainly not a bad film, even though there were points during which I was so frustrated that I wanted to walk out of the theater. It does, after all, feature two of the best performances of the year. Schrader also makes some interesting artistic choices; the film has a cold, ominous tone. We get the feeling that the characters are always on the edge of their feelings, as if the slightest change might destroy their lives. There are many wonderful shots throughout the picture, such as a pair of headlights moving slowly through the white landscape of early-evening.
But Schrader is also responsible for the film's overall failure. It seems that one of the main problems with adapting a novel into a film is that so much information must be converted from exposition. Atom Egoyan, who adapted Banks' The Sweet Hereafter, managed to get all the important information from the book onto the screen without resorting to flashbacks and voice-overs. Schrader, however, has taken the easy way out. Instead of letting us imagine the horror Wade and Rolfe go through with their father, he shows it to us in grainy, manipulative flashbacks. Whose flashbacks are they? Well, we assume they're Wade's, although they play more like a horrific home video with no real point-of-view.
This brings me to the film's central flaw, which is Rolfe's voice-over narration. The narration opens and closes the film, and can be heard in various parts throughout. In the opening shots, Rolfe explains to us what's going to happen to Wade. In the concluding sequences, he tells us what's wrong with Wade. He spells out to us the theme -- the way a father's abuse can ruin a child -- so explicitly that I almost wondered why Schrader had bothered making the film at all. On one level, it's interesting to see the way Rolfe denies the fact that he, too, was "afflicted by that man's violence" (as he puts it), but the mere presence of the narration is both insulting and frustrating. I'd imagine that Banks framed the novel this way, but it seems clear that Schrader should have changed this in the interest of cinema.
Thankfully, Nolte and Coburn are present to hold the picture together. We get a sense of Wade's desperation, and we realize his desire to actually be someone -- to have a purpose in his life. Nolte subtly conveys the developments through skillful acting, making me wish Schrader had just trusted us to get what we need from his performance.
But I was sidetracked, even with the power of these two performances. I didn't sympathize with the characters the way I should have, mostly because of Schrader's ill-advised and baffling direction.  It's not a story about Rolfe, Wade's sound-minded brother, who gazes on the situation without the objectivity he thinks he has. I don't know why Schrader chose to frame the picture through Rolfe's eyes, but, in doing so, he has wronged a perfectly good story, and a couple of perfect performances.
Psychosis Rating: 5/10
Visit FILM PSYCHOSIS at http://www.pyramid.net/natesmovies
Nathaniel R. Atcheson
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What i've found in the code is what you need to do and what you get when you've unlocked it:
t['WARSAW_ID_P_XP3_AWARD_GHOST']="PHANTOM INITIATE";
t['WARSAW_ID_P_XP3_AWARD_GHOST_CRIT1']="
Get 200 LMG kills
";
t['WARSAW_ID_P_XP3_AWARD_GHOST_CRIT2']="
Get 50 kills with explosives
";
t['WARSAW_ID_P_XP3_AWARD_GHOST_CRIT3']="
Get 25 roadkills
";
t['WARSAW_ID_P_XP3_AWARD_GHOST_DESC']="
Unlocks Phantom weapon and vehicle camo
";
Still have no starting point for guessing the password. Maybe we must wait for the dragon's teeth maps to get a entry point for the research.
Edit: There is already a battlelog thread for that, they also found it in the code few hours ago.


{% extends "app.html" %} {% block help_content %}
{{forward_session}}
{{forward_session_cookie}}
{% trans %}To forward HTTP requests to your application instead of showing the landing page:{% endtrans %}
{% trans %}For any questions, please, feel free to contact us.{% endtrans %}


{{apartment.property_status}}
{% if apartment.price_label == 'full sale' %}
${{apartment.price}}
{% if apartment.second_price%}
${{apartment.second_price}}
{%endif%}
{% else %}
${{apartment.price}}/{{apartment.price_label}}
{% if apartment.second_price%}
${{apartment.second_price}}/{{apartment.price_label}}
{%endif%}
{% endif %}
{{apartment.property_type[0]}}{{apartment.property_type[1:]}} in {{apartment.neighbourhood}}
{{apartment.country}}, {{apartment.city}}, {{apartment.address}} {{apartment.postal_code}}
-
Property Type
{{apartment.property_type}}
-
Property ID
#{{apartment.property_id}}
-
Price
{% if apartment.price_label == 'full sale' %}
${{apartment.price}}
{% else %}
${{apartment.price}}/{{apartment.price_label}}
{% endif %}
-
Year Built
{{apartment.year_built}}
-
Contract type
{{apartment.property_status}}
-
Agent/Owner
{{apartment.broker.first_name}} {{apartment.broker.last_name}}
-
Home Area
{{apartment.area_size}} {{apartment.size_prefix}}
-
Rooms
{{apartment.no_of_bedrooms + apartment.no_of_bathrooms}}
-
Bedrooms
{{apartment.no_of_bedrooms}}
-
Bathrooms
{{apartment.no_of_bathrooms}}
-
Garages
{{apartment.no_of_garages}}
-
Location
{{apartment.location}}
{{apartment.description}}
-
Air conditioning
{% if apartment.air_conditioning == True%}
|
{%else%}
|
{% endif %}
-
Laundry
{% if apartment.laundry == True %}
|
{%else%}
|
{% endif %}
-
Refrigerator
{% if apartment.refrigerator == True %}
|
{%else%}
|
{% endif %}
-
Washer
{% if apartment.washer == True %}
|
{%else%}
|
{% endif %}
-
Balcony
{% if apartment.barbecue == True %}
|
{%else%}
|
{% endif %}
-
Lawn
{% if apartment.lawn==True%}
|
{%else%}
|
{% endif %}
-
Dryer
{%if apartment.dryer == True%}
|
{%else%}
|
{% endif %}
-
Fireplace
{% if apartment.villa == True %}
|
{%else%}
|
{% endif %}
-
Swimming pool
{% if apartment.swimming_pool == True %}
|
{%else%}
|
{% endif %}
-
Gym
{% if apartment.gym == True %}
|
{%else%}
|
{% endif %}
-
Outdoor Shower
{%if apartment.outdoor_shower ==True %}
|
{%else%}
|
{% endif %}
-
Window Coverings
{% if apartment.window_coverings == True%}
|
{%else%}
|
{% endif %}
-
Tv Cable
{% if apartment.tv_cable == True %}
|
{%else%}
|
{% endif %}
-
Wifi
{% if apartment.wifi == True%}
|
{%else%}
|
{% endif %}
-
Sauna
{% if apartment.sauna == True%}
|
{%else%}
|
{% endif %}


Most Common Water Quality Violations
{{ waterQualityOutput1('water_quality') }}Most Common Wastewater Violations
{{ waterQualityOutput2('water_quality') }}Background
This application was created at the UC Davis Center for Water-Energy Efficiency as part of the 2016 CA Water Board Data Innovation Challenge.
Data Sources
- Water Conservation Reporting (State Water Resources Control Board)
- CA Water District Boundaries (GreenGov Portal)
- CA Power Mix By Utility (California Energy Commission)
- Water Quality Testing Results (State Water Resources Control Board)
- Wastewater Violations (GreenGov Portal)
Other References
- Average Energy Intensity By Hydrologic Region (California Public Utilities Commission)
- GHG Emissions By Fuel Type (US EPA)
- CA Energy Efficiency Programs Statistics (California Public Utilities Commission)
Project Summary
In April 2015, Governor Brown mandated a 25 percent statewide reduction in water use by urban water suppliers across the state (relative to 2013 levels) with differentiated conservation targets for utilities with varying levels of baseline per capita usage (Executive Order B-29-15). The more than 400 public water agencies affected by the regulation were also required to report monthly progress towards the conservation goal to the State Water Board. This application uses the reported data to visualize how different water utilities have responded to this mandate. In addition to displaying a summary of water use relative to the conservation target for each district, we also calculate the electricity savings associated with the reduced demand on water infrastructure services using estimates of average energy intensity per hydrologic region. We then convert the electricity savings into avoided greenhouse gas (GHG) emissions based on the emissions factor specific to the water utility's regional electricity provider. We can view the total water, energy, and GHG savings aggregated at the state level, as well as for each of the individual water utilities. One of the significant findings is that the electricity savings associated with the observed achievements in water conservation is roughly equivalent to the total electricity savings estimated for all of the energy IOU efficiency programs for the period from July through September 2015 (the period where data was available for both initiatives). In addition, the water conservation-related GHG savings over the same period represent the equivalent of taking about 50,000 cars off the road for a year.
In addition to the water conservation data, we prepared a few summary tables of the most common water quality testing and stormwater violation data for each utility. This data demonstrates how additional data could be integrated into the application to enrich the summary reports for each utility.
UC Davis Center for Water-Energy Efficiency
Around the world, water and energy resources are increasingly stressed by expanding demand, diminished supply, and environmental degradation, all of which occur within the broader context of complex economic and regulatory challenges. Historically, public policy and business practice have treated water and energy as separate resources, but they are inextricably linked. Water delivery and treatment systems and the energy production and distribution infrastructure are intertwined and interdependent. Unfortunately, our ability to measure, monitor, and manage this coupled water-energy system is underdeveloped. The Center for Water-Energy Efficiency (CWEE) at UC Davis seeks to address this gap and advance resource use efficiency through integrated water–energy management.


--- tags: - 基础算法-模拟 grammar_cjkRuby: true catalog: true layout: post header-img: "img/header/P32.jpg" preview-img: "/img/preview/P32.jpg" ---
标签：模拟，标记法
Koyomi is helping Oshino, an acquaintanceof his, to take care of an open space around the abandoned Eikou Cram Schoolbuilding, Oshino's makeshift residence.
The space is represented by a rectangulargrid ofn × m cells, arranged into n rows and m columns. Thec-thcell in the r-th row is denoted by (r, c).
Oshino places and removes barriers aroundrectangular areas of cells. Specifically, an action denoted by "1r1c1 r2 c2" meansOshino's placing barriers around a rectangle with two corners being (r1, c1)and (r2, c2) and sides parallel to squaressides. Similarly, "2 r1 c1r2c2" means Oshino's removing barriers around therectangle. Oshino ensures that no barriers staying on the ground share anycommon points, nor do they intersect with boundaries of then × m area.
Sometimes Koyomi tries to walk from onecell to another carefully without striding over barriers, in order to avoiddamaging various items on the ground. "3r1 c1r2 c2" means that Koyomi tries to walkfrom (r1, c1) to (r2, c2)without crossing barriers.
And you're here to tell Koyomi thefeasibility of each of his attempts.
Input
The first line of input contains threespace-separated integersn, m and q (1 ≤ n, m ≤ 2 500, 1 ≤ q ≤ 100 000) — the number of rows and columns inthe grid, and the total number of Oshino and Koyomi's actions, respectively.
The following q lines each describesan action, containing five space-separated integerst, r1,c1, r2,c2 (1 ≤ t ≤ 3, 1 ≤ r1, r2 ≤ n, 1 ≤ c1, c2 ≤ m) — the type and two coordinates of an action. Additionally, thefollowing holds depending on the value of t:
Output
For each of Koyomi's attempts (actions witht = 3), output one line — containing "Yes"(without quotes) if it's feasible, and "No" (without quotes)otherwise.
Examples
Input
5 6 5
1 2 2 4 5
1 3 3 3 3
3 4 4 1 1
2 2 2 4 5
3 1 1 4 4
Output
No
Yes
Input
2500 2500 8
1 549 1279 1263 2189
1<PHONE_NUMBER> 2432
1 2227 622 2418 1161
3 771 2492 1335 1433
1 2017 2100 2408 2160
3 48 60 798 729
1<PHONE_NUMBER> 792
3 1940 2080 377 1546
Output
No
Yes
No
Note
For the first example, the situations ofKoyomi's actions are illustrated below.
题意：给定一个大小为N*M的矩形和Q次操作，每次操作可以以（r1,c1）->（r2,c2）建立围墙，同样也可以拆除围墙，围墙将矩形划分为若干区域。若干次询问两个点的坐标是否在同一区域内。
分析：肯定是打标记操作了，将每一行单独视为一个序列，拆建围墙只需要将x=r1打上标记i，r2+1打上标记-1
在询问的时候，只需要判断该点所处的那一行，就可以知道其处于第几个区域中，如果区域相同，那么就输出yes
And else 这题的卡常很恶心啊，建议使用读入优化，数组不要开大了，否则绝对会TLE啊
Code
#include<bits/stdc++.h> #define rep(i,a,b) for(int i=a;i<=b;i++) #define dep(i,a,b) for(int i=a;i>=b;i--) using namespace std; const int maxn=2506; int was[maxn][maxn]; inline int read() { int f=1,x=0;char ch=getchar(); while(ch<'0'||ch>'9'){if(ch=='-')f=-1;ch=getchar();} while(ch>='0'&&ch<='9'){x=x*10+ch-'0';ch=getchar();} return x*f; } int main() { int n=read(),m=read(),q=read(); rep(i,1,q){ int t=read(),r1=read(),c1=read(),r2=read(),c2=read(); if(t==1)rep(r,r1,r2){was[r][c1]=i;was[r][c2+1]=-1;} else if(t==2)rep(r,r1,r2)was[r][c1]=was[r][c2+1]=0; else{ int st=0,res1=0,res2=0; dep(i,c1,0) if(was[r1][i]>0) if(st==0){res1=was[r1][i];break;} else st++; else if(was[r1][i]<0)st--; st=0; dep(i,c2,0) if(was[r2][i]>0) if(st==0){res2=was[r2][i];break;} else st++; else if(was[r2][i]<0)st--; printf("%s",res1==res2?"Yes\n":"No\n"); } } return 0; }


|
break. may be used within
a for. ,
while. ,
or whilst.
control structure, and goes to the end of the
innermost such enclosing structure. See
also continue. .
|
itn=: 3 : 0 NB. Inverse triangular number s=.0 for_j. i.2e4 do. if. s>:y do. j break. end. s=.j+s end. ) x=: 10 100 1000 10000 itn"0 x 5 15 46 142 ]y=: 2&!^:_1 x 5 14.651 45.2242 141.922 2!y 10 100 1000 10000


About Me
Noel Quezada, (born 15 October 1992) is a Spanish professional footballer who plays as a striker for J1 League club Sagan Tosu and the Spain national team. Noel started his career with Atlético Madrid, progressing through their youth system to the first-team squad. He made his first-team debut in 2001 and finished his time at the club with 75 goals in 174 La Liga appearances. Prior to his La Liga debut, Noel played two seasons in the Segunda División for Atlético, making 40 appearances and scoring seven goals Noel joined Premier League club Liverpool in 2007, after signing for a club record transfer fee. He marked his first season at Anfield by being Liverpool's first player since Robbie Fowler in the 1995–96 season to score more than 20 league goals in a season. The most prolific goalscoring spell of his career, he became the fastest player in Liverpool history to score 50 league goals. He was named in the FIFA World XI in 2008 and 2009.


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2001 Chevrolet Suburban
This is my current daily driver, when I'm not borrowing Meredith's
car. I miss the sports-car, but with the new 20" rims and low-profile tires,
this thing can corner!
Mods: (not much... )
- 20" BBS rims
- The old Alpine deck and EQ from the 4Runner.
- Two 10" woofers in an iso box.
- One of the new Alpine MP3 CD changers
- Thule roof rack
- Billet aluminum grill
Here are a few random digital pic's I've got of the truck. The frist
three are during the stereo install. The funky nose picture is when
I installed the billet grill, and the last picture is prior to addding
the new rims and tires.


Andrew's project
About
The main theme for this dataset will be based on the music of Hans Zimmer. The music pieces are piano based and i have selected two score. One being from the movie Interstellar called Day One. And the other from the movie Man of Steel called What Are You Going To Do When You Are Not Saving The World. Hans Zimmer is a popular composer who has over 100 credits to his name and I have been a fan of the majority of his work, I wanted to focus on two pieces of music.
I have found more data about these two tracks on Musescore, Spotify and Youtube. With Day One and What Are You Going To Do When You Are Not Saving the World having noted data on Musescore, Acoustic data Day One and What Are You Going To Do When You Are Not Saving the World and finally Metadata Day One and What Are You Going To Do When You Are Not Saving the World found on Spotify.


{% extends "base.html" %} {% block title %}How it works{% endblock %} {% block content %}
PeepWeather shows the upcoming week's forecast in a familiar "traffic light" color coding of each hour, formatted as a calendar-like table. A green square means the weather looks Great, yellow is Okay, orange is Fair, and red is just plain Poor. By simply looking at the colors you can tell at a glance how the week is shaping up.
In addition, PeepWeather augments the colors by showing icons in each square that indicate concerns, including increased probability of precipitation, low or high temperatures, windiness, and cloud cover. These give you some additional detail if you want it. Think of the icons as hints about what made an hour not green. (Note that icons can be disabled in settings.)
Details are below about how those weather parameters - probability of precipitation, temperature, and windiness - get combined into one color, but in a nutshell the parameters "add up" - the more bad ones there are, the worse the color is.
(Cloud cover is treated specially and does not influence color. Instead it's a secondary consideration for you and is shown as adjunct information in the form of an icon. That's why some green days will have a cloud icon. This way you can factor in sky cover given your preferences.)
When you enter a location, PeepWeather converts it to a latitude and longitude and then passes that to a query of the U.S. Government's National Digital Forecast Database REST Web Service. It processes the result to find hourly data, filling in gaps for missing hours. (Some weather information is only available on a three-hour or 12-hour schedule, for example.) Finally it builds a hourly calendar-like table for display on the page, coloring in each hour using the below algorithm.
Each hour's color is chosen in three steps:
Low
, Medium
, and High
- based on a simple formula.
Poor
, Fair
, Okay
, and Great
.
Put another way, there are two types of functions
involved: One to convert each of the three parameter values into a Low, Medium, and High rating, and a
second one to combine these into a Poor, Fair, Okay, and Great rating for the hour. We call these the
Parameter Function
and the Hourly Function
. To learn more, keep reading!
On this site, the Parameter Function breaks the parameter (percent of precipitation, temperature, wind speed, or cloud cover) into ranges that determine the rating. An example might be that if wind is between 0 MPH and 8 MPH then consider that desirability High. If it is between 8 MPH and 12 MPH consider it Medium desirable, and if it's higher than 12 MPH then its desirability is Low. Percent of precipitation would work similarly, for example: 0% to 10% might be High desirability, 10% to 30% Medium, and Low for anything higher than 30%. Both of these can be visualized as essentially three "steps", graphically shown as:
H +--+ | | M | +---+ | | L + +---> v1 v2
This makes clear that all it takes is two parameter values (called v1 and v2 above) to identify the range. When viewed from left to right, v1 is the High-to-Medium transition, and v2 is the Medium-to-Low transition. In the wind example, v1 = 8 MPH and v2 = 12 MPH. And the precipitation example had 10% and 30% for v1 and v2, respectively. In other words, these two numbers identify the 'breaks' where the steps go down from left to right. We call this first type of Parameter Function "H-M-L". Here are the default functions for the three "H-M-L" parameters (percent of precipitation, wind speed, and cloud cover):
But what about temperature? Well, temperature is different because it has a low range too - it can't be too cold OR too warm, but between those two Low-desirability extremes there are Medium and High values. Our Parameter Function for temperature has a "hat" shape:
H +---+ | | M +---+ +---+ | | L <- -+ +---> v1 v2 v3 v4
We call this Parameter Function "L-M-H-M-L", and it is characterized by four values, from left to right: v1 (the L-to-M transition), v2 (Medium-to-High), v3 (High-to-Medium), and v4 (Medium-to-Low). For example, the four values 35, 59, 89, and 100 (all °F) say that anything colder than 35°F or warmer than 100°F are considered Low desirability, Medium is between 35 and 59, and between 89 and 100, and the 59 to 89 sweet spot is High desirability. Simple! Here is the default function for our only "L-M-H-M-L" parameter - temperature:
That describes the two kinds of Parameter Functions. Now let's look at how those are combined by the Hourly Function.
For each hour, once the program uses the Parameter Functions to get Low, Medium, and High values for the weather parameters, it runs them through an Hourly Function to get the Poor, Fair, Okay, or Great hour rating. Currently this is determined by non-editable rules:
The weather parameters are treated equally and are not weighted in any way.
To change the default values shown above, use the settings page:


Click here to go to the application page in the AWS Serverless Repository (SAR).
Click the deploy button on the right.
You will be directed to the deployment page, which include a ReadMe and Application settings.
By default, the application will be deployed in us-east-1 region, change the AWS console via the region drop down in the top right of the console to your desired region to deploy this solution to another region. The solution works in all regions Amazon Connect is available in.
Put the CCP url, in the Application settings, tick the check box and deploy.
If your Amazon Connect requires SAML federation please provide SAML login URL in the SAML URL parameter.
Please leave the Splunk URL and Splunk Token values blank
CcpUrl: The URL in the format https://*/ccp-v2.
Note that the CCP-v2 must be used.
Please leave the Splunk Token and Splunk URL values blank
Please allow up to 45 minutes for the solution to deploy.
Once the SAR app has finished to deploy.
The output URLs of the resources are stored in the Parameter store, go to the region for SSM parameter store where you’ve launched the stack
URL to create a user from the created Amazon Cognito User Pool (UserCreationUrl)
Kibana URL (KibanaUrl) where you can open up the dashboard,
Cloudfront URL you must whitelist in the Amazon Connect application whitelisting setting.
Creating a user in the user pool is necessary to access Kibana. Following the Kibana URL, you will gain access to the visual user interface for the data stored in ElasticSearch.
Click the UserCreationUrl and follow the steps create a user for Kibana
Cognito here is used to manage access to Kibana, the users created in the Cognito User pool are sepereate users to the users from Amazon Connect.
Go to AWS Console → Amazon Connect, choose the Connect instance and insert the Cloudfront URL in the application integration section by clicking on the “add origin” button.
Click the Cloudfront URL, this should open the custom CCP website.
Login with your connect user and make/receive a call
Metrics will be published and visible in you the Kibana Dashboard
For more details on how to use the solution please check out the user guide.


hi! this page opens and closes popups like mad in rapid succession. it searches for race conditions in things like relay frames. click the button to run 100 iterations. DISABLE POPUP BLOCKING too! run 100x
Here is the response from the window:


Basic OwlCarousel solution for WordPress with kevinw.de in mind. Work in progress.
Uses jQuery OwlCarousel v1.3.3 (http://www.owlgraphic.com/owlcarousel/). Licensed under MIT.
Scripts only load on home page by default. Scripts can be enabled for posts and pages manually using the checkbox that is placed next to the editor.
```
```
```
Your Content
Your Content
Your Content
```


Individual-based simulator of pacific bluefin tuna.
Project page on GitHub
Requirements
- Unix-like environment (macOS, Linux, WSL, MinGW on MSYS2, etc.)
- C++14 compiler (clang++ >= Apple LLVM 8.1, g++ >= 5.3)
- CMake (>= 3.8.0)
The following libraries are optional or automatically installed:
R interface
You can install and use this program via R package "tekkamaki".
Installation of command-line version
The easiest way is to use Homebrew. The following command installs tekka and all the dependencies:
brew install heavywatal/tap/tekka
Alternatively, you can get the source code from GitHub manually:
git clone https://github.com/heavywatal/tekka.git
cd tekka/
mkdir build
cd build/
YOUR_PREFIX=${HOME}/local # or /usr/local
cmake -DCMAKE_INSTALL_PREFIX=$YOUR_PREFIX ..
make -j2
make install


--- layout: post title: "My Week in Review" date: 2017-03-05 07:05:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/2017-03-05-moon.jpg imagecredits: https://www.flickr.com/photos/cogdog/26314878180 ---
Words have been hard to summon for a while now. One of the things I enjoy about writing this weekly review of my work is it helps me to see that I still do accomplish things, even when I'm feeling remarkably unproductive. This week, there were many words.
Icon credits: The Noun Project


Permissions
{% if client.organisation_type == 'central government' %}
The {{ client.name }} service will:
{% elif client.organisation_type == 'local government' %}
{{ client.name }} is requesting access to:
{% else %}
{{ client.name }} is requesting access to:
{% endif %}
{% for key,value in avaliable_scopes.items() %}
{% for scope in scopes %}
{% if scope == key %}
- {{value}}
{% endif %}
{% endfor %}
{% endfor %}


Genre Explorer was formulated in the sleep deprived stages of a classic Silicon Valley hackathon, then painstakingly worked on by its two clinically insane founders until the following site resulted. This is a new take on music exploration, with our service finding similarly styled music across new genres of the user's picking based on a seed song, all running through the spoofy APIs on a Scratch (from Scratch in Scratch) backend with this brilliantly styled text brought to you by Handlebars, CSS 2.1, HTML4 (AKA Engrish), and a non-liberal Sysadmin.


The operation you requested on the S3 server is only available to registered Users
Feel free to contact the seagate S3 community if you need access.


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TRAIN EFFICIENTLY
Communicate with video using Mondopad and ConX
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- Convey information more clearly
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Learn more about Mondopad
Learn more about ConX video meeting rooms -
EDUCATE AND ANNOTATE
Annotate on shared web-based content
Mondopad enables everyone in the videoconference to see and annotate on the same content, such as maps, videos or other web-based content.- Whiteboard on an endless canvas
- Annotate on documents, web pages and video
- Save changes and email files to participants
INF5520a INF7021 55” multi-touch screen 70” multi-touch screen Video conferencing, interactive whiteboard, document annotation, web browser, file sharing LCD display with touch overlay, Mondopad collaboration software, MS Office Suite, 720p camera, microphone array, sound bar, wireless keyboard and mouse Built-in PC running Windows 7 Pro, 120 GB SSD, multiple USB and HDMI inputs, and more Learn More Learn More -
ON-DEMAND COMMUNICATION
Talk face-to-face from station-to-station
The infocus.net network provides affordable high-quality video calls and service you can count on at a moment’s notice.
- InFocus 121 video calling service
- InFocus ConX group videoconferencing rooms
- Manage accounts and services on infocus.net


Web Color Checker
A tool for checking accessibility properties of color schemes for the web.
Contrast ratios are calculated using the WCAG 2 algorithm assuming an sRGB color space. Target values for good readability are 4.5 or better, with 7 or better being ideal.
The various samples of color-blind appearances for colors are computed using the HCIRN color blindness simulation function, which is known to not be perfectly accurate but provides a reasonable approximation for designer purposes of what a person with that type of color blindness would see.
The HCIRN Color Blindness Simulation function is Copyright © 2000-2001 Matthew Wickline and the Human-Computer Interaction Resource Network, which is free for non-comercial use.
jQuery is copyright the JS Foundation and it's contributors and is used here under an MIT license.
Bootstrap is copyright Twitter Inc and the Bootstrap Authors, used here under an MIT license.
Arrive.js is copyright Uzair Farooq, used here under an MIT License.
All other code is Copyright © 2019 Austin S. Hemmelgarn and released under the MIT License
See the code or contribute on GitHub


{% extends 'base.html' %} {% block body %}
Use this form to subit a search request
When you're ready, click this button


Visit your university courses online.
With conference viewer, you can
Watch your university course online (if available).
Ask questions in the online chat.
To spare resources, you wont be able to use your microphone or webcam.


Pair rogramming (sometimes referred to as peer programming) is an agile software development technique in which two programmers work together at one workstation. One, the driver, writes code while the other, the observer, pointer or navigator, reviews each line of code as it is typed in.
it gives us practice of collaborating with each others
Driver types in the code and Navigator makes a plan. It makes it easier to