Search Results: Need For Speed Heat

When players break our rules, some games let you report abuse, content, and cheating right in the game or service. This is the best way to report issues because it automatically grabs some of the information we need to investigate.

However, there is one exception to this rule: We do need to comply with laws governing search engines, including CSAM, copyright takedown (DMCA), right to be forgotten (GDPR), and nation-state orders.

The player is busted when the driver stops and is close to a PCPD unit for a certain amount of time, is completely immobilised during a pursuit, or has depleted their strength bar. Being busted will reward the player with any rep they have earned during the current night session, but will not be multiplied based on their heat level. They will also have to pay a fine using bank. Players busted by the PCPD will not have an impound strike applied to their vehicle or any other form of marks that would result in them losing their vehicle. They will only be fined and have their current night session consequently concluded. The game also features a storyline in which the players interact with the city's police force, led by authority figure Lt. Frank Mercer.[2][3][4] Players can smash neon flamingos hidden within the map, which rewards them with a small amount of money or rep depending on the time of day. They can also find graffiti, referred to as \"Street Art\" in the game, and send it to the livery editor to use it on their cars. Lastly, they can complete activities around the open world such as smashing billboards, beating scores on drift zones, getting the highest speeds passing through speed traps, and going the longest distances when performing long jumps. Players may complete \"Crew Time Trials\" which allows them to complete short timed events in an attempt to get the #1 spot on the leaderboard in their crew.

It's not the most reliable method of losing the cops, but it can be one of the easiest. This won't require any sharp turns or city-street maneuvering. Players only need to memorize where the longest straight roads are located in Palm City. They also need a car that can go well past 200 MPH (322 KMH). Because it seems that most cop cars can't go past that top speed.

You may want to use the Verbose mode if you are putting together a transforming search but are not exactly sure what fields you need to report on, or if you need to verify that you are summarizing the correct events.

All reports run in Smart mode, the default search mode, after they are first created. By design, the Smart mode returns the best results for whatever search or report you run. If you search on events, you get all the event information you need. If you run a transforming search, the Splunk software favors speed over thoroughness and brings you straight to the report result table or visualization.

Emerging research suggests that internet search patterns may provide timely, actionable insights into adverse health impacts from, and behavioral responses to, days of extreme heat, but few studies have evaluated this hypothesis, and none have done so across the United States. We used two-stage distributed lag nonlinear models to quantify the interrelationships between daily maximum ambient temperature, internet search activity as measured by Google Trends, and heat-related emergency department (ED) visits among adults with commercial health insurance in 30 US metropolitan areas during the warm seasons (May to September) from 2016 to 2019. Maximum daily temperature was positively associated with internet searches relevant to heat, and searches were in turn positively associated with heat-related ED visits. Moreover, models combining internet search activity and temperature had better predictive ability for heat-related ED visits compared to models with temperature alone. These results suggest that internet search patterns may be useful as a leading indicator of heat-related illness or stress.

We hypothesized that aggregated and anonymized data on internet search patterns can provide novel insights into the health effects or behavioral responses to extreme heat, as illustrated in the conceptual diagram shown in Fig. 1. Although there is ample evidence that days of extreme heat are linked to higher risk of death or healthcare utilization, there is typically little visibility into the experience of the much larger pool of people experiencing discomfort or preclinical or subclinical signs and symptoms of heat-related illness (typically unobserved variables are depicted in the square of Fig. 1). We posit that internet search activity can provide novel insights into these typically unobservable states.

Conceptual framework highlighting the relationship between exposure to heat and health outcomes highlighting the additional, previously unmeasurable intermediate vulnerability that helps identify increased exposure, behavior change, and susceptibility measured using internet search activity.

If this conceptual model is correct, internet search patterns could provide a marker of the impact of heat on a given population that is available nearly everywhere and in near real-time. Specifically, internet searches may serve as a marker of risk perception (e.g., searches for dehydration or heat stroke) or intent to change behavior (e.g., searches for swimming pools or air conditioning), and thus, may provide important insight into periods of time when or where populations are experiencing acute concern about heat. To test this hypothesis, we linked aggregated and anonymized data on internet search patterns provided by Google Trends with weather data and ED visit data for heat-related illnesses among adults across the US with commercial health insurance.

Because heat stress is experienced differently across individuals and communities, a single metric of heat is unlikely to be optimal in every population, location, or circumstance. Ambient temperature has been shown to be associated with heat-related morbidity and is a commonly used marker of thermal discomfort in the epidemiological literature22, but heat index (HI) and wet bulb globe temperature (WBGT) may provide a more accurate physiological representation of thermal discomfort or heat stress. Specifically, HI, which is frequently used as the basis for issuing heat warnings and advisories in the US, is a combination of air temperature and relative humidity and provides a representation of how heat feels to the human body23. WBGT accounts for air temperature, relative humidity, wind speed, and solar radiation and is thus thought to better depict heat stress in direct sunlight24. Given that no single meteorological index may fully represent the population experience of heat stress, we assessed three possible metrics of heat.

The statistical analysis was divided into three parts: (1) assessment of the association between temperature and internet search activity, (2) assessment of the association between internet search activity and heat-related ED visits, and (3) evaluation of a series of models for predicting heat-related ED visits based on combinations of internet search activity and weather metrics.

In the second stage, we used a multivariate random effects meta-analytic model to estimate the overall cumulative association between maximum daily temperature and heat-related internet searches across the 30 DMAs. We report the exponentiated coefficient derived from this model as the ratio of the proportion of searches for extreme heat at the 95th versus 1st percentile of the DMA-specific temperature distribution, loosely referred to throughout as the relative risk (RR). We repeated these analyses using maximum WBGT and HI instead of temperature, with similar results (see supplement).

We next examined the association between Google search activity and heat-related ED visits in each DMA by conducting an analogous two-stage analysis as described above, but with Google searches as the exposure of interest rather than daily maximum temperature. We continued to adjust for the same time varying factors described above. We report the RR for heat-related ED visits for the 95th versus 1st percentile of DMA-specific Google search activity.

We next evaluated the correlations between daily maximum ambient temperature and Google searches on the same day within each DMA (Fig. 4a). Maximum daily temperature tended to be most strongly correlated with searches for air conditioning (FBID), although the strength of the correlation varied considerably across DMAs. Google searches were also associated with ED visits for heat-related illness on the same day among adults with commercial health insurance (Fig. 4b). Searches for air conditioning (FBID) tended to be most strongly correlated with ED visits, but with notable heterogeneity among DMAs.

Spearman correlation distribution of (a) daily maximum ambient temperature and same day heat-health related Google searches and (b) heat-health related Google searches and same day heat-related ED visits across 30 DMAs.

We next used distributed lag nonlinear models to assess the association between percentiles of maximum daily ambient temperature and Google searches for selected terms, adjusting for temporal trends (Fig. 5). Ambient temperature was positively associated with the probability of Google searches for the selected terms. The association was most pronounced for searches for heat exhaustion (RR: 20.5; 95% CI: 14.7, 28.4) comparing the 95th to the 1st percentile of the DMA-specific distribution of warm-season maximum daily temperature); followed by searches for heat stroke (RR: 9.8; 95% CI: 7.8, 12.2); and searches for air conditioning using the FBID (RR: 8.2; 95% CI: 6.3, 10.6) (Supplementary Table S2). In other words, internet searches for air conditioning and heat stroke were nearly 10-times more common on the hottest versus the coolest days, while searches for heat exhaustion were approximately 20-times more common.

Finally, we fit a series of models to assess the relative abilities of temperature alone (Model 1), Google search activity alone (Model 2), or both together (Model 3) to predict heat-related ED visits. We assessed model fit using both pseudo R2 (higher is better) and mean squared error (MSE; lower is better) (Table 1). The median pseudo R2 was consistently highest for models that included both temperature and search terms (Model 3) and was lowest for the model with temperature only (Model 1). Results were qualitatively similar when considering the MSE (Table 1) or the sum of quasi-AIC (Supplementary Table S3) rather than the pseudo-R2, suggesting that models that included both temperature and search terms tended to perform better in predicting heat-related ED visits versus models with temperature or search terms alone. For example, comparing Model 1 and Model 3, we found that the pseudo R2 increased by 11.4% on average across 11 different Google searches, while mean square error decreased by 2.3%. 59ce067264

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