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Northern Arizona Recumbent Riders

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James Waves We Are Not In Control [CRACKED]



The COVID-19 pandemic has had an unprecedented impact on human health and society1,2, with high-income, urban and temperate areas often the most severely affected3,4,5. The impacts of the virus are felt directly through its substantial infection-related mortality6,7 and post-infection sequelae8, as well as through the often highly restrictive public health measures needed to achieve control9.




James Waves We Are Not In Control



Australia was relatively successful in controlling COVID-19 throughout 202010, with all jurisdictions of the country achieving good control of the first wave of imported cases through March and April. However, the southern state of Victoria suffered a substantial second wave of locally-transmitted cases, reaching around 600 notifications per day, predominantly in Metropolitan Melbourne in winter.


As noted previously, stage 3 restrictions were associated with a reduction in the effective reproduction number16, although significant case rates persisted throughout July, and further reductions in mobility were observed with stage 4. An agent-based model with detailed social networks, consideration of multiple intervention types, and without geographical structure was calibrated to the Victorian epidemic17. This model emphasised the importance of associations between individuals who would not otherwise be in regular contact to the epidemic. Another agent-based simulation found that earlier activation of social distancing interventions could halve the total epidemic size18. By contrast to previous work, our model captures both the temporal and spatial implementation of the policy changes in Victoria to allow inference of the effect of each intervention. As concern increased that epidemic control had not been achieved over the course of July, the policy changed rapidly in an attempt to bring the epidemic under control. Testing numbers increased following a nadir in early June and lockdown measures were implemented differently in twelve Melbourne postcodes, the remaining postcodes of Greater Melbourne, Mitchell Shire (immediately north of Greater Melbourne) and the remainder of regional Victoria. We captured these complicated geographical patterns of restriction by scaling our mixing matrices using Google mobility data, which are available at the LGA level for Victoria. School closure and face-covering policy changes were captured according to the dates of policy changes.


In earlier versions of the model we included an effect of seasonal forcing. While good fits were also achieved with this effect included, the posterior estimate of the effect of seasonality was not markedly constrained through fitting to data. The minimal information provided on seasonal forcing was likely attributable to our simulation period spanning less than four months and so covering a small proportion of the cycling period. Therefore, while a potentially important seasonal effect would be consistent with our analysis and with evidence from elsewhere19, it was not possible to draw conclusions as to its strength. The effect of face coverings was similar to or greater than is typically estimated at the individual level20,21, but is consistent with the dominant importance of the respiratory route to transmission22. The finding was also not unexpected given the marked shift in population use of face coverings at this time and the timing of the policy change in late July relative to the dramatic reversal in case numbers occurring around one week later. The significant estimated effect of behavioural changes suggests that reductions in interpersonal associations (macro-distancing) alone were not solely responsible for the dramatic reversal in the epidemic trajectory observed. However, the Google mobility functions used to capture macro-distancing simulated falls in attendance at workplaces and other non-household locations to considerably below baseline values in several services (Fig. 6), emphasising their importance. The dramatic effect of each of these interventions on the epidemic trajectory (relative to the parameter estimates that suggest relatively modest individual-level efficacy) is partly attributable to our implementation of these processes as applying to both the infectious cases and the exposed individual. This approach is analogous to simulating the use of bed-nets for malaria control, where the overall effect of the intervention is quadratic (i.e., the scaled transmission rate is effectively the square of the complement of the intervention effect), as it affects both the infection vector and recipient23.


Many objects in the universe are too cool and faint to be detected in visible light but can be detected in the infrared. Scientists are beginning to unlock the mysteries of cooler objects across the universe such as planets, cool stars, nebulae, and many more, by studying the infrared waves they emit.


The Cassini spacecraft captured this image of Saturn's aurora using infrared waves. The aurora is shown in blue, and the underlying clouds are shown in red. These aurorae are unique because they can cover the entire pole, whereas aurorae around Earth and Jupiter are typically confined by magnetic fields to rings surrounding the magnetic poles. The large and variable nature of these aurorae indicates that charged particles streaming in from the Sun are experiencing some type of magnetism above Saturn that was previously unexpected.


Infrared waves have longer wavelengths than visible light and can pass through dense regions of gas and dust in space with less scattering and absorption. Thus, infrared energy can also reveal objects in the universe that cannot be seen in visible light using optical telescopes. The James Webb Space Telescope (JWST) has three infrared instruments to help study the origins of the universe and the formation of galaxies, stars, and planets.


Smart components comprise the sensors, microprocessors, data storage, controls, software, and, typically, an embedded operating system and enhanced user interface. In a car, for example, smart components include the engine control unit, antilock braking system, rain-sensing windshields with automated wipers, and touch screen displays. In many products, software replaces some hardware components or enables a single physical device to perform at a variety of levels.


The capabilities of smart, connected products can be grouped into four areas: monitoring, control, optimization, and autonomy. Each builds on the preceding one; to have control capability, for example, a product must have monitoring capability.


Control through software embedded in the product or the cloud allows the customization of product performance to a degree that previously was not cost effective or often even possible. The same technology also enables users to control and personalize their interaction with the product in many new ways. For example, users can adjust their Philips Lighting hue lightbulbs via smartphone, turning them on and off, programming them to blink red if an intruder is detected, or dimming them slowly at night. Doorbot, a smart, connected doorbell and lock, allows customers to give visitors access to the home remotely after screening them on their smartphones.


The rich flow of monitoring data from smart, connected products, coupled with the capacity to control product operation, allows companies to optimize product performance in numerous ways, many of which have not been previously possible. Smart, connected products can apply algorithms and analytics to in-use or historical data to dramatically improve output, utilization, and efficiency. In wind turbines, for instance, a local microcontroller can adjust each blade on every revolution to capture maximum wind energy. And each turbine can be adjusted to not only improve its performance but minimize its impact on the efficiency of those nearby.


Smart, connected products offer major improvements in predictive maintenance and service productivity. New service organizational structures and delivery processes are required to take advantage of product data that can reveal existing and future problems and enable companies to make timely, and sometimes remote, repairs. Real-time product usage and performance data allows substantial reductions in field-service dispatch costs and major efficiencies in spare-parts inventory control. Early warnings about impending failure of parts or components can reduce breakdowns and allow more efficient service scheduling. Data on product usage and performance can feed insights back to product design, so that firms can reduce future product failures and associated service required. Product usage data can also be used to validate warranty claims and identify warranty agreement violations.


Smart, connected products create the need for robust security management to protect the data flowing to, from, and between products; protect products against unauthorized use; and secure access between the product technology stack and other corporate systems. This will require new authentication processes, secure storage of product data, protections against hackers for both product data and customer data, definition and control of access privileges, and protections for products themselves from hackers and unauthorized use.


We believe that as smart, connected products evolve, more human-machine interface capabilities may well move out of the product and into the cloud. However, the complexity facing users in operating these interfaces will increase. User interfaces may often overshoot in complexity, and user backlash may drive firms to restore simpler, easy-to-use interfaces for common functions, including on/off controls.


A fully open system enables any entity to participate in and interface with the system. When Philips Lighting introduced the hue smart, connected lightbulb, for example, it included a basic smartphone application that allowed users to control the color and intensity of individual bulbs. Philips also published the application programming interface, which led independent software developers to quickly release dozens of applications that extended the utility of the hue bulbs, boosting sales. The open approach enables a faster rate of applications development and system innovation as multiple entities contribute. It can also result in a de facto industry standard, but one from which no company gains a proprietary benefit. 041b061a72


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