On October 22, 2020, yesterday, Dexter Johnson posted The Lithium-Ion Battery With Built-In Fire Suppression . Within this topic, Dexter Johnson regards a Stanford University research team and the SLAC National Accelerator Laboratory (its former name was the Stanford Linear Accelerator Center [1] ). Johnson stated: Now [Yi] Cui and his research team, in collaboration with SLAC National Accelerator Laboratory, have offered some exciting new capabilities for lithium-ion batteries based around a new polymer material they are using in the current collectors for them. The researchers claim this new design to current collectors increases efficiency in Li-ion batteries and reduces the risk of fires associated with these batteries. [2] Johnson was saying this: fires are a current Li-ion battery threat that has been realized, but a new design can secure client use-case safety, and this required this battery redesigned. As this technology approaches marketplace entry points, this shall c
COVID-19
On 17 April, 2020, 5:00 AM Chicago, IL time, Greg Stewart, Klaske van Heusden, and Guy A. Dumont released the IEEE SPECTRUM article How Control Theory Can Help Us Control COVID-19 (link). Stewart, et. al. wrote, “As we write these words, several billion people, the majority of the world’s population, are confined to their homes or subject to physical-distancing policies in an attempt to contain on of the worst pandemics of modern times” (Stewart, Heusden, Dumont, 17 Apr, 2020). This is a reflection against the COVID-19 pandemic’s recorded measures.
The Reproduction Number of COVID-19
The Cost of Easing Social-Distancing Restrictions
About easing social-distancing standardization, Stewart, et. al. wrote a commonly cited proposition is binary in scope because some blocks would be removed only when intensive care cases do not extend beyond a certain number.For a Holistic Model: Simulating a Highly Infectious Disease
Against non-binary nonpharmaceutical interventions, Stewart, et. al simulated recovery strategy policies to contend against a commonly used infectious-disease model. As a result, the most dangerous time an intensive care patient can experience, wrote Stewart et. al., was inclusively between the first and third months of virtually any deadly and highly contagious illness due to patients’ need of critical care relatively far outnumbered all of the available beds in the intensive care unit. Due to easing social-distancing restrictions, the second surge, said Stewart, et. al. of this is inclusively between the third and fifth months, so the disease effectively doubles its dangerousness.
Next, Stewart, et. al. displayed: although the binary, on-off, approach, began relatively dangerous inclusively during the first two months, its patient population effectively halved inclusively from the third month to the seventh month. Also, the patients’ critical care need peaked every month, but valleyed every two months. Plus, the R0 values are inversely proportional to the binary on-off approach’s number of patients or beds, so this means intensive care had been historically capable of successfully mitigating highly infectious diseases.
According to Stewart, et. al. their third experiment had a different goal: ninety percent occupancy in hospital intensive care units, and they designed a control systems theory-based policy based on simple feedback. In summary of Stewart, et. al., the Ro being high warrants many restrictions implemented. In contrast, later conditions began improvement, and other social services are allowed. This process enabled the maximum rate of recovery. During the first month that the pandemic model began its simulation, the patients needing critical care decreased against the threshold, and this model prevented any further threshold exceeder months, inclusively, from the second month to the seventh. Also, the R0 number is inversely proportional to the patients who needed intensive care because of uncertain patients since many reports of a highly infectious disease were self-reported. Even noncompliance, said Stewart, et. al., is not a great enough deterrent to withdraw the feedback policy because the variance is, in summary, logarithmic, so eventually little change in control effectiveness transpired. This is a Web-based tool, and the biggest challenge, said Stewart, et. al. would be onboarding nonspecialists because the epidemiological model has various components, and these components work with the feedback policy selections and different model noncompliances.
Slaves to Christ and Feedback
In the Apostle Paul’s (INT) Epistle to the Ephesians, the Apostle Paul recommended slavery. The Apostle Paul said (link), “not with eye-service as men-pleasers, but as servants of Christ, doing the will - of God from [the] heart” (6:6). Given that many of the Bishop, Paul’s, epistles include feedback such as to Timothy, and also to Timothy and Silvanus, together (1 Thess 1:1), this indicates that Paul had countered noncompliance with a substantial amount of feedback, and this is evident because Paul authored most of the New Testament. Since Christianity is one of the world’s most popular religions (link), the adoption of a feedback model is crucially dependent on limiting noncompliance. For onboarding nonspecialists of the infectious disease model, then, the goal ought to be a mentor-catechism program. This is better than another Master-slave relationship because the Christianity controls are already mostly in place, worldwide.
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