As professionals return to they are avoiding public transit en masse

Boxcar

This paper is available on arxiv under CC 4.0 license. Authors: (1) Eleonora Alei, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (2) Björn S. Konrad, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (3) Daniel Angerhausen, ETH Zurich, Institute for Particle Physics & Astrophysics, National Center of Competence in Research PlanetS & Blue Marble Space Institute of Science; (4) John Lee Grenfell, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR) (5) Paul Mollière, Max-Planck-Institut für Astronomie; (6) Sascha P. Quanz, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (7) Sarah Rugheimer, Department of Physics, University of Oxford; (8) Fabian Wunderlich, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR); (9) LIFE collaboration, www.life-space-mission.com. Table of Links Abstract & Introduction Methods Results Discussion Conclusions Next Steps & References Appendix A: Scattering of terrestrial exoplanets Appendix B: Corner Plots Appendix C: Bayes’ factor analysis: other epochs Appendix D: Cloudy scenarios: additional figures 2. Methods We start by discussing the details of the input spectra that were used in this study (Section 2.1). We then discuss the updates on the Bayesian retrieval routine (Section 2.2). We describe the assumptions that our model takes into account and the main potential source of systematic errors in the retrievals in Section 2.3. 2.1. Input spectra and scenarios We consider spectra corresponding to four different evolutionary epochs of Earth: the prebiotic Earth (3.9 Ga), the Earth shortly after the Great Oxygenation Event (2.0 Ga), the Neoproterozoic Oxygenation Event (0.8 Ga), and Modern Earth. All considered Earth spectra were produced by Rugheimer & Kaltenegger (2018). These self-consistent spectra were produced using a 1D convective-radiative transfer model loosely coupled with a 1D climate model and a 1D photochemistry model. The authors accounted for the thermal chemistry and the photochemistry of more than 55 species. The atmospheres were modeled up to 10−4 bar and split into 100 layers. The radiative forcing of clouds is included by adjusting the surface albedo of the planet. The results of the photochemistry-climate-radiative model were then fed to a line-by-line radiative transfer model to produce emission spectra. The line lists and the pressure broadening coefficients were from to the HITRAN 2016 database (Gordon et al. 2017). Surface scattering was included in the calculations, assuming 70% ocean, 2% coast, and 28% land. In some scenarios, a partial cloud coverage was directly included in the calculation of the emission spectrum. In these cloudy cases, the authors assumed a 60% cloud coverage (split into 40% water clouds at 1 km altitude, 40% water clouds at 6 km altitude, and 20% ice clouds at 12 km altitude) consistent with an averaged Earth cloud model. Aerosol was not included in the calculation. The model has been validated from the visual to the mid-infrared wavelength ranges with observations of Earth (Kaltenegger et al. 2007; Kaltenegger & Traub 2009; Rugheimer et al. 2013). For further details, we refer the reader to Rugheimer & Kaltenegger (2018). For each epoch, we consider both clear sky and cloudy sky spectra, which yields a total of 8 scenarios. We assigned every modeled scenario an identifier and a specific color, as listed in Table 1. We will use these identifiers throughout the remainder of the paper. We simulate observations with LIFE via the LIFEsim tool (see Paper II for a description of the simulator). LIFEsim estimates the wavelength-dependent S/N considering all major astrophysical noise sources (stellar leakage, local zodiacal dust emission, and exo-zodiacal dust emission). We consider an Earth-sized planet on a 1 AU orbit around a Sun-like star at 10 pc distance. For our baseline analyses we assumed the nominal simulation parameters for LIFEsim as summarized in Table 2 (see Paper I and Paper II for details). We consider an exo-zodi level of 3 times the local zodiacal dust density, based on the results from the HOSTS survey (Ertel et al. 2020). 2.2. Updates on the Bayesian retrieval framework We denote the input spectra from Section 2.1 as the "true spectra." To simulate a LIFE-like observation of these targets, we run LIFEsim on the true spectra, thus obtaining simulated "observed spectra." We then perform a retrieval on the observed spectra using petitRADTRANS (Mollière et al. 2019) as "forward model" in the retrieval routine, and the Bayesian sampler model pyMultiNest (Buchner et al. 2014) as "parameter estimation routine" (cf. Paper III). The theoretical 1D atmospheric model petitRADTRANS (Mollière et al. 2019) applies the radiative transfer equation to calculate spectra corresponding to a set of parameters. These parameters describe the bulk parameters (planetary mass and radius), the pressure-temperature (P-T) structure (approximated by a fourth-order polynomial), and the chemical composition of the atmosphere. Our Bayesian retrieval framework recursively draws combinations of parameters from a set of "priors" that describe the "a priori" probability distribution of each parameter (listed in Table 3) and uses the forward model to compute the corresponding spectra. Then, the Bayesian framework tests how well these calculated spectra fit the observed one using a "likelihood" function (see Eq. (3) in Paper III). In order to sample the prior space efficiently, our retrieval relies on the parameter estimation routine pyMultiNest (Buchner et al. 2014), which is based on MultiNest (Feroz et al. 2009). This routine applies the Nested Sampling algorithm (Skilling 2006) to fit the theoretical spectral model to the observed spectrum, and thereby yields estimates and uncertainties for the model parameters. These estimates are the "posterior probability distributions" (or "posteriors"). The posteriors contain the information on which combinations of model parameters best describe the observed spectrum. For more details about the Bayesian retrieval framework, we refer the reader to Paper III. In our previous work, we argued that effects of scattering on simulated mid-infrared spectra are negligible at the considered resolutions and LIFEsim noise patterns. In that study, we had to be particularly mindful of the computing time. The version of petitRADTRANS used in Paper III only allowed to calculate spectra at R = 1000, which were subsequently binned down to the resolution of the input spectrum (R = 35 − 100 in Paper III). Calculating a spectrum at R = 1000 excluding scattering required ≈ 0.5 seconds, whereas including scattering required ≈ 18 seconds. Since millions of spectra have to be calculated for a single Bayesian retrieval, including scattering was prohibitive with respect to the computing time, especially when considering a large grid of retrieval runs. Recent updates to petitRADTRANS have enabled us to compute spectra at any resolution, provided a grid of correlated-k tables at that resolution is available. These tables can be produced with petitRADTRANS by binning down the R = 1000 opacity tables. We compiled a correlated-k opacity database for R = 50 and used it to produce spectra directly at this resolution. This reduces the computation time per spectrum significantly and allows us to compute emission spectra in ≈ 0.04 seconds when excluding scattering, and in ≈ 0.5 seconds when including scattering. This reduction in computing time allows us to include scattering in the theoretical spectral model. Other updates on petitRADTRANS were performed. The treatment of collision-induced absorption (CIA) was modified by updating the interpolation of the CIA tables, originally in FORTRAN, to Python. Minor variations in the interpolation options (log-linear compared to nearest neighbor in Paper III) make the new model not directly comparable with its previous version. However, at Earth-like conditions, the differences in the CIA signature on the spectrum remain negligible. Also, the CIA features impact mostly the short wavelengths (λ < 6 µm), where the LIFEsim noise is large. Furthermore, the treatment of scattering was updated. Originally implemented in petitRADTRANS only for gaseous planets (see Mollière et al. 2019), it was adapted to include the scattering by a rocky surface. More information on the new implementation of scattering can be found in Appendix A, as well as the petitRADTRANS documentation[3] . This new version of petitRADTRANS is available in the main GitLab repository[4] . Using the updated retrieval framework, we run retrievals for the eight different spectra introduced in Section 2.1. We retrieve the same parameters as in Paper III and leave most prior distributions unchanged. An exhaustive list of the parameters and priors used, and the corresponding expected values for the different epochs, are listed in Table 3. Most priors are represented by a boxcar function (hereafter "uniform" priors): every value is equally probable if within a certain range. Two notable exceptions are the priors for the planetary radius Rpl and the logarithm of the planetary mass log10(Mpl), whose priors are Gaussian. The prior we assumed on Rpl is based on the radius estimate expected from observing a terrestrial planet with LIFE during its search phase (see Paper II for details). The log10(Mpl) prior was inferred from Rpl using the statistical mass-radius relation presented in Chen & Kipping (2016) (see Paper III for details). 2.3. Assumptions and discrepancies When performing retrievals, we are limited by the maximum number of parameters retrieved in a reasonable computing time. For this reason, we need to make a few simplifications: 1. As in Paper III, we parameterize the P-T profile in the retrieval by a fourth order polynomial. Table 2: Simulation parameters used in LIFEsim for the baseline analyses. 2. We assume the abundances of the considered species to be independent of altitude. 3. Our retrieval framework does not model clouds for any of the considered scenarios. This is a strong simplification for the cases where we retrieve cloudy input spectra. However, this retrieval approach allows us to investigate the biases on the results obtained when retrieving a cloudy spectrum assuming a cloud-free atmosphere. The addition of a cloud model to our retrieval framework will be tackled in a future study 4. A surface reflectance of 0.1 is assumed for all the wavelength range. This is a common value for water-rich habitable terrestrial planets, dominated by the low IR reflectance of oceans and ice. In contrast, Rugheimer & Kaltenegger (2018) used P-T profiles that were self-consistently calculated by their climatephotochemistry model to generate the input spectra. The chemical abundance profiles were also altitude-dependent and calculated self-consistently by the climate-photochemistry model. In order to compare the results of the retrievals with the input, we approximate the input P-T and abundance profiles to calculate expected values of the parameters considered in the retrievals. Regarding the thermal structure of the atmosphere, we determine the expected values of the polynomial coefficients (a4, a3, a2, a1, a0) listed in Table 3 by fitting a fourth-order polynomial to the self-consistent P-T profiles. As for the abundances, we assume the weighted means over the pressure grid of the altitude-dependent abundance profiles. This is sensible, since the denser layers of the atmosphere (which corresponds to higher pressures) are generally the ones that contribute more to the spectrum. The expected values for the considered species are listed in Table 3 as well. For what concerns the surface reflectance, Rugheimer & Kaltenegger (2018) considered a wavelength-dependent reflectance. However, the reflectance still averaged to 0.1 in the wavelength range of interest. We would therefore not expect large variations due to this parameter. There are also differences between the opacity tables that the two models use. We used the default set of opacities for petitRADTRANS as presented in Mollière et al. (2019). We added the N2O opacity from the ExoMol database (Chubb et al. 2021). The CIA opacities are taken from the HITRAN database. Details and reference papers corresponding to the opacity linelists are shown in Tables 4 and 5. In contrast, the input spectra were calculated using HITRAN 2016 opacities (Rugheimer & Kaltenegger 2018). Differences in line-lists and broadening coefficients are therefore to be expected and may cause biases in the results. Due to these differences in the atmospheric models, we would imagine to find some small discrepancies between the spectra that were published in Rugheimer & Kaltenegger (2018) and the ones that our framework can calculate. We will discuss this particular aspect in Section 4.4. [3] https://petitradtrans.readthedocs.io [4] https://gitlab.com/mauricemolli/petitRADTRANS This paper is available on under CC 4.0 license. arxiv

The Methods in Which We're Researching Exoplanets

The Go-Getter: A Story That Tells You How to be One by Peter B. Kyne is part of the HackerNoon Books Series. You can jump to any chapter in this book . III here III At twelve-thirty, as Cappy was hurrying up California Street to luncheon at the Commercial Club, he met Bill Peck limping down the sidewalk. The ex-soldier stopped him and handed him a card. "What do you think of that, sir?" he queried. "Isn't it a neat business card?" Cappy read: Cappy Ricks ran a speculative thumb over Comrade Peck's business card. It was engraved. And copper plates or steel dies are not made in half an hour! "By the Twelve Ragged Apostles!" This was Cappy's most terrible oath and he never employed it unless rocked to his very foundations. "Bill, as one bandit to another--come clean. When did you first make up your mind to go to work for us?" "A week ago," Comrade Peck replied blandly. "And what was your grade when Kaiser Bill went A.W.O.L.?" "I was a buck." "I don't believe you. Didn't anybody ever offer you something better?" "Frequently. However, if I had accepted I would have had to resign the nicest job I ever had. There wasn't much money in it, but it was filled with excitement and interesting experiments. I used to disguise myself as a Christmas tree or a box car and pick off German sharp-shooters. I was known as Peck's Bad Boy. I was often tempted to quit, but whenever I'd reflect on the number of American lives I was saving daily, a commission was just a scrap of paper to me." "If you'd ever started in any other branch of the service you'd have run John J. Pershing down to lance corporal. Bill, listen! Have you ever had any experience selling skunk spruce?" Comrade Peck was plainly puzzled. He shook his head. "What sort of stock is it?" he asked. "Humboldt County, California, spruce, and it's coarse and stringy and wet and heavy and smells just like a skunk directly after using. I'm afraid Skinner's going to start you at the bottom--and skunk spruce is it. "Can you drive nails in it, Mr. Ricks?" "Oh, yes." "Does anybody ever buy skunk spruce, sir?" "Oh, occasionally one of our bright young men digs up a half-wit who's willing to try anything once. Otherwise, of course, we would not continue to manufacture it. Fortunately, Bill, we have very little of it, but whenever our woods boss runs across a good tree he hasn't the heart to leave it standing, and as a result, we always have enough skunk spruce on hand to keep our salesmen humble." "I can sell anything--at a price," Comrade Peck replied unconcernedly, and continued on his way back to the office. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Peter B. Kyne (2004). The Go-Getter: A Story That Tells You How to be One. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/12257/pg12257-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

At twelve-thirty, as Cappy was hurrying up California Street to luncheon at the Commercial Club

BARNEY RETURNS TO LUTHA

The Jungle: Chapter IX

This story was originally published on by by and . ProPublica Jennifer Smith Richards Jodi S. Cohen Prosecutors in Illinois have dropped at least 15 court cases that hinged on the word of a former Chicago police officer who’s now charged with perjury and forgery after he got dozens of traffic tickets dismissed by testifying each time that his girlfriend had stolen his car. Jeffrey Kriv is accused of lying under oath 44 times to get out of speeding, parking and red light camera tickets involving his personal vehicles. In his 26 years as a Chicago officer, Kriv was known for being one of the city’s most prolific drunken-driving enforcers, which means many more pending cases in which he was an arresting officer could be in peril. The most recent DUI arrests he made involve allegations of dangerous driving. On just one morning this month, an assistant state’s attorney made motions declining to prosecute seven cases where Kriv was the arresting officer, some of which dated to 2021. The assistant state’s attorney offered no reason, but in court, defense attorneys mentioned Kriv’s alleged credibility issues. “This is the officer who has been charged with a felony,” one defense lawyer told Judge Chloe Pedersen after his client’s DUI case was called. “This is a Kriv matter,” another defense attorney said as his client’s case was dismissed. While Kriv retired in January just before he was charged criminally, he had faced nearly 100 misconduct complaints from citizens and fellow officers in his tenure on the force. ProPublica and the Chicago Tribune previously detailed Kriv’s long history of alleged . misconduct as an officer and his current legal trouble Prosecutors charged him in January with four counts of perjury and five counts of forgery, all felonies, in connection with four of the traffic tickets he accrued. He has pleaded not guilty. The Chicago Police Department knew in early 2022 that the Office of Inspector General was investigating Kriv for lying to get out of tickets. Then, on Oct. 28, the state’s attorney’s office added Kriv to its list of officers whose credibility issues meant the office would not call them to testify in criminal cases, sometimes called the do-not-call list. But the Police Department would not answer ProPublica when asked why it kept Kriv on the streets until January, when it stripped him of police powers. In those few months, Kriv made two dozen arrests and issued seven traffic citations, according to Police Department records obtained by ProPublica. He was the primary arresting officer in 18 of those cases; charges already have been dropped in seven of them. A spokesperson for the Police Department declined to comment. The state’s attorney’s office said there are 10 pending felony cases in which Kriv was the arresting officer. Two cases filed after he was put on the do-not-call list are moving forward; the other eight cases remain under review, a spokesperson said. There may be other cases — most of them likely misdemeanor DUIs — involving Kriv that are still pending. The state’s attorney’s office said it does not track misdemeanors, and the Cook County clerk’s office and chief judge’s office also said they did not have that information. Most DUI cases are misdemeanors, however, and Kriv regularly made about 100 arrests a year on DUI charges, according to annual counts published by an anti-drunken-driving group. Kriv estimated last year that hundreds of his cases might be awaiting an outcome. Kriv’s attorney, Tim Grace, told reporters that he and his client would not comment for this story, though Kriv has twice emailed reporters to defend himself against misconduct complaints and call ProPublica’s reporting biased, including in an email last week. In a 2,000-word email sent in June, he said that he had nearly 150 honorable mentions and two life-saving awards and was consistently one of the city’s top arresting officers for DUI charges. He also criticized other officers, a judge and the Police Department as a whole. “I’ve been on the job for 27 years and I was a worker. Any officer that works will get complaints. Some false, some true,” Kriv wrote. “I could do what many, many officers do and that would be nothing.” Kriv also sent a strongly worded email to a prosecutor last November, while he was still working as a police officer, after he learned he would not be allowed to testify about arrests he made. In the email, he defended his honesty and threatened to “not go out of my way to arrest people for DUI” going forward. “My reputation as a Chicago Police Officer is immaculate and all my testimony on any case that I have been involved in is spot on,” he wrote to Assistant State’s Attorney Emily Leuin, after calling her a coward. ProPublica obtained the email through a public records request. “Go ahead and dismiss my cases where someone was seriously hurt because the offender was drunk. This is something you have to live with but by all accounts, you obviously have no qualms about doing so. I’m sure [anti-drunk-driving groups] will like to know what is going on with the dozens, if not hundreds of cases of mine that are currently pending an outcome,” he wrote. The beginning of an email that Kriv sent to Assistant State’s Attorney Emily Leuin. Credit: Obtained by ProPublica. Redacted by ProPublica Leuin did not respond to a request for comment. Despite his threats, Kriv did make DUI arrests for the rest of his tenure as an officer. He arrested a Chicago man in December 2022 after he said he watched the driver go the wrong way on a one-way street and then go through a stop sign. “Very strong odor of alcoholic beverage on breath, slurred/thick-tongued speech, glassy eyes, confused,” Kriv wrote in the charging documents. That case was dismissed in April. Other cases, meanwhile, are moving forward despite Kriv’s indictment. In traffic court this month, defense attorney Steve Roach asked the prosecutor whether Kriv, who made the arrest, would be subpoenaed in his client’s DUI case. The prosecutor said no, leaving Roach to wonder how the state could continue with its case. “Because you’ve got a primary arresting officer who has an indictment against him which involves questions of one’s honesty, I wouldn’t be playing games with these cases,” Roach said in an interview. The Cook County public defender’s office is representing several people in Kriv’s arrest cases. A spokesperson said that in cases the state continues to pursue, the office will demand trials and the state will have to decide whether to make its cases without Kriv. The collapse of Kriv’s cases shares some similarities with the problematic DUI arrests made by Chicago police officer John Haleas nearly 20 years ago. Even after the police administration confirmed that Haleas had falsified reports in DUI arrests, he remained on duty. (In fact, he is still a Chicago police officer, though he was later convicted of attempted obstruction of justice and the department tried to fire him.) There’s also a key difference between Haleas and Kriv: how the state’s attorney has reacted to pending court cases. The state’s attorney dismissed more than 150 of Haleas’ pending cases when he was found to have lied. The state’s attorney’s office in July made public its list of officers in Cook County who it has barred from testifying in criminal cases — more than 300 officers in all, the vast majority of them from the Chicago Police Department. About 60 officers were added this year, and the office adopted a new policy last month that spells out prosecutors’ duty to disclose its problematic witnesses to defendants. “The culture that allowed disreputable law enforcement officers to testify in court propelled Cook County’s reputation as the wrongful conviction capital of the country,” State’s Attorney Kim Foxx said in a press release about the disclosure list. Chicago has a long history of scandalous cops whose cases fell apart when their own corruption or misdeeds were revealed. Over the years, officials have grappled with how to handle the fallout in court, as well as what to disclose to defendants and the public about these cases. A spokesperson for the state’s attorney’s office said the list of officers with credibility issues is public and that disclosures to defendants would be made when evidence is typically shared. ProPublica contacted more than a dozen defense attorneys currently representing clients arrested by Kriv, and only one said he had been notified by a prosecutor that Kriv was barred from testifying. Other lawyers were unaware that Kriv would not be called as a witness. One said the state’s attorney’s office had left his case “in limbo” and he is skeptical that the prosecutor can mount a case without Kriv. “Every time I show up, the state’s attorney doesn’t know what’s going on,” said defense attorney Marco Rodriguez, who is representing a client in a DUI case. Among the Kriv cases that have been dismissed is one from October 2022 involving a woman who was idling in a crosswalk but asleep, according to Kriv’s police report. He noted in the report that there was vomit on the car seat and on the woman’s pants. Her blood alcohol content was 0.19%, more than twice the legal limit to drive, according to the report, obtained through a public records request. Prosecutors dropped the case against her in March. Even in his last weeks as a police officer, Kriv arrested at least five people over four days on DUI charges, including a 41-year-old driver he said he’d found on Dec. 10 passed out behind the wheel of his running Honda Accord in the middle of a dead-end street. The man “seemed confused” and the “whole front end” of his car was damaged and the tires shredded. Kriv noted that the driver had an explanation for the car’s damage. He wrote that the man “stated his car was like that from his girlfriend as it was her fault previously.” That case also has been dismissed. Photo by on Giammarco Boscaro Unsplash

Prosecutors Were Forced to Drop Cases Because They Were Linked to Ex-Cop Accused of Perjury

The Protestants

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The above-mentioned VPNs will give full freedom over Tor and onion nodes without risking your privacy. You don't have to compromise with your internet speed while accessing any website or app. You may not get a free trial with any of these, but they offer a risk-free money-back guarantee, so you'll get a full refund if you are not happy with their services. So don't wait and try it by yourself. FAQs What is the use of the Dark Web? Cyber Crime hackers, government, and private organizations use the Dark Web to monitor unusual and illegal activities. Some people also use it to sell drugs and weapons illegally. How dangerous can the Dark web be? The usage of the Dark Web took cybercrime to a new height and set records for ransomware, hacking, and phishing. Is it safe to use VPN for Dark Web? Tor websites and browsers are banned in many countries. To access these websites, you'll need a powerful VPN like ExpressVPN or CyberGhost. 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5 Best VPNs for Privacy and Security on the Dark Web

Asyncio in Python is deceptively intuitive, and with its pseudo real time / event processing /single threaded nature allows most features of concurrent programming while avoiding or hiding most of the complexities associated with multi-threaded/multi-processing programming.  In this article the versatility of asyncio program is examined by looking at a very specific problem: Assume that there is a task list and each task is being worked on.  When one or more tasks complete, another task or tasks should start. This is a very typical problem where there is a limit on the number of concurrent tasks in execution, but the number of tasks are not. Here is a contemporary example: Imagine the Covid 19 vaccine administration in a Parking lot.  Four vaccination stations have been set up. Each station administers a vaccine shot which takes between 1-3 minutes. The task is to complete boxes of vaccines with each box having 100 doses. Figure 1: Schematic of the parking lot vaccine administration Let us see how Python asyncio will administer the shots! Before you get too eager, let me reveal and briefly discuss the real title of this article: ' '. Dynamic addition of new tasks to a list of awaited tasks : A list of tasks are being executed. Situations will arise where you want to add another task to the list dynamically. Using Python asyncio we write this problem in pseudo code: Problem description True:
    #a tasklist t1,t2,t3 is being executed 
    asyncio.wait(t1(),t2(),t3(),return_when=asyncio.FIRST_COMPLETED) 
    <some tasks completes and execution gets here..
    Now we want another task or run the same task again into the list> while of To solve this problem, we maintain a dict of the running tasks, and insert new tasks into the list as tasks get completed. This is coded below: #Create a dict running tasks. These are { :t1, :t2, :t3} #

    #start the tasks to run concurrently. Here the functions are called or initiated
    tasks={ :asyncio.create_task(v(),name=k ) k,v taskDict.items()}
    #wait one or more tasks to complete and insert tasks into the dict True:
        done,pending = asyncio.wait(tasks.values(),return_when=asyncio.FIRST_COMPLETED) t done:  #we are only concerned about the tasks done. Only these need to be restarted not t.exception():
                print(f )
                tasks[tNm]=asyncio.create_task(taskDict[tNm](),name=tNm) :  #some exception occurred during task execution and has to be resolved
                    print(f )
                    #recover/restart the task
                    tNm=t.get_name()
                    tasks[tNm]=asyncio.create_task(taskDict[tNm](),name=tNm) of . = function names only and hence they will not be called but a list is maintained runningTasks 't1' 't2' 't3' async k for in for new while await for in if 'Task {t.get_name()} is done! Starting the Next Run of the task..' else 'Task {t.get_name()} got exception {t.exception()}. Please take care of it!' I recommend reading the above code about 3 times and refer asyncio docs, especially if you are not familiar with the the asyncio functions used above. Now back to the problem of Vaccination. The above 'Dynamic addition of new tasks to a list of awaited tasks' is used to manage the allocation of vaccines to each station. This in in the async procedure  dynamicTaskList. The vaccination station is simulated by the async procedure .  Each time a vaccinateStation uses up a box of vaccines, it returns, and then is restarted with a new box of vaccines. vaccinateStation random, asyncio,time def vaccinateStation():
    #Task that administers vaccine one by one a box vaccines
    boxCapacity= #capacity truck
    vaccinesLeft=boxCapacity #initially all are left to administer True: asyncio.sleep(random.uniform( , )) # to seconds each shot
        vaccinesLeft -= vaccinesLeft == : #box is now empty boxCapacity # number vaccines administered def dynamicTaskList():
    taskDict={ :vaccinateStation, :vaccinateStation, :vaccinateStation} # stations
    #start the tasks. Here the functions are called or initiated
    tasks={ :asyncio.create_task(v(),name=k ) k,v taskDict.items()}
    # boxes Vaccines
    noOfVaccineBoxes= #time tracker
    timeNow=time.time() i range(noOfVaccineBoxes):
        # the tasks
        done,pending = asyncio.wait(tasks.values(),return_when=asyncio.FIRST_COMPLETED) t done:  #we are only concerned about the tasks done. Only these need to be restarted
            tNm=t.get_name() t.exception():
                    print(f )
                    #recover/restart the task
                    tasks[tNm]=asyncio.create_task(taskDict[tNm](),name=tNm) :
                print(f )
                tasks[tNm]=asyncio.create_task(taskDict[tNm](),name=tNm)

asyncio.run(dynamicTaskList()) import async from of 100 100 of while await 0.001 0.1 0.001 0.1 for 1 if 0 break return return of async 'b1' 'b2' 'b3' 3 async k for in 100 of 100 for in await await for in if 'Task {tNm} got exception {t.exception()}. Please take care of it!' else 'vaccine box {i} is done! Vaccinated  {t.result()} and and time is {(time.time() - timeNow):.2f} seconds. Starting on the Next box..' Running this program gives the following output: (venv) vv@vv-GL63 RD:~ vv3/test$ python dynamicVaccinator.py 
vaccine box is done! Vaccinated and and time is seconds. Starting on the Next box..
vaccine box is done! Vaccinated and and time is seconds. Starting on the Next box..
vaccine box is done! Vaccinated and and time is seconds. Starting on the Next box..
vaccine box is done! Vaccinated and and time is seconds. Starting on the Next box..
vaccine box is done! Vaccinated and and time is seconds. Starting on the Next box.. -8 /git/ 0 100 5.11 1 100 5.26 2 100 5.33 3 100 10.31 4 100 10.37 Here we notice that about 100 vaccinations are being administered in about 5 seconds. We are shooting out vaccines from a machine gun!  The vaccination rate is controlled in line 8: asyncio.sleep(random.uniform( , )) # to seconds each shot await 0.001 0.1 0.001 0.1 for Other parameters like number of vaccines per box are also defined. Try executing this program (written/tested on Python 3.8), change parameters, add slow/fast vaccinateStation's etc. to explore the asyncio features. Please note that if you put in realistic vaccination rates (1-3 minutes), this program will take hours to execute, since it operates in pseudo real-time. Enjoy!

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