Fair allocation of multiple resources using a non-monetary allocation mechanism

The fair allocation of scarce resources is relevant to a wide field of applications. For example, cloud resources, such as CPU, RAM, disk space, and bandwidth, have to be shared. This paper presents a mechanism to find fair allocations of multiple divisible resources, which, contrary to other mechanisms, is applicable to but not limited to the example above. Wide applicability of the mechanism is achieved by designing it (1) to scale with the number of consumers and resources, (2) to allow for arbitrary preference functions of consumers, and (3) to not rely on monetary compensation. The mechanism uses a mathematical definition of greediness to balance resources consumers receive and thereby to compute a fair allocation

Nitrogen compounds and ozone in the stratosphere: comparison of MIPAS satellite data with the chemistry climate model ECHAM5/MESSy1

The chemistry climate model ECHAM5/MESSy1 (E5/M1) in a setup extending from the surface to 80 km with a vertical resolution of about 600m near the tropopause with nudged tropospheric meteorology allows a direct comparison with satellite data of chemical species at the same time and location. Here we present results out of a transient 10 years simulation for the period of the Antarctic vortex split in September 2002, where data of MIPAS on the ENVISATsatellite are available. For the first time this satellite instrument opens the opportunity, to evaluate all stratospheric nitrogen containing species simultaneously with a good global coverage, including the source gas N2O and ozone which allows an estimate for NOx-production in the stratosphere. We show correlations between simulated and observed species in the altitude region between 10 and 50 hpa for different latitude belts, together with the Probability Density Functions (PDFs) of model results and observations. This is supplemented by global maps on pressure levels showing the comparison between the satellite and the simulated data sampled at the same time and location. We demonstrate that the model in most cases captures the partitioning in the nitrogen family, the diurnal cycles and the spatial distribution within experimental uncertainty. This includes even variations due to tropospheric clouds. There appears to be, however, a problem to reproduce the observed nighttime partitioning between N2O5 and NO2 in the middle stratosphere using the recommended set of reaction coefficients and photolysis data

Continual learning autoencoder training for a particle-in-cell simulation via streaming

The upcoming exascale era will provide a new generation of physics simulations. These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. Therefore, we need to rethink the training of machine learning models for simulations for the upcoming exascale era. This work presents an approach that trains a neural network concurrently to a running simulation without storing data on a disk. The training pipeline accesses the training data by in-memory streaming. Furthermore, we apply methods from the domain of continual learning to enhance the generalization of the model. We tested our pipeline on the training of a 3d autoencoder trained concurrently to laser wakefield acceleration particle-in-cell simulation. Furthermore, we experimented with various continual learning methods and their effect on the generalization

Population dynamics and harvest management of eastern mallards

Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision‐making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2‐season banding and recovery data, and hunterharvest data from 1998 to 2018.When developing the model, we used novel model selection methods to test various forms of a submodel for survival including estimating the degree of harvest additivity and any age‐specific trends. The top survival sub‐model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America

Population dynamics and harvest management of eastern mallards

Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision‐making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2‐season banding and recovery data, and hunterharvest data from 1998 to 2018.When developing the model, we used novel model selection methods to test various forms of a submodel for survival including estimating the degree of harvest additivity and any age‐specific trends. The top survival sub‐model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America

After-hours respiratory physiotherapy for intubated and mechanically ventilated patients with community-acquired pneumonia: An Australian perspective

Introduction: Community acquired pneumonia (CAP) is a common reason for admission to an intensive care unit for intubation and mechanical ventilation, and results in high morbidity and mortality. The primary aim of the study was to investigate availability and provision of respiratory physiotherapy, outside of normal business hours, for intubated and mechanically ventilated adults with CAP in Australian hospitals. Materials and methods: A cross-sectional, mixed methods online survey was conducted. Participants were senior intensive care unit physiotherapists from 88 public and private hospitals. Main outcome measures included presence and nature of an after-hours physiotherapy service and factors perceived to influence the need for after-hours respiratory physiotherapy intervention, when the service was available, for intubated adult patients with CAP. Data were also collected regarding respiratory intervention provided after-hours by other ICU professionals. Results: Response rate was 72% (n = 75). An after-hours physiotherapy service was provided by n = 31 (46%) hospitals and onsite after-hours physiotherapy presence was limited (22%), with a combination of onsite and on-call service reported by 19%. Treatment response (83%) was the most frequent factor for referring patients with CAP for after-hours physiotherapy intervention by the treating day-time physiotherapist. Nurses performing respiratory intervention (77%) was significantly associated with no available after-hours physiotherapy service (p = 0.04). Discussion: Physiotherapy after-hours service in Australia is limited, therefore it is common for intubated patients with CAP not to receive any respiratory physiotherapy intervention outside of normal business hours. In the absence of an after-hours physiotherapist, nurses were most likely to perform after-hours respiratory intervention to intubated patients with CAP. Conclusion: Further research is required to determine whether the frequency of respiratory physiotherapy intervention, including after-hours provision of treatment, influences outcomes for ICU patients intubated with pneumonia

A survey of clinicians regarding respiratory physiotherapy intervention for intubated and mechanically ventilated patients with community‐acquired pneumonia. What is current practice in Australian ICUs?

Rationale, aims, and objectives: Community-acquired pneumonia (CAP) is a common cause for intensive care unit (ICU) admission resulting in high morbidity and mortality. There is a paucity of evidence regarding respiratory physiotherapy for intubated and mechanically ventilated patients with CAP, and anecdotally clinical practice is variable in this cohort. The aims of this study were to identify the degree of variability in physiotherapy practice for intubated adult patients with CAP and to explore ICU physiotherapist perceptions of current practice for this cohort and factors that influence physiotherapy treatment mode, duration, and frequency. Method: A survey was developed based on common aspects of assessment, clinical rationale, and intervention for intubated and mechanically ventilated patients. Senior ICU physiotherapists across 88 Australian public and private hospitals were recruited. Results: The response rate was 72%. Respondents (n = 75) stated their main rationale for providing a respiratory intervention were improved airway clearance (98%, n = 60/61), alveolar recruitment (74%, n = 45/61), and gas exchange (33%, n = 20/61). Respondents estimated that average intervention lasted between 16 and 30 minutes (70% of respondents, n = 41/59) and would be delivered once (44%) or twice (44%) daily. Results indicated large variability in reported practice; however, trends existed regarding positioning in alternate side-lying (81%, n = 52/64) or affected lung uppermost (83%, n = 53/64) and use of hyperinflation techniques (81%, 52/64). Decisions regarding duration were reported to be based on sputum volume (95%), viscosity (93%) and purulence (88%), cough effectiveness (95%), chest X-ray (87%), and auscultation (84%). Sixty percent reported that workload and staffing affected intervention duration and frequency. Intervention time was more likely increased when there was greater staffing (P = .03). Conclusion: Respiratory physiotherapy treatment varies for intubated patients with CAP. Further research is required to determine what is considered best practice for this patient population

The SPARC water vapour assessment II: comparison of annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour observed from satellites

In the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), the amplitudes and phases of the annual, semi-annual and quasi-biennial variation in stratospheric and lower mesospheric water were compared using 30 data sets from 13 different satellite instruments. These comparisons aimed to provide a comprehensive overview of the typical uncertainties in the observational database which can be considered in subsequent observational and modelling studies. For the amplitudes, a good agreement of their latitude and altitude distribution was found. Quantitatively there were differences in particular at high latitudes, close to the tropopause and in the lower mesosphere. In these regions, the standard deviation over all data sets typically exceeded 0.2 ppmv for the annual variation and 0.1 ppmv for the semi-annual and quasi-biennial variation. For the phase, larger differences between the data sets were found in the lower mesosphere. Generally the smallest phase uncertainties can be observed in regions where the amplitude of the variability is large. The standard deviations of the phases for all data sets were typically smaller than a month for the annual and semi-annual variation and smaller than 5 months for the quasi-biennial variation. The amplitude and phase differences among the data sets are caused by a combination of factors. In general, differences in the temporal variation of systematic errors and in the observational sampling play a dominant role. In addition, differences in the vertical resolution of the data, the considered time periods and influences of clouds, aerosols as well as non-local thermodynamic equilibrium (NLTE) effects cause differences between the individual data sets