The departure process of a quorum queueing system

AbstractWe study in this paper the departure process of a bulk service queueing system. The server operates under a minimum batch size strategy. We characterize the departure process through the distribution of the interdeparture times of batches and of customers, the distribution of the number of customers in a batch, and the coefficient of correlation between the interdeparture time of a batch and the number of customers in the batch. A numerical illustration is presented

A New Lifetime Distribution and Its Power Transformation

New one-parameter and two-parameter distributions are introduced in this paper. The failure rate of the one-parameter distribution is unimodal (upside-down bathtub), while the failure rate of the two-parameter distribution can be decreasing, increasing, unimodal, increasing-decreasing-increasing, or decreasing-increasing-decreasing, depending on the values of its two parameters. The two-parameter distribution is derived from the one-parameter distribution by using a power transformation. We discuss some properties of these two distributions, such as the behavior of the failure rate function, the probability density function, the moments, skewness, and kurtosis, and limiting distributions of order statistics. Maximum likelihood estimation for the two-parameter model using complete samples is investigated. Different algorithms for generating random samples from the two new models are given. Applications to real data are discussed and compared with the fit attained by some one- and two-parameter distributions. Finally, a simulation study is carried out to investigate the mean square error of the maximum likelihood estimators, the coverage probability, and the width of the confidence intervals of the unknown parameters

The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come

The Lancet Countdown: tracking progress on health and climate change was established to provide an independent, global monitoring system dedicated to tracking the health dimensions of the impacts of, and the response to, climate change. The Lancet Countdown tracks 41 indicators across five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; finance and economics; and public and political engagement. This report is the product of a collaboration of 27 leading academic institutions, the UN, and intergovernmental agencies from every continent. The report draws on world-class expertise from climate scientists, ecologists, mathematicians, geographers, engineers, energy, food, livestock, and transport experts, economists, social and political scientists, public health professionals, and. doctors. The Lancet Countdown’s work builds on decades of research in this field, and was first proposed in the 2015 Lancet Commission on health and climate change,1 which documented the human impacts of climate change and provided ten global recommendations to respond to this public health emergency and secure the public health benefits available (panel 1)

Deployment of AI-based RBF network for photovoltaics fault detection procedure

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions

The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

The Lancet Countdown is an international collaboration, established to provide an independent, global monitoring system dedicated to tracking the emerging health profile of the changing climate. The 2020 report presents 43 indicators across five sections: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. This report represents the findings and consensus of the 35 leading academic institutions and UN agencies that make up the Lancet Countdown, and draws on the expertise of climate scientists, geographers, and engineers; of energy, food, and transport experts; and of economists, social and political scientists, data scientists, public health professionals, and doctors

Numerical and experimental analysis of convective heat transfer in a heated greenhouse

Internal convective flows, resulted from buoyancy forces, in a greenhouse tunnel were numerically and experimentally analysed and discussed. Experiments were performed in an experimental arc type tunnel greenhouse with a tomato crop, which was heated by a network of heating pipes or by an air heater or by their combination according to the outside climate conditions. A three dimensional (3D) sonic anemometer and fast response sensors for air temperature and humidity were used in order to map climate variables in different locations inside the greenhouse. For the numerical part of the study a commercially available computational fluid dynamics code was used A 3D model was built and average values form experimental data were used for boundary conditions. The crop was modelled by means of the concept of porous medium approach. In general a good quantitative and qualitative agreement was found between experimental and numerical results. Results highlight the influence of heating system on greenhouse microclimate

Convective heat transfer in a heated greenhouse tunnel

Convective transfers determine for a large part the micro-climate inside a greenhouse. These processes are very complicated because they combine free and forced convection modes, most often turbulent with very different characteristic scales. The aim of the present study is to analyze the internal convective flows generated in a closed greenhouse by thermal buoyancy forces. A commercially available computational fluid dynamics (CFD) code (CFX-5) was used for this purpose in a tunnel greenhouse with crop heated by pipes. Three common heating pipes positions were studied: (a) heating pipes positioned on the soil close to the root of the crop; (b) heating pipes positioned at the middle height of the crop and (c) heating pipes positioned overhead of the crop. All the simulations were carried out in three dimensions. The standard k-σ model was adopted to describe the turbulent transport. The crop was modelled by means of the concept of porous medium and the Darcy-Forcheimer equation. For the same energy released from the heating pipes, results show that there were not significant differences in mean air temperature for the three tested positions of heating pipes. Higher air velocities, in the crop cover, were achieved with the first configuration resulting in an increase on crop aerodynamic resistance and thus in an increase to the heat and mass transfer between crop and air