Zipf’s law for atlas models

A set of data with positive values follows a Pareto distribution if the log–log plot of value versus rank is approximately a straight line. A Pareto distribution satisfies Zipf’s law if the log–log plot has a slope of $-1$. Since many types of ranked data follow Zipf’s law, it is considered a form of universality. We propose a mathematical explanation for this phenomenon based on Atlas models and first-order models, systems of strictly positive continuous semimartingales with parameters that depend only on rank. We show that the stationary distribution of an Atlas model will follow Zipf’s law if and only if two natural conditions, conservation and completeness, are satisfied. Since Atlas models and first-order models can be constructed to approximate systems of time-dependent rank-based data, our results can explain the universality of Zipf’s law for such systems. However, ranked data generated by other means may follow non-Zipfian Pareto distributions. Hence, our results explain why Zipf’s law holds for word frequency, firm size, household wealth, and city size, while it does not hold for earthquake magnitude, cumulative book sales, and the intensity of wars, all of which follow non-Zipfian Pareto distributions.

Evaluation of the ATLAS model for remote access to database resident information for LHC Run 3

The ATLAS model for remote access to database resident information relies upon a limited set of dedicated and distributed Oracle database repositories complemented with the deployment of Frontier system infrastructure on the WLCG (Worldwide LHC Computing Grid). ATLAS clients with network access can get the database information they need dynamically by submitting requests to a Squid proxy cache server in the Frontier network which provides results from its cache or passes new requests along the network to launchpads co-located at one of the Oracle sites (the master Oracle database at CERN or one of the Tier 1 Oracle database replicas). Since the beginning of LHC Run 1, the system has evolved in terms of client, Squid, and launchpad optimizations but the distribution model has remained fundamentally unchanged. On the whole, the system has been broadly successful in providing data to clients with relatively few disruptions even while site databases were down due to overall redundancy. At the same time, its quantitative performance characteristics, such as the global throughput of the system, the load distribution between sites, and the constituent interactions that make up the whole, were largely unknown. But more recently, information has been collected from launchpad and Squid logs into an Elasticsearch repository which has enabled a wide variety of studies of various aspects of the system. This contribution*** will describe dedicated studies of the data collected in Elasticsearch over the previous year to evaluate the efficacy of the distribution model. Specifically, we will quantify any advantages that the redundancy of the system offers as well as related aspects such as the geographical dependence of wait times seen by clients in getting a response to its requests. These studies are essential so that during LS2 (the long shutdown between LHC Run 2 and Run 3), we can adapt the system in preparation for the expected increase in the system load in the ramp up to Run 3 operations.

Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study

A drug repurposing strategy could be a potential approach to overcoming the economic costs for diabetes mellitus (DM) treatment incurred by most countries. DM has emerged as a global epidemic, and an increase in the outbreak has led developing countries like Mexico, India, and China to recommend a prevention method as an alternative proposed by their respective healthcare sectors. Incretin-based therapy has been successful in treating diabetes mellitus, and inhibitors like sitagliptin, vildagliptin, saxagliptin, and alogliptin belong to this category. As of now, drug repurposing strategies have not been used to identify existing therapeutics that can become dipeptidyl peptidase-4 (DPP-4) inhibitors. Hence, this work presents the use of bioinformatics tools like the Activity Atlas model, flexible molecular docking simulations, and three-dimensional reference interaction site model (3D-RISM) calculations to assist in repurposing Food and Drug Administration (FDA)-approved drugs into specific nonsteroidal anti-inflammatory medications such as DPP-4 inhibitors. Initially, the Activity Atlas model was constructed based on the top scoring DPP-4 inhibitors, and then the model was used to understand features of nonsteroidal anti-inflammatory drugs (NSAIDs) as a function of electrostatic, hydrophobic, and active shape features of DPP-4 inhibition. The FlexX algorithm was used to infer protein–ligand interacting residues, and binding energy, to predict potential draggability towards the DPP-4 mechanism of action. 3D-RISM calculations on piroxicam-bound DPP-4 were used to understand the stability of water molecules at the active site. Finally, piroxicam was chosen as the repurposing drug to become a new DPP-4 inhibitor and validated experimentally using fluorescence spectroscopy assay. These findings are novel and provide new insights into the role of piroxicam as a new lead to inhibit DPP-4 and, taking into consideration the biological half-life of piroxicam, it can be proposed as a possible therapeutic strategy for treating diabetes mellitus.

Improving Urban Runoff in Multi-Basin Hydrological Simulation by the HYPE Model Using EEA Urban Atlas: A Case Study in the Sege River Basin, Sweden

In this study, the high-resolution polygonal land cover data of EEA Urban Atlas was applied for land-use characterization in the dynamic multi-basin hydrological model, HYPE. The objective of the study was to compare this dedicated urban land cover data in semi-distributed hydrological modelling with the widely used but less detailed EEA CORINE. The model was set up for a basin including a small town named Svedala in southern Sweden. In order to verify the ability of the HYPE model to reproduce the observed flow rate, the simulated flow rate was evaluated based on river flow time series, statistical indicators and flow duration curves. Flow rate simulated by the model based on Urban Atlas generally agreed better with observations of summer storm events than the CORINE-based model, especially when the daily rainfall amount was 10 mm/day or more, or the flow exceedance probability was 0.02 to 0.5. It suggests that the added value of the Urban Atlas model is higher for heavy-to-medium storm events dominated by direct runoff. To conclude, the effectiveness of the proposed approach, which aims at improving the accuracy of hydrological simulations in urbanized basins, was supported.

The Extrapolar SWIFT model (version 1.0): fast stratospheric ozone chemistry for global climate models

The Extrapolar SWIFT model is a fast ozone chemistry scheme for interactive calculation of the extrapolar stratospheric ozone layer in coupled general circulation models (GCMs). In contrast to the widely used prescribed ozone, the SWIFT ozone layer interacts with the model dynamics and can respond to atmospheric variability or climatological trends. The Extrapolar SWIFT model employs a repro-modelling approach, in which algebraic functions are used to approximate the numerical output of a full stratospheric chemistry and transport model (ATLAS). The full model solves a coupled chemical differential equation system with 55 initial and boundary conditions (mixing ratio of various chemical species and atmospheric parameters). Hence the rate of change of ozone over 24 h is a function of 55 variables. Using covariances between these variables, we can find linear combinations in order to reduce the parameter space to the following nine basic variables: latitude, pressure altitude, temperature, overhead ozone column and the mixing ratio of ozone and of the ozone-depleting families (Cly, Bry, NOy and HOy). We will show that these nine variables are sufficient to characterize the rate of change of ozone. An automated procedure fits a polynomial function of fourth degree to the rate of change of ozone obtained from several simulations with the ATLAS model. One polynomial function is determined per month, which yields the rate of change of ozone over 24 h. A key aspect for the robustness of the Extrapolar SWIFT model is to include a wide range of stratospheric variability in the numerical output of the ATLAS model, also covering atmospheric states that will occur in a future climate (e.g. temperature and meridional circulation changes or reduction of stratospheric chlorine loading). For validation purposes, the Extrapolar SWIFT model has been integrated into the ATLAS model, replacing the full stratospheric chemistry scheme. Simulations with SWIFT in ATLAS have proven that the systematic error is small and does not accumulate during the course of a simulation. In the context of a 10-year simulation, the ozone layer simulated by SWIFT shows a stable annual cycle, with inter-annual variations comparable to the ATLAS model. The application of Extrapolar SWIFT requires the evaluation of polynomial functions with 30–100 terms. Computers can currently calculate such polynomial functions at thousands of model grid points in seconds. SWIFT provides the desired numerical efficiency and computes the ozone layer 104 times faster than the chemistry scheme in the ATLAS CTM.

An overview of the wind power potential in the Romanian coastal environment - moving from onshore to offshore

In the past few years in Romania the total installed capacity reached 3025MW. Because the Europe Union is pursuing to obtain an increase in renewable energy by 2020 to cover 20% of the energy consumption with renewable energy maybe in the near future in Romania the wind energy parks will start to be build off shore where the wind energy is more powerful. From this perspective, the present work is trying to provide a better picture to the wind energy resources at the Romanian shore. The measured data was provided by NOAA [1] from different points (weather stations) four along the Romanian shore and one off-shore. The measured data was compared with data from the Global Wind Atlas model [2]. According to the analysis, there are discrepancies between the measured results and those provided by the model, but the two sets of data show that in the northern part of the off-shore the power density and the wind speed is higher than in the south.