Search results for " Modeling"

An over-the-distance wireless battery charger based on RF energy harvesting

2017

An RF powered receiver silicon IC (integrated circuit) for RF energy harvesting is presented as wireless battery charger. This includes an RF-to-DC energy converter specifically designed with a sensitivity of -18.8 dBm and an energy conversion efficiency of ∼45% at 900 MHz with a transmitting power of 0.5 W in free space. Experimental results concerned with remotely battery charging using a complete prototype working in realistic scenarios will be shown.

Multi Architecture Optimization of a Hybrid Electric Vehicle Using Object-Oriented Programming

2017

This article presents an energetic macroscopic representation multi-architecture model for hybrid vehicles using object-oriented programming. This approach is successfully used to evaluate the power performance and fuel consumption of different vehicles on different driving cycles. An optimization of power source sizing (ICE, EM and Battery) and system control, based on simulation results, is carried. Different architectures are compared for given cycles and optimization of hybrid architecture will also be possible.

Bayesian Methodology in Statistics

2009

Bayesian methods provide a complete paradigm for statistical inference under uncertainty. These may be derived from an axiomatic system and provide a coherent methodology which makes it possible to incorporate relevant initial information, and which solves many of the difficulties that frequentist methods are known to face. If no prior information is to be assumed, the more frequent situation met in scientific reporting, a formal initial prior function, the reference prior, mathematically derived from the assumed model, is used; this leads to objective Bayesian methods, objective in the precise sense that their results, like frequentist results, only depend on the assumed model and the data…

Pharmacophore modeling e screening in silico di nuovi inibitori della proteina antiapoptotica Bcl-xl

2008

How far the substituent effects in disubstituted cyclohexa-1,3-diene derivatives differ from those in bicyclo[2.2.2]octane and benzene?

2018

Substituents effects in cyclic diene derivatives are studied using quantum chemical modeling and compared to the corresponding effects in aromatic (benzene) and fully saturated (bicyclo[2.2.2]octane) compounds. In particular, electronic properties of the fixed group Y in a series of 3- and 4-X-substituted cyclohexa-1,3-diene-Y derivatives (where Y = NO2, COOH, COO− OH, O−, NH2, and X = NMe2, NH2, OH, OMe, Me, H, F, Cl, CF3, CN, CHO, COMe, CONH2, COOH, NO2, NO) are examined using the B3LYP/6-311++G(d,p) method. For this purpose, quantum chemistry models of the substituent effect: cSAR (charge of the substituent active region) and SESE (substituent effect stabilization energy) as well as trad…

Big Data in metagenomics: Apache Spark vs MPI.

2020

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when…

Deep learning and process understanding for data-driven Earth system science

2017

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybri…

Cropland and grassland management

2014

According to the latest National Inventory, the Italian agricultural sector is a source of GHGs with 34.5 Mt of CO2 eq in 2009, corresponding to 7 % of the total emissions (excluding LULUCF). In particular, more than half (19.1 Mt of CO2 eq) are N2O emissions from soils. Although the national methodology is in accordance with Tier 1 and 2 approaches proposed by the IPCC (2006), still empirical emission factors are used to assess the emission from fertilizer (e.g. 0.0125 kg N2O–N kg−1 N from synthetic fertilizers). Disaggregated data at sub-national level, including models and inventory measurement systems required by higher order methods (i.e. Tier 3), are not available in Italy so far and …

Multiscale modeling on biological systems

2018

PET: Theoretical Background and Practical Aspects

2012

Positron emission tomography (PET) is a nuclear medicine imaging tool utilized for investigation of physiological processes in vivo. PET uses the decay characteristics of positron-emitting radionuclides which are produced in a cyclotron and then used to label compounds involved in physiological processes. Usually, the labeled compound—the tracer—is administered intravenously and distributed in the tissue. The radionuclide decays and the emitted photons are detected by the PET scanner. PET then offers the possibility to compute three-dimensional images of the biodistribution and kinetics of the regional radioactivity concentration. There are several options to analyze reconstructed PET image…