Search results for " absolute"
showing 10 items of 44 documents
A new tuning parameter selector in lasso regression
2019
Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning parameter that balances the trade-off between model fitting and sparsity. In this work we introduce a new tuning parameter selction criterion based on the maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to a real data on prostate cancer disease.
Acid rearrangment of epoxy-germacranolides and absolute configuration of 1beta, 10alpha-epoxy-salonitenolide
2010
The acid-catalyzed cyclization of mono epoxides of cnicin acetonide (3) was investigated. Several 6,12-eudesmanolides were obtained, and their stereochemistry established by extensive spectroscopic analyses. Chemical correlations also led to the assignment of the absolute configuration of 1beta,10alpha-epoxy-salonitenolide (13), a previously isolated natural product. The cytotoxic activities of some compounds were determined against A549 and MCF-7 tumor cell lines. The esterified germacranolides 2-6 were selectively cytotoxic against the MCF-7 breast cancer cell line.
Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 20…
2020
Artículo con numerosos autores. Sólo se hace referencia al primero que coincide con el de la UAM y al colectivo
Real Time Stereo Matching Using Two Step Zero-Mean SAD and Dynamic Programing
2018
Dense depth map extraction is a dynamic research field in a computer vision that tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed. The local methods based on block matching that are prevalent due to the linear computational complexity and easy implementation. This local cost is used on global methods as graph cut and dynamic programming in order to reduce sensitivity to local to occlusion and uniform texture. This paper proposes a new method for matching images based on a two-stage of block matching as local cost function and dynamic programming as energy optimization approach. In our work introduce the two stage of th…
Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area
2020
Abstract The Norwegian rural distributed power network is mainly designed for Holiday Cabins with limited electrical loading capacity. Load prediction analysis, within such type of network, is necessary for effective operation and to manage the increasing demand of new appliances (e. g. electric vehicles and heat pumps). In this paper, load prediction of a distributed power network (i.e. a typical Norwegian rural area power network of 125 cottages with 478 kW peak demand) is carried out using regression analysis techniques for establishing autocorrelations and correlations among weather parameters and occurrence time in the period of 2014–2018. In this study, the regression analysis for loa…
Experimental study on triangular central baffle flume
2019
Abstract In this paper the results of the experiments performed to study the flow through a Triangular Central Baffle Flume (TCBF) are reported. The investigated flume consists of a triangular baffle of the apex angle of 75° with a given base width. The theoretical stage-discharge formula was deduced by applying the Buckingham's Theorem and incomplete self-similarity hypothesis and was calibrated using the laboratory measurements carried out in this investigation. The proposed stage-discharge formula is characterized by a mean absolute relative error of 7.4% and 72% of the data points are in an error range of ±5%. The results indicate that TCBF flume is characterized by a flow capacity high…
Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs
2021
Sediments accumulated in check dams are a valuable measure to estimate soil erosion rates. Here, geographic information systems (GIS) and three machine learning techniques (MARS-multivariate adaptive regression splines, RF-random forest and SVM-support vector machine) were used, for the first time, to predict sediment deposition rate (SR) in check-dams located in six watersheds in SW Spain. There, 160 dry-stone check dams (~ 77.8 check-dams km−2), accumulated sediments during a period that varied from 11 to 23 years. The SR was estimated in former research using a topographical method and a high-resolution Digital Elevation Model (DEM) (average of 0.14 m3 ha−1 year−1). Nine environmental-to…
Mapping daily global solar irradiation over Spain: A comparative study of selected approaches
2011
Abstract Three methods to estimate the daily global solar irradiation are compared: the Bristow–Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005–2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absol…
General Equilibrium Models of Monopolistic Competition: CRRA Versus CARA
2005
We analyze a class of "large group" Chamberlinian monopolistic competition models using multiplicatively quasi-separable (MQS) and additively quasi-separable (AQS) functions. We first prove that the MQS and AQS functions are equivalent to the "constant relative risk aversion" (CRRA) and "constant absolute risk aversion" (CARA) classes of functions, respectively. Whereas both approaches allow for closed-form solutions, only the AQS functions yield profit-maximizing prices that decrease in the mass of competing firms. We then characterize the equilibrium in both cases and discuss some possible applications of the AQS framework to trade, growth, and development.
A genetic algorithm for scratch removal in static images
2002
This paper investigates the removal of line scratches from old moving pictures and gives a twofold contribution. First, it presents a simple technique for detecting the scratches, based on an analysis of the statistics of the grey levels. Second, the scratch removal is approached as an optimisation problem, which is solved by using a genetic algorithm. The method can be classified as a static approach, as it works independently on each single frame of the sequence. It does not require any a-priori knowledge of the absolute position of the scratch, nor an external starting population of chromosomes for the genetic algorithm. The central column of the line scratch once detected is changed wit…