Hasil untuk "Modern"

Aneel Bhangu, K. Søreide, S. Saverio et al.

Shen Yin, Xianwei Li, Huijun Gao et al.

Fuxiu Jiang, Kenneth A. Kim

C. Dupraz, R. Reid, O. Braissant et al.

D. Bertsimas, Angela King, R. Mazumder

In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving Mixed Integer Optimization (MIO) problems. We present a MIO approach for solving the classical best subset selection problem of choosing $k$ out of $p$ features in linear regression given $n$ observations. We develop a discrete extension of modern first order continuous optimization methods to find high quality feasible solutions that we use as warm starts to a MIO solver that finds provably optimal solutions. The resulting algorithm (a) provides a solution with a guarantee on its suboptimality even if we terminate the algorithm early, (b) can accommodate side constraints on the coefficients of the linear regression and (c) extends to finding best subset solutions for the least absolute deviation loss function. Using a wide variety of synthetic and real datasets, we demonstrate that our approach solves problems with $n$ in the 1000s and $p$ in the 100s in minutes to provable optimality, and finds near optimal solutions for $n$ in the 100s and $p$ in the 1000s in minutes. We also establish via numerical experiments that the MIO approach performs better than {\texttt {Lasso}} and other popularly used sparse learning procedures, in terms of achieving sparse solutions with good predictive power.

Shang‐keng Ma

Stuart Russell, Peter Norvig

S. Woosley, A. Heger, T. Weaver

John, Doyle, Gunter Stein

I. Wallerstein

N. S. Landkof

J. Gusfield, Barrington. Moore

L. Reichl

M. Reed, B. Simon

Thomas R. Karl, K. Trenberth

Brigitte Maier

G. Williamson

Abstract Polyphenols are found in plant‐based foods and beverages, notably apples, berries, citrus fruit, plums, broccoli, cocoa, tea and coffee and many others. There is substantial epidemiological evidence that a diet high in polyphenol‐rich fruit, vegetables, cocoa and beverages protects against developing cardiovascular disease and type 2 diabetes. The absorption and metabolism of these compounds have been well described and, for many, the gut microbiota play a critical role in absorption; taking into consideration the parent compound and the metabolites from colon bacteria catabolism, more than 80% of a dose can be absorbed and ultimately excreted in the urine. Common polyphenols in the diet are flavanols (cocoa, tea, apples, broad beans), flavanones (hesperidin in citrus fruit), hydroxycinnamates (coffee, many fruits), flavonols (quercetin in onions, apples and tea) and anthocyanins (berries). Many intervention studies, mechanistic in vitro data and epidemiological studies support a role for polyphenols against the development of chronic diseases. For example, flavanols decrease endothelial dysfunction, lower blood pressure and cholesterol, and modulate energy metabolism. Coffee and tea both reduce the risk of developing type 2 diabetes, through action of their constituent polyphenols. Despite extensive research, the exact mechanisms of action of polyphenols in the human body have not been decisively proven, but there is strong evidence that some targets such as nitric oxide metabolism, carbohydrate digestion and oxidative enzymes are important for health benefits. Consumption of polyphenols as healthy dietary components is consistent with the advice to eat five or more portions of fruit and vegetables per day, but it is currently difficult to recommend what ‘doses’ of specific polyphenols should be consumed to derive maximum benefit.

C. Bae, K. Douka, M. Petraglia

Chadi Nohra, Bechara Nehme, Raymond Ghandour et al.

The integration of communication networks into modern power systems introduces variable time delays that degrade the performance of traditional Load Frequency Control (LFC), while the shift towards renewable energy sources increases system vulnerability through parametric uncertainties. Existing methods, predominantly based on Lyapunov-Krasovskii Functionals, involve a complexity&#x2013;conservatism trade-off and may not provide a unified and tractable solution for this multi-domain robustness challenge. This paper addresses this gap by proposing a novel control framework based on <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-analysis. The methodology models communication delays as structured uncertainties using a Pad&#x00E9; approximation and integrates them with parametric variations within a unified <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synthesis design process. A detailed comparative analysis indicates that unlike Lyapunov-based approaches, which require guaranteeing system smoothness at every delay subinterval, the proposed method efficiently stabilizes the system under the worst-case conditions, quantified by the structured singular value. Simulation results demonstrate improved robustness compared to conventional H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> control under concurrent delay and parametric uncertainties. While the conventional H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> controller exhibits degraded stability margins when delays exceed 15 ms, the proposed <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synthesis controller maintains stability and performance under extreme concurrent disturbances, including time-varying delays of 0.5&#x2013;5 s, 40% load changes, and over 80% variation in tie-line reactance and turbine-governor time constants. The proposed controller drives the Area Control Error (ACE) below 0.01 pu within two minutes for a 40% load change under these conditions. These results indicate that <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-analysis provides a systematic framework for achieving multi-domain robustness in Load Frequency Control under large simultaneous uncertainties.