Results 191 to 200 of about 52,825 (304)

Another attempt to quantify the benefits of reducing inflation

This article estimates the benefits of reducing U.S. inflation below its current level when the government simultaneously raises another distortionary tax.
R. Anton Braun
core  

Quantitative Easing: A Rationale and Some Evidence from Japan [PDF]

This paper reviews the rationale for quantitative easing when central bank policy rates reach near zero levels in light of recent announcements regarding direct asset purchases by the Bank of England, the Bank of Japan, the U.S.
Volker Wieland
core  

Too Complex to Choose? The Role of Heuristics in Shaping Farmers' Willingness to Pay for Income Stabilization Tool in Italy

Agribusiness, EarlyView.
ABSTRACT European agriculture is increasingly exposed to economic instability driven by extreme weather events, market volatility, and geopolitical tensions. To manage these growing risks, farmers are encouraged to adopt innovative risk management strategies such as the Income Stabilization Tool (IST), which offers protection against severe income ...
Alice Stiletto, Ruggiero Rippo, Samuele Trestini, Elisa Giampietri, Roberta Raffaelli, Simone Cerroni   +5 more
wiley   +1 more source

Emergency Cholecystectomy in Patients Classified as High Risk According to the Tokyo Guidelines 2018: A Real‐World Analysis

Annals of Gastroenterological Surgery, EarlyView.
Emergency cholecystectomy was evaluated in patients with acute cholecystitis classified as non‐recommended for surgery by the Tokyo Guidelines 2018. Major postoperative complications, rather than mortality, better reflected operative risk. Physiological instability, particularly ASA‐PS ≥ 3 and shock status, identified high‐risk patients, suggesting ...
Satoshi Mii, So Yamaki, Daisuke Hashimoto, Kazuki Matsumura, Hiroyuki Ishida, Hidetaka Miyazaki, Yuki Matsui, Yuji Ikeda, Tsukasa Ikeura, Sohei Satoi   +9 more
wiley   +1 more source

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Annals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males, Marko Kumric, Zvonimir Boban, Josip Vrdoljak, Dario Pecenkovic, Matej Ivanda, Marko Grahovac, Zenon Pogorelic, Josko Bozic   +8 more
wiley   +1 more source

MIXED MODEL AND ESTIMATING EQUATION APPROACHES FOR ZERO INFLATION IN CLUSTERED BINARY RESPONSE DATA WITH APPLICATION TO A DATING VIOLENCE STUDY. [PDF]

Ann Appl Stat, 2015
Fulton KA, Liu D, Haynie DL, Albert PS.
europepmc   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

Using next-generation DNA sequence data for genetic association tests based on allele counts with and without consideration of zero inflation. [PDF]

BMC Proc, 2016
González Silos R, Karadag Ö, Peil B, Fischer C, Kabisch M, Legrand C, Lorenzo Bermejo J.   +6 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source