Results 101 to 110 of about 17,889,928 (308)

Bayesian Inference for the Mixed-Frequency VAR Model [PDF]

In this paper a mixed-frequency VAR à la Mariano & Murasawa (2004) with Markov regime switching in the parameters is estimated by Bayesian inference. Unlike earlier studies, that used the pseuo-EM algorithm of Dempster, Laird & Rubin (1977) to estimate ...
Paul Viefers
core  

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Bidding Strategy for Wind and Thermal Power Joint Participation in the Electricity Spot Market Considering Uncertainty

Energies
As the proportion of new energy sources, such as wind power, in the electricity system rapidly increases, their participation in spot market competition has become an inevitable trend. However, the uncertainty of clearing price and wind power output will
Zhiwei Liao, Wenjuan Tao, Bowen Wang, Ye Liu   +3 more
doaj   +1 more source

Interfacial Failure and Self‐Healing in Solid‐State Batteries

Advanced Materials, EarlyView.
Dynamic interfacial self‐healing offers an adaptive route to mitigate coupled mechanical, chemical, and electrochemical degradation in solid‐state batteries. This review connects evolving interfacial failure mechanisms with physical‐flow, chemical‐restoration, stimuli‐responsive, and electric‐field‐assisted repair strategies, highlighting targeted self‐
Xinxin Zhu, Tengfei Dai, Wendi Dou, Zhiheng Li, Kaihua Wen, Yanqun Lv, Ming Zhao, Jun Lu, Zaiping Guo   +8 more
wiley   +1 more source

Advanced Materials for Biologics Delivery to Brain Tumors

Advanced Materials, EarlyView.
Material innovation is central to unlocking the therapeutic potential of biologics against many central nervous system diseases, including brain cancer. By engineering carriers with controlled transport, targeting, and release properties, advanced materials can overcome the blood–brain barrier and tumor microenvironment, improving the delivery of ...
Yuran Feng, Yuxue Cao, Maria Kavallaris, Taskeen Janjua, Amirali Popat   +4 more
wiley   +1 more source

A marketing mix model for a complex and turbulent environment

Acta Commercii, 2007
Purpose: This paper is based on the proposition that the choice of marketing tactics is determined, or at least significantly influenced, by the nature of the company’s external environment. It aims to illustrate the type of marketing mix tactics that are suggested for a complex and turbulent environment when marketing and the environment are ...
Mason, Roger Bruce, Staude, Gavin
openaire   +4 more sources

Mixed oligopoly and the choice of capacity [PDF]

We analyze the capacity choice of firms under different time structures in a mixed oligopoly market, in which firms decide not only production quantities but also capacity scales.
Yuanzhu Lu, Sougata Poddar
core  

One-sided tolerance interval in a two-way balanced nested model with mixed effects [PDF]

, 2013
In many research areas (such as public health, environmental contamination, and others) one deals with the necessity of using data to infer whether some proportion (%) of a population of interest is (or one wants it to be) below and/or over some ...

core  

Electrolyte Engineering Challenges and Opportunities for Next‐Generation Aqueous Ammonium‐Ion Batteries

Advanced Materials, EarlyView.
Aqueous ammonium‐ion batteries (AAIBs) face a hydrogen‐bond paradox: the HB network enables fast NH4+ transport but triggers water decomposition. This review dissects this dilemma, evaluates multiple electrolyte engineering strategies, and outlines four future directions for next‐generation AAIB design ABSTRACT Aqueous ammonium‐ion batteries (AAIBs ...
Zi‐Hang Huang, Xiaoyu Liu, Shuai Mao, Lingfeng Zhu, Zhijun Wu, Hongge Pan, Tianyi Ma   +6 more
wiley   +1 more source

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

Proceedings of the 17th ACM International Conference on Web Search and Data Mining
In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing.
Chang Gong 0001, Di Yao 0001, Lei Zhang 0206, Sheng Chen, Wenbin Li 0012, Yueyang Su, Jingping Bi   +6 more
openaire   +3 more sources