Results 61 to 70 of about 84,563 (261)

Analyzing Uncertainty in Complex Socio-Ecological Networks

Entropy, 2020
Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain.
Ana D. Maldonado, María Morales, Pedro A. Aguilera, Antonio Salmerón   +3 more
doaj   +1 more source

Achieving career satisfaction through fostering innovation: lessons from the engineering profession in the Australian public sector

Engineering Management in Production and Services, 2021
This paper proposes a novel approach that integrates the capability of empirical validation of structural equation modelling (SEM) and the prediction ability of Bayesian networks (BN).
Wipulanusat Warit, Panuwatwanich Kriengsak, Stewart Rodney A., Sunkpho Jirapon, Thamsatitdej Poomporn   +4 more
doaj   +1 more source

Defect Evolution and Mechanical Performance of Fused Filament Fabrication‐Manufactured 17‐4PH Stainless Steel Revealed by X‐Ray Computed Tomography

Advanced Engineering Materials, EarlyView.
X‐ray computed tomography reveals how process‐induced defects evolve from green to sintered states in Fused Filament Fabrication (FFF)‐manufactured 17‐4PH stainless steel. Internal porosity, weakest cross‐sections, and fracture locations show strong correlation with tensile performance, demonstrating the potential of computed tomography (CT)‐based ...
György Ledniczky, Márk Sárközi, Ibolya Zsoldos, Zoltán Weltsch   +3 more
wiley   +1 more source

The variance of causal effect estimators for binary v-structures

Journal of Causal Inference, 2022
Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Quantum inference on Bayesian networks [PDF]

Physical Review A, 2014
Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values $e$ of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on $n$ variables with at most $m$ parents per node in
Low, Guang Hao, Yoder, Theodore James, Chuang, Isaac L.   +2 more
openaire   +4 more sources

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Relational Bayesian Networks

CoRR, 1997
A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probability distribution over the relations is directly represented by a Bayesian network.
openaire   +4 more sources