Results 151 to 160 of about 16,631 (258)

Leveraging Big Multitemporal Multisource Satellite Data and Artificial Intelligence for the Detection of Complex and Invisible Features: The Case of Extensive Irrigation Mapping

Archaeological Prospection, EarlyView.
ABSTRACT The detection of buried or obscured archaeological features remains a central challenge in landscape archaeology, particularly in the irrigated floodplains of Mesopotamia where levees and canals formed the basis of complex agrarian systems. This study presents a deep learning–based approach for the large‐scale, automated detection of ancient ...
Nazarij Buławka, Hector A. Orengo, Felipe Lumbreras Ruiz, Iban Berganzo‐Besga, Ekta Gupta   +4 more
wiley   +1 more source

An Indicator‐Based Decision Framework for Circular Bioeconomy Transition in the Steel Industry: Integrating Multiphase Learning and Cooperative Game Modelling

Business Strategy and the Environment, EarlyView.
ABSTRACT Despite growing attention to the circular bioeconomy (CBE), the steel industry currently lacks a standardised, sectoral measurement framework to facilitate a low‐carbon transition. In this study, a decision‐support framework for evaluating CBE performance in the steel industry is proposed.
Ali Zamani Babgohari, Mohammadreza Sadeghi‐Moghadam, Hannan Amoozad Mahdiraji   +2 more
wiley   +1 more source

The Tree of Life Synagogue Attack: A Terrorist Radicalization Assessment Protocol‐18 Examination of Pre‐Attack Warnings and Post‐Attack Contagion and Copycat Effects

Behavioral Sciences &the Law, EarlyView.
ABSTRACT This is a retrospective case study of an antisemitic lone actor terrorist who completed the deadliest attack against the Jewish community in American history. The analysis through the lens of the Terrorist Radicalization Assessment Protocol (TRAP‐18) finds that 72% of the warning indicators were present, including four proximal warning ...
Molly Amman, Julia Kupper, J. Reid Meloy
wiley   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

NOISE RADAR OF MILLIMETER RANGE [PDF]

Radioelectronics. Nanosystems. Information Technologies, 2018
Vladimir V. Kolesov, Evgeny A. Myasin
openaire   +1 more source

An ultra high resolution FMCW radar

, 1992
Bibliography: leaves 127-128.There is a great need for real-time non-intrusive measurements in industry. A short-range radar system can be used to make these measurements. A standard requirement for these type of applications is high resolution.
Bas, Alon
core  

Tetrazine Ligation in Living Systems: Beyond Fast Kinetics to Effective Bioorthogonality

Chemistry – A European Journal, EarlyView.
Rapid reaction kinetics (k2) alone do not ensure successful tetrazine ligation in living systems. This review introduces ‘effective orthogonality’—focusing on chemical survival, availability, and encounter—as a practical framework. We highlight how bottlenecks shift from cellular sinks to in vivo delivery limits, offering design strategies for reliable
Junhyeong Yim, Junyoung Park, Youngjun Lee, Jongmin Park   +3 more
wiley   +1 more source

Spotlight on the Nucleotide: Solid‐State NMR for the Investigation of ATP Hydrolysis in the ATPase SmsC

Chemistry – A European Journal, EarlyView.
Nucleotide‐detected solid‐state NMR has been applied to probe nucleotide conformations and dynamics during ATP hydrolysis using the dimeric P‐loop ATPase SmsC as a model protein. The different stages of ATP hydrolysis have been mimicked by ATP analogues for which different degrees of conformational heterogeneity have been observed. ABSTRACT Solid‐state
Nina Wehr, Laure Decamps, Julius Schlüter, Koen Linssen, Georg Künze, Kushal Sengupta, Serena DeBeer, Thomas Wiegand   +7 more
wiley   +1 more source

Random forest regression for catalyst performance prediction and validation of tri‐reforming of methane (TRM)

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Carbon dioxide‐reduced hydrogen can be synthesized through various methods such as dry‐reforming (DRM), steam reforming (SMR), and partial oxidation (POX). Tri‐reforming of methane (TRM) is a promising technology which combines all the above‐mentioned processes for the simultaneous production of hydrogen and syngas with high energy efficiency.
Paulo A. L. de Souza, Raja Muhammad Afzal, Felipe Gomes Camacho, Nader Mahinpey   +3 more
wiley   +1 more source

Hybrid machine learning and genetic algorithm approach for catalyst and process optimization in Fischer–Tropsch synthesis toward sustainable fuel production

The Canadian Journal of Chemical Engineering, EarlyView.
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan, Ryan Gosselin, Nicolas Abatzoglou   +2 more
wiley   +1 more source