Results 161 to 170 of about 4,435,585 (324)

Risk Factors for Small‐for‐Size Syndrome Grade B/C After Simultaneous Splenectomy in Adult Living‐Donor Liver Transplantation

Annals of Gastroenterological Surgery, EarlyView.
In a single‐center cohort of 577 adult LDLT recipients who underwent simultaneous splenectomy, clinically significant SFSS grade B/C (ILTS‐iLDLT‐LTSI 2023) occurred in 18.2% and was associated with inferior graft survival. Multivariate analysis identified MELD ≥ 30, NLR ≥ 4.5, and donor age ≥ 50 years as independent risk factors, which risk rising ...
Kyohei Yugawa, Takeo Toshima, Shinji Itoh, Takashi Motomura, Shohei Yoshiya, Norifumi Iseda, Tomoharu Yoshizumi   +6 more
wiley   +1 more source

A Pediatric-Focused Self-Assessment Tool on Vulnerabilities to Aid Regional Disaster Planning. [PDF]

Disaster Med Public Health Prep
Pintea M, Dahl Grove D.
europepmc   +1 more source

Using tweets to support disaster planning, warning and response

, 2016
Peter Landwehr, Wei Wei, Michael Kowalchuck, Kathleen M. Carley   +3 more
semanticscholar   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Emergency planning – IFLA DISASTER. Preparedness and Planning

Conservar Património, 2006
Isabel Raposo Magalhães
doaj   +1 more source

Collaborative planning principles for disaster preparedness

International Journal of Disaster Risk Reduction, 2020
D. Shmueli, C. Ozawa, S. Kaufman
semanticscholar   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Where to Build-Back-Better? Analyzing changing risk for post-disaster reconstruction planning

, 2018
C.J. van Westen
openalex   +1 more source

Community-informed Decisions for Efficient, Costeffective, and Integrative Disaster Resilience planning (Co-DECIDR) :

Jennifer Helgeson, Steven A Gray, Mahdi Zareei, Carissa Knox, Richard Sadler, Laura Schmitt Olabisi, Chelsea Wentworth, Nathan Brugnone   +7 more
openalex   +1 more source

Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck, Samuel Yanes, Manuel Perales, Daniel Gutiérrez, Sergio Toral   +4 more
wiley   +1 more source