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Valuation of marine integrated disaster management amidst global warming in Southern Coast of Java, Indonesia.
Mar Pollut Bull
December 2024
Department of Environmental Engineering, Faculty of Infrastructure Planning, Universitas Pertamina, Jalan Sinabung II, Terusan Simprug, Jakarta 12220, Indonesia; Center for Environmental Solution (CVISION), Universitas Pertamina, Jalan Sinabung II, Terusan Simprug, Jakarta, 12220, Indonesia; Center for Interdisciplinary Research on Ecology and Sustainability, College of Environmental Studies and Oceanography, National Dong Hwa University, Hualien 97401, Taiwan, ROC. Electronic address:
This research explores the valuation of integrated disaster management in the coastal regions of Southern Java, Indonesia, a locale increasingly threatened by the impacts of global warming, which exacerbates marine disasters such as earthquakes and tsunamis. Employing a choice experiment methodology, the study assesses the willingness to pay among local households for various strategies designed to enhance earthquake preparedness. Three distinct scenarios are examined, each reflecting varying levels of integration and sophistication: (1) Educational empowerment and localized alert integration, which emphasizes community education and rapid, self-directed evacuation practices tailored to the immediate onset of tsunamis following seismic events.
View Article and Find Full Text PDFQuick mining in dense data: applying probabilistic support prediction in depth-first order.
PeerJ Comput Sci
October 2024
Computer Science and Engineering Department, Yanbu Industrial College, Yanbu, Saudi Arabia.
Article Synopsis
- Frequent itemset mining (FIM) is crucial for association rule mining but is computationally challenging, especially with large and dense datasets.
- Researchers have developed various methods to approximate itemset support using small subsets of data to maintain both efficiency and accuracy.
- The new ProbDF algorithm optimizes memory use by discarding transaction data after determining small itemsets and utilizes a probabilistic support prediction model for larger itemsets, shown to improve performance in experiments.
A comprehensive phylogeny and revised taxonomy of Diadectomorpha with a discussion on the origin of tetrapod herbivory.
R Soc Open Sci
July 2024
Humboldt-Universität zu Berlin, Unter den Linden 6, 10117 Berlin, Germany.
Among terrestrial tetrapods, the origin of herbivory marked a key evolutionary event that allowed for the evolution of modern terrestrial ecosystems. A 100 Ma gap separates the oldest terrestrial tetrapods and the first undisputed herbivorous tetrapods. While four clades of early tetrapod herbivores are undisputed amniotes, the phylogenetic position of Diadectomorpha with respect to Amniota has long been controversial.
View Article and Find Full Text PDFNew World dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) colonization of a recent Miocene insular territory: The case of Costa Rica.
Ecol Evol
June 2024
BioAlfa Barcoding Project Santo Domingo de Heredia Costa Rica.
Costa Rica emerged from the seas as a new geological territory during the Miocene as an insular archipelago. It later became part of a continental area once it became a segment of Central America. Two dung beetle genera that colonized this new territory from South and North America, and (Coleoptera: Scarabaeidae: Scarabaeinae), are here studied, in the first analysis of a volcanic paleo-archipelago, colonized from its emergence, and then later becoming .
View Article and Find Full Text PDFInformation bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates.
Front Comput Neurosci
May 2024
Electrical and Computer Engineering Department, University of Wisconsin-Madison, Madison, WI, United States.
Deep neural feedforward networks are effective models for a wide array of problems, but training and deploying such networks presents a significant energy cost. Spiking neural networks (SNNs), which are modeled after biologically realistic neurons, offer a potential solution when deployed correctly on neuromorphic computing hardware. Still, many applications train SNNs , and running network training directly on neuromorphic hardware is an ongoing research problem.
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