Results 211 to 220 of about 167,829 (332)

A Significant Unification of Checksums and Hash Tables Using AIT

, 2018
Qinglin Yang, Wen-biao QIAN, Xiao-lei YANG, Jin-ping Mo   +3 more
openalex   +2 more sources

Green Sea Turtle Recruitment in the Eastern North Pacific: Patterns Identified Using Geochemical Signatures in Bones

Ecology and Evolution, Volume 16, Issue 1, January 2026.
We present vital life‐history and demographic data including the oceanic stage duration, timing of ontogenetic habitat shifts, and multi‐year foraging patterns of eastern North Pacific green sea turtles found in Southern California. The findings were obtained by analyzing sea turtle bones (age and growth via skeletochronology; and habitat and diet via ...
Calandra N. Turner Tomaszewicz, Erin LaCasella, Garrett E. Lemons, Robin LeRoux, Jeffrey A. Seminoff   +4 more
wiley   +1 more source

Neural Radiance Fields: Driven Exploration of Visual Communication and Spatial Interaction Design for Immersive Digital Installations. [PDF]

J Imaging
Li W, Hu Y.
europepmc   +1 more source

Perfect Hamming code with a hash table for faster genome mapping. [PDF]

BMC Genomics, 2011
Takenaka Y, Seno S, Matsuda H.
europepmc   +1 more source

Replica-aware, multi-dimensional range queries in Distributed Hash Tables

, 2010
Antony Chazapis, Athanasia Asiki, Georgios Tsoukalas, Dimitrios Tsoumakos, Nectarios Koziris   +4 more
openalex   +1 more source

Explainable AI With Imbalanced Learning Strategies for Blockchain Transaction Fraud Detection

Engineering Reports, Volume 8, Issue 1, January 2026.
Research methodology pipeline for blockchain fraud detection. ABSTRACT Blockchain networks now support billions of dollars in daily transactions, making reliable and transparent fraud detection essential for maintaining user trust and financial stability.
Ahmed Abbas Jasim Al‐Hchaimi, M. A. Khalifa, Walid El‐Shafai   +2 more
wiley   +1 more source

Distributed Hash Tables [PDF]

, 2010
openaire   +2 more sources

IBS-ECDHE: A blockchain-enhanced lightweight protocol for secure cloud-IoT in biomedical HCPS. [PDF]

Comput Struct Biotechnol J
Rehman AU, Lu S, Usman M, Moqurrab SA, Srivastava G, Yoo J, Al-Aqrabi H.   +6 more
europepmc   +1 more source

Survey on Distributed Hash Tables

, 2006
Filipe Araújo, Luı́s Rodrigues
openalex   +1 more source

Quantitative Assessment Model for Technology Transfer Risks in University‐Enterprise Collaborative Innovation: Based on Multi‐Objective Optimization Strategy of Deep Adversarial Reinforcement Learning

Engineering Reports, Volume 8, Issue 1, January 2026.
This study develops a deep adversarial reinforcement learning framework for dynamic technology transfer risk assessment. The research outcome DAGAN‐MORL has a misjudgment rate 6.3%, a delay of 1.8 h, and a profit error of 7.9%, leading at the industrial level, providing intelligent support for university‐enterprise collaborative innovation. ABSTRACT As
Guojun Wang
wiley   +1 more source