Context. The problem of optimizing computational load balancing in hybrid IoT networks combining cloud, edge, and embedded nodes under limited resources and dynamic topology conditions is addressed. The object of the study is the processes and mathematical models of load balancing in heterogeneous IoT environments.Obje...
I. Rozlomii, E. Faure, A. Yarmilko et al.· Radio Electronics, Computer...· 0 citations
Multi-Lane Free Flow (MLFF) systems have emerged as a promising approach to reducing congestion at toll gates by enabling uninterrupted toll transactions without requiring vehicles to stop. To support this implementation, this study proposes a YOLOv9-based vehicle detection and classification system optimized using gri...
Nihayatus Sa'adah, Faridatun Nadziroh, R. Sudibyo et al.· Journal of Electrical and In...· 0 citations
The El-Rakhawi Protocol for Ecological Resilience and Wildfire Mitigation presents a proactive, bio-computational framework to combat wildfires. Moving beyond reactive suppression, it integrates four core innovations: (1) Mycelial-assisted early warning sensor networks (MEWN) using neuromorphic edge computing to detect...
mohamed kamal arafa el-rakhawi· Zenodo (CERN European Organi...· 0 citations
Added Released together with progeny-selector (https://github.com/piercetaylor/progeny-selector/releases/tag/v0.1.0); both implement input data contract 1.12.0. Each GitHub Release carries backcross- -site.zip, the built site with a relative base that serves from any static folder (with a one-line server instruction, s...
Pierce Taylor· Zenodo (CERN European Organi...· 0 citations
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# LeMARLA **L**arge Language Model-**E**nhanced **M**ulti-**A**gent **R**einforcement **L**earning for **A**utonomous enterprise workflow orchestration via graph semantic routing. LeMARLA couples a large language model, a heterogeneous graph attention network and a multi-agent policy under centralized training with dec...
Peizhi He· Zenodo (CERN European Organi...· 0 citations
Understanding and recognizing human activities from low-cost wearable sensors has attracted increasing attention in recent years. To achieve this goal, numerous learning models and augmentation approaches have been developed. While effective in certain scenarios, their performance is often limited due to the distributi...
Yi-Ze Cai, Rui-Xiang Feng, Kun-Lin Cai et al.· Proceedings of the ACM on In...· 0 citations
QuantNature Monograph Series on Sovereign Systems (Vol. 4)Document ID: QN-SAR2026-V1.0Permanent DOI: 10.5281/zenodo.23060890Publication Date: September 30, 2026Author: Steven K (QuantNature Global) Executive Summary & Abstract For more than a decade and a half, small unmanned aerial systems (sUAS) have remained trapped...
Steven K· Zenodo (CERN European Organi...· 0 citations
DEGAS 2 [1], like its predecessor, DEGAS [2], uses the Monte Carlo approach to integrating the Boltzmann equation, allowing the treatment of complex geometries, atomic physics, and wall interactions. DEGAS 2 is written in a "macro-enhanced'' version of FORTRAN via the FWEB library, providing an object oriented capabili...
Daren P. Stotler, Charles F. F. Karney· OSTI OAI (U.S. Department of...· 0 citations
AviGPT-250M-Instruct is a 250M-parameter autoregressive small language model (SLM) introducing a Semi-Parametric Decoupling paradigm for resource-constrained edge computing. Rather than overloading transformer weights with static encyclopedic memorization and floating-point arithmetic approximation, AviGPT-250M delegat...
Avinash Ricky Yadlapalli· Zenodo (CERN European Organi...· 0 citations
A state has two indicators. Compliance is decided by the posterior mean of the first, while a verifier reads each disclosed posterior through a per-coordinate spectral statistic of both marginals — a lower Expected Shortfall or a lower quantile of each indicator — and the sender minimises the expected read subject to a...
Mikio Hanaeda· Zenodo (CERN European Organi...· 0 citations
Electronic skin (E-skin) has been a key enabling technology for robots that need to work safely, intelligently, and in physical proximity with humans. The domain has progressed from fingertip tactile arrays to large-area, full-body systems that can now sense pressure, shear, high-frequency vibration, proximity (i.e., t...
Ioannis Papadongonas, Alexandros Gazis, Vasileios Fanidis et al.· Academia Engineering· 0 citations
Engineering quality control is shifting from sampling-based acceptance and manual review to online perception, process intervention, and evidence traceability. Taking detailed quality problems such as prefabricated-building joints, rebar tying, component seams, and apparent concrete defects as research objects, this pa...
Cheng-Peng Mao· Innovative Applications of A...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
