This dataset supports the systematic review article "Sustainable Agro-Food Systems in the Digital Era: A Systematic Review of Economic, Technological, and Managerial Perspectives", which synthesises evidence from sixteen (16) studies examining how economic instruments, technological innovations, and managerial governan...
Muzamil Muzamil, Obel Obel, A. (Ade Tika) Herawati et al.· Zenodo (CERN European Organi...· 0 citations
Dynamic Geometry for Adaptive Machine Learning addresses the limitations of static machine learning models by introducing a mechanism for dynamically adjusting geometric properties during training. This novel approach leverages quantum annealing to optimize for robustness and efficiency, ultimately enhancing generaliza...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Abstract Digital therapeutics (DTx) represent an emerging category of evidence-based healthcare interventions that use software to prevent, manage, or treat diseases and disorders. Unlike conventional wellness applications, DTx are designed around therapeutic objectives and are expected to demonstrate clinical benefits...
Zahid Hussain· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to parameter optimization utilizing adaptive neural networks, specifically designed to model complex function structures. The core innovation lies in integrating function structure modeling directly into the optimization process, enabling automated learning and optimization of the c...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This study provides technical support for trajectory planning and coordinated vehicle control of intelligent vehicles, contributing to high-precision obstacle avoidance and multi-objective coordinated control of connected and automated vehicles.
Yi Luo, A.-J. Niu· Frontiers of Mechanical Engi...· 0 citations
Relational Reinforcement Learning (RRL) aims to address the challenges posed by environments where actions influence the state, creating complex dependencies between states. However, traditional RRL methods often struggle to scale effectively, particularly in environments with intricate relational structures. This pape...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to designing distributed control systems utilizing Multi-Agent Reinforcement Learning (MARL). Traditional distributed control systems often rely on manually engineered control strategies, leading to limitations in adaptability, robustness, and scalability. This work introduces a fra...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
MATLAB source code supporting the research article “QMDP-PER-DDQN Trajectory Tracking for Mecanum Robots under Partial Observability.” This repository provides the MATLAB implementation of a belief-aware trajectory-tracking framework for a four-Mecanum-wheel mobile robot operating under noisy and partially observable s...
Oorappan G M, Lakshmanan S A, S Vignesh· Zenodo (CERN European Organi...· 0 citations
This paper introduces Adaptive Neuron Growth Networks (ANGNs), a novel neural network architecture designed for dynamic adaptation to varying input complexities and network performance. ANGNs utilize a reinforcement learning-based mechanism where individual neurons dynamically adjust their parameters – both synaptic we...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This theoretical blueprint introduces the Urooj Arshad AI-Driven Autonomous Nano-Electroceutical Framework [UA-ANEF-2026]. The model critiques current AI oncology systems for ignoring in vivo hydrodynamic drag—the "In Vivo Delivery Gap"—which leads to premature hepatic clearance of nanoparticles. We propose using 100 n...
Urooj Arshad· Zenodo (CERN European Organi...· 0 citations
Smart cities rely on Internet of Things (IoT) sensing and Edge-to-Cloud computing infrastructures to deliver intelligent urban services with heterogeneous requirements. Safetycritical control loops require low latency and high availability, while planning and analytics workloads tolerate relaxed constraints. A single d...
Walid Abdallaoui, Mohammed Naas, Adyson M. Maia et al.· 0 citations
This study examines how the location of reinforcement-learning (RL) compensation affects the control of a low-voltage dual-active-bridge converter using triple-phase-shift modulation. A proportional–integral triple-phase-shift controller is used as the baseline, and two deep deterministic policy gradient schemes are co...
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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