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Waste-to-Energy Advances Using Domain-Specific AI Models and IoT for Scalable Biofuel Production 2026 introduces an innovative framework that leverages tailored artificial intelligence algorithms and Internet of Things infrastructure to transform heterogeneous organic waste streams into high-yield biofuels at industrial scales. By integrating graph neural networks for predictive modelling of biochemical reaction pathways and reinforcement learning for dynamic process optimization, the approach addresses longstanding inefficiencies in traditional waste-to-energy systems, such as variable feedstock quality and suboptimal reactor conditions. IoT-enabled […]

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The Maximum Independent Set (MIS) problem, a core NP-hard problem in graph theory, seeks the largest subset of vertices in an undirected graph $G = (V, E)$ with $n$ vertices and $m$ edges such that no two vertices are adjacent. We present a hybrid approximation algorithm that combines a vertex-cover complement approach with greedy selections based on minimum and maximum degrees, plus a low-degree induced subgraph heuristic, implemented using NetworkX. The algorithm preprocesses the graph to handle trivial […]

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The rapid proliferation of the Internet of Things (IoT) has positioned the Message Queuing Telemetry Transport (MQTT) protocol as a fundamental communication standard for large-scale, resource-constrained systems. Despite its lightweight design and scalability advantages, modern MQTT deployments operate under increasingly complex conditions characterized by intensive security enforcement, dynamic traffic patterns, and widespread use of wildcard subscriptions. These factors introduce tightly coupled challenges related to system performance, congestion, and security, which are often addressed independently in existing literature. This […]

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Introduction: Educational systems have historically adapted to incorporate technological innovations, but the success of these processes may vary greatly. Some tools become embedded in established processes, while others lead to radical transformation. In this paper, we examine the development of educational technology and recommend and critique certain technologies that facilitate existing industrial education approaches and others that require the restructuring of how educational aims and methods are defined and implemented. Purpose: This study aims to evaluate this optimization-restructuring […]

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Class-incremental learning (CIL) is typically evaluated under predefined schedules with equal-sized tasks, leaving more realistic and complex cases unexplored. However, a practical CIL system should learns immediately when any number of new classes arrive, without forcing fixed-size tasks. We formalize this setting as Free-Flow Class-Incremental Learning (FFCIL), where data arrives as a more realistic stream with a highly variable number of unseen classes each step. It will make many existing CIL methods brittle and lead to clear performance […]

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Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, […]

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MLOps A hands-on, beginner-friendly guide to deploying, monitoring, and optimizing a Machine Learning model in production — from API creation to data drift detection, using the real Home Credit Default Risk dataset from Kaggle. Last Saturday morning, I was scrolling through freelance gig postings on Fiverr when I stumbled upon something that caught my attention. A small fintech startup was looking for someone to “take our trained credit scoring model and make it production-ready — API, Docker, CI/CD, […]

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arXiv:2604.01442v1 Announce Type: new Abstract: Modern generator-based fuzzing techniques combine lightweight input generators with coverage-guided mutation as a method of exploring deep execution paths in a target program. A complimentary approach in prior research focuses on creating highly customized, domain-specific generators that encode structural and semantic logic sufficient enough to reach deep program states; the challenge comes from the overhead of writing and testing these complex generators. We investigate whether AI coding agents can automatically synthesize such target-specific […]

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arXiv:2604.01440v1 Announce Type: new Abstract: The shift toward IoT-enabled, sensor-driven systems has transformed how operational data is generated, favoring continuous, real-time event streams (ES) over static event logs. This evolution presents new challenges for Streaming Process Mining (SPM), which must cope with out-of-order events, concurrent activities, incomplete cases, and concept drifts. Yet, the evaluation of SPM algorithms remains rooted in outdated practices, relying on static logs or artificially streamified data that fail to reflect the complexities of real-world […]

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arXiv:2604.01438v1 Announce Type: new Abstract: Personal AI agents like OpenClaw run with elevated privileges on users’ local machines, where a single successful prompt injection can leak credentials, redirect financial transactions, or destroy files. This threat goes well beyond conventional text-level jailbreaks, yet existing safety evaluations fall short: most test models in isolated chat settings, rely on synthetic environments, and do not account for how the agent framework itself shapes safety outcomes. We introduce CLAWSAFETY, a benchmark of 120 […]

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