OpenPonk is a Pharo-based metamodeling platform and modeling workbench supporting UML, BPMN, Petri nets, ER diagrams and other notations. The post OpenPonk – metamodeling platform and modeling workbench appeared first on LinuxLinks.

Jjodel is an open source language workbench for the browser.

This release continues on Application Logical layer, finishing "application service", "application function", "application provider", adding relationships also cross map to Technology Layer.

**Abstract:** Gödel's incompleteness theorems underscore inherent limitations in axiomatic systems, highlighting the impossibility of proving all true statements within a consistent, formalized system. This paper presents a novel methodology leveraging formalized metamodeling and automated constraint resolution to rigorously verify instances of Gödel-esque incompleteness within specific mathematical systems. Our approach, termed “Hyperformal Verification,” employs a multi-layered architecture for ingesting, decomposing, evaluating, and self-correcting formal models, achieving a tenfold increase in complexity resolution compared to existing theorem proving and model checking methodologies.

MetaEdit+ 5.6 provides refactoring and multi-user metamodeling features and tens of other new features

Alzheimer Disease Reimagined Through Computational Metamodeling Personalized briefing Discovery of the day · Neurology Alzheimer disease in the computational era: from a deterministic disease to a multifaceted disorder Dear Damien Boorman, this is your personalized scientific intelligence briefing — curated for your work in Neurology. Key finding Medicine · Neurology Discovery of the day This paper reconceptualizes Alzheimer's disease as a multifaceted, heterogeneous disorder whose evolving definitions demand a data-driven, patient-tailored framework rather than a monolithic diagnostic category.

Several approaches have been proposed in the past for conducting conceptual modeling and model-driven engineering using virtual or augmented reality. However, traditional metamodeling platforms only c...

**Abstract:** This paper details a novel approach to bioelectrochemical reactor (BER) design and operation focused on maximizing butanol production from carbon dioxide (CO₂) using microbial electrosynthesis. The method leverages adaptive metamodeling, real-time sensor data, and a multi-objective optimization framework to dynamically tune reactor parameters, ultimately resulting in a 15-20% increase in butanol yield compared to traditional batch and fed-batch approaches. The system's architecture is modular, designed for rapid prototyping and deployment within existing industrial infrastructure.

**Abstract:** The dynamics of lipid domain percolation in supported bilayer membranes are crucial for understanding various biological processes and designing advanced biomaterials. Current computational and experimental approaches struggle to simultaneously capture the complexity of lipid interactions, membrane topology, and environmental factors. This paper introduces a metamodeling framework leveraging multi-modal data fusion, automated theorem proving, and reinforcement learning to predict lipid domain percolation behavior with unprecedented accuracy.

**Abstract:** This research proposes a novel technique for dynamically controlling quantum dot (QD) emission wavelengths and efficiencies by precisely engineering heterostructures through computational metamodeling. Departing from traditional fabrication methods, we utilize a closed-loop optimization process governed by a hybrid reinforcement learning and Bayesian optimization algorithm acting on a multi-fidelity simulation framework, yielding unprecedented control over QD optical properties. This approach promises significant advancements in display technology, solid-state lighting, and quantum information processing.

**Abstract:** This paper introduces a novel approach to enhancing spin-charge conversion efficiency in heterostructure devices using dynamically controlled lattice strain and a feedback-driven optimization loop. We leverage established piezoelectric material properties and advanced metamodeling techniques to create a self-adjusting lattice configuration minimizing conversion losses. The system employs a machine learning algorithm to predict optimal strain profiles in real-time based on measured photocurrent and voltage characteristics, resulting in a projected 35% improvement in energy conversion efficiency compared to existing passive strain engineering methods.

**Abstract:** This paper introduces a novel framework for accelerating the discovery of optimal compositions for lead-zirconate titanate (PZT) nanocomposites exhibiting targeted mechanical properties. Leveraging gradient information from finite element analysis (FEA) simulations integrated within a Bayesian optimization (BO) loop, combined with a multi-fidelity metamodeling approach, we demonstrate a 5-7x speedup in identifying compositions providing desired Young's modulus and hardness compared to traditional BO without gradient enhancement.

**Randomly Selected Sub-Field:** *Adaptive CRISPR Guide RNA Design for Enhanced Gene Editing Specificity in Mammalian Cell Lines* **Combined Research Topic:** *Automated Metamodeling of CRISPR Guide RNA Fitness Landscapes for Predictive Off-Target Effect Mitigation* This research paper proposes a novel framework for automated design of CRISPR guide RNAs (gRNAs) that minimizes off-target effects by metamodeling the complex fitness landscape of gRNA-target interactions, exceeding current rule-based and deep learning approaches in predictive accuracy and efficiency.

**Abstract:** This paper presents an automated framework for Signal Integrity (SI) verification of High-Speed Printed Circuit Board (PCB) traces leveraging a multi-modal data ingestion and normalization layer, semantic decomposition, and a meta-self-evaluation loop. Existing SI verification tools rely heavily on manual model construction and iterative simulations, limiting scalability and accuracy. Our framework ingests PCB design data (Gerber, Netlist) alongside material properties and manufacturing specifications, parses these into a structured representation, and utilizes advanced machine learning techniques to deconvolve complex signal distortions and construct predictive metamodels with superior accuracy and significantly reduced simulation time—up to a 10x speedup.