Inorganic–Organic Hybrid Dielectrics for Energy Conversion: Mechanism,Strategy, and Applications

Dielectric materials with higher energy storage and electromagnetic (EM) energy conversion are in high demand to advance electronic devices, military stealth, and mitigate EM wave pollution. Existing dielectric materials for high-energy-storage electronics and dielectric loss electromagnetic wave absorbers are studied toward realizing these goals, each aligned with the current global grand challenges. Libraries of dielectric materials with desirable permittivity, dielectric loss, and/or dielectric breakdown strength potentially meeting the device requirements are reviewed here. Regardless, aimed at translating these into energy storage devices, the oft-encountered shortcomings can be caused by either of two confluences: a) low permittivity, high dielectric loss, and low breakdown strength; b) low permittivity, low dielectric loss, and process complexity. Contextualizing these aspects and the overarching objectives of enabling high-efficiency energy storage and EM energy conversion, recent advances in by-design inorganic–organic hybrid materials are reviewed here, with a focus on design approaches, preparation methods, and characterization techniques. In light of their strengths and weaknesses, potential strategies to foster their commercial adoption are critically interrogated.     

AiCEF: an AI-assisted cyber exercise content generation framework using named entity recognition

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any cyber security exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors’ tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework. Our methodology was assessed by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise.

     

Implementation of 3D mesh streaming and compression techniques in NVEs

In this paper, we present a framework that integrates three‐dimensional (3D) mesh streaming and compression techniques and algorithms into our EVE‐II networked virtual environments (NVEs) platform, in order to offer support for large‐scale environments as well as highly complex world geometry. This framework allows the partial and progressive transmission of 3D worlds as well as of separate meshes, achieving reduced waiting times for the end‐user and improved network utilization. We also present a 3D mesh compression method focused on network communication, which is designed to support progressive mesh transmission, offering a fast and effective means of reducing the storage and transmission needs for geometrical data. This method is integrated in the above framework and utilizes prediction to achieve efficient lossy compression of 3D geometry. Copyright © 2006 John Wiley & Sons, Ltd.