Self-generated oxygen gradients control collective aggregation of photosynthetic microbes

For billions of years, photosynthetic microbes have evolved under the variable exposure to sunlight in diverse ecosystems and microhabitats all over our planet. Their abilities to dynamically respond to alterations of the luminous intensity, including phototaxis, surface association and diurnal cell cycles, are pivotal for their survival. If these strategies fail in the absence of light, the microbes can still sustain essential metabolic functionalities and motility by switching their energy production from photosynthesis to oxygen respiration. For suspensions of motile C. reinhardtii cells above a critical density, we demonstrate that this switch reversibly controls collective microbial aggregation. Aerobic respiration dominates over photosynthesis in conditions of low light, which causes the microbial motility to sensitively depend on the local availability of oxygen. For dense microbial populations in self-generated oxygen gradients, microfluidic experiments and continuum theory based on a reaction–diffusion mechanism show that oxygen-regulated motility enables the collective emergence of highly localized regions of high and low cell densities.     

Modal analysis of an operational offshore wind turbine using enhanced Kalman filter-based subspace identification

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches.     

An autoML network traffic analyzer for cyber threat detection

International Journal of Information Security - Timely detection and effective treatment of cyber-attacks for protecting personal and sensitive data from unauthorized disclosure constitute a core...     

Enabling technologies for the ‘always best connected’ concept

‘Always Best Connected’ (ABC) is considered one of the main requirements for next generation networks. The ABC concept allows a person to have access to applications using the devices and network technologies that best suits his or her needs or profile at any time. Clearly, this requires the combination of a set of existing and new technologies, at all levels of the protocol stack, into one integrated system. In this paper, a considerable set of the technologies, that are expected to play a key role towards the ABC vision, are presented. Starting from a reference architecture, the paper describes the required enhancements at certain levels of a traditional protocol stack, as well as technologies for mobility and end‐to‐end Quality of Service (QoS) support. The paper concludes with a case study that reveals the advantages of the ABC concept. This article replaces a previously published version (Wireless Communications and Mobile Computing; 5 (2): 175‐191. [DOI: 10.1002/wcm.207]). Retraction notice DOI: 10.1002/wcm.426 . Copyright © 2006 John Wiley & Sons, Ltd.     

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.