Hybrid Sensible/Thermochemical Solar Energy Storage Concepts Based on Porous Ceramic Structures and Redox Pair Oxides Chemistry

AbstractThe enthalpy effects of reversible chemical reactions can be exploited for the so-called thermochemical storage of solar energy. Oxides of multivalent metals in particular, capable of being reduced and oxidized under air atmosphere with significant heat effects are perfect candidates for air-operated Concentrated Solar Power plants since in this case air can be used as both the heat transfer fluid and the reactant (O2) and therefore can come to direct contact with the storage material (oxide).Based on the characteristics of the oxide redox pair Co3O4/CoO as a thermochemical heat storage medium and the advantages of porous ceramic structures like honeycombs and foams in heat exchange applications, the idea of employing such structures either coated with or entirely made of a redox material like Co3O4, as a hybrid sensible-thermochemical solar energy storage system in air-operated Concentrated Solar Power plants has been set forth and tested. At first, small-scale, redox-inert, cordierite foams and honeycombs were coated with Co3O4 and tested for cyclic reduction-oxidation operation via Thermo-Gravimetric Analysis. Such Co3O4-coated supports exhibited repeatable operation within the temperature range 800-1000oC for many cycles, employing all the redox material incorporated, even at very high redox oxide loading levels. To improve the volumetric heat storage capacity of such reactors, ceramic foams made entirely of Co3O4 were manufactured. Such foams exhibited satisfactory structural integrity and were comparatively tested vs. the “plain” Co3O4 powder and the Co3O4-coated, cordierite supports under the same cyclic redox conditions up to 15 consecutive cycles. The Co3O4-made porous foams were proved also capable of cyclic reduction–oxidation, exploiting the entire amount of Co3O4 used in their manufacture, maintaining simultaneously their structural integrity

The Data that Drives Cyber Insurance: A Study into the Underwriting and Claims Processes

Cyber insurance is a key component in risk management, intended to transfer risks and support business recovery in the event of a cyber incident. As cyber insurance is still a new concept in practice and research, there are many unanswered questions regarding the data and economic models that drive it, the coverage options and pricing of premiums, and its more procedural policy-related aspects. This paper aims to address some of these questions by focusing on the key types of data which are used by cyber-insurance practitioners, particularly for decision-making in the insurance underwriting and claim processes. We further explore practitioners' perceptions of the challenges they face in gathering and using data, and identify gaps where further data is required. We draw our conclusions from a qualitative study by conducting a focus group with a range of cyber-insurance professionals (including underwriters, actuaries, claims specialists, breach responders, and cyber operations specialists) and provide valuable contributions to existing knowledge. These insights include examples of key data types which contribute to the calculation of premiums and decisions on claims, the identification of challenges and gaps at various stages of data gathering, and initial perspectives on the development of a pre-competitive dataset for the cyber insurance industry. We believe an improved understanding of data gathering and usage in cyber insurance, and of the current challenges faced, can be invaluable for informing future research and practice

Insider threat response and recovery strategies in financial services firms

Insiders have become some of the most widely cited culprits of cybercrime. Over the past decade, the scale of attacks carried out by insiders has steadily increased. Financial services firms, in particular, have been frequent targets of insider at-tacks. While insider-threat awareness levels have grown over the years, threat management strategies remain to be better understood. This article analyses how financial services institutions address insider threat, and how they respond to, and recover from insider-threat incidents. It is argued that response and recovery strategies of financial services organisations are still nascent. Combining industry reports, academic literature, and semi-structured interviews with senior financial services security professionals, the research offers a practice-oriented perspective on insider-threat response and recovery strategies, and identifies best practices

Catching the Phish: Detecting Phishing Attacks using Recurrent Neural Networks (RNNs)

The emergence of online services in our daily lives has been accompanied by a range of malicious attempts to trick individuals into performing undesired actions, often to the benefit of the adversary. The most popular medium of these attempts is phishing attacks, particularly through emails and websites. In order to defend against such attacks, there is an urgent need for automated mechanisms to identify this malevolent content before it reaches users. Machine learning techniques have gradually become the standard for such classification problems. However, identifying common measurable features of phishing content (e.g., in emails) is notoriously difficult. To address this problem, we engage in a novel study into a phishing content classifier based on a recurrent neural network (RNN), which identifies such features without human input. At this stage, we scope our research to emails, but our approach can be extended to apply to websites. Our results show that the proposed system outperforms state-of-the-art tools. Furthermore, our classifier is efficient and takes into account only the text and, in particular, the textual structure of the email. Since these features are rarely considered in email classification, we argue that our classifier can complement existing classifiers with high information gain

A New Take on Detecting Insider Threats: Exploring the use of Hidden Markov Models

The threat that malicious insiders pose towards organisations is a significant problem. In this paper, we investigate the task of detecting such insiders through a novel method of modelling a user's normal behaviour in order to detect anomalies in that behaviour which may be indicative of an attack. Specifically, we make use of Hidden Markov Models to learn what constitutes normal behaviour, and then use them to detect significant deviations from that behaviour. Our results show that this approach is indeed successful at detecting insider threats, and in particular is able to accurately learn a user's behaviour. These initial tests improve on existing research and may provide a useful approach in addressing this part of the insider-threat challenge

Postero-apical thoracic schwannoma with cervical extension resected by complete video-assisted thoracoscopic surgery

Schwannomas or neurilemmomas are benign tumors developed from the peripheral nervous system. Complete video-assisted thoracic surgery (cVATS) has set itself over the years as the preferred approach for the removal of small mediastinal neurogenic tumors. However, in case of apical location, complete VATS seems challenging because of proximity with the subclavian artery and/or elements of the brachial plexus. In case of a cVATS procedure, some authors prefer enucleation instead of resection, with a higher risk of relapse. We present two cases of cVATS resection of thoracic apical schwannomas

A Self-Organizing Algorithm for Modeling Protein Loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies

Mapping the Coverage of Security Controls in Cyber Insurance Proposal Forms

Policy discussions often assume that wider adoption of cyber insurance will promote information security best practice. However, this depends on the process that applicants need to go through to apply for cyber insurance. A typical process would require an applicant to fill out a proposal form, which is a self-assessed questionnaire. In this paper, we examine 24 proposal forms, offered by insurers based in the UK and the US, to determine which security controls are present in the forms. Our aim is to establish whether the collection of security controls mentioned in the analysed forms corresponds to the controls defined in ISO/IEC 27002 and the CIS Critical Security Controls; these two control sets are generally held to be best practice. This work contains a novel research direction as we are the first to systematically analyse cyber insurance proposal forms. Our contributions include evidence regarding the assumption that the insurance industry will promote security best practice. To address the problem of adverse selection, we suggest the number of controls that proposal forms should include to be in alignment with the two information security frameworks. Finally, we discuss the incentives that could lead to this disparity between insurance practice and information security best practice, emphasising the importance of information security economics in studying cyber insurance

Mediastinal silicone lymphadenopathy revealed after thymectomy for autoimmune myasthenia gravis

Breast reconstruction is a very popular surgical intervention performed either for cosmetic reasons or after oncological resections. Even though silicone is considered to be an inert material, there are side effects that have been reported, such as silicone lymphadenopathy. In the case reported herein, a silicone lymphadenopathy of the internal mammary and the anterior mediastinal lymph nodes were revealed after a thymectomy for autoimmune myasthenia gravis. Silicone lymphadenopathy should always be part of the differential diagnosis of enlarged lymph nodes, in patients with previous cosmetic or oncoplastic surgery with the use of silicone gel breast implants. Special attention should be paid in case of previous breast cancer in order to rule out metastasis