Psycho-social perspectives on living and working with violence in distressed and traumatised (dis-) organisations

The published work presented in this submission examines the nature and form of psychosocial processes that lead towards and away from mental health, social security and community and organisational cohesion. It demonstrates the application of psychosocial research methods to theproblem of living and working with violence in a range of clinical and educational settings. The emergent work is described in 14 pieces of work; 8 peer-reviewed articles, 4 book chapters and 2 edited volumes. Each of these pieces of work is accompanied by short discussion and commentary on its impact and dissemination.The published work presented extends over a 15 year period and demonstrates learning derived from a 30 year professional and academic commitment to an in-depth exploration of the ways in which structural andcultural processes of inclusion/exclusion give rise to personal and interpersonal violence that poses significant risks of psychosocial harm. The work also explores the reciprocal nature of the violence played out between‘identified clients’, the systems of care tasked with helping them and the wider society from whom these systems of care take their authority. A central concern of the presented work is to consider the often distressing andtraumatising ways in which this reciprocal structural and behavioural violence impacts frontline workers and teams that comprise these organisations.The submission also draws upon psychosocial, group analytic, systems psychodynamic and educational theories of practice, to explore the ways in which reflective practice and team development interventions may be deployed to equip multi-disciplinary teams with the necessary resilience andreflective capacity to work with this psychosocial violence in more creative, thoughtful and collaborative ways. The impact of the published work and the implication for future professional clinical, educational and consultancy practice is also discussed

TraceGen: user activity emulation for digital forensic test image generation

Digital forensic test images are commonly used across a variety of digital forensic use cases including education and training, tool testing and validation, proficiency testing, malware analysis, and research and development. Using real digital evidence for these purposes is often not viable or permissible, especially when factoring in the ethical and in some cases legal considerations of working with individuals' personal data. Furthermore, when using real data it is not usually known what actions were performed when, i.e., what was the ‘ground truth’. The creation of synthetic digital forensic test images typically involves an arduous, time-consuming process of manually performing a list of actions, or following a ‘story’ to generate artefacts in a subsequently imaged disk. Besides the manual effort and time needed in executing the relevant actions in the scenario, there is often little room to build a realistic volume of non-pertinent wear-and-tear or ‘background noise’ on the suspect device, meaning the resulting disk images are inherently limited and to a certain extent simplistic. This work presents the TraceGen framework, an automated system focused on the emulation of user actions to create realistic and comprehensive artefacts in an auditable and reproducible manner. The framework consists of a series of actions contained within scripts that are executed both externally and internally to a target virtual machine. These actions use existing automation APIs to emulate a real user's behaviour on a Windows system to generate realistic and comprehensive artefacts. These actions can be quickly scripted together to form complex stories or to emulate wear-and-tear on the test image. In addition to the development of the framework, evaluation is also performed in terms of the ability to produce background artefacts at scale, and also the realism of the artefacts compared with their human-generated counterparts

Understanding the electronic structure of Y2Ti2O5S2 for green hydrogen production: a hybrid- DFT and GW study

Combined hybDFT and GW study reveals surface properties and optoelectronic behaviour of Y2Ti2O5S2 for green hydrogen production

Spatial Electron-hole Separation in a One Dimensional Hybrid Organic-Inorganic Lead Iodide.

The increasing efficiency of the inorganic-organic hybrid halides has revolutionised photovoltaic research. Despite this rapid progress, the significant issues of poor stability and toxicity have yet to be suitably overcome. In this article, we use Density Functional Theory to examine (Pb2I6) · (H2DPNDI) · (H2O) · (NMP), an alternative lead-based hybrid inorganic-organic solar absorber based on a photoactive organic cation. Our results demonstrate that optical properties suitable for photovoltaic applications, in addition to spatial electron-hole separation, are possible but efficient charge transport may be a limiting factor

Understanding the Photocatalytic Activity of La₅Ti₂AgS₅O₇ and La₅Ti₂CuS₅O₇ for Green Hydrogen Production:Computational Insights

[Image: see text] Green production of hydrogen is possible with photocatalytic water splitting, where hydrogen is produced while water is reduced by using energy derived from light. In this study, density functional theory (DFT) is employed to gain insights into the photocatalytic performance of La(5)Ti(2)AgS(5)O(7) and La(5)Ti(2)CuS(5)O(7)—two emerging candidate materials for water splitting. The electronic structure of both bulk materials was calculated by using hybrid DFT, which indicated the band gaps and charge carrier effective masses are suitable for photocatalytic water splitting. Notably, the unique one-dimensional octahedral TiO(x)S(6–x) and tetragonal MS(4) channels formed provide a structural separation for photoexcited charge carriers which should inhibit charge recombination. Band alignments of surfaces that appear on the Wulff constructions of 12 nonpolar symmetric surface slabs were calculated by using hybrid DFT for each of the materials. All surfaces of La(5)Ti(2)AgS(5)O(7) have band edge positions suitable for hydrogen evolution; however, the small overpotentials on the largest facets likely decrease the photocatalytic activity. In La(5)Ti(2)CuS(5)O(7), 72% of the surface area can support oxygen evolution thermodynamically and kinetically. Based on their similar electronic structures, La(5)Ti(2)AgS(5)O(7) and La(5)Ti(2)CuS(5)O(7) could be effectively employed in Z-scheme photocatalytic water splitting

ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown

The disruptive application of ChatGPT (GPT-3.5, GPT-4) to a variety of domains has become a topic of much discussion in the scientific community and society at large. Large Language Models (LLMs), e.g., BERT, Bard, Generative Pre-trained Transformers (GPTs), LLaMA, etc., have the ability to take instructions, or prompts, from users and generate answers and solutions based on very large volumes of text-based training data. This paper assesses the impact and potential impact of ChatGPT on the field of digital forensics, specifically looking at its latest pre-trained LLM, GPT-4. A series of experiments are conducted to assess its capability across several digital forensic use cases including artefact understanding, evidence searching, code generation, anomaly detection, incident response, and education. Across these topics, its strengths and risks are outlined and a number of general conclusions are drawn. Overall this paper concludes that while there are some potential low-risk applications of ChatGPT within digital forensics, many are either unsuitable at present, since the evidence would need to be uploaded to the service, or they require sufficient knowledge of the topic being asked of the tool to identify incorrect assumptions, inaccuracies, and mistakes. However, to an appropriately knowledgeable user, it could act as a useful supporting tool in some circumstances

ChatGPT for digital forensic investigation: The good, the bad, and the unknown

The disruptive application of ChatGPT (GPT-3.5, GPT-4) to a variety of domains has become a topic of much discussion in the scientific community and society at large. Large Language Models (LLMs), e.g., BERT, Bard, Generative Pre-trained Transformers (GPTs), LLaMA, etc., have the ability to take instructions, or prompts, from users and generate answers and solutions based on very large volumes of text-based training data. This paper assesses the impact and potential impact of ChatGPT on the field of digital forensics, specifically looking at its latest pre-trained LLM, GPT-4. A series of experiments are conducted to assess its capability across several digital forensic use cases including artefact understanding, evidence searching, code generation, anomaly detection, incident response, and education. Across these topics, its strengths and risks are outlined and a number of general conclusions are drawn. Overall this paper concludes that while there are some potential low-risk applications of ChatGPT within digital forensics, many are either unsuitable at present, since the evidence would need to be uploaded to the service, or they require sufficient knowledge of the topic being asked of the tool to identify incorrect assumptions, inaccuracies, and mistakes. However, to an appropriately knowledgeable user, it could act as a useful supporting tool in some circumstances