8,403 research outputs found
The Most Important Thing in IPV Right Now : The Intersection of Intimate Partner Violence and Brain Injury
The intersection of intimate partner violence (IPV) and brain injury (BI) has been almost entirely overlooked in research, practice, and policy, despite the known risks associated with the two conditions. Individually, IPV and BI are associated with elevated rates of unemployment, poverty, and homelessness, as well as increased mental health challenges. These social determinants of health, employment status, and income impact womenâs wellbeing through access to safe accommodations, food security, and (dis)ability supports. These determinants are also related to an increased likelihood of experiencing addictions, mental health challenges, and physical danger, potentially leaving women vulnerable to ongoing violence. This qualitative study sought to explore the complex interconnections between work environments and the needs of women survivors of IPV-related BI (IPV-BI). Semi-structured interviews were conducted with twenty-four stakeholder participants from four groups: women survivors, executive director/program managers, direct service providers, and employer/union representatives. The overarching goal of this project was to provide in-depth information about the intersection between IPV and BI, and the implications for womenâs employment. The research provided an opportunity for women survivors to share their lived experiences of employment within the context of their exposure to IPV and BI, amplifying their voices through a participatory model of qualitative research. The study was informed by Critical Disability Theory, Intersectionality, and the authorâs own theoretical advancement of the Considered Inclusive Framework. The work concludes with a discussion of the findings, including the extraordinary complexity within the intersection of IPV-BI itself, the impact of a socially derived culture of shame and stigma that shapes the experience of IPV-BI, and the recognition/consideration of the complex layers of power that survivors are exposed to, both structural and individual. A significant and serious gap in awareness, knowledge, and understanding of IPV-BI, combined with an underfunded support system, is also discussed. Recommendations for practice and future research are presented, and the unique role of social work is considered in the context of moving toward an integrated response
An Integrated Deep Learning Model with Genetic Algorithm (GA) for Optimal Syngas Production Using Dry Reforming of Methane (DRM)
The dry reforming of methane is a chemical process transforming two primary sources of greenhouse gases, i.e., carbon dioxide (CO2) and methane (CH4), into syngas, a versatile precursor in the industry, which has gained significant attention over the past decades. Nonetheless, commercial development of this eco-friendly process faces barriers such as catalyst deactivation and high energy demand. Artificial intelligence (AI), specifically deep learning, accelerates the development of this process by providing advanced analytics. However, deep learning requires substantial training samples and collecting data on a bench scale encounters cost and physical constraints. This study fills this research gap by employing a pretraining approach, which is invaluable for small datasets. It introduces a software sensor for regression (SSR) powered by deep learning to estimate the quality parameters of the process. Moreover, combining the SSR with a genetic algorithm offers a prescriptive analysis, suggesting optimal thermodynamic parameters to improve the process efficiency
DEVELOPING COMPETENCE FOR INNOVATION IN KNOWLEDGE PRACTICE: an exploration of the sustainability science-policy interface
This thesis is motivated by institutional claims for a ânew type of knowledgeâ in the sustainability science-policy interface. It thus explores the thinking and practice of experts in the field about professional competencies necessary to induce required innovations in their knowledge practice.
The thesis proposes a novel conceptual framework, synthesising (1) five key features informing claimed innovations in knowledge practice of science-policy sustainability âboundary organisationsâ, (2) a set of ten differentiating individual competencies deemed critical to induce such type of innovations and (3) required approaches to effective development of such competencies. In doing so, this thesis suggests an operative framework to engage with a competence-based approach in response to the need for innovations in knowledge practice within boundary organisations.
Under the conceptual framework above, the thesis engages in an empirical work exploring the thinking and praxis of experts in the field, around three key questions: (1) How do practitioners in the field perceive the need for and the pertinence of such type of innovations, (2) How do they relate to the notion of individual competence and the need for different types of competencies to induce innovations in their own knowledge practice and (3) How can professionals working in the science-policy interface most effectively learn and develop such new set of competences, given their specific organisational / institutional contexts?
Methodologically, this thesis engaged a combined set of empirical research instruments, mostly including semi-structured interviews with professionals operating in the sustainability sciencepolicy interface, three focus-groups in The Netherlands, Portugal and the UK, with actors operating within the remit of sustainability boundary organizations, and participatory observation within the European Environment Agency.
Outcomes of this research indicate that, while the need for a new type of knowledge is clearly acknowledged by practitioners in âboundary organisationsâ, notions associated with required innovations in knowledge practice â such as co-creation, systems thinking, transdisciplinarity, reflexivity and action-orientated knowledge â are still subject to ambiguity and controversy within the institutional context they operate. As practitioners struggle to engage the notion of individual competence in this debate, the type of competencies deemed critical to induce required innovations in their knowledge practice resonates with their own experience. Experts in boundary organisations identify though a lack of institutional frameworks to support their efforts to generate innovations in knowledge practice. While this research synthetises and presents existing examples of learning programmes and approaches to help develop such type of competencies, practitioners in the field manifest scepticism on the extent to which such learning approaches are feasible in their given institutional settings
Efficient network management and security in 5G enabled internet of things using deep learning algorithms
The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model
Configuration Management of Distributed Systems over Unreliable and Hostile Networks
Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems.
This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration.
Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture.
The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn.
Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts
A Holistic Analysis of Internet of Things (IoT) Security : Principles, Practices, and New Perspectives
Peer reviewedPublisher PD
An examination of the verbal behaviour of intergroup discrimination
This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with oneâs freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with oneâs thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies.
The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity
Distributed Ledger Technology (DLT) Applications in Payment, Clearing, and Settlement Systems:A Study of Blockchain-Based Payment Barriers and Potential Solutions, and DLT Application in Central Bank Payment System Functions
Payment, clearing, and settlement systems are essential components of the financial markets and exert considerable influence on the overall economy. While there have been considerable technological advancements in payment systems, the conventional systems still depend on centralized architecture, with inherent limitations and risks. The emergence of Distributed ledger technology (DLT) is being regarded as a potential solution to transform payment and settlement processes and address certain challenges posed by the centralized architecture of traditional payment systems (Bank for International Settlements, 2017). While proof-of-concept projects have demonstrated the technical feasibility of DLT, significant barriers still hinder its adoption and implementation. The overarching objective of this thesis is to contribute to the developing area of DLT application in payment, clearing and settlement systems, which is still in its initial stages of applications development and lacks a substantial body of scholarly literature and empirical research. This is achieved by identifying the socio-technical barriers to adoption and diffusion of blockchain-based payment systems and the solutions proposed to address them. Furthermore, the thesis examines and classifies various applications of DLT in central bank payment system functions, offering valuable insights into the motivations, DLT platforms used, and consensus algorithms for applicable use cases. To achieve these objectives, the methodology employed involved a systematic literature review (SLR) of academic literature on blockchain-based payment systems. Furthermore, we utilized a thematic analysis approach to examine data collected from various sources regarding the use of DLT applications in central bank payment system functions, such as central bank white papers, industry reports, and policy documents. The study's findings on blockchain-based payment systems barriers and proposed solutions; challenge the prevailing emphasis on technological and regulatory barriers in the literature and industry discourse regarding the adoption and implementation of blockchain-based payment systems. It highlights the importance of considering the broader socio-technical context and identifying barriers across all five dimensions of the social technical framework, including technological, infrastructural, user practices/market, regulatory, and cultural dimensions. Furthermore, the research identified seven DLT applications in central bank payment system functions. These are grouped into three overarching themes: central banks' operational responsibilities in payment and settlement systems, issuance of central bank digital money, and regulatory oversight/supervisory functions, along with other ancillary functions. Each of these applications has unique motivations or value proposition, which is the underlying reason for utilizing in that particular use case
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