Exploring 'dual diagnosis' treatment motivation

Section A reviews the clinical and risk implications of dual diagnosis along with the treatment context. The value of gathering firsthand accounts of service users to inform the planning and delivery of healthcare is touched on. The second part of the paper centres on theories of motivation and how they might be applied to help explain low rates of dual diagnosis treatment uptake and engagement. Finally, gaps in the literature are highlighted with recommendations for further research. Section B There is an emerging evidence base to support the use of integrated approaches that treat co-existing mental health and substance use disorders simultaneously. However, low rates of treatment uptake and engagement remain a concern. To address this, it would seem important to understand dual diagnosis treatment motivation and engagement, an area that has received little attention from the research community. The aim of this study was to explore service users’ and clinicians' understandings of how treatment motivation and its relationship with treatment engagement relate specifically to people with dual diagnosis. Transcripts from semi-structured interviews with four service users and four clinicians were analysed using narrative methodology. The study suggests that the factors underpinning treatment motivation and engagement among people viewed as having dual diagnosis are similar to those thought to be associated with addictions and mental health disorders generally although their relative influence and interaction effect might be different. It is suggested that negative perceptions of services, difficulties with trust, and therapeutic relationship are particularly important issues among dual diagnosis populations. Clinical and theoretical implications of the study are discussed in relation to the literature as well as recommendations for future research. Section C: Critical Appraisal. This paper provides a general overview of narrative research, including strengths and limitations as they relate to this study. With reference to the literature, clinical and theoretical implications are elaborated along with recommendations for future research. The author’s critical self-reflections regarding the process of initiating, carrying out and completing the study are highlighted. Following this, there is a section on the ethical considerations of the study. Finally, the measures taken to ensure the quality of the study and maximise internal consistency are presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Antigovernment networks in civil conflicts : how network structures affect conflictual behavior

In this article, we combine a game-theoretic treatment of public goods provision in networks with a statistical network analysis to show that fragmented opposition network structures lead to an increase in conflictual actions. Current literature concentrates on the dyadic relationship between the government and potential challengers. We shift the focus toward exploring how network structures affect the strategic behavior of political actors. We derive and examine testable hypotheses and use latent space analysis to infer actors’ positions vis-à-vis each other in the network. Network structure is examined and used to test our hypotheses with data on conflicts in Thailand from 2001 to 2010. We show the influential role of network structure in generating conflictual behavior

Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation

Active Directory (AD) is the default security management system for Windows domain networks. We study a Stackelberg game model between one attacker and one defender on an AD attack graph. The attacker initially has access to a set of entry nodes. The attacker can expand this set by strategically exploring edges. Every edge has a detection rate and a failure rate. The attacker aims to maximize their chance of successfully reaching the destination before getting detected. The defender's task is to block a constant number of edges to decrease the attacker's chance of success. We show that the problem is #P-hard and, therefore, intractable to solve exactly. We convert the attacker's problem to an exponential sized Dynamic Program that is approximated by a Neural Network (NN). Once trained, the NN provides an efficient fitness function for the defender's Evolutionary Diversity Optimisation (EDO). The diversity emphasis on the defender's solution provides a diverse set of training samples, which improves the training accuracy of our NN for modelling the attacker. We go back and forth between NN training and EDO. Experimental results show that for R500 graph, our proposed EDO based defense is less than 1% away from the optimal defense

Back to the Future: The City of the Future and its Architecture in Science Fiction Films

This paper investigates the portrayal of future city and its architecture in science fiction films tracing how this image has changed over time. Films such as Metropolis (1927) and Blade Runner (1982) predicted a future not far from our current time. Comparing the images in these films to our current built environment would give insights about how accurate these predictions were and how reliable science fiction films are in predicting the future. Whether these visions were close to reality or not, it is evident that seeing cities through the eyes of filmmakers opens many theoretical debates about the future of the city and its architecture giving important insights to architects and planners to read and manage their cities in a different critical way. Since the production of the earliest science fiction films in the silent film era, few films have been produced again, this paper identifies these films and examines different versions of future cities in these films through a case study. The investigated case study is Blade Runner (1982 and 2017) where a comparative analysis between the architectural signifiers and their significances is conducted. It can be seen that the prediction of future architecture has changed over time which is attributed to the differences in motives and significances behind implementing these architectural elements across different versions of the same film. This paper attempts to raise attention toward the mutual relationship between film and architecture and the role science fiction films play in predicting the future of our city and its architecture

Synergistic action of thermophilic pectinases for pectin bioconversion into D-galacturonic acid

Large amounts of pectin-rich biomass are generated worldwide yearly, which can be hydrolysed by pectinases to obtain bio-based chemical building blocks such as D-galacturonic acid (GalA). The aim of this work was to investigate thermophilic pectinases and explore their synergistic application in the bioconversion of pectic substrates into GalA. Two exo-polygalacturonases (exo-PGs) from Thermotoga maritima (TMA01) and Bacillus licheniformis (BLI04) and two pectin methylesterases (PMEs) from Bacillus licheniformis (BLI09) and Streptomyces ambofaciens (SAM10) were cloned and expressed in Escherichia coli BL21 (DE3), purified and fully characterised. These pectinases exhibited optimum activity at temperatures above 50 °C and good stability at high temperature (40-90 °C) for up to 24 h. Exo-PGs preferred non-methylated substrates, suggesting that previous pectin demethylation by PMEs was necessary to achieve an efficient pectin monomerisation into GalA. Synergistic activity between PMEs and exo-PGs was tested using pectin from apple, citrus and sugar beet. GalA was obtained from apple and citrus pectin in a concentration of up to 2.5 mM after 4 h reaction at 50 °C, through the combined action of BLI09 PME with either TMA01 or BLI04 exo-PGs. Overall, this work contributes to expand the knowledge of pectinases from thermophiles and provides further insights into their application in the initial valorisation of sustainable pectin-rich biomass feedstocks

Learning from the past and stepping into the future : toward a new generation of conflict prediction

Developing political forecasting models not only increases the ability of political scientists to inform public policy decisions, but is also relevant for scientific advancement. This article argues for and demonstrates the utility of creating forecasting models for predicting political conflicts in a diverse range of country settings. Apart from the benefit of making actual predictions, we argue that predictive heuristics are one gold standard of model development in the field of conflict studies. As such, they shed light on an array of important components of the political science literature on conflict dynamics. We develop and present conflict predictions that have been highly accurate for past and subsequent events, exhibiting few false-negative and false-positive categorizations. Our predictions are made at the monthly level for 6-month periods into the future, taking into account the social–spatial context of each individual country. The model has a high degree of accuracy in reproducing historical data measured monthly over the past 10 years and has approximately equal accuracy in making forecasts. Thus, forecasting in political science is increasingly accurate. At the same time, by providing a gold standard that separates model construction from model evaluation, we can defeat observational research designs and use true prediction as a way to evaluate theories. We suggest that progress in the modeling of conflict research depends on the use of prediction as a gold standard of heuristic evaluation

Training Spiking Neural Networks Using Lessons From Deep Learning

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks; the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.htm