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Recently Added
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Mobile drawing methods in landscape research: collaborative drawing in Kathmandu Valley, NepalIn this paper, we show how mobile drawing methodologies can bring the dynamic, relational and non-representational qualities of landscape encounters to the foreground. The research paper discusses a mobile drawing project that took place in the Kathmandu Valley, Nepal. The project entitled ‘Taxi Guff-Gaff’ invited participants to undertake a collaborative drawing and conversational journey. Mobile drawing together on a bumpy taxi journey required artist participants to move together and literally ‘pay attention to the moment at hand’. In so doing it produced imagery that foregrounds the inherent dynamic quality of all our landscape encounters. We propose that mobile drawing offers an immersive way to relate to the urban landscape and each other and can open up spaces of landscape research that centre on speculative forms of thinking, being, drawing and conversation.
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An insight into the impact of thermal process on dissolution profile and physical characteristics of theophylline tablets made through 3D printing compared to conventional methodsThe dissolution profile is of great importance in drug delivery and is affected by the manufacturing method. Thus, it is important to study the influence of the thermal process on drug release in emerging technologies such as 3D printing-fused deposition modeling (FDM). For this purpose, the characteristics of 3D printed tablets were compared to those of tablets prepared by other thermal methods such as hot-melt extrusion (HME) and non-thermal methods such as physical mixture (PM). Theophylline was used as a drug model and blends of ethyl cellulose (EC) and hydroxypropyl cellulose (HPC) were used as a matrix former. The solid state of the drug in all formulations was investigated by differential scanning calorimetry, X-ray powder diffraction, and Fourier-transformed infrared spectroscopy. All studied tablets had the same weight and surface area/volume (SA/V). Dissolution data showed that, for some formulations, printed tablets interestingly had a faster release profile despite having the highest hardness values (>550 N) compared to HME and PM tablets. Porosity investigations showed that 100% infill printed tablets had the highest porosity (~20%) compared to HME (<10%) and PM tablets (≤11%). True density records were the lowest in printed tablets (~1.22 g/m3) compared to tablets made from both HME and PM methods (~1.26 g/m3), reflecting the possible increase in polymer specific volume while printing. This increase in the volume of polymer network may accelerate water and drug diffusion from/within the matrix. Thus, it is a misconception that the 3D printing process will always retard drug release based on increased tablet hardness. Hardness, porosity, density, solid-state of the drug, SA/V, weight, and formulation components are all factors contributing to the release profile where the total balance can either slow down or accelerate the release profile.
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Sustainability opportunities and challenges in the UK HE sectorThis presentation aims to provide some insightful thoughts on how sustainability research is integrated into developing the HE sector. It explores the main challenges and opportunities of sustainable teaching and learning research. It illustrates teaching and research resources from leading sustainable business organizations in the UK. These organizations represent a new sustainable business model. This model focuses on commercializing social and environmental projects. Furthermore, this business model involves a new form of accountability that could be used for the HE services for the future generation. The main research objectives of this research paper are: 1. Explain how sustainability could offer more opportunities in the HE sector. 2. Explore the dilemmas of developing sustainable resources 3. Provide some examples (teaching resources and research impact case studies) to help institutions to grow and provide a more significant impact. The new sustainable business models (Cases) This presentation illustrates teaching and research resources from leading sustainable business organizations in the UK. A number of academic attempts developed to address the main themes of sustainable business models in some HE institutions. The main focus of these attempts was centered on examining the levels of social and environmental involvement within the HE sector. So, it seems more relevant for HE institutions to consider social and environmental issues (including sustainability practices) as a core component to create a more coherent sustainable business model. In addition, this presentation provides educational resources to analyze a number of different conceptual sustainability issues. These conceptual issues involve the need to answer some of the main challenges of sustainable business model in business organizations: - Sustainability for what? - Sustainability for whom? - Sustainability in what way? - Sustainability for how long? - Sustainability at what level of resolution?
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The role of universities in the achievement of the UN sustainable development goalsThis poster aims to illustrate some approaches that could be used to manage the achievement of the UN sustainable development goals in higher education institutions. More importantly, it suggests some institutional tools to manage the achievements of these goals and help business organizations to explore more profitable business opportunities from achieving these goals.
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Behavior-neutral smart charging of plugin electric vehicles: reinforcement learning approachHigh-powered electric vehicle (EV) charging can significantly increase charging costs due to peak-demand charges. This paper proposes a novel charging algorithm which exploits typically long plugin sessions for domestic chargers and reduces the overall charging power by boost charging the EV for a short duration, followed by low-power charging for the rest of the plugin session. The optimal parameters for boost and low-power charging phases are obtained using reinforcement learning by training on EV’s past charging sessions. Compared to some prior work, the proposed algorithm does not attempt to predict the plugin session duration, which can be difficult to accurately predict in practice due to the nature of human behavior, as shown in the analysis. Instead, the charging parameters are controlled directly and are adapted transparently to the user’s charging behavior over time. The performance evaluation on a UK dataset of 3.1 million charging sessions from 22,731 domestic charge stations, demonstrates that the proposed algorithm results in 31% of aggregate peak reduction. The experiments also demonstrate the impact of history size on learning behavior and conclude with a case study by applying the algorithm to a specific charge point.
