Experimental study of formwork tightness as a function of rheological properties of SCC

Several studies relating formwork pressure to rheology exist, however the relationship between rheology and leakage through formwork joints remains to be investigated. In practice, standard documents are used to define formwork tightness requirements, typically using a qualitative approach. To try bridge this gap in knowledge, we developed a test set-up to study tightness of formwork joints under pressure as a function of varying rheological properties. Coupled with standard rheology tests, this new test set-up provides means of linking flow rate, formwork pressure, flow area, and the rheological properties. The study seeks to provide insight on measurable governing parameters and thus inform formwork tightness requirements in a more quantifiable manner. This paper presents a test set-up designed to study the flow of fresh paste through small openings. It highlights a preliminary study on the pressure-driven flow of limestone paste through a bottom orifice in a cylindrical container. While this new device may not be directly representative of the actual conditions in formwork, it provides a good base for a fundamental study that can then be extrapolated to a more representative test operation. Preliminary results show a linear relationship between the flow rate and the applied pressure. The results also show that increasing the flow area by a factor of 2.33 had a higher impact than an increase in yield stress and viscosity by a factor of 2.54 and 3.80 respectively. However, more tests need to be carried out to obtain clear trends

Scattering from supramacromolecular structures

We study theoretically the scattering imprint of a number of branched supramacromolecular architectures, namely, polydisperse stars and dendrimeric, hyperbranched structures. We show that polydispersity and nature of branching highly influence the intermediate wavevector region of the scattering structure factor, thus providing insight into the morphology of different aggregates formed in polymer solutions.Comment: 20 pages, 8 figures To appear in PR

Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data

Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on Artificial Intelligence and Mobile Services) on June 2

Non-diffractive mechanisms in the ϕ\phi meson photoproduction on nucleons

We examine the non-diffractive mechanisms in the $\phi$ meson photoproduction from threshold up to a few GeV using an effective Lagrangian in a constituent quark model. The new data from CLAS at large angles can be consistently accounted for in terms of {\it s}- and {\it u}-channel processes. Isotopic effects arising from the reactions $\gamma p\to \phi p$ and $\gamma n\to \phi n$, are investigated by comparing the cross sections and polarized beam asymmetries. Our result highlights an experimental means of studying non-diffractive mechanisms in $\phi$ meson photoproduction.Comment: 4 eps figures, version accepted by Phys. Lett.

Controlled release from zein matrices: Interplay of drug hydrophobicity and pH

Purpose: In earlier studies, the corn protein zein is found to be suitable as a sustained release agent, yet the range of drugs for which zein has been studied remains small. Here, zein is used as a sole excipient for drugs differing in hydrophobicity and isoelectric point: indomethacin, paracetamol and ranitidine. Methods: Caplets were prepared by hot-melt extrusion (HME) and injection moulding (IM). Each of the three model drugs were tested on two drug loadings in various dissolution media. The physical state of the drug, microstructure and hydration behaviour were investigated to build up understanding for the release behaviour from zein based matrix for drug delivery. Results: Drug crystallinity of the caplets increases with drug hydrophobicity. For ranitidine and indomethacin, swelling rates, swelling capacity and release rates were pH dependent as a consequence of the presence of charged groups on the drug molecules. Both hydration rates and release rates could be approached by existing models. Conclusion: Both the drug state as pH dependant electrostatic interactions are hypothesised to influence release kinetics. Both factors can potentially be used factors influencing release kinetics release, thereby broadening the horizon for zein as a tuneable release agent