Statistical modelling of the rheological and mucoadhesive properties of aqueous poly(methylvinylether-co-maleic acid) networks: Redefining biomedical applications and the relationship between viscoelasticity and mucoadhesion

Poly(methylvinylether-co-maleic acid) (PMVE/MA) is commonly used as a component of pharmaceutical platforms, principally to enhance interactions with biological substrates (mucoadhesion). However, the limited knowledge on the rheological properties of this polymer and their relationships with mucoadhesion has negated the biomedical use of this polymer as a mono-component platform. This study presents a comprehensive study of the rheological properties of aqueous PMVE/MA platforms and defines their relationships with mucoadhesion using multiple regression analysis. Using dilute solution viscometry the intrinsic viscosities of un-neutralised PMVE/MA and PMVE/MA neutralised using NaOH or TEA were 22.32 ± 0.89 dL g-1, 274.80 ± 1.94 dL g-1 and 416.49 ± 2.21 dL g-1 illustrating greater polymer chain expansion following neutralisation using Triethylamine (TEA). PMVE/MA platforms exhibited shear-thinning properties. Increasing polymer concentration increased the consistencies, zero shear rate (ZSR) viscosities (determined from flow rheometry), storage and loss moduli, dynamic viscosities (defined using oscillatory analysis) and mucoadhesive properties, yet decreased the loss tangents of the neutralised polymer platforms. TEA neutralised systems possessed significantly and substantially greater consistencies, ZSR and dynamic viscosities, storage and loss moduli, mucoadhesion and lower loss tangents than their NaOH counterparts. Multiple regression analysis enabled identification of the dominant role of polymer viscoelasticity on mucoadhesion (r > 0.98). The mucoadhesive properties of PMVE/MA platforms were considerable and were greater than those of other platforms that have successfully been shown to enhance in vivo retention when applied to the oral cavity, indicating a positive role for PMVE/MA mono-component platforms for pharmaceutical and biomedical applications.

Threat Analysis of BlackEnergy Malware for Synchrophasor based Real-time Control and Monitoring in Smart Grid

The BlackEnergy malware targeting critical infrastructures has a long history. It evolved over time from a simple DDoS platform to a quite sophisticated plug-in based malware. The plug-in architecture has a persistent malware core with easily installable attack specific modules for DDoS, spamming, info-stealing, remote access, boot-sector formatting etc. BlackEnergy has been involved in several high profile cyber physical attacks including the recent Ukraine power grid attack in December 2015. This paper investigates the evolution of BlackEnergy and its cyber attack capabilities. It presents a basic cyber attack model used by BlackEnergy for targeting industrial control systems. In particular, the paper analyzes cyber threats of BlackEnergy for synchrophasor based systems which are used for real-time control and monitoring functionalities in smart grid. Several BlackEnergy based attack scenarios have been investigated by exploiting the vulnerabilities in two widely used synchrophasor communication standards: (i) IEEE C37.118 and (ii) IEC 61850-90-5. Specifically, the paper addresses reconnaissance, DDoS, man-in-the-middle and replay/reflection attacks on IEEE C37.118 and IEC 61850-90-5. Further, the paper also investigates protection strategies for detection and prevention of BlackEnergy based cyber physical attacks.

Optimized operations of Distributed Renewable Energy Sources in Smart Grid

The renewable energy sources (RES) will play a vital role in the future power needs in view of the increasing demand of electrical energy and depletion of fossil fuel with its environmental impact. The main constraints of renewable energy (RE) generation are high capital investment, fluctuation in generation and requirement of vast land area. Distributed RE generation on roof top of buildings will overcome these issues to some extent. Any system will be feasible only if it is economically viable and reliable. Economic viability depends on the availability of RE and requirement of energy in specific locations. This work is directed to examine the economic viability of the system at desired location and demand.

Electric Vehicles as Grid Resources

Thesis (Ph.D.)--University of Washington, 2016-08

Introduction to the Special Issue on Biomedical and Bioelectronic Circuits for Enhanced Diagnosis and Therapy

The 22 papers in this special issue focus on biomedical and biolectronic circuits for enhanced diagnosis and therapy.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Federated architecture for biomedical data integration

The last decades have been characterized by a continuous adoption of IT solutions in the healthcare sector, which resulted in the proliferation of tremendous amounts of data over heterogeneous systems. Distinct data types are currently generated, manipulated, and stored, in the several institutions where patients are treated. The data sharing and an integrated access to this information will allow extracting relevant knowledge that can lead to better diagnostics and treatments. This thesis proposes new integration models for gathering information and extracting knowledge from multiple and heterogeneous biomedical sources. The scenario complexity led us to split the integration problem according to the data type and to the usage specificity. The first contribution is a cloud-based architecture for exchanging medical imaging services. It offers a simplified registration mechanism for providers and services, promotes remote data access, and facilitates the integration of distributed data sources. Moreover, it is compliant with international standards, ensuring the platform interoperability with current medical imaging devices. The second proposal is a sensor-based architecture for integration of electronic health records. It follows a federated integration model and aims to provide a scalable solution to search and retrieve data from multiple information systems. The last contribution is an open architecture for gathering patient-level data from disperse and heterogeneous databases. All the proposed solutions were deployed and validated in real world use cases.

An iterative analytical design framework for the optimal designing of an off-grid renewable energy based hybrid smart micro-grid

Creative ways of utilising renewable energy sources in electricity generation especially in remote areas and particularly in countries depending on imported energy, while increasing energy security and reducing cost of such isolated off-grid systems, is becoming an urgently needed necessity for the effective strategic planning of Energy Systems. The aim of this research project was to design and implement a new decision support framework for the optimal design of hybrid micro grids considering different types of different technologies, where the design objective is to minimize the total cost of the hybrid micro grid while at the same time satisfying the required electric demand. Results of a comprehensive literature review, of existing analytical, decision support tools and literature on HPS, has identified the gaps and the necessary conceptual parts of an analytical decision support framework. As a result this research proposes and reports an Iterative Analytical Design Framework (IADF) and its implementation for the optimal design of an Off-grid renewable energy based hybrid smart micro-grid (OGREH-SμG) with intra and inter-grid (μG2μG & μG2G) synchronization capabilities and a novel storage technique. The modelling design and simulations were based on simulations conducted using HOMER Energy and MatLab/SIMULINK, Energy Planning and Design software platforms. The design, experimental proof of concept, verification and simulation of a new storage concept incorporating Hydrogen Peroxide (H2O2) fuel cell is also reported. The implementation of the smart components consisting Raspberry Pi that is devised and programmed for the semi-smart energy management framework (a novel control strategy, including synchronization capabilities) of the OGREH-SμG are also detailed and reported. The hybrid μG was designed and implemented as a case study for the Bayir/Jordan area. This research has provided an alternative decision support tool to solve Renewable Energy Integration for the optimal number, type and size of components to configure the hybrid μG. In addition this research has formulated and reported a linear cost function to mathematically verify computer based simulations and fine tune the solutions in the iterative framework and concluded that such solutions converge to a correct optimal approximation when considering the properties of the problem. As a result of this investigation it has been demonstrated that, the implemented and reported OGREH-SμG design incorporates wind and sun powered generation complemented with batteries, two fuel cell units and a diesel generator is a unique approach to Utilizing indigenous renewable energy with a capability of being able to synchronize with other μ-grids is the most effective and optimal way of electrifying developing countries with fewer resources in a sustainable way, with minimum impact on the environment while also achieving reductions in GHG. The dissertation concludes with suggested extensions to this work in the future.

Hybrid materials for biomedical applications

The increased longevity of humans and the demand for a better quality of life have led to a continuous search for new implant materials. Scientific development coupled with a growing multidisciplinarity between materials science and life sciences has given rise to new approaches such as regenerative medicine and tissue engineering. The search for a material with mechanical properties close to those of human bone produced a new family of hybrid materials that take advantage of the synergy between inorganic silica (SiO4) domains, based on sol-gel bioactive glass compositions, and organic polydimethylsiloxane, PDMS ((CH3)2.SiO2)n, domains. Several studies have shown that hybrid materials based on the system PDMS-SiO2 constitute a promising group of biomaterials with several potential applications from bone tissue regeneration to brain tissue recovery, passing by bioactive coatings and drug delivery systems. The objective of the present work was to prepare hybrid materials for biomedical applications based on the PDMS-SiO2 system and to achieve a better understanding of the relationship among the sol-gel processing conditions, the chemical structures, the microstructure and the macroscopic properties. For that, different characterization techniques were used: Fourier transform infrared spectrometry, liquid and solid state nuclear magnetic resonance techniques, X-ray diffraction, small-angle X-ray scattering, smallangle neutron scattering, surface area analysis by Brunauer–Emmett–Teller method, scanning electron microscopy and transmission electron microscopy. Surface roughness and wettability were analyzed by 3D optical profilometry and by contact angle measurements respectively. Bioactivity was evaluated in vitro by immersion of the materials in Kokubos’s simulated body fluid and posterior surface analysis by different techniques as well as supernatant liquid analysis by inductively coupled plasma spectroscopy. Biocompatibility was assessed using MG63 osteoblastic cells. PDMS-SiO2-CaO materials were first prepared using nitrate as a calcium source. To avoid the presence of nitrate residues in the final product due to its potential toxicity, a heat-treatment step (above 400 °C) is required. In order to enhance the thermal stability of the materials subjected to high temperatures titanium was added to the hybrid system, and a material containing calcium, with no traces of nitrate and the preservation of a significant amount of methyl groups was successfully obtained. The difficulty in eliminating all nitrates from bulk PDMS-SiO2-CaO samples obtained by sol-gel synthesis and subsequent heat-treatment created a new goal which was the search for alternative sources of calcium. New calcium sources were evaluated in order to substitute the nitrate and calcium acetate was chosen due to its good solubility in water. Preparation solgel protocols were tested and homogeneous monolithic samples were obtained. Besides their ability to improve the bioactivity, titanium and zirconium influence the structural and microstructural features of the SiO2-TiO2 and SiO2-ZrO2 binary systems, and also of the PDMS-TiO2 and PDMS-ZrO2 systems. Detailed studies with different sol-gel conditions allowed the understanding of the roles of titanium and zirconium as additives in the PDMS-SiO2 system. It was concluded that titanium and zirconium influence the kinetics of the sol-gel process due to their different alkoxide reactivity leading to hybrid xerogels with dissimilar characteristics and morphologies. Titanium isopropoxide, less reactive than zirconium propoxide, was chosen as source of titanium, used as an additive to the system PDMS-SiO2-CaO. Two different sol-gel preparation routes were followed, using the same base composition and calcium acetate as calcium source. Different microstructures with high hydrophobicit were obtained and both proved to be biocompatible after tested with MG63 osteoblastic cells. Finally, the role of strontium (typically known in bioglasses to promote bone formation and reduce bone resorption) was studied in the PDMS-SiO2-CaOTiO2 hybrid system. A biocompatible material, tested with MG63 osteoblastic cells, was obtained with the ability to release strontium within the values reported as suitable for bone tissue regeneration.