1522 research outputs found
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Mathematical Models of Magnetic Nanoparticles in Hyperthermia and Targeted Delivery
Get PDFMagnetic nanoparticles (MNPs), mainly iron oxide particles, have the advantage of being controllable by magnetic fields. MNPs show promise in biomedical applications, typically as carriers for biological or therapeutic entities or for their hyperthermic properties. Mathematical modelling assists in the design of MNP applications. However, the role of interparticle interactions is frequently ignored due to computational complexity, despite the general acceptance of the importance of interactions. Magnetic hyperthermia and magnetic drug delivery are two important clinical applications of MNPs where magnetic dipole interaction can be expected to have a significant role in the behaviour and thus be important in any potential medical applications. Good design of magnetic hyperthermia treatment approaches a thorough understanding of the complexities of the heating mechanisms. There are typically two mechanisms which lead to heating: Debye and Néel relaxation. Most models of hyperthermia consider only Debye relaxation and typically interparticle interaction is ignored. Targeted drug delivery aims to reduce the undesired side effects of drug usage by directing or capturing the active agents near a desired site within the body. This is particularly beneficial in, for instance, cancer chemotherapy, where the side effects of general drug administration can be severe. Although a number of mathematical models exist in literature, certain differences in the theoretical and experimental results have been noted. This thesis presents mathematical models of magnetic hyperthermia and magnetic delivery along with detailed analysis of three other mathematical models of magnetic interaction available in the literature. In this thesis, chapter 1 overviews some general information concerning the role of magnetic nanoparticles in biomedicine and the motivation for this work. Chapter 2 presents a mathematical model of hyperthermia which includes interparticle interactions, and offers empirical approximations to estimate the optimum heating for a chain of MNPs. Chapters 3–5 present replications and in some cases corrections of the models published by various authors. Chapter 6 presents a model investigating the aggregation of MNPs in parabolic flow. Here MNPs are considered whose initial positions are always above or below each other along the vertical axis of the vessel. A critical distance is then found between the MNPs within the vessel. If the MNPs begin their motion within this critical distance, then over time aggregation occurs. This critical distance is found to depend upon the initial position along the diameter of the vessel and also the fluid velocity. Analytic expressions for the upper and lower bounds are obtained and validated with the numerical results. Also, an empirical approximation of the critical distance is given, which gives close agreement with the numerical results
Using embedded energy-harvesting nanodevices for neural data communications in the human body
Get PDFImplanted biomedical devices are an important part of the diagnosis and treatment of human illnesses. Such devices need electrical power for operation, transmission systems for data communications and a high level of bio-compatibility to reduce the possibility of inflammation. Powering by battery is widely used but requires removal of the device from the body for battery renewal. Wireless electromagnetic (EM) systems are also in common use but are subject to tissue absorption and potential tissue heating. It would be preferable to use some form of energy-harvesting for power and a more biocompatible method for data communications. This Thesis proposes the use of ultrasound as a method of providing in-body energy harvesting for an implanted device at a shallow depth of tissue. The medical use of ultrasound for imaging is widespread, well understood and has recommended safety levels. Arrays of devices containing piezoelectric nanowires can convert incident ultrasound energy into electrical pulses. These pulses can stimulate a nerve to generate a stream of modulated signals along the nerve and deliver data packets to a more deeply embedded receiver. The maximum bit rate is 200 bit/s, limited by the rate at which nerves can generate electrical signals. The proposed modulation is simple on-off keying (OOK) to create a stream of logic “ones" and “zeroes". The send and receive timing is asynchronous and the direction of transmission is one-way so no re-sending of faulty packets can be supported. We model a specific scenario of a stimulus system on the vagus nerve in the neck sending modulated data pulses to an embedded, multi-reservoir drug-delivery system in the brain. The drug-delivery system could use cerebrospinal glucose as a source for energy harvesting. Forward error correction is analysed as a potential method to improve transmission performance. The overall energy-harvesting and communications system is simple, biocompatible and safe
Development and Validation of an Instrument to Measure the Service Innovation Capability Maturity of SMEs
Get PDFSmall and medium-sized enterprises in the service sector must continuously innovate and adapt to remain competitive and ensure their survival. Key to this is their service innovation capability, a dynamic capability underpinning the repeated generation of new services and improvement of existing ones. However, despite the significance of this capability within the literature, there are serious gaps in how it is understood, and no measure exists of its effectiveness or maturity. As a result, practitioners are unaware of their service innovation capability performance or where resources should be directed for its improvement. Informed by a positivistic research philosophy, this study addressed this gap by constructing and validating a formative measure of service innovation capability maturity for SMEs. As the maturity score for this capability is caused by its three subdimensions; User Involvement, Strategising, and Networking capabilities; an index construction procedure was synthesised and applied to its development. Using a cross-sectional online survey methodology, the responses of 284 service organisations located in the Republic of Ireland were used to test the instrument. Collected data were utilised to subject the index to rigorous testing that confirmed the acceptability of goodness-of-fit statistics, variance explained, the validity of individual indicators, the absence of excessive multicollinearity, and the validity of its structural model. The novel and original measure developed in this study is the first of its kind and makes several major contributions to theory and practice. Specifically, the study’s findings provide empirical support for the three subdimensions as predictors of service innovation capability performance, its synthesis and execution of a best practice index construction procedure offer a valuable template to researchers with similar objectives, and the managers of SMEs are provided with a tool to quantitatively understand their capability maturity and learn where their effort or attention ought to be directed to achieve improvements
Application of Machine Learning and Analytics for Cattle Behaviour Classification within an Internet of Things Deployment
Get PDFLameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs, vendor incompatibility and complex equipment in currently available solutions, and as a result, this work presents a hybrid model and an end-to-end Internet of Things (IoT) application that leverages machine learning and data analytics techniques to predict lameness in dairy cattle. As part of a real world trial in Waterford, Ireland, 150 cows were each fitted with a long range pedometer. The mobility data from sensors attached to the front leg (left leg for 50% of the cows and right leg for the other 50%) of each cow is aggregated to formtime series of behavioral activities (Step count, lying time and swaps per hour). These are analyzed in the cloud and alerts of predicted lame animals are sent to the farmer’s mobile device using push notifications. The application and model automaticallymeasure and can gather data continuously such that cows can bemonitored daily. This means there is no need for herding the cows as this would bias the results because cows are stoic in nature. Furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application is extended to other farms. The initial results indicate that the application can predict lameness 3 days before it can be visually seen by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated (usually by administering antibiotics) immediately to avoid any further effects of lameness. The application designed in this study is based on a fog-to-cloud architecture. In this architecture, some of the cloud services and applications are run closer to the physical IoT devices at the network edge. The application also implements a microservices based design approach. The solution can therefore be decoupled as a single service which can be accessed via an Application Programming Interface (API) either by the farmer seeking such a service or an agri-tech service provider who wants to provide such a service to his exiting customers. This also aids data preprocessing and aggregating between the fog node and the cloud. The result of this show an overall data reduction from 10.1MB to 1.62MB exchanged between the fog node and cloud node daily. This is the first time such an approach is implemented for lameness detection and generally for welfare monitoring for dairy cattle
Reconfigurable Filtering of Neuro-Spike Communications Using Synthetically Engineered Logic Circuits
Get PDFHigh-frequency firing activity can be induced either naturally in a healthy brain as a result of the processing of sensory stimuli or as an uncontrolled synchronous activity characterizing epileptic seizures. As part of this work, we investigate how logic circuits that are engineered in neurons can be used to design spike filters, attenuating high-frequency activity in a neuronal network that can be used to minimize the effects of neurodegenerative disorders such as epilepsy. We propose a reconfigurable filter design built from small neuronal networks that behave as digital logic circuits. We developed a mathematical framework to obtain a transfer function derived from a linearization process of the Hodgkin-Huxley model. Our results suggest that individual gates working as the output of the logic circuits can be used as a reconfigurable filtering technique. Also, as part of the analysis, the analytical model showed similar levels of attenuation in the frequency domain when compared to computational simulations by fine-tuning the synaptic weight. The proposed approach can potentially lead to precise and tunable treatments for neurological conditions that are inspired by communication theory
A GUIDE FOR ENACTING AN APPRENTICESHIP EDUCATION MODEL AS A MECHANISM FOR FACILITATING HIGHER EDUCATION AND INDUSTRY COLLABORATION
Get PDFThis study seeks to create a process for enacting an apprenticeship education framework as a mechanism for facilitating higher education and industry collaboration (HE-IC). A review of the extant literature in the areas of apprenticeship, higher education and industry collaboration exhibits prior research in these areas. A preliminary conceptual framework is developed based on this review, drawing upon the frameworks of Engestrom (1987) and Sternlieb et al (2013), and underpinned by boundary organisation theory which aligns with the researcher’s interpretivist philosophical approach to the study. The resultant research questions are: (a) what is the process for developing, implementing and enacting a HE apprenticeship education model? (b) How can this model serve as a mechanism for higher education institute (HEI) and industry collaboration? A single interpretive sector study of the International Financial Services suite of apprenticeships underpinned the primary research. The ensuing sectoral study involved semi-structured interviews with apprenticeship consortium members and policy stakeholders, supported by a review of relevant documentation and researcher reflective log entries. The findings suggest that successful HEI and industry collaboration is core to the achievement of successful apprenticeship outcomes. The key drivers have been explored in the literature and combined with the insights from the participants. These drivers have been identified as: trust; transparency; mutual understanding; necessity; reciprocity; efficiency; stability; legitimacy and asymmetry (Schilke & Cook, 2013; Vanneste, Puranam and Kretschmer, 2014; Ankrah and Al-Tabaa, 2015) which can combine in different ways at different stages of the collaboration relationship (Plewa et al., 2015). A revised conceptual framework provides greater insight into creating a process for enacting an apprenticeship education model as a mechanism for facilitating higher education and industry collaboration. This framework can serve as a mechanism for broader HEI and industry collaboration, thereby extending boundary organisation theory. The findings have practical relevance to those interested in the range of benefits of HEI and industry collaborations; learners, the HEI, industry and regional and national socio-economic stakeholders. The ambiguity that existed on the apprenticeship landscape when this study commenced has been somewhat clarified by the relevant state agencies, but formal guidance for industry representatives contemplating in developing an apprenticeship, is still missing. This motivated the researcher to produce outputs which draw attention to matters for consideration for industry representatives, considering developing a new apprenticeship. The guide produced by the researcher, as an output from this study, aims to close the guidance gap. While the study was carried out in the context of a higher education and industry collaboration specific to apprenticeship, it may also have relevance to further education and industry collaboration and also to broader education and industry collaborations outside of the apprenticeship setting
A Novel Outlier Detection Method for Multivariate Data
Get PDFDetecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions. This paper proposes a novel, yet effective outlier learning algorithm that is based on decomposing the full attributes space into different combinations of subspaces, in which the 3D-vectors, representing the data points per 3D-subspace, are rotated about the geometric median, using Rodrigues rotation formula, to construct the overall outlying score. The proposed approach is parameter-free, requires no distribution assumptions and easy to implement. Extensive experimental study and comparison are conducted on both synthetic and real-world datasets with six popular outlier detection algorithms, each from different category. The comparison is evaluated based on the precision @s, average precision, rank power, AUC ROC and time complexity metrics. The results show that the performance of the proposed method is competitive and promising
Herd and animal-level management tools generated from national databases and national genetic evaluations
Get PDFIrish cattle-based decision support tools (DSTs) currently focus on breeding and identifying genetically superior parents for future generations and the appropriate bull teams to use; however, one exception being a dairy management DST which ranks dairy cows on their remaining lifetime profitability for voluntary culling. The animal’s additive genetic merit forms the basis of all genetic-based DSTs and is estimated by disentangling an observed phenotype into the additive genetic effects from the environmental effects (i.e., BLUEs) and, in doing so, estimates are generated for both. Yet, to date only the additive genetic contribution to an animal performance has been exploited. Moreover, there are clear voids in beef management genetic-based DSTs. The objectives of this thesis were therefore to: 1) characterise best linear unbiased estimated (BLUEs) and quantify the response to selection for additive and non-additive genetic merit by herd BLUEs, 2) construct the framework for a DST for predicting the expected carcass revenue for growing cattle, and 3) develop the framework for a DST to predict the expected remaining lifetime profitability of beef females to identify candidates for culling. Data used within this thesis originated from the national cattle database and the national genetic evaluations. This thesis demonstrated that the response to genetic selection varied depending on the herd BLUE and therefore potential exists for herd BLUEs to be used when tailoring breeding values and DSTs for each individual. Results also substantiate that although the carcass value of an animal is commonly predicted from their recorded breed composition, using the transaction index framework developed, the accuracy of the carcass revenue prediction doubled. This thesis also validated that when beef females were ranked on their expected lifetime profitability, the females identified for voluntary culling contributed €32 less per calving to the herd’s profitability relative to the highest ranked females
An Investigation of cold chain management system breaches during exportation on the quality and shelf life of fresh fish
Get PDFChilling fish involves establishing and maintaining consistent chilled storage (cold chain) of between 0 and 2 °C using a combination of ice and refrigeration. This process offers logistical advantages to fish producers, maintaining a high quality for longer during distribution. Megrim (Lepidorhombus whiffiagonis) and pollack (Pollachius pollachius) are typically caught in Irish waters, with the majority of each catch (approximately 90%) exported to Europe. As part of this research and to ensure high quality exports from Ireland to continental Europe, temperatures were assessed and breaches during the distribution of whole chilled fish were identified. To better understand the effect of temperature on product quality these breaches were replicated under controlled laboratory conditions, where both species were evaluated to determine the resultant changes in fish quality and microbial growth. Samples were analysed using proximate composition, microbial analysis, colour, texture, total volatile basic nitrogen (TVB-N), and sensory analysis. Both species had low microbial counts on day 1 of storage, this gradually increased over time, as spoilage occurred. It was observed that when fish samples were stored outside of optimum conditions (0-2 °C), the rate of spoilage increased, with unacceptable microbial level thresholds exceeded in within two days. Similar results were encountered for both species across all other quality testing parameters (colour, texture, TVB-N and sensory), however proximate composition was not significantly affected. In conclusion, the main findings of this study suggest that temperature breaches can easily occur throughout the cold chain, drastically reducing the shelf life and quality of fish. Once fish was exposed to temperatures outside of the recommended (0-2 °C) for chilled fish, quality was severely degraded
EXPLORING THE IMPACT OF AUGMENTED REALITY AND VIRTUAL REALITY TECHNOLOGIES ON BUSINESS MODEL INNOVATION
Get PDFThe entrepreneurial environment of the 21st century is becoming increasingly complex. Consequently, firms that strive to be successful in the long run need to employ new concepts to continuously reinvent themselves. Business model innovation (BMI) is such a concept. Furthermore, BMI can be impacted by the technologies augmented reality (AR) and virtual reality (VR). However, little is known about the effect of this impact. Therefore, this study’s aim is to better understand the impact of AR/VR on BMI. The formal objective of the study is “to explore the impact of AR and VR technologies on BMI”. To meet the research objective, the researcher constructs a theoretical research framework based on a business model (BM) and BMI literature review; designs and operationalises a phenomenologist, subjectivist, interpretivist research approach using a multiple case holistic “Type 3” case study design; and implements case data collection in form of semi-structured interviews in five companies that employed AR/VR for BMI. Case study data was collected between January and September 2019. The study indicates that AR/VR are well perceived. The technologies are used for short-term marketing benefits, to optimise company internal processes, and for the development of new products and services. However, working with AR/VR is challenging due to a low maturity level of the technology. Furthermore, BMI through AR/VR requires demanding decisions from management. The study further reveals that AR/VR are still young technologies that do not yet lead to new revenue streams, in most cases. However, research participants expect that the relevance of AR/VR will continue to grow in the future. The impact of AR/VR on BMI is a push in BM newness resulting in twofold consequences: it presents firms with new business opportunities, as well as with significant organisational challenges. Consequently, BMI efforts for AR/VR technology should be strategically guided. Keywords: business model innovation, business models, augmented reality, virtual reality, case studies