1522 research outputs found
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A Critical Analysis of Bernice Morgan’s Portrayal of Female Characters and their Experiences
Get PDFDue to the decline of the oral tradition and the omission of women’s stories from the official narrative, records of those living in the rural Outport communities of Newfoundland were rapidly vanishing. Within the sub-genre of Newfoundland women’s writing, and classed as part of a settler literature, Bernice Morgan’s fictional novels Random Passage and Waiting for Time offer insights into the daily lives of 19th and 20th century women living in the Outports, thus retrieving their stories from the margins of the male-dominated literary canon to ensure their survival. As Morgan’s novels focus on women’s narratives, this thesis provides a critical analysis of her portrayal of female characters, and their experiences. It contends that these characters negotiate the boundaries of transcendence and immanence and explore the possibility for a partial transcendence. In particular, the thesis investigates how Morgan’s fictional account of female experience within the 19th and 20th centuries reflects the social and cultural mores of these periods, and analyses how the novels explore the attempts made by women to extend their legacy and impact beyond the confines of the domestic space. Informed by the existentialist feminist theories of Simone de Beauvoir, the study considers the characters within three stages of their lives – as young girls, married and unmarried women, and as mothers. The incorporation of socio-cultural and historical elements further informs contextual detail relating to women’s situation during the timeframes of the novels. By depicting both characters who conform to and diverge from conventional female roles, Morgan both facilitates a more encompassing representation of life experience in the Outports, and illustrates how her female characters’ choices and attempts to extend their legacies enable them to occupy liminal spaces of partial transcendence
Distributed Decomposed Data Analytics in Fog Enabled IoT Deployments
Get PDFThe edge of the network plays a vital role in an IoT system, serving as an optimal site to perform operation on data before transmitting it over the network. We present the fog specific decomposition of multivariate linear regression as the predictive analytic model in our work using Statistical Query Model and Summation Form. The decomposition method used is not the contribution, but applying the decomposition method to the analytics model to run in a distributed manner in fog enabled IoT deployments is the contribution. What is novel is the decomposition made on a fog based distributed setting. To test the performance, our proposed approach has been applied to a real-world dataset and evaluated using a fog computing testbed. The proposed method avoids sending raw data to the cloud, and offers balanced computation in the infrastructure. The results show an 80% reduction in amount of data transferred to the cloud using the proposed fog based distributed data analytics approach as compared to the conventional cloud based approach. Furthermore, by adopting the proposed distributed approach, we observed a 98% drop in the time taken to arrive to the final result as compared to the cloud centric approach. We also present the results on quality of analytics solution obtained in both approaches, and they suggest that fog based distributed analytics approach can serve as equally as the traditional cloud centric approach
Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
Get PDFSignificant efforts have been made and are still being made on short-term traffic prediction methods, specially for highway traffic based on punctual measurements. Literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This work presents a novel data-driven prediction algorithm based on Random Forests regression over spatio-temporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate future distribution of V2X traffic demand, providing a valuable input for a dynamic management of radio resources in small cells. Radio Access Networks (RAN) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high absorption coefficients values and low reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand, estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive a minimum signal power
Quality and Capacity Analysis of Molecular Communications in Bacterial Synthetic Logic Circuits
Get PDFSynthetic logic circuits have been proposed as potential solutions for theranostics of biotechnological problems. One proposed model is the engineering of bacteria cells to create logic gates, and the communication between the bacteria populations will enable the circuit operation. In this paper, we analyse the quality of bacteria-based synthetic logic circuit through molecular communications that represent communication along a bus between three gates. In the bacteria-based synthetic logic circuit, the system receives environmental signals as molecular inputs and will process this information through a cascade of synthetic logic gates and free diffusion channels. We analyse the performance of this circuit by evaluating its quality and its relationship to the channel capacity of the molecular communications links that interconnect the bacteria populations. Our results show the effect of the molecular environmental delay and molecular amplitude differences over both the channel capacity and circuit quality. Furthermore, based on these metrics we also obtain an optimum region for the circuit operation resulting in an accuracy of 80% for specific conditions. These results show that the performance of synthetic biology circuits can be evaluated through molecular communications, and lays the groundwork for combined systems that can contribute to future biomedical and biotechnology applications
Investigation of Plant Growth and Associated Soil Microbial Stimulation by Digestate Fertilisers
Get PDFAbstract: The main aim of this PhD thesis was to investigate how different types of liquid anaerobic digestates fertilisers affect plant growth responses, and if these plant growth responses can be associated with a microbial stimulation of the soil due to repeated applications of these biofertilisers. Recent field and laboratory trials have indicated that anaerobic digestates may stimulate the soil/plant interaction in a different way to other fertilisers, with growth enhancement effects sometimes being higher than expected for the amount of nutrients supplied, especially for grass species. The mechanism for this stimulation of plant growth is not fully clear, but it is thought that digestate may stimulate complex interactions between the plant, soil, and soil microorganisms. The thesis is subdivided into five research chapters, where, in two of them described the characterisation of the physical-chemical and microbial properties of different types of anaerobic digestates. The second part is divided into three chapters, based on the results of a one-season fertilisation trial in a glasshouse using different types of plants combination with perennial ryegrass (Lolium perenne L.) and white-clover (Trifolium repens L.), and a two-season fertilisation trial performed in field conditions using a ryegrass sward. Effects of the digestates on plant growth and soil physical-chemical and microbial properties were investigated. Different types of liquid anaerobic digestates exhibited significant differences for most of the physical and chemical traits evaluated, with higher variability found for dry matter (DM) and K (CV= 17.2 and 16.8 respectively), and lower variation for pH and P (CV= 1.78 and 3.55 respectively). Anaerobic digestates exhibited varied quantities and fertiliser potential in terms of plant macro and micronutrients. Most of the anaerobic digestates met the recommendations of Irish standards on the quantity of pathogen indicators and potentially toxic elements. Bacterial and fungal colony-forming units (CFU) ranged widely in liquid anaerobic digestates (105 to 1010; 0 to 105 g-1 DW, respectively). Bacterial, archaeal and fungal gene copies numbers (GCN) showed narrower ranges v than CFU (108 to 1010; 107 to 109; 104 to 106 g-1 dry weight (DW), respectively) between different commercial anaerobic digestates. Microorganisms with agronomic importance were detected in all anaerobic digestates, including N-fixing bacteria, plant-growth-promoting bacteria (PGPB), nitrifying and denitrifying bacteria, arbuscular mycorrhizal fungi (AMF), cellulolytic microbes, methanogens and saprotrophic organisms; however, most of them were found in very low abundances. Digestates with different chemical composition, when equally balanced in terms of dry matter, drove comparable forage yield responses in ryegrass and mixed ryegrass/white clover pots. In the glasshouse trial, the soil bacterial (16S) GCN responded to the interaction between fertiliser/vegetation (p<0.05), while archaeal (16S) and fungal (18S) GCN only to the type of vegetation (p<0.05). No detectable effect of the digestates on soil GCN was observed. In the field trial, different digestates, when balanced in terms of dry matter, also drove comparable forage yield responses in ryegrass. Plant growth responses were strongly associated with the amounts of NPK supplied. In the field trial no detectable effect of the repeated applications of anaerobic digestates on soil microbial abundance and diversity could be observed. The dominant microbial community from the biofertilisers failed to replace the native microbial populations of the soil, possibly due to niche incompatibilities and competitiveness of indigenous soil microbes. In conclusion, most of the plant-growth effects associated with anaerobic digestate application were due to nutrients supplied, especially NPK; no evident biostimulation of the soil could be confirmed
Novel real-time PCR species identification assays for British and Irish bats and their application to a non-invasive survey of bat roosts in Ireland
Get PDFDetection and monitoring of extant bat populations are crucial for conservation success. Non-invasive genetic analysis of bat droppings collected at roosts could be very useful in this respect as a rapid, cost‐efficient monitoring tool. We developed species‐specific real-time PCR assays for 18 British and Irish bat species to enable non‐invasive, large‐scale distribution monitoring, which were then applied to a field survey in Ireland. One hundred and sixty-four DNA samples were collected from 95 bat roosts, of which 73% of samples were identified to species, and the resident bat species were identified at 89% of roosts. However, identification success varied between roost types, ranging from 22% for underground sites to 92% for bat boxes. This panel of DNA tests will be especially useful in cases where roosts contain multiple species, where the number of bats present is small, or bats are otherwise difficult to directly observe. The methodology could be applied to the surveillance of proposed development sites, post development mitigation measures, distribution surveys, bat box schemes and the evaluation of agri-environmental bat box schemes
Computational Synthetic Biology for Molecular Communications
Get PDFBacteria-based synthetic biology systems have been proposed in the past twenty years
as solutions for biotechnology and the design of novel therapeutics. In parallel to the field
of synthetic biology, a new field has emerged where engineers can characterize and design
communications systems through the exchange of molecules. This field is known as molecular
communications, and has taken the paradigm from conventional communication networks
and applied it to biological systems. This paradigm shift has numerous challenges, and in
particular due to the characteristics of the molecular signal propagation behaviour that is very
different from electromagnetic signals. Since the birth of this new field, numerous research
works have concentrated on characterizing the communication channels and developing
theoretical models to lay the groundwork for novel applications. Both Synthetic Biology and
Molecular Communications fields have evolved since then, and the current challenges reside
in the ability to combine these two fields together to create novel applications. The aim of
integrating these two fields is to enable implementation of complex synthetic circuits that are
able to autonomously operate in the long-term with high accuracy levels and reliability.
In this PhD thesis, synthetic biology and molecular communications systems are integrated through computational methods for a number of applications that utilizes bacteria as
the main cell lines to be programmed. This novel combination can provide novel biotechnology solutions such as biofilm prevention, bio-sensor synthetic gates, as well as synthetic
logic circuits. This synergistic integration was proven in this PhD thesis, and can provide a
new direction for the molecular communications community
An Investigation of Underpricing and the Role of Clusters in Initial Public Offerings in the UK
Get PDFThe concept of efficiency is central to finance as it relates to the primary role of capital markets, the efficient allocation of capital. The persistence of anomalies in stock markets, such as the abnormalities relating to the equity trading: the underpricing, the long-term underperformance of the new issues, and the waves in the issuing activity contradicts the efficient market hypothesis and causes continuous debate. Furthermore, the behaviours and roles of the different groups of market participants involved in the IPO process are constantly being questioned and analysed. Understanding these behaviours can help avoid speculations leading to losses, and, thus, devise an appropriate wealth management strategy. To this end, this dissertation investigates the three anomalies relating to the IPO settings and IPO performance in the UK market: short-term underpricing, long-term underperformance and clustering of new issues. Furthermore, it aims to fill the research gap relating to the formation and development of IPO clusters by analysing factors facilitating the creation of a wave and examining how investment and issuing decisions are affected by the stage of a wave development. The dissertation shows that the performance of IPOs in the UK is changing. The average level of IPO underpricing is 19 percent and it is primarily driven by the AIM market with the average IPO underpricing on that market of 21 percent. It is higher for local offerings and is reduced with geographical dispersion of the investor base. Also, the IPO issuance follows highly seasonal patterns. The findings indicate that the majority of the waves begin in just two periods: quarter one or quarter four. The study identifies innovation and technological change as the most influential factors in facilitating a wave. Additionally, the changing role of underwriters in the overall process and in marketing of IPOs depending on the prevailing market conditions is illuminated. Furthermore, the study draws attention to the quality of issuing firms, suggesting that firms having an IPO issued during a wave are more likely to delist or bankrupt than those issued out of the wave or as pioneering IPOs. The study concludes that the issues of uncertainty, informational asymmetry among market participants, and the role of underwriters in the IPO process remain critical. The quality of the disclosed information, the seasonality of the IPO patterns, the IPO marketing strategy, the industry characteristics, the use of price as an incentive or a reward, and the use of IPO proceeds should be implemented as guidelines in designing the regulatory requirements for the IPO process
Human Resources Data Analytics – Evidence from an Irish Manufacturing Perspective
Get PDFIndustry is propelled by measurement and the transformative potential of data analysis as a driver of business success.
Human Resource (HR) departments have not escaped this impetus, indeed it has gained momentum over the last decade.
The promise of analytics is significant: to replace gut and intuition with data-based decision making and evidence-based
strategies. HR analytics hails itself as a framework to temper HR intuition with objectivity. It promises rigour and validity to guide and prioritise human capital expenditure. Despite enormous interest, evidence of practical application has been scarce. This research adopts an inductive, interpretivist approach, using multiple case studies of Irish manufacturing firms, underpinned by interviews with HR Managers and industry experts. It contributes to research and practitioner knowledge with insights of industry led practical applications of HR analytics and the levels and application of HR analytics within companies. Furthermore, it reveals the factors impacting application outcomes in firms
Wireless Sensor Based Data Analytics for Precision Farming
Get PDFWith advances in the Internet of Things, the use of Wireless Sensor Networks (WSN) has been widely proposed for monitoring and automation of farm processes under the umbrella of Precision Farming. In conventional WSN systems, data gathered by sensors is transmitted to remote cloud servers for analysis. These systems, however, incur delay in getting insights into the processes due to the high volume of data generated on the farms coupled with the poor Internet connectivity. This negatively affects the delay-sensitive applications that require immediate response. The Fog Computing paradigm suggests a shift in intelligence from the cloud towards the network edges to cater to the requirements of delay-sensitive applications. It proposes the use of compute, memory and networking resources available at edge devices such as gateways, routers and sensors to reduce dependency on cloud and, thereby, improve the responsiveness of the system. In this work, we focus our attention on the development of on-board intelligence for sensor devices in the context of Precision Farming. Firstly, we identify gaps in the current WSN-based Precision Farming technologies and examine the suitability of Edge Mining, an instance of Fog Computing, for real-time event detection in farm processes. In addition, we propose an extension of the Edge Mining approach to allow for context-aware operation of sensor devices in farms. A WSN prototype consisting of a plug-n-play universal sensor device and gateway node has been designed to validate the performance of these algorithms. Next, we develop two cooperative frameworks - Collaborative Edge Mining and Iterative Edge Mining, to represent the analytic problems as a set of cooperative Edge Mining-based tasks for parallel and sequential analysis respectively within WSN. The cooperation between tasks allows for scaling of analysis within and across devices to improve computational capability of the network. Finally, we discuss resource management through cooperative computing within WSN. Cooperation between devices is considered to improve accuracy and timeliness of in-network analytics while optimizing the use of energy resources of sensor devices for improved network longevity