Investigation of polyhydroxyalkanoate production by an activated sludge microbial consortium treating artificial dairy wastewater

Petrochemical plastics/polymers are a common feature of day to day living as they occur in packaging, furniture, mobile phones, computers, construction equipment etc. However, these materials are produced from non-renewable materials and are resistant to microbial degradation in the environment. Considerable research has therefore been carried out into the production of sustainable, biodegradable polymers, amenable to microbial catabolism to CO2 and H2O. A key group of microbial polyesters, widely considered as optimal replacement polymers, are the Polyhydroxyalkaonates (PHAs). Primary research in this area has focused on using recombinant pure cultures to optimise PHA yields, however, despite considerable success, the high costs of pure culture fermentation have thus far hindered the commercial viability of PHAs thus produced. In more recent years work has begun to focus on mixed cultures for the optimisation of PHA production, with waste incorporations offering optimal production cost reductions. The scale of dairy processing in Ireland, and the high organic load wastewaters generated, represent an excellent potential substrate for bioconversion to PHAs in a mixed culture system. The current study sought to investigate the potential for such bioconversion in a laboratory scale biological system and to establish key operational and microbial characteristics of same. Two sequencing batch reactors were set up and operated along the lines of an enhanced biological phosphate removal (EBPR) system, which has PHA accumulation as a key step within repeated rounds of anaerobic/aerobic cycling. Influents to the reactors varied only in the carbon sources provided. Reactor 1 received artificial wastewater with acetate alone, which is known to be readily converted to PHA in the anaerobic step of EBPR. Reactor 2 wastewater influent contained acetate and skim milk to imitate a dairy processing effluent. Chemical monitoring of nutrient remediation within the reactors as continuously applied and EBPR consistent performances observed. Qualitative analysis of the sludge was carried out using fluorescence microscopy with Nile Blue A lipophillic stain and PHA production was confirmed in both reactors. Quantitative analysis via HPLC detection of crotonic acid derivatives revealed the fluorescence to be short chain length Polyhydroxybutyrate, with biomass dry weight accumulations of 11% and 13% being observed in reactors 1 and 2, respectively. Gas Chromatography-Mass Spectrometry for medium chain length methyl ester derivatives revealed the presence of hydroxyoctanoic, -decanoic and -dodecanoic acids in reactor 1. Similar analyses in reactor 2 revealed monomers of 3-hydroxydodecenoic and 3-hydroxytetradecanoic acids. Investigation of the microbial ecology of both reactors as conducted in an attempt to identify key species potentially contributing to reactor performance. Culture dependent investigations indicated that quite different communities were present in both reactors. Reactor 1 isolates demonstrated the following species distributions Pseudomonas (82%), Delftia acidovorans (3%), Acinetobacter sp. (5%) Aminobacter sp., (3%) Bacillus sp. (3%), Thauera sp., (3%) and Cytophaga sp. (3%). Relative species distributions among reactor 2 profiled isolates were more evenly distributed between Pseudoxanthomonas (32%), Thauera sp (24%), Acinetobacter (24%), Citrobacter sp (8%), Lactococcus lactis (5%), Lysinibacillus (5%) and Elizabethkingia (2%). In both reactors Gammaproteobacteria dominated the cultured isolates. Culture independent 16S rRNA gene analyses revealed differing profiles for both reactors. Reactor 1 clone distribution was as follows; Zooglea resiniphila (83%), Zooglea oryzae (2%), Pedobacter composti (5%), Neissericeae sp. (2%) Rhodobacter sp. (2%), Runella defluvii (3%) and Streptococcus sp. (3%). RFLP based species distribution among the reactor 2 clones was as follows; Runella defluvii (50%), Zoogloea oryzae (20%), Flavobacterium sp. (9%), Simplicispira sp. (6%), Uncultured Sphingobacteria sp. (6%), Arcicella (6%) and Leadbetterella bysophila (3%). Betaproteobacteria dominated the 16S rRNA gene clones identified in both reactors. FISH analysis with Nile Blue dual staining resolved these divergent findings, identifying the Betaproteobacteria as dominant PHA accumulators within the reactor sludges, although species/strain specific allocations could not be made. GC analysis of the sludge had indicated the presence of both medium chain length as well short chain length PHAs accumulating in both reactors. In addition the cultured isolates from the reactors had been identified previously as mcl and scl PHA producers, respectively. Characterisations of the PHA monomer profiles of the individual isolates were therefore performed to screen for potential novel scl-mcl PHAs. Nitrogen limitation driven PHA accumulation in E2 minimal media revealed a greater propensity among isoates for mcl-pHA production. HPLC analysis indicated that PHB production was not a major feature of the reactor isolates and this was supported by the low presence of scl phaC1 genes among PCR screened isolates. A high percentage distribution of phaC2 mcl-PHA synthase genes was recorded, with the majority sharing high percentage homology with class II synthases from Pseudomonas sp. The common presence of a phaC2 homologue was not reflected in the production of a common polymer. Considerable variation was noted in both the monomer composition and ratios following GC analysis. While co-polymer production could not be demonstrated, potentially novel synthase substrate specificities were noted which could be exploited further in the future.

Automatic detection of learner-style for adaptive eLearning

The advent of modern wireless technologies has seen a shift in focus towards the design and development of educational systems for deployment through mobile devices. The use of mobile phones, tablets and Personal Digital Assistants (PDAs) is steadily growing across the educational sector as a whole. Mobile learning (mLearning) systems developed for deployment on such devices hold great significance for the future of education. However, mLearning systems must be built around the particular learner’s needs based on both their motivation to learn and subsequent learning outcomes. This thesis investigates how biometric technologies, in particular accelerometer and eye-tracking technologies, could effectively be employed within the development of mobile learning systems to facilitate the needs of individual learners. The creation of personalised learning environments must enable the achievement of improved learning outcomes for users, particularly at an individual level. Therefore consideration is given to individual learning-style differences within the electronic learning (eLearning) space. The overall area of eLearning is considered and areas such as biometric technology and educational psychology are explored for the development of personalised educational systems. This thesis explains the basis of the author’s hypotheses and presents the results of several studies carried out throughout the PhD research period. These results show that both accelerometer and eye-tracking technologies can be employed as an Human Computer Interaction (HCI) method in the detection of student learning-styles to facilitate the provision of automatically adapted eLearning spaces. Finally the author provides recommendations for developers in the creation of adaptive mobile learning systems through the employment of biometric technology as a user interaction tool within mLearning applications. Further research paths are identified and a roadmap for future of research in this area is defined.

The s-mote: a versatile heterogeneous multi-radio platform for wireless sensor networks applications

This paper presents a novel architecture and its implementation for a versatile, miniaturised mote which can communicate concurrently using a variety of combinations of ISM bands, has increased processing capability, and interoperability with mainstream GSM technology. All these features are integrated in a small form factor platform. The platform can have many configurations which could satisfy a variety of applications’ constraints. To the best of our knowledge, it is the first integrated platform of this type reported in the literature. The proposed platform opens the way for enhanced levels of Quality of Service (QoS), with respect to reliability, availability and latency, in addition to facilitating interoperability and power reduction compared to existing platforms. The small form factor also allows potential of integration with other mobile platforms including smart phones.

A cycle route planner mobile-app for Dublin city

In a road network, cyclists are the group exposed to the maximum amount of risk. Route choice of a cyclist is often based on level of expertise, perceived or actual road risks, personal decisions, weather conditions and a number of other factors. Consequently, cycling tends to be the only significant travel mode where optimised route choice is not based on least-path or least-time. This paper presents an Android platform based mobile-app for personalised route planning of cyclists in Dublin. The mobile-app, apart from its immediate advantage to the cyclists, acts as the departure point for a number of research projects and aids in establishing some critical calibration values for the cycling network in Dublin. 

Examining the post-adoptive infusion of mobile technology in a healthcare domain: determinants and outcomes

The healthcare industry is beginning to appreciate the benefits which can be obtained from using Mobile Health Systems (MHS) at the point-of-care. As a result, healthcare organisations are investing heavily in mobile health initiatives with the expectation that users will employ the system to enhance performance. Despite widespread endorsement and support for the implementation of MHS, empirical evidence surrounding the benefits of MHS remains to be fully established. For MHS to be truly valuable, it is argued that the technological tool be infused within healthcare practitioners work practices and used to its full potential in post-adoptive scenarios. Yet, there is a paucity of research focusing on the infusion of MHS by healthcare practitioners. In order to address this gap in the literature, the objective of this study is to explore the determinants and outcomes of MHS infusion by healthcare practitioners. This research study adopts a post-positivist theory building approach to MHS infusion. Existing literature is utilised to develop a conceptual model by which the research objective is explored. Employing a mixed-method approach, this conceptual model is first advanced through a case study in the UK whereby propositions established from the literature are refined into testable hypotheses. The final phase of this research study involves the collection of empirical data from a Canadian hospital which supports the refined model and its associated hypotheses. The results from both phases of data collection are employed to develop a model of MHS infusion. The study contributes to IS theory and practice by: (1) developing a model with six determinants (Availability, MHS Self-Efficacy, Time-Criticality, Habit, Technology Trust, and Task Behaviour) and individual performance-related outcomes of MHS infusion (Effectiveness, Efficiency, and Learning), (2) examining undocumented determinants and relationships, (3) identifying prerequisite conditions that both healthcare practitioners and organisations can employ to assist with MHS infusion, (4) developing a taxonomy that provides conceptual refinement of IT infusion, and (5) informing healthcare organisations and vendors as to the performance of MHS in post-adoptive scenarios.

The cloud personal assistant for providing services to mobile clients

This paper introduces the original concept of a cloud personal assistant, a cloud service that manages the access of mobile clients to cloud services. The cloud personal assistant works in the cloud on behalf of its owner: it discovers services, invokes them, stores the results and history, and delivers the results to the mobile user immediately or when the user requests them. Preliminary experimental results that demonstrate the concept are included.

User experience of mobile cloud applications - current state and future directions

The increasing penetration rate of feature rich mobile devices such as smartphones and tablets in the global population has resulted in a large number of applications and services being created or modified to support mobile devices. Mobile cloud computing is a proposed paradigm to address the resource scarcity of mobile devices in the face of demand for more computing intensive tasks. Several approaches have been proposed to confront the challenges of mobile cloud computing, but none has used the user experience as the primary focus point. In this paper we evaluate these approaches in respect of the user experience, propose what future research directions in this area require to provide for this crucial aspect, and introduce our own solution.

Mobile cloud application models facilitated by the CPA

This paper describes implementations of two mobile cloud applications, file synchronisation and intensive data processing, using the Context Aware Mobile Cloud Services middleware, and the Cloud Personal Assistant. Both are part of the same mobile cloud project, actively developed and currently at the second version. We describe recent changes to the middleware, along with our experimental results of the two application models. We discuss challenges faced during the development of the middleware and their implications. The paper includes performance analysis of the CPA support for the two applications in respect to existing solutions.

The mobile cloud - more than a cloud

Nearly one billion smart mobile devices are now used for a growing number of tasks, such as browsing the web and accessing online services. In many communities, such devices are becoming the platform of choice for tasks traditionally carried out on a personal computer. However, despite the advances, these devices are still lacking in resources compared to their traditional desktop counterparts. Mobile cloud computing is seen as a new paradigm that can address the resource shortcomings in these devices with the plentiful computing resources of the cloud. This can enable the mobile device to be used for a large range of new applications hosted in the cloud that are too resource demanding to run locally. Bringing these two technologies together presents various difficulties. In this paper, we examine the advantages of the mobile cloud and the new approaches to applications it enables. We present our own solution to create a positive user experience for such applications and describe how it enables these applications.

Designing tools that enhance communication and understanding between multiple stakeholders: the case of mobile payment value networks

The pervasive use of mobile technologies has provided new opportunities for organisations to achieve competitive advantage by using a value network of partners to create value for multiple users. The delivery of a mobile payment (m-payment) system is an example of a value network as it requires the collaboration of multiple partners from diverse industries, each bringing their own expertise, motivations and expectations. Consequently, managing partnerships has been identified as a core competence required by organisations to form viable partnerships in an m-payment value network and an important factor in determining the sustainability of an m-payment business model. However, there is evidence that organisations lack this competence which has been witnessed in the m-payment domain where it has been attributed as an influencing factor in a number of failed m-payment initiatives since 2000. In response to this organisational deficiency, this research project leverages the use of design thinking and visualisation tools to enhance communication and understanding between managers who are responsible for managing partnerships within the m-payment domain. By adopting a design science research approach, which is a problem solving paradigm, the research builds and evaluates a visualisation tool in the form of a Partnership Management Canvas. In doing so, this study demonstrates that when organisations encourage their managers to adopt design thinking, as a way to balance their analytical thinking and intuitive thinking, communication and understanding between the partners increases. This can lead to a shared understanding and a shared commitment between the partners. In addition, the research identifies a number of key business model design issues that need to be considered by researchers and practitioners when designing an m-payment business model. As an applied research project, the study makes valuable contributions to the knowledge base and to the practice of management.

Efficient delivery of scalable media streaming over lossy networks

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet, exposing challenges in coping with heterogeneous device capabilities and varying network throughput. When we couple this rise in streaming with the growing number of portable devices (smart phones, tablets, laptops) we see an ever-increasing demand for high-definition videos online while on the move. Wireless networks are inherently characterised by restricted shared bandwidth and relatively high error loss rates, thus presenting a challenge for the efficient delivery of high quality video. Additionally, mobile devices can support/demand a range of video resolutions and qualities. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to available bandwidth and heterogeneity, and can provide us with graceful changes in video quality, all while respecting our viewing satisfaction. In this context the use of well-known scalable media streaming techniques, commonly known as scalable coding, is an attractive solution and the focus of this thesis. In this thesis we investigate the transmission of existing scalable video models over a lossy network and determine how the variation in viewable quality is affected by packet loss. This work focuses on leveraging the benefits of scalable media, while reducing the effects of data loss on achievable video quality. The overall approach is focused on the strategic packetisation of the underlying scalable video and how to best utilise error resiliency to maximise viewable quality. In particular, we examine the manner in which scalable video is packetised for transmission over lossy networks and propose new techniques that reduce the impact of packet loss on scalable video by selectively choosing how to packetise the data and which data to transmit. We also exploit redundancy techniques, such as error resiliency, to enhance the stream quality by ensuring a smooth play-out with fewer changes in achievable video quality. The contributions of this thesis are in the creation of new segmentation and encapsulation techniques which increase the viewable quality of existing scalable models by fragmenting and re-allocating the video sub-streams based on user requirements, available bandwidth and variations in loss rates. We offer new packetisation techniques which reduce the effects of packet loss on viewable quality by leveraging the increase in the number of frames per group of pictures (GOP) and by providing equality of data in every packet transmitted per GOP. These provide novel mechanisms for packetizing and error resiliency, as well as providing new applications for existing techniques such as Interleaving and Priority Encoded Transmission. We also introduce three new scalable coding models, which offer a balance between transmission cost and the consistency of viewable quality.

Mobile cloud contextual awareness with the cloud personal assistant

This paper presents our efforts to bridge the gap between mobile context awareness, and mobile cloud services, using the Cloud Personal Assistant (CPA). The CPA is a part of the Context Aware Mobile Cloud Services (CAMCS) middleware, which we continue to develop. Specifically, we discuss the development and evaluation of the Context Processor component of this middleware. This component collects context data from the mobile devices of users, which is then provided to the CPA of each user, for use with mobile cloud services. We discuss the architecture and implementation of the Context Processor, followed by the evaluation. We introduce context profiles for the CPA, which influence its operation by using different context types. As part of the evaluation, we present two experimental context-aware mobile cloud services to illustrate how the CPA works with user context, and related context profiles, to complete tasks for the user.

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources.

Designing and evaluating an interactive assessment instrument to improve the process for mobile service application innovation

Published Version

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.