Investigations on the feasability of using phytoremediation for treatment of hydrocarbon-contaminated sediments at Horsea Lagoon

This project investigates phytoremediation at a disused fire training runoff lagoon at an ecologically sensitive area in Southern England called Horsea Island. The sediments in the semi-saline lagoon were highly contaminated with over 145,000 mg/kg Dry Weight (DW) Diesel Range Organics (DRO) and were classed as carcinogenic and unsafe for human exposure. Phytoremediation was attractive as an inexpensive and in situ remediation method. However there were limited field studies examining such extremely contaminated conditions. Initial mesocosm studies indicated that both Phragmites australis and Typha latifolia were able to grow in this sediment without the need for additional nutrients and P. australis was subsequently used for test planting in the lagoon. Ex situ greenhouse microcosms as well as an in situ full scale trial was carried out and the response of DRO and microbial populations were investigated over a 15 month period. Using a novel colourmetric plate test developed during this study, planted sediments in both the microcosms and Horsea lagoon showed not only an increased diversity but also an increased number of hydrocarbon-degrading bacterial isolates when compared to unplanted sediments. However, although the overall DRO concentrations fell in both the in situ and ex situ systems in times of hot weather and low water levels, there were no significant differences between planted and unplanted sites. There was also no difference between microbial numbers in planted and unplanted sites in either the microcosms or Horsea lagoon however, the mesocosms that had been growing for three years showed significantly higher numbers of bacteria, hydrocarbon-tolerant bacteria and hydrocarbon-tolerant fungi as well as elevated numbers of fungi in the planted sites. This indicates that phytoremediation may require a longer period of time to enhance degradation in such conditions.EThOS - Electronic Theses Online ServiceMinistry of Defence (MOD)GBUnited Kingdo

Compost Application Affects Metal Uptake in Plants Grown in Urban Garden Soils and Potential Human Health Risk

Purpose This study explores the effect of varying organic matter content on the potential human health risk of consuming vegetables grown in urban garden soils. Materials and methods Metal accumulation among edible tissues of green bean (Phaseolus vulgaris L.), lettuce (Lactuca sativa L.) and carrot (Daucus carota L.) was determined for plants grown in five urban garden soils amended with 0, 9, or 25% (v/v) compost. Potential risk to human health was assessed by calculating a bioconcentration factor and a hazard quotient. Results and discussion Overall, the consumption of lettuce and green bean pods grown in some urban gardens posed a potential human health risk due to unacceptably high concentrations of cadmium or lead. In many cases, compost amendment increased the accumulation of metals in the vegetables. Even in soils considered uncontaminated by current guidelines, some hazard quotients exceeded the threshold value of 1. The compost used in this study had a high fulvic acid to humic acid ratio, which may explain increased concentrations of metals in plants grown in compost-amended soils. Conclusions These results indicate a need to include soil characteristics, specifically organic matter quality, when setting threshold criteria for metal content of urban garden soils

Wi-Fi fingerprinting based on collaborative confidence level training

Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%

Collaborative Wi-Fi fingerprint training for indoor positioning

As the scope of location-based applications and services further reach into our everyday lives, the demand for more robust and reliable positioning becomes ever more important. However indoor positioning has never been a fully resolved issue due to its complexity and necessity to adapt to different situations and environment. Inertial sensor and Wi-Fi signal integrated indoor positioning have become good solutions to overcome many of the problems. Yet there are still problems such as inertial heading drift, wireless signal fluctuation and the time required for training a Wi-Fi fingerprint database. The collaborative Wi-Fi fingerprint training (cWiDB) method proposed in this paper enables the system to perform inertial measurement based collaborative positioning or Wi-Fi fingerprinting alternatively according to the current situation. It also reduces the time required for training the fingerprint database. Different database training methods and different training data size are compared to demonstrate the time and data required for generating a reasonable database. Finally the fingerprint positioning result is compared which indicates that the cWiDB is able to achieve the same positioning accuracy as conventional training methods but with less training time and a data adjustment option enabled

Navigating in large hospitals

Navigating around large hospitals can be a stressful and time-consuming experience for all users of the hospital infrastructure. Navigation difficulties encountered by patients and visitors can result in missed appointments or simply create a poor impression of the hospital organisation. When staff encounter navigation difficulties this can lead to cost and efficiency issues and potentially put patient safety at risk. Despite the provision of an array of in-hospital navigational aids, ‘getting lost’ continues to be an everyday problem in these large complex environments. This study aims to to identify factors which affect navigation in hospitals. We do not seek to evaluate the effectiveness of a single navigation aid, instead the objective of this study was to understand the environment in which a new system must operate and the gaps in provision left by existing navigation aids. This study is intended to be used to inform the development of new in hospital navigational aids, be they technological or otherwise. Eleven participants, all users of a large hospital site, were asked to describe specific first hand experiences of navigating in a hospital. The ‘Critical Incidence Technique’ was applied in a series of semi-structured interviews to elicit information about a participants navigation experience. This work presents the results of these interviews, with concepts identified and organised into five themes: The ‘Impact’ of poor navigation, ‘Barriers’ to effective navigation, ‘Enhancers’ for effective navigation, ‘Types of Navigation Aids’ and user groups with ‘Specific Navigational Needs’. The number of navigation aids available to participants was identified as an issue in itself, we found examples of thirty seven distinct sources of information available to a hospital user. We begin by introducing previous work on in-hospital navigation before describing the study design employed in this research. The themes and categories identified from the interview data are enumerated and described, with examples given from the interview transcripts. Finally we go on to give a discussion of some potential navigation solutions in light of the identified factors. This study highlights that a candidate navigation aid must be carefully designed and implemented if it is to compliment the thirty seven other sources of navigation information available to the hospital user

PRO: confronting resistance to rule-based medicine is essential to improving outcomes

The past 20 years have seen two great changes in the practice of medicine: the widespread adoption of evidence-based medicine, and the increasing challenge of managing complex multimorbid patients. Both these developments have resulted in clinical rules and protocols becoming ever more abundant and increasingly critical to delivering safe and effective patient care. These evidence-based clinical rules perform at least as well as expert opinion, and the increasing volume and quality of available clinical data suggests their performance could continue to improve. This article considers why clinicians deviate from effective rules, highlighting key issues such as the persisting culture of heroism, institutional inertia, deference to authority and personal heuristics. We argue that better rules can be created, and that clinical improvements will follow if there is a ‘common knowledge’ of these rules. Furthermore, we argue that there is a ceiling to the effectiveness of any rule, even one as simple as ensuring hand hygiene, unless individuals are held accountable for transgressions

The potential of electromyography to aid personal navigation

This paper reports on research to explore the potential for using electromyography (EMG) measurements in pedestrian navigation. The aim is to investigate whether the relationship between human motion and the activity of skeletal muscles in the leg might be used to aid other positioning sensors, or even to determine independently the path taken by a pedestrian. The paper describes an exercise to collect sample EMG data alongside leg motion data, and the subsequent analysis of this data set using machine learning techniques to infer motion from a set of EMG sensors. The sample data set included measurements from multiple EMG sensors, a camera-based motion tracking system and a foot mounted inertial sensor. The camera based motion tracking system at MMU allowed many targets on the subjects lower body to be tracked in a small (3m x 3m x 3m) volume to millimetre accuracy. Processing the data revealed a strong, but not trivial, relation-ship between leg muscle activity and motion. Each type of motion involves many different muscles, and it is not possible to conclude merely from the triggering of any single muscle that a particular type of motion has occurred. For instance, a similar set of leg muscles is involved in both forward and backward steps. It is the precise sequencing, duration and magnitude of multiple muscle activity that allows us to determine what type of motion has occurred. Preliminary analyses of the data suggest that subsets of the EMG sensors can be used to distinguish, for instance, forward motion from backward motion, and it is expected that further analysis will reveal additional correlations that will be useful in inferring the subjects motion in more detail. This paper will introduce the EMG personal navigation con-cept, describe the data collected, explore the machine learning techniques applied to the dataset, and present the results of the analysis

Examining the impact of early longitudinal patient exposure on medical students’ career choices

Background: Medical schools include career direction experiences to help students make informed career decisions. Most experiences are short, precluding students from attaining adequate exposure to long-term encounters within medicine. We investigated the impact of the First Patient Program (FPP), which fosters longitudinal patient exposure by pairing junior medical students with chronically ill patients through their healthcare journey, in instilling career direction. Methods: Medical students who completed at least 6-months in the FPP participated in a cross-sectional survey. Students’ answers were analyzed with respect to the number of FPP appointments attended. Thematic analysis was conducted to explore qualitative responses.Results: One hundred and forty-eight students participated in the survey. Only 28 (19%) students stated that the FPP informed their career decisions. Thirty-nine percent of students who attended four or more appointments indicated that the FPP informed their career decisions, compared to 16% of students who attended less (p=0.021). Thematic analysis revealed two themes: 1) Students focused mainly on patient encounters within FPP; and 2) Students sought career directions from other experiences.Conclusion: The majority of students did not attain career guidance from the FPP, but rather used the program to understand the impact of chronic illness from the patient’s perspective

A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors

Learning capacity: predicting user decisions for vehicle-to-grid services

The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-to-grid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities