21,984 research outputs found
Visualisation of Fundamental Movement Skills (FMS): An Iterative Process Using an Overarm Throw
Fundamental Movement Skills (FMS) are precursor gross motor skills to more complex or specialised skills and are recognised as important indicators of physical competence, a key component of physical literacy. FMS are predominantly assessed using pre-defined manual methodologies, most commonly the various iterations of the Test of Gross Motor Development. However, such assessments are time-consuming and often require a minimum basic level of training to conduct. Therefore, the overall aim of this thesis was to utilise accelerometry to develop a visualisation concept as part of a feasibility study to support the learning and assessment of FMS, by reducing subjectivity and the overall time taken to conduct a gross motor skill assessment. The overarm throw, an important fundamental movement skill, was specifically selected for the visualisation development as it is an acyclic movement with a distinct initiation and conclusion. Thirteen children (14.8 ± 0.3 years; 9 boys) wore an ActiGraph GT9X Link Inertial Measurement Unit device on the dominant wrist whilst performing a series of overarm throws. This thesis illustrates how the visualisation concept was developed using raw accelerometer data, which was processed and manipulated using MATLAB 2019b software to obtain and depict key throw performance data, including the trajectory and velocity of the wrist during the throw. Overall, this thesis found that the developed visualisation concept can provide strong indicators of throw competency based on the shape of the throw trajectory. Future research should seek to utilise a larger, more diverse, population, and incorporate machine learning. Finally, further work is required to translate this concept to other gross motor skills
Increased lifetime of Organic Photovoltaics (OPVs) and the impact of degradation, efficiency and costs in the LCOE of Emerging PVs
Emerging photovoltaic (PV) technologies such as organic photovoltaics (OPVs) and perovskites (PVKs) have the potential to disrupt the PV market due to their ease of fabrication (compatible with cheap roll-to-roll processing) and installation, as well as their significant efficiency improvements in recent years. However, rapid degradation is still an issue present in many emerging PVs, which must be addressed to enable their commercialisation. This thesis shows an OPV lifetime enhancing technique by adding the insulating polymer PMMA to the active layer, and a novel model for quantifying the impact of degradation (alongside efficiency and cost) upon levelized cost of energy (LCOE) in real world emerging PV installations.
The effect of PMMA morphology on the success of a ternary strategy was investigated, leading to device design guidelines. It was found that either increasing the weight percent (wt%) or molecular weight (MW) of PMMA resulted in an increase in the volume of PMMA-rich islands, which provided the OPV protection against water and oxygen ingress. It was also found that adding PMMA can be effective in enhancing the lifetime of different active material combinations, although not to the same extent, and that processing additives can have a negative impact in the devices lifetime.
A novel model was developed taking into account realistic degradation profile sourced from a literature review of state-of-the-art OPV and PVK devices. It was found that optimal strategies to improve LCOE depend on the present characteristics of a device, and that panels with a good balance of efficiency and degradation were better than panels with higher efficiency but higher degradation as well. Further, it was found that low-cost locations were more favoured from reductions in the degradation rate and module cost, whilst high-cost locations were more benefited from improvements in initial efficiency, lower discount rates and reductions in install costs
Effectiveness of Lateral Auditory Collision Warnings: Should Warnings Be Toward Danger or Toward Safety?
Objective. The present study investigated the design of spatially oriented auditory collision warning signals to facilitate drivers’ responses to potential collisions.
Background. Prior studies on collision warnings have mostly focused on manual driving. It is necessary to examine the design of collision warnings for safe take-over actions in semi-autonomous driving.
Method. In a video-based semi-autonomous driving scenario, participants responded to pedestrians walking across the road, with a warning tone presented in either the avoidance direction or the collision direction. The time interval between the warning tone and the potential collision was also manipulated. In Experiment 1, pedestrians always started walking from one side of the road to the other side. In Experiment 2, pedestrians appeared in the middle of the road and walked toward either side of the road.
Results. In Experiment 1, drivers reacted to the pedestrian faster with collision-direction warnings than with avoidance-direction warnings. In Experiment 2, the difference between the two warning directions became non-significant. In both experiments, shorter time intervals to potential collisions resulted in faster reactions but did not influence the effect of warning direction.
Conclusion. The collision-direction warnings were advantageous over the avoidance-direction warnings only when they occurred at the same lateral location as the pedestrian, indicating that this advantage was due to the capture of attention by the auditory warning signals.
Application. The present results indicate that drivers would benefit most when warnings occur at the side of potential collision objects rather than the direction of a desirable action during semi-autonomous driving
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Exploring the effects of spinal cord stimulation for freezing of gait in parkinsonian patients
Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for axial motor symptoms including gait dysfunction and freezing of gait (FOG) in Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy (PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant gait and FOG in parkinsonian patients. Further studies investigating the mechanism of SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are warranted. The hypothesis of the research described in this thesis is that mid-thoracic, dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait and may have a dopaminergic effect in individuals with FOG. The primary objective was to understand the relationship between FOG reduction, improvements in upper limb visual-motor performance, modulation of cortical activity and striatal dopaminergic innervation in 7 PD participants. FOG reduction was associated with changes in upper limb reaction time, speed and accuracy measured using robotic target reaching choice tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using electroencephalography, was significantly associated with FOG reduction while participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical activity associated with motor planning and sensory perception. Changes to striatal dopaminergic innervation, measured using a dopamine transporter marker, were associated with visual-motor performance improvements. Axial and appendicular motor features may be mediated by non-dopaminergic and dopaminergic pathways, respectively. The secondary objective was to demonstrate the short- and long-term effects of SCS for alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS participants without back/leg pain. SCS programming was individualized based on which setting best improved gait and/or FOG responses per participant using objective gait analysis. Significant improvements in stride velocity, step length and reduced FOG frequency were observed in all PD participants with up to 3-years of SCS. Similar gait and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is a promising therapeutic option for parkinsonian patients with FOG by possibly influencing cortical and subcortical structures involved in locomotion physiology
Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges
5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools
Non-Print Information Resources and The Preservation Approaches Recommendation in Tanzanian Academic Libraries
Background: Non-print information resources are increasingly becoming more important as vital learning materials in higher learning institutions. Academic libraries therefore, have to acquire, process, organize and preserve them for current and future use. Purpose: This paper aims to assess the factors affecting the non-print information resources and their recommended preservation approaches in academic libraries. Method: The study adopted a convergent parallel mixed approach which collects and analyses data to produce integrated findings by using both qualitative and quantitative techniques in a single study. Data was collected by means of questionnaire and in-depth interview. Result: The study revealed that dust, loss of data on disc and hard disc, loss of data due to server failure, high heat, and excessive light, fading of disc surface, high humidity, fungus on disc surface, atmospheric pollutants and virus attack were factors affecting non-print information resources. It was also revealed that highly recommended preservation approaches were good cleanliness of library where information resources are kept, educating library users on how to handle and use information resources, migrating information resources from obsolete storage media to modern storage media, technology preservation and refreshing. Conclusion: The study concludes that library staff need to adopt recommended preservation approaches to safeguard the important information in academic libraries but also system librarians in academic libraries need to be employed to assist in trouble shooting issues.
Keywords: Non-Print Information Resources; Information Resources; Information Resources Preservation; Preservation Approaches; Academic Library
Abstrak
Latar Belakang: Sumber informasi non-cetak sekarang ini menjadi semakin penting sebagai bahan pembelajaran vital di perguruan tinggi. Oleh karena itu, perpustakaan akademik harus memperoleh, memproses, mengatur, dan melestarikannya untuk penggunaan saat ini dan masa depan. Tujuan: Makalah ini bertujuan untuk menilai faktor-faktor yang mempengaruhi sumber informasi non-cetak dan pendekatan pelestarian yang direkomendasikan di perpustakaan akademik. Metode: Studi ini mengadopsi pendekatan campuran paralel konvergen yang mengumpulkan dan menganalisis data untuk menghasilkan temuan yang terintegrasi dengan menggunakan teknik kualitatif dan kuantitatif dalam satu studi. Pengumpulan data dilakukan dengan kuesioner dan wawancara mendalam. Temuan: Hasil penelitian menunjukkan bahwa debu, hilangnya data pada disk/hard disk, hilangnya data karena kegagalan server, panas yang tinggi, dan cahaya yang berlebihan, memudarnya permukaan disk, kelembaban tinggi, jamur pada permukaan disk, polutan atmosfer dan serangan virus adalah faktor yang mempengaruhi sumber informasi non-cetak. Diungkapkan juga bahwa pendekatan pelestarian yang sangat direkomendasikan adalah kebersihan perpustakaan tempat sumber informasi disimpan, mendidik pengguna perpustakaan tentang cara menangani dan menggunakan sumber informasi, migrasi sumber informasi dari media penyimpanan usang ke media penyimpanan modern, pelestarian teknologi dan penyegaran koleksi. Kesimpulan: Studi ini menyimpulkan bahwa staf perpustakaan perlu mengadopsi pendekatan pelestarian yang direkomendasikan untuk melindungi informasi penting di perpustakaan akademik, tetapi juga pustakawan di perpustakaan akademik perlu dioptimalkan untuk membantu memecahkan masalah yang ada.
Kata kunci: Sumber Informasi Non-Cetak; Sumber Daya Informasi; Pelestarian Sumber Daya Informasi; Pendekatan Pelestarian; Perpustakaan Akademik 
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
Interactive Sonic Environments: Sonic artwork via gameplay experience
The purpose of this study is to investigate the use of video-game technology in the design and implementation of interactive sonic centric artworks, the purpose of which is to create and contribute to the discourse and understanding of its effectiveness in electro-acoustic composition highlighting the creative process. Key research questions include: How can the language of electro-acoustic music be placed in a new framework derived from videogame aesthetics and technology? What new creative processes need to be considered when using this medium? Moreover, what aspects of 'play' should be considered when designing the systems? The findings of this study assert that composers and sonic art practitioners need little or no coding knowledge to create exciting applications and the myriad of options available to the composer when using video-game technology is limited only by imagination. Through a cyclic process of planning, building, testing and playing these applications the project revealed advantages and unique sonic opportunities in comparison to other sonic art installations. A portfolio of selected original compositions, both fixed and open are presented by the author to complement this study. The commentary serves to place the work in context with other practitioners in the field and to provide compositional approaches that have been taken
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Conflict and climate factors and the risk of child acute malnutrition among children aged 24–59 months: a comparative analysis of Kenya, Nigeria, and Uganda
Acute malnutrition affects a sizeable number of young children around the world, with serious repercussions for mortality and morbidity. Among the top priorities in addressing this problem are to anticipate which children tend to be susceptible and where and when crises of high prevalence rates would be likely to arise. In this article, we highlight the potential role of conflict and climate conditions as risk factors for acute malnutrition, while also assessing other vulnerabilities at the individual- and household-levels. Existing research reflects these features selectively, whereas we incorporate all the features into the same study. The empirical analysis relies on integration of health, conflict, and environmental data at multiple scales of observation to focuses on how local conflict and climate factors relate to an individual child’s health. The centerpiece of the analysis is data from the Demographic and Health Surveys conducted in several different cross-sectional waves covering 2003–2016 in Kenya, Nigeria, and Uganda. The results obtained from multi-level statistical models indicate that in Kenya and Nigeria, conflict is associated with lower weight-for-height scores among children, even after accounting for individual-level and climate factors. In Nigeria and Kenya, conflict lagged 1–3 months and occurring within the growing season tends to reduce WHZ scores. In Uganda, however, weight-for-height scores are primarily associated with individual-level and household-level conditions and demonstrate little association with conflict or climate factors. The findings are valuable to guide humanitarian policymakers and practitioners in effective and efficient targeting of attention, interventions, and resources that lessen burdens of acute malnutrition in countries prone to conflict and climate shocks
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