On the Efficiency of Multi-Source Energy Harvesters

Energy harvesters can be used to provide small amounts of power in remote locations. Applications include powering wireless sensor networks and powering microelectromechanical systems. A wealth of different designs exists for harvesting energy from different sources, including designs which harvest from multiple sources simultaneously. However, there are no universally accepted metrics for assessing the performance of energy harvesters; this can make it impossible to compare designs in any meaningful way. The first part of this thesis develops a domain-neutral framework for describing and analysing the behaviour of energy harvesters. This involves introducing a system of dimensionally consistent analogies into energy harvesting. Using this domain-neutral and dimensionally consistent framework, it is possible to come up with general expressions for the behaviour of single-source energy harvesting systems. This approach is then validated experimentally for single-source energy harvesters. The second part of this thesis involves extending the theoretical analysis to multi-source energy harvesters. Using the system of analogies defined in the first part of the thesis it is possible to create an n-degree-of-freedom matrix representation of a multi-source energy harvester. This enables us to derive expressions which are valid for both single-source and multi-source energy harvesters. The expressions for the maximum power absorbed by an energy harvesting device are shown to be independent of the number of sources, as well as any static coupling or coupling through material effects (e.g. piezoelectric). Numerical simulations are used to explore the validity of these expressions for various system configurations driven with a mixed stochastic-deterministic input signal. From the results of these numerical simulations, a practical approach for estimating the efficiency of an energy harvester using the maximum power absorbed as a theoretical limit is described. The third part of this thesis describes experiments which validate the theoretical analysis. These experiments are used to provide an example of how to calculate and compare the efficiency of energy harvesting designs

Foundations of population-based SHM, part III : heterogeneous populations – mapping and transfer

This is the third and final paper in a series laying foundations for a theory/methodology of Population-Based Structural Health Monitoring (PBSHM). PBSHM involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a source domain structure, where labels are known for given feature sets, and mapping these onto the unlabelled feature space of a different, target domain structure. This mapping means a classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e. a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined here via domain adaptation, a subcategory of transfer learning. A mathematical underpinning for when domain adaptation is possible in a structural dynamics context is provided, with reference to topology within a graphical representation of structures. Subsequently, a novel procedure for performing domain adaptation on topologically different structures is outlined

Foundations of population-based SHM, part II : heterogeneous populations – graphs, networks, and communities

This paper is the second in a series of three which aims to provide a basis for Population-Based Structural Health Monitoring (PBSHM); a new technology which will allow transfer of diagnostic information across a population of structures, augmenting SHM capability beyond that applicable to individual structures. The new PBSHM can potentially allow knowledge about normal operating conditions, damage states, and even physics-based models to be transferred between structures. The first part in this series considered homogeneous populations of nominally-identical structures. The theory is extended in this paper to heterogeneous populations of disparate structures. In order to achieve this aim, the paper introduces an abstract representation of structures based on Irreducible Element (IE) models, which capture essential structural characteristics, which are then converted into Attributed Graphs (AGs). The AGs form a complex network of structure models, on which a metric can be used to assess structural similarity; the similarity being a key measure of whether diagnostic information can be successfully transferred. Once a pairwise similarity metric has been established on the network of structures, similar structures are clustered to form communities. Within these communities, it is assumed that a certain level of knowledge transfer is possible. The transfer itself will be accomplished using machine learning methods which will be discussed in the third part of this series. The ideas introduced in this paper can be used to define precise terminology for PBSHM in both the homogeneous and heterogeneous population cases

A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

On the application of population-based structural health monitoring in aerospace engineering

One of the major obstacles to the widespread uptake of data-based Structural Health Monitoring so far, has been the lack of damage-state data for the (mostly high-value) structures of interest. To address this issue, a methodology for sharing data and models between structures has been developed–Population-Based Structural Health Monitoring (PBSHM). PBSHM works on the principle that, if populations of structures are sufficiently similar, or share sections which can be considered similar, then data and models can be shared between them for use in diagnostic inference. The PBSHM methodology therefore relies on two key components: firstly, identifying whether structures are sufficiently similar for successful transfer of diagnostics; this is achieved by the use of an abstract representation of structures. Secondly, machine learning techniques are exploited to effectively transfer information between the structures in a way that improves damage detection and classification across the whole population. Although PBSHM has been conceived to deal with large and general classes of structures, much of the detailed developments presented so far have concerned bridges; the aim of this paper is to provide similarly detailed discussions in the aerospace context. The overview here will examine data transfer between aircraft components, as well as illustrating how one might construct an abstract representation of a full aircraft

Towards population-based structural health monitoring, Part II : heterogeneous populations and structures as graphs

Information about the expected variation in the normal condition and various damage states of a structure is crucial in structural health monitoring. In an ideal case, the behaviour associated with each possible type of damage would be known and classification would be possible. However, it is not realistic to obtain data for every possible damage state in an individual structure. Examining a population of structures gives a much larger pool of data to work with. Machine learning can then potentially allow inferences across the population using algorithms from transfer learning. The degree of similarity between structures determines the level of possible knowledge transfer between different structures. It is also useful to quantify in which ways two structures are similar, and where these similarities lie. This information determines whether or not certain the transfer learning approaches are applicable in a given situation. It is therefore necessary to develop a method for analysing the similarities between structures. First, it must be decided which properties of the structure to use when measuring the similarity. For example, comparing 3D CAD models or Finite Element models is not a suitable approach, since these contain a lot of irrelevant information. It is better to abstract this information into a form that contains only the relevant information. This paper proposes Irreducible Element (IE) models, which are designed to capture the features that are crucial in determining whether or not transfer learning is possible. This information is then converted into an Attributed Graph (AG). The Attributed Graph for a structure contains the same information as the Irreducible Element model; however, the graph carries this information as a list of attributes attached to nodes. Organising the information in this manner makes it easier for graph-matching algorithms to perform a comparison between two structures. This comparison can then be used to generate a measure of similarity between the two structures and determine the most appropriate transfer learning method

Foundations of population-based SHM, Part I : homogeneous populations and forms

In Structural Health Monitoring (SHM), measured data that correspond to an extensive set of operational and damage conditions (for a given structure) are rarely available. One potential solution considers that information might be transferred, in some sense, between similar systems. A population-based approach to SHM looks to both model and transfer this missing information, by considering data collected from groups of similar structures. Specifically, in this work, a framework is proposed to model a population of nominally-identical systems, such that (complete) datasets are only available from a subset of members. The SHM strategy defines a general model, referred to as the population form, which is used to monitor a homogeneous group of systems. First, the framework is demonstrated through applications to a simulated population, with one experimental (test-rig) member; the form is then adapted and applied to signals recorded from an operational wind farm

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Change detection and deformation monitoring is an active area of research within the field of engineering surveying as well as overlapping areas such as structural and civil engineering. The application of Terrestrial Laser Scanning (TLS) techniques for change detection and deformation monitoring of concrete structures has increased over the years as illustrated in the past studies. This paper presents a review of literature on TLS application in the monitoring of structures and discusses registration and georeferencing of TLS point cloud data as a critical issue in the process chain of accurate deformation analysis. Past TLS research work has shown some trends in addressing issues such as accurate registration and georeferencing of the scans and the need of a stable reference frame, TLS error modelling and reduction, point cloud processing techniques for deformation analysis, scanner calibration issues and assessing the potential of TLS in detecting sub-centimetre and millimetre deformations. However, several issues are still open to investigation as far as TLS is concerned in change detection and deformation monitoring studies such as rigorous and efficient workflow methodology of point cloud processing for change detection and deformation analysis, incorporation of measurement geometry in deformation measurements of high-rise structures, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. Even though some studies have attempted to address these issues, some gaps exist as information is still limited. Some methods reviewed in the case studies have been applied in landslide monitoring and they seem promising to be applied in engineering surveying to monitor structures. Hence the proposal of a three-stage process model for deformation analysis is presented. Furthermore, with technological advancements new TLS instruments with better accuracy are being developed necessitating more research for precise measurements in the monitoring of structures

Resveratrol and Pterostilbene Inhibit SARS-CoV-2 Replication in Air-Liquid Interface Cultured Human Primary Bronchial Epithelial Cells

The current COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has an enormous impact on human health and economy. In search for therapeutic options, researchers have proposed resveratrol, a food supplement with known antiviral, anti-inflammatory, and antioxidant properties as an advantageous antiviral therapy for SARS-CoV-2 infection. Here, we provide evidence that both resveratrol and its metabolically more stable structural analog, pterostilbene, exhibit potent antiviral properties against SARS-CoV-2 in vitro. First, we show that resveratrol and pterostilbene antiviral activity in African green monkey kidney cells. Both compounds actively inhibit virus replication within infected cells as reduced virus progeny production was observed when the compound was added at post-inoculation conditions. Without replenishment of the compound, antiviral activity was observed up to roughly five rounds of replication, demonstrating the long-lasting effect of these compounds. Second, as the upper respiratory tract represents the initial site of SARS-CoV-2 replication, we also assessed antiviral activity in air–liquid interface (ALI) cultured human primary bronchial epithelial cells, isolated from healthy volunteers. Resveratrol and pterostilbene showed a strong antiviral effect in these cells up to 48 h post-infection. Collectively, our data indicate that resveratrol and pterostilbene are promising antiviral compounds to inhibit SARS-CoV-2 infection. Because these results represent laboratory findings in cells, we advocate evaluation of these compounds in clinical trials before statements are made whether these drugs are advantageous for COVID-19 treatment

Dasatinib impairs long-term expansion of leukemic progenitors in a subset of acute myeloid leukemia cases

A number of signaling pathways might be frequently disrupted in acute myeloid leukemia (AML). We questioned whether the dual SRC/ABL kinase inhibitor dasatinib can affect AML cells and whether differences can be observed with normal CD34+ cells. First, we demonstrated that normal cord blood (CB) CD34+ cells were unaffected by dasatinib at a low concentration (0.5 nM) in the long-term culture on MS5 stromal cells. No changes were observed in proliferation, differentiation, and colony formation. In a subset of AML cases (3/15), a distinct reduction in cell proliferation was observed, ranging from 48% to 91% inhibition at 0.5 nM of dasatinib, in particular, those characterized by BCR–ABL or KIT mutations. Moreover, the inhibitory effects of dasatinib were cytokine specific. Stem cell factor-mediated proliferation was significantly impaired, associated with a reduced phosphorylation of ERK1/2 and STAT5, whereas no effect was observed on interleukin-3 and thrombopoietin-mediated signaling despite SRC activation. In conclusion, this study demonstrates that dasatinib is a potential inhibitor in a subgroup of AML, especially those that express BCR–ABL or KIT mutations