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Background: The cumulative effect of taking anticholinergic medicines (anticholinergic burden) is associated with adverse outcomes for older people. Prevalence of anticholinergic prescribing is increasing, and there is a need for tools to proactively identify at-risk patients for medication reviews. Aim: To explore the need for, and feasibility of, an integrated case-finding tool that predicts risks using electronic health records (EHRs), facilitating the review of anticholinergic medicines for frail older people. Methods: Mixed methods, adopting a pragmatic approach. A systematic review, prediction modelling of cohort study data, and qualitative interviews were undertaken. Results: The systematic review found anticholinergic exposure was associated with adverse outcomes for the frail; poorer physical function, falls, and mortality, indicating a need for a risk reducing intervention. In the prediction modelling study, predicting risks using composite measures of anticholinergic burden and frailty indicated limited feasibility. Neither enhanced the performance of best subset models using cohort study data. Their predictive utility needs to be investigated using EHR data, to determine their feasibility within primary care. The qualitative study found healthcare professionals needed a proactive tool, supporting risk prediction as a feasible approach. Factors influencing future implementation were; upskilling requirements, deprescribing confidence, patient reluctance, motivation, holistic care, interoperability, trust in risk prediction, remuneration, among other barriers and facilitators. Conclusions: Through identifying a need, and potential feasibility, foundations towards the future developments of a case-finding tool have been provided, informing an early tool prototype (AC-FRAIL). Recommendations for further work suggest a roadmap ahead, to maximise the potential for integrated solutions to proactively reduce anticholinergic risks. / NIHR Yorkshire and Humber Patient Safety Translational Research Centre (NIHR YHPSTRC)

While art can be an intensification of perception, this nature center recognizes the presence of nature through symbiotic connotations of its primordial elements as architectural form. Deepened experiences of both nature and architecture are to be achieved through a clarified ordering of architectonic elements and their relationship to each other, nature, and humanity. / Master of Architecture

No / An experimental study of coupling compensation for AOA estimation using compact low profile antenna arrays with element separations of a quarter wavelength has been conducted. Two circular arrays of low profile miniaturised logarithmic spiral antennas deployed on a circular metal plate were used for data acquisition. Using the MUSIC direction-finding algorithm, the AOA estimation errors in receiving mode were observed before and after compensation: the errors were significantly decreased by coupling compensation.

This thesis investigates the computing capabilities of three distinct platforms for radio direction finding (RDF) applications in electronic warfare (EW) systems: the Raspberry Pi 4 Model B, Intel NUC NUC7i5BNH, and NVIDIA Jetson AGX Orin 64GB. RDF plays a critical role in locating radio emitters, demanding real-time processing for precise signal data analysis. The study aims to determine the maximum sampling frequency that each platform can maintain while meeting real-time requirements and identifies the most suitable RDF algorithm for platform assessment. The best-suited algorithm was found to be Phase Interferometry. Results indicate that the Raspberry Pi 4 Model B achieves a sampling frequency of 13.08 MHz, the Intel NUC NUC7i5BNH maintains 12.68 MHz, and the NVIDIA Jetson AGX Orin 64GB performs at 399.45 MHz (60W), 229.82 MHz (50W), 83.88 MHz (30W), and 54.12 MHz (15W).

Networks are a mathematical abstraction of the interactions between a set of entities, with extensive applications in social science, epidemiology, bioinformatics, and cybersecurity, among others. There are many fundamental problems when analyzing network data, such as anomaly detection, dense subgraph mining, motif finding, information diffusion, and epidemic spread. A common underlying task in all these problems is finding an "interesting subgraph"; that is, finding a part of the graph---usually small relative to the whole---that optimizes a score function and has some property of interest, such as connectivity or a minimum density. Finding subgraphs that satisfy common constraints of interest, such as the ones above, is computationally hard in general, and state-of-the-art algorithms for many problems in network analysis are heuristic in nature. These methods are fast and usually easy to implement. However, they come with no theoretical guarantees on the quality of the solution, which makes it difficult to assess how the discovered subgraphs compare to an optimal solution, which in turn affects the data mining task at hand. For instance, in anomaly detection, solutions with low anomaly score lead to sub-optimal detection power. On the other end of the spectrum, there have been significant advances on approximation algorithms for these challenging graph problems in the theoretical computer science community. However, these algorithms tend to be slow, difficult to implement, and they do not scale to the large datasets that are common nowadays. The goal of this dissertation is developing scalable algorithms with theoretical guarantees for various network analysis problems, where the underlying task is to find subgraphs with constraints. We find interesting subgraphs with guarantees by adapting techniques from parameterized complexity, convex optimization, and submodularity optimization. These techniques are well-known in the algorithm design literature, but they lead to slow and impractical algorithms. One unifying theme in the problems that we study is that our methods are scalable without sacrificing the theoretical guarantees of these algorithm design techniques. We accomplish this combination of scalability and rigorous bounds by exploiting properties of the problems we are trying to optimize, decomposing or compressing the input graph to a manageable size, and parallelization. We consider problems on network analysis for both static and dynamic network models. And we illustrate the power of our methods in applications, such as public health, sensor data analysis, and event detection using social media data. / Ph. D. / Networks are a mathematical abstraction of the interactions between a set of entities, with extensive applications in social science, epidemiology, bioinformatics, and cybersecurity, among others. There are many fundamental problems when analyzing network data, such as anomaly detection, dense subgraph mining, motif finding, information diffusion, and epidemic spread. A common underlying task in all these problems is finding an “interesting subgraph”; that is, finding a part of the graph—usually small relative to the whole—that optimizes a score function and has some property of interest, such as being connected. Finding subgraphs that satisfy common constraints of interest is computationally hard, and existing techniques for many problems of this kind are heuristic in nature. Heuristics are fast and usually easy to implement. However, they come with no theoretical guarantees on the quality of the solution, which makes it difficult to assess how the discovered subgraphs compare to an optimal solution, which in turn affects the data mining task at hand. For instance, in anomaly detection, solutions with low anomaly score lead to sub-optimal detection power. On the other end of the spectrum, there have been significant progress on these challenging graph problems in the theoretical computer science community. However, these techniques tend to be slow, difficult to implement, and they do not scale to the large datasets that are common nowadays. The goal of this dissertation is developing scalable algorithms with theoretical guarantees for various network analysis problems, where the underlying task is to find subgraphs with constraints. One unifying theme in the problems that we study is that our methods are scalable without sacrificing theoretical guarantees. We accomplish this combination of scalability and rigorous bounds by exploiting properties of the problems we are trying to optimize, decomposing or compressing the input graph to a manageable size, and parallelization. We consider problems on network analysis for both static and dynamic network models. And we illustrate the power of our methods in applications, such as public health, sensor data analysis, and event detection using social media data.

Many animal species are known for their unparalleled abilities to encode sensory information that supports fast, reliable action in complex environments, but the mechanisms remain often unclear. Through fast ear motions, bats can encode information on target direction into time-frequency Doppler signatures. These species were thought to be evolutionarily tuned to Doppler shifts generated by a prey's wing beat. Self-generated Doppler shifts from the bat's own flight motion were for the most part considered a nuisance that the bats compensate for. My findings indicate that these Doppler-based biosonar systems may be more complicated than previously thought because the animals can actively inject Doppler shifts into their input signals. The work in this dissertation presents a novel nonlinear principle for sensory information encoding in bats. Up to now, sound-direction finding has required either multiple signal frequencies or multiple pressure receivers. Inspired by bat species that add Doppler shifts to their biosonar echoes through fast ear motions, I present a source-direction finding paradigm based on a single frequency and a single pressure receiver. Non-rigid ear motions produce complex Doppler signatures that depend on source direction but are difficult to interpret. To demonstrate that deep learning can solve this problem, I have combined a soft-robotic microphone baffle that mimics a deforming bat ear with a CNN for regression. With this integrated cyber-physical setup, I have able to achieve a direction-finding accuracy of 1 degree based on a single baffle motion. / Doctor of Philosophy / Bats are well-known for their intricate biosonar system that allow the animals to navigate even the most complex natural environments. While the mechanism behind most of these abilities remains unknown, an interesting observation is that some bat species produce fast movements of their ears when actively exploring their surroundings. By moving their pinna, the bats create a time-variant reception characteristic and very little research has been directed at exploring the potential benefits of such behavior so far. One hypothesis is that the speed of the pinna motions modulates the received biosonar echoes with Doppler-shift patterns that could convey sensory information that is useful for navigation. This dissertation intends to explore this hypothetical dynamic sensing mechanism by building a soft-robotic biomimetic receiver to replicate the dynamics of the bat pinna. The experiments with this biomimetic pinna robot demonstrate that the non-rigid ear motions produce Doppler signatures that contain information about the direction of a sound source. However, these patterns are difficult to interpret because of their complexity. By combining the soft-robotic pinna with a convolutional neural network for processing the Doppler signatures in the time-frequency domain, I have been able to accurately estimate the source direction with an error margin of less than one degree. This working system, composed of a soft-robotic biomimetic ear integrated with a deep neural net, demonstrates that the use of Doppler signatures as a source of sensory information is a viable hypothesis for explaining the sensory skills of bats.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第25186号 / 医博第5072号 / 新制||医||1072(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 安達 泰治, 教授 森本 尚樹, 教授 羽賀 博典 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM