683 research outputs found

    The Quality of Life of Menopausal Women With Polycystic Ovary Syndrome

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    Polycystic ovarian syndrome (PCOS) is a severe public and clinical health issue that puts a strain on health care resources and adversely affects the health of women throughout their life span. The syndrome has adverse reproductive and metabolic features linked to infertility, heart disease, cancer, diabetes, and psychological problems. The literature provides evidence that the quality of life (QoL) and psychological well-being of PCOS reproductive-age women is compromised due to comorbidities related to both the reproductive and metabolic features of the syndrome. Little research has been conducted to investigate the QoL of menopausal women ages 48–65 who were diagnosed with PCOS during their reproductive years, ages 18 to 45. The purpose of this phenomenological study was to understand the QoL of PCOS menopausal women diagnosed during their reproductive years. The conceptual framework the World Health Organization developed to conceptualize QoL was used in this study. Participants were recruited and interviewed via online conferencing. Data were collected from 10 semi-structured interviews and were analyzed using thematic analysis. Results indicate that the QoL of women with PCOS is aggravated. Participants described several psychological and physical conditions related to PCOS and menopause. The findings of this study could have implications for positive social change by leading to the development, implementation, and practice of interventions in health settings that may improve the QoL of menopausal women who were diagnosed with PCOS in their reproductive years

    Viscous Thin-film Models of Nanoscale Self-organization Under Ion Bombardment

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    For decades, it has been observed that broad-beam irradiation of semiconductor surfaces can lead to spontaneous self-organization into highly regular patterns, sometimes at length scales of only a few nanometers. Initial theory was largely based on erosion and redistribution of material occurring on fast time scales, which are able to produce good agreement with certain aspects of surface evolution. However, further experimental and theoretical work eventually led to the realization that numerous effects are active in the irradiated target, including stresses associated with ion-implantation and the accumulation of damage leading to the development of a disordered, amorphous layer atop the substrate. It was also shown that relaxation of this amorphous layer proceeds in a manner closer to viscous flow ratherthan surface diffusion on a crystal lattice. Observing the viscous character of the amorphous layer, it is natural to consider whether stress-based continuum models might help explain pattern formation under ion bombardment and the observations described above. Indeed, there are early indications from the experimental literature that this may be the case, and, at low energies (∌ 1keV), at least one experimental-theoretical study has shown that they may even dominate erosive and redistributive effects in their contribution to surface evolution. In this thesis, we develop a continuum model based on viscous thin-film flow and ion-induced stresses within the amorphous layer. This model is a composite of, and significant generalization of, a previously-studied “anisotropic plastic flow” (APF) mechanism and a previously-studied “ion-induced isotropic swelling” (IIS) mechanism. Previous work has shown that, with certain simplifying assumptions about the amorphous-crystalline interface and spatial homogeneity of anisotropic plastic flow, this mechanism produces an instability capable of predicting pattern formation beginning at 45◩ angle of incidence against the macroscopically-flat substrate, consistent with some experimental systems. Under similar simplifying assumptions, ion-induced swelling has been shown to be capable of suppressing pattern formation. Our generalizations allow the use of simulation data to inform both linear and nonlinear surface evolution due to the spatial localization of APF and IIS to certain regions of the bulk, improved treatment of the amorphous-crystalline geometry, andboundary conditions suitable to the physical systems of interest. We are then able to provide insight into several phenomena that have previously been difficult to explain, but seem to emerge naturally from a more detailed treatment of the physical system

    Entrepreneurship innovation using social robots in tourism: a social listening study

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    The tourism sector has been one of the most impacted by the COVID-19 pandemic, due to restrictions on mobility and fear of social contact. In this context, business innovation through digital transformation is presented as a great opportunity for the tourism industry and the inclusion of social robots in service tasks is an example. This transformation requires new methodologies, skills and talent that must be promoted to improve the innovative tourism ecosystem. With this research, we try to determine how the inclusion of social or service robots in hotels can improve the image and perception held by clients or guests. For that, we frst analyse the degree of knowledge and sentiment generated by social robots through a social listening study in social networks. In addition, we determine whether these perceptions on the subject are in tune with other more formal felds, such as scientifc research, or with the strategies followed at a national or international level by companies, agencies and organisations related to the technology and innovation of social robotics. For both objectives, we use the Simbiu social listening tool, a software-based program on Talkwalker, and we obtain interesting results. Basically, people on Twitter have a neutral or positive feeling about the use of social robots, and people who write in English have a more positive attitude towards social robots than Spanish speakers. After COVID-19, are necessary changes in strategic decisions of the hospitality and it is essential to continue investigating the role of social robots in this new context.Funding for open access charge: CRUE-Universitat Jaume

    Analog Photonics Computing for Information Processing, Inference and Optimisation

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    This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

    Model-agnostic network inference enhancement from noisy measurements via curriculum learning

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    Noise is a pervasive element within real-world measurement data, significantly undermining the performance of network inference models. However, the quest for a comprehensive enhancement framework capable of bolstering noise resistance across a diverse array of network inference models has remained elusive. Here, we present an elegant and efficient framework tailored to amplify the capabilities of network inference models in the presence of noise. Leveraging curriculum learning, we mitigate the deleterious impact of noisy samples on network inference models. Our proposed framework is model-agnostic, seamlessly integrable into a plethora of model-based and model-free network inference methods. Notably, we utilize one model-based and three model-free network inference methods as the foundation. Extensive experimentation across various synthetic and real-world networks, encapsulating diverse nonlinear dynamic processes, showcases substantial performance augmentation under varied noise types, particularly thriving in scenarios enriched with clean samples. This framework's adeptness in fortifying both model-free and model-based network inference methodologies paves the avenue towards a comprehensive and unified enhancement framework, encompassing the entire spectrum of network inference models. Available Code: https://github.com/xiaoyuans/MANIE

    MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT

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    This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions: First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control. Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle. Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence. The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios

    Study of Climate Variability Patterns at Different Scales – A Complex Network Approach

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    Das Klimasystem der Erde besteht aus zahlreichen interagierenden Teilsystemen, die sich ĂŒber verschiedene Zeitskalen hinweg verĂ€ndern, was zu einer Ă€ußerst komplizierten rĂ€umlich-zeitlichen KlimavariabilitĂ€t fĂŒhrt. Das VerstĂ€ndnis von Prozessen, die auf verschiedenen rĂ€umlichen und zeitlichen Skalen ablaufen, ist ein entscheidender Aspekt bei der numerischen Wettervorhersage. Die VariabilitĂ€t des Klimas, ein sich selbst konstituierendes System, scheint in Mustern auf großen Skalen organisiert zu sein. Die Verwendung von Klimanetzwerken hat sich als erfolgreicher Ansatz fĂŒr die Erkennung der rĂ€umlichen Ausbreitung dieser großrĂ€umigen Muster in der VariabilitĂ€t des Klimasystems erwiesen. In dieser Arbeit wird mit Hilfe von Klimanetzwerken gezeigt, dass die KlimavariabilitĂ€t nicht nur auf grĂ¶ĂŸeren Skalen (Asiatischer Sommermonsun, El Niño/Southern Oscillation), sondern auch auf kleineren Skalen, z.B. auf Wetterzeitskalen, in Mustern organisiert ist. Dies findet Anwendung bei der Erkennung einzelner tropischer WirbelstĂŒrme, bei der Charakterisierung binĂ€rer Wirbelsturm-Interaktionen, die zu einer vollstĂ€ndigen Verschmelzung fĂŒhren, und bei der Untersuchung der intrasaisonalen und interannuellen VariabilitĂ€t des Asiatischen Sommermonsuns. Schließlich wird die Anwendbarkeit von Klimanetzwerken zur Analyse von Vorhersagefehlern demonstriert, was fĂŒr die Verbesserung von Vorhersagen von immenser Bedeutung ist. Da korrelierte Fehler durch vorhersagbare Beziehungen zwischen Fehlern verschiedener Regionen aufgrund von zugrunde liegenden systematischen oder zufĂ€lligen Prozessen auftreten können, wird gezeigt, dass Fehler-Netzwerke helfen können, die rĂ€umlich kohĂ€renten Strukturen von Vorhersagefehlern zu untersuchen. Die Analyse der Fehler-Netzwerk-Topologie von Klimavariablen liefert ein erstes VerstĂ€ndnis der vorherrschenden Fehlerquelle und veranschaulicht das Potenzial von Klimanetzwerken als vielversprechendes Diagnoseinstrument zur Untersuchung von Fehlerkorrelationen.The Earth’s climate system consists of numerous interacting subsystems varying over a multitude of time scales giving rise to highly complicated spatio-temporal climate variability. Understanding processes occurring at different scales, both spatial and temporal, has been a very crucial problem in numerical weather prediction. The variability of climate, a self-constituting system, appears to be organized in patterns on large scales. The climate networks approach has been very successful in detecting the spatial propagation of these large scale patterns of variability in the climate system. In this thesis, it is demonstrated using climate network approach that climate variability is organized in patterns not only at larger scales (Asian Summer Monsoon, El Niño-Southern Oscillation) but also at shorter scales, e.g., weather time scales. This finds application in detecting individual tropical cyclones, characterizing binary cyclone interaction leading to a complete merger, and studying the intraseasonal and interannual variability of the Asian Summer Monsoon. Finally, the applicability of the climate network framework to understand forecast error properties is demonstrated, which is crucial for improvement of forecasts. As correlated errors can arise due to the presence of a predictable relationship between errors of different regions because of some underlying systematic or random process, it is shown that error networks can help to analyze the spatially coherent structures of forecast errors. The analysis of the error network topology of a climate variable provides a preliminary understanding of the dominant source of error, which shows the potential of climate networks as a very promising diagnostic tool to study error correlations

    Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions

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    Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. But what exactly in the training data causes a model to make a certain prediction? We seek to answer this question by providing a language for describing how training data influences predictions, through a causal framework. Importantly, our framework bypasses the need to retrain expensive models and allows us to estimate causal effects based on observational data alone. Addressing the problem of extracting factual knowledge from pretrained language models (PLMs), we focus on simple data statistics such as co-occurrence counts and show that these statistics do influence the predictions of PLMs, suggesting that such models rely on shallow heuristics. Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.Comment: We received a criticism regarding the validity of the causal formulation in this paper. We will address them in an upcoming versio
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