6,009 research outputs found

    Musculoskeletal complaints in primary care:Constraining healthcare costs, rethinking the deployment of healthcare professionals

    Get PDF
    Worldwide policy makers are challenged to account for rising healthcare costs and increased healthcare demand. Also, in the Netherlands there is a growing concern how to maintain high-quality and accessible care while keeping costs in check. Access to care is under pressure as the demand for care is rising fast, due to an aging population and an increasing number of chronically ill people. Not only at the policy level, but also in clinical practice challenges exist. The workload in the health care sector is high, causing health workers, such as general practitioners (GPs), to leave this sector. To keep costs in check available resources need be allocated as efficiently as possible. A good starting point for evaluating healthcare costs may be assessing large patient groups that are responsible for high resource use and costs, such as patients with musculoskeletal conditions treated in general practice. Another point may be identifying prognostic factors for higher healthcare costs. Besides lowering costs, it is also of importance to keep GP care accessible by lowering GPs’ workload. One of the ways to address GPs’ high workload is task reallocation. Internationally, positive effects have been found for an Advanced Physiotherapy Practitioner (APP) model of care, in which APPs take over tasks from a physician in the care for patients with musculoskeletal conditions. This model of care could potentially be of value in reducing the workload of Dutch GPs and keeping GP care accessible. Besides lowering healthcare cost and decreasing GPs’ workload maintaining good quality care is essential. One of the most widely used Patient Reported Outcome Measures (PROMs) in assessing quality of healthcare is the EQ-5D, a preference-based measurement instrument that measures health related quality of life and is used to estimate utility values that represent the preferences of the general population of a country for given health states. These utility values are needed for estimating Quality-Adjusted Life-Years (QALYs) in cost effectiveness analysis. However, quality-of-life measurements are generally not available when data are collected for clinical purposes, such as data from GP electronic medical records. Therefore, researchers are exploring ways to estimate EQ-5D based utility values by means of outcomes on other available health related outcome measures. This thesis aimed to explore some of the challenges in Dutch primary care by evaluating 1) healthcare utilization and associated cost of GP-guided care in patients with musculoskeletal complaints, 2) the introduction of an APP model of care, and 3) different approaches to estimate missing EQ-5D based utility values

    Survey on detecting and preventing web application broken access control attacks

    Get PDF
    Web applications are an essential component of the current wide range of digital services proposition including financial and governmental services as well as social networking and communications. Broken access control vulnerabilities pose a huge risk to that echo system because they allow the attacker to circumvent the allocated permissions and rights and perform actions that he is not authorized to perform. This paper gives a broad survey of the current research progress on approaches used to detect access control vulnerabilities exploitations and attacks in web application components. It categorizes these approaches based on their key techniques and compares the different detection methods in addition to evaluating their strengths and weaknesses. We also spotted and elaborated on some exciting research gaps found in the current literature, Finally, the paper summarizes the general detection approaches and suggests potential research directions for the future

    The Childfree Phenomenon in Indonesia: An Analysis of Sentiments on YouTube Video Comments

    Get PDF
    Childfree is a condition in which a person or couple decides not to have children in marriage. Childfree became popular in Indonesia when YouTuber and influencer Gita Savitri uploaded an Instagram story about it. This brought many pros and cons among the people towards the freedom to have children. Many TV broadcasts and YouTube videos cover this phenomenon. Several YouTube channels that broadcast this phenomenon are Menjadi Manusia and Analisa Channel. We collect YouTube comment data using web scraping techniques. From September 2021 to September 2022, 674 sample data points were obtained from two YouTube videos. Data is labelled (positive, negative, and neutral) using the Indonesian language lexicon approach as well as the Support Vector Machine (SVM) and Random Forest algorithms to determine the best model for classifying YouTube comments. The purpose of this research is to understand the public's perception of childfree and to compare the accuracy and AUC values of the two methods. Based on the results of the analysis, 128 comments are classified as positive, the remaining 39 comments are classified as neutral, and 503 comments are classified as negative. This shows that that the commentators on YouTube do not support or give a negative stigma to people who adhere to childfree. The solution to the balanced data problem for each sentiment class uses the random oversampling (ROS) approach. The RBF kernel SVM classification algorithm is a suitable method for classifying commentary data with an accuracy of 98.01% and an AUC of 98.58%, while the Random Forest algorithm only obtains an accuracy of 94.37% and an AUC of 95.87%

    Self-supervised learning for transferable representations

    Get PDF
    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Rather Than to Seem: Black and Indigenous Narratives in a Stormy, Swampy South

    Get PDF
    I examine the narratives of the South that have been historically overlooked, ignored, or hidden in order to establish a dominant narrative of the region. The narratives examined here are by southern Black and Indigenous authors who restore lost knowledge and offer histories that help complete the South culturally and ecologically. The conceptual methodology develops from LeAnne Howe’s tribalography which explains that Indigenous people create stories and histories that transform the space around them and offer an understanding of the world around us. Another methodology used is Anthony Wilson’s ecocritical swamp studies; each chapter analyzes a narrative centered around a swamp, a low-lying area, or a hurricane. I also use the concept of ecotones or spaces of transition, necessary spaces of invisible work as each author supplies a space created by knowledge, history, and story to do the necessary work of leading their readers through the middle of a dominant narrative to the unburied truth. The selected texts offer perspectives of a violent past and how this violence affected the ecology of their home as well as how the natural world was seen and cared for. Traditional Ecological Knowledge (TEK) is an example used in one chapter that illuminates what has been lost due to colonization and white supremacy. I examine Black and Indigenous authors as these two people groups have suffered the most under colonization in the South and have witnessed the most violence. Their voices offer the South a way to confront its past and renew both land and human relations with access to hidden stories and forgotten knowledge

    The Individual And Their World

    Get PDF

    Mechanical characterization, constitutive modeling and applications of ultra-soft magnetorheological elastomers

    Get PDF
    Mención Internacional en el título de doctorSmart materials are bringing sweeping changes in the way humans interact with engineering devices. A myriad of state-of-the-art applications are based on novel ways to actuate on structures that respond under different types of stimuli. Among them, materials that respond to magnetic fields allow to remotely modify their mechanical properties and macroscopic shape. Ultra-soft magnetorheological elastomers (MREs) are composed of a highly stretchable soft elastomeric matrix in the order of 1 kPa and magnetic particles embedded in it. This combination allows large deformations with small external actuations. The type of the magnetic particles plays a crucial role as it defines the reversibility or remanence of the material magnetization. According to the fillers used, MREs are referred to as soft-magnetic magnetorheological elastomers (sMREs) and hard-magnetic magnetorheological elastomers (hMREs). sMREs exhibit strong changes in their mechanical properties when an external magnetic field is applied, whereas hMREs allow sustained magnetic effects along time and complex shape-morphing capabilities. In this regard, end-of-pipe applications of MREs in the literature are based on two major characteristics: the modification of their mechanical properties and macrostructural shape changes. For instance, smart actuators, sensors and soft robots for bioengineering applications are remotely actuated to perform functional deformations and autonomous locomotion. In addition, hMREs have been used for industrial applications, such as damping systems and electrical machines. From the analysis of the current state of the art, we identified some impediments to advance in certain research fields that may be overcome with new solutions based on ultrasoft MREs. On the mechanobiology area, we found no available experimental methodologies to transmit complex and dynamic heterogeneous strain patterns to biological systems in a reversible manner. To remedy this shortcoming, this doctoral research proposes a new mechanobiology experimental setup based on responsive ultra-soft MRE biological substrates. Such an endeavor requires deeper insights into the magneto-viscoelastic and microstructural mechanisms of ultra-soft MREs. In addition, there is still a lack of guidance for the selection of the magnetic fillers to be used for MREs and the final properties provided to the structure. Eventually, the great advances on both sMREs and hMREs to date pose a timely question on whether the combination of both types of particles in a hybrid MRE may optimize the multifunctional response of these active structures. To overcome these roadblocks, this thesis provides an extensive and comprehensive experimental characterization of ultra-soft sMREs, hMREs and hybrid MREs. The experimental methodology uncovers magneto-mechanical rate dependences under numerous loading and manufacturing conditions. Then, a set of modeling frameworks allows to delve into such mechanisms and develop three ground-breaking applications. Therefore, the thesis has lead to three main contributions. First and motivated on mechanobiology research, a computational framework guides a sMRE substrate to transmit complex strain patterns in vitro to biological systems. Second, we demonstrate the ability of remanent magnetic fields in hMREs to arrest cracks propagations and improve fracture toughness. Finally, the combination of soft- and hard-magnetic particles is proved to enhance the magnetorheological and magnetostrictive effects, providing promising results for soft robotics.Los materiales inteligentes están generando cambios radicales en la forma que los humanos interactúan con dispositivos ingenieriles. Distintas aplicaciones punteras se basan en formas novedosas de actuar sobre materiales que responden a diferentes estímulos. Entre ellos, las estructuras que responden a campos magnéticos permiten la modificación de manera remota tanto de sus propiedades mecánicas como de su forma. Los elastómeros magnetorreológicos (MREs) ultra blandos están compuestos por una matriz elastomérica con gran ductilidad y una rigidez en torno a 1 kPa, reforzada con partículas magnéticas. Esta combinación permite inducir grandes deformaciones en el material mediante la aplicación de campos magnéticos pequeños. La naturaleza de las partículas magnéticas define la reversibilidad o remanencia de la magnetización del material compuesto. De esta manera, según el tipo de partículas que contengan, los MREs pueden presentar magnetización débil (sMRE) o magnetización fuerte (hMRE). Los sMREs experimentan grandes cambios en sus propiedades mecánicas al aplicar un campo magnético externo, mientras que los hMREs permiten efectos magneto-mecánicos sostenidos a lo largo del tiempo, así como programar cambios de forma complejos. En este sentido, las aplicaciones de los MREs se basan en dos características principales: la modificación de sus propiedades mecánicas y los cambios de forma macroestructurales. Por ejemplo, los campos magnéticos pueden emplearse para inducir deformaciones funcionales en actuadores y sensores inteligentes, o en robótica blanda para bioingeniería. Los hMREs también se han aplicado en el ámbito industrial en sistemas de amortiguación y máquinas eléctricas. A partir del análisis del estado del arte, se identifican algunas limitaciones que impiden el avance en ciertos campos de investigación y que podrían resolverse con nuevas soluciones basadas en MREs ultra blandos. En el área de la mecanobiología, no existen metodologías experimentales para transmitir patrones de deformación complejos y dinámicos a sistemas biológicos de manera reversible. En esta investigación doctoral se propone una configuración experimental novedosa basada en sustratos biológicos fabricados con MREs ultra blandos. Dicha solución requiere la identificación de los mecanismos magneto-viscoelásticos y microestructurales de estos materiales, según el tipo de partículas magnéticas, y las consiguientes propiedades macroscópicas del material. Además, investigaciones recientes en sMREs y hMREs plantean la pregunta sobre si la combinación de distintos tipos de partículas magnéticas en un MRE híbrido puede optimizar su respuesta multifuncional. Para superar estos obstáculos, la presente tesis proporciona una caracterización experimental completa de sMREs, hMREs y MREs híbridos ultra blandos. Estos resultados muestran las dependencias del comportamiento multifuncional del material con la velocidad de aplicación de cargas magneto-mecánicas. El desarrollo de un conjunto de modelos teórico-computacionales permite profundizar en dichos mecanismos y desarrollar aplicaciones innovadoras. De este modo, la tesis doctoral ha dado lugar a tres bloques de aportaciones principales. En primer lugar, este trabajo proporciona un marco computacional para guiar el diseño de sustratos basados en sMREs para transmitir patrones de deformación complejos in vitro a sistemas biológicos. En segundo lugar, se demuestra la capacidad de los campos magnéticos remanentes en los hMRE para detener la propagación de grietas y mejorar la tenacidad a la fractura. Finalmente, se establece que la combinación de partículas magnéticas de magnetización débil y fuerte mejora el efecto magnetorreológico y magnetoestrictivo, abriendo nuevas posibilidades para el diseño de robots blandos.I want to acknowledge the support from the Ministerio de Ciencia, Innovación y Universidades, Spain (FPU19/03874), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947723, project: 4D-BIOMAP).Programa de Doctorado en Ingeniería Mecánica y de Organización Industrial por la Universidad Carlos III de MadridPresidente: Ramón Eulalio Zaera Polo.- Secretario: Abdón Pena Francesch.- Vocal: Laura de Lorenzi

    The Knowledge Graph Construction in the Educational Domain: Take an Australian School Science Course as an Example

    Get PDF
    The evolution of the Internet technology and artificial intelligence has changed the ways we gain knowledge, which has expanded to every aspect of our lives. In recent years, Knowledge Graphs technology as one of the artificial intelligence techniques has been widely used in the educational domain. However, there are few studies dedicating the construction of knowledge graphs for K-10 education in Australia, and most of the existing studies only focus on at the theory level, and little research shows practical pipeline steps to complete the complex flow of constructing the educational knowledge graph. Apart from that, most studies focused on concept entities and their relations but ignored the features of concept entities and the relations between learning knowledge points and required learning outcomes. To overcome these shortages and provide the data foundation for the development of downstream research and applications in this educational domain, the construction processes of building a knowledge graph for Australian K-10 education were analyzed at the theory level and implemented in a practical way in this research. We took the Year 9 science course as a typical data source example fed to the proposed method called K10EDU-RCF-KG to construct this educational knowledge graph and to enrich the features of entities in the knowledge graph. In the construction pipeline, a variety of techniques were employed to complete the building process. Firstly, the POI and OCR techniques were applied to convert Word and PDF format files into text, followed by developing an educational resources management platform where the machine-readable text could be stored in a relational database management system. Secondly, we designed an architecture framework as the guidance of the construction pipeline. According to this architecture, the educational ontology was initially designed, and a backend microservice was developed to process the entity extraction and relation extraction by NLP-NER and probabilistic association rule mining algorithms, respectively. We also adopted the NLP-POS technique to find out the neighbor adjectives related to entitles to enrich features of these concept entitles. In addition, a subject dictionary was introduced during the refinement process of the knowledge graph, which reduced the data noise rate of the knowledge graph entities. Furthermore, the connections between learning outcome entities and topic knowledge point entities were directly connected, which provides a clear and efficient way to identify what corresponding learning objectives are related to the learning unit. Finally, a set of REST APIs for querying this educational knowledge graph were developed

    Deploying Artificial Intelligence to Combat Covid-19 Misinformation on Social Media: Technological and Ethical Considerations

    Get PDF
    This paper reports on research into online misinformation pertaining to the COVID-19 pandemic using artificial intelligence. This is part of our longer-term goal, i.e., the development of an artificial intelligence (machine-learning) tool to assist social media platforms, online service providers and government agencies in identifying and responding to misinformation on social media. We report herein on the predictive accuracy accomplished by applying a combination of technologies, including a custom-designed web-crawler, The Dark Crawler (TDC) and the Posit toolkit, a text-reading software solution designed by George Weir of University of Strathclyde. Overall, we found that performance of models based upon Posit-derived textual features showed high levels of correlation to the pre-determined (manual and machine-driven) data classifications. We further argue that the harms associated with COVID-19 misinformation — e.g., the social and economic damage, and the deaths and severe illnesses — outweigh the right to personal privacy and freedom of speech considerations
    corecore