1,539 research outputs found

    Planetary Hinterlands:Extraction, Abandonment and Care

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    This open access book considers the concept of the hinterland as a crucial tool for understanding the global and planetary present as a time defined by the lasting legacies of colonialism, increasing labor precarity under late capitalist regimes, and looming climate disasters. Traditionally seen to serve a (colonial) port or market town, the hinterland here becomes a lens to attend to the times and spaces shaped and experienced across the received categories of the urban, rural, wilderness or nature. In straddling these categories, the concept of the hinterland foregrounds the human and more-than-human lively processes and forms of care that go on even in sites defined by capitalist extraction and political abandonment. Bringing together scholars from the humanities and social sciences, the book rethinks hinterland materialities, affectivities, and ecologies across places and cultural imaginations, Global North and South, urban and rural, and land and water

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Інформаційна система попередження наближення землі сучасного літака

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    Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник дипломної роботи: доцент кафедри авіоніки, Чужа Олексій ОлександровичIn the late 1960s, a series of Controlled Flight Into Terrain (CFIT) accidents, in which the pilots did not lose control of the aircraft, killed hundreds of passengers. Controlled flight into terrain (CFIT) is an accident in which an aircraft strikes the ground, water, or an obstacle without the pilot flying losing control. Although a mechanical problem can be the cause of a CFIT, pilot error is the most common factor. It may be due to navigational error, weather misjudgement, lack of awareness of terrain height, or spatial disorientation. Accidents resulting from a voluntary action by the person flying, such as an act of terrorism or pilot suicide, are not considered CFIT, nor are situations where the aircraft is out of control at the time of impact.The term was invented by Boeing engineers in the late 1970s. According to Boeing, for the period 2003 to 2012, CFIT type of accident was the second most deadly after LOC (Lost Of Control), causing almost a thousand deaths in aircraft and among outsiders during this period. So, during the 1970s, numerous studies were conducted to discover the causes of these accidents. These accidents could have been avoided if the aircraft had been equipped with ground proximity warning systems (GPWS). In 1974, the Federal Aviation Administration declared GPWS mandatory on large aircraft to prevent accidents. In 2000, the FAA amended its operating rules to require that all US-registered turboprop aircraft with six or more passenger seats (excluding pilot and co-pilot seats) be equipped with an FAA-approved ground proximity warning system. The distance between the aircraft and the ground is measured by the radiosonde (or radio altimeter). Depending on the height and the flight configuration, the computer can inform the pilot of a danger by audio or visual messages. Nowadays, manufacturers and airlines are still constantly working to reduce accidents connected with CFIT. The most common solutions are improved pilot training, mainly by asking pilots to pay attention to their on-board instrumentation, but also the develop and improve of efficient safety systems, such as the ground proximity warning system (GPWS) that today became mandatory for all commercial aircrafts and not only. The main input data of modern GPWS systems are values from the radar altimeter and barometric altimeter sensors. When entering the GPWS system, these data are analyzed according to certain algorithms (which also take into account the current position of the mechanization of the wheels, the position of the chassis, etc.) by the on-board computer of the system, which then issues the appropriate visual and sound signals to the pilot.Наприкінці 1960-х років серія аварій з контрольованим польотом на землю (CFIT), в яких пілоти не втратили керування літаком, загинули сотні пасажирів. Контрольований політ на землю (CFIT) — це нещасний випадок, під час якого повітряне судно стикається з землею, вода або перешкода без втрати керування пілотом. Хоч і механічний проблема може бути причиною CFIT, помилка пілота є найпоширенішим фактором. Це може бути через до навігаційної помилки, неправильної оцінки погоди, відсутності інформації про висоту місцевості або просторову дезорієнтація. Аварії, що сталися внаслідок добровільних дій особи, яка здійснює політ, наприклад дії тероризм чи самогубство пілота не вважаються CFIT, а також ситуації, коли літак знаходиться поза контролем у момент зіткнення. Термін був винайдений інженерами Boeing наприкінці 1970-ті роки. За даними компанії Boeing, за період з 2003 по 2012 роки тип аварії CFIT був другий за смертоносністю після LOC (втрата контролю), спричинивши майже тисячу смертей у літаків і серед сторонніх осіб у цей період. Тому протягом 1970-х років було проведено численні дослідження, щоб виявити причини цього аварії. Цих аварій можна було б уникнути, якби літак був оснащений системи попередження про наближення до землі (GPWS). У 1974 році Федеральна авіаційна адміністрація оголосила GPWS обов'язковою для великих літаків щоб запобігти нещасним випадкам. У 2000 році FAA внесло зміни до своїх правил експлуатації, вимагаючи, щоб усі зареєстровані в США турбогвинтові двигуни повітряне судно з шістьма або більше пасажирськими місцями (за винятком місць пілота та другого пілота). із схваленою FAA системою попередження про наближення до землі. Відстань між літаком і землею вимірюється радіозондом (або радіо висотомір). Залежно від висоти та конфігурації польоту комп’ютер може інформувати пілот про небезпеку за допомогою звукових або візуальних повідомлень. Сьогодні виробники та авіакомпанії все ще постійно працюють над зменшенням кількості аварій пов'язаний з CFIT. Найпоширенішими рішеннями є вдосконалення підготовки пілотів, головним чином просять пілотів звернути увагу на свої бортові прилади, а також на розробку та вдосконалення ефективних систем безпеки, таких як система попередження про наближення до землі (GPWS), яка сьогодні стала обов'язковою для всіх комерційних літаків і не тільки. Основними вхідними даними сучасних систем GPWS є значення радіолокаційного висотоміра та датчики барометричного висотоміра. При вході в систему GPWS ці дані аналізуються за певними алгоритмами (які також враховують поточну позицію механізація коліс, положення шасі тощо) за допомогою бортового комп'ютера система, яка потім подає відповідні візуальні та звукові сигнали пілоту

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
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