208 research outputs found

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023

    Towards a circular economy: fabrication and characterization of biodegradable plates from sugarcane waste

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    Bagasse pulp is a promising material to produce biodegradable plates. Bagasse is the fibrous residue that remains after sugarcane stalks are crushed to extract their juice. It is a renewable resource and is widely available in many countries, making it an attractive alternative to traditional plastic plates. Recent research has shown that biodegradable plates made from Bagasse pulp have several advantages over traditional plastic plates. For example, they are more environmentally friendly because they are made from renewable resources and can be composted after use. Additionally, they are safer for human health because they do not contain harmful chemicals that can leach into food. The production process for Bagasse pulp plates is also relatively simple and cost-effective. Bagasse is first collected and then processed to remove impurities and extract the pulp. The pulp is then molded into the desired shape and dried to form a sturdy plate. Overall, biodegradable plates made from Bagasse pulp are a promising alternative to traditional plastic plates. They are environmentally friendly, safe for human health, and cost-effective to produce. As such, they have the potential to play an important role in reducing plastic waste and promoting sustainable practices. Over the years, the world was not paying strict attention to the impact of rapid growth in plastic use. As a result, uncontrollable volumes of plastic garbage have been released into the environment. Half of all plastic garbage generated worldwide is made up of packaging materials. The purpose of this article is to offer an alternative by creating bioplastic goods that can be produced in various shapes and sizes across various sectors, including food packaging, single-use tableware, and crafts. Products made from bagasse help address the issue of plastic pollution. To find the optimum option for creating bagasse-based biodegradable dinnerware in Egypt and throughout the world, researchers tested various scenarios. The findings show that bagasse pulp may replace plastics in biodegradable packaging. As a result of this value-added utilization of natural fibers, less waste and less of it ends up in landfills. The practical significance of this study is to help advance low-carbon economic solutions and to produce secure bioplastic materials that can replace Styrofoam in tableware and food packaging production

    Deep learning for clinical decision support in oncology

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    In den letzten Jahrzehnten sind medizinische Bildgebungsverfahren wie die Computertomographie (CT) zu einem unersetzbaren Werkzeug moderner Medizin geworden, welche eine zeitnahe, nicht-invasive Begutachtung von Organen und Geweben ermöglichen. Die Menge an anfallenden Daten ist dabei rapide gestiegen, allein innerhalb der letzten Jahre um den Faktor 15, und aktuell verantwortlich für 30 % des weltweiten Datenvolumens. Die Anzahl ausgebildeter Radiologen ist weitestgehend stabil, wodurch die medizinische Bildanalyse, angesiedelt zwischen Medizin und Ingenieurwissenschaften, zu einem schnell wachsenden Feld geworden ist. Eine erfolgreiche Anwendung verspricht Zeitersparnisse, und kann zu einer höheren diagnostischen Qualität beitragen. Viele Arbeiten fokussieren sich auf „Radiomics“, die Extraktion und Analyse von manuell konstruierten Features. Diese sind jedoch anfällig gegenüber externen Faktoren wie dem Bildgebungsprotokoll, woraus Implikationen für Reproduzierbarkeit und klinische Anwendbarkeit resultieren. In jüngster Zeit sind Methoden des „Deep Learning“ zu einer häufig verwendeten Lösung algorithmischer Problemstellungen geworden. Durch Anwendungen in Bereichen wie Robotik, Physik, Mathematik und Wirtschaft, wurde die Forschung im Bereich maschinellen Lernens wesentlich verändert. Ein Kriterium für den Erfolg stellt die Verfügbarkeit großer Datenmengen dar. Diese sind im medizinischen Bereich rar, da die Bilddaten strengen Anforderungen bezüglich Datenschutz und Datensicherheit unterliegen, und oft heterogene Qualität, sowie ungleichmäßige oder fehlerhafte Annotationen aufweisen, wodurch ein bedeutender Teil der Methoden keine Anwendung finden kann. Angesiedelt im Bereich onkologischer Bildgebung zeigt diese Arbeit Wege zur erfolgreichen Nutzung von Deep Learning für medizinische Bilddaten auf. Mittels neuer Methoden für klinisch relevante Anwendungen wie die Schätzung von Läsionswachtum, Überleben, und Entscheidungkonfidenz, sowie Meta-Learning, Klassifikator-Ensembling, und Entscheidungsvisualisierung, werden Wege zur Verbesserungen gegenüber State-of-the-Art-Algorithmen aufgezeigt, welche ein breites Anwendungsfeld haben. Hierdurch leistet die Arbeit einen wesentlichen Beitrag in Richtung einer klinischen Anwendung von Deep Learning, zielt auf eine verbesserte Diagnose, und damit letztlich eine verbesserte Gesundheitsversorgung insgesamt.Over the last decades, medical imaging methods, such as computed tomography (CT), have become an indispensable tool of modern medicine, allowing for a fast, non-invasive inspection of organs and tissue. Thus, the amount of acquired healthcare data has rapidly grown, increased 15-fold within the last years, and accounts for more than 30 % of the world's generated data volume. In contrast, the number of trained radiologists remains largely stable. Thus, medical image analysis, settled between medicine and engineering, has become a rapidly growing research field. Its successful application may result in remarkable time savings and lead to a significantly improved diagnostic performance. Many of the work within medical image analysis focuses on radiomics, i. e. the extraction and analysis of hand-crafted imaging features. Radiomics, however, has been shown to be highly sensitive to external factors, such as the acquisition protocol, having major implications for reproducibility and clinical applicability. Lately, deep learning has become one of the most employed methods for solving computational problems. With successful applications in diverse fields, such as robotics, physics, mathematics, and economy, deep learning has revolutionized the process of machine learning research. Having large amounts of training data is a key criterion for its successful application. These data, however, are rare within medicine, as medical imaging is subject to a variety of data security and data privacy regulations. Moreover, medical imaging data often suffer from heterogeneous quality, label imbalance, and label noise, rendering a considerable fraction of deep learning-based algorithms inapplicable. Settled in the field of CT oncology, this work addresses these issues, showing up ways to successfully handle medical imaging data using deep learning. It proposes novel methods for clinically relevant tasks, such as lesion growth and patient survival prediction, confidence estimation, meta-learning and classifier ensembling, and finally deep decision explanation, yielding superior performance in comparison to state-of-the-art approaches, and being applicable to a wide variety of applications. With this, the work contributes towards a clinical translation of deep learning-based algorithms, aiming for an improved diagnosis, and ultimately overall improved patient healthcare

    Review of Particle Physics

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    The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE

    Artificial intelligence driven anomaly detection for big data systems

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    The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces

    Development of a Tomographic Atmospheric Monitoring System based on Differential Optical Absorption Spectroscopy

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    The aim of this thesis is to describe the design and development of a proof of concept for a commercially viable large area atmospheric analysis tool, for use in trace gas concentration mapping and quanti cation. Atmospheric monitoring is a very well researched eld, with dozens of available analytical systems and subsystems. However, current systems require a very important compromise between spatial and operational complexity. We address this issue asking how we could integrate the Di erential Optical Absorption Spectroscopy (DOAS) atmospheric analysis technique in a Unmanned Aerial Vehicle (UAV) with tomographic capabilities. Using a two-part methodology, I proposed two hypotheses for proving the possibility of a miniaturised tomographic system, both related to how the spectroscopic data is acquired. The rst hypothesis addresses the projection forming aspect of the acquisition, its matrix assembly and the resolution of the consequent equations. This hypothesis was con rmed theoretically by the development of a simulation platform for the reconstruction of a trace gas concentration mapping. The second hypothesis deals with the way in which data is collected in spectroscopic terms. I proposed that with currently available equipment, it should be possible to leverage a consequence of the Beer-Lambert law to produce molecular density elds for trace gases using passiveDOAS. This hypothesiswas partially con rmed, with de nite conclusions being possible only through the use of complex autonomous systems for improved accuracy. This work has been a very important rst step in the establishment of DOAS tomography as a commercially viable solution for atmospheric monitoring, although further studies are required for de nite results. Moreover, this thesis has conducted to the development of a DOAS software library for Python that is currently being used in a production environment. Finally, it is important to mention that two journal articles were published from pursuing this work, both in important journals with Impact Factors over 3.0.Era o objectivo deste trabalho descrever o processo de desenho e implementação de uma prova de conceito para um sistema de avaliação atmosférica comercialmente viável, para uso no mapeamento das concentrações de compostos traço na atmosfera. A avaliação atmosférica é um campo muito estudado, estando no presente momento disponíveis para instalação diversos sistemas e subsistemas com estas capacidades. No entanto, é marcante o compromisso que se veri ca entre a resolução espacial e a complexidade operacional destes equipamentos. Nesta tese, desa o este problema e levanto a questão sobre como se poderia desenvolver um sistema com os mesmos ns, mas sem este premente compromisso. Usando uma metodologia a duas partes, proponho duas hipóteses para comprovar a exequibilidade deste sistema. A primeira diz respeito à formação da matriz tomográ ca e à resolução das equações que dela derivam e que formam a imagem que se pretende. Con rmei esta hipótese teoricamente através do desenvolvimento de uma plataforma de simulação para a reconstrução tomográ ca de um campo de concentrações fantoma. A segunda é dirigida a aquisição de dados espectroscópicos. Proponho que com o material presentemente disponível comercialmente, deverá ser possível aproveitar uma consequência da lei de Beer-Lambert para retirar os valores de concentração molecular de gases traço na atmosfera. Foi apenas possível validar esta hipótese parcialmente, sendo que resultados mais conclusivos necessitariam de equipamentos automatizados dos quais não foi possível dispôr. No nal, este trabalho constitui um importante primeiro passo no estabelecimento da técnica de DOAS tomográ co como uma alternativa comercialmente viável para a análise atmosférica. Ademais, o desenvolvimento desta tese levou à escrita de uma biblioteca em Python para análise de dados DOAS actualmente usada em ambiente de produção. Por m, importa realçar que dos trabalhos realizados no decorrer da tese foram publicados dois artigos em revistas cientí cas com Impact Factor acima de 3

    ECOS 2012

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    The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

    Review of Particle Physics

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    The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app

    Mining Safety and Sustainability I

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    Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry
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