42 research outputs found
Operational Research: methods and applications
This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Operational research:methods and applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
Appetite and Satiety Control-Gut Mechanisms
The prevalence of obesity and its comorbidities, particularly type 2 diabetes, cardiovascular and hepatic disease and certain cancers, continues to rise worldwide. Paradoxically, despite an increasingly obesogenic environment, particularly in Western societies, undernutrition is also extremely common. The application of novel, sophisticated techniques, particularly related to imaging and molecular biology, has substantially advanced our understanding of the mechanisms controlling appetite and energy intake. This has led to a redefinition of many concepts, including the relative importance of central versus peripheral mechanisms, recognising that the gastrointestinal (GI) tract, particularly gut hormones, plays a critical role. Given the major advance in knowledge in the field, this Special Issue provides a comprehensive overview of the GI mechanisms underlying the regulation of appetite and energy intake, as a series of definitive reviews by international authorities. The reviews address gut-related mechanisms, including nutrient sensing, gut hormones and GI motility, gut-brain communication, including the roles of the vagus and the modulation of reward perception, the roles of diet and the microbiota, as well as the abnormalities associated with eating disorders, specifically obesity and anorexia of ageing, and the beneficial effects of bariatric surgery. The reviews cover both preclinical research and studies in humans, and are complemented by a number of important original papers
Medición de la eficiencia y la productividad: Aspectos computacionales
Programa de Doctorado en EconomÃa (DECiDE)The purpose of efficiency and productivity problems is based on evaluating whether the use of the resources available (inputs) by a company or public institution (in general, any decision-making unit) corresponds or not with the optimal way of operating in such a way as to generate the largest possible number of outputs. To carry out this type of calculations, several mathematical models have already been proposed in the specialized literature that can be used, all of which are based on Mathematical Programming problems, and, in particular, some of them correspond to Mixed Integer Linear Programming problems (MILP). These types of problems combine several types of variables, continuous and discrete, in the same mathematical model as well as numerous restrictions, depending on the nature of the problem; features that can make the resolution process somewhat difficult. In addition, it is worth noting that these problems tend to be combinatorial in practice (NP-hard). Throughout this work, the analysis and study will focus on a field within the area of Operations Research called Data Envelopment Analysis (DEA), whose main objective is the estimation of production frontiers and the measurement of productive efficiency. Different optimization models belonging to this field will be put to the test in this thesis from a purely computational perspective, being solved through different techniques, both 2 exact and approximate, analyzing the performance and the difficulty of the same. The main objective of this work does not lie in the development and modeling of new problems in the field of DEA, but in how to achieve optimal solutions in a reasonable time for certain problems of a combinatorial nature, given that being NP-hard type problems, as the size of the problem grows, so does the difficulty of obtaining optimal solutions, especially in a short time. At this point, we will focus on the study and design of approximation techniques, known in the literature as Metaheuristics, closely linked to Machine Learning or Artificial Intelligence methodologies. In addition to these methodologies, based on learning and improving the solutions obtained, parallelization techniques have also been incorporated, capable of efficiently reducing the time needed to obtain optimal solutions in complex problems.La finalidad de los problemas de eficiencia y productividad se basan en evaluar si el uso de los recursos (entradas o inputs, en inglés) disponibles por parte de una empresa o institución pública (en general, cualquier unidad tomadora de decisiones) se corresponde o no con la forma óptima de operar de dicha entidad, generando la mayor cantidad de salidas posible (outputs en inglés). Para llevar a cabo este tipo de cálculos, varios modelos matemáticos han sido ya planteados en la literatura especializada que pueden ser utilizados, teniendo en común todos ellos que están basados en problemas de Programación Matemática, y, en particular, algunos de ellos se corresponden con problemas de Programación Matemática Lineal Mixta (Mixed Integer Linear Programming en inglés – MILP). Este tipo de problemas combinan en un mismo modelo matemático varios tipos de variables, continuas y discretas, asà como numerosas restricciones, dependiendo de la naturaleza del problema, siendo estas restricciones caracterÃsticas que pueden hacer que el proceso de resolución resulte ser algo difÃcil. Además, cabe destacar la caracterÃstica de que estos problemas suelen ser en la práctica de tipo combinatorio (NP-duros). A lo largo de este trabajo, el análisis y el estudio se va a centrar en un campo dentro del área de Investigación Operativa denominado Análisis Envolvente de Datos (Data Envelopment Analysis en inglés - DEA), cuyo principal objetivo es el de la estimación de fronteras de producción y la medición de la eficiencia productiva. Diferentes modelos de optimización pertenecientes a este ámbito serán puestos a prueba en esta tesis desde una perspectiva puramente computacional, siendo resueltos a través de diferentes técnicas, tanto exactas como de aproximación, analizando el rendimiento y la dificultad del mismo.
El objetivo principal de este trabajo no reside en el desarrollo y modelado de nuevos problemas en el ámbito del DEA, sino en cómo conseguir soluciones óptimas y eficientes en un tiempo razonable para ciertos problemas de naturaleza combinatoria, dado que al ser problemas de tipo NP-duro, a medida que el tamaño del problema crece, también lo hace la dificultad de obtener soluciones óptimas, sobre todo en un tiempo reducido. En este punto, centraremos la atención en el estudio y diseño de técnicas de aproximación, conocidas en la literatura como MetaheurÃsticas, estando muy ligadas a metodologÃas de Machine Learning o Artificial Inteligence. Además de estas metodologÃas, basadas en el aprendizaje y la mejora de las soluciones obtenidas, también se han incorporado técnicas de paralelismo, capaces de reducir de forma eficiente el tiempo necesario para obtener soluciones óptimas en problemas complejos
Vincristine-Induced Peripheral Neuropathy: Assessing Preventable Strategies in Paediatric Acute Lymphoblastic Leukaemia
Background: Acute Lymphoblastic Leukaemia is the most common cancer experienced by children with overall survival rates now exceeding 90%. However, most children will experience vincristine-induced peripheral neuropathy (VIPN) during treatment resulting in sensory-motor abnormalities. To date, there are no approved preventative therapeutics or mitigation strategies for VIPN. This body of work set out to: (1) establish a high-throughput and high-content assay with the capacity to identify neuroprotective compounds, (2) test the feasibility of repurposing olesoxime as a neuroprotectant, and (3) compare traditional statistical methods with machine learning models to identify patients at risk of VIPN.
Methods: (1) In vitro neuronal cultures were exposed to vincristine to recapitulate the VIPN phenotype and olesoxime assessed as a positive control. The neurotoxicity assay was miniaturised in 384-well microplates with automation steps to reduce manual handling. (2) Olesoxime and vincristine were applied to proliferating malignant cell lines to ensure the efficacy of vincristine was maintained. (3) Machine learning algorithms were developed using data from a local retrospective cohort to predict VIPN.
Results: (1) Neurite length was reduced in a dose-responsive manner with vincristine. Assay miniaturisation and automation steps helped facilitate a high-throughput workflow. An optimised multiplexed dye solution enabled image acquisition and neurite quantification. Further, olesoxime was found to protect neurites and deemed suitable as a positive control (2) Cell viability assays confirmed olesoxime did not interfere with vincristine efficacy in leukemia cells. (3) Machine learning algorithms showed equivalency to traditional univariate analysis. The observation of severe class imbalance meant that patients who were least susceptible to VIPN could be identified.
Conclusions: This body of work demonstrates the successful development of a neurotoxicity assay suitable for neuroprotectant drug discovery. Olesoxime was found suitable as a positive control in the assay. Further, viability studies indicated that vincristine retains it efficacy with olesoxime, opening the possibility of its use as an adjunctive therapy. Finally, this work developed machine learning models with the capacity to identify patients with VIPN-free survival. The utility of this model may mean that it can be used to stratify patients prospectively in the clinic based on favourable clinical features
pHealth 2021. Proc. of the 18th Internat. Conf. on Wearable Micro and Nano Technologies for Personalised Health, 8-10 November 2021, Genoa, Italy
Smart mobile systems – microsystems, smart textiles, smart implants, sensor-controlled medical devices – together with related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and the next generation of healthcare services. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, the improvement of patient experience.
This book presents the proceedings of pHealth 2021, the 18th in a series of conferences on wearable micro and nano technologies for personalized health with personal health management systems, hosted by the University of Genoa, Italy, and held as an online event from 8 – 10 November 2021. The conference focused on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The book contains 46 peer-reviewed papers (1 keynote, 5 invited papers, 33 full papers, and 7 poster papers). Subjects covered include the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, the Health Internet of Things (HIoT), as well as potential risks for security and privacy, and the motivation and empowerment of patients in care processes.
Providing an overview of current advances in personalized health and health management, the book will be of interest to all those working in the field of healthcare today