4,794 research outputs found
Study Guide Mathematical Modeling for Decision Making II DA 3410
The mission of the U.S. Army Special Operations Command is to organize, train, educate, man, equip, fund, administer, mobilize, deploy and sustain Army special operations forces to successfully conduct worldwide special operations, across the range of military operations, in support of regional combatant commanders, American ambassadors and other agencies as directed
Fuzzy Logic
The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
Co-constructing a new framework for evaluating social innovation in marginalized rural areas
The EU funded H2020 project \u2018Social Innovation in Marginalised Rural Areas\u2019 (SIMRA; www.simra-h2020.eu) has the overall objective of advancing the state-of-the-art in social innovation. This paper outlines the process for co- developing an evaluation framework with stakeholders, drawn from across Europe and the Mediterranean area, in the fields of agriculture, forestry and rural development. Preliminary results show the importance of integrating process and outcome-oriented evaluations, and implementing participatory approaches in evaluation practice. They also raise critical issues related to the comparability of primary data in diverse regional contexts and highlight the need for mixed methods approaches in evaluation
Multispace & Multistructure. Neutrosophic Transdisciplinarity (100 Collected Papers of Sciences), Vol. IV
The fourth volume, in my book series of âCollected Papersâ, includes 100 published and unpublished articles, notes, (preliminary) drafts containing just ideas to be further investigated, scientific souvenirs, scientific blogs, project proposals, small experiments, solved and unsolved problems and conjectures, updated or alternative versions of previous papers, short or long humanistic essays, letters to the editors - all collected in the previous three decades (1980-2010) â but most of them are from the last decade (2000-2010), some of them being lost and found, yet others are extended, diversified, improved versions. This is an eclectic tome of 800 pages with papers in various fields of sciences, alphabetically listed, such as: astronomy, biology, calculus, chemistry, computer programming codification, economics and business and politics, education and administration, game theory, geometry, graph theory, information fusion, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, psychology, quantum physics, scientific research methods, and statistics. It was my preoccupation and collaboration as author, co-author, translator, or cotranslator, and editor with many scientists from around the world for long time. Many topics from this book are incipient and need to be expanded in future explorations
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Artificial Intelligence for detection and prevention of mold contamination in tomato processing
openIl presente elaborato si propone di analizzare l'uso dell'intelligenza artificiale attraverso il
riconoscimento di immagini per rilevare la presenza di muffa nei pomodori durante il processo
di essiccazione. La muffa nei pomodori rappresenta un rischio sia per la salute umana sia per
l'industria alimentare, comportando, anche, una serie di problemi che vanno oltre l'aspetto
estetico. Essa è causata principalmente da funghi che si diffondono rapidamente sulla
superficie dei pomodori. Tale processo compromette cosĂŹ la qualitĂ con la conseguente
produzione di tossine che possono influire sulla salute umana.
L'obiettivo sperimentale di questo lavoro è il problema dello spreco e della perdita di prodotto
nell'industria alimentare. Quando i pomodori sono colpiti da muffe, infatti, diventano inadatti
al consumo, con conseguente perdita di cibo. Lo spreco di pomodori a causa delle muffe
rappresenta anche la perdita di preziose risorse, utili alla produzione, come terra, acqua,
energia e tempo. Il proposito è testare, anche nella fase iniziale, la capacità di un algoritmo di
rilevamento degli oggetti per identificare la muffa, e adottare misure preventive. L'analisi
sperimentale ha previsto l'addestramento dell'algoritmo con un'ampia serie di foto, tra cui
pomodori sani e rovinati di diversi tipi, forme e consistenze. Per etichettare le immagini e
creare le epoche di addestramento è stato quindi utilizzato YOLOv7, l'algoritmo di
rilevamento degli oggetti scelto, basato su reti neurali. Per valutare le prestazioni sono state
utilizzate metriche di valutazione, tra cui âPrecisionâ e âRecallâ.
L'ipotesi di applicazione dell'intelligenza artificiale in futuro sarĂ un grande potenziale per
migliorare i processi di produzione alimentare, facilitando, cosĂŹ, l'identificazione delle muffe.
Il rilevamento rapido delle muffe faciliterebbe la separazione tempestiva dei prodotti
contaminati, riducendo cosĂŹ il rischio di diffusione delle tossine e preservando la qualitĂ degli
alimenti non contaminati. Questo approccio contribuirebbe a ridurre al minimo gli sprechi
alimentari e le inefficienze delle risorse associate allo scarto di grandi quantitĂ di prodotto.
Inoltre, l'integrazione della computer vision nel contesto dell'HACCP (Hazard Analysis
Critical Control Points) potrebbe migliorare i protocolli di sicurezza alimentare grazie a un
rilevamento accurato e tempestivo. Questa tecnologia potrĂ offrire, dando prioritĂ alla
prevenzione, una promettente opportunitĂ per migliorare la qualitĂ , l'efficienza e la
sostenibilitĂ dei futuri processi di produzione alimentare.This study investigates the use of computer vision couples with artificial intelligence to detect
mold in tomatoes during the drying process.
Mold presence in tomatoes poses threats to human health and the food industry as it leads to
several issues beyond appearance. It is primarily caused by fungi that spread rapidly over the
tomato surface, compromising their quality, and potentially producing toxins that can harm
human health.
The experimental aim of this work focused on the issue of wastage and loss within the food
industry. When tomatoes succumb to mold, they become unsuitable for consumption, resulting
in a loss of food and resources. Considering that tomato production requires resources such as
land, water, energy, and time, wasting tomatoes due to mold also represents a waste of these
valuable resources.
The goal was to evaluate the mold detection capabilities of an object detection algorithm,
particularly in its early stages, to facilitate preventative measures. This experimental analysis
entailed training the algorithm with an extensive array of images, encompassing a variety of
healthy and spoiled tomatoes of different shapes, types, textures and drying stages. The chosen
object detection algorithm, YOLOv7, is convolutional neural network-based and was utilized
for image labeling and training epochs. Evaluation metrics, including precision and recall,
were utilized to assess the algorithm's performance.
The implementation of artificial intelligence in the future has significant potential for
enhancing food production processes by streamlining mold identification. Prompt mold
detection would expedite segregation of contaminated products, thus reducing the risk of toxin
dissemination and preserving the quality of uncontaminated food. This approach could
minimize food waste and resource inefficiencies linked to discarding significant product
amounts. Furthermore, integrating computer vision in the HACCP (Hazard Analysis Critical
Control Points) context could enhance food safety protocols via accurate and prompt
detection. By prioritizing prevention, this technology offers a promising chance to optimize
quality, efficiency, and sustainability of future food production processes
Network Centralities in Polycentric Urban Regions: Methods for the Measurement of Spatial Metrics
The primary aim of this thesis is to explain the complex spatial organisations of polycentric urban regions (PURs). PURs are a form of regional morphology that often evolves from post-industrial structures and describe a subnational area featuring a plurality of urban centres. As of today, the analysis of the spatial organisation of PURs constitutes a hitherto uncharted territory. This is due to PURsâ inherent complexity that poses challenges for their conceptualisation. In this context, this thesis reviews theories on the spatial organisation of regions and cities and seeks to make a foundational methodological contribution by joining space syntax and central place theory in the conceptualisation of polycentric urban regions. It takes into account human agency embedded in the physical space, as well as the reciprocal effect of the spatial organisation for the emergence of centralities and demonstrates how these concepts can give insights into the fundamental regional functioning. The thesis scrutinises the role that the spatial organisation plays in such regions, in terms of organising flows of goods and people, ordering locational occupation and fostering centres of commercial activity. It proposes a series of novel measurements and techniques to analyse large and messy datasets. This includes a method for the application of large-scale volunteered geographic information in street network analysis. This is done, in the context of two post-industrial regions: the German Ruhr Valley and the British Nottinghamshire, Derbyshire and Yorkshire region. The thesisâ contribution to the understanding of regional spatial organisation and the study of regional morphology lies in the identification of spatial structural features of socio-economic potentials of regions and particular areas within them. It constitutes the first comparative study of comprehensive large-scale regional spatial networks and presents a framework for the analysis of regions and the evaluation of the predictive potential of spatial networks for socio-economic patterns and the location of centres in regional contexts
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