Melothria scabra [Naudin] Provides New Opportunities for Agronomic Research

This manuscript attempts to bringMelothria scabra[Naudin] to the attention of agronomic researchers as an increasingly popularand economically important crop and to identify gaps in research that should beinvestigatedwithfuturestudies.Allrelevantpeerreviewedpublications were examined in this study, with 79% of the studies published since 2000 c.e., while the remainder of the studies provide historical context.Major gaps in the research involvingM. scabraoffers a new frontier in agronomic studies, and will increase agronomist’s knowledge of this uniquely meso-American crop species. In conclusion,M. scabrais an understudied crop with world-widecultivation, and offers many opportunities foragronomists to research the genetics, physiology, and morphology of this small melo

Classifying Road Debris Using Deep Learning Technique in Artificial Intelligence

According to a study done by AAA Foundation for Traffic Safety in 2016, road debris was a factor in an average number of 50,658 police-reported crashes between the years 2011-2014. This work addresses the critical problem of road debris detection and classification, a major threat to road safety, especially on highways. Road debris, such as barrels, car parts, puddles, salts, and trees, can cause accidents. Leveraging deep learning, we explored three pre-trained convolutional neural network (CNN) models - VGG16, MobileNetV2, and InceptionResNetV2 - to classify five types of road debris. We divided our dataset into training, validation, and testing sets, initially with 146, 73, and 49 images. After augmenting the dataset, we increased it to 875 training thermal images, 375 validation thermal images, and 114 testing thermal images. We evaluated the models\u27 performance over various epochs with a learning rate of 0.0001, an Adam optimizer, and a batch size of 10. The VGG16 model emerged as the top performer, boasting a 100% training accuracy and a 96.65% validation accuracy. In testing, it correctly classified 90.35% of the images. Visualized confusion matrices showed consistent superiority for the VGG16 model across all debris types. This work underscores the efficacy of deep learning models in detecting and classifying road debris, with VGG16 as the most accurate and efficient model. It also emphasizes the importance of image augmentation, significantly improving model performance by expanding the training dataset\u27s size and diversity. These findings have substantial implications for road safety. Implementing deep learning models for road debris detection can substantially reduce accidents, making roads safer for all users. Road authorities and safety organizations can leverage this research to develop automated systems for timely debris detection and removal, enhancing road safety

Evaluation of 3D Printed Immobilisation Shells for Head and Neck IMRT

This paper presents the preclinical evaluation of a novel immobilization system for patients undergoing external beam radiation treatment of head and neck tumors. An immobilization mask is manufactured directly from a 3-D model, built using the CT data routinely acquired for treatment planning so there is no need to take plaster of Paris moulds. Research suggests that many patients find the mould room visit distressing and so rapid prototyping could potentially improve the overall patient experience. Evaluation of a computer model of the immobilization system using an anthropomorphic phantom shows that >99% of vertices are within a tolerance of ±0.2 mm. Hausdorff distance was used to analyze CT slices obtained by rescanning the phantom with a printed mask in position. These results show that for >80% of the slices the median “worse-case” tolerance is approximately 4 mm. These measurements suggest that printed masks can achieve similar levels of immobilization to those of systems currently in clinical use

Optimization Across Decomposition : A Multiagent Model of Collaboration in Decomposed System Design

Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams--instead representing designers as agents which control all variables of the problem. This paper presents a new model of design which captures the distributed nature of complex systems design by decomposing the ability to control variables of the design to individual computational designers acting on a design problem with shared constraints. These designers are represented as a multiagent learning system which is shown in this paper to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space to achieve the best design outcome. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration across disciplinary boundaries in complex systems design

A Computational Framework for Resilience-Informed Design

It is desirable for complex engineered systems to perform missions efficiently and economically, even when these missions' complex, variable, long-term operational profiles make it likely for hazards to arise. It is thus important to design these systems to be resilient so that they will actively prevent and recover from hazards when they occur. To most effectively design a system to be resilient, the resilience of each design alternative should be quantified and valued so that it can be incorporated in the decision-making process. However, considering resilience in early design is challenging because resilience is a dynamic and stochastic property characterizing how the system performs over time in a set of unlikely-but-salient hazardous scenarios. Quantifying these properties thus requires a model to simulate the system's dynamic behavior and performance over the set of hazardous scenarios. Thus, to be able to incorporate resilience in the design process, there is a need to develop a framework which implements and integrates these models with design exploration and decision-making. This dissertation fulfills this need by defining resilience to enable fault simulations to be incorporated in decision-making, devising and implementing a modelling framework for early assessment of system resilience attributes, and exploring optimization architectures to efficiently structure the design exploration of resilience variables. Additionally, this dissertation provides a validity testing framework to determine when the resilient design process has been effective given the uncertainties present in the design problem. When each of these parts are used together, they comprise an overall framework that can be used to consider and incorporate system resilience in the early design process

CONHECENDO A REALIDADE DE UM ASSENTAMENTO RURAL

O projeto de extensão PRODUTERRA A&V Consultoria Junior é uma ação voluntária que visa levar informações e capacitação à pequenos produtores rurais, localizados nos arredores de Guarapuava-PR, além de enriquecer os conhecimentos teórico-práticos dos acadêmicos dos cursos de ciências agrárias e torná-los mais aptos ao futuro mercado de trabalho. Objetivo:conhecer a realidade e oferecer assistência técnica aos pequenos produtores da agricultura familiar, por meio da ProduTerra A&V Consultoria Junior, buscando o seu desenvolvimento econômico e social, além do desenvolvimento técnico dos alunos, pela aplicação prática de conhecimentos teóricos relativos à área de formação profissional. Metodologia:as atividades desenvolvidas ao longo do projeto foram baseadas no Diagnóstico Rural Participativo (DRP), com realização de atividades técnicas coletivas e individuais, de acordo com a necessidade da comunidade. Processos avaliativos:a realização de reuniões periódicasentre alunos e professoresao longo do projeto permitiram a avaliação das ações que estavam sendo desenvolvidas, possibilitando adequações ao planejamento das atividades subsequentes. Conclusões:projeto proporcionou assistência técnica aos agricultores participantes do projeto, incluindo a sua capacitação nos dias de campo e palestras realizados. Ao aluno permitiu o contato com o mercado de trabalho, mostrou uma pequena parcela da realidade rural, promovendo seu desenvolvimento técnico e também pessoal

Modeling multidisciplinary design with multiagent learning

Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem in a coordinated way. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams - instead either representing designers as agents which control all variables, measuring organizational outcomes instead of design outcomes, or representing different aspects of distributed design, such as negotiation. This paper presents a new model which captures the distributed nature of complex systems design by decomposing the ability to control design variables to individual computational designers acting on a problem with shared constraints. These designers are represented as a multiagent learning system which is shown to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration in complex systems design

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience-the ability to avoid or quickly recover from faults-is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures

An Australian rental housing conditions research infrastructure

Each year the proportion of Australians who rent their home increases and, for the first time in generations, there are now as many renters as outright homeowners. Researchers and policy makers, however, know very little about housing conditions within Australia's rental housing sector due to a lack of systematic, reliable data. In 2020, a collaboration of Australian universities commissioned a survey of tenant households to build a data infrastructure on the household and demographic characteristics, housing quality and conditions in the Australian rental sector. This data infrastructure was designed to be national (representative across all Australian States and Territories), and balanced across key population characteristics. The resultant Australian Rental Housing Conditions Dataset (ARHCD) is a publicly available data infrastructure for researchers and policy makers, providing a basis for national and international research

FMDTOOLS: A Fault propagation Toolkit for Resilience Assessment in Early Design

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one’s ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many theoretical frameworks have been presented for early hazard assessment, most currently-available modelling tools are meant for the later stages of design. Given the wide adoption of Python in the broader research community, there is an opportunity to create an environment for researchers to study the resilience of different PHM technologies in the early phases of design. This paper describes fmdtools, an attempt to realize this opportunity with a set of modules which may be used to construct different design models, simulate system behaviors over a set of fault scenarios and analyze the resilience of the resulting simulation results. This approach is demonstrated in the hazard analysis and architecture design of a multi-rotor drone, showing how the toolkit enables a large number of analyses to be performed on a relatively simple model as it progresses through the early design process