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วารสารสังคมศาสตร์ มหาวิทยาลัยนเรศวร, 18, 2, 39-72

วารสารดุษฎีบัณฑิตทางสังคมศาสตร์, 11, 2, 536-550

Presentation for Digital Hub Ostfriesland

Nanoparticles (NPs) have been shown to deliver micronutrients to plants to improve health, increase biomass, and suppress disease. Nanoscale properties such as morphology, size, composition, and surface chemistry have all been shown to impact nanomaterial interactions with plant systems. An organic-ligand-free synthesis method was used to prepare positively charged copper oxide (CuO) nanospikes, negatively charged CuO nanospikes, and negatively charged CuO nanosheets with exposed (001) crystal faces. X-ray photoelectron spectroscopy measurements show that the negative charge correlates to increased surface concentration of O on the NP surface, whereas relatively higher Cu concentrations are observed on the positively charged surfaces. The NPs were then used to treat tomato (

Localization algorithms that rely on 3D LiDAR scanners often encounter temporary failures due to various factors, such as sensor faults, dust particles, or drifting. These failures can result in a misalignment between the robot’s estimated pose and its actual position in the global map. To address this issue, the process of global re-localization becomes essential, as it involves accurately estimating the robot’s current pose within the given map. In this article, we propose a novel global re-localization framework that addresses the limitations of current algorithms heavily reliant on scan matching and direct point cloud feature extraction. Unlike most methods, our framework eliminates the need for an initial guess and provides multiple top-� candidates for selection, enhancing robustness and flexibility. Furthermore, we introduce an event-based re-localization trigger module, enabling autonomous robotic missions. Focusing on subterranean environments with low features, we leverage range image descriptors derived from 3D LiDAR scans to preserve depth information. Our approach enhances a state-of-the-art data-driven descriptor extraction framework for place recognition and orientation regression by incorporating a junction detection module that utilizes the descriptors for classification purposes. The effectiveness of the proposed approach was evaluated across three distinct real-life subterranean environments. Validerad;2023;Nivå 2;2023-09-22 (joosat);CC BY 4.0 License

In a realm where publications explicitly addressing the integration of remote sensing and GIS with architecture and planning are currently scarce, this exceptional book offers an unparalleled platform to demonstrate your profound expertise in this burgeoning field. It presents an exclusive opportunity to showcase the forefront of research, present compelling case studies, and exemplify the seamless fusion of remote sensing and Geographic Information Systems (GIS) within the realm of architecture and planning. By harnessing the power of geospatial data, this book exemplifies how remote sensing and GIS integration significantly influence the architectural design process, urban planning, and the pursuit of sustainable development. It serves as a beacon of knowledge, providing a global perspective on the integration of these ground-breaking technologies, with a particular emphasis on their profound impact on architecture. Important Dates: 30th June 2023 - Abstract Submission Deadline 16th July 2023 - Abstract Acceptance/Rejection Info 17th September 2023 - Complete Chapter Submission Deadline 1st October 2023 - Chapter Acceptance/ Rejection Info 3rd December 2023 - Chapter Review Results/ Corrections 24th December 2023 - Final Chapter Submission The book actively seeks contributions that capture diverse perspectives from around the world, both from established architectural powerhouses and burgeoning economies. By doing so, it aims to cultivate a rich tapestry of ideas, experiences, and best practices, contributing to a more comprehensive understanding of the global implications of remote sensing and GIS integration within architecture and planning.

Resumo: A pecuária leiteira familiar é relevante para a agropecuária nacional que, embora contribua com a geração de riqueza e renda, enfrenta baixas eficiência, produtividade e custos de produção elevados. Objetivou-se analisar a eficiência técnica de propriedades leiteiras familiares no Estado de Minas Gerais. Utilizou-se a Análise por Envoltória de Dados (DEA) com modelo BCC orientado a inputs. Quanto maior foi o nível de eficiência das DMU’s, menos recursos foram utilizados em excesso. Foi identificada, ainda, a inexistência de DMU’s que realizaram manutenção preventiva de máquinas, implementos e benfeitorias, justificando o aumento da utilização, principalmente, do insumo MIB (manutenção de máquinas, implementos e benfeitorias). Com esta pesquisa, sugeriu-se o Índice de Comprometimento de Insumos em Leite (ICIL), que evidencia o percentual da produção que está comprometida para comprar um determinado insumo. Quanto maior foi o nível de eficiência das DMU’s, menor foi o ICIL, que passou de 49,36% (eficientes) para 67,47% (com eficiência baixa). Este se mostrou um importante indicador de eficiência de propriedades leiteiras, responsável por evidenciar a quantidade de leite produzido que está comprometida com o pagamento dos insumos utilizados na atividade leiteira. Também se mostrou valioso instrumento a ser utilizado como benchmark em propriedades leiteiras.

Resumo Este trabalho estudou o comportamento da pobreza multidimensional e de renda, bem como seus determinantes no Brasil, evidenciando as diferenças entre as áreas rurais e urbanas com base nas informações das Pesquisas Nacionais por Amostra de Domicílios Contínua (PNADC) de 2019. Foi utilizada a metodologia Alkire-Foster na construção de um Índice de Pobreza Multidimensional (IPM), além do modelo logit para estimar os impactos na pobreza de determinantes relacionados a características domiciliares. A dimensão saúde e saneamento foi a que mais contribuiu com o IPM, seguida da dimensão educação e condições habitacionais. A pobreza unidimensional foi maior do que a multidimensional, além disso, a pobreza nas áreas rurais foi também maior. Entre os determinantes da pobreza, estar em domicílios com chefes não brancos, mais jovens, não casados, desempregados, menos escolarizados e ser das regiões Norte e Nordeste aumentaram as chances de pobreza. Além disso, domicílios com chefes mulheres tiveram menores chances de pobreza multidimensional, embora mais chances de pobreza unidimensional. Abstract This paper studied the behavior of multidimensional and income poverty, as well as its determinants in Brazil, in addition to rural and urban areas, based on information from the 2019 National Continuous Household Sample Survey (PNADC). The Alkire-Foster methodology was used in the construction of a Multidimensional Poverty Index (MPI), in addition to the logit model to estimate the impacts of determinants related to household characteristics on poverty. The health and sanitation dimension was the one that contributed the most to the MPI, followed by the education and housing dimensions. Unidimensional poverty was greater than multidimensional and rural poverty was also greater. Among the determinants of poverty, being in households with non-white heads, younger, unmarried, unemployed, less educated and in the North and Northeast regions increased the chances of poverty. In addition, while female heads reduced the chances of multidimensional poverty, it increased the chances of the unidimensional one.