MANAGEMENT OF THE ROOT-KNOT NEMATODE MELOIDOGYNE INCOGNITAON TOMATO WITH DIFFERENT COMBINATIONS OF NEMATICIDESAND A RESISTANT ROOTSTOCK: PRELIMINARY DATA

In south Italy, tomato growers commonly face severe root-knot nematode infestations. Alternative methods ofcontrol are required because of the high toxicity of current pesticides. Therefore, the potential of an integrated pestmanagement strategy for the control of root-knot nematodes on tomato in greenhouse was investigated. The nematodesusceptible tomato cv. Ikram, non-grafted or grafted onto the tomato rootstock cv. Armstrong, with intermediate resistanceto the nematode, in combination with soil applications of the nematicides fosthiazate, oxamyl, and abamectin were tested.The resistant rootstock significantly reduced nematode soil population levels and root galling index until one month aftertransplanting, when soil temperature was below 28°C, but not by harvest due to increased soil temperature. Fosthiazate,abamectin and oxamyl increased tomato yield and reduced root galling caused by Meloidogyne incognita. The synergisticeffect of the rootstock resistant to root-knot nematodes and soil treatments of fosthiazate in combination with abamectin oroxamyl could successfully be employed in integrated pest management programs to control M. incognita in tomato

Vertical farming systems - analysis of their contribution for sustainable building and circular economy cities

Dissertação de mestrado em Sustainable Built EnvironmentAccording to the United Nations (UN), two out of three people will be living in cities by 2050. As cities grow, more resources will be used to support billions of lives and keep cities functioning, which results in environmental and social impact. Food insecurity is also seen as a major challenge for humanity seeing that providing people with safe, nutritious, and sufficient food can be threaded by population growth and climate change. When it comes to cities, it is evident that the building sector plays an important role in the urban built environment. Based on the United Nations Environment Program (UNEP), the building sector is responsible for 36% of energy consumption and 39% of greenhouse gas emissions. In this respect, green building standards, certifications, and rating systems have emerged to tackle the impact of buildings on the natural environment through sustainable approaches on how buildings are designed, renovated, used, and managed. Besides, some measures have to be taken to promote circularity in cities and to mitigate the problems that an overpopulated world can create. The growing importance of the concept of the circular economy (CE) as a way to attain sustainable development has led to different strategies to promote it. This dissertation focuses on a real project that is being undertaken by the company Bios that builds an Indoor Vertical Farming system inside existing buildings that have unused space available with the purpose of creating a technology that promotes a high resource use efficiency. To that end, a literature review was carried out combined with a methodology approach to validate whether applying urban agriculture technologies in buildings can contribute to green building status and help make cities more circular economy-oriented. Five potential outcomes of the Bios project were selected to analyse whether they meet the dissertation goal. Globally, the study identifies that the Bios project can contribute to making cities more circular-oriented since it is aligned with the circular city actions framework - rethink, regenerate, reuse, reduce and recover. Moreover, the Bios IVF project by itself is considered circular, since the main circular principles are applied in the project. When it comes to making a building green, the Bios IVF meet several recognized standards: by minimizing energy use; improving water efficiency and management; by integrating renewable and low-carbon technologies to supply buildings’ energy needs; by promoting health and wellbeing by delivering good indoor air quality; and by promoting ways to make urban areas more productive. Finally, This study fills this gap by identifying whether the use of IVF in existing buildings can contribute to CE implementation at a macro level.De acordo com as Nações Unidas (ONU), duas em cada três pessoas viverão em cidades até 2050. À medida que as cidades crescem, mais recursos serão utilizados para suportar biliões de vidas e manter as cidades a funcionar, o que resulta em impacto ambiental e social. A insegurança alimentar é também vista como um grande desafio para a humanidade, uma vez que o fornecimento de alimentos seguros, nutritivos, e suficientes às pessoas pode ser ameaçado pelo crescimento populacional e pelas alterações climáticas. Quando se trata de cidades, é evidente que o sector da construção desempenha um papel importante no ambiente urbano construído. Com base no Programa das Nações Unidas para o Ambiente (PNUA), o sector da construção é responsável por 36% do consumo de energia e 39% das emissões de gases com efeito de estufa. A este respeito, surgiram normas, certificações e sistemas de classificação de edifícios verdes para combater o impacto dos edifícios no ambiente natural através de abordagens sustentáveis sobre a forma como os edifícios são concebidos, renovados, utilizados e geridos. Além disso, algumas medidas têm de ser tomadas para promover a circularidade nas cidades e para mitigar os problemas que um mundo superpovoado pode criar. A crescente importância do conceito de economia circular (EC) como forma de alcançar o desenvolvimento sustentável levou a diferentes estratégias para a sua promoção. Esta dissertação centra-se num projecto real que está a ser levado a cabo pela empresa Bios que constrói um sistema de Agricultura Vertical Interior (AVI) dentro de edifícios existentes que têm espaço não utilizado disponível com o objectivo de criar uma tecnologia que promove uma elevada eficiência na utilização de recursos. Para o efeito, foi realizada uma revisão bibliográfica combinada com uma abordagem metodológica para validar se a aplicação de tecnologias de agricultura urbana em edifícios pode contribuir para o estatuto de edifício verde e ajudar a tornar as cidades mais circulares e orientadas para a economia. Cinco resultados potenciais do projecto Bios foram selecionados para analisar se cumprem o objectivo da dissertação. Este estudo preenche esta lacuna ao identificar se a utilização de agricultura vertical interior em edifícios existentes pode contribuir para a implementação da económica circular a um nível macro. Globalmente, o estudo identifica que o projecto Bios pode contribuir para tornar as cidades mais circulares, uma vez que está alinhado com o quadro de acções da cidade circular - repensar, regenerar, reutilizar, reduzir e recuperar. Além disso, o projecto Bios IVF por si só é considerado circular, uma vez que os principais princípios circulares são aplicados no projecto. Quando se trata de tornar um edifício verde, a AVI Bios cumpre várias normas reconhecidas: minimizando a utilização de energia; melhorando a eficiência e gestão da água; integrando tecnologias renováveis e de baixo carbono para suprir as necessidades energéticas dos edifícios; promovendo a saúde e o bem-estar através do fornecimento de boa qualidade do ar interior; e promovendo formas de tornar as áreas urbanas mais produtivas. Finalmente, este estudo preenche esta lacuna ao identificar se a utilização de AVI em edifícios existentes pode contribuir para a implementação da EC a um nível macro

Environmental Enrichment Effects on Development of Retinal Ganglion Cell Dendritic Stratification Require Retinal BDNF

A well-known developmental event of retinal maturation is the progressive segregation of retinal ganglion cell (RGC) dendrites into a and b sublaminae of the inner plexiform layer (IPL), a morphological rearrangement crucial for the emergence of the ON and OFF pathways. The factors regulating this process are not known, although electrical activity has been demonstrated to play a role. Here we report that Environmental Enrichment (EE) accelerates the developmental segregation of RGC dendrites and prevents the effects exerted on it by dark rearing (DR). Development of RGC stratification was analyzed in a line of transgenic mice expressing plasma-membrane marker green fluorescent protein (GFP) under the control of Thy-1 promoter; we visualized the a and b sublaminae of the IPL by using an antibody selectively directed against a specific marker of cholinergic neurons. EE precociously increases Brain Derived Neurotrophic Factor (BDNF) in the retina, in parallel with the precocious segregation of RGC dendrites; in addition, EE counteracts retinal BDNF reduction in DR retinas and promotes a normal segregation of RGC dendrites. Blocking retinal BDNF by means of antisense oligos blocks EE effects on the maturation of RGC dendritic stratification. Thus, EE affects the development of RGC dendritic segregation and retinal BDNF is required for this effect to take place, suggesting that BDNF could play an important role in the emergence of the ON and OFF pathways

Spot the Difference: A Novel Task for Embodied Agents in Changing Environments

Embodied AI is a recent research area that aims at creating intelligent agents that can move and operate inside an environment. Existing approaches in this field demand the agents to act in completely new and unexplored scenes. However, this setting is far from realistic use cases that instead require executing multiple tasks in the same environment. Even if the environment changes over time, the agent could still count on its global knowledge about the scene while trying to adapt its internal representation to the current state of the environment. To make a step towards this setting, we propose Spot the Difference: a novel task for Embodied AI where the agent has access to an outdated map of the environment and needs to recover the correct layout in a fixed time budget. To this end, we collect a new dataset of occupancy maps starting from existing datasets of 3D spaces and generating a number of possible layouts for a single environment. This dataset can be employed in the popular Habitat simulator and is fully compliant with existing methods that employ reconstructed occupancy maps during navigation. Furthermore, we propose an exploration policy that can take advantage of previous knowledge of the environment and identify changes in the scene faster and more effectively than existing agents. Experimental results show that the proposed architecture outperforms existing state-of-the-art models for exploration on this new setting.Comment: Accepted by 26TH International Conference on Pattern Recognition (ICPR 2022

Out of the Box: Embodied Navigation in the Real World

The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances? In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world

Embodied Navigation at the Art Gallery

Embodied agents, trained to explore and navigate indoor photorealistic environments, have achieved impressive results on standard datasets and benchmarks. So far, experiments and evaluations have involved domestic and working scenes like offices, flats, and houses. In this paper, we build and release a new 3D space with unique characteristics: the one of a complete art museum. We name this environment ArtGallery3D (AG3D). Compared with existing 3D scenes, the collected space is ampler, richer in visual features, and provides very sparse occupancy information. This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information. Additionally, we annotate the coordinates of the main points of interest inside the museum, such as paintings, statues, and other items. Thanks to this manual process, we deliver a new benchmark for PointGoal navigation inside this new space. Trajectories in this dataset are far more complex and lengthy than existing ground-truth paths for navigation in Gibson and Matterport3D. We carry on extensive experimental evaluation using our new space for evaluation and prove that existing methods hardly adapt to this scenario. As such, we believe that the availability of this 3D model will foster future research and help improve existing solutions.Comment: Accepted by 21st International Conference on Image Analysis and Processing (ICIAP 2021

Explore and Explain: Self-supervised Navigation and Recounting

Embodied AI has been recently gaining attention as it aims to foster the development of autonomous and intelligent agents. In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path. In this context, the agent needs to navigate the environment driven by an exploration goal, select proper moments for description, and output natural language descriptions of relevant objects and scenes. Our model integrates a novel self-supervised exploration module with penalty, and a fully-attentive captioning model for explanation. Also, we investigate different policies for selecting proper moments for explanation, driven by information coming from both the environment and the navigation. Experiments are conducted on photorealistic environments from the Matterport3D dataset and investigate the navigation and explanation capabilities of the agent as well as the role of their interactions

Focus on Impact: Indoor Exploration with Intrinsic Motivation

Exploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on simulated environments. Current state-of-the-art methods employ a dense extrinsic reward that requires the complete a priori knowledge of the layout of the training environment to learn an effective exploration policy. However, such information is expensive to gather in terms of time and resources. In this work, we propose to train the model with a purely intrinsic reward signal to guide exploration, which is based on the impact of the robot's actions on its internal representation of the environment. So far, impact-based rewards have been employed for simple tasks and in procedurally generated synthetic environments with countable states. Since the number of states observable by the agent in realistic indoor environments is non-countable, we include a neural-based density model and replace the traditional count-based regularization with an estimated pseudo-count of previously visited states. The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts. We also show that a robot equipped with the proposed approach seamlessly adapts to point-goal navigation and real-world deployment.Comment: Published in IEEE Robotics and Automation Letters. To appear in ICRA 202