683 research outputs found

    Non-Market Food Practices Do Things Markets Cannot: Why Vermonters Produce and Distribute Food That\u27s Not For Sale

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    Researchers tend to portray food self-provisioning in high-income societies as a coping mechanism for the poor or a hobby for the well-off. They describe food charity as a regrettable band-aid. Vegetable gardens and neighborly sharing are considered remnants of precapitalist tradition. These are non-market food practices: producing food that is not for sale and distributing food in ways other than selling it. Recent scholarship challenges those standard understandings by showing (i) that non-market food practices remain prevalent in high-income countries, (ii) that people in diverse social groups engage in these practices, and (iii) that they articulate diverse reasons for doing so. In this dissertation, I investigate the persistent pervasiveness of non-market food practices in Vermont. To go beyond explanations that rely on individual motivation, I examine the roles these practices play in society. First, I investigate the prevalence of non-market food practices. Several surveys with large, representative samples reveal that more than half of Vermont households grow, hunt, fish, or gather some of their own food. Respondents estimate that they acquire 14% of the food they consume through non-market means, on average. For reference, commercial local food makes up about the same portion of total consumption. Then, drawing on the words of 94 non-market food practitioners I interviewed, I demonstrate that these practices serve functions that markets cannot. Interviewees attested that non-market distribution is special because it feeds the hungry, strengthens relationships, builds resilience, puts edible-but-unsellable food to use, and aligns with a desired future in which food is not for sale. Hunters, fishers, foragers, scavengers, and homesteaders said that these activities contribute to their long-run food security as a skills-based safety net. Self-provisioning allows them to eat from the landscape despite disruptions to their ability to access market food such as job loss, supply chain problems, or a global pandemic. Additional evidence from vegetable growers suggests that non-market settings liberate production from financial discipline, making space for work that is meaningful, playful, educational, and therapeutic. Non-market food practices mend holes in the social fabric torn by the commodification of everyday life. Finally, I synthesize scholarly critiques of markets as institutions for organizing the production and distribution of food. Markets send food toward money rather than hunger. Producing for market compels farmers to prioritize financial viability over other values such as stewardship. Historically, people rarely if ever sell each other food until external authorities coerce them to do so through taxation, indebtedness, cutting off access to the means of subsistence, or extinguishing non-market institutions. Today, more humans than ever suffer from chronic undernourishment even as the scale of commercial agriculture pushes environmental pressures past critical thresholds of planetary sustainability. This research substantiates that alternatives to markets exist and have the potential to address their shortcomings

    A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges

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    Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

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    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

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    This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    SET2022 : 19th International Conference on Sustainable Energy Technologies 16th to 18th August 2022, Turkey : Sustainable Energy Technologies 2022 Conference Proceedings. Volume 4

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    Papers submitted and presented at SET2022 - the 19th International Conference on Sustainable Energy Technologies in Istanbul, Turkey in August 202

    Modeling and Robust Control of Flying Robots Using Intelligent Approaches Modélisation et commande robuste des robots volants en utilisant des approches intelligentes

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    This thesis aims to modeling and robust controlling of a flying robot of quadrotor type. Where we focused in this thesis on quadrotor unmanned Aerial Vehicle (QUAV). Intelligent nonlinear controllers and intelligent fractional-order nonlinear controllers are designed to control. The QUAV system is considered as MIMO large-scale system that can be divided on six interconnected single-input–single-output (SISO) subsystems, which define one DOF, i.e., three-angle subsystems with three position subsystems. In addition, nonlinear models is considered and assumed to suffer from the incidence of parameter uncertainty. Every parameters such as mass, inertia of the system are assumed completely unknown and change over time without prior information. Next, basing on nonlinear, Fractional-Order nonlinear and the intelligent adaptive approximate techniques a control law is established for all subsystems. The stability is performed by Lyapunov method and getting the desired output with respect to the desired input. The modeling and control is done using MATLAB/Simulink. At the end, the simulation tests are performed to that, the designed controller is able to maintain best performance of the QUAV even in the presence of unknown dynamics, parametric uncertainties and external disturbance

    Apprenticeship Bootstrapping for Autonomous Aerial Shepherding of Ground Swarm

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    Aerial shepherding of ground vehicles (ASGV) musters a group of uncrewed ground vehicles (UGVs) from the air using uncrewed aerial vehicles (UAVs). This inspiration enables robust uncrewed ground-air coordination where one or multiple UAVs effectively drive a group of UGVs towards a goal. Developing artificial intelligence (AI) agents for ASGV is a non-trivial task due to the sub-tasks, multiple skills, and their non-linear interaction required to synthesise a solution. One approach to developing AI agents is Imitation learning (IL), where humans demonstrate the task to the machine. However, gathering human data from complex tasks in human-swarm interaction (HSI) requires the human to perform the entire job, which could lead to unexpected errors caused by a lack of control skills and human workload due to the length and complexity of ASGV. We hypothesise that we can bootstrap the overall task by collecting human data from simpler sub-tasks to limit errors and workload for humans. Therefore, this thesis attempts to answer the primary research question of how to design IL algorithms for multiple agents. We propose a new learning scheme called Apprenticeship Bootstrapping (AB). In AB, the low-level behaviours of the shepherding agents are trained from human data using our proposed hierarchical IL algorithms. The high-level behaviours are then formed using a proposed gesture demonstration framework to collect human data from synthesising more complex controllers. The transferring mechanism is performed by aggregating the proposed IL algorithms. Experiments are designed using a mixed environment, where the UAV flies in a simulated robotic Gazebo environment, while the UGVs are physical vehicles in a natural environment. A system is designed to allow switching between humans controlling the UAVs using low-level actions and humans controlling the UAVs using high-level actions. The former enables data collection for developing autonomous agents for sub-tasks. At the same time, in the latter, humans control the UAV by issuing commands that call the autonomous agents for the sub-tasks. We baseline the learnt agents against Str\"{o}mbom scripted behaviours and show that the system can successfully generate autonomous behaviours for ASGV
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