1,425 research outputs found

    Special economic zones: The global frontlines of neoliberalism’s value regime

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    This chapter develops an anthropological theory and ethnographic research paradigm to capture the role of special economic zones (SEZs) as frontlines in neoliberalism’s global value regime. Based on global ethnographic and archival research, the lineage of today’s more than 5,000 zones with more than 100 million workers across over 140 nations can be traced back to late 1940s economic development policy innovations in the US-American dependency Puerto Rico. From there, early zone policies spread with remarkable continuity as frontlines of a singular political-economic value regime with novel relations between capital, state, and labor across the diverse geopolitical constellations of capitalist aggression against socialism, decolonization, and non-alignment. Carried by a dynamic global alliance of US-American, various United Nations agencies, private sector pressure groups, and postcolonial comprador bourgeoisies’ development policies, the zones shaped neoliberal export-oriented industrialization by way of implementing gendered and racialized (super-)exploitation of workers in a new international division of labor. The chapter identifies the zones’ prevalent singular value regime as a global labor arbitrage that pits workers in less-developed nations against workers in advanced capitalist nations, while post-colonial (and nowadays all) nation-states provide subsidies for transnational capital in exchange for the provision of employment, contributions to gross domestic product, and incorporation into global value chains. Alongside this, persistent zone operations have established a plural value regime that portrays investors as benevolent donors of employment despite the fact that they operate SEZ factories on the basis of gendered and other super-exploitation that has pushed labor standards into a global race to the bottom

    Temporal evolution of roof collapse from tephra fallout during the 2021-Tajogaite eruption (La Palma, Spain)

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    Although dominantly effusive, the 2021 Tajogaite eruption from Cumbre Vieja volcano (La Palma, Spain) produced a wide tephra blanket over 85 days of activity. About one month after the eruption onset, clean-up operations were implemented to mitigate the impact of tephra load on primary buildings. Here, we present a post-event impact assessment of 764 primary buildings, which expands our empirical knowledge of building vulnerability to tephra fallout to include impacts from long-lasting eruptions. Field observations are analyzed in the perspective of existing fragility curves, high-resolution satellite imagery and a reconstruction of the spatio-temporal evolution of the tephra blanket to characterize the evolution of roof collapse due to static loads over time. Thanks to a chronological correlation between the temporal evolution of tephra sedimentation and the timing of clean-up operations, we quantified their effectiveness in mitigating roof collapse. If no clean-up measures had been taken 11% of the surveyed buildings would have exceeded a 75% probability of roof collapse, while only 10 roof collapses have been observed (1.3% of the analysed buildings). This work provides key insights for further development of emergency plans for the management of long-lasting eruptions characterised by the sustained emission of tephra over weeks to months

    Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

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    For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models

    Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads

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    Optimal decision-making compels us to anticipate the future at different horizons. However, in many domains connecting together predictions from multiple time horizons and abstractions levels across their organization becomes all the more important, else decision-makers would be planning using separate and possibly conflicting views of the future. This notably applies to smart grid operation. To optimally manage energy flows in such systems, accurate and coherent predictions must be made across varying aggregation levels and horizons. With this work, we propose a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors and established hierarchical reconciliation taxonomy. A generic formulation of multi-dimensional hierarchies, reconciling spatial and temporal hierarchies under a common frame is initially defined. Next, a coherency-informed hierarchical learner is developed built upon a custom loss function leveraging optimal reconciliation methods. Coherency of the produced hierarchical forecasts is then secured using similar reconciliation technics. The outcome is a unified and coherent forecast across all examined dimensions. The method is evaluated on two different case studies to predict building electrical loads across spatial, temporal, and spatio-temporal hierarchies. Although the regressor natively profits from computationally efficient learning, results displayed disparate performances, demonstrating the value of hierarchical-coherent learning in only one setting. Yet, supported by a comprehensive result analysis, existing obstacles were clearly delineated, presenting distinct pathways for future work. Overall, the paper expands and unites traditionally disjointed hierarchical forecasting methods providing a fertile route toward a novel generation of forecasting regressors

    ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

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    Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

    The main processes responsible for landscape transformation in post-industrial urban areas in Central Europe

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    In recent years, the dynamic of spatial change has been increasing, influenced by processes linked to the transformation of traditional industrial regions into metropolitan areas. This is related to changes in function and administrative status, but above all to spatial changes. Examples of cities experiencing dynamic landscape changes from coal mining cities to modern metropolises can be found in the former coal basins of Central Europe – the Upper Silesian Metropolis in Poland and the Ostrava-Karviná Region in the Czechia. This study analysed the transformation of the landscape on the basis of land cover data from the years 2000, 2006, 2012 and 2018. The index of landscape change and the index of change of individual cover types were calculated, and on the basis of these indices the main processes responsible for the transformation of the landscape were determined. In the two study areas, similar changes in the landscape are taking place but at different rates. The main processes changing the landscape are suburbanization, reindustrialization and agricultural land abandonment. In space, they are manifested in an increase in the areas of residential, commercial and service development, the densification of the road network, and an increase in land allocated for new industrial plants. At the same time, the acreage of agricultural land (mainly arable fields, orchards and plantations but also open landscapes) is decreasing

    Stochastic Occupancy Grid Map Prediction in Dynamic Scenes

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    This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a range of possible future states of the environment. The algorithm takes full advantage of the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene to improve prediction accuracy. Three simulated and real-world datasets collected by different robot models are used to demonstrate that the proposed algorithm is able to achieve more accurate and robust prediction performance than other prediction algorithms. Furthermore, a predictive uncertainty-aware planner is proposed to demonstrate the effectiveness of the proposed predictor in simulation and real-world navigation experiments. Implementations are open source at https://github.com/TempleRAIL/SOGMP.Comment: Accepted by 7th Annual Conference on Robot Learning (CoRL), 202

    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
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