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This thesis by Klervie Toczé explores latency-aware resource management within the edge computing paradigm. It addresses the challenges of efficiently managing finite resources of edge devices, which are crucial for applications requiring ultra-low latency and privacy beyond what current cloud systems offer. The work includes a review of existing research, proposes a taxonomy for resource management, and presents the ORCH framework designed to orchestrate a mix of mobile and stationary edge devices. The thesis also studies the workload of a mixed reality application, using the MR-Leo prototype to model and understand the end-to-end latency and throughput requirements. The research is aimed at advancing the deployment of edge computing solutions, with a focus on enhancing quality of service for latency-critical tasks.

Edge computing. --- Resource allocation.

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We are currently experiencing a climate crisis that is associated with extreme weather events worldwide. Some of its most noticeable effects are increases in temperatures, droughts, and desertification. These effects are already making whole regions unsuitable for agriculture. Therefore, we urgently need global measures to mitigate the effects of climate breakdown as well as crop alternatives that are more stress-resilient. These crop alternatives can come from breeding new varieties of well-established crops, such as wheat and barley. They can also come from promoting underutilized crop species that are naturally tolerant to some stresses, such as quinoa. Either way, we need to gather more knowledge on how plants respond to stresses related to climate breakdown, such as heat, water-deficit, flooding high salinity, nitrogen, and heavy metal stress. This Special Issue provides a timely collection of recent advances in the understanding of plant responses to these stresses. This information will definitely be useful to the design of new strategies to prevent the loss of more cultivable land and to reclaim the land that has already been declared unsuitable.

Plant resource allocation.

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Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

Mathematical optimization. --- Probabilities. --- Decision making --- Resource allocation --- Algorithms. --- Mathematical models.

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This paper examines within-sector resource misallocation in three Southeast Asian countries - Indonesia, Malaysia, and Vietnam. The methodology accounts for measurement error in revenues and costs. The firm-level evidence suggests that measurement error is substantial, resulting in an overestimation of misallocation by as much as 30 percent. Nevertheless, resource misallocation across firms within a sector remains large, albeit declining. The findings imply that there are considerable potential gains from efficient reallocation - above 80 percent for Indonesia and around 20 to 30 percent for Malaysia and Vietnam. Private domestic firms and firms with higher productivity appear to face larger distortions that prevent them from expanding.

Economic Distortion --- Private Sector Development --- Private Sector Economics --- Productivity --- Public Sector Development --- Resource Allocation

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China's economy grew by an impressive 10 percent per year over four decades. Productivity improvements within sectors and gains from resource reallocation between sectors and ownership groups drove that expansion. However, productivity growth has declined markedly in recent years. This paper extends previous macro and firm-level studies to show that domestic factors and policies contributed to the slowdown. The analysis finds that limited market entry and exit and lack of resource allocation to more productive firms were associated with slower manufacturing total factor productivity growth. Earlier reforms led to state-owned enterprises catching up to private sector productivity levels in manufacturing, but convergence stalled after 2007. Furthermore, the allocation of a larger share of credit and investment to infrastructure and housing led to lower returns to capital, a rapid buildup in debt, and higher risks to growth. China's growth potential remains high, but its long-term growth prospects depend on reversing the recent decline in total factor productivity growth.

Economic Growth --- Economic Theory and Research --- Local Government --- Macroeconomics and Economic Growth --- Private Sector Development --- Private Sector Economics --- Productivity --- Productivity Growth --- Resource Allocation --- State-Owned Enterprises --- Total Factor Productivity