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For the mitigation of long-term pollution threats, one must consider that both the process of environmental degradation and the switchover to new and cleaner technologies are dynamic. We develop a model of a uniform good that can be produced by either a polluting technology or a clean one; the latter is more expensive and requires investment in capacity. We derive the socially optimal pollution stock accumulation and creation of nonpolluting production capacity, weighing the tradeoffs among consumption, investment and djustment costs, and environmental damages. We consider the effects of changes in the pollution decay rate, the capacity depreciation rate, and the initial state of the environment on both the steady state and the transition period. The optimal transition path looks quite different with a clean or dirty initial environment. With the former, investment is slow and the price of pollution may overshoot the long-run optimum before converging. With the latter, capacity may overshoot.

Snowmelt is complex under heterogeneous forest cover due to spatially variable snow surface energy and mass balances and snow accumulation. Forest canopies influence the under-canopy snowpack net total radiation energy balance by enhancing longwave radiation, shading the surface from shortwave radiation, in addition to intercepting snow, and protecting the snow surface from the wind. Despite the importance of predicting snowmelt timing for water resources, there are limited observations of snowmelt timing in heterogeneous forest cover across the Intermountain West. This research seeks to evaluate the processes that control snowmelt timing and magnitude at two paired forested and open sites in semi-arid southern Idaho, USA. Snow accumulation, snowmelt, and snow energy balance components were measured at a marginal snowpack and seasonal snowpack location in the forest, sparse vegetation, forest edge, and open environments. At both locations, the snow disappeared either later in the forest or relatively uniformly in the open and forest. At the upper elevation location, a later peak in maximum snow depth resulted in more variable snow disappearance timing between the open and forest sites with later snow disappearance in the forest. Snow disappearance timing at the marginal snowpack location was controlled by the magnitude and duration of a late season storm increasing snow depth variability and reducing the shortwave radiation energy input. Here, a shorter duration spring storm resulted in more uniform snowmelt in the forest and open. At both locations, the low-density forests shaded the snow surface into the melt period slowing the melt rate in the forest. However, the forest site had less cold content to overcome before melting started, partially canceling out the forest shading effect. Our results highlight the regional similarities and differences of snow surface energy balance controls on the timing and duration of snowmelt.

AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges and opportunities for operation in the NTC paradigm. By performing rigorous simulations in TPU systolic array, we reveal the severity of timing errors and its impact on inference accuracy at NTC. We analyze various attributes—such as data–delay relationship, delay disparity within arithmetic units, utilization pattern, hardware homogeneity, workload characteristics—and uncover unique localized and global techniques to deal with the timing errors in NTC.

AbstractThe term Blue Carbon (BC) was first coined a decade ago to describe the disproportionately large contribution of coastal vegetated ecosystems to global carbon sequestration. The role of BC in climate change mitigation and adaptation has now reached international prominence. To help prioritise future research, we assembled leading experts in the field to agree upon the top-ten pending questions in BC science. Understanding how climate change affects carbon accumulation in mature BC ecosystems and during their restoration was a high priority. Controversial questions included the role of carbonate and macroalgae in BC cycling, and the degree to which greenhouse gases are released following disturbance of BC ecosystems. Scientists seek improved precision of the extent of BC ecosystems; techniques to determine BC provenance; understanding of the factors that influence sequestration in BC ecosystems, with the corresponding value of BC; and the management actions that are effective in enhancing this value. Overall this overview provides a comprehensive road map for the coming decades on future research in BC science.

Mechanistic understanding of the interaction of copper-based nanomaterials with crops is crucial for exploring their application in precision agriculture and their implications on plant health. We investigated the biological response of soybean (Glycine max) plants to the foliar application of copper hydroxide nanowires (CNWs) at realistic exposure concentrations. A commercial copper based-fungicide (Kocide), dissolved copper ions, and untreated controls were used for comparison to identify unique features at physiological, cellular, and molecular levels. After 32 d of exposure to CNW (0.36, 1.8, and 9 mg CNW/plant), the newly developed tissues accumulated significantly high levels of Cu (18-60 μg/g) compared to Kocide (10 μg/g); however, the rate of Cu translocation from the site of CNW treatment to other tissues was slower compared to other Cu treatments. Like Kocide, CNW exposure at medium and high doses altered Co, Mn, Zn, and Fe accumulation in the tissues and enhanced photosynthetic activities. The proteomic and metabolomic analyses of leaves from CNW-treated soybean plants suggest a dose-dependent response, resulting in the activation of major biological processes, including photosynthesis, energy production, fatty acid metabolism, lignin biosynthesis, and carbohydrate metabolism. In contrast to CNW treatments, Kocide exposure resulted in increased oxidative stress response and amino acid metabolism activation.