Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)each with multiple Total Maximum Daily Loads (TMDL) targetswere selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of \$67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of \$67.7 millionmarginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.
The brain site of perceptual learning has been frequently debated. Recent psychophysical evidence for complete learning transfer to new retinal locations and orientations/directions suggests that perceptual learning may mainly occur in high-level brain areas. Contradictorily, ERP C1 changes associated with perceptual learning are cited as evidence for training-induced plasticity in the early visual cortex. However, C1 can be top-down modulated, which suggests the possibility that C1 changes may result from top-down modulation of the early visual cortex by high-level perceptual learning. To single out the potential top-down impact, we trained observers with a peripheral orientation discrimination task and measured C1 changes at an untrained diagonal quadrant location where learning transfer was previously known to be significant. Our assumption was that any C1 changes at this untrained location would indicate top-down modulation of the early visual cortex, rather than plasticity in the early visual cortex. The expected learning transfer was indeed accompanied with significant C1 changes. Moreover, C1 changes were absent in an untrained shape discrimination task with the same stimuli. We conclude that ERP C1 can be top-down modulated in a task-specific manner by high-level perceptual learning, so that C1 changes may not necessarily indicate plasticity in the early visual cortex. Moreover, learning transfer and associated C1 changes may indicate that learning-based top-down modulation can be remapped to early visual cortical neurons at untrained locations to enable learning transfer.
The environmental degradation of lakes in China has become increasingly serious over the last 30 years and eutrophication resulting from enhanced nutrient inputs is considered a top threat. In this study, a quasi-mass balance method, net anthropogenic N inputs (NANI), was introduced to assess the human influence on N input into three typical Chinese lake basins. The resultant NANI exceeded 10 000 kg N km(-2) year(-1) for all three basins, and mineral fertilizers were generally the largest sources. However, rapid urbanization and shrinking agricultural production capability may significantly increase N inputs from food and feed imports. Higher percentages of NANI were observed to be exported at urban river outlets, suggesting the acceleration of NANI transfer to rivers by urbanization. Over the last decade, the N inputs have declined in the basins dominated by the fertilizer use but have increased in the basins dominated by the food and feed import. In the foreseeable future, urban areas may arise as new hotspots for nitrogen in China while fertilizer use may decline in importance in areas of high population density.
Ozone concentrations in the Po Valley of northern Italy often exceed international regulations. As both a source of radicals and an intermediate in the oxidation of most volatile organic compounds (VOCs), formaldehyde (HCHO) is a useful tracer for the oxidative processing of hydrocarbons that leads to ozone production. We investigate the sources of HCHO in the Po Valley using vertical profile measurements acquired from the airship Zeppelin NT over an agricultural region during the PEGASOS 2012 campaign. Using a 1-D model, the total VOC oxidation rate is examined and discussed in the context of formaldehyde and ozone production in the early morning. While model and measurement discrepancies in OH reactivity are small (on average 3.4 ± 13%), HCHO concentrations are underestimated by as much as 1.5 ppb (45%) in the convective mixed layer. A similar underestimate in HCHO was seen in the 2002–2003 FORMAT Po Valley measurements, though the additional source of HCHO was not identified. Oxidation of unmeasured VOC precursors cannot explain the missing HCHO source, as measured OH reactivity is explained by measured VOCs and their calculated oxidation products. We conclude that local direct emissions from agricultural land are the most likely source of missing HCHO. Model calculations demonstrate that radicals from degradation of this non-photochemical HCHO source increase model ozone production rates by as much as 0.6 ppb h−1 (12%) before noon.
Particle shape plays a crucial role in determining packing characteristics. Real particles in nature usually have rounded corners. In this work, we systematically investigate the rounded corner effect on the dense packings of spherotetrahedral particles. The evolution of dense packing structure as the particle shape continuously deforms from a regular tetrahedron to a sphere is investigated, starting both from the regular tetrahedron and the sphere packings. The dimer crystal and the quasicrystal approximant are used as initial configurations, as well as the two densest sphere packing structures. We characterize the evolution of spherotetrahedron packings from the ideal tetrahedron (s = 0) to the sphere (s = 1) via a single roundness parameter s. The evolution can be partitioned into seven regions according to the shape variation of the packing unit cell. Interestingly, a peak of the packing density Φ is first observed at s ≈ 0.16 in the Φ-s curves where the tetrahedra have small rounded corners. The maximum density of the deformed quasicrystal approximant family (Φ ≈ 0.8763) is slightly larger than that of the deformed dimer crystal family (Φ ≈ 0.8704), and both of them exceed the densest known packing of ideal tetrahedra (Φ ≈ 0.8563).