A robust method for axis identification

The present paper proposes a new method for axis identification in discrete axially symmetrical geometric models. This method is based on-a-never-used-before property of the axially symmetrical surfaces for which the symmetry line of any section curve of the surface (or of a portion of it in the case of an incomplete axially symmetrical surface) always intersects the axis of symmetry of the surface. Thus the working principle of the method makes it very robust to local defectiveness, measurement noise and outliers.In order to compare it with the most cited methods presented in literature, several types of tests have been designed and performed. The robustness of those methods, on the one hand, has been evaluated by defining the Statistical Confidence Boundary at 1σ confidence level. The trueness of the method, on the other hand, has been evaluated on geometric models obtained by measuring real objects. The high robustness, which characterizes the proposed method, makes it particularly suitable for product geometric inspection where high accuracy is required.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises

The relationship between location and land use patterns is one of the classic theoretical issues in urban studies. Classic models based on the monocentricity hypothesis have limitations in the interpretation of modern urban structure. China has experienced institutional transformation in recent decades, and the interaction of national government policy, market forces and the natural environment has influenced urban planning in Chinese metropolises, resulting in urban structures with special characteristics. This paper examines the distribution of location and land use intensity, and tested the Alonso model by the relationship between them in five Chinese metropolises using Point of Interest data, space syntax methodology, the grid weighted statistical method and the Geographically Weighted Regression (GWR) model. Universal patterns about the scaling relation between intensity of land use types and the centrality of location are revealed. The elasticity of land use types to location, from high to low sensitivity, is commercial, residential then industrial land in most of the five metropolises studied. The sensitivity sequence of land use studied suggests that the hypothetical model based on the classical Alonso model can explain the spatial structure of modern metropolises in China to some extent, especially for the commercial land. But the order of sensitivity of residential land and industrial land to location does not conform to the model. The spatial heterogeneity in land use intensity and centrality were explored and the factors embedded were discussed. It can be found that the relation between centrality and land use intensity conforms to power law. In most of the metropolises studied, when the scaling relation between land use intensity and centrality is super linear, the sequence of the frequency value from high to low are commercial, residential and industrial land; when the scaling relation is sublinear, the sequence of the frequency value is industrial, residential and commercial land.     

Rip currents near coastal structures in Lake Michigan: Characterization and assessment for warnings

Rip currents near coastal structures commonly occur in Lake Michigan in the Great Lakes region of the United States. Lack of timely warning due to undocumented characteristics of rip currents and no assessment tool can contribute to tragic drownings incidents. In this paper, we characterized rip current occurrences near breakwater structures and developed an assessment tool for providing timely rip current warnings to beachgoers at the study site, City of Port Washington, WI. Characteristics of rip currents near the structure were observed from field measurements or visual images. Deflection rip currents had speeds of ～ 0.2 m/s and lasted for several hours. The rip current occurrences were associated with environmental proxies. It was found that rip currents can occur even when the water appears calm near the structure. A Structure Rip Checklist and Assessment Matrix (SRiCAM) with a four-tiered risk was developed and validated using observations. Furthermore, the SRiCAM was integrated into cyberinfrastructure with a data contingency plan to provide real-time warnings to the public. The applicability of the SRiCAM to other locations across Lake Michigan was further tested and results are promising. Overall, the SRiCAM has the potential to be widely extended to foster recreational water safety and resilience to rip current hazards in the Great Lakes.     

Stable Oxygen and Carbon Isotopic Compositions of Lates stappersii Otoliths from Lake Tanganyika,East Africa

In this study, we measured growth trends in oxygen and carbon isotopic ratios in whole sagittal otoliths from three adult centropomid fish (Lates stappersii) from each of three sub-basins of Lake Tanganyika, East Africa. Sampling density was 20 to 50 samples per otolith. Both δ¹⁸O and δ¹³C values increase with age. The δ¹⁸O data suggest that otoliths were precipitated near the expected equilibrium with the ambient environment (ca. +3.5‰) and support a migration pattern from surface waters during larval stages to deeper waters (40 to 80 m) for mature fish. Relatively high δ¹⁸O values in the southern sub-basin are consistent with cooler temperatures in the region during seasonal upwelling. The δ13C increase from otolith core to edge is large (up to 4‰) and is interpreted as due to ontogenetic changes in diet and contributions from a decrease in the proportion of respired CO₂ incorporated into otolith carbonate as metabolic rates of the fish dropped with maturity. The data seem to successfully reveal life strategy and migration patterns of L. stappersii, document regional differences in lake conditions, and provide a record of temperature within the water column during which the fish lived. Higher resolution studies and analyses of historical samples could be used to constrain modern and past growth patterns, and to reconstruct past temperature gradients and productivity patterns in the lake.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

Evaluation of landscape and instream modeling to predict watershed nutrient yields

The project goal was to loosely couple the SWAT model and the QUAL2E model and compare their combined ability to predict total phosphorus (TP) and NO₃-N plus NO₂-N yields to the ability of the SWAT model with its completely coupled water quality components to predict TP and NO₃-N plus NO₂-N yields from War Eagle Creek watershed in Northwest Arkansas. Model predictions were compared using a statistical approach to identify significant differences between the two modeling methods. Results from two variations of the Pearson product-moment correlation (p < 0.05) indicated that correlation coefficients and regression slopes for the two data sets were not significantly different. This implies that neither modeling method was significantly better in predicting monthly TP and NO₃-N plus NO₂-N yields from the watershed. Additionally, no significant differences were present between predicted outputs of the SWAT model with instream components active compared with when instream components were inactive, indicating a need for further testing and refinement of the SWAT algorithms simulating instream processes. We can further infer that the instream processes available in SWAT may not be enhancing its predictive abilities as far as simulating instream components.     

A matrix population model to aid agency response to grass carp (Ctenopharyngodon idella) in the Great Lakes Basin - Lake Erie

Managers and researchers have identified a reproducing population of grass carp (Ctenopharyngodon idella) in the western basin of Lake Erie, generating concern over the potential threat to ecosystem function in the Great Lakes Basin. Capture histories indicate that grass carp may be present at low levels in other areas of Lake Erie, necessitating a large scale, multi-jurisdictional response. As a result, a group of experts and decision makers began a structured decision making exercise to collaboratively address the threat and identify potential response actions. To aid this process, we developed a spatially-explicit periodic matrix population model to project grass carp abundance, and probabilistically evaluate specific management actions. We evaluated four potential management response actions ranging from no action, diffuse removal efforts, and concentrated removal efforts with and without a barrier on the Sandusky River to reduce spawning success. Based on our current knowledge, concentrated removal including a barrier on the Sandusky River provides the most likely path to achieving and maintaining a management target of no more than 10 fish/ha. Our understanding of grass carp ecology in Lake Erie is growing. This model and parameter development methods were designed to flexibly accommodate new information as our understanding of grass carp ecology evolves, or management objectives change. Ultimately, this modeling framework and use of Bayesian methods could facilitate management response efforts for other invasive species occurring over large scales and multiple jurisdictions.