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.     

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.     

Lead-free sodium niobate nanowires with strong piezo-catalysis for dye wastewater degradation

In this work, the hydrothermally-synthesized sodium niobate nanowires were used to decompose Rhodamine B dye solution through the piezo-catalytic effect. With the sodium niobate catalyst, a high piezo-catalytic degradation ratio of ～80% was achieved under the excitation of vibration for the Rhodamine B dye solution (～5?mg/l). These active species in the catalytic process, hydroxyl radicals and superoxide radicals with the strong oxidation ability, were also observed, which confirmed the key role of piezoelectric effect for piezo-catalysis. The piezo-catalysis of sodium niobate nanowires provides a high-efficiency and reusable tool in application in depredating the dye wastewater.     

Model-based detection of critical driving situations with fuzzy logic decision making

A precise knowledge about the current driving condition is getting increasingly important for future driver assistance systems like global chassis control or collision avoidance systems for avoiding any critical driving situation. Moreover a precise knowledge about the driving situation can be used in testing, in evaluation, and for comparison of new passenger cars. A two degree of freedom model of vehicle lateral dynamics is used to derive a characteristic velocity stability indicator (CVSI). The CVSI is used to distinguish between different driving and stability conditions (i.e. understeering, oversteering, and neutralsteering). This forms the basis for a driving condition detection system with fixed thresholds. It is then extended to a detection system with fuzzy logic thresholds. The CVSI and the fuzzy systems are compared experimentally using (i) a slalom test drive on an icy road and (ii) a stationary circular test drive on a dry asphalt road.     

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.     

Groundwater-surface water interactions and agricultural nutrient transport in a Great Lakes clay plain system

Nutrient export from agricultural land to surface waters is a significant environmental concern within the Great Lakes Basin (GLB). A field-based watershed-scale study was completed to investigate spatial and temporal variations of phosphorus and nitrate to assess nutrient transport pathways and groundwater-surface water interactions in an agriculturally dominated clay plain system. This was conducted in the 127 km² Upper Parkhill Watershed, near Lake Huron in southwestern Ontario, Canada. Data collection occurred from June 2018 to May 2019 via continuous sensor deployment and discrete sampling of stream water, groundwater, hyporheic zone, and tile drainage water. Samples were analyzed for various nutrient species (total, total dissolved, soluble reactive, and particulate phosphorus, and nitrate-N) to examine the hydrological dynamics of principal transport pathways of agriculturally-derived nutrients. Total phosphorus and nitrate concentrations in stream water ranged from 0.007 to 0.324 mg/L and 0.32 to 13.13 mg NO₃^?-N/L, respectively. Tile drainage water total phosphorous concentrations varied from 0.006 to 0.066 mg/L. Groundwater total dissolved phosphorus concentrations ranged from <0.003 to 0.085 mg/L. Transport of phosphorus through tile drainage was observed to be greater than through groundwater over the study period. No distinct relationship was observed between nutrient concentrations in the hyporheic zone and the vertical hydraulic gradient within this zone in the studied stream reach. Preliminary correlations were discerned between water quality observations and recognized land management practices. Given the elevated stream nutrient concentrations, these results are consequential for the continual improvement of strategies and programs devised to conserve water resources within the GLB.