Effects of a Spatially and Temporally Predictable Chlorophyll Maximum on Bottlenose Dolphin Distribution in a South Carolina Estuary

Numerous studies have focused on the complex relationship between phytoplankton and zooplankton in estuarine environments, but few have scrutinized the effects of this connection on organisms in higher trophic levels. This study examined chlorophyll a concentrations and zooplankton densities in North Inlet, South Carolina, a site where a stable chlorophyll a maximum has been documented to exist at low tide, to determine if they influenced the distribution of resident bottlenose dolphins (Tursiops truncatus). We hypothesized that patterns of estuarine circulation in the salt marsh serve to concentrate phytoplankton and zooplankton predictably in time and space, and that these patterns influence the distribution of organisms at all trophic levels, including apex predators, in the marsh. During surveys in September through November of 2008, water samples for chlorophyll and tows for zooplankton were taken at two-hour intervals throughout the tidal cycle along a gradient of five sites centered around the historic chlorophyll maximum. Correlations between zooplankton densities and phytoplankton concentration were unexpectedly low and the chlorophyll a maxima were more spatially unpredictable than in previous studies. However, the distribution of dolphin sightings, both present and from 1999 through 2003, suggests that chlorophyll a maxima influence dolphin distribution in North Inlet, particularly during the warmer months out of the year

Algorithms for Bohemian Matrices

This thesis develops several algorithms for working with matrices whose entries are multivariate polynomials in a set of parameters. Such parametric linear systems often appear in biology and engineering applications where the parameters represent physical properties of the system. Some computations on parametric matrices, such as the rank and Jordan canonical form, are discontinuous in the parameter values. Understanding where these discontinuities occur provides a greater understanding of the underlying system. Algorithms for computing a complete case discussion of the rank, Zigzag form, and the Jordan canonical form of parametric matrices are presented. These algorithms use the theory of regular chains to provide a unified framework allowing for algebraic or semi-algebraic constraints on the parameters. Corresponding implementations for each algorithm in the Maple computer algebra system are provided. In some applications, all entries may be parameters whose values are limited to finite sets of integers. Such matrices appear in applications such as graph theory where matrix entries are limited to the sets {0, 1}, or {-1, 0, 1}. These types of parametric matrices can be explored using different techniques and exhibit many interesting properties. A family of Bohemian matrices is a set of low to moderate dimension matrices where the entries are independently sampled from a finite set of integers of bounded height. Properties of Bohemian matrices are studied including the distributions of their eigenvalues, symmetries, and integer sequences arising from properties of the families. These sequences provide connections to other areas of mathematics and have been archived in the Characteristic Polynomial Database. A study of two families of structured matrices: upper Hessenberg and upper Hessenberg Toeplitz, and properties of their characteristic polynomials are presented

Dairy Cow Ownership and Child Nutritional Status in Kenya

This study examines the hypothesis that dairy cow ownership improves child nutritional status. Using household data from coastal and highland Kenya, three econometric model formulations are estimated. Positive impacts on chronic malnutrition are observed for coastal Kenya. No negative effects on acute or chronic malnutrition are found for either region.Food Consumption/Nutrition/Food Safety, Livestock Production/Industries,

Graduate Level Distance Learning: Enhanced Student Experience, Significant Scalability Challenges: A Multiyear Case Study

This article describes our experiences and lessons learned providing degree-based distance (online) education to graduate students (studying business, law, and policy related to government contracts or public procurement). Temporal note: our pilot, and the five years of experience described in this case study, predate the 2020 Coronavirus Pandemic emergency distance teaching transition. Among other things, we discuss our experiences with regard to fundamentally rethinking our pedagogical approach, flipping the classroom, chunking, and scaffolded learning. We extol the benefits of working with, and being open to, advice from experienced instructional designers. We conclude that embracing distance education, at least in a hybrid form, offers exciting opportunities for more effective teaching and student learning. If thoughtfully and responsibly managed, the student learning experience in distance education not only compares favorably with, but may surpass, that found in the classic, amphitheater, quasi-Socratic or lecture-centric law course. Conversely, preparing to deliver and delivering quality distance education is time consuming, labor intensive, and, potentially, expensive and difficult. To reap the benefits and achieve the promise of distance education, law schools must embrace paradigm-shifting cultural change, a significant barrier for many faculty and institutions

A guided analytics tool for feature selection in steel manufacturing with an application to blast furnace top gas efficiency

In knowledge intensive industries such as steel manufacturing, application of data analytics to optimise process performance, requires effective knowledge transfer between domain experts and data scientists. This is often an inefficient path to follow, requiring much iteration whilst being suboptimal with regard to organisational knowledge capture for the long term. With the ‘initial Guided Analytics for parameter Testing and controlband Extraction (iGATE)’ tool we created a feature selection framework that finds influential process parameters and their optimal control bands and which can easily be made available to process operators in the form of guided analytics tool, while allowing them to modify the analysis according to their expertise. The method is embedded in a work flow whereby the extracted parameters and control bands are verified by the domain expert and a report of the analysis is automatically generated. The approach allows us to combine the power of suitable statistical analysis with process-expertise, whilst dramatically reducing the time needed for conducting the feature selection. We regard this application as a stepping stone to gain user confidence in advance of introduction of more autonomous analytics approaches. We present the statistical foundations of iGATE and illustrate its effectiveness in the form of a case study of Tata Steel blast furnace data. We have made the iGATE core functionality freely available in the igate package for the R programming language

Adsorption isotherm determination and heavy metal removal by acid-washed softwood biochar

Heavy metal concentrations above critical range in soils may pose environmental and economic problems by hindering plant growth and limiting land usage. Biochar used as soil amendment is a low-cost, natural remediation technique, which has the capability to immobilize and reduce metals (exchangeable, oxide-bound, and organic matter-bound) bioavailable for plant uptake. Its large surface area provides high capacity for binding metals through sorption reactions. Please click on the file below for full content of the abstract

A continental phenology model for monitoring vegetation responses to interannual climatic variability

Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km × 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990–1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability