Environmental lead contamination in Miami inner-city area

The purpose of the study was to evaluate the magnitude of environmental lead contamination in the downtown area of Miami. Lead inspections took place at 121 homes in Little Haiti and Liberty City and involved the collection ofrepresentative samples from floors, window wells, tap water, soil and air. Community health workers (CHWs) trained in interview and safety techniques went from door to door to enlist participation. On-site investigations were tailored to areas most utilized by children underthe age of6 years. The presence of lead-containing paint was also investigated in situ via X-ray fluorescence (XRF) analysis. Results: Of the sampling areas, the window wells area had the most abundant occurrence of lead. On analysis, 24% of sites returned window well samples with lead levels above Department of Housing and Urban Development (HUD) guidelines. Of the soil samples, the playgrounds around the house had the highest concentration of lead. Soil sampling demonstrated that 27.5% of sites returned samples with lead levels (400 to 1600 ppm) inexcess of HUD/Environmental Protection Agency (EPA) standards. Positive XRF readings in one or more components were returned by 18% of sites. Conclusions: More than half of the houses in these two neighborhoods exhibited unacceptably high levels of lead dust and soil in areas where children live and play. Limitations of this study did not allow the assessment of how many children in this area are affected. A more comprehensive study including other areas of Miami-Dade County with older housing stock is recommended.

Assessing the construct validity of a measure of learning agility

This study examined the construct validity of the Choices questionnaire that purported to support the theory of Learning Agility. Specifically, Learning Agility attempts to predict an individual's potential performance in new tasks. The construct validity will be measured by examining the convergent/discriminant validity of the Choices Questionnaire against a cognitive ability measure and two personality measures. The Choices Questionnaire did tap a construct that is unique to the cognitive ability and the personality measures, thus suggesting that this measure may have considerable value in personnel selection. This study also examined the relationship of this pew measure to job performance and job promotability. Results of this study found that the Choices Questionnaire predicted job performance and job promotability above and beyond cognitive ability and personality. Data from 107 law enforcement officers, along with two of their co-workers and a supervisor resulted in a correlation of .08 between Learning Agility and cognitive ability. Learning Agility correlated .07 with Learning Goal Orientation and. 17 with Performance Goal Orientation. Correlations with the Big Five Personality factors ranged from −.06 to. 13 with Conscientiousness and Openness to Experience, respectively. Learning Agility correlated .40 with supervisory ratings of job promotability and correlated .3 7 with supervisory ratings of overall job performance. Hierarchical regression analysis found incremental validity for Learning Agility over cognitive ability and the Big Five factors of personality for supervisory ratings of both promotability and overall job performance. A literature review was completed to integrate the Learning Agility construct into a nomological net of personnel selection research. Additionally, practical applications and future research directions are discussed. ^

The order selection and lot sizing problem in the make-to-order environment

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three subproblems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

Assessment of the occurrence and potential effects of pharmaceuticals and personal care products in South Florida waters and sediments

A LLE-GC-MS method was developed to detect PPCPs in surface water samples from Big Cypress National Park, Everglades National Park and Biscayne National Park in South Florida. The most frequently found PPCPs were caffeine, DEET and triclosan with detected maximum concentration of 169 ng/L, 27.9 ng/L and 10.9 ng/L, respectively. The detection frequencies of hormones were less than PPCPs. Detected maximal concentrations of estrone, 17β-estradiol, coprostan-3-ol, coprostane and coprostan-3-one were 5.98 ng/L, 3.34 ng/L, 16.5 ng/L, 13.5 ng/L and 6.79 ng/L, respectively. An ASE-SPE-GC-MS method was developed and applied to the analysis of the sediment and soil area where reclaimed water was used for irrigation. Most analytes were below detection limits, even though some of analytes were detected in the reclaimed water at relatively high concentrations corroborating the fact that PPCPs do not significantly partition to mineral phases. An online SPE-HPLC-APPI-MS/MS method and an online SPE-HPLC-HESI-MS/MS method were developed to analyze reclaimed water and drinking water samples. In the reclaimed water study, reclaimed water samples were collected from the sprinkler for a year-long period at Florida International University Biscayne Bay Campus, where reclaimed water was reused for irrigation. Analysis results showed that several analytes were continuously detected in all reclaimed water samples. Coprostanol, bisphenol A and DEET's maximum concentration exceeded 10 μg/L (ppb). The four most frequently detected compounds were diphenhydramine (100%), DEET (98%), atenolol (98%) and carbamazepine (96%). In the study of drinking water, 54 tap water samples were collected from the Miami-Dade area. The maximum concentrations of salicylic acid, ibuprofen and DEET were 521 ng/L, 301 ng/L and 290 ng/L, respectively. The three most frequently detected compounds were DEET (93%), carbamazepine (43%) and salicylic acid (37%), respectively. Because the source of drinking water in Miami-Dade County is the relatively pristine Biscayne aquifer, these findings suggest the presence of wastewater intrusions into the delivery system or the onset of direct influence of surface waters into the shallow aquifer.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.

Multi-media filtration for removal of petroleum contamination

A pilot scale multi-media filtration system was used to evaluate the effectiveness of filtration in removing petroleum hydrocarbons from a source water contaminated with diesel fuel. Source water was artificially prepared by mixing bentonite clay and tap water to produce a turbidity range of 10-15 NTU. Diesel fuel concentrations of 150 ppm or 750 ppm were used to contaminate the source water. The coagulants used included Cat Floc K-10 and Cat Floc T-2. The experimental phase was conducted under direct filtration conditions at constant head and constant rate filtration at 8.0 gpm. Filtration experiments were run until the filter reached its clogging point as noted by a measured peak pressure loss of 10 psi. The experimental variables include type of coagulant, oil concentration and source water. Filtration results were evaluated based on turbidity removal and petroleum hydrocarbon (PHC) removal efficiency as measured by gas chromatography. Experiments indicated that clogging was controlled by the clay loading on the filter and that inadequate destabilization of the contaminated water by the coagulant limited the PHC removal. ^