Crystal structures of a family of layered coordination polymers containing divalent metal ions (Mn2+, Co2+, Ni2+ and Zn2+) and the 3-amino 4-methyl benzoate ion

Peer reviewed

Crystal structures of two one-dimensional coordination polymers constructed from Mn2+ ions, chelating anions and flexible bipyridyl bridging ligands

Peer reviewed

Crystal structures of a family of layered coordination polymers containing divalent metal ions (Mn2+, Co2+, Ni2+ and Zn2+) and the 3-amino 4-methyl benzoate ion

Peer reviewed

Scientific Discovery in a Space of Structural Models: An Example from the History of Solution Chemistry

Postprint

Referring heroin users from compulsory detoxification centers to community methadone maintenance treatment: a comparison of three models.

BACKGROUND: Both compulsory detoxification treatment and community-based methadone maintenance treatment (MMT) exist for heroin addicts in China. We aim to examine the effectiveness of three intervention models for referring heroin addicts released from compulsory detoxification centers to community methadone maintenance treatment (MMT) clinics in Dehong prefecture, Yunnan province, China. METHODS: Using a quasi-experimental study design, three different referral models were assigned to four detoxification centers. Heroin addicts were enrolled based on their fulfillment to eligibility criteria and provision of informed consent. Two months prior to their release, information on demographic characteristics, history of heroin use, and prior participation in intervention programs was collected via a survey, and blood samples were obtained for HIV testing. All subjects were followed for six months after release from detoxification centers. Multi-level logistic regression analysis was used to examine factors predicting successful referrals to MMT clinics. RESULTS: Of the 226 participants who were released and followed, 9.7% were successfully referred to MMT(16.2% of HIV-positive participants and 7.0% of HIV-negative participants). A higher proportion of successful referrals was observed among participants who received both referral cards and MMT treatment while still in detoxification centers (25.8%) as compared to those who received both referral cards and police-assisted MMT enrollment (5.4%) and those who received referral cards only (0%). Furthermore, those who received referral cards and MMT treatment while still in detoxification had increased odds of successful referral to an MMT clinic (adjusted OR = 1.2, CI = 1.1-1.3). Having participated in an MMT program prior to detention (OR = 1.5, CI = 1.3-1.6) was the only baseline covariate associated with increased odds of successful referral. CONCLUSION: Findings suggest that providing MMT within detoxification centers promotes successful referral of heroin addicts to community-based MMT upon their release.

On the Advancement of Probabilistic Models of Decompression Sickness

The work presented in this dissertation is focused on applying engineering methods to develop and explore probabilistic survival models for the prediction of decompression sickness in US NAVY divers. Mathematical modeling, computational model development, and numerical optimization techniques were employed to formulate and evaluate the predictive quality of models fitted to empirical data. In Chapters 1 and 2 we present general background information relevant to the development of probabilistic models applied to predicting the incidence of decompression sickness. The remainder of the dissertation introduces techniques developed in an effort to improve the predictive quality of probabilistic decompression models and to reduce the difficulty of model parameter optimization.

The first project explored seventeen variations of the hazard function using a well-perfused parallel compartment model. Models were parametrically optimized using the maximum likelihood technique. Model performance was evaluated using both classical statistical methods and model selection techniques based on information theory. Optimized model parameters were overall similar to those of previously published Results indicated that a novel hazard function definition that included both ambient pressure scaling and individually fitted compartment exponent scaling terms.

We developed ten pharmacokinetic compartmental models that included explicit delay mechanics to determine if predictive quality could be improved through the inclusion of material transfer lags. A fitted discrete delay parameter augmented the inflow to the compartment systems from the environment. Based on the observation that symptoms are often reported after risk accumulation begins for many of our models, we hypothesized that the inclusion of delays might improve correlation between the model predictions and observed data. Model selection techniques identified two models as having the best overall performance, but comparison to the best performing model without delay and model selection using our best identified no delay pharmacokinetic model both indicated that the delay mechanism was not statistically justified and did not substantially improve model predictions.

Our final investigation explored parameter bounding techniques to identify parameter regions for which statistical model failure will not occur. When a model predicts a no probability of a diver experiencing decompression sickness for an exposure that is known to produce symptoms, statistical model failure occurs. Using a metric related to the instantaneous risk, we successfully identify regions where model failure will not occur and identify the boundaries of the region using a root bounding technique. Several models are used to demonstrate the techniques, which may be employed to reduce the difficulty of model optimization for future investigations.

Spatial structures of manufacturing clusters in Cambodia, Lao People's Democratic Republic, and Thailand

Examining the spatial structure of clusters is essential for deriving regional development policy implications. In this study, we identify the manufacturing clusters in Cambodia, the Lao People's Democratic Republic, and Thailand, using two indices—global extent (GE) and local density (LD)—as proposed by Mori and Smith (2013). We also analyze four different combinations of these indices to highlight the spatial structures of industrial agglomerations. Since industrial clusters often spread over administrative boundaries, the GE and LD indices—along with cluster mapping—display how the detected clusters fit into specific spatial structures.

Validation of evacuation models

Too often, validation of computer models is considered as a "once and forget" task. In this paper a systematic and graduated approach to evacuation model validation is suggested. This involves, (i) component testing, (ii) functional validation, (iii) qualitative validation and (iv) quantitative validation. Viewed in this manner, validation is considered an on-going activity and an integral part of the life cycle of the software. While the first three components of the validation protocol pose little or no significant problems, the task of quantitative validation poses a number of challenges, the most significant being a shortage of suitable experimental data. Finally, the validation protocol used in the development of the EXODUS suite of evacuation models is examined.

A review of the methodologies and critical appraisal of computer models used in the simulation of evacuation from built environment

Abstract not available

Case-based reasoning as a tool to improve the usability of numerical models

There may be advantages to be gained by combining Case-Based Reasoning (CBR) techniques with numerical models. In this paper we consider how CBR can be used as a flexible query engine to improve the usability of numerical models. Particularly they can help to solve inverse and mixed problems, and to solve constraint problems. We discuss this idea with reference to the illustrative example of a pneumatic conveyor. We describe a model of the problem of particle degradation in such a conveyor, and the problems faced by design engineers. The solution of these problems requires a system that allows iterative sharing of control between user, CBR system, and numerical model. This multi-initiative interaction is illustrated for the pneumatic conveyor by means of Unified Modeling Language (UML) collaboration and sequence diagrams. We show approaches to the solution of these problems via a CBR tool.

Development and Application of Predictive Models for Survival, Growth, and Death of Enteric Pathogens in Leafy Greens Supply Chain

Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

Influences and benefits of role models on social media.

The current paper examined three research questions. First, what are the perceived benefits for social network users who have role models online? Second, to what extent does having role models online influence one’s self-presentation on social media? And finally, are users who expect more in return (greater reciprocity) more likely to have role models on social media? Using two opportunity survey samples and exploratory analyses, study 1 (N = 236) demon-strated that having role models was associated with greater perceived support for one’s career aspirations, and perceived access to information. The results of study 2 (N = 192) revealed that participants who had role models online reported that their online profile presented a more realistic self-presentation of values and pri-orities, as well as having higher reciprocity expectation.

Enhancing the usability of numerical models with case-based reasoning

Abstract not available