Investigating the effect of modeling single-vehicle and multi-vehicle crashes separately on confidence intervals of Poisson-gamma models

Crash prediction models still constitute one of the primary tools for estimating traffic safety. These statistical models play a vital role in various types of safety studies. With a few exceptions, they have often been employed to estimate the number of crashes per unit of time for an entire highway segment or intersection, without distinguishing the influence different sub-groups have on crash risk. The two most important sub-groups that have been identified in the literature are single- and multi-vehicle crashes. Recently, some researchers have noted that developing two distinct models for these two categories of crashes provides better predicting performance than developing models combining both crash categories together. Thus, there is a need to determine whether a significant difference exists for the computation of confidence intervals when a single model is applied rather than two distinct models for single- and multi-vehicle crashes. Building confidence intervals have many important applications in highway safety.This paper investigates the effect of modeling single- and multi-vehicle (head-on and rear-end only) crashes separately versus modeling them together on the prediction of confidence intervals of Poisson-gamma models. Confidence intervals were calculated for total (all severities) crash models and fatal and severe injury crash models. The data used for the comparison analysis were collected on Texas multilane undivided highways for the years 1997-2001. This study shows that modeling single- and multi-vehicle crashes separately predicts larger confidence intervals than modeling them together as a single model. This difference is much larger for fatal and injury crash models than for models for all severity levels. Furthermore, it is found that the single- and multi-vehicle crashes are not independent. Thus, a joint (bivariate) model which accounts for correlation between single- and multi-vehicle crashes is developed and it predicts wider confidence intervals than a univariate model for all severities. Finally, the simulation results show that separate models predict values that are closer to the true confidence intervals, and thus this research supports previous studies that recommended modeling single- and multi-vehicle crashes separately for analyzing highway segments.     

Enhanced intestinal absorption and bioavailability of raloxifene hydrochloride via lyophilized solid lipid nanoparticles

The current oral therapy with raloxifene hydrochloride (RXH) is less effective due to its poor bioavailability (only 2%). Henceforth, an attempt was made to investigate the utility of triglyceride (trimyristin, tripalmitin and tristearin) based solid lipid nanoparticles (SLNs) for improved oral delivery of RXH. The SLN formulations prepared were evaluated for particle size, zeta potential and % entrapment and the optimized formulation was lyophilized. Solid state characterization studies unravel the transformation of RXH to amorphous or molecular state from the native crystalline form. Further the in situ perfusion studies carried out in rat intestine reveal the potential of SLN for enhanced permeation of raloxifene HCl across gastrointestinal barrier. To derive the conclusions, in vivo pharmacokinetic study was conducted in rats to assess the bioavailability of RXH from SLN formulation compared to drug suspension. Overall a twofold increase in bioavailability with SLN formulations confer their potential for improved oral delivery of RXH.     

Optimal supply contract selection

Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include long-term, medium-term, and short-term contracts. While extant literature has stressed the importance of such contracts, few methodologies have been proposed for optimally selecting contracts under various business conditions. To this end, this paper proposes a methodology for optimal contract selection based on a mixed-integer programming approach. We present specific insights to manufacturing managers on choosing the right contracts in the presence of market price uncertainty, supplier discounts, investment costs, and supplier capacity restrictions.     

A methodology for monitoring system performance

Monitoring and improving manufacturing processes involves identifying, investigating and eliminating problems responsible for inefficiencies in production operations. While statistical process control tools, such as control charts, are available for process monitoring at the operational level, methods for evaluating system performance from more strategic and tactical levels are limited. The traditional control charts that monitor a single process parameter at a time may not be appropriate in situations where interrelationships among various system measures exist. Although multivariate process control techniques allow for simultaneous monitoring of several process parameters, they require assumptions of independence and multivariate normality of data. In addition, their application has mostly been at an operational level. In order to assist managers in monitoring and improving manufacturing system performance, this paper proposes an individual control chart that monitors an integrated performance index generated from a non-parametric method, which effectively considers multiple performance measures and the relationships between them. The primary advantages of this method are that a single integrated measure can be monitored, does not require assumptions of independence and multivariate normality of data, and allows for the integration of decision-maker's input when the system measures that are monitored have unequal importance.     

Transmuting performance on manufacturing dimensions into business performance: An exploratory analysis of efficiency using DEA

This research examines efficiency in the transformation of performance on manufacturing competitive dimensions into business performance in the automotive supplier industry. Specifically, the study explores efficiency produced from data envelopment analysis (DEA) using cost, quality, time, flexibility and innovativeness performance as inputs and return on investment (ROI) and return on assets (ROA) as outputs. First tier suppliers to the 'Big 3' in North America were sampled. The 51 suppliers were categorized into three groups (high, medium and low) based on their efficiency. No significant group differences in quality, time, flexibility and innovativeness performance were found, but a significant difference does exist for cost. The results were further investigated by testing between-group differences for various cost reduction programmes, thereby identifying the levers and benchmarks for improving low efficiency firms.     

Traffic analysis zone level crash estimation models based on land use characteristics

The objective of this paper is to develop crash estimation models at traffic analysis zone (TAZ) level as a function of land use characteristics. Crash data and land use data for the City of Charlotte, Mecklenburg County, North Carolina were used to illustrate the development of TAZ level crash estimation models. Negative binomial count models (with log-link) were developed as data was observed to be over-dispersed. Demographic/socio-economic characteristics such as population, the number of household units and employment, traffic indicators such as trip productions and attractions, and, on-network characteristics such as center-lane miles by speed limit were observed to be correlated to land use characteristics, and, hence were not considered in the development of TAZ level crash estimation models. Urban residential commercial, rural district and mixed use district land use variables were observed to be correlated to other land use variables and were also not considered in the development of the models. Results obtained indicate that land use characteristics such as mixed use development, urban residential, single-family residential, multi-family residential, business and, office district are strongly associated and play a statistically significant role in estimating TAZ level crashes. The coefficient for single-family residential area was observed to be negative, indicating a decrease in the number of crashes with an increase in single-family residential area. Models were also developed to estimate these crashes by severity (injury and property damage only crashes). The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, and, to proactively apply safety treatments in high risk TAZs.     

Control of Slag Formation, Foaming, Slopping, and Chaos in BOF

Decarburization, slag formation, foaming, and slopping in basic oxygen furnace (BOF) can now be partly understood, and to some extent predicted with the help of several on-line measurements and on-line control models. The principal reaction in BOF is decarburization. The bulk of the decarburization takes place in the turbulent region of jet impact irrespective of the fact whether the slag is solid, liquid or foamy. Metal droplets are ejected from the jet impact zone but it is difficult to distinguish the decarburization occurring in the bulk metal from that occurring in the droplets. Slag in BOF is heterogeneous and always contains some entrained gas bubbles and solid material (either un-dissolved or precipitated). At no stage the slag is 100 % liquid. A significant part of the metal droplets fall back and travel through the semi liquid slag. Through this mechanism the droplets can cause slag foaming and slopping in the BOF. Phenomenon of slag foaming and slopping can be monitored and controlled by following, dynamically, the audio signals, gas flow rate and composition, by tracking ‘Chaos’ in chemical reactions, by manipulating the chaotic attractors, and by monitoring the lance water temperature and weight. Relative stability potential (RSP) diagrams are found to be a good indicator of the dynamics of process inside the BOF and help in advance prediction of the impending chaos.     

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ) - a direct classification approach - for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards.     

Particle size dependence on magnetic and electrical properties of (Ni0.8Fe0.2)10C90 granular composites

The granular structure and electrical transport behavior of ball milled magnetic permalloy particles and graphite forming (Ni0.8Fe0.2)10C90 granular composites have been reported. Retaining the composite composition to be 90:10 and varying the particle size of permalloy, the electronic transport properties have been carried out down to 4 K under the external applied magnetic field of 50 kOe. All the samples show semiconducting like behavior and positive magnetoresistance (MR) in the temperature range 4-300 K. A strong anisotropic magnetoresistance in these samples has also been observed. The highest 31% longitudinal and 6.8% transverse magnetoresistance values have been observed in 40 hrs ball milled (Ni0.8Fe0.2)10C90 composite. From these studies, we suggest that the magnetic component present in the sample may not be playing a major role in obtaining large positive MR values, which is in deviation with the earlier reports.