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As agencies consider strategies for meeting the livability goals (e.g., public health, mobility, and access) of communities, they often repurpose the existing right-of-way of arterials to meet traffic operation or safety goals. Agencies need more definitive guidance on when this repurposing of existing right-of-way can be done without negatively affecting safety. Researchers around the world have considered the safety efficacy of lane width, but none defines lane width design recommendations of urban arterials. This study uses treed regression analysis on crash data collected from four cities of Nebraska to develop predictive models for identifying the crash modification factors of different lane widths. The analysis from five treed regression models indicates that narrower lanes increase the likelihood and the frequency of injury and noninjury crashes on high-volume segments; however more specifically, the results also show that narrower lane widths can be adopted for segments with lower volumes, a sufficient number of through lanes, and lower speed limits without degrading safety. Based on these crash modification factors, the research team creates design recommendations for arterial lane widths using combinations of speed limit, average annual daily traffic per lane, and number of through lanes. [ABSTRACT FROM AUTHOR]

Maritime logistics operations are full of uncertainty such as severe weather, mechanical problems, strikes, and fluctuating freight rates. Traditional ship-scheduling models ignore uncertainty, even in highly volatile markets. Although ship operators can increase revenue by delivering many spot cargoes, they have to embrace the risk of the fluctuation of spot rates. We present a set-packing model that limits risk using a quadratic variance constraint. We use a traditional Kelley's cutting plane algorithm and a delayed column-and-cut generation (branch-and-price-and-cut) algorithm on medium-sized ship-scheduling problems with restricted variance. We develop a second set of cuts that are more restrictive under certain conditions. Computational results show that the variance of profit can be significantly reduced with a reasonable increase in cost. [ABSTRACT FROM AUTHOR]

Presents a solution to the faculty salaries problem in the 1994 Mathematical Contest in Modeling. Assumptions; Analysis of the problem; Design of the model; Variables; Inflation; Finite budgets; Model verification; Sensitivity analysis.

The article focuses on a simulation model for evaluating patient-to-nurse assignments and enabling nurses to use an evidence-based practice (EBP). It mentions about the traditional nursing system under which nurse budgeting is done before planning nurses' schedule and workloads and informs that the simulation model was developed after collecting data on nurses' workflow and patient in a North Texas hospital. It reveals that the data collection based on the simulation model revealed that the nurses liked the speed and appropriateness with which patient-to-nurse assignments are determined. Also discussed are the limitations of the use of an evidence-based approach of patient-to-nurse assignments.

• Two novel methods, PHCM and HCM, for MARS knot positioning are proposed. • Traditional and two novel methods are similarly accurate and robust with noise. • The proposed methods are 10% to 70% faster than traditional method. • PHCM is 70% faster than HCM for higher dimensional datasets. • The Python code will be made open-source on GitHub to help other researchers. Multivariate adaptive regression splines (MARS) is a statistical modeling approach with wide-ranging real-world applications. In the MARS model building process, knot positioning is a critical step that potentially affects the accuracy of the final MARS model. Identifying well-positioned knots entails assessing the quality of many knots in each model building iteration, which requires intensive computational effort. By exploring the change in the residual sum of squares (RSS) within MARS, we find that local optima from previous iterations can be very close to those of the current iteration. In our approach, the prior change in RSS information is used to "warm start" an optimal knot positioning. We propose two methods for MARS knot positioning. The first method is a hill climbing method (HCM), which ignores prior change in RSS information. The second method is a hill climbing method using prior change in RSS information (PHCM). Numerical experiments are conducted on data with up to 30 dimensions. Our results show that both versions of hill climbing methods outperform a standard MARS knot selection method on datasets with different noise levels. Further, PHCM using prior change in RSS information performs best in both accuracy and computational speed. In addition, an open source Python code will be available upon acceptance of the paper on GitHub (https://github.com/JuXinglong/MARSHC). [ABSTRACT FROM AUTHOR]

Ultraviolet (UV) absorption spectroscopy is a widely used tool for quantitative and qualitative analyses of chemical compounds. In the gas phase, vacuum UV (VUV) and UV absorption spectra are specific and diagnostic for many small molecules. An accurate prediction of VUV/UV absorption spectra can aid the characterization of new or unknown molecules in areas such as fuels, forensics, and pharmaceutical research. An alternative to quantum chemical spectral prediction is the use of artificial intelligence. Here, different molecular feature representation techniques were used and developed to encode chemical structures for testing three machine learning models to predict gas-phase VUV/UV absorption spectra. Structure data files (.sdf) and VUV/UV absorption spectra for 1397 volatile and semivolatile chemical compounds were used to train and test the models. New molecular features (termed ABOCH) were introduced to better capture pi-bonding, aromaticity, and halogenation. The incorporation of these new features benefited spectral prediction and demonstrated superior performance compared to computationally intensive molecular-based deep learning methods. Of the machine learning methods, the use of a Random Forest regressor returned the best accuracy score with the shortest training time. The developed machine learning prediction model also outperformed spectral predictions based on the time-dependent density functional theory.

Sustainable mobility options such as electric vehicles (EVs) have the potential to improve the quality of life for Americans as well as those in other countries, as they can enhance the quality of the air we breathe, while reducing greenhouse gas emissions, fossil fuel consumption, and the adverse impacts of global warming. Despite their many benefits, however, the demand for EVs remains low. Therefore, this study aims to identify the barriers that affect the widespread EV adoption in the United States. Seventeen barriers were identified from the literature, and a questionnaire survey was designed and distributed to potential consumers of EVs. The survey yielded 733 responses, and various statistical tests like cluster analysis and chi-squared tests were performed. The results revealed that the high purchase price of the vehicle, high battery replacement cost, and the lack of public infrastructures for charging them were the primary concerns. The results also revealed that middle-aged men with high education and income are more enthusiastic about adopting EVs. The results presented in this study indicate a range of developments that different stakeholders could implement. To surmount the economic barriers to EV adoption, policymakers should strengthen incentives countrywide, and automakers should introduce more affordable EVs to the market. To overcome the challenges associated with charging, it is necessary to make investments in rapid charging infrastructure along the primary toll routes., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This pilot program is a software-based prototype providing a nurse-to-patient assignment presented to two groups of RNs enrolled in a nursing research course in a North Texas university. The goal of the pilot program was to obtain input regarding the assessment, functionality, and practicality of a nurse-to-patient electronic prototype. Registered nurse students were given a presurvey, instructions, and details on the use of the prototype, followed by a postsurvey. Prototype speed and lack of bias were reported as most favorable. Registered nurse students requested additions of multiple diagnoses, patient acuity, and experience level of the nurse to enhance the prototype. Seventy-three percent (n = 24) of the participants said that they would use the prototype, and 15% (n = 5) said that they would not.