Household Income and Vehicle Fuel Economy in California

This white paper presents the findings from an analysis of the fiscal implications for vehicle owners of changing from the current statewide fuel tax to a “road user charge” (RUC) based on vehicle-miles traveled (VMT). Since 1923, California’s motor vehicle fuel tax has provided revenue used to plan, construct, and maintain the state’s publicly funded transportation systems. Over time, improvements in vehicle fuel efficiency and the effects of inflation have reduced both the revenue from the fuel tax and its purchasing power. Thus, there is growing interest among policy makers for replacing the state’s per-gallon fuel tax with an RUC based on VMT. This study analyzes the 2010-2011California Household Travel Survey (CHTS) to identify the potential effects this policy change would be likely to have on households across the state. The analysis found that while daily household fuel consumption and VMT both appear to increase with household income, urban and rural households show roughly the same amount of fuel consumption and VMT. No statistically significant difference in cost was found between the two programs in any income group. This suggests that an RUC designed to collect the same amount of revenues statewide as the current fuel tax would not place a significant financial burden on California households

Eastern Promises: Poland\u27s Role as a Regional Actor in the European Union\u27s Eastern Policy-- the Example of Belarus

The present thesis will analyze Poland\u27s current and potential role as an effective regional actor in mediating the European Union\u27s (EU) relations with its easternmost neighbors. In order to most accurately assess this, this thesis will examine Poland\u27s relationship with Belarus, specifically democratization efforts and forging a strong, resolute association with the European Union. Being a successful post-communist transition state and recent EU Member State, Poland has for some time seen itself to be the most-qualified country to bridge Eastern and Western Europe. Belarus in particular is a country of concern for the Polish government because it is a bordering country with a long historical relationship to Poland and contains an ethnic Polish minority. This thesis will discuss and evaluate the previous and current initiatives the European Union and Poland are taking in Belarus, their level of success, and will postulate who would be poised to be the most efficacious (external) player in the country\u27s progress towards democratic consolidation and good governance. By using the example of Belarus this thesis will also expand the current literature on the ability of Member States (particularly newer ones) to influence policymaking in Brussels

Condition Survey of the Circulating Collection: Joseph Anderson Cook Memorial Library, University Of Southern Mississippi

Building on the foundation of condition surveys that span the last 30 years, the current research provides library administrators at The University of Southern Mississippi’s Joseph Anderson Cook Memorial Library with information that can be used to plan mitigation strategies, create preservation policies, and further develop the existing preservation services to ensure that the collection remains accessible for future students and scholars. This survey serves as a pilot project to determine whether additional research in this area is warranted. It also provides a statistically valid set of data that indicate the current condition of the collection and an estimate of the collection’s future deterioration

Altered intrinsic functional connectivity in language-related brain regions in association with verbal memory performance in euthymic bipolar patients

Potential abnormalities in the structure and function of the temporal lobes have been studied much less in bipolar disorder than in schizophrenia. This may not be justified because language-related symptoms, such as pressured speech and flight of ideas, and cognitive deficits in the domain of verbal memory are amongst the hallmark of bipolar disorder (BD), and contribution of temporal lobe dysfunction is therefore likely. In the current study, we examined resting-state functional connectivity (FC) between the auditory cortex (Heschl’s gyrus [HG], planum temporale [PT]) and whole brain using seed correlation analysis in n = 21 BD euthymic patients and n = 20 matched healthy controls and associated it with verbal memory performance. In comparison to controls BD patients showed decreased functional connectivity between Heschl’s gyrus and planum temporale and the left superior and middle temporal gyrus. Additionally, fronto-temporal functional connectivity with the right inferior frontal/precentral gyrus and the insula was increased in patients. Verbal episodic memory deficits in the investigated sample of BD patients and language-related symptoms might therefore be associated with a diminished FC within the auditory/temporal gyrus and a compensatory fronto-temporal pathway

Final Report: California Tribal Nations Transportation Planning Needs Assessment Study

The Tribal Transportation Planning Needs Assessment Study was a collaborative project conducted by Caltrans and the Mineta Transportation Institute, San Jose State University. The primary goal of the project was to identify the current state of transportation planning activities and partnerships within Tribal governments in California, so that Caltrans can meaningfully engage more actively with Tribal governments early in the planning process and better meet the transportation needs of Tribal communities. The study focused on the 109 federally recognized Tribal Nations in California, and collected data through a survey questionnaire. The key task undertaken by the study team was to engage and support Tribes to ensure that their opinions were heard and to provide as much or as little technical assistance on the questionnaire as they needed. The findings of the study clearly demonstrate that Tribal Nations have challenges related to technical and staff capacity, funding, lack of resources to meet transportation needs, and lack of collaborative partnerships at the local, state and federal levels, among other issues. This study identifies specific needs and provides recommendations for future engagement between Caltrans, local, state and federal agencies with the Tribal Nations

A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification

Introduction: Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. Objectives: We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. Methods: We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. Results: There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. Conclusion: The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm

Migrating from partial least squares discriminant analysis to artificial neural networks: A comparison of functionally equivalent visualisation and feature contribution tools using Jupyter Notebooks

Introduction: Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods. Objectives: We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN. Methods: We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub. Results: The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach. Conclusion: We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures

Metropolitan Transportation Commission Discretionary Transit Funding Methods Evaluation

In 2021, the Santa Clara Valley Transportation Authority (VTA) approached the Mineta Transportation Institute (MTI) with a proposal to have MTI provide an evaluation of the Metropolitan Transportation Commission’s (MTC’s) operational discretionary funding allocation policies and methods for Bay Area transit operators. The research was done in two parts. Part 1 investigated MTC’s past and current allocation methods for discretionary operational transit funding programs; Part 2 involved the evaluation of outcomes if MTC employed alternative allocation methods. After the Part 1 review of MTC’s various transit funding programs, the federal pandemic relief funds and the Transportation Development Act/State Transit Assistance (TDA/STA) funding programs were selected and evaluated in Part 2 using a set of five alternative allocation metrics and compared to actual MTC allocations. Key findings include: (1) the population-based metric produced the largest increase for VTA’s pandemic relief funds, with VTA receiving 221 percent more than MTC actually allocated in 2020 and 2021, but the San Francisco Municipal Transportation Agency (SFMTA) receiving 64 percent less; (2) the ridership-based metric yielded the smallest amount of VTA pandemic funding, but high ridership operators such as SFMTA would have a 41 percent increase; (3) the population-based metric produced the largest increase in STA funding to VTA but would come at the expense of other transit operators, with Sonoma County receiving 51 percent less; and (4) the ridership-based metric yielded the smallest amount of STA funds for VTA, with 50 percent less funding than actual, while high ridership operators such as SFMTA, would see a roughly 400 percent increase. Thoroughly investigating current and alternative funding allocation methods and policies is critical to understanding their effects on transit agencies and the communities they serve

Defining and Measuring Equity in Public Transportation

Transit should serve all users, regardless of age, race, ability, or any other identity. Policies and planning must be conscious of inequities when defining and measuring equity in public transportation. This study was done to aid the California Department of Transportation (Caltrans) and the state’s transit agencies in assessing transit service equity and assisting with evaluating past, existing, and future inequities. This report identifies and evaluates policies and practices associated with equity measurement in public transit from extant academic and professional literature sources. These include the Federal laws and regulations addressing Title VI of the 1964 Civil Rights Act and the measurement tools (i.e., metrics) that are used to identify and evaluate equity impacts related to transit benefits and costs. The report identifies a list of candidate metrics and applies them to a test case in Santa Cruz County, California, and compares their results to those generated by the metrics required by Title VI (race and income) for transit equity analysis. From this comparison, the study evaluated the need for new metrics in transit equity. Findings suggest that these traditional Title VI measures do not correlate well with other potential measures of inequity. Hence, transit inequity is a multifaceted problem with several potential different measures, each revealing an aspect of inequity. Caltrans and other transit-related agencies need to reach beyond these traditional measures, finding metrics that address the specific, context-appropriate equity conditions of the communities they are measuring to ensure fair and equal public transportation for all

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Background A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. Aim of Review To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. Key Scientific Concepts of Review This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform