Ohlone College (Ohlone or OC) is a public community college with its main campus in Fremont, California and a second campus in Newark. It is part of the California Community College System. The Ohlone Community College District serves the cities of Fremont and Newark, as well as parts of Union City.Ohlone offers 61 associate degrees leading to university transfer or careers and over 100 vocational certificate programs that provide job skill training..
Marriage equality in the United States has not automatically yielded parenthood equality. Although same-sex couples disproportionately shoulder adoption and foster-care responsibilities, access to front-end services remains uneven. This paper maps the legal gap between marriage and adoption equality, evaluates how institutional design mediates equality in practice, and proposes federal solutions. Methodologically, it conducts doctrinal analysis of constitutional and statutory frameworks (including marriage and parentage recognition, interstate judgment recognition, and Free Exercise constraints after recent Supreme Court decisions), examines spending-clause conditions and 2024 administrative rules in child welfare, and undertakes a comparative case study of California and Texas. The analysis finds that constitutional rules secure marriage and core incidents of parental recognition and that interstate recognition preserves the portability of adoption judgments; however, state control over eligibility, licensing, contracting, and vital-records administration, combined with religious-exemption statutes and contract design, produces unequal access. California's aligned legislative, judicial, and administrative architecture normalizes dual legal parentage and nondiscrimination in practice, while Texas's exemption architecture sustains gatekeeping and reduces system capacity. Data blind spots in federal reporting further blunt oversight. The paper recommends a federal adoption-equality statute tied to funding conditions, generally applicable nondiscrimination contract terms, restoration and modernization of child-welfare data elements, targeted training and audits, and embedded evaluation to convert symbolic equality into operational parity centered on permanency for children.
INTRODUCTION:Prior to California's law restricting sales of flavoured tobacco, 129 jurisdictions in the state had local sales restrictions. This study assesses compliance with the state law and tests whether time since effective dates of local flavour laws predicted lower odds of a sales violation. METHODS:Between June and August 2024, data collectors completed 332 purchase attempts (215 for menthol cigarettes, 117 for flavoured vapes) in 223 licensed tobacco retailers clustered in San Francisco Bay Area jurisdictions with a comprehensive local law (n=17) and without a local law (n=5). Data collectors were randomly assigned to try to purchase menthol cigarettes or a flavoured vape. Data collectors completed a survey including open-ended items about clerks' responses. Generalised linear mixed models tested for a relationship between time since adoption of local laws and sales violation, adjusting for store type and data collector. Open-ended responses were categorised by theme. RESULTS:Violation rate was 2.3% for menthol cigarettes and 19.7% for flavoured vapes. Although requests for flavoured vapes were not brand-specific, 83% of purchased vapes were FLUM. Tobacco specialty shops had the highest violation rate. Controlling for store type and data collector, there was no significant relationship between flavoured vape purchase and duration or presence of local law. CONCLUSIONS:Menthol cigarette sales were rare, but sales of flavoured vapes persisted. Targets for further enforcement are tobacco specialty shops and FLUM. Contrary to expectation, duration of local laws was not associated with lower odds of violation, but the sample was limited to San Francisco Bay Area counties.
The recent increase in the popularity of sailboats calls for efficient prediction of sailboat prices, a feature that would help buyers, sellers, businesses, and investors. Machine Learning (ML) has been identified as an efficient tool for price prediction tasks. In this paper we propose new, efficient sailboat price prediction methodologies by employing various machine learning (linear, boosting) and deep learning techniques. The linear models used were linear regression, decision tree, random forest, support vector regression, and K nearest regressor; the boosting models used were AdaBoost, CatBoost, and XGBoost; and the deep learning models used were multilayer perceptron and 1-dimensional Convolutional Neural Networks. After training all our models, test results demonstrate great performance, with the highest R2 score being 1-dimensional Convolutional Neural Network’s 0.887.
