Westwood College

Many young people in the United States lack the exposure, confidence, and skills necessary to use public transportation independently. Our nonprofit in Austin, Texas addresses this gap by integrating daily public transit use into its summer camp experience. Each day, children (campers) and teens (volunteers) receive training and are led by camp leaders through the city using public transportation to reach educational destinations. This hands-on approach aligns with experiential learning theory, which emphasizes learning through action and reflection. It also reflects the PRECEDE-PROCEED model, prioritizing a real community need-low youth transit engagement-by reducing barriers and embedding evaluation into program design. The program builds practical navigation skills, city awareness, independence, and social confidence. Both age groups benefit: children develop early transit literacy, while teens gain leadership skills, practical knowledge, and increased civic engagement. Teaching youth to use public transportation increases the likelihood of confident, regular adult transit use, shaping future riders and strengthening public transit systems.

Effective network intrusion detection using anomaly scores from unsupervised machine learning models depends on the performance of the models. Although unsupervised models do not require labels during the training and testing phases, the assessment of their performance metrics during the evaluation phase still requires comparing anomaly scores against labels. In real-world scenarios, the absence of labels in massive network datasets makes it infeasible to calculate performance metrics. Therefore, it is valuable to develop an algorithm that calculates robust performance metrics without using labels. In this paper, we propose a novel algorithm, Expectation Maximization-Area Under the Curve (EM-AUC), to derive the Area Under the ROC Curve (AUC-ROC) and the Area Under the Precision-Recall Curve (AUC-PR) by treating the unavailable labels as missing data and replacing them through their posterior probabilities. This algorithm was applied to two network intrusion datasets, yielding robust results. To the best of our knowledge, this is the first time AUC-ROC and AUC-PR, derived without labels, have been used to evaluate network intrusion detection systems. The EM-AUC algorithm enables model training, testing, and performance evaluation to proceed without comprehensive labels, offering a cost-effective and scalable solution for selecting the most effective models for network intrusion detection.

This paper examines emotional resilience, and independence in “left behind children,” defined as those growing up with one or more parents working and living away from home. In considering existing literature on the subject, as well as drawing from oral interviews conducted, this paper suggests that “left behind children” are indeed more emotionally resilient and independent than their peers who do not fall into this category. The participants in this study are five high-school students, two from China and three from America, who grew up with one or more parents living outside of the household. This research complicates broader assumptions regarding “left behind” identity, a term typically associated with low socioeconomic rural Chinese children of migrant workers. The findings of this paper highlight the interviewees’ minimal emotional distress and relatively apathetic dispositions toward their circumstances, challenging canonical assumptions of adverse mental and physical effects.

BackgroundHuman Immunodeficiency Virus (HIV) remains a significant public health concern worldwide. Women living with HIV/AIDS (WLHA) have the additional and unique need to seek sexual and reproductive health services. WLHA’s maternal health journeys can be shaped by the cultural norms and resources that exist in their society. This study sought to understand if and how WLHA’s family planning, pregnancy, and motherhood experiences could be influenced by the patriarchal culture, gender roles, and HIV stigma in Vietnam, specifically.MethodsBetween December 2021 and March 2022, 30 WLHA with diverse socioeconomic backgrounds and childbirth experiences were interviewed in Hanoi, Vietnam. These semi-structured interviews covered topics including HIV stigma, gender norms, pregnancy experiences, and child-rearing challenges. Interviews were audio recorded, transcribed, and analysed using ATLAS.ti.ResultsQualitative analyses of participant quotes revealed how limited information on one’s health prospects and reproductive options posed a significant challenge to family planning. Societal and familial expectations as well as economic circumstances also influenced reproductive decision-making. WLHA often encountered substandard healthcare during pregnancy, labor, and delivery. Stigma and lack of provider attentiveness resulted in cases where women were denied pain relief and other medical services. Communication breakdowns resulted in failure to administer antiretroviral therapy for newborns. Motherhood for WLHA was shadowed by concerns for not only their own health, but also the wellbeing of their children, as HIV stigma affected their children at school and in society as well. Many WLHA highlighted the constructive or destructive role that family members could play in their childbirth decision-making and care-giving experiences.ConclusionsOverall, this study underscores the complex ways that cultural expectations, family support, and stigma in healthcare impact WLHA. Efforts to educate and engage families and healthcare providers are warranted to better understand and address the needs of WLHA, ultimately improving their reproductive and maternal health.

Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA’s superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities.

During the past 50 years, medical and behavioral scientists have made great progress in understanding the variables which influence the development of sexual orientation, identity, and consequent behavior. In most instances, homosexuality is influenced by hormonal, genetic, and immunologic variables during fetal development, and the effects cannot usually be altered without consequence. The recent struggle within The United Methodist Church in the USA reflects the difficulty that society in general has with accepting homosexuality as part of the spectrum of sexuality. Hopefully, understanding the factors influencing sexual orientation will aid in reducing prejudice and eventually bring an end to the pain endured by the LGBTQ community, and the conflict within The United Methodist Church, a prototype of the struggle.

ABSTRACT Fly ash from the coal combustion at electric plants is commonly used for partially replacing portland cement in concrete production. Because of the varying nature of the coal source and the different processing protocols, different fly ashes exhibit wide ranges of physical and chemical characteristics, resulting in distinct impacts on concrete strength. Thus far, the most adopted method for assessing a given fly ash is specified by ASTM C618, Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete, wherein a series of influential chemical and physical features can be correlated to fly ash’s strength activity index (SAI). However, limited knowledge is available on how exactly the individual material attribute affects SAI, so accurately predicting the SAI remains out of reach. Here, we take advantage of recent advances in machine learning to reveal the origins of fly ash’s SAI. Leveraging a data set comprising 2,158 fly ash samples, we trained neural network models to predict 28-day SAI based on the sole knowledge of ASTM C618 material attributes. The results demonstrate that SAI is a complex property that does not systematically follow the conventional Class C/F classification. To gain a deeper insight into this matter, we further quantify the influence of each attribute on SAI as captured by the machine learning model.