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A School Effects Analysis of First-Generation, Working-Class Students’ Long-Term Outcomes
No full textSociologists have long recognized schools as important factors in student outcomes, but prior work often takes institutional forces for granted when analyzing class inequality in higher education, focusing instead on students’ skills and resources. This study applies the Critical Cultural Wealth Model to argue that institutions differentially impact long-term academic, professional, and social-emotional outcomes of first-generation, working-class (FGWC) students and their peers. School effects analyses of data from the College and Beyond II Study reveal several key findings. First, colleges and universities differentially affect student outcomes. Second, these institutions shape academic, professional, and social-emotional disparities between FGWC students and their peers. Finally, the institutions that most positively affect academic outcomes for FGWC students have more negative impacts on these students’ social psychological outcomes. These results affirm that shows higher education institutions matter for student success and class inequality but show they may do so in contradictory ways for different outcomes
Phage biocontrol in water treatment and reuse systems: a nascent field with significant innovation opportunities
No full textWhile the use of phages in the food and biomedical sectors occurs commercially, their application in the water sector is less common and is typically demonstrated at a lower technological readiness level. This is so despite the potential that phages have to enhance the control of problematic bacteria (including pathogens) and protect infrastructure within the water sector. Fulfilling the great potential of this nascent field requires more research and development. Here, we highlight innovation opportunities and discern critical knowledge gaps and research needs to facilitate the use of phages as precise biocontrol agents in the water sector. First, while the advent of sequencing technologies made it easier to identify bacterial communities and understand their functional roles, identifying and cultivating the appropriate phages that can be effective against the bacterial target requires more research. The large volumes of water to be spiked with phages also require optimizing the phage biocontrol strategy, minimizing the associated costs and enhancing scaling up. In addition, bacterial hosts may gain phage resistance after long-term exposure, which is common in most water-engineered systems, and strategies to minimize or delay resistance must be considered. In this opinion, we provide an overview of pertinent literature and bioinformatic tools that help identify appropriate bacterial hosts and phages for water systems applications. We then discuss strategies that can aid in prolonging the efficacy and enhancing the feasibility of phage biocontrol approaches
The biophysics of neuron-astrocyte-vascular modeling in conditions of normalcy, Intracerebral Hemorrhagic (ICH) stroke and electrical stimulation
No full textIntracerebral Hemorrhagic (ICH) stroke is the second most common type of stroke, but the deadliest. Nearly of patients succumb to complications, while the ones surviving suffer a high degree of morbidity and lose the previous quality of life. Neuromodulation has been used in past as a part of therapeutic regimens for post ischemic stroke (most common type of stroke) rehabilitation during the chronic stages. The idea of neuromodulation as a rehabilitation technique has been not formally studied from the first principles for ICH strokes whose outcomes are way more severe than ischemia. While our experimental work focuses on understanding whether neuromodulation can be applied in a practical setting of ICH during the acute phase to control outcome of the patient, this project explores the theoretical underpinnings of the effect of neuromodulation at a cellular systemic level of neuron-astrocyte-vascular system in the normal and acute conditions post ICH. We improvise the Hodgkin-Huxley neuron model to incorporate a presynaptic neuron, calcium transients in a neuron, the tripartite synapse along with the astrocytes, astrocytic calcium signaling, the post synaptic neuron, the cerebral blood flow as well as the oxygen and energy consumption dynamics. We simulate the various biochemical pathways that set in during the acute phase post ICH and implement electrical stimulation both in the normal and post stroke settings. The goal of this work is to understand qualitatively, the effects of electrical stimulation paradigms as a therapeutic strategy by analysing a set of non-linear Ordinary Differential Equations(ODEs). This is the first work of its kind, wherein electrical stimulation has been studied in such an elaborate setting incorporating not only the biophysics but also the bioenergetic effects of neurostimulation. The solution for the ODEs consists of traces and limit cycles, which exhibit the behaviour of different components under normal conditions, during stroke and during electrical stimulation. From these, it is safe to say that we get a closer look at the effects of various electrical neuromodulation paradigms under the given assumptions. This will be used to look into the gaps of theoretical understanding of such complex phenomena, paving the way for better future modeling of neurodegenerative diseases and various treatments for it
Drama in LCTL Classrooms: Example of an Experiential Learning Project in Modern Standard Arabic
No full textThe present article describes an experiential learning drama project in Modern Standard Arabic that took place at a large, public university in the United States, as an effective way of consolidating learners’ oral skills both productively and receptively at various sublevels of proficiency. After briefly discussing methods and benefits of employing authentic drama texts in a language classroom, the article moves on to describe how the project was structured and conducted through the various phases of play selection, reading sessions, and abridging process and its implications for the lexical and syntactic aspects of the text. Furthermore, the article discusses CALL tools that were employed with a particular focus placed on components such as pronunciation and intonation, and finally, the rehearsal phase as an opportunity for meaningful interaction between learners. The article aims to provide a detailed model of how drama can be employed in second language classrooms with particular focus on less commonly taught languages
Financial Knowledge, Banking, and Fintech in Houston and Harris County
No full textFinancial literacy and access to capital through a bank are both related to improved economic well-being. To better understand these issues, the Greater Houston Community Panel (GHCP) asked residents of Houston and Harris County, Texas, to report on their financial knowledge, use of banks, and use of more modern financial technology applications (fintech). This report provides a snapshot of the findings
Sunrise Centers — Strengths, Challenges, and Recommendations to Enhance Family and Student Well-Being
No full textThis four-part series examines the first-year implementation of the Houston Independent School District’s (HISD) Sunrise Centers. Launched
in 2023-2024, the Sunrise Centers provide essential non-instructional resources to students and families across seven centers in the district. This series, a collaboration between the Kinder Institute for Urban Research's Houston Education Research Consortium (HERC) and HISD’s Family and Community Engagement (FACE) department, explores initial strengths, challenges, and recommendations to enhance the program’s impact on family and student well-being
Leveraging Graph Networks for Health and Wellbeing Prediction
No full textHealth and well-being prediction plays an essential role in mental healthcare and well-being-aware computing. The complex nature of well-being, resulting from its dependency on a person’s physiological health, mental state, and surroundings, makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported well-being metrics. In addition to a person’s physiology, we incorporate the environment’s impact through weather and social network data. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users within the graph network and integrates it with the temporal dynamics of data to predict well-being outcomes for all users.
To address the dynamic nature of social networks, we introduce GEDD (Graph Extraction for Dynamic Distribution), an approach that automatically adapts to fluctuating network sizes. GEDD utilizes graph properties, including connectivity and components, to transform variable-sized graphs into a standardized format, ensuring no user data is discarded. The proposed architecture supports online learning, making it feasible to scale to large networks without adding ecological momentary assessments (EMAs) or additional data collection burdens, thus preserving user privacy.
Through extensive evaluations, we show that social network incorporation improves prediction accuracy, although node influence, especially in users with high eigenvector centrality, can amplify noise. To address this, we propose a robust system that leverages attention and social contagion in well-being behaviors through graph networks and integrates it with physiological and phone data from ubiquitous mobile and wearable devices. This system is designed to predict well-being outcomes, such as sleep duration and other health metrics while mitigating the challenges posed by noisy and incomplete data.
Finally, we further leverage the graph structure to reduce the user burden associated with collecting health and well-being metrics, which are often captured at a much lower resolution than sensing data through surveys and EMAs. To this end, we introduce a benchmark framework to evaluate existing state-of-the-art graph-based active learning (AL) strategies in dynamic sensing environments. Our framework assesses AL strategies in terms of adaptability to real-time, user-centric data by evaluating performance over time in a stream-based setting. We also introduce new metrics, including sampling entropy, coverage ratio, and time-gap analysis, to quantify user burden, sampling diversity, and generalization performance. These metrics provide a holistic view of the AL strategies’ effectiveness, helping to identify those that best balance predictive accuracy and user engagement. This comprehensive evaluation framework supports scalable and efficient health prediction systems, facilitating practical, large-scale deployment
Cosmological and astrophysical probes of axionlike particles
No full textAxionlike particles (ALPs), pseudo Nambu-Goldstone bosons arising from the spontaneous breaking of global U(1) symmetries, appear in solutions to open issues in fundamental physics and are ubiquitous in string theory compactifications. Furthermore, ALPs have a rich phenomenology that provides numerous ways to search for evidence of their existence. This work explores two potential discovery channels for ALPs. The first considers the possibility that hyperlight ALPs, with masses less than 10^(-28) eV and a Chern-Simons coupling to electromagnetism, formed a cosmic string network in the early Universe that survives beyond recombination. In this scenario, cosmic microwave background (CMB) photons passing through string loops in the network experience a rotation in their plane of polarization, an effect known as CMB birefringence that may be within reach of future CMB probes. I use existing CMB birefringence power spectrum data to constrain axion string network parameters, then discuss non-Gaussian features of axion string-induced CMB birefringence maps, and finally explore how a neural network could estimate axion string network parameters from these maps. The second potential discovery channel examines how ALPs with lepton flavor-violating couplings and masses less than 1 MeV affect the cooling rates of neutron stars. Through these studies, I develop tools that would assist in identifying signatures of ALPs in cosmological and astrophysical observations
Symmetry projection methods in strongly correlated systems
No full textQuantum chemistry aims to understand and predict molecular structure and properties by solving the time-independent Schr\"odinger equation within fixed nuclei (Born-Oppenheimer) approximation. The Hamiltonian may have continuous symmetries (e.g., spin) or discrete symmetries (for example, point group). Mean-field wave functions may break some or all of these symmetries. By taking the symmetry-adapted pieces of these broken symmetry states, one can restore the physical character of the wave function while also treating relevant strong correlations. On the other hand, mean-field methods lack dynamic correlation arising from short-range interactions. Still, single-reference methods such as configuration interaction or coupled cluster theory can handle these correlations efficiently.
Ideally, we would like to combine these approaches: performing configuration interaction or coupled cluster theory on a broken symmetry reference while restoring the desired symmetries. This work introduces a new approach to integrating symmetry projection and coupled cluster theory. By borrowing techniques from previous research, the new method can restore continuous symmetries like (1) and (2) and discrete symmetries like point group and time reversal. This method achieves accurate results in challenging systems and has the same computational scaling as traditional coupled cluster theory.
Jordan-Wigner transformation is a potent tool in spin models, but its application is challenging due to the inherent strings. The same techniques we use to combine symmetry projection and coupled cluster theory can be used to combine Jordan-Wigner transformation and coupled cluster. The strings are the ' symmetry projection ' operator when constructing the Hartree-Fock and coupled cluster wave functions for a Jordan-Wigner transformed Hamiltonian. Leveraging the method of symmetry projection, we obtain excellent results for both 1-D and 2-D spin models