California State University, Los Angeles
  • Los Angeles, California, United States
Recent publications
The environmental problems of global warming and fossil fuel depletion are increasingly severe, and the demand for energy conversion and storage is increasing. Ecological issues such as global warming and fossil fuel depletion are increasingly stringent, increasing energy conversion and storage needs. The rapid development of clean energy, such as solar energy, wind energy and hydrogen energy, is expected to be the key to solve the energy problem. Several excellent literature works have highlighted quantum dots in supercapacitors, lithium-sulfur batteries, and photocatalytic hydrogen production. Here, we outline the latest achievements of quantum dots and their composites materials in those energy storage applications. Moreover, we rationally analyze the shortcomings of quantum dots in energy storage and conversion, and predict the future development trend, challenges, and opportunities of quantum dots research.
Objectives Research examining the age of diagnosis of autism spectrum disorder (ASD) and its influencing factors mostly originate from developed Western countries, providing little to no systematic information about the understanding and management of ASD in the rest of the world. The present exploratory study examined the influence of child and family characteristics on the age of ASD diagnosis in Saudi Arabia. Results The median age at diagnosis was 3.0 years and was associated with some child and family characteristics. A 1 year increase in child’s age was associated with a 0.1 year increase in age of diagnosis (95% CI 0.05, 0.12). Children who did not respond to their name were diagnosed 0.3 years earlier than other children (95% CI − 0.60, − 0.05), and engaging in challenging behavior was associated with a 0.5 year increase in age of diagnosis (95% CI 0.20, 0.81). A lack of comorbidity was associated with a 0.6 year increase in the age of diagnosis compared to the diagnosis age of children with comorbidity (95% CI 0.13, 1.01). Finally, those residing outside of Saudi Arabia were diagnosed with ASD 0.9 years earlier than those residing in Saudi Arabia (95% CI − 0.171, − 0.11).
Many middle school students perform below grade-level standards in reading (National Center for Education Statistics, Washington, 2019), and recent observation studies demonstrate middle school teachers’ limited use of reading comprehension practices within content area instruction (e.g., science and social studies; as reported by Greenleaf (in: Hinchman (ed) Adolescent literacies: A handbook of practice-based research, Guilford Press, 2017)). In this experimental pilot study, we aimed to boost middle schoolers’ reading comprehension outcomes by providing schoolwide professional development (PD) on integrating reading comprehension practices within content instruction for English language arts, social studies, and science teachers. Six schools were matched into pairs and randomized to the schoolwide PD or a business-as-usual (BAU) condition. Content area teachers in schools assigned to the PD condition received distributed PD resources to support implementation, and coaching in one reading comprehension practice in the fall (i.e., get the gist) and one in the spring (i.e., asking and answering questions). Contrary to traditional PD, this PD was implemented across three content areas, was narrow in scope but long in duration (one practice per semester), focused on practices that could feasibly be integrated into content area instruction, and included ongoing coaching in content area teams. Students in schools assigned to the PD condition significantly outperformed those in the BaU condition on a measure of main idea generation (ES = 0.29) but not on measures of asking and answering questions (ES = 0.11) and general reading comprehension (ES = − 0.09). Findings suggest promise for implementing schoolwide approaches embedded within content area instruction to improve reading comprehension performance for middle school students.
Purpose Risk assessments have been constructed using a variety of algorithms, from bivariate associations, to regression, to advanced machine learning (ML) approaches. While promising greater accuracy, agencies are hesitant to adopt tools using newer ML approaches, noting concerns of bias and transparency. Research is needed to identify optimal scenarios for algorithm use in assessment development. Methods We compared regression models (logistic, boosted, and penalized) to more advanced, techniques (neural networks, support vector machines, random forests, and K-nearest neighbors); while also introducing ‘stacking’, a method that combines algorithms to create an optimized model. Using a multi-state sample of 258,464 youth assessments, we varied prediction scenarios by sample size and base rate. Results While performance generally improved with greater sample size, a set of ‘top performing’ algorithms was identified. Among top performers, a ‘saturation point’ was observed, where algorithm type had little impact when samples exceeded 5000 subjects. Conclusions In an era of big data and artificial intelligence, it is tantalizing to explore new approaches. While we do not hasten exploration, our findings demonstrate that sample size trumps algorithm type. Agencies and providers should consider this finding when adopting or developing tools, as algorithms that offer transparency may also be top performers.
Rocking walls continue to gain interest in seismic regions due to their ability of re-centering the building system after major earthquakes. However, they have low inherent damping, thus they are often supplemented with energy dissipating elements. A rocking wall supplemented with partially debonded longitudinal bars is named as a hybrid rocking wall (HRW). While HRWs combine re-centering with adequate energy dissipation, inspection and replacement of the energy dissipating bars may not be economically achieved after a major earthquake. This paper proposes an improvement to HRWs with the use of partially debonded longitudinal bars that are locally heat-treated to strategically concentrate hysteretic action in replaceable bar segments. The effect of the heat treatment protocol on the stress-strain properties and microstructure of reinforcing bars is investigated experimentally and a procedure for locally heat-treating standard reinforcing bars is established. Combining the experimental data of heat treatment with an analytical model for HRWs, the reversed-cyclic behavior of the proposed HRW is investigated to characterize the energy dissipation through the heat-treated bar segments, the re-centering capability of the HRW, and other key design parameters of the proposed wall system.
Recent studies suggest a critical yet unexplored role for local places such as cities and communities in category research. In this study, I investigate how places, as the nexus of cultural, political, and material influences, can shape category dynamics, especially in the early phases of category emergence. I synthesize insights from category work with recent conceptualizations of places as geographically bounded spaces, imbued with meanings and material forms. I conduct an in-depth qualitative field study of the emergence and expansion of a new category, transitional micro-housing villages, also known as Tiny Home Villages, in Eugene, a mid-size city in the United States in 2011 - 2019. The study unpacks the role of nested places in triggering, enabling, and constraining actors and their work to create, legitimate, and expand a category. Specifically, I highlight the role of local material forms and how actors can mobilize local spaces, technologies, and practices to advance their goals in contested category dynamics.
This article examines the historical, institutional, and interactional processes by which “Poly” (i.e., Polynesian) has come to be understood as a race and language within a context in the California Bay Area. Rather than understanding “races” as discrete categories—as well as sociolinguistic features as permanently attributable and patterned to specific racialized groups—I argue that racialization is ever‐changing and rooted in power relations that are (re)produced from interaction to interaction, and moment to moment. I primarily draw upon a semi‐structured interview with a Tongan young woman (“Maklea”), and more broadly ethnographic research conducted within her local language context, and argue that a racialized Polyness (i.e., Polynesianness) is becoming raciolinguistically enregistered due to experiences with White supremacy and processes of colonialism. That is, Polyness is in the process of being rendered mutually perceivable as a racial category and coherent set of semiotic practices as Polynesian diasporic peoples in this community are confronting policing, gentrification, and an ideology of oppressionlessness. The raciolinguistic enregisterment of Polyness is occurring as Maklea, and more broadly Polynesian young people, are grappling with and challenging the ways White supremacist institutions and systems are seeking to violently structure their lives and ways of knowing, being, valuing, and speaking.
Aim It is reported that problem drinking is severe among the elderly. The family environment has been regarded as a significant effecting factor in alcohol consumption of the drinker. With the increasing number of older people, paying more attention to this vulnerable group's drinking status and its' influencing factors is substantial for improving older adults' health and the quality of health services. Methods This study used data from the Chinese Longitudinal Healthy and Longevity Study (CLHLS), which was a representative survey covering 23 provinces in mainland China. Cross-sectional analyses were conducted with 15,142 older individuals (aged ≥65 years). Three self-reported questions about drinking behavior were examined to calculate alcohol consumption and categorize problem drinkers. Three multi-level models were utilized while adjusting for numerous socio-demographic and self-reported health factors to analyze the effect of family factors associated with problem drinking among the elderly. Results A total of 1,800 problem drinkers (12%) were identified in the sample. Key factors for the problem drinker were assessed such as Hukou (governmental household registration system), current marital status, years of schooling, primary caregivers, and financial sources of living were associated with problem drinking. The older population who live in rural areas (OR = 1.702, CI = 1.453, 1.994), with advanced years of education (OR = 1.496, CI = 1.284, 1.744), and making life by themselves (OR = 1.330, CI = 1.139, 1.552) were more likely to engage in problem drinking while those participants who are widowed (OR = 0.678, CI = 0.574, 0.801), cared for by children or other relatives (OR = 0.748, CI = 0.642, 0.871), adult care giver (OR = 0.348, CI = 0.209, 0.578) or by no one (OR = 0.539, CI = 0.348, 0.835), provided with financial support from their children (OR = 0.698, CI = 0.605, 0.806), other relatives (OR = 0.442, CI = 0.332, 0.587), or the government/community (OR = 0.771, CI = 0.650, 0.915), with insufficient financial support (OR = 0.728, CI = 0.608, 0.872) were at lower risk of problem drinking. Conclusions This study provides a strong correlation of various family factors that were associated with problem drinking among the elderly. The findings underscore the effort to promote healthy behaviors, including the importance of positive family factors and appropriate levels of alcohol consumption.
The ongoing pandemic has led to substantial volatility in residential housing markets. However, relatively little is known about whether the volatility is dominated by housing demand or supply, and how different priced markets contribute to the volatility. This article first examines the temporal effect of COVID-19 on house prices, housing demand, and supply in Los Angeles, and second explores the effect heterogeneity in luxury and low-end housing markets within the city. For identification, the article employs a revised difference-in-differences (DID) method that controls more rigorously for unobservables and improves on the traditional DID with smaller prior trends. Using individual level data, the result first shows that, in response to the outbreak, house prices, demand, and supply all decreased in March to May 2020 and increased in July and August 2020, with demand dominating the process. Second, the heterogeneity exploration identifies diverging COVID-19 impacts in higher- and lower- priced markets. Particularly, the decline in overall price and demand before June originates mainly from the lower-priced market while the higher-priced one experienced limited changes in demand. After July, higher-priced markets led housing market’s surge in price, demand, and supply, whereas the lower-priced market has not fully recovered from decreases in house prices and housing demand. Finally, a larger price decline in lower-priced markets is found to be associated with higher service shares and lower homeownership rates. The results not only facilitate market participants in their decision making but also aid local governments in formulating policies and allocating subsidies to mitigate the effects of the outbreak.
Multi-responsive functional materials with simultaneous photothermal and photoluminescent properties have broad applicational prospects in the field of flexible electronics, devices and remote force detection and monitor. Here, a feasible method is developed by introducing MoO3-x quantum dots (MoO3-x QDs) and blue fluorescent carbon dots (B-CDs) into a strong hydrogel matrix to fabricate a novel composite hydrogel with concurrent photothermal and photoluminescent properties. Besides rendering the peculiar multifunctionalities, we found that the addition of photothermal MoO3-x QDs enhances the mechanical properties and self-healing properties of the composite hydrogel. The temperature of the MoO3-x-CDs-PVA (Polyvinyl Alcohol) hydrogel can rise by 30 °C within 1 minute after 808 nm infrared laser irradiation and the self-healing efficiency could double after 40 seconds of irradiation. Moreover, there is a good linear relationship between the fluorescence intensity of the composite hydrogel and the external force, which can be used to monitor the force within a certain range. The monitoring range and sensitivity can be further improved by adjusting the infrared laser to for small force detection. Finally, the novel composite hydrogel is successfully applied to fracture monitoring in fracturing and force monitoring at different locations in the fluid model in the field of petroleum engineering.
Current studies on the effect of thank-you gifts on charitable giving are primarily based on the conclusion of a milestone paper, “The counterintuitive effects of thank-you gifts on charitable giving” which argued that thank-you gifts are mainly driven by lower feelings of altruism. This article argues that the question design in “The counterintuitive effects of thank-you gifts on charitable giving” may lead to a biased conclusion. This article added an extra treatment group to the original study and found that the authors neglected the critical impact of participants’ inference about the usage of the money.
This study explores the demographic/background, academic, and environmental factors that predicted student retention after stopping out at a large Hispanic-serving institution in Southern California. The results show that (a) gender, (b) academic background, experience, readiness, and performance, and (c) personal and financial issues predicted retention. We interpret these findings in the context of existing literature and provide suggestions to help institutions develop intervention strategies to promote student retention and “servingness” at HSIs.
This article examines how Bogotá has developed civic engagement with street artists to design and implement a program promoting the responsible practice of graffiti by engaging in a thought experiment. Bogotá used participatory policymaking and public outreach that is carried out by street artists as forms of civic engagement. This article contributes to our understanding of how government officials can engage groups who typically do not participate in the policymaking process. The research reveals there is some effort from the government to build bonds and bridges with the program for the responsible practice of graffiti and the artists. The findings suggest that participatory policymaking and public outreach are useful methods to engage members of the public who are not typically represented in the policymaking process. The article provides key lessons that can be applied to other cities that wish to engage different groups of people who are unrepresented in traditional forms of civic engagement in local government.
Data-driven decision making (DDDM) in public child welfare (PCW) has become increasingly important with the passage of the Family First Prevention Services Act (FFPSA), making PCW agencies across the U.S. examine their various programs to ensure that they meet the service requirements of FFPSA. Family Preservation (FP) is an important program that is offered by PCW agencies nationwide, yet little is known about how programs like FP can implement DDDM to examine outcomes to improve practice. This study describes how one of the largest PCW agencies nationwide adopted DDDM in their FP program and presents preliminary findings along with lessons learned as part of the process to meet FFPSA requirements. For example, FP established a baseline recurrence rate using the standard federal definition of the recurrence of maltreatment adapted for FP; this rate was 6.6% for families receiving FP in the target county compared to 8.4% for families not receiving FP services. Subsequent case reviews revealed issues related to engagement, family expectations, and termination codes, which led to standardized definitions and practice changes. Several lessons learned are provided as part of the incorporation of DDDM in FP as well as implications for practice and research.
The impact of COVID-19 on job displacement in the United States has been unevenly experienced by race, ethnicity, and the socioeconomically disadvantaged. Although unemployment benefits may mitigate the effects of job displacement, this social safety net is also unevenly distributed across workers. We examine racial/ethnic differences in receiving unemployment benefits among workers displaced by the pandemic. We use data from the US Census Household Pulse Survey (HPS), which is specifically designed to capture the real time effects of the pandemic across a wide spectrum of social issues. (US Census, 2020) Unlike the Current Population Survey (CPS) data used in the monthly unemployment rate calculations, the HPS data allow us to identify workers directly displaced from their jobs by the pandemic. We analyze over 1.3 million HPS interviews from the first stage of the pandemic when the disruptions to the labor market were the most severe, covering the period from June 11, 2020 to December 22, 2020. We contribute to the literature on the labor market effects of the pandemic in two ways. One, the HPS data allow us to identify workers who directly experienced job loss as a result of the disruptions created by COVID-19 and to determine who did not receive unemployment insurance. Two, we present both bivariate and multivariate analyses to examine racial/ethnic disparities for five groups: non-Hispanic whites, Blacks, Hispanic, Asian, and non-Hispanic Other workers. We find that Black and Hispanic workers are more likely to be unemployed without Unemployment Insurance (UI). Black workers are 12.0% of the employed but 17.5% of displaced workers without UI. Hispanic workers are even more affected. Hispanic workers are 15.6% of the employed, but are 23.4% of all displaced workers without UI. Although there are limitations to using the HPS data—the survey was administered online in only English and Spanish and occupational and industry data are not available for displaced workers, the results still provide valuable insights informing the current policy debate about the effects of expanding UI.
Drawing a direct analogy with the well-studied vibration or elastic modes, we introduce an object’s fracture modes , which constitute its preferred or most natural ways of breaking. We formulate a sparsified eigenvalue problem, which we solve iteratively to obtain the n lowest-energy modes. These can be precomputed for a given shape to obtain a prefracture pattern that can substitute the state of the art for realtime applications at no runtime cost but significantly greater realism. Furthermore, any realtime impact can be projected onto our modes to obtain impact-dependent fracture patterns without the need for any online crack propagation simulation. We not only introduce this theoretically novel concept, but also show its fundamental and practical advantages in a diverse set of examples and contexts.
Students who have remained classified as English Learners (ELs) for more than six years are often labeled “Long-term English Learners” (LTELs). The present study examined the English Language Development (ELD) test scores and demographic information in a group of 560 students identified as LTELs. Despite assumptions that these students are still learning English, results showed many students who are labeled LTELs exhibited advanced English skills, especially on measures of expressive and receptive oral language (i.e., speaking and listening subtests). At the same time, ELD assessments showed many of these students struggled with literacy skills, especially reading. Perhaps due to these overlapping circumstances, we found many LTELs were also identified with learning disabilities. Based on these findings, we explored the impact of restricting domains needed for reclassification as English proficient on reclassification rates. Compared with existing decision rules in the students’ state, proposed models allow many more LTELs to reclassify as English proficient, and most LTELs not reclassifying are students in special education. Discussion focuses on interpreting ELD scores for students who have remained classified as ELs for more than a few years.
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4,893 members
Sunil Sapra
  • Department of Economics and Statistics
Nathan Lanning
  • Department of Biological Sciences
Miwako Hisagi
  • Department of Communication Disorders
Robert M Nissen
  • Department of Biological Sciences
Marina Mondin
  • Department of Electrical and Computer Engineering
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President William A. Covino
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