California State University, San Bernardino
  • San Bernardino, CA, United States
Recent publications
This study presents the recollections of 12 successful women in science during their school years before postsecondary education. The participants shared detailed descriptions of their science experiences through three semi‐structured interviews. An identity works conceptual framework consisting of figured worlds, positioning, and agency constructs to portray the complex dynamics of their experiences was used to analyze the data. The following four themes emerged from the data analysis: participants had an early interest in mathematics and science; they were “stubbornly” persistent in science‐figured worlds; they engaged in science‐figured worlds beyond school; and they positioned themselves as science leaders. These findings add to the evolution of science identity development theoretical models because they are from a nondeficit perspective. Participants engaged in identity work that advanced their science identities despite the gender biases in science‐figured worlds. From a practical stance, girls and women could employ the agentic and positive positioning identity work that the findings show to develop their science identity in educational contexts. Science educators and researchers are encouraged to structure figured worlds where girls feel empowered to enact identity work to build strong science identities.
Prior to the COVID-19 pandemic, Computer Science and STEM-related fields were among the most resistant to online courses. This is because of a perception of the need for more hands-on instruction with labs, clinicals, field studies, etc. Additionally, many STEM students had perceptions based on limited experience of an online STEM course. Therefore, investigating how the pandemic affected students’ perceptions over time is very important. This study investigates the evolution of student perceptions after one and a half years relative to synchronous courses, asynchronous courses, overall satisfaction with online courses, and lab and project-based courses. Our analysis is based on two surveys conducted in the Spring 2020 and Spring 2021 terms, i.e., the first and last semesters that the university converted to a fully online mode. We hypothesize why there were significant empirical shifts in some areas and not in others, and make recommendations based on the qualitative student responses relative to best, acceptable, and poor practices. Our main findings include: 1) Students’ perceptions of online classes have improved but are far from equivalent for a lot of the students. 2) Lab resources have improved a great deal, but lab experiences have only improved modestly. 3) Although students’ preference between synchronous and asynchronous online modalities were evenly divided, it did not significantly affect students’ perception of their learning experience. 4) Grading policies have left many students anxious and confused. Recommendations are provided at the end of the paper.
Heart failure with preserved ejection fraction (HFpEF) encompasses nearly half of heart failure (HF) worldwide, and still remains a poor prognostic indicator. It commonly coexists in patients with vascular disease and needs to be recognized and managed appropriately to reduce morbidity and mortality. Due to the heterogeneity of HFpEF as a disease process, targeted pharmacotherapy to this date has not shown a survival benefit among this population. This article serves as a comprehensive historical review focusing on the management of HFpEF by reviewing past, present, and future randomized controlled trials that attempt to uncover a therapeutic value. With a paradigm shift in the pathophysiology of HFpEF as an inflammatory, neurohormonal, and interstitial process, a phenotypic approach has increased in popularity focusing on the treatment of HFpEF as a systemic disease. This article also addresses common comorbidities associated with HFpEF as well as current and ongoing clinical trials looking to further elucidate such links.
Objective: Parkinson's disease (PD) is a neurodegenerative movement disorder that is a result of dopamine depletion in the basal ganglia. Individuals with a PD diagnosis experience motor symptoms (e.g., tremors) and nonmotor symptoms (e.g., cognitive decline). Previous studies suggest that progression of cognitive dysfunction in other neurologic populations can be predicted by cumulative head injuries. The study examined the association between lifelong number of head injuries and nonmotor outcomes (cognitive complaints, depression, and quality of life). Methods: Participants consisted of 3,483 individuals with PD diagnoses who were enrolled in the Fox Insight study. Participants completed a self-report questionnaire to quantify the number of head injuries experienced throughout life. Participants also completed measures of nonmotor outcomes (cognitive complaints, depression, and quality of life) every 6 months over a 3-year period. Results: Cognitive complaints were more common among those experiencing more head injuries. Further, more severe depression and greater difficulties in quality of life were reported among individuals experiencing a greater number of head injuries. Additional analyses revealed the effect between cognitive complaints and number of head injuries was driven by individuals who experienced five or more head injuries in their lifetime. Conclusions: Among individuals with PD, a patient report of past head injuries may have prognostic implications for important nonmotor outcomes. Report of multiple head injuries may be particularly concerning.
Background: Due to improved coverage and scale-up of antiretroviral therapy (ART), patients are increasingly transferring between ART-providing sites. Self-transfers may constitute a high proportion of patients considered lost to follow-up (LTFU), and if overlooked when reporting patients who have dropped out of HIV care, may result in an incorrect estimation of retention. We determined the prevalence of self-transfers, and successful tracing, and identified associated factors among people living with HIV (PLHIV) LTFU from care at public health facilities in Sheema District, Southwestern Uganda. Methods: We conducted a cross-sectional retrospective medical records review during February and March 2022. We included records of all PLHIV who were LTFU from 2017 to 2021, and who were registered at government-owned ART clinics in Sheema District. LTFU was considered for those who were not taking ART refills for a period of ≥ 3 months. We abstracted demographic and clinical data from medical records at the selected clinics. Participants were traced via phone calls or in-person to ascertain the outcomes of LTFU. We performed multivariate modified Poisson regression to identify factors associated with self-transfer, and successful tracing. Results: Overall, 740 patients were identified as LTFU from three ART-providing clinics; of these, 560 (76%) were self-transfers. The mean age was 30 (SD ± 10) years, and most (69%, n = 514) were female; the majority (87%, 641/740) were successfully traced. Age (adjusted prevalence ratio [aPR] = 1.13, 95% CI 1.01-1.25, P = 0.026 for those aged 18-30 years compared to > 30 years), female sex (aPR = 1.18, 95% CI 1.11-1.25, P < 0.001), and having WHO clinical stage 1-2 (aPR = 2.34, 95% CI 1.89-3.91, P < 0.001) were significantly associated with self-transfer. Presence of a phone contact in the patient's file (aPR = 1.10, 95% CI 1.01-1.90, P = 0.026) was associated with successful tracing of the patients considered LTFU. Conclusion: Self-transfers accounted for the majority of patients recorded as LTFU, highlighting the need to account for self-transfers among patients considered LTFU, to accurately estimate retention in care. ART-providing facilities should regularly update contact information for PLHIV to enable successful tracing, in the event that the patients are LTFU. This calls for a health-tracking system that easily identifies self-transfers across ART-providing clinics using unique patient identifiers.
Recently, purchasing intention towards green products has gained global attention due to their extensive use and high environmental issues. Thus, the current article investigates environmental concern, environmental knowledge, green product, and ecoinnovation influence on the green purchase intention of green products in Taiwan industry. The research also examines the mediating role of consumers’ attention among environmental concerns environmental knowledge, green products eco-innovation, and green purchase intention. The article used questionnaires to collect the primary data from the sampled population. To examine the hypothesis, the PLS methodology was adopted. The results indicated that environmental concern, environmental knowledge, green product, and eco-innovation have a positive association with green purchase intention. The findings also exposed that consumer attention significantly and positively mediates environmental concern, environmental knowledge, green product, eco-innovation, and green purchase intention. The paper also proposed some guidelines to the practitioners which help them in the development of green-related policies to increase purchase intention.
Sustainable energy technology adoption has become a global requirement due to environmental degradation, and this phenomenon needs researchers' and regulators' emphasis. Hence, the present study examines the various factors such as low cost, awareness of the environment, attribution of responsibility, habitual energy-saving behavior, and social norms impact on the sustainable energy technologies adoption in ASEAN countries. The article has taken the questionnaires to collect the data from the chosen respondents. The research has also applied structural equation models (SEM) using SPSS-AMOS to test the hypotheses and association among the variables. The results indicated that the low cost, awareness of the environment, attribution of responsibility, and habitual energy-saving behavior are positively correlated with sustainable energy technologies adoption in a sample of ASEAN economies. The findings also exposed that the social norms and sustainable energy technologies adoption are negatively correlated in the chosen sample. This study guides the regulators in establishing the policies related to sustainable energy technologies adoption by examining the features mentioned in the study.
A large body of research has found that people judge bad foreseen side effects to be more intentional than good. While the standard interpretation of this Side-Effect Effect (SEE) takes it to show that the ordinary concept of intentionality is influenced by normative considerations, a competing account holds that it is the result of pragmatic pressure to express moral censure and, thus, that the SEE is an experimental artifact. Attempts to reveal this have previously been unsuccessful, however. That is until recently, when Lindauer and Southwood (2021) detailed a study purporting to cancel the SEE. We are not convinced. Here, we detail three studies testing their interpretation. The results indicate that it is the purported cancellation, rather than the SEE, that is an experimental artifact.'cus. N.L. neut. n. methanum, methane; L. masc. adj. caldus, hot; Gr. masc. n. kokkos, a grain, a seed; N.L. masc. n. Methanocaldococcus a coccus producing methane at hyperthermophilic growth temperatures. Euryarchaeota / Methanococci / Methanococcales / Methanocaldococcaceae / Methanocaldococcus Regular to irregular cocci, 1–3 μm in diameter. Cells stain Gram‐negative and lyse rapidly in aqueous solutions with low osmotic strength. Motile by means of polar tufts of flagella. Obligately anaerobic. Hyperthermophilic (temperature optima: 80–90°C). pH optima between 6.0 and 7.0. NaCl required for growth, optimal concentration 2.5–3.0% (w/v). Obligately methanogenic; H2 serves as the electron donor. Formate, acetate, methanol, and methylamines are not substrates for methanogenesis. Autotrophic growth in mineral medium. Selenium is required and tungsten is stimulatory for growth. Complex carbon sources such as yeast extract are sometimes stimulatory. Nitrogen sources include ammonium, nitrate, and N2 gas. Sulfur sources include sulfide, sulfite, and S0. Isolated from deep‐sea hydrothermal vents and surrounding sediments. DNA G + C content (mol%): 29.5–33.6 (genome, LC). Type species: Methanocaldococcus jannaschii Whitman 2001, VL85 (basonym: Methanococcus jannaschii Jones et al. 1983, VL16).
This work provides historiographical surveys of four transformative historians: Barthold Niebuhr, Theodor Mommsen, Friedrich Münzer, and Matthias Gelzer. Close analysis of each author reveals their innovations – methodological, narrative, and philosophical – to be foundational to modern historical praxis, particularly within studies of the Roman Republic: these maestri rendered political history susceptible to ‘scientific’ inquiry, systematized available evidence, and crafted frameworks for reimagining premodernity. Likewise, their interventions on Republican political culture still define the discipline. Much, in other words, is owed to their efforts. Yet, the field has forgotten these scholars. Engagement, where it exists, consists of perfunctory review and repudiation. In response, this work advocates an alternative historiography balancing critical retrospection with pragmatic revitalization. Our four scholars are reevaluated. Standard critiques are refuted, and emphasis is placed on their texts’ utility: as exemplars, untapped fonts, but also cautionary models, whose establishment of conventional historicism demands scrutiny. In agreement with voices from related fields, the book calls for (re)considerations of ‘ideology’ and an ‘ontological turn’ in Roman studies.
In this paper, we consider several families of closed-form estimators of the two parameters of the Generalized Pareto Distribution (GPD). These estimators are easy to compute and have high efficiency when compared to previously proposed methods. We also consider some estimators which are not of closed-form. All methods are based on certain order statistics. The proposed procedures are best for extreme values of the shape parameters and sample sizes of 100 or larger. Monte Carlo simulations are conducted to investigate the performance of the proposed parameter estimation procedures. Our findings suggest that the proposed estimation methods are competitive compared to the existing methods. We provide a real data application to illustrate the utilization of the proposed methods in estimating the GPD parameters.
This study examines the Socially Responsible (SR) exchange‐traded funds (ETFs) by comparing their risk‐adjusted performance with a matched group of conventional ETFs in the U.S. equity market. In contrast to prior studies that focus on actively managed mutual funds, we find that the risk‐adjusted returns of SR ETFs are significantly lower than those of conventional ETFs during the 2005–2020 period. Such underperformance is only observed in non‐crisis periods but not in economic crisis periods (i.e., the 2020 pandemic recession and 2008 financial turmoil). We attribute the observed underperformance of SR ETFs during the non‐crisis periods to their limited diversification of unsystematic risks resulting from various negative or positive screens employed in the funds. We also find that net fund flows of the SR ETFs are less sensitive to past negative performance than are conventional fund flows. Collectively, our findings suggest that, instead of seeking wealth maximization, socially conscious investors may choose SR ETFs to gain non‐economic utility.
The messenger RNA (mRNA) vaccines for COVID-19, Pfizer-BioNTech and Moderna, were authorized in the US on an emergency basis in December of 2020. The rapid distribution of these therapeutics around the country and the world led to millions of people being vaccinated in a short time span, an action that decreased hospitalization and death but also heightened the concerns about adverse effects and drug-vaccine interactions. The COVID-19 mRNA vaccines are of particular interest as they form the vanguard of a range of other mRNA therapeutics that are currently in the development pipeline, focusing both on infectious diseases as well as oncological applications. The Vaccine Adverse Event Reporting System (VAERS) has gained additional attention during the COVID-19 pandemic, specifically regarding the rollout of mRNA therapeutics. However, for VAERS, absence of a reporting platform for drug-vaccine interactions left these events poorly defined. For example, chemotherapy, anticonvulsants, and antimalarials were documented to interfere with the mRNA vaccines, but much less is known about the other drugs that could interact with these therapeutics, causing adverse events or decreased efficacy. In addition, SARS-CoV-2 exploitation of host cytochrome P450 enzymes, reported in COVID-19 critical illness, highlights viral interference with drug metabolism. For example, patients with severe psychiatric illness (SPI) in treatment with clozapine often displayed elevated drug levels, emphasizing drug-vaccine interaction.
Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models' training, validating, and testing, while Well C dataset is applied for evaluating the models' generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R 2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset.
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Marilyn Stoner
  • Department of Nursing
Sara Callori
  • Department of Physics
Kenneth S Shultz
  • Department of Psychology
Eric Scott
  • Department of Biology
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