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Advanced Day-Ahead Scheduling of HVAC Demand Response Control Using Novel Strategy of Q-Learning, Model Predictive Control, and Input Convex Neural Networks
No full textIn this paper, we present a Q-Learning optimization algorithm for smart home HVAC systems. The proposed algorithm combines new convex deep neural network models with model predictive control (MPC) techniques. More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. As a novel RL approach, the proposed algorithm generates day-ahead HVAC demand response (DR) signals in smart homes that optimally reduce and/or shift peak energy usage, reduce electricity costs, minimize user discomfort, and honor in a best-effort way the recommendations from utility/aggregator, which in turn has impact on the overall well being of the distribution network controlled by the aggregator. The proposed Q-Learning optimization algorithm, based on epsilon-model predictive control (-MPC), can be implemented as a control agent that is executed by the smart house energy management (SHEM) system that we assume exists in the smart home, which can interact with the energy provider of the distribution network, i.e., utility/aggregator, via the smart meter. The output generated by the proposed control agent represents day-ahead local DR signals in the form of temperature setpoints for the HVAC system that are found by the optimization process to lead to desired trade-offs between electricity cost and user discomfort. The proposed algorithm can be used in smart homes with passive HVAC controllers, which solely react to end-user setpoints, to transform them into smart homes with active HVAC controllers. Such systems not only respond to the preferences of the end-user but also incorporate an external control signal provided by the utility or aggregator. Simulation experiments conducted with a custom simulation tool demonstrate that the proposed optimization framework can offer significant benefits. It achieves 87% higher success rate in optimizing setpoints in the desired range, thereby resulting in up to 15% energy savings and zero temperature discomfort
Participant Personal Characteristics and Adherence to Oral Capsules: A Secondary Analysis of a Randomized Placebo-Controlled Trial of Antenatal Probiotics
No full textBackground
Adherence to study interventions is critical to the conduct of randomized controlled trials (RCTs). The relationships between participant characteristics and intervention adherence are understudied in pregnant populations. The purpose of this study was to conduct a secondary analysis of adherence to study capsules in a double-masked, placebo-controlled RCT of a probiotic intervention to reduce antenatal Group B Streptococcus colonization, in relationship to participant characteristics. Methods
We analyzed the relationship between capsule adherence rates and demographic characteristics among 81 RCT participants. Categorical variables were reported using counts and percentages, and continuous variables were expressed as means along with their standard deviations. For the univariate analyses, we compared demographic variables with adherence scores. A multivariate linear regression model was used to identify predictors of adherence. Results
Average adherence was similar for control and probiotic group participants (P = .86) Univariate analysis showed that average adherence increased directly with age, education, and income. Participants who were partnered or living with others had higher average adherence compared with those who were single and living alone. Asian and White participants had the highest and Black participants had the lowest average, and there was no difference based on Hispanic ethnicity. Adjusting for all the variables in the regression, participants who identified as Black were significantly less likely to adhere to capsules than White participants, and those who were married or living with partners were more likely to adhere than the single participants. Discussion
Diverse participants are critically important to RCTs. This secondary analysis provides evidence that participant characteristics and the social determinants of health play an important role in adherence to self-administered interventions in RCTs, although more research is needed. Our findings suggest that intentional consideration of RCT participant characteristics may allow for the development and tailoring of strategies to enhance intervention adherence. The study was registered on ClinicalTrials.gov (NCT03696953) on January 10, 2018
Engaging Minoritized Communities in Clinical Trials Through Social Media: Recommendations from Community-Based Participatory Research
No full textThe current study aims to understand what Black and Latino community members know about clinical trials and develop effective messaging to generate interest, improve access, and encourage participation among minoritized populations through social media. Employing community-based participatory research, we formed a community research advisory team and conducted focus group interviews with Black and Latino community members about elements of social media messages that might reduce hesitancy about and increase engagement in clinical trials. From the interview transcripts, we extracted seven key themes: transparency, familiarity, altruism, adaptability, flexibility, recognition, and safety. We suggest leveraging these themes as strategies to craft targeted recruitment messages addressing barriers to participation in clinical trials among Black and Latino community members
Discrimination and Adverse Birth Outcomes Among Latina Women: The Protective Role of Social Support.
No full textInterpersonal discrimination has been associated with adverse birth outcomes among Black populations, but few studies have examined the impact of discrimination among Latinx/Hispanic populations in the United States, especially in conjunction with resources that could be protective. The present study examined (a) if exposure to discrimination is associated with adverse birth outcomes for Latina/Hispanic women and (b) if prenatal social support buffers these links. Method: In two independent prospective studies of Latina/Hispanic women in Southern California (N = 84 and N = 102), the relation between maternal experience of discrimination and birth outcomes (length of gestation and birth weight) was examined. Additionally, social support was tested as a moderator of these relations. Results: In both Studies 1 and 2, exposures to discrimination predicted adverse birth outcomes. Specifically, lifetime experiences of major discrimination predicted lower birth weight. Additionally, in Study 2, chronic experiences of everyday discrimination were linked to lower birth weight. In Study 1, major discrimination also predicted shorter gestational length. Importantly, in both studies, the presence of prenatal social support buffered associations between discrimination and poorer birth outcomes. Conclusions: Findings implicate discrimination as an important risk factor for adverse birth outcomes among women of Latina/Hispanic descent. Further policies, practice, and research on reducing discrimination and enhancing factors that promote resilience such as social support are needed to facilitate healthy births among Latina/Hispanic women, mitigate intergenerational harm of discrimination-related stress, and advance health equity at birth and across the lifespan
Relationships Among Peri-Traumatic Circulating Endocannabinoids and Long-Term, Negative Outcomes Following Traumatic Injury
No full textRationale
Traumatically injured individuals can develop chronic negative psychological sequelae. Improved understanding of contributing, peri-traumatic risk factors is essential to reduce the risk of these consequences. Previous studies have found that peri-traumatic, circulating endocannabinoid concentrations are positively associated with development of post-traumatic stress disorder (PTSD), chronic pain and depression months later, particularly in members of racial/ethnic groups that have been historically marginalized. Objectives
This replication study examined relationships among peri-trauma serum endocannabinoid concentrations and long-term consequences in a cohort comprised primarily of individuals from marginalized racial and ethnic groups. Methods
Participants (n = 100; 81% from marginalized racial and ethnic groups) were traumatically injured adults presenting to the ED of an urban tertiary care hospital. Endocannabinoids N-arachidonoylethanolamine (AEA) and 2-arachidonoylglycerol (2-AG) were measured in serum collected within days (peri-trauma) and 6–10 months following injury (follow-up). Assessments, including PTSD, depression, pain and quality of life were completed. Statistical approaches, including multivariate, hierarchical regressions, were used to determine associations among serum endocannabinoid concentrations and long-term outcomes. Results
Although it did not survive correction for multiple comparisons, peri-trauma serum 2-AG concentrations. Peri-trauma serum 2-AG concentrations were also positively associated with PTSD, pain severity, and functional engagement scores at follow-up. There were no significant associations between circulating 2-AG or AEA and depression. Conclusions
These findings generally replicate earlier studies demonstrating that serum 2-AG concentrations are biomarkers of risk for PTSD and pain and uncover an additional association with poor functional quality of life. Further studies are needed to determine the underlying mechanisms of these relationships
A Scoping Review of Oral Feeding Skill Development in Typically Developing Children Part I: Methodologies, Populations, and Normative Data
No full textPurpose: This scoping review is the first in a two-part series aimed at synthesizing literature on oral feeding skills and informing the development of a classification system of observable skills. This article consolidates research on feeding skill development in typically developing children. The second paper analyzes individual skills identified. This review addresses three questions: (a) What methods have been used to study feeding skill development? (b) What populations of typically developing children without feeding disorders have been studied? (c) What normative data on feeding skills are available? Method: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, studies were included if they examined oral feeding skills in typically developing children born at ≥ 37 weeks gestation, aged at least 4 months, with a focus on skills related to drinking liquids by cup and eating solids, using direct observation. Results: Fourteen studies met inclusion criteria. Findings revealed significant methodological variability, particularly in the number of skills assessed, feeding procedures used, and participant characteristics. While some normative data exist, they were limited and inconsistently reported. A key challenge was the lack of standardized definitions and categorization of feeding skills, which limited cross-study comparisons. Conclusions: Multiple approaches have been used to study typical feeding skill development, presenting an opportunity for methodological standardization. Greater clarity around individual feeding skills, addressed in Part 2, may help resolve inconsistencies in developmental timelines and support the development of an observational clinical measure
Optimization-Based Distributed Controller for Multi-Agents System in Microgrid Secondary Control
No full textMicro-grids function to connect to power system power produced by the renewable energy resources. In islanded micro-grids, grid-forming units collaborate to maintain the micro-grids voltage and frequency by utilizing droop control technique that includes primary, secondary and tertiary levels. Secondary control intervenes to improve power sharing and restore voltage and frequency to their nominal levels. However, the conventional droop control applied to a grid with mismatched line parameters experiences a trade-off between reactive power sharing and voltage regulations. This paper applies real-time trajectory tracking convex optimization to ensure by communicating power sharing between units in a consensus topology. The optimization function is designed with local frequency and voltage constraints to maintain the frequency at its nominal value and ensure the voltage remains within a 5% tolerance range.. The proposed controller maintains power sharing among all units at the global consensus average value with constraints to within the limits. When the voltage limit is reached, the reactive power automatically deviates from the agreed global average in an optimal manner. The performance of the controller is shown using MATLAB/SIMULINK for different control parameters. The performance is compared to centralized-based topology. Finally, the controller is tested for grids with different line-parameters mismatches. The results show the reactive power sharing in an optimized manner
Predicting Seizure Onset Zones From Interictal Intracranial EEG Using Functional Connectivity and Machine Learning
No full textFunctional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as epileptic spikes and high-frequency oscillations (HFOs). Using interictal iEEG data from 26 patients, we estimated FC across eight frequency bands (4–290 Hz) using amplitude envelope correlation (AEC) and phase locking value (PLV). From the resulting FC-matrices, we estimated two graph metrics each to derive 32 FC-based features. We also extracted features related to spikes, HFOs, and power spectral densities (PSD). A trained support vector machine (SVM) classifier predicted seizure onset zones (SOZs) with an area under the ROC curve (AUC) of 0.91 for node-level 4-fold cross-validation (CV), 0.69 for patient-level 4-fold CV, and 0.73 for patient-level leave-one-out CV. Notably, gamma-band graph features from AECs outperformed spikes and HFOs in SOZ prediction when using an equivalent number of features. Our results strongly suggest that AEC-based features may provide more information about epileptogenicity compared to PLV-based features. Furthermore, machine learning provides a robust approach for identifying useful FC-based features and integrating information from putative biomarkers of epilepsy to better localize epileptogenic networks
Examining Racial and Ethnic Disparities in Nurse Burnout, Intent to Leave, and Student Debt Burden: A Cross-Sectional Study
No full textBackground:
A racially and ethnically diverse nursing workforce can only enhance the profession\u27s ability to serve the widely diverse U.S. population. Embracing differences allows nurses to develop a broader understanding of each other and of their patients. Yet many nurses of color face unique challenges that can adversely impact their well-being and their longevity in this profession. Purpose:
The purpose of this descriptive cross-sectional study was to examine levels of burnout, intent to leave the profession, and student debt burden among nurses who self-identified as Asian, Black or African American, or Hispanic, and compare these to the levels reported by their White counterparts. Methods:
A survey of U.S. hospital nurses took place from January through March 2023, measuring levels of burnout, intent to leave the profession, and student debt burden. Differences regarding self-identified race and ethnicity were then examined using regression analyses. Results:
Asian participants had significantly higher burnout scores and were more likely to report intent to leave the profession than their White counterparts. Hispanic participants were also more likely to report intent to leave. Black and African American participants reported significantly higher student debt burdens than participants from other groups. Conclusions:
This study\u27s findings attest to racial and ethnic disparities regarding nurse burnout, intent to leave the profession, and student debt burden. They raise concerns about the long-term retention of Asian and Hispanic nurses and about barriers to entering the profession among Black and African American nurses. Failure to effectively address racial and ethnic disparities in the workplace jeopardizes workforce diversity. It\u27s our hope that the study findings will spark a larger conversation about how the experiences of nurses of color differ from those of their White counterparts and how disparities can be effectively redressed. The nursing profession can only benefit from having a diverse workforce, one truly capable of serving various populations. In recognition of this need, in 2023, the American Nurses Association (ANA) and two sister organizations jointly released the ANA Enterprise 2023-2025 Strategic Plan, which prioritizes the advancement of “diversity, equity, inclusion, belonging, and anti-racism to improve nursing practice and work environments.”1 To successfully advance these laudable goals, it must be acknowledged that nurses of color—those belonging to racial and ethnic minority groups—often aren\u27t afforded the same opportunities and face more adverse circumstances compared to their White counterparts. For nurses of color, examples of inequity include less availability of mentorship, fewer promotional opportunities in both clinical and academic arenas, and experiences of discrimination in the workplace.2-4 In this study, we examine three variables that might reflect inequity: nurse burnout, intent to leave the profession, and student debt burden