Regulatory Mechanisms in Biosystems (E-Journal - Dnipro National University)
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Effects of primary care provider characteristics on changes in behavioral health delivery during a collaborative care trial
No full textObjective: Pediatric primary care providers (PCPs) are increasingly expected to deliver behavioral health (BH) services, yet PCP characteristics that facilitate or hinder BH service delivery are poorly understood. This study examined how PCP characteristics and collaborative care participation influenced changes in BH-related effort and competency over time.
Methods: Pediatric PCPs (N = 74) participating in a cluster randomized trial (8 practices) of a collaborative care intervention for disruptive behavior problems completed self-report measures at 0, 6, 12, and 18 months. Latent growth curve models tested the impact of PCP characteristics (ie, age, gender, negative BH beliefs, BH burden, BH competency) on changes in identification/treatment of disruptive behavior disorders and competency over the course of the trial.
Results: Participation in collaborative care was associated with increases in identification/treatment, with no evidence that PCP characteristics moderated changes in identification/treatment. For competency, however, older PCPs (>50 years) in collaborative care exhibited steep increases over time, while older PCPs in the comparison condition exhibited steep decreases, suggesting differential benefits of collaborative care participation by PCP age. In both conditions, PCPs with more negative BH beliefs reported less identification/treatment over time. Baseline competency was positively associated with identification/treatment and associations weakened over time. Gender and perceived burden had little impact.
Conclusions: PCP characteristics are associated with changes in PCPs' BH-related effort and competency over time. Participation in a collaborative care model appears to be especially beneficial for older PCPs. Implementation of collaborative care can promote growth in BH-related effort and competency for PCPs.This work was supported by the National Institutes of Health [NIMH 063272; NIMH 2T32MH018951–24; NIMH 2R25MH054318; NCATS TL1TR001858]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health
Thirteen people pose for a photograph on a beach.
No full textThirteen people pose for a photograph on a beach
1962 Buc Days Queens Greeted
No full text1962 Buc Days Queens Greeted by the Red Carpet Commssion as she exited a Eastern Airline airplane with flower
Age-optimal mobile elements scheduling for recharging and data collection in green IoT
No full textEnsuring real-time reporting of fresh information and maintaining the sustainability of power supply is of great importance in time-critical green Internet of Things (IoT). In this paper, we investigate the mobile element scheduling problem in a network with multiple independent and rechargeable sensors, in which mobile elements are dispatched to collect data packets from the sensor nodes and to recharge them. The age of information (AoI) is used to measure the time elapsed of the most recently delivered packet since the generation of the packet. We propose an age-optimal mobile elements scheduling (AMES), which decides the trajectories of mobile elements based on a cooperative enforcement game and completes the time-slot allocation in each meeting point, to minimize the average AoI and maximize the energy efficiency. The cooperative enforcement game enables the mobile elements to make optimal visiting decisions and avoid the visiting conflicts, and the outcome of the game is pareto-optimal. Compared to the existing approaches, i.e., greedy algorithm (GA), greedy-neighborhood algorithm (GA-neighborhood), simulation results demonstrate that AMES can achieve a lower average AoI and a higher energy efficiency with a higher successful visiting ratio of the sensor node.Ensuring real-time reporting of fresh information and maintaining the sustainability of power supply is of great importance in time-critical green Internet of Things (IoT). In this paper, we investigate the mobile element scheduling problem in a network with multiple independent and rechargeable sensors, in which mobile elements are dispatched to collect data packets from the sensor nodes and to recharge them. The age of information (AoI) is used to measure the time elapsed of the most recently delivered packet since the generation of the packet. We propose an age-optimal mobile elements scheduling (AMES), which decides the trajectories of mobile elements based on a cooperative enforcement game and completes the time-slot allocation in each meeting point, to minimize the average AoI and maximize the energy efficiency. The cooperative enforcement game enables the mobile elements to make optimal visiting decisions and avoid the visiting conflicts, and the outcome of the game is pareto-optimal. Compared to the existing approaches, i.e., greedy algorithm (GA), greedy-neighborhood algorithm (GA-neighborhood), simulation results demonstrate that AMES can achieve a lower average AoI and a higher energy efficiency with a higher successful visiting ratio of the sensor node
QoS driven optimal mobile edge server placement in mobile edge cloud
No full textMobile edge cloud is an emerging technology to enhance the Quality of Service (QoS)
for mobile users' applications, especially for computation resource-consuming applications. A
challenge in mobile edge cloud is the problem of mobile edge server placement, which concerns
where to place the mobile edge servers to reduce the transmission delay and computation delay
for tasks generated by mobile device users. In this essay, we work on the deployment of edge
servers in the mobile edge cloud. We formulate the deployment problem as an integer linear
problem and propose a density-based deployment of the MECs algorithm, which combines the
K-means approach and integer linear programming. To evaluate the performance of our
proposed approach, we conduct experiments using the telecom dataset of Shanghai. From the
result of the experiment, we demonstrate that our proposed method could reduce the delay per
task by around 10%.Computing SciencesCollege of Science and Engineerin
Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters
No full textThe Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop
The expanded footprint of the Deepwater Horizon oil spill in the Gulf of Mexico deep-sea benthos.
No full textThe 2010 Deepwater Horizon blowout off the coast of Louisiana caused the largest marine oil spill on record. Samples were collected 2–3 months after the Macondo well was capped to assess damage to macrofauna and meiofauna communities. An earlier analysis of 58 stations demonstrated severe and moderate damage to an area of 148 km2. An additional 58 archived stations have been analyzed to enhance the resolution of that assessment and determine if impacts occurred further afield. Impacts included high levels of total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAH) in the sediment, low diversity, low evenness, and low taxonomic richness of the infauna communities. High nematode to copepod ratios corroborated the severe disturbance of meiofauna communities. Additionally, barium levels near the wellhead were very high because of drilling activities prior to the accident. A principal component analysis (PCA) was used to summarize oil spill impacts at stations near the Macondo well, and the benthic footprint of the DWH oil spill was estimated using Empirical Bayesian Kriging (EBK) interpolation. An area of approximately 263 km2 around the wellhead was affected, which is 78% higher than the original estimate. Particularly severe damages to benthic communities were found in an area of 58 km2, which is 142% higher than the original estimate. The addition of the new stations extended the area of the benthic footprint map to about twice as large as originally thought and improved the resolution of the spatial interpolation. In the future, increasing the spatial extent of sampling should be a top priority for designing assessment studies