Factors Associated with Help-Seeking Behaviors Among Persons with Serious Psychological Distress

Serious psychological distress (SPD) is an important individual and public health concern, but inconsistent results exist in the literature in terms of help-seeking behaviors and health-related outcomes among persons with SPD. The purpose of this study was to address this issue and understand clinical factors, access and quality of care factors, and sociodemographic factors associated with help-seeking behaviors among adults with SPD using the California Health Interview Survey (CHIS). CHIS data were collected in 2015 as a part of a two-year cycle (2015-2016) and surveyed 42,089 adults. For multivariable analyses, logistic regression analyses were performed. Participants with chronic health conditions had nearly twice the odds of having had a doctor visit compared to those who did not (adjusted odds ratio [aOR] 1.94, 95% confidence interval [CI] [1.08, 3.48], p = 0.03). Those with a general health condition had lower odds of delaying care (aOR 0.72, 95% CI [0.61, 0.84], p \u3c 0.001). The odds of having a doctor visit among those who had issues with access to healthcare were 2.68 times higher than for those who did not (aOR 2.68, 95% CI [1.38, 5.19], p \u3c 0.004). The odds of forgoing care among those who were not insured were 13% higher than for those that were insured (aOR 1.13, 95% CI [1.04, 1.24], p \u3c 0.005). Females had lower odds than males in terms of delaying care (aOR 0.71, 95% CI [0.51, 0.99], p = 0.04). Compared to White Americans, Asians and African Americans had higher odds of forgoing care (aOR 4.34, 95% CI [1.48-12.76]; aOR 2.80, 95% CI [1.34, 5.86] respectively). To promote positive health outcomes and prevent disease, it is important to devise optimal intervention programs that incorporate factors associated with healthcare seeking decisions for adults with SPD

Energy efficiency performance improvements for ant-based routing algorithm in wireless sensor networks

The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1) a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2) intelligent update of routing tables in case of a node or link failure, and (3) reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC) and Beesensor

Visual Saliency Based on Fast Nonparametric Multidimensional Entropy Estimation

Bottom-up visual saliency can be computed through information theoretic models but existing methods face significant computational challenges. Whilst nonparametric methods suffer from the curse of dimensionality problem and are computationally expensive, parametric approaches have the difficulty of determining the shape parameters of the distribution models. This paper makes two contributions to information theoretic based visual saliency models. First, we formulate visual saliency as center surround conditional entropy which gives a direct and intuitive interpretation of the center surround mechanism under the information theoretic framework. Second, and more importantly, we introduce a fast nonparametric multidimensional entropy estimation solution to make information theoretic-based saliency models computationally tractable and practicable in realtime applications. We present experimental results on publicly available eyetracking image databases to demonstrate that the proposed method is competitive to state of the art

Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks

Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.Comment: 40 pages, 9 figures, 5 tables, Book chapte

Gene expression dynamics underlying cell fate emergence in 2D micropatterned human embryonic stem cell gastruloids

Human embryonic stem cells cultured in 2D micropatterns with BMP4 differentiate into a radial arrangement of germ layers and extraembryonic cells. Single-cell transcriptomes demonstrate generation of cell types transcriptionally similar to their in vivo counterparts in Carnegie stage 7 human gastrula. Time-course analyses indicate sequential differentiation, where the epiblast arises by 12 h between the prospective ectoderm in the center and the cells initiating differentiation toward extraembryonic fates at the edge. Extraembryonic and mesendoderm precursors arise from the epiblast by 24 h, while nascent mesoderm, endoderm, and primordial germ cell-like cells form by 44 h. Dynamic changes in transcripts encoding signaling components support a BMP, WNT, and Nodal hierarchy underlying germ-layer specification conserved across mammals, and FGF and HIPPO pathways being active throughout differentiation. This work also provides a resource for mining genes and pathways expressed in a stereotyped 2D gastruloid model, common with other species or unique to human gastrulation

Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest

This paper investigated the utility of drone-based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The use of drone-derived images and the subsequent application of digital photogrammetry to obtain information about PNFs are underinvestigated in southeast Queensland vegetation types. In this study, we used image processing to separate individual trees and digital photogrammetry to derive a canopy height model (CHM). The study was supported with tree height data collected in the field for one site. The paper addressed the research question “How well do drone-derived point clouds estimate the height of trees in PNF ecosystems?” The study indicated that a drone with a basic RGB camera can estimate tree height with good confidence. The results can potentially be applied across multiple land tenures and similar forest types. This informs the development of drone-based and remote-sensing image-processing methods, which will lead to improved forest inventories, thereby providing forest managers with recent, accurate, and efficient information on forest resources