BACKGROUND The morbidity and mortality associated with COPD exacts a considerable economic burden. Comorbidities in COPD are associated with poor health outcomes and increased costs. Our objective ...was to assess the impact of comorbidities on COPD-associated costs in a large administrative claims dataset. METHODS This was a retrospective observational study of data from the Truven Health MarketScan Commercial Claims and Encounters and the MarketScan Medicare Supplemental Databases from January 1, 2009, to September 30, 2012. Resource consumption was measured from the index date (date of first occurrence of non-rule-out COPD diagnosis) to 360 days after the index date. Resource use (all-cause and disease-specific ie, COPD- or asthma-related ED visits, hospitalizations, office visits, other outpatient visits, and total length of hospital stay) and health-care costs (all-cause and disease-specific costs for ED visits, hospitalizations, office visits, and other outpatient visits and medical, prescription, and total health-care costs) were assessed. Generalized linear models were used to evaluate the impact of comorbidities on total health-care costs, adjusting for age, sex, geographic location, baseline health-care use, employment status, and index COPD medication. RESULTS Among 183,681 patients with COPD, the most common comorbidities were cardiovascular disease (34.8%), diabetes (22.8%), asthma (14.7%), and anemia (14.2%). Most patients (52.8%) had one or two comorbidities of interest. The average all-cause total health-care costs from the index date to 360 days after the index date were highest for patients with chronic kidney disease ($41,288) and anemia ($38,870). The impact on total health-care costs was greatest for anemia ($10,762 more, on average, than a patient with COPD without anemia). CONCLUSIONS Our analysis demonstrated that high resource use and costs were associated with COPD and multiple comorbidities.
Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional ...nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse auto encoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification.
The gas-liquid two-phase flow is an important problem in the proton exchange membrane (PEM) electrolysis cell, however the detailed modeling is still difficult due to the complex reaction process. In ...this work, a comprehensive, three-dimensional, two-phase PEM electrolysis cell model is established and validated by the tested data. The electrochemical model coupled with the mass and heat transfer model is used to capture the temperature, gas fraction and current density distribution, and to explore two-phase flow effects on the cell performance. The results show that this model has a better fitting effect in the range of test current density (0–1.2A/cm2). The coverage of bubbles on the catalyst active area is the leading cause of mass transport loss. Adjusting the wettability can promote gas discharge. The combination of hydrophilic CL and hydrophobic PTL shows better performance (12.6 times the combination of hydrophilic PTL and hydrophobic CL) due to the capillary pressure. The simulation results indicate that excessive water flow will inhibit gas discharge, resulting in an increase of gas accumulation and a decrease of cell performance at high current density. Further, this model can be extended to the whole stack, and used to optimize the cell design and control strategy.
•Comprehensive 3D two-phase PEM electrolysis cell model.•High accuracy to predict polarization curve than traditional single-phase model.•Mass transport loss caused by bubble coverage of catalyst active sites.•Adjusting the wettability combination of PTL and CL to promote the gas discharge.•Explanation for the role of anode water flow rate on cell performance.
The adhesion of water droplets to leaves is important in controlling rainfall interception, and affects a variety of hydrological processes. Leaf water drop adhesion (hereinafter, adhesion) depends ...not only on droplet formulation and parameters but also on the physical (leaf roughness) and physico-chemical (surface free energy, its components, and work-of-adhesion) properties of the leaf surface. We selected 60 plant species from Shaanxi Province, NW China, as experimental materials with the goal of gaining insight into leaf physical and physico-chemical properties in relation to the adhesion of water droplets on leaves. Adhesion covered a wide range of area, from 4.09 to 88.87 g/m(2) on adaxial surfaces and 0.72 to 93.35 g/m(2) on abaxial surfaces. Distinct patterns of adhesion were observed among species, between adaxial and abaxial surfaces, and between leaves with wax films and wax crystals. Adhesion decreased as leaf roughness increased (r = -0.615, p = 0.000), but there were some outliers, such as Salix psammophila and Populus simonii with low roughness and low adhesion, and the abaxial surface of Hyoscyamus pusillus and the adaxial surface of Vitex negundo with high roughness and high adhesion. Meanwhile, adhesion was positively correlated with surface free energy (r = 0.535, p = 0.000), its dispersive component (r = 0.526, p = 0.000), and work of adhesion for water (r = 0.698, p = 0.000). However, a significant power correlation was observed between adhesion and the polar component of surface free energy (p = 0.000). These results indicated that leaf roughness, surface free energy, its components, and work-of-adhesion for water played important roles in hydrological characteristics, especially work-of-adhesion for water.
This study investigates the neck/shoulder pain (NSP) and low back pain (LBP) among current high school students in Shanghai and explores the relationship between these pains and their possible ...influences, including digital products, physical activity, and psychological status.
An anonymous self-assessment was administered to 3,600 students across 30 high schools in Shanghai. This questionnaire examined the prevalence of NSP and LBP and the level of physical activity as well as the use of mobile phones, personal computers (PC) and tablet computers (Tablet). The CES-D (Center for Epidemiological Studies Depression) scale was also included in the survey. The survey data were analyzed using the chi-square test, univariate logistic analyses and a multivariate logistic regression model.
Three thousand sixteen valid questionnaires were received including 1,460 (48.41%) from male respondents and 1,556 (51.59%) from female respondents. The high school students in this study showed NSP and LBP rates of 40.8% and 33.1%, respectively, and the prevalence of both influenced by the student's grade, use of digital products, and mental status; these factors affected the rates of NSP and LBP to varying degrees. The multivariate logistic regression analysis revealed that Gender, grade, soreness after exercise, PC using habits, tablet use, sitting time after school and academic stress entered the final model of NSP, while the final model of LBP consisted of gender, grade, soreness after exercise, PC using habits, mobile phone use, sitting time after school, academic stress and CES-D score.
High school students in Shanghai showed high prevalence of NSP and LBP that were closely related to multiple factors. Appropriate interventions should be implemented to reduce the occurrences of NSP and LBP.
The pursuit of optoelectronic devices operating in the mid-infrared regime is driven by both fundamental interests and envisioned applications ranging from imaging, sensing to communications. Despite ...continued achievements in traditional semiconductors, notorious obstacles such as the complicated growth processes and cryogenic operation preclude the usage of infrared detectors. As an alternative path towards high-performance photodetectors, hybrid semiconductor/graphene structures have been intensively explored. However, the operation bandwidth of such photodetectors has been limited to visible and near-infrared regimes. Here we demonstrate a mid-infrared hybrid photodetector enabled by coupling graphene with a narrow bandgap semiconductor, Ti
O
(E
= 0.09 eV), which achieves a high responsivity of 300 A W
in a broadband wavelength range up to 10 µm. The obtained responsivity is about two orders of magnitude higher than that of the commercial mid-infrared photodetectors. Our work opens a route towards achieving high-performance optoelectronics operating in the mid-infrared regime.
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•Heavy metals were divided into two groups with different temporal–spatial distribution.•Each heavy metal source was synthetically and clearly differentiated.•Health indices of the ...eight metals fall within three gradients.
The temporal–spatial changes in the concentration and health risk of eight dissolved heavy metals in the Yangtze Estuary over a 5-year period were discussed based on large-scale sampling data. Special attention was paid to the differentiation of metal sources. Concentrations of the metals were present in the following order: Zn≫As>Cu>Cr>Ni>Pb>Cd>Hg, but the hazard quotient indices could be obviously divided into three gradients. More attention should be paid to As, Ni, Pb, and Cr because they increased yearly. Cu, Ni, Pb and As had higher health risks in the nearshore zones, while higher health risks of Zn, Cr, Cd, and Hg were observed in the estuarine channel. Correlations and hierarchical cluster analysis results of metal sources were consistent well with those obtained by temporal–spatial distributions. Shipping activities were the largest contributor to the elevated Zn concentrations in the estuary, while Megacity Shanghai significantly affected the Ni, Cu and As pollution. Yangtze River runoff was the primary source of Cu and As in the estuary. Cd and Cr pollution were closely related to the sediment release under the drive of the “salt-out effect”.
NiO nanoflakes are created with a simple hydrothermal method on 3D (three‐dimensional) graphene scaffolds grown on Ni foams by microwave plasma enhanced chemical vapor deposition (MPCVD). Such ...as‐grown NiO‐3D graphene hierarchical composites are then applied as monolithic electrodes for a pseudo‐supercapacitor application without needing binders or metal‐based current collectors. Electrochemical measurements impart that the hierarchical NiO‐3D graphene composite delivers a high specific capacitance of ≈1829 F g−1 at a current density of 3 A g−1 (the theoretical capacitance of NiO is 2584 F g−1). Furthermore, a full‐cell is realized with an energy density of 138 Wh kg−1 at a power density of 5.25 kW kg−1, which is much superior to commercial ones as well as reported devices in asymmetric capacitors of NiO. More attractively, this asymmetric supercapacitor exhibits capacitance retention of 85% after 5000 cycles relative to the initial value of the 1st cycle.
Hierarchical nickel oxide nanoflake 3D graphene electrodes are developed by growing NiO nanoflakes atop 3D architecture of graphene on Ni foam. The optimum structure enables the 3‐electrode pseudocapacitors and 2‐electrode full cells to deliver outstanding electrochemical performance. In a full cell configuration, the achieved power density is much higher than that of commercially available asymmetric capacitors.
Traditional human emotion recognition is based on electroencephalogram (EEG) data collection technologies which rely on plenty of rigid electrodes and lack anti‐interference, wearing comfort, and ...portability. Moreover, a significant distribution difference in EEG data also results in low classification accuracy. Here, on‐skin biosensors with adhesive and hydrophobic bilayer hydrogel (AHBH) as interfaces for high accuracy emotion classification are proposed. The AHBH achieves remarkable adhesion (59.7 N m−1) by combining the adhesion mechanism of catechol groups and electrostatic attraction. Meanwhile, based on the synergistic effects of hydrophobic group rearrangements and surface energy reduction, the AHB‐hydrophobic layer exhibits 133.87° water contact angles through hydrophobic treatment of only 0.5 h. Hydrogen and electrostatic bonds are also introduced to form a seamless adhesive‐hydrophobic hydrogel interface and inhibit adhesion attenuation, respectively. With the AHBH as an ideal device/skin interface, the biosensor can reliably collect high‐quality electrophysiological signals even under vibration, sweating, and long‐lasting monitoring condition. Furthermore, the on‐skin electrodes, data processing, and wireless modules are integrated into a portable headband for EEG‐based emotion classification. A domain adaptive neural network based on the transfer learning technique is introduced to alleviate the effect of domain shift and achieve high classification accuracy.
A novel adhesive and hydrophobic bilayer hydrogel (AHBH) is developed as ideal device/skin interfaces for on‐skin electrodes. The biosensors can reliably collect high‐quality electrophysiological signals even under harsh conditions. By further combining domain adaptive neural network algorithms, a portable headband integrated with AHB hydrogel electrodes and wireless modules achieves high‐accuracy electroencephalogram‐based emotion classification.
Currently, medical institutes generally use EMR to record patient’s condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable ...resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.
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