SOCIO-ECOLOGICAL PREDICTORS OF INTERCOURSE FREQUENCY AND NUMBER OF SEXUAL PARTNERS AMONG MALE AND FEMALE AFRICAN AMERICAN ADOLESCENTS

PubMed Central

Ritchwood, Tiarney D.; Traylor, Amy C.; Howell, Rebecca J.; Church, Wesley T.; Bolland, John M.

2015-01-01

The current study examined 14 waves of data derived from a large, community-based study of the sexual behavior of impoverished youth between 12 and 17 years of age residing in the Deep South. We used multilevel linear modeling to identify ecological predictors of intercourse frequency and number of sexual partners among gender-specific subsamples. Results indicated that predictors of adolescent sexual behavior differed by both type of sexual behavior and gender. For males, age, maternal warmth, parental knowledge, curfew, self-worth, and sense of community predicted intercourse frequency, while age, parental knowledge, curfew, self-worth, friend support, and sense of community were significantly associated with having multiple sexual partners. Among females, age, curfew, and self-worth exerted significant effects on intercourse frequency, while age, parental knowledge, curfew, and self-worth exerted significant effects on having multiple sexual partners. Implications and future directions are discussed. PMID:26401060

SOCIO-ECOLOGICAL PREDICTORS OF INTERCOURSE FREQUENCY AND NUMBER OF SEXUAL PARTNERS AMONG MALE AND FEMALE AFRICAN AMERICAN ADOLESCENTS.

PubMed

Ritchwood, Tiarney D; Traylor, Amy C; Howell, Rebecca J; Church, Wesley T; Bolland, John M

2014-09-01

The current study examined 14 waves of data derived from a large, community-based study of the sexual behavior of impoverished youth between 12 and 17 years of age residing in the Deep South. We used multilevel linear modeling to identify ecological predictors of intercourse frequency and number of sexual partners among gender-specific subsamples. Results indicated that predictors of adolescent sexual behavior differed by both type of sexual behavior and gender. For males, age, maternal warmth, parental knowledge, curfew, self-worth, and sense of community predicted intercourse frequency, while age, parental knowledge, curfew, self-worth, friend support, and sense of community were significantly associated with having multiple sexual partners. Among females, age, curfew, and self-worth exerted significant effects on intercourse frequency, while age, parental knowledge, curfew, and self-worth exerted significant effects on having multiple sexual partners. Implications and future directions are discussed.

Predictors of self-rated health: a 12-month prospective study of IT and media workers.

PubMed

Hasson, Dan; Arnetz, Bengt B; Theorell, Töres; Anderberg, Ulla Maria

2006-07-31

The aim of the present study was to determine health-related risk and salutogenic factors and to use these to construct prediction models for future self-rated health (SRH), i.e. find possible characteristics predicting individuals improving or worsening in SRH over time (0-12 months). A prospective study was conducted with measurements (physiological markers and self-ratings) at 0, 6 and 12 months, involving 303 employees (187 men and 116 women, age 23-64) from four information technology and two media companies. There were a multitude of statistically significant cross-sectional correlations (Spearman's Rho) between SRH and other self-ratings as well as physiological markers. Predictors of future SRH were baseline ratings of SRH, self-esteem and social support (logistic regression), and SRH, sleep quality and sense of coherence (linear regression). The results of the present study indicate that baseline SRH and other self-ratings are predictive of future SRH. It is cautiously implied that SRH, self-esteem, social support, sleep quality and sense of coherence might be predictors of future SRH and therefore possibly also of various future health outcomes.

Monitoring spatial variations in soil organic carbon using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Jaber, Salahuddin M.

Soil organic carbon (SOC) sequestration is a component of larger strategies to control the accumulation of greenhouse gases that may be causing global warming. To implement this approach, it is necessary to improve the methods of measuring SOC content. Among these methods are indirect remote sensing and geographic information systems (GIS) techniques that are required to provide non-intrusive, low cost, and spatially continuous information that cover large areas on a repetitive basis. The main goal of this study is to evaluate the effects of using Hyperion hyperspectral data on improving the existing remote sensing and GIS-based methodologies for rapidly, efficiently, and accurately measuring SOC content on farmland. The study area is Big Creek Watershed (BCW) in Southern Illinois. The methodology consists of compiling a GIS database (consisting of remote sensing and soil variables) for 303 composite soil samples collected from representative pixels along the Hyperion coverage area of the watershed. Stepwise procedures were used to calibrate and validate linear multiple regression models where SOC was regarded as the response and the other remote sensing and soil variables as the predictors. Two models were selected. The first was the best all variables model and the second was the best only raster variables model. Map algebra was implemented to extrapolate the best only raster variables model and produce a SOC map for the BGW. This study concluded that Hyperion data marginally improved the predictability of the existing SOC statistical models based on multispectral satellite remote sensing sensors with correlation coefficient of 0.37 and root mean square error of 3.19 metric tons/hectare to a 15-cm depth. The total SOC pool of the study area is about 225,232 metric tons to 15-cm depth. The nonforested wetlands contained the highest SOC density (34.3 metric tons/hectare/15cm) with total SOC content of about 2,003.5 metric tons to 15-cm depth, where croplands had the lowest SOC density (21.6 metric tons/hectare/15cm) with total SOC content of about 44,571.2 metric tons to 15-cm depth.

Spatial Irrigation Management Using Remote Sensing Water Balance Modeling and Soil Water Content Monitoring

NASA Astrophysics Data System (ADS)

Barker, J. Burdette

Spatially informed irrigation management may improve the optimal use of water resources. Sub-field scale water balance modeling and measurement were studied in the context of irrigation management. A spatial remote-sensing-based evapotranspiration and soil water balance model was modified and validated for use in real-time irrigation management. The modeled ET compared well with eddy covariance data from eastern Nebraska. Placement and quantity of sub-field scale soil water content measurement locations was also studied. Variance reduction factor and temporal stability were used to analyze soil water content data from an eastern Nebraska field. No consistent predictor of soil water temporal stability patterns was identified. At least three monitoring locations were needed per irrigation management zone to adequately quantify the mean soil water content. The remote-sensing-based water balance model was used to manage irrigation in a field experiment. The research included an eastern Nebraska field in 2015 and 2016 and a western Nebraska field in 2016 for a total of 210 plot-years. The response of maize and soybean to irrigation using variations of the model were compared with responses from treatments using soil water content measurement and a rainfed treatment. The remote-sensing-based treatment prescribed more irrigation than the other treatments in all cases. Excessive modeled soil evaporation and insufficient drainage times were suspected causes of the model drift. Modifying evaporation and drainage reduced modeled soil water depletion error. None of the included response variables were significantly different between treatments in western Nebraska. In eastern Nebraska, treatment differences for maize and soybean included evapotranspiration and a combined variable including evapotranspiration and deep percolation. Both variables were greatest for the remote-sensing model when differences were found to be statistically significant. Differences in maize yield in 2015 were attributed to random error. Soybean yield was lowest for the remote-sensing-based treatment and greatest for rainfed, possibly because of overwatering and lodging. The model performed well considering that it did not include soil water content measurements during the season. Future work should improve the soil evaporation and drainage formulations, because of excessive precipitation and include aerial remote sensing imagery and soil water content measurement as model inputs.

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

PubMed

Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

2017-12-01

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics

PubMed Central

Chuang, Ting-Wu; Henebry, Geoffrey M.; Kimball, John S.; VanRoekel-Patton, Denise L.; Hildreth, Michael B.; Wimberly, Michael C.

2012-01-01

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems. PMID:23049143

Predictors of Sense of Belonging for Students with Psychological Conditions

ERIC Educational Resources Information Center

Mackie, Michele Matteo

2013-01-01

The purpose of this study was to develop a portrait of students with psychological conditions, to determine the predictors of sense of belonging for these students, and to draw comparisons between the collegiate experiences of students with, and those without, psychological conditions. Using data from the 2009 Multi-Institutional Study of…

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

USGS Publications Warehouse

Ackers, Steven H.; Davis, Raymond J.; Olsen, K.; Dugger, Catherine

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Ingroup Rejection among Women: The Role of Personal Inadequacy

ERIC Educational Resources Information Center

Cowan, Gloria; Ullman, Jodie B.

2006-01-01

We examined predictors and outcomes of women's hostility toward other women. Based on a projection model, we hypothesized and tested the theory via structural equation modeling that women's sense of personal inadequacy, the tendency to stereotype, and general anger would predict hostility toward women, and hostility toward women would predict…

Applying Lidar and High-Resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic

NASA Astrophysics Data System (ADS)

Greaves, Heather E.

Climate change is disproportionately affecting high northern latitudes, and the extreme temperatures, remoteness, and sheer size of the Arctic tundra biome have always posed challenges that make application of remote sensing technology especially appropriate. Advances in high-resolution remote sensing continually improve our ability to measure characteristics of tundra vegetation communities, which have been difficult to characterize previously due to their low stature and their distribution in complex, heterogeneous patches across large landscapes. In this work, I apply terrestrial lidar, airborne lidar, and high-resolution airborne multispectral imagery to estimate tundra vegetation characteristics for a research area near Toolik Lake, Alaska. Initially, I explored methods for estimating shrub biomass from terrestrial lidar point clouds, finding that a canopy-volume based algorithm performed best. Although shrub biomass estimates derived from airborne lidar data were less accurate than those from terrestrial lidar data, algorithm parameters used to derive biomass estimates were similar for both datasets. Additionally, I found that airborne lidar-based shrub biomass estimates were just as accurate whether calibrated against terrestrial lidar data or harvested shrub biomass--suggesting that terrestrial lidar potentially could replace destructive biomass harvest. Along with smoothed Normalized Differenced Vegetation Index (NDVI) derived from airborne imagery, airborne lidar-derived canopy volume was an important predictor in a Random Forest model trained to estimate shrub biomass across the 12.5 km2 covered by our lidar and imagery data. The resulting 0.80 m resolution shrub biomass maps should provide important benchmarks for change detection in the Toolik area, especially as deciduous shrubs continue to expand in tundra regions. Finally, I applied 33 lidar- and imagery-derived predictor layers in a validated Random Forest modeling approach to map vegetation community distribution at 20 cm resolution across the data collection area, creating maps that will enable validation of coarser maps, as well as study of fine-scale ecological processes in the area. These projects have pushed the limits of what can be accomplished for vegetation mapping using airborne remote sensing in a challenging but important region; it is my hope that the methods explored here will illuminate potential paths forward as landscapes and technologies inevitably continue to change.

An application of remote sensing data in mapping landscape-level forest biomass for monitoring the effectiveness of forest policies in northeastern China.

PubMed

Wang, Xinchuang; Shao, Guofan; Chen, Hua; Lewis, Bernard J; Qi, Guang; Yu, Dapao; Zhou, Li; Dai, Limin

2013-09-01

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3 × 3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77 × 10(6) Mg in 1999 to 2.73 × 10(6) Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9 × 10(3) ha in 1999 to 18.1 × 10(3) ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.

Re-entry Adjustment and Job Embeddedness: The Mediating Role of Professional Identity in Indonesian Returnees.

PubMed

Andrianto, Sonny; Jianhong, Ma; Hommey, Confidence; Damayanti, Devi; Wahyuni, Honey

2018-01-01

The present study examined the relationship between difficulty in re-entry adjustment and job embeddedness, considering the mediating role of sense of professional identity. The online data on demographic characteristics, difficulty on re-entry adjustment, sense of professional identity, and job embeddedness were collected from 178 Indonesian returnees from multiple organizations. The results showed that difficulty in re-entry adjustment was a significant predictor of a sense of professional identity; a sense of professional identity was a significant predictor of job embeddedness. Furthermore, sense of professional identity is an effective mediating variable, bridging the relationship between post-return conditions to the home country and work atmosphere. Finally, the key finding of this study was that sense of professional identity mediated the effect of difficulty in re-entry adjustment on job embeddedness. The theoretical and practical implications, study limitations, and future research needs of our findings are noted.

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

Volunteerism as Purpose: Examining the Long-Term Predictors of Continued Community Engagement

ERIC Educational Resources Information Center

Barber, Carolyn; Mueller, Conrad T.; Ogata, Sachiko

2013-01-01

This study frames continued long-term participation in community engagement activities as indicative of a sense of "purpose" as defined by Damon, Menon, and Cotton Bronk (2003). Using data from US-based National Longitudinal Study of Adolescent Health, we examined factors that predict whether students participating in civic engagement…

Predictors of mental health in female teachers.

PubMed

Seibt, Reingard; Spitzer, Silvia; Druschke, Diana; Scheuch, Klaus; Hinz, Andreas

2013-12-01

Teaching profession is characterised by an above-average rate of psychosomatic and mental health impairment due to work-related stress. The aim of the study was to identify predictors of mental health in female teachers. A sample of 630 female teachers (average age 47 ± 7 years) participated in a screening diagnostic inventory. Mental health was surveyed with the General Health Questionnaire GHQ-12. The following parameters were measured: specific work conditions (teacher-specific occupational history), scales of the Effort-Reward-Imbalance (ERI) Questionnaire as well as cardiovascular risk factors, physical complaints (BFB) and personal factors such as inability to recover (FABA), sense of coherence (SOC) and health behaviour. First, mentally fit (MH(+)) and mentally impaired teachers (MH(-)) were differentiated based on the GHQ-12 sum score (MH(+): < 5; MH(-): ≥ 5); 18% of the teachers showed evidence of mental impairment. There were no differences concerning work-related and cardiovascular risk factors as well as health behaviour between MH(+) and MH(-). Binary logistic regressions identified 4 predictors that showed a significant effect on mental health. The effort-reward-ratio proved to be the most relevant predictor, while physical complaints as well as inability to recover and sense of coherence were identified as advanced predictors (explanation of variance: 23%). Contrary to the expectations, classic work-related factors can hardly contribute to the explanation of mental health. Additionally, cardiovascular risk factors and health behaviour have no relevant influence. However, effort-reward-ratio, physical complaints and personal factors are of considerable influence on mental health in teachers. These relevant predictors should become a part of preventive arrangements for the conservation of teachers' health in the future.

Remote-sensing supported monitoring of global biodiversity change

NASA Astrophysics Data System (ADS)

Jetz, W.; Tuanmu, M. N.; W, A.; Melton, F. S.; Parmentier, B.; Amatulli, G.; Guzman, A.

2016-12-01

Remote sensing combined with biodiversity observation offers an unrivalled tool for understanding and predicting species distributions and their changes at the planetary scale. I will illustrate recently developed high-resolution remote-sensing based layers targeted for spatiotemporal biodiversity modeling, addressing climate, environment, topography, and habitat heterogeneity. In particular, I will illustrate the development and use of global MODIS-derived environmental layers for biodiversity assessment and change monitoring. Remote-sensing based capture of these putative predictors of biodiversity dynamics provides more a reliable signal than spatially interpolated layers and avoids inflated spatial autocorrelation. The layers result in more accurate models of species occurrence and are more readily able to address the scale of processes underpinning species distributions, e.g. when combined with emerging hierarchical, cross-scale models. I illustrate the multiple ways in which this type of information, based on continuously collected data, supports the prediction of not just spatial but also temporal variation in biodiversity. Using implementations in the Map of Life infrastructure I will showcase new indicators of species distribution and change that demonstrate these new opportunities.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Optimal Periodic Cooperative Spectrum Sensing Based on Weight Fusion in Cognitive Radio Networks

PubMed Central

Liu, Xin; Jia, Min; Gu, Xuemai; Tan, Xuezhi

2013-01-01

The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time. PMID:23604027

Improved disturbance rejection for predictor-based control of MIMO linear systems with input delay

NASA Astrophysics Data System (ADS)

Shi, Shang; Liu, Wenhui; Lu, Junwei; Chu, Yuming

2018-02-01

In this paper, we are concerned with the predictor-based control of multi-input multi-output (MIMO) linear systems with input delay and disturbances. By taking the future values of disturbances into consideration, a new improved predictive scheme is proposed. Compared with the existing predictive schemes, our proposed predictive scheme can achieve a finite-time exact state prediction for some smooth disturbances including the constant disturbances, and a better disturbance attenuation can also be achieved for a large class of other time-varying disturbances. The attenuation of mismatched disturbances for second-order linear systems with input delay is also investigated by using our proposed predictor-based controller.

Relating forest attributes with area- and tree-based light detection and ranging metrics for western Oregon

Treesearch

Michael E. Goerndt; Vincente J. Monleon; Hailemariam. Temesgen

2010-01-01

Three sets of linear models were developed to predict several forest attributes, using stand-level and single-tree remote sensing (STRS) light detection and ranging (LiDAR) metrics as predictor variables. The first used only area-level metrics (ALM) associated with first-return height distribution, percentage of cover, and canopy transparency. The second alternative...

Adult Life Stage and Crisis as Predictors of Curiosity and Authenticity: Testing Inferences from Erikson's Lifespan Theory

ERIC Educational Resources Information Center

Robinson, Oliver C.; Demetre, James D.; Litman, Jordan A.

2017-01-01

During periods of developmental crisis, individuals experience uncomfortable internal incongruence and are motivated to reduce this through forms of exploration of self, other and world. Based on this, we inferred that being in a crisis would relate positively to curiosity and negatively to a felt sense of authenticity. A quasi-experimental design…

Modelling the standing timber volume of Baden-Württemberg-A large-scale approach using a fusion of Landsat, airborne LiDAR and National Forest Inventory data

NASA Astrophysics Data System (ADS)

Maack, Joachim; Lingenfelder, Marcus; Weinacker, Holger; Koch, Barbara

2016-07-01

Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r2 = 0.56, NRMSE = 9.65%), the approach with balanced training data (r2 = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r2 = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy.

Interference Information Based Power Control for Cognitive Radio with Multi-Hop Cooperative Sensing

NASA Astrophysics Data System (ADS)

Yu, Youngjin; Murata, Hidekazu; Yamamoto, Koji; Yoshida, Susumu

Reliable detection of other radio systems is crucial for systems that share the same frequency band. In wireless communication channels, there is uncertainty in the received signal level due to multipath fading and shadowing. Cooperative sensing techniques in which radio stations share their sensing information can improve the detection probability of other systems. In this paper, a new cooperative sensing scheme that reduces the false detection probability while maintaining the outage probability of other systems is investigated. In the proposed system, sensing information is collected using multi-hop transmission from all sensing stations that detect other systems, and transmission decisions are based on the received sensing information. The proposed system also controls the transmit power based on the received CINRs from the sensing stations. Simulation results reveal that the proposed system can reduce the outage probability of other systems, or improve its link success probability.

Burnout Syndrome Among Health Care Students: The Role of Type D Personality.

PubMed

Skodova, Zuzana; Lajciakova, Petra; Banovcinova, Lubica

2016-07-18

The aim of this study was to examine the effect of Type D personality, along with other personality traits (resilience and sense of coherence), on burnout syndrome and its counterpart, engagement, among students of nursing, midwifery, and psychology. A cross-sectional study was conducted on 97 university students (91.9% females; M age = 20.2 ± 1.49 years). A Type D personality subscale, School Burnout Inventory, Utrecht Work Engagement Scale, Sense of Coherence Questionnaire, and Baruth Protective Factor Inventory were used. Linear regression models, Student's t test, and Pearson's correlation analysis were employed. Negative affectivity, a dimension of Type D personality, was a significant personality predictor for burnout syndrome (β = .54; 95% CI = [0.33, 1.01]). The only significant personality predictor of engagement was a sense of coherence. Students who were identified as having Type D personality characteristics scored significantly higher on the burnout syndrome questionnaire (t = -2.58, p < .01). In health care professions, personality predictors should be addressed to prevent burnout. © The Author(s) 2016.

Linking Satellite Remote Sensing Based Environmental Predictors to Disease: AN Application to the Spatiotemporal Modelling of Schistosomiasis in Ghana

NASA Astrophysics Data System (ADS)

Wrable, M.; Liss, A.; Kulinkina, A.; Koch, M.; Biritwum, N. K.; Ofosu, A.; Kosinski, K. C.; Gute, D. M.; Naumova, E. N.

2016-06-01

90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.

Resolution-improved in situ DNA hybridization detection based on microwave photonic interrogation.

PubMed

Cao, Yuan; Guo, Tuan; Wang, Xudong; Sun, Dandan; Ran, Yang; Feng, Xinhuan; Guan, Bai-ou

2015-10-19

In situ bio-sensing system based on microwave photonics filter (MPF) interrogation method with improved resolution is proposed and experimentally demonstrated. A microfiber Bragg grating (mFBG) is used as sensing probe for DNA hybridization detection. Different from the traditional wavelength monitoring technique, we use the frequency interrogation scheme for resolution-improved bio-sensing detection. Experimental results show that the frequency shift of MPF notch presents a linear response to the surrounding refractive index (SRI) change over the range of 1.33 to 1.38, with a SRI resolution up to 2.6 × 10(-5) RIU, which has been increased for almost two orders of magnitude compared with the traditional fundamental mode monitoring technique (~3.6 × 10(-3) RIU). Due to the high Q value (about 27), the whole process of DNA hybridization can be in situ monitored. The proposed MPF-based bio-sensing system provides a new interrogation method over the frequency domain with improved sensing resolution and rapid interrogation rate for biochemical and environmental measurement.

The impact of parent-delivered intervention on parents of very young children with autism.

PubMed

Estes, Annette; Vismara, Laurie; Mercado, Carla; Fitzpatrick, Annette; Elder, Lauren; Greenson, Jessica; Lord, Catherine; Munson, Jeffrey; Winter, Jamie; Young, Gregory; Dawson, Geraldine; Rogers, Sally

2014-02-01

This study investigated the impact of a parent-coaching intervention based on the Early Start Denver Model (P-ESDM) on parenting-related stress and sense of competence. This was part of a multisite, randomized trial comparing P-ESDM (n = 49) with community intervention (n = 49) for children aged 12 and 24 months. The P-ESDM group reported no increase in parenting stress, whereas the Community group experienced an increase over the same 3-month period. Parental sense of competence did not differ. Number of negative life events was a significant predictor of parenting stress and sense of competence across both groups. This suggests that a parent-coaching intervention may help maintain parental adjustment directly after a child is diagnosed with ASD.

The impact of parent-delivered intervention on parents of very young children with autism

PubMed Central

Estes, Annette; Vismara, Laurie; Mercado, Carla; Fitzpatrick, Annette; Elder, Lauren; Greenson, Jessica; Lord, Catherine; Munson, Jeffrey; Winter, Jamie; Young, Gregory; Dawson, Geraldine; Rogers, Sally

2013-01-01

This study investigated the impact of a parent-coaching intervention based on the Early Start Denver Model (P-ESDM) on parenting-related stress and sense of competence. This was part of a multisite, randomized trial comparing P-ESDM (n=49) with community intervention (n=49) for children aged 12 and 24 months. The P-ESDM group reported no increase in parenting stress, whereas the Community group experienced an increase over the same 3-month period. Parental sense of competence did not differ. Number of negative life events was a significant predictor of parenting stress and sense of competence across both groups. This suggests that a parent-coaching intervention may help maintain parental adjustment directly after a child is diagnosed with ASD. PMID:23838727

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China

NASA Astrophysics Data System (ADS)

Wu, Hao; Ye, Lu-Ping; Shi, Wen-Zhong; Clarke, Keith C.

2014-10-01

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.

A comparative analysis of predictors of sense of place dimensions: attachment to, dependence on, and identification with lakeshore properties.

PubMed

Jorgensen, Bradley S; Stedman, Richard C

2006-05-01

Sense of place can be conceived as a multidimensional construct representing beliefs, emotions and behavioural commitments concerning a particular geographic setting. This view, grounded in attitude theory, can better reveal complex relationships between the experience of a place and attributes of that place than approaches that do not differentiate cognitive, affective and conative domains. Shoreline property owners (N=290) in northern Wisconsin were surveyed about their sense of place for their lakeshore properties. A predictive model comprising owners' age, length of ownership, participation in recreational activities, days spent on the property, extent of property development, and perceptions of environmental features, was employed to explain the variation in dimensions of sense of place. In general, the results supported a multidimensional approach to sense of place in a context where there were moderate to high correlations among the three place dimensions. Perceptions of environmental features were the biggest predictors of place dimensions, with owners' perceptions of lake importance varying in explanatory power across place dimensions.

Investigating kindergarteners' number sense and self-regulation scores in relation to their mathematics and Turkish scores in middle school

NASA Astrophysics Data System (ADS)

İvrendi, Asiye

2016-09-01

Number sense and self-regulation are considered foundational skills for later school learning. This study aimed to investigate the predictive power of kindergarten children's number sense and self-regulation scores on their mathematics and Turkish language examination scores in the 5th and 6th grades. The participants in this study were 5th grade ( n = 46) and 6th grade ( n = 28) students, whose number sense and self-regulation skills were measured when they were in kindergarten in 2009 and 2010. Data were analyzed through multiple regression. The results showed positive and mid-level correlations. The children's kindergarten number sense and self-regulation scores significantly predicted their 5th and 6th grade mathematics and Turkish language examination scores. Self-regulation was the stronger predictor of mathematics scores, whereas number sense scores were the better predictor of Turkish language examination scores. The findings from this study provide further evidence as to the critical role of children's early skills in middle school mathematics and language achievement.

Baseline Predictors for Success Following Strategy-Based Cognitive Remediation Group Training in Schizophrenia.

PubMed

Farreny, Aida; Aguado, Jaume; Corbera, Silvia; Ochoa, Susana; Huerta-Ramos, Elena; Usall, Judith

2016-08-01

Our aim was to examine predictive variables associated with the improvement in cognitive, clinical, and functional outcomes after outpatient participation in REPYFLEC strategy-based Cognitive Remediation (CR) group training. In addition, we investigated which factors might be associated with some long-lasting effects at 6 months' follow-up. Predictors of improvement after CR were studied in a sample of 29 outpatients with schizophrenia. Partial correlations were computed between targeted variables and outcomes of response to explore significant associations. Subsequently, we built linear regression models for each outcome variable and predictors of improvement. The improvement in negative symptoms at posttreatment was linked to faster performance in the Trail Making Test B. Disorganization and cognitive symptoms were related to changes in executive function at follow-up. Lower levels of positive symptoms were related to durable improvements in life skills. Levels of symptoms and cognition were associated with improvements following CR, but the pattern of resulting associations was nonspecific.

Predictors of participation in parenting workshops for improving adolescent behavioral and mental health: results from the common sense parenting trial.

PubMed

Fleming, Charles B; Mason, W Alex; Haggerty, Kevin P; Thompson, Ronald W; Fernandez, Kate; Casey-Goldstein, Mary; Oats, Robert G

2015-04-01

Engaging and retaining participants are crucial to achieving adequate implementation of parenting interventions designed to prevent problem behaviors among children and adolescents. This study examined predictors of engagement and retention in a group-based family intervention across two versions of the program: a standard version requiring only parent attendance for six sessions and an adapted version with two additional sessions that required attendance by the son or daughter. Families included a parent and an eighth grader who attended one of five high-poverty schools in an urban Pacific Northwest school district. The adapted version of the intervention had a higher rate of engagement than the standard version, a difference that was statistically significant after adjusting for other variables assessed at enrollment in the study. Higher household income and parent education, younger student age, and poorer affective quality in the parent-child relationship predicted greater likelihood of initial attendance. In the adapted version of the intervention, parents of boys were more likely to engage with the program than those of girls. The variables considered did not strongly predict retention, although retention was higher among parents of boys. Retention did not significantly differ between conditions. Asking for child attendance at workshops may have increased engagement in the intervention, while findings for other predictors of attendance point to the need for added efforts to recruit families who have less socioeconomic resources, as well as families who perceive they have less need for services.

The Effects of Geographic Isolation and Social Support on the Health of Wisconsin Women.

PubMed

Tittman, Sarah M; Harteau, Christy; Beyer, Kirsten M M

2016-04-01

Rural residents are less likely to receive preventive health screening, more likely to be uninsured, and more likely to report fair to poor health than urban residents. Social disconnectedness and perceived isolation are known to be negative predictors of self-rated physical health; however, the direct effects of geographic isolation and social support on overall health have not been well elucidated. A cross-sectional survey of women (n = 113) participating in Wisconsin Rural Women's initiative programming was conducted, which included measures of geographic isolation, an assessment of overall health, and social support using the validated Interpersonal Support Evaluation List with 3 subscales, including belonging support, tangible support, and appraisal support. Geographic isolation was shown to be a negative predictor of belonging support (P = .0064) and tangible support (P = .0349); however, geographic isolation was not a statistically significant predictor of appraisal support. A strong and direct relationship was observed between social support and self-perceived health status among this population of Wisconsin women, and hospital access based on geographic proximity was positively correlated (P = .028) with overall health status. The direct relationship between social support and overall health demonstrated here stresses the importance of developing and maintaining strong social support networks, which can be improved through rural support groups that have the unique ability to assist rural residents in fostering social support systems, advocating stress management techniques, and achieving a greater sense of well-being.

Predictors of Participation in Parenting Workshops for Improving Adolescent Behavioral and Mental Health: Results from the Common Sense Parenting Trial

PubMed Central

Fleming, Charles B.; Mason, W. Alex; Haggerty, Kevin P.; Thompson, Ronald W.; Fernandez, Kate; Casey-Goldstein, Mary; Oats, Robert G.

2015-01-01

Engaging and retaining participants are crucial to achieving adequate implementation of parenting interventions designed to prevent problem behaviors among children and adolescents. This study examined predictors of engagement and retention in a group-based family intervention across two versions of the program: a standard version requiring only parent attendance for six sessions and an adapted version with two additional sessions that required attendance by the son or daughter. Families included a parent and an eighth grader who attended one of five high-poverty schools in an urban Pacific Northwest school district. The adapted version of the intervention had a higher rate of engagement than the standard version, a difference that was statistically significant after adjusting for other variables assessed at enrollment in the study. Higher household income and parent education, younger student age, and poorer affective quality in the parent-child relationship predicted greater likelihood of initial attendance. In the adapted version of the intervention, parents of boys were more likely to engage with the program than those of girls. The variables considered did not strongly predict retention, although retention was higher among parents of boys. Retention did not significantly differ between conditions. Asking for child attendance at workshops may have increased engagement in the intervention, while findings for other predictors of attendance point to the need for added efforts to recruit families who have less socioeconomic resources, as well as families who perceive they have less need for services. PMID:25656381

A comparison of two above-ground biomass estimation techniques integrating satellite-based remotely sensed data and ground data for tropical and semiarid forests in Puerto Rico

NASA Astrophysics Data System (ADS)

Iiames, J. S.; Riegel, J.; Lunetta, R.

2013-12-01

Two above-ground forest biomass estimation techniques were evaluated for the United States Territory of Puerto Rico using predictor variables acquired from satellite based remotely sensed data and ground data from the U.S. Department of Agriculture Forest Inventory Analysis (FIA) program. The U.S. Environmental Protection Agency (EPA) estimated above-ground forest biomass implementing methodology first posited by the Woods Hole Research Center developed for conterminous United States (National Biomass and Carbon Dataset [NBCD2000]). For EPA's effort, spatial predictor layers for above-ground biomass estimation included derived products from the U.S. Geologic Survey (USGS) National Land Cover Dataset 2001 (NLCD) (landcover and canopy density), the USGS Gap Analysis Program (forest type classification), the USGS National Elevation Dataset, and the NASA Shuttle Radar Topography Mission (tree heights). In contrast, the U.S. Forest Service (USFS) biomass product integrated FIA ground-based data with a suite of geospatial predictor variables including: (1) the Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; (2) NLCD land cover proportions; (3) topographic variables; (4) monthly and annual climate parameters; and (5) other ancillary variables. Correlations between both data sets were made at variable watershed scales to test level of agreement. Notice: This work is done in support of EPA's Sustainable Healthy Communities Research Program. The U.S EPA funded and conducted the research described in this paper. Although this work was reviewed by the EPA and has been approved for publication, it may not necessarily reflect official Agency policy. Mention of any trade names or commercial products does not constitute endorsement or recommendation for use.

Landsat time series and lidar as predictors of live and dead basal area across five bark beetle-affected forests

Treesearch

Benjamin C. Bright; Andrew T. Hudak; Robert E. Kennedy; Arjan J. H. Meddens

2014-01-01

Bark beetle-caused tree mortality affects important forest ecosystem processes. Remote sensing methodologies that quantify live and dead basal area (BA) in bark beetle-affected forests can provide valuable information to forest managers and researchers. We compared the utility of light detection and ranging (lidar) and the Landsat-based detection of trends in...

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

Calling for the Development of Children's Number Sense in Primary Schools in Malaysia

ERIC Educational Resources Information Center

Kuldas, Seffetullah; Sinnakaudan, Santi; Hashim, Shahabuddin; Ghazali, Munirah

2017-01-01

Although the early development of children's number sense is a strong predictor of their later mathematics achievements, it has been overlooked in primary schools in Malaysia. Mainly attributable to underdeveloped number sense of Malaysian primary and secondary school children, their inability to handle simple mathematics tasks, which require the…

Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring

NASA Astrophysics Data System (ADS)

Nieland, Simon; Kleinschmit, Birgit; Förster, Michael

2015-05-01

Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalisation and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, "bottom-up" engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalisation and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class description, is a key factor for the future success of using semantics for interoperability in remote sensing.

Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes.

PubMed

Leong, Misha; Roderick, George K

2015-01-01

Global change has led to shifts in phenology, potentially disrupting species interactions such as plant-pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators.

Improvement of mechanical performance for vibratory microgyroscope based on sense mode closed-loop control

NASA Astrophysics Data System (ADS)

Xiao, Dingbang; Su, Jianbin; Chen, Zhihua; Hou, Zhanqiang; Wang, Xinghua; Wu, Xuezhong

2013-04-01

In order to improve its structural sensitivity, a vibratory microgyroscope is commonly sealed in high vacuum to increase the drive mode quality factor. The sense mode quality factor of the microgyroscope will also increase simultaneously after vacuum sealing, which will lead to a long decay time of free response and even self-oscillation of the sense mode. As a result, the mechanical performance of the microgyroscope will be seriously degraded. In order to solve this problem, a closed-loop control technique is presented to adjust and optimize the sense mode quality factor. A velocity feedback loop was designed to increase the electric damping of the sense mode vibration. A circuit was fabricated based on this technique, and experimental results indicate that the sense mode quality factor of the microgyroscope was adjusted from 8052 to 428. The decay time of the sense mode free response was shortened from 3 to 0.5 s, and the vibration-rejecting ability of the microgyroscope was improved obviously without sensitivity degradation.

Gender as a predictor of posttraumatic stress symptoms and externalizing behavior problems in sexually abused children

PubMed Central

Gauthier-Duchesne, Amélie; Daspe, Marie-Ève

2017-01-01

Despite the proliferation of studies documenting outcomes in sexually abused victims, gender differences remain understudied. The bulk of studies have relied on retrospective samples of adults with insufficient representation of male victims to explore gender specificities. This study examined differential outcomes among boy and girl victims of sexual abuse. A predictive model of outcomes including abuse characteristics and sense of guilt as mediators was proposed. Path analysis was conducted with a sample of 447 sexually abused children (319 girls and 128 boys), aged 6 to 12. Being a girl was a predictor of posttraumatic stress symptoms, while being a boy was a predictor of externalizing problems. Being a boy was also associated with more severe abuse, which in turn predicted posttraumatic stress symptoms. Child’s gender was not related to perpetrator’s relationship to the child or sense of guilt. However, sense of guilt predicted posttraumatic stress symptoms and externalizing problems while perpetrator’s relationship to the child predicted externalizing problems. Gender specificities should be further studied among sexually abused children, as boys and girls appear to manifest different outcomes. Sense of guilt should be a target in intervention for sexually abused children, as results highlight its link to heightened negative outcomes. PMID:28040616

Human mobility prediction from region functions with taxi trajectories.

PubMed

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

PubMed

Hu, Xuefei; Waller, Lance A; Lyapustin, Alexei; Wang, Yujie; Liu, Yang

2014-10-16

Multiple studies have developed surface PM 2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM 2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM 2.5 . In this paper, we examined whether remotely sensed fire count data could improve PM 2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM 2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM 2.5 across the models considered. Cross validation (CV) generated an R 2 of 0.69, a mean prediction error of 2.75 µg/m 3 , and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m 3 , indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m 3 , exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM 2.5 concentration estimation, especially in areas and seasons prone to fire events.

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S

PubMed Central

Hu, Xuefei; Waller, Lance A.; Lyapustin, Alexei; Wang, Yujie; Liu, Yang

2017-01-01

Multiple studies have developed surface PM2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM2.5. In this paper, we examined whether remotely sensed fire count data could improve PM2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM2.5 across the models considered. Cross validation (CV) generated an R2 of 0.69, a mean prediction error of 2.75 µg/m3, and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m3, indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m3, exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM2.5 concentration estimation, especially in areas and seasons prone to fire events. PMID:28967648

Predictor-Based Model Reference Adaptive Control

NASA Technical Reports Server (NTRS)

Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.

2010-01-01

This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now classical model reference adaptive control. A simulation example is presented in which numerical evidence indicates that the proposed controller yields improved transient characteristics.

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Treesearch

Ram Deo; Matthew Russell; Grant Domke; Hans-Erik Andersen; Warren Cohen; Christopher Woodall

2017-01-01

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-...

Investigation of a Photoelectrochemical Passivated ZnO-Based Glucose Biosensor

PubMed Central

Lee, Ching-Ting; Chiu, Ying-Shuo; Ho, Shu-Ching; Lee, Yao-Jung

2011-01-01

A vapor cooling condensation system was used to deposit high quality intrinsic ZnO thin films and intrinsic ZnO nanorods as the sensing membrane of extended-gate field-effect-transistor (EGFET) glucose biosensors. The sensing sensitivity of the resulting glucose biosensors operated in the linear range was 13.4 μA mM−1 cm−2. To improve the sensing sensitivity of the ZnO-based glucose biosensors, the photoelectrochemical method was utilized to passivate the sidewall surfaces of the ZnO nanorods. The sensing sensitivity of the ZnO-based glucose biosensors with passivated ZnO nanorods was significantly improved to 20.33 μA mM−1 cm−2 under the same measurement conditions. The experimental results verified that the sensing sensitivity improvement was the result of the mitigation of the Fermi level pinning effect caused by the dangling bonds and the surface states induced on the sidewall surface of the ZnO nanorods. PMID:22163867

A Number Sense Intervention for Low-Income Kindergartners at Risk for Mathematics Difficulties

ERIC Educational Resources Information Center

Dyson, Nancy I.; Jordan, Nancy C.; Glutting, Joseph

2013-01-01

Early number sense is a strong predictor of later success in school mathematics. A disproportionate number of children from low-income families come to first grade with weak number competencies, leaving them at risk for a cycle of failure. The present study examined the effects of an 8-week number sense intervention to develop number competencies…

Sense of Community in Student Retention at a Tertiary Technical Institution in Bogotá: An Ecological Approach

ERIC Educational Resources Information Center

Mendoza, Pilar; Suarez, Juan Diego; Bustamante, Eileen

2016-01-01

Objective: This study probes the reasons for high attrition rates and sense of community among students enrolled at a technical institution serving low-income students in Bogotá, Colombia. Although sense of community on campus is the strongest predictor of a student's thriving, scholars in higher education have studied mainly minority students'…

Building medical ethics education to improve Japanese medical students' attitudes toward respecting patients' rights.

PubMed

Saito, Yukiko; Kudo, Yasushi; Shibuya, Akitaka; Satoh, Toshihiko; Higashihara, Masaaki; Aizawa, Yoshiharu

2011-08-01

In medical education, it is important for medical students to develop their ethics to respect patients' rights. Some physicians might make light of patients' rights, because the increased awareness of such rights might make it more difficult for them to conduct medical practice. In the present study, predictors significantly associated with "a sense of resistance to patients' rights" were examined using anonymous self-administered questionnaires. For these predictors, we produced original items with reference to the concept of ethical development and the teachings of Mencius. The subjects were medical students at the Kitasato University School of Medicine, a private university in Japan. A total of 518 students were analyzed (response rate, 78.4%). The average age of enrolled subjects was 22.5 ± 2.7 years (average age ± standard deviation). The average age of 308 male subjects was 22.7 ± 2.8 years, while that of 210 female subjects was 22.1 ± 2.5 years. The item, "Excessive measures to pass the national examination for medical practitioners," was significantly associated with "a sense of resistance to patients' rights." However, other items, including basic attributes such as age and gender, were not significant predictors. If students spent their school time only focusing on the national examination, they would lose the opportunity to receive the ethical education that would allow them to respect patients' rights. That ethical development cannot easily be evaluated with written exams. Thus, along with the acquisition of medical knowledge, educational programs to promote medical students' ethics should be developed.

The role of social capital and community belongingness for exercise adherence: An exploratory study of the CrossFit gym model.

PubMed

Whiteman-Sandland, Jessica; Hawkins, Jemma; Clayton, Debbie

2016-08-01

This is the first study to measure the 'sense of community' reportedly offered by the CrossFit gym model. A cross-sectional study adapted Social Capital and General Belongingness scales to compare perceptions of a CrossFit gym and a traditional gym. CrossFit gym members reported significantly higher levels of social capital (both bridging and bonding) and community belongingness compared with traditional gym members. However, regression analysis showed neither social capital, community belongingness, nor gym type was an independent predictor of gym attendance. Exercise and health professionals may benefit from evaluating further the 'sense of community' offered by gym-based exercise programmes.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

PubMed

Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

2010-07-01

PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

PubMed

Peng, Yi; Xiong, Xiong; Adhikari, Kabindra; Knadel, Maria; Grunwald, Sabine; Greve, Mogens Humlekrog

2015-01-01

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l'Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the 'upland model' was able to more accurately predict SOC compared with the 'upland & wetland model'. However, the separately calibrated 'upland and wetland model' did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

PubMed Central

Peng, Yi; Xiong, Xiong; Adhikari, Kabindra; Knadel, Maria; Grunwald, Sabine; Greve, Mogens Humlekrog

2015-01-01

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM). PMID:26555071

Gas-sensing enhancement methods for hydrothermal synthesized SnO2-based sensors

NASA Astrophysics Data System (ADS)

Zhao, Yalei; Zhang, Wenlong; Yang, Bin; Liu, Jingquan; Chen, Xiang; Wang, Xiaolin; Yang, Chunsheng

2017-11-01

Gas sensing for hydrothermal synthesized SnO2-based gas sensors can be enhanced in three ways: structural improvement, composition optimization, and processing improvement. There have been zero-dimensional, one-dimensional, and three-dimensional structures reported in the literature. Controllable synthesis of different structures has been deployed to increase specific surface area. Change of composition would intensively tailor the SnO2 structure, which affected the gas-sensing performance. Furthermore, doping and compounding methods have been adopted to promote gas-sensing performance by adjusting surface conditions of SnO2 crystals and constructing heterojunctions. As for processing area, it is very important to find the optimal reaction time and temperature. In this paper, a gas-solid reaction rate constant was proposed to evaluate gas-sensing properties and find an excellent hydrothermal synthesized SnO2-based gas sensor.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Human mobility prediction from region functions with taxi trajectories

PubMed Central

Wang, Minjie; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions. PMID:29190708

Investigation related to multispectral imaging systems

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Erickson, J. D.

1974-01-01

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

PubMed

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

Remote sensing image ship target detection method based on visual attention model

NASA Astrophysics Data System (ADS)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

PubMed

Bernecker, Samantha L; Rosellini, Anthony J; Nock, Matthew K; Chiu, Wai Tat; Gutierrez, Peter M; Hwang, Irving; Joiner, Thomas E; Naifeh, James A; Sampson, Nancy A; Zaslavsky, Alan M; Stein, Murray B; Ursano, Robert J; Kessler, Ronald C

2018-04-03

High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

Time banking and health: the role of a community currency organization in enhancing well-being.

PubMed

Lasker, Judith; Collom, Ed; Bealer, Tara; Niclaus, Erin; Young Keefe, Jessica; Kratzer, Zane; Baldasari, Lauren; Kramer, Ethan; Mandeville, Rachel; Schulman, Julia; Suchow, Danielle; Letcher, Abby; Rogers, Anne; Perlow, Kathy

2011-01-01

Time banking is an international movement that seeks to transform traditional asymmetric social service models into social networks in which members both provide and receive services that are assigned equal value. Time banks have been shown to enhance social capital, and there is some evidence for improved health. This article, based on a survey of 160 members of a hospital-affiliated time bank, examines the likelihood and predictors of improvement in physical and mental health as a result of membership. Men, people with lower income, and those who were not working full-time reported highest levels of participation in exchanging services; attachment to the organization was greatest among women, older members, people with less education, and those with the highest participation levels. Multivariate analyses revealed that physical health improvement attributed to membership was significantly predicted by attachment to the organization and living alone; mental health gains were predicted by general health changes, average number of exchanges, and attachment to the organization. We conclude that a sense of belonging, a dimension of social capital, is key to improved well-being and that time banking may be particularly valuable in promoting health and belonging among older and lower-income individuals and those who live alone.

Bullying and Middle School Students with and without Specific Learning Disabilities: An Examination of Social-Ecological Predictors

ERIC Educational Resources Information Center

Rose, Chad Allen; Espelage, Dorothy L.; Monda-Amaya, Lisa E.; Shogren, Karrie A.; Aragon, Steven R.

2015-01-01

The current study investigated demographic variables, sense of belonging, and social supports as predictors for involvement in bullying for students with specific learning disabilities (SLD) and students without disabilities. Although these student groups are characteristically different, results suggested involvement in bullying was invariant.…

Self-Referenced Smartphone-Based Nanoplasmonic Imaging Platform for Colorimetric Biochemical Sensing.

PubMed

Wang, Xinhao; Chang, Te-Wei; Lin, Guohong; Gartia, Manas Ranjan; Liu, Gang Logan

2017-01-03

Colorimetric sensors usually suffer due to errors from variation in light source intensity, the type of light source, the Bayer filter algorithm, and the sensitivity of the camera to incoming light. Here, we demonstrate a self-referenced portable smartphone-based plasmonic sensing platform integrated with an internal reference sample along with an image processing method to perform colorimetric sensing. Two sensing principles based on unique nanoplasmonics enabled phenomena from a nanostructured plasmonic sensor, named as nanoLCA (nano Lycurgus cup array), were demonstrated here for colorimetric biochemical sensing: liquid refractive index sensing and optical absorbance enhancement sensing. Refractive indices of colorless liquids were measured by simple smartphone imaging and color analysis. Optical absorbance enhancement in the colorimetric biochemical assay was achieved by matching the plasmon resonance wavelength with the chromophore's absorbance peak wavelength. Such a sensing mechanism improved the limit of detection (LoD) by 100 times in a microplate reader format. Compared with a traditional colorimetric assay such as urine testing strips, a smartphone plasmon enhanced colorimetric sensing system provided 30 times improvement in the LoD. The platform was applied for simulated urine testing to precisely identify the samples with higher protein concentration, which showed potential point-of-care and early detection of kidney disease with the smartphone plasmonic resonance sensing system.

Investigating Prospective Teachers' Teaching-Specific Hopes as Predictors of Their Sense of Personal Responsibility

ERIC Educational Resources Information Center

Eren, Altay

2017-01-01

This study examined whether prospective teachers' teaching-specific hopes significantly predicted their sense of personal responsibility. A total of 503 prospective teachers voluntarily participated in the study. Correlation and structural equation modelling analyses were conducted to examine the links between prospective teachers'…

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Potential impact of remote sensing data on sea-state analysis and prediction

NASA Technical Reports Server (NTRS)

Cardone, V. J.

1983-01-01

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

PubMed

Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

2016-11-01

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

Improved ground-based remote-sensing systems help monitor plant response to climate and other changes

USGS Publications Warehouse

Dye, Dennis G.; Bogle, Rian

2016-05-26

Scientists at the U.S. Geological Survey are improving and developing new ground-based remote-sensing instruments and techniques to study how Earth’s vegetation responds to changing climates. Do seasonal grasslands and forests “green up” early (or late) and grow more (or less) during unusually warm years? How do changes in temperature and precipitation affect these patterns? Innovations in ground-based remote-sensing instrumentation can help us understand, assess, and mitigate the effects of climate change on vegetation and related land resources.

Visual working memory and number sense: Testing the double deficit hypothesis in mathematics.

PubMed

Toll, Sylke W M; Kroesbergen, Evelyn H; Van Luit, Johannes E H

2016-09-01

Evidence exists that there are two main underlying cognitive factors in mathematical difficulties: working memory and number sense. It is suggested that real math difficulties appear when both working memory and number sense are weak, here referred to as the double deficit (DD) hypothesis. The aim of this study was to test the DD hypothesis within a longitudinal time span of 2 years. A total of 670 children participated. The mean age was 4.96 years at the start of the study and 7.02 years at the end of the study. At the end of the first year of kindergarten, both visual-spatial working memory and number sense were measured by two different tasks. At the end of first grade, mathematical performance was measured with two tasks, one for math facts and one for math problems. Multiple regressions revealed that both visual working memory and symbolic number sense are predictors of mathematical performance in first grade. Symbolic number sense appears to be the strongest predictor for both math areas (math facts and math problems). Non-symbolic number sense only predicts performance in math problems. Multivariate analyses of variance showed that a combination of visual working memory and number sense deficits (NSDs) leads to the lowest performance on mathematics. Our DD hypothesis was confirmed. Both visual working memory and symbolic number sense in kindergarten are related to mathematical performance 2 years later, and a combination of visual working memory and NSDs leads to low performance in mathematical performance. © 2016 The British Psychological Society.

Development of a Global Evaporative Stress Index Based on Thermal and Microwave LST towards Improved Monitoring of Agricultural Drought

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Otkin, J.; Holmes, T. R.; Gao, F.

2017-12-01

This presentation will describe the development of a global agricultural monitoring tool, with a focus on providing early warning of developing vegetation stress for agricultural decision-makers and stakeholders at relatively high spatial resolution (5-km). The tool is based on remotely sensed estimates of evapotranspiration, retrieved via energy balance principals using observations of land surface temperature. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET generated with the ALEXI surface energy balance model. The LST inputs to ESI have been shown to provide early warning information about the development of vegetation stress with stress-elevated canopy temperatures observed well before a decrease in greenness is detected in remotely sensed vegetation indices. As a diagnostic indicator of actual ET, the ESI requires no information regarding antecedent precipitation or soil moisture storage capacity - the current available moisture to vegetation is deduced directly from the remotely sensed LST signal. This signal also inherently accounts for both precipitation and non-precipitation related inputs/sinks to the plant-available soil moisture pool (e.g., irrigation) which can modify crop response to rainfall anomalies. Independence from precipitation data is a benefit for global agricultural monitoring applications due to sparseness in existing ground-based precipitation networks, and time delays in public reporting. Several enhancements to the current ESI framework will be addressed as requested from project stakeholders: (a) integration of "all-sky" MW Ka-band LST retrievals to augment "clear-sky" thermal-only ESI in persistently cloudy regions; (b) operational production of ESI Rapid Change Indices which provide important early warning information related to onset of actual vegetation stress; and (c) assessment of ESI as a predictor of global yield anomalies; initial studies have shown the ability of intra-seasonal ESI to provide an early indication of at-harvest agricultural yield anomalies.

Predictors in Internet-delivered cognitive behavior therapy and behavioral stress management for severe health anxiety.

PubMed

Hedman, Erik; Andersson, Erik; Lekander, Mats; Ljótsson, Brjánn

2015-01-01

Severe health anxiety can be effectively treated with exposure-based Internet-delivered cognitive behavior therapy (ICBT), but information about which factors that predict outcome is scarce. Using data from a recently conducted RCT comparing ICBT (n = 79) with Internet-delivered behavioral stress management (IBSM) (n = 79) the presented study investigated predictors of treatment outcome. Analyses were conducted using a two-step linear regression approach and the dependent variable was operationalized both as end state health anxiety at post-treatment and as baseline-to post-treatment improvement. A hypothesis driven approach was used where predictors expected to influence outcome were based on a previous predictor study by our research group. As hypothesized, the results showed that baseline health anxiety and treatment adherence predicted both end state health anxiety and improvement. In addition, anxiety sensitivity, treatment credibility, and working alliance were significant predictors of health anxiety improvement. Demographic variables, i.e. age, gender, marital status, computer skills, educational level, and having children, had no significant predictive value. We conclude that it is possible to predict a substantial proportion of the outcome variance in ICBT and IBSM for severe health anxiety. The findings of the present study can be of high clinical value as they provide information about factors of importance for outcome in the treatment of severe health anxiety. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integrated Strategy Improves the Prediction Accuracy of miRNA in Large Dataset

PubMed Central

Lipps, David; Devineni, Sree

2016-01-01

MiRNAs are short non-coding RNAs of about 22 nucleotides, which play critical roles in gene expression regulation. The biogenesis of miRNAs is largely determined by the sequence and structural features of their parental RNA molecules. Based on these features, multiple computational tools have been developed to predict if RNA transcripts contain miRNAs or not. Although being very successful, these predictors started to face multiple challenges in recent years. Many predictors were optimized using datasets of hundreds of miRNA samples. The sizes of these datasets are much smaller than the number of known miRNAs. Consequently, the prediction accuracy of these predictors in large dataset becomes unknown and needs to be re-tested. In addition, many predictors were optimized for either high sensitivity or high specificity. These optimization strategies may bring in serious limitations in applications. Moreover, to meet continuously raised expectations on these computational tools, improving the prediction accuracy becomes extremely important. In this study, a meta-predictor mirMeta was developed by integrating a set of non-linear transformations with meta-strategy. More specifically, the outputs of five individual predictors were first preprocessed using non-linear transformations, and then fed into an artificial neural network to make the meta-prediction. The prediction accuracy of meta-predictor was validated using both multi-fold cross-validation and independent dataset. The final accuracy of meta-predictor in newly-designed large dataset is improved by 7% to 93%. The meta-predictor is also proved to be less dependent on datasets, as well as has refined balance between sensitivity and specificity. This study has two folds of importance: First, it shows that the combination of non-linear transformations and artificial neural networks improves the prediction accuracy of individual predictors. Second, a new miRNA predictor with significantly improved prediction accuracy is developed for the community for identifying novel miRNAs and the complete set of miRNAs. Source code is available at: https://github.com/xueLab/mirMeta PMID:28002428

Correlation Study of Physics Achievement, Learning Strategy, Attitude and Gender in an Introductory Physics Course

ERIC Educational Resources Information Center

Sezgin Selcuk, Gamze

2010-01-01

This study investigates the relationship between multiple predictors of physics achievement including reported use of four learning strategy clusters (elaboration, organization, comprehension monitoring and rehearsal), attitudes towards physics (sense of care and sense of interest) and a demographic variable (gender) in order to determine the…

Is sense of coherence a predictor of lifestyle changes in subjects at risk for type 2 diabetes?

PubMed

Nilsen, V; Bakke, P S; Rohde, G; Gallefoss, F

2015-02-01

To determine whether the sense of coherence (SOC) could predict the outcome of an 18-month lifestyle intervention program for subjects at risk of type 2 diabetes. Subjects at high risk of type 2 diabetes mellitus were recruited to a low-intensity lifestyle intervention program by their general practitioners. Weight reduction ≥ 5% and improvement in exercise capacity of ≥ 10% from baseline to follow-up indicated a clinically significant lifestyle change. SOC was measured using the 13-item SOC questionnaire. The study involved 213 subjects with a mean body mass index of 37 (SD ± 6). Complete follow-up data were obtained for 131 (62%). Twenty-six participants had clinically significant lifestyle changes. There was a 21% increase in the odds of a clinically significant lifestyle change for each point increase in the baseline SOC score (odds ratio = 1.21; confidence interval = 1.11-1.32). The success rate was 14 times higher in the highest SOC score tertile group compared with the lowest. High SOC scores were good predictors of successful lifestyle change in subjects at risk of type 2 diabetes. SOC-13 can be used in daily practice to increase clinical awareness on the impact of mastery on the outcome of life-style intervention programs. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Research of Improving the Particleboard Glue Dosing Process Based on TRIZ Analysis

NASA Astrophysics Data System (ADS)

Yu, Huiling; Fan, Delin; Zhang, Yizhuo

This research creates a design methodology by synthesizing the Theory of Inventive Problem Solving (TRIZ) and cascade control based on Smith predictor. The particleboard glue supplying and dosing system case study defines the problem and the solution using the methodology proposed in the paper. Status difference existing in the gluing dosing process of particleboard production usually causes gluing volume inaccurately. In order to solve the problem above, we applied the TRIZ technical contradiction and inventive principle to improve the key process of particleboard production. The improving method mapped inaccurate problem to TRIZ technical contradiction, the prior action proposed Smith predictor as the control algorithm in the glue dosing system. This research examines the usefulness of a TRIZ based problem-solving process designed to improve the problem-solving ability of users in addressing difficult or reoccurring problems and also testify TRIZ is practicality and validity. Several suggestions are presented on how to approach this problem.

Characterization and cysteine sensing performance of nanocomposites based on up-conversion excitation host and rhodamine-derived probes

NASA Astrophysics Data System (ADS)

Yuqing, Zhao; Yi, Xing; Lihua, Li; Juanjuan, Ma

2018-02-01

Optical sensing for cysteine (Cys) recognition is an interesting topic due to Cys biological participation. In this paper, two rhodamine-based chemosensors were designed for Cys optical sensing. For chemosensor photostability improvement, up-conversion nanocrystals were synthesized and used as excitation host. These nanocrystals were modified with a phase transfer reagent α-cyclodextrin (α-CD) to improve their compatibility with chemosensors. An efficient energy transfer from these nanocrystals to chemosensors under 980 nm radiation was observed and confirmed by spectral match analysis, energy transfer radius calculation and emission decay lifetime comparison. A direct bonding mechanism between Cys and chemosensors with bonding stoichiometry of 1:1 was established by Job's plot experiment. Given the presence of Cys, chemosensor emission was increased, showing emission turn on effect. These two chemosensors showed good selectivity, improved photostability and linear sensing response towards Cys.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-04-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other organic soils are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new dataset comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip well specific long-term annual mean water level (WL) as well as a transformed form of it (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insights into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying e.g. on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

NASA Astrophysics Data System (ADS)

Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.

2014-09-01

Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

PubMed

Zhou, Qingtao; Flores, Alejandro; Glenn, Nancy F; Walters, Reggie; Han, Bangshuai

2017-01-01

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3-0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling.

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

PubMed Central

Flores, Alejandro; Glenn, Nancy F.; Walters, Reggie; Han, Bangshuai

2017-01-01

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for estimating daily downward solar radiation flux at the land surface over complex terrain using MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing data. The model-building technique makes use of a unique network of 16 solar flux measurements in the semi-arid Reynolds Creek Experimental Watershed and Critical Zone Observatory, in southwest Idaho, USA. Based on a composite RF model built on daily observations from all 16 sites in the watershed, the model simulation of downward solar radiation matches well with the observation data (r2 = 0.96). To evaluate model performance, RF models were built from 12 of 16 sites selected at random and validated against the observations at the remaining four sites. Overall root mean square errors (RMSE), bias, and mean absolute error (MAE) are small (range: 37.17 W/m2-81.27 W/m2, -48.31 W/m2-15.67 W/m2, and 26.56 W/m2-63.77 W/m2, respectively). When extrapolated to the entire watershed, spatiotemporal patterns of solar flux are largely consistent with expected trends in this watershed. We also explored significant predictors of downward solar flux in order to reveal important properties and processes controlling downward solar radiation. Based on the composite RF model built on all 16 sites, the three most important predictors to estimate downward solar radiation include the black sky albedo (BSA) near infrared band (0.858 μm), BSA visible band (0.3–0.7 μm), and clear day coverage. This study has important implications for improving the ability to derive downward solar radiation through a fusion of multiple remote sensing datasets and can potentially capture spatiotemporally varying trends in solar radiation that is useful for land surface hydrologic and terrestrial ecosystem modeling. PMID:28777811

Neuropsychological predictors of performance-based measures of functional capacity and social skills in individuals with severe mental illness.

PubMed

Mahmood, Zanjbeel; Burton, Cynthia Z; Vella, Lea; Twamley, Elizabeth W

2018-04-13

Neuropsychological abilities may underlie successful performance of everyday functioning and social skills. We aimed to determine the strongest neuropsychological predictors of performance-based functional capacity and social skills performance across the spectrum of severe mental illness (SMI). Unemployed outpatients with SMI (schizophrenia, bipolar disorder, or major depression; n = 151) were administered neuropsychological (expanded MATRICS Consensus Cognitive Battery), functional capacity (UCSD Performance-Based Skills Assessment-Brief; UPSA-B), and social skills (Social Skills Performance Assessment; SSPA) assessments. Bivariate correlations between neuropsychological performance and UPSA-B and SSPA total scores showed that most neuropsychological tests were significantly associated with each performance-based measure. Forward entry stepwise regression analyses were conducted entering education, diagnosis, symptom severity, and neuropsychological performance as predictors of functional capacity and social skills. Diagnosis, working memory, sustained attention, and category and letter fluency emerged as significant predictors of functional capacity, in a model that explained 43% of the variance. Negative symptoms, sustained attention, and letter fluency were significant predictors of social skill performance, in a model explaining 35% of the variance. Functional capacity is positively associated with neuropsychological functioning, but diagnosis remains strongly influential, with mood disorder participants outperforming those with psychosis. Social skill performance appears to be positively associated with sustained attention and verbal fluency regardless of diagnosis; however, negative symptom severity strongly predicts social skills performance. Improving neuropsychological functioning may improve psychosocial functioning in people with SMI. Published by Elsevier Ltd.

Remote sensing image segmentation based on Hadoop cloud platform

NASA Astrophysics Data System (ADS)

Li, Jie; Zhu, Lingling; Cao, Fubin

2018-01-01

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

Bullying among Adolescents in North Cyprus and Turkey: Testing a Multifactor Model

ERIC Educational Resources Information Center

Bayraktar, Fatih

2012-01-01

Peer bullying has been studied since the 1970s. Therefore, a vast literature has accumulated about the various predictors of bullying. However, to date there has been no study which has combined individual-, peer-, parental-, teacher-, and school-related predictors of bullying within a model. In this sense, the main aim of this study was to test a…

Family Interaction Patterns and Locus of Control as Predictors of the Presence and Severity of Anorexia Nervosa.

ERIC Educational Resources Information Center

Harding, Thomas P.; Lachenmeyer, Juliana Rasic

1986-01-01

Overprotection, enmeshment, and rigidity and locus of control were contrasted in terms of their relative effectiveness in predicting both the presence or absence and severity of the disorder. The best predictor of both measures was locus of control. Results support Bruch's contention that underlying anorexia nervosa is a sense of personal…

Motion-compensated optical coherence tomography using envelope-based surface detection and Kalman-based prediction

NASA Astrophysics Data System (ADS)

Irsch, Kristina; Lee, Soohyun; Bose, Sanjukta N.; Kang, Jin U.

2018-02-01

We present an optical coherence tomography (OCT) imaging system that effectively compensates unwanted axial motion with micron-scale accuracy. The OCT system is based on a swept-source (SS) engine (1060-nm center wavelength, 100-nm full-width sweeping bandwidth, and 100-kHz repetition rate), with axial and lateral resolutions of about 4.5 and 8.5 microns respectively. The SS-OCT system incorporates a distance sensing method utilizing an envelope-based surface detection algorithm. The algorithm locates the target surface from the B-scans, taking into account not just the first or highest peak but the entire signature of sequential A-scans. Subsequently, a Kalman filter is applied as predictor to make up for system latencies, before sending the calculated position information to control a linear motor, adjusting and maintaining a fixed system-target distance. To test system performance, the motioncorrection algorithm was compared to earlier, more basic peak-based surface detection methods and to performing no motion compensation. Results demonstrate increased robustness and reproducibility, particularly noticeable in multilayered tissues, while utilizing the novel technique. Implementing such motion compensation into clinical OCT systems may thus improve the reliability of objective and quantitative information that can be extracted from OCT measurements.

Improving transmembrane protein consensus topology prediction using inter-helical interaction.

PubMed

Wang, Han; Zhang, Chao; Shi, Xiaohu; Zhang, Li; Zhou, You

2012-11-01

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html. Copyright © 2012 Elsevier B.V. All rights reserved.

IRB Process Improvements: A Machine Learning Analysis.

PubMed

Shoenbill, Kimberly; Song, Yiqiang; Cobb, Nichelle L; Drezner, Marc K; Mendonca, Eneida A

2017-06-01

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Blogging as a Social Medium in Undergraduate Courses: Sense of Community Best Predictor of Perceived Learning

ERIC Educational Resources Information Center

Top, Ercan

2012-01-01

The purpose of the study was to examine pre-service teachers' sense of community, perception of collaborative learning, and perceived learning. Fifty pre-service teachers from two undergraduate ICT courses which incorporated blogs participated in this study. The data were obtained via three online questionnaires (Collaborative Learning scale,…

Early Life Adversity as a Predictor of Sense of Purpose during Adulthood

ERIC Educational Resources Information Center

Hill, Patrick L.; Turiano, Nicholas A.; Burrow, Anthony L.

2018-01-01

Feeling a sense of purpose in life appears to hold consistent benefits for positive aging and well-being. As such, it is important to consider the potential factors that promote or hinder the development of purposefulness over the lifespan. For instance, it remains unclear whether early life experiences, particularly adverse ones, may hold lasting…

The Asian American College Experience at a Diverse Institution: Campus Climate as a Predictor of Sense of Belonging

ERIC Educational Resources Information Center

Wells, Alison V.; Horn, Catherine

2015-01-01

Assessing campus climate is an important factor in understanding the persistence and satisfaction of all students. This investigation extends this research stream by examining the relationship between perceptions of campus and overall sense of belonging of Asian American students on a diverse campus. Administrators may use the information gained…

Spirituality as a Predictor of Guilt and Shame among Lesbian and Gay Adults

ERIC Educational Resources Information Center

Oliver, Jonie

2016-01-01

The purpose of the study was to examine the relationship among constructs related to spirituality (religious/spiritual practice, religious/spiritual belief, sense of purpose/connection, and sense of hope/control) and reported degree of likelihood to feel guilt and shame among individuals who are lesbian, gay, bisexual, or queer. If clear…

Sense of Community in Academic Communities of Practice: Predictors and Effects

ERIC Educational Resources Information Center

Nistor, Nicolae; Daxecker, Irene; Stanciu, Dorin; Diekamp, Oliver

2015-01-01

Sense of community (SoC) in communities of practice (CoP) seems to play a similar role to that of group cohesion in small groups: Both sustain participants' knowledge sharing, which in turn substantiates the socio-cognitive structures that make up the CoP such as scholar identities, practical repertoires in research and teaching or relationships…

Identifying gnostic predictors of the vaccine response.

PubMed

Haining, W Nicholas; Pulendran, Bali

2012-06-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis of their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of vaccine prediction. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. Copyright © 2012 Elsevier Ltd. All rights reserved.

Identifying gnostic predictors of the vaccine response

PubMed Central

Haining, W. Nicholas; Pulendran, Bali

2012-01-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis for their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of outcome classification. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. PMID:22633886

A static predictor of seismic demand on frames based on a post-elastic deflected shape

USGS Publications Warehouse

Mori, Y.; Yamanaka, T.; Luco, N.; Cornell, C.A.

2006-01-01

Predictors of seismic structural demands (such as inter-storey drift angles) that are less time-consuming than nonlinear dynamic analysis have proven useful for structural performance assessment and for design. Luco and Cornell previously proposed a simple predictor that extends the idea of modal superposition (of the first two modes) with the square-root-of-sum-of-squares (SRSS) rule by taking a first-mode inelastic spectral displacement into account. This predictor achieved a significant improvement over simply using the response of an elastic oscillator; however, it cannot capture well large displacements caused by local yielding. A possible improvement of Luco's predictor is discussed in this paper, where it is proposed to consider three enhancements: (i) a post-elastic first-mode shape approximated by the deflected shape from a nonlinear static pushover analysis (NSPA) at the step corresponding to the maximum drift of an equivalent inelastic single-degree-of-freedom (SDOF) system, (ii) a trilinear backbone curve for the SDOF system, and (iii) the elastic third-mode response for long-period buildings. Numerical examples demonstrate that the proposed predictor is less biased and results in less dispersion than Luco's original predictor. Copyright ?? 2006 John Wiley & Sons, Ltd.

SHORT-TERM SOLAR FLARE PREDICTION USING MULTIRESOLUTION PREDICTORS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu Daren; Huang Xin; Hu Qinghua

2010-01-20

Multiresolution predictors of solar flares are constructed by a wavelet transform and sequential feature extraction method. Three predictors-the maximum horizontal gradient, the length of neutral line, and the number of singular points-are extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. A maximal overlap discrete wavelet transform is used to decompose the sequence of predictors into four frequency bands. In each band, four sequential features-the maximum, the mean, the standard deviation, and the root mean square-are extracted. The multiresolution predictors in the low-frequency band reflect trends in the evolution of newly emerging fluxes. The multiresolution predictors in the high-frequencymore » band reflect the changing rates in emerging flux regions. The variation of emerging fluxes is decoupled by wavelet transform in different frequency bands. The information amount of these multiresolution predictors is evaluated by the information gain ratio. It is found that the multiresolution predictors in the lowest and highest frequency bands contain the most information. Based on these predictors, a C4.5 decision tree algorithm is used to build the short-term solar flare prediction model. It is found that the performance of the short-term solar flare prediction model based on the multiresolution predictors is greatly improved.« less

Dual-modal cancer detection based on optical pH sensing and Raman spectroscopy

NASA Astrophysics Data System (ADS)

Kim, Soogeun; Lee, Seung Ho; Min, Sun Young; Byun, Kyung Min; Lee, Soo Yeol

2017-10-01

A dual-modal approach using Raman spectroscopy and optical pH sensing was investigated to discriminate between normal and cancerous tissues. Raman spectroscopy has demonstrated the potential for in vivo cancer detection. However, Raman spectroscopy has suffered from strong fluorescence background of biological samples and subtle spectral differences between normal and disease tissues. To overcome those issues, pH sensing is adopted to Raman spectroscopy as a dual-modal approach. Based on the fact that the pH level in cancerous tissues is lower than that in normal tissues due to insufficient vasculature formation, the dual-modal approach combining the chemical information of Raman spectrum and the metabolic information of pH level can improve the specificity of cancer diagnosis. From human breast tissue samples, Raman spectra and pH levels are measured using fiber-optic-based Raman and pH probes, respectively. The pH sensing is based on the dependence of pH level on optical transmission spectrum. Multivariate statistical analysis is performed to evaluate the classification capability of the dual-modal method. The analytical results show that the dual-modal method based on Raman spectroscopy and optical pH sensing can improve the performance of cancer classification.

Predicting outcome of Internet-based treatment for depressive symptoms.

PubMed

Warmerdam, Lisanne; Van Straten, Annemieke; Twisk, Jos; Cuijpers, Pim

2013-01-01

In this study we explored predictors and moderators of response to Internet-based cognitive behavioral therapy (CBT) and Internet-based problem-solving therapy (PST) for depressive symptoms. The sample consisted of 263 participants with moderate to severe depressive symptoms. Of those, 88 were randomized to CBT, 88 to PST and 87 to a waiting list control condition. Outcomes were improvement and clinically significant change in depressive symptoms after 8 weeks. Higher baseline depression and higher education predicted improvement, while higher education, less avoidance behavior and decreased rational problem-solving skills predicted clinically significant change across all groups. No variables were found that differentially predicted outcome between Internet-based CBT and Internet-based PST. More research is needed with sufficient power to investigate predictors and moderators of response to reveal for whom Internet-based therapy is best suited.

Optimization of strand displacement amplification-sensitized G-quadruplex DNAzyme-based sensing system and its application in activity detection of uracil-DNA glycosylase.

PubMed

Du, Yi-Chen; Jiang, Hong-Xin; Huo, Yan-Fang; Han, Gui-Mei; Kong, De-Ming

2016-03-15

As an isothermal nucleic acid amplification technique, strand displacement amplification (SDA) reaction has been introduced in G-quadruplex DNAzyme-based sensing system to improve the sensing performance. To further provide useful information for the design of SDA-amplified G-quadruplex DNAzyme-based sensors, the effects of nicking site number in SDA template DNA were investigated. With the increase of the nicking site number from 1 to 2, enrichment of G-quadruplex DNAzyme by SDA is changed from a linear amplification to an exponential amplification, thus greatly increasing the amplification efficiency and subsequently improving the sensing performance of corresponding sensing system. The nicking site number cannot be further increased because more nicking sites might result in high background signals. However, we demonstrated that G-quadruplex DNAzyme enrichment efficiency could be further improved by introducing a cross-triggered SDA strategy, in which two templates each has two nicking sites are used. To validate the proposed cross-triggered SDA strategy, we used it to develop a sensing platform for the detection of uracil-DNA glycosylase (UDG) activity. The sensor enables sensitive detection of UDG activity in the range of 1 × 10(-4)-1 U/mL with a detection limit of 1 × 10(-4)U/mL. Copyright © 2015 Elsevier B.V. All rights reserved.

A Survey on Gas Sensing Technology

PubMed Central

Liu, Xiao; Cheng, Sitian; Liu, Hong; Hu, Sha; Zhang, Daqiang; Ning, Huansheng

2012-01-01

Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches. PMID:23012563

First-Grade Predictors of Mathematical Learning Disability: A Latent Class Trajectory Analysis

PubMed Central

Geary, David C.; Bailey, Drew H.; Littlefield, Andrew; Wood, Phillip; Hoard, Mary K.; Nugent, Lara

2009-01-01

Kindergarten to 3rd grade mathematics achievement scores from a prospective study of mathematical development were subjected to latent growth trajectory analyses (n = 306). The four corresponding classes included children with mathematical learning disability (MLD, 6% of sample), and low (LA, 50%), typically (TA, 39%) and high (HA, 5%) achieving children. The groups were administered a battery of intelligence (IQ), working memory, and mathematical-cognition measures in 1st grade. The children with MLD had general deficits in working memory and IQ, and potentially more specific deficits on measures of number sense. The LA children did not have working memory or IQ deficits, but showed moderate deficits on these number sense measures and for addition fact retrieval. The distinguishing features of the HA children were a strong visuospatial working memory, a strong number sense, and frequent use of memory-based processes to solve addition problems. Implications for the early identification of children at risk for poor mathematics achievement are discussed. PMID:20046817

Design and Development Computer-Based E-Learning Teaching Material for Improving Mathematical Understanding Ability and Spatial Sense of Junior High School Students

NASA Astrophysics Data System (ADS)

Nurjanah; Dahlan, J. A.; Wibisono, Y.

2017-02-01

This paper aims to make a design and development computer-based e-learning teaching material for improving mathematical understanding ability and spatial sense of junior high school students. Furthermore, the particular aims are (1) getting teaching material design, evaluation model, and intrument to measure mathematical understanding ability and spatial sense of junior high school students; (2) conducting trials computer-based e-learning teaching material model, asessment, and instrument to develop mathematical understanding ability and spatial sense of junior high school students; (3) completing teaching material models of computer-based e-learning, assessment, and develop mathematical understanding ability and spatial sense of junior high school students; (4) resulting research product is teaching materials of computer-based e-learning. Furthermore, the product is an interactive learning disc. The research method is used of this study is developmental research which is conducted by thought experiment and instruction experiment. The result showed that teaching materials could be used very well. This is based on the validation of computer-based e-learning teaching materials, which is validated by 5 multimedia experts. The judgement result of face and content validity of 5 validator shows that the same judgement result to the face and content validity of each item test of mathematical understanding ability and spatial sense. The reliability test of mathematical understanding ability and spatial sense are 0,929 and 0,939. This reliability test is very high. While the validity of both tests have a high and very high criteria.

Predictors of treatment attrition as indicators for program improvement not offender shortcomings: a study of sex offender treatment attrition.

PubMed

Beyko, Michelle J; Wong, Stephen C P

2005-10-01

This study classified potential attrition predictors under the domains of risk, need and responsivity (D. Andrews & J. Bonta, 2003). Non-sexual criminogenic needs (e.g. aggression, rule violating behaviors) and responsivity factors (e.g. lack of motivation and denial) were the two main clusters of predictors that correctly classified 95.3% of program completers and non-completers using discriminant function analysis in a sample of high-risk male sexual offenders treated in an accredited inpatient sex offender treatment program. Rapists were more aggressive than other types of sex offenders and were more likely to drop out of treatment. Some studies of predictors of treatment attrition have used offender problem behaviors or psychopathologies to predict attrition and then use the information to exclude offenders from treatment. Others have argued, and we concur, that results of attrition research should not be used to develop an "attrition profile" to exclude offenders from treatment. Predictors of attrition should be seen as markers for program improvement, rather than shortcomings of the offender. Suggestions for program improvements to reduce the rate of attrition, based on results of research, are presented.

Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

USDA-ARS?s Scientific Manuscript database

Remote sensing based evapotranspiration (ET) mapping is an important improvement for water resources management. Hourly climatic data and reference ET are crucial for implementing remote sensing based ET models such as METRIC and SEBAL. In Turkey, data on all climatic variables may not be available ...

Change in Age-Specific, Psychosocial Correlates of Risky Sexual Behaviors Among Youth: Longitudinal Findings From a Deep South, High-Risk Sample

PubMed Central

Howell, Rebecca J.; Traylor, Amy C.; Church, Wesley T.; Bolland, John M.

2015-01-01

The current study examined psychosocial predictors of change in intercourse frequency and number of sexual partners among youth within a socio-ecological framework and assessed whether these determinants vary by stage of adolescent development. Longitudinal data were derived from a large, community study of adolescent risky behavior among predominantly high-risk, African American youth. Significant predictors of intercourse frequency for early adolescents included age, gender, self-worth, and familial factors; for older youth, age, gender, self-worth, curfews, and sense of community exerted significant effects. Among early adolescents, age, gender, self-worth, familial factors, and sense of community predicted change in the number of sexual partners in the previous year, while age, gender, self-worth, parental knowledge, curfews, and sense of community were predictive of change in the number of sexual partners in the previous year among older youth. Study implications and future directions are discussed. PMID:26388682

Moderate-resolution data and gradient nearest neighbor imputation for regional-national risk assessment

Treesearch

Kenneth B. Jr. Pierce; C. Kenneth Brewer; Janet L. Ohmann

2010-01-01

This study was designed to test the feasibility of combining a method designed to populate pixels with inventory plot data at the 30-m scale with a new national predictor data set. The new national predictor data set was developed by the USDA Forest Service Remote Sensing Applications Center (hereafter RSAC) at the 250-m scale. Gradient Nearest Neighbor (GNN)...

Segmentation of remotely sensed data using parallel region growing

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Cox, S. C.

1983-01-01

The improved spatial resolution of the new earth resources satellites will increase the need for effective utilization of spatial information in machine processing of remotely sensed data. One promising technique is scene segmentation by region growing. Region growing can use spatial information in two ways: only spatially adjacent regions merge together, and merging criteria can be based on region-wide spatial features. A simple region growing approach is described in which the similarity criterion is based on region mean and variance (a simple spatial feature). An effective way to implement region growing for remote sensing is as an iterative parallel process on a large parallel processor. A straightforward parallel pixel-based implementation of the algorithm is explored and its efficiency is compared with sequential pixel-based, sequential region-based, and parallel region-based implementations. Experimental results from on aircraft scanner data set are presented, as is a discussioon of proposed improvements to the segmentation algorithm.

Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

PubMed

Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

2011-08-21

Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Relating remotely sensed optical variability to marine benthic biodiversity.

PubMed

Herkül, Kristjan; Kotta, Jonne; Kutser, Tiit; Vahtmäe, Ele

2013-01-01

Biodiversity is important in maintaining ecosystem viability, and the availability of adequate biodiversity data is a prerequisite for the sustainable management of natural resources. As such, there is a clear need to map biodiversity at high spatial resolutions across large areas. Airborne and spaceborne optical remote sensing is a potential tool to provide such biodiversity data. The spectral variation hypothesis (SVH) predicts a positive correlation between spectral variability (SV) of a remotely sensed image and biodiversity. The SVH has only been tested on a few terrestrial plant communities. Our study is the first attempt to apply the SVH in the marine environment using hyperspectral imagery recorded by Compact Airborne Spectrographic Imager (CASI). All coverage-based diversity measures of benthic macrophytes and invertebrates showed low but statistically significant positive correlations with SV whereas the relationship between biomass-based diversity measures and SV were weak or lacking. The observed relationships did not vary with spatial scale. SV had the highest independent effect among predictor variables in the statistical models of coverage-derived total benthic species richness and Shannon index. Thus, the relevance of SVH in marine benthic habitats was proved and this forms a prerequisite for the future use of SV in benthic biodiversity assessments.

Community characteristics as predictors of perceived HMO quality.

PubMed

Ahern, M M; Hendryx, M S

1998-06-01

We model the impact of community characteristics on people's perceptions of the quality of their health care experiences in HMOs. We focus on three community characteristics: sense of community, population density, and population diversity. Sense of community refers to people's perception of interconnection, shared responsibility, and common goals. Population density and population diversity are community characteristics that affect transactions costs in terms of time and energy, and affect people's perceptions of their community. We use data from a 1993 Florida poll to estimate the relationship between HMO members' perceptions of problems with health care experiences (cost, choice, access, satisfaction) and community characteristics. We find that all three community variables are significantly associated with perceptions of health care problems. We also find that effects of community variables operate differently for those in HMOs vs. those under traditional insurance. This study is consistent with research showing that community characteristics impact the health status of community institutions. Results suggest that providers may be able to improve care by being more responsive to individuals' need for community, that providers and communities can mutually gain by collaborating to improve community health, and that it may be cost-beneficial to factor community issues more strongly into health care policy.

Synthesis of integrated primary production in the Arctic Ocean: II. In situ and remotely sensed estimates

NASA Astrophysics Data System (ADS)

Hill, Victoria J.; Matrai, Patricia A.; Olson, Elise; Suttles, S.; Steele, Mike; Codispoti, L. A.; Zimmerman, Richard C.

2013-03-01

Recent warming of surface waters, accompanied by reduced ice thickness and extent may have significant consequences for climate-driven changes of primary production (PP) in the Arctic Ocean (AO). However, it has been difficult to obtain a robust benchmark estimate of pan-Arctic PP necessary for evaluating change. This paper provides an estimate of pan-Arctic PP prior to significant warming from a synthetic analysis of the ARCSS-PP database of in situ measurements collected from 1954 to 2007 and estimates derived from satellite-based observations from 1998 to 2007. Vertical profiles of in situ chlorophyll a (Chl a) and PP revealed persistent subsurface peaks in biomass and PP throughout the AO during most of the summer period. This was contradictory with the commonly assumed exponential decrease in PP with depth on which prior satellite-derived estimates were based. As remotely sensed Chl a was not a good predictor of integrated water column Chl a, accurate satellite-based modeling of vertically integrated primary production (IPPsat), requires knowledge of the subsurface distribution of phytoplankton, coincident with the remotely sensed ocean color measurements. We developed an alternative approach to modeling PP from satellite observations by incorporating climatological information on the depths of the euphotic zone and the mixed layer that control the distribution of phytoplankton that significantly improved the fidelity of satellite derived PP to in situ observations. The annual IPP of the Arctic Ocean combining both in situ and satellite based estimates was calculated here to be a minimum of 466 ± 94 Tg C yr-1 and a maximum of 993 ± 94 Tg C yr-1, when corrected for subsurface production. Inflow shelf seas account for 75% of annual IPP, while the central basin and Beaufort northern sea were the regions with the lowest annual integrated productivity, due to persistently stratified, oligotrophic and ice-covered conditions. Although the expansion of summertime ice retreat should stimulate photosynthesis by exposing more of the AO to solar irradiance, total PP is ultimately limited by nutrient availability. Therefore, changes in AO PP will be forced by the balance between stratification and mixing, the effects of which are not yet quantified.

Identifying Opportunities for Grade One Children to Acquire Foundational Number Sense: Developing a Framework for Cross Cultural Classroom Analyses

ERIC Educational Resources Information Center

Andrews, Paul; Sayers, Judy

2015-01-01

It is known that an appropriately developed foundational number sense (FONS), or the ability to operate flexibly with number and quantity, is a powerful predictor of young children's later mathematical achievement. However, until now not only has FONS been definitionally elusive but instruments for identifying opportunities for children to acquire…

Community Social and Place Predictors of Sense of Community: A Multilevel and Longitudinal Analysis

ERIC Educational Resources Information Center

Long, D. Adam; Perkins, Douglas D.

2007-01-01

Sense of community (SOC) is empirically "unpacked" as a multilevel construct with place and social elements. SOC has been studied primarily at the individual level despite researchers acknowledging its effects at the community level. Little attention has been given to the roles of place and place attitudes in SOC. We argue that place and…

Exploring Sense of Belonging and Perceived Heritage Community Language Proficiency as Predictors of Bicultural Identity Integration in U.S

ERIC Educational Resources Information Center

Nour, Mona Dina

2016-01-01

The recent influx of immigration to the United States has naturally led to a population increase of U.S. born children with immigrant parents. These bicultural individuals undertake the complex task of constructing identities drawn from dual, and sometimes multiple, cultural foundations. This study examined sense of belonging and perceived…

Advising and Acculturation Variables as Predictors of Satisfaction, Sense of Belonging, and Persistence among International Undergraduates

ERIC Educational Resources Information Center

Mataczynski, Lisa

2013-01-01

Guided by the work of Hurtado and Carter (1997) as an alternative to Tinto's theory of student departure (1993), the purpose of this quantitative study was to explore the relationship of institutional and cultural factors to satisfaction with academic advising, sense of belonging to campus and retention among international undergraduate…

Personal investment, culture and learning: insights into school achievement across Anglo, Aboriginal, Asian and Lebanese students in Australia.

PubMed

McInerney, Dennis M

2008-10-01

Personal investment theory is a multifaceted theory of motivation, in which three key components: achievement goals (mastery, performance, social, and extrinsic), sense of self (sense of purpose, self-reliance, negative self-concept, positive self-concept), and facilitating conditions (parent support, teacher support, peer support), engage students in the process of learning. Four cultural groups (Anglo Australian, n = 852, Aboriginal Australian, n = 343, Lebanese Australian, n = 372, and Asian Australian, n = 283) of students were compared on these personal investment components and on several outcome measures (engagement, affect, achievement, participation). A series of MANOVAs, followed up by univariate tests, indicated ethnic differences and similarities in the endorsement of the personal investment theory components as well as in the outcome measures. Multiple regression analyses showed that each of the three sets of predictors (achievement goals, sense of self, facilitating conditions) explained a significant amount of the variance in almost all of the outcome measures. Across cultural groups, students' mastery goal and sense of purpose were consistently found to be significant predictors of their intention for further education, positive affect for schooling, and valuing of schooling.

Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

USDA-ARS?s Scientific Manuscript database

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance t...

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Predictor-corrector framework for the sequential assembly of optical systems based on wavefront sensing.

PubMed

Schindlbeck, Christopher; Pape, Christian; Reithmeier, Eduard

2018-04-16

Alignment of optical components is crucial for the assembly of optical systems to ensure their full functionality. In this paper we present a novel predictor-corrector framework for the sequential assembly of serial optical systems. Therein, we use a hybrid optical simulation model that comprises virtual and identified component positions. The hybrid model is constantly adapted throughout the assembly process with the help of nonlinear identification techniques and wavefront measurements. This enables prediction of the future wavefront at the detector plane and therefore allows for taking corrective measures accordingly during the assembly process if a user-defined tolerance on the wavefront error is violated. We present a novel notation for the so-called hybrid model and outline the work flow of the presented predictor-corrector framework. A beam expander is assembled as demonstrator for experimental verification of the framework. The optical setup consists of a laser, two bi-convex spherical lenses each mounted to a five degree-of-freedom stage to misalign and correct components, and a Shack-Hartmann sensor for wavefront measurements.

Distributed Optical Fiber Sensors Based on Optical Frequency Domain Reflectometry: A review

PubMed Central

Wang, Chenhuan; Liu, Kun; Jiang, Junfeng; Yang, Di; Pan, Guanyi; Pu, Zelin; Liu, Tiegen

2018-01-01

Distributed optical fiber sensors (DOFS) offer unprecedented features, the most unique one of which is the ability of monitoring variations of the physical and chemical parameters with spatial continuity along the fiber. Among all these distributed sensing techniques, optical frequency domain reflectometry (OFDR) has been given tremendous attention because of its high spatial resolution and large dynamic range. In addition, DOFS based on OFDR have been used to sense many parameters. In this review, we will survey the key technologies for improving sensing range, spatial resolution and sensing performance in DOFS based on OFDR. We also introduce the sensing mechanisms and the applications of DOFS based on OFDR including strain, stress, vibration, temperature, 3D shape, flow, refractive index, magnetic field, radiation, gas and so on. PMID:29614024

Distributed Optical Fiber Sensors Based on Optical Frequency Domain Reflectometry: A review.

PubMed

Ding, Zhenyang; Wang, Chenhuan; Liu, Kun; Jiang, Junfeng; Yang, Di; Pan, Guanyi; Pu, Zelin; Liu, Tiegen

2018-04-03

Distributed optical fiber sensors (DOFS) offer unprecedented features, the most unique one of which is the ability of monitoring variations of the physical and chemical parameters with spatial continuity along the fiber. Among all these distributed sensing techniques, optical frequency domain reflectometry (OFDR) has been given tremendous attention because of its high spatial resolution and large dynamic range. In addition, DOFS based on OFDR have been used to sense many parameters. In this review, we will survey the key technologies for improving sensing range, spatial resolution and sensing performance in DOFS based on OFDR. We also introduce the sensing mechanisms and the applications of DOFS based on OFDR including strain, stress, vibration, temperature, 3D shape, flow, refractive index, magnetic field, radiation, gas and so on.

Mental health and positive change among Japanese mothers of children with intellectual disabilities: Roles of sense of coherence and social capital.

PubMed

Kimura, Miyako; Yamazaki, Yoshihiko

2016-12-01

We investigated predictors of mental health and positive change among mothers of children with intellectual disabilities in Japan based on the concept of the Double ABCX model. We used variables of having a child with autism spectrum disorder (ASD) and dissatisfaction with systems as stressors, availability of social support and social capital (SC) as existing resources, sense of coherence (SOC) as appraisal of the stressor, and mental health and positive change as adaptation. A self-administered questionnaire was distributed to 10 intellectual disability-oriented special needs schools in Tokyo, and obtained 613 responses from mothers of children under age 20 attending these schools. The results showed that our Double ABCX model explained 46.0% of the variance in mothers' mental health and 38.9% of the variance in positive change. The most powerful predictor of this model was SOC, and SC may be directly and indirectly related to maternal mental health and positive change through mothers' SOC. Increasing opportunity for interaction between neighbors and family of children with disabilities may be one effective way to enhance SOC through SC. Since maternal SOC, SC, mental health, and positive change were significantly correlated with each other, synergy among these elements could be expected. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

NASA Astrophysics Data System (ADS)

Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

2018-04-01

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

The role of NASA's Water Resources applications area in improving access to water quality-related information and water resources management

NASA Astrophysics Data System (ADS)

Lee, C. M.

2016-02-01

The NASA Applied Sciences Program plays a unique role in facilitating access to remote sensing-based water information derived from US federal assets towards the goal of improving science and evidence-based decision-making in water resources management. The Water Resources Application Area within NASA Applied Sciences works specifically to develop and improve water data products to support improved management of water resources, with partners who are faced with real-world constraints and conditions including cost and regulatory standards. This poster will highlight the efforts and collaborations enabled by this program that have resulted in integration of remote sensing-based information for water quality modeling and monitoring within an operational context.

The role of NASA's Water Resources applications area in improving access to water quality-related information and water resources management

NASA Astrophysics Data System (ADS)

Lee, C. M.

2016-12-01

The NASA Applied Sciences Program plays a unique role in facilitating access to remote sensing-based water information derived from US federal assets towards the goal of improving science and evidence-based decision-making in water resources management. The Water Resources Application Area within NASA Applied Sciences works specifically to develop and improve water data products to support improved management of water resources, with partners who are faced with real-world constraints and conditions including cost and regulatory standards. This poster will highlight the efforts and collaborations enabled by this program that have resulted in integration of remote sensing-based information for water quality modeling and monitoring within an operational context.

Improving national-scale invasion maps: Tamarisk in the western United States

USGS Publications Warehouse

Jarnevich, C.S.; Evangelista, P.; Stohlgren, T.J.; Morisette, J.

2011-01-01

New invasions, better field data, and novel spatial-modeling techniques often drive the need to revisit previous maps and models of invasive species. Such is the case with the at least 10 species of Tamarix, which are invading riparian systems in the western United States and expanding their range throughout North America. In 2006, we developed a National Tamarisk Map by using a compilation of presence and absence locations with remotely sensed data and statistical modeling techniques. Since the publication of that work, our database of Tamarix distributions has grown significantly. Using the updated database of species occurrence, new predictor variables, and the maximum entropy (Maxent) model, we have revised our potential Tamarix distribution map for the western United States. Distance-to-water was the strongest predictor in the model (58.1%), while mean temperature of the warmest quarter was the second best predictor (18.4%). Model validation, averaged from 25 model iterations, indicated that our analysis had strong predictive performance (AUC = 0.93) and that the extent of Tamarix distributions is much greater than previously thought. The southwestern United States had the greatest suitable habitat, and this result differed from the 2006 model. Our work highlights the utility of iterative modeling for invasive species habitat modeling as new information becomes available. ?? 2011.

Modeling vehicle interior noise exposure dose on freeways: Considering weaving segment designs and engine operation.

PubMed

Li, Qing; Qiao, Fengxiang; Yu, Lei; Shi, Junqing

2018-06-01

Vehicle interior noise functions at the dominant frequencies of 500 Hz below and around 800 Hz, which fall into the bands that may impair hearing. Recent studies demonstrated that freeway commuters are chronically exposed to vehicle interior noise, bearing the risk of hearing impairment. The interior noise evaluation process is mostly conducted in a laboratory environment. The test results and the developed noise models may underestimate or ignore the noise effects from dynamic traffic and road conditions and configuration. However, the interior noise is highly associated with vehicle maneuvering. The vehicle maneuvering on a freeway weaving segment is more complex because of its nature of conflicting areas. This research is intended to explore the risk of the interior noise exposure on freeway weaving segments for freeway commuters and to improve the interior noise estimation by constructing a decision tree learning-based noise exposure dose (NED) model, considering weaving segment designs and engine operation. On-road driving tests were conducted on 12 subjects on State Highway 288 in Houston, Texas. On-board Diagnosis (OBD) II, a smartphone-based roughness app, and a digital sound meter were used to collect vehicle maneuvering and engine information, International Roughness Index, and interior noise levels, respectively. Eleven variables were obtainable from the driving tests, including the length and type of a weaving segment, serving as predictors. The importance of the predictors was estimated by their out-of-bag-permuted predictor delta errors. The hazardous exposure level of the interior noise on weaving segments was quantified to hazard quotient, NED, and daily noise exposure level, respectively. Results showed that the risk of hearing impairment on freeway is acceptable; the interior noise level is the most sensitive to the pavement roughness and is subject to freeway configuration and traffic conditions. The constructed NED model shows high predictive power (R = 0.93, normalized root-mean-square error [NRMSE] < 6.7%). Vehicle interior noise is usually ignored in the public, and its modeling and evaluation are generally conducted in a laboratory environment, regardless of the interior noise effects from dynamic traffic, road conditions, and road configuration. This study quantified the interior exposure dose on freeway weaving segments, which provides freeway commuters with a sense of interior noise exposure risk. In addition, a bagged decision tree-based interior noise exposure dose model was constructed, considering vehicle maneuvering, vehicle engine operational information, pavement roughness, and weaving segment configuration. The constructed model could significantly improve the interior noise estimation for road engineers and vehicle manufactures.

Post partum depression and the psychosocial predictors in first-time fathers from northwestern China.

PubMed

Zhang, Yin-Ping; Zhang, Lu-Lu; Wei, Huan-Huan; Zhang, Yao; Zhang, Chun-Li; Porr, Caroline

2016-04-01

there is growing evidence that fathers also experience post partum depression (PPD). However, paternal PPD has been less studied than maternal PPD. Very few studies have investigated PPD in first-time fathers from northwestern China. the purpose of this study was to investigate the occurrence and predictors of depressive symptoms in first-time fathers from northwestern China. a longitudinal study was conducted involving 180 couples who were assessed at three time periods: 3 days, 2 weeks and 6 weeks after childbirth. Self-reported questionnaires including Edinburgh Postnatal Depression Scale (EPDS), Parenting Sense of Competence Scale (PSOC), and Kansas Marital Satisfaction Scale (KMSS) were administered to all participants during each time period. after childbirth 35 (21.1%) of the fathers at 3 days, 32 (20.4%) at 2 weeks and 20 (13.6%) at 6 weeks, indicated that they suffered from PPD. Paternal parental sense of competence, paternal marital satisfaction, and maternal depressive symptoms were among the main predictors for paternal PPD. the study results suggest that paternal PPD is a significant public health concern. Health professionals should focus attention on the psychological health among new fathers during the postpartum period; and, the psychosocial predictors should be considered and incorporated into clinical assessment and intervention of paternal PPD. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dual-modal cancer detection based on optical pH sensing and Raman spectroscopy.

PubMed

Kim, Soogeun; Lee, Seung Ho; Min, Sun Young; Byun, Kyung Min; Lee, Soo Yeol

2017-10-01

A dual-modal approach using Raman spectroscopy and optical pH sensing was investigated to discriminate between normal and cancerous tissues. Raman spectroscopy has demonstrated the potential for in vivo cancer detection. However, Raman spectroscopy has suffered from strong fluorescence background of biological samples and subtle spectral differences between normal and disease tissues. To overcome those issues, pH sensing is adopted to Raman spectroscopy as a dual-modal approach. Based on the fact that the pH level in cancerous tissues is lower than that in normal tissues due to insufficient vasculature formation, the dual-modal approach combining the chemical information of Raman spectrum and the metabolic information of pH level can improve the specificity of cancer diagnosis. From human breast tissue samples, Raman spectra and pH levels are measured using fiber-optic-based Raman and pH probes, respectively. The pH sensing is based on the dependence of pH level on optical transmission spectrum. Multivariate statistical analysis is performed to evaluate the classification capability of the dual-modal method. The analytical results show that the dual-modal method based on Raman spectroscopy and optical pH sensing can improve the performance of cancer classification. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

Internet use as a predictor of sense of community in older people.

PubMed

Sum, Shima; Mathews, R Mark; Pourghasem, Mohsen; Hughes, Ian

2009-04-01

The Internet opens new options for communication and may change the extent to which older people use other modes of communication. The importance of older adults' participation in cyberspace has increased as Internet use for commerce and communication has increased. The present study explores how older adults' Internet use affects their sense of community. An online survey was conducted at the University of Sydney to determine the associations between Internet use and seniors' sense of community and well-being. Participants were recruited online. There was a positive association between a sense of belonging to an online community, sense of community, and well-being. Seniors' use of the Internet for communication and information, and the frequency and history of their Internet use, were consistently related to a greater sense of community.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

An Improved Unsupervised Image Segmentation Evaluation Approach Based on - and Over-Segmentation Aware

NASA Astrophysics Data System (ADS)

Su, Tengfei

2018-04-01

In this paper, an unsupervised evaluation scheme for remote sensing image segmentation is developed. Based on a method called under- and over-segmentation aware (UOA), the new approach is improved by overcoming the defect in the part of estimating over-segmentation error. Two cases of such error-prone defect are listed, and edge strength is employed to devise a solution to this issue. Two subsets of high resolution remote sensing images were used to test the proposed algorithm, and the experimental results indicate its superior performance, which is attributed to its improved OSE detection model.

Resonant efficiency improvement design of piezoelectric biosensor for bacteria gravimetric sensing.

PubMed

Tsai, Jang-Zern; Chen, Ching-Jung; Shie, Dung-Ting; Liu, Jen-Tsai

2014-01-01

The piezoelectric biosensor have been widely used in ultra-small mass detection of biomolecular, based on PZT piezoelectric material can create a variety of compositions geometrically; it could widely develop a high-frequency resonator and measure the change of the slightest mass while improve the limited detection simultaneously. Therefore, the piezoelectric biosensor of this study was fabricated by a spin-coating method and backside etching process for improving the characteristic of piezoelectric biosensor. The result exhibited that the 250 μm × 250 μm working size has the most favorable piezoelectric characteristic. The tunability was approximately 38.56 % and it showed that reducing the substrate thickness could obtain a clear resonance signal in a range of 60 to 380 MHz. In theory calculated for gravimetric sensing, it could achieve 0.1 ng sensing sensitivity. In gravimetric sensing, the sensing range was between 50,000~100,000 CFU/ml. Sensing range was lower in clinical urinary tract infection (100,000 CFU/ml), thus demonstrating its usefulness for preventive medicine. It can understand the piezoelectric sensor of this study has potential application in the future for biomedical gravimetric sensing.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications.

PubMed

Dinh, Thanh; Kim, Younghan; Lee, Hyukjoon

2017-03-01

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications †

PubMed Central

Dinh, Thanh; Kim, Younghan; Lee, Hyukjoon

2017-01-01

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model. PMID:28257067

Dissolved organic carbon and its potential predictors in eutrophic lakes.

PubMed

Toming, Kaire; Kutser, Tiit; Tuvikene, Lea; Viik, Malle; Nõges, Tiina

2016-10-01

Understanding of the true role of lakes in the global carbon cycle requires reliable estimates of dissolved organic carbon (DOC) and there is a strong need to develop remote sensing methods for mapping lake carbon content at larger regional and global scales. Part of DOC is optically inactive. Therefore, lake DOC content cannot be mapped directly. The objectives of the current study were to estimate the relationships of DOC and other water and environmental variables in order to find the best proxy for remote sensing mapping of lake DOC. The Boosted Regression Trees approach was used to clarify in which relative proportions different water and environmental variables determine DOC. In a studied large and shallow eutrophic lake the concentrations of DOC and coloured dissolved organic matter (CDOM) were rather high while the seasonal and interannual variability of DOC concentrations was small. The relationships between DOC and other water and environmental variables varied seasonally and interannually and it was challenging to find proxies for describing seasonal cycle of DOC. Chlorophyll a (Chl a), total suspended matter and Secchi depth were correlated with DOC and therefore are possible proxies for remote sensing of seasonal changes of DOC in ice free period, while for long term interannual changes transparency-related variables are relevant as DOC proxies. CDOM did not appear to be a good predictor of the seasonality of DOC concentration in Lake Võrtsjärv since the CDOM-DOC coupling varied seasonally. However, combining the data from Võrtsjärv with the published data from six other eutrophic lakes in the world showed that CDOM was the most powerful predictor of DOC and can be used in remote sensing of DOC concentrations in eutrophic lakes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Magnesium Oxide (MgO) pH-sensitive Sensing Membrane in Electrolyte-Insulator-Semiconductor Structures with CF4 Plasma Treatment.

PubMed

Kao, Chyuan-Haur; Chang, Chia Lung; Su, Wei Ming; Chen, Yu Tzu; Lu, Chien Cheng; Lee, Yu Shan; Hong, Chen Hao; Lin, Chan-Yu; Chen, Hsiang

2017-08-03

Magnesium oxide (MgO) sensing membranes in pH-sensitive electrolyte-insulator-semiconductor structures were fabricated on silicon substrate. To optimize the sensing capability of the membrane, CF 4 plasma was incorporated to improve the material quality of MgO films. Multiple material analyses including FESEM, XRD, AFM, and SIMS indicate that plasma treatment might enhance the crystallization and increase the grain size. Therefore, the sensing behaviors in terms of sensitivity, linearity, hysteresis effects, and drift rates might be improved. MgO-based EIS membranes with CF 4 plasma treatment show promise for future industrial biosensing applications.

Observability-Based Guidance and Sensor Placement

NASA Astrophysics Data System (ADS)

Hinson, Brian T.

Control system performance is highly dependent on the quality of sensor information available. In a growing number of applications, however, the control task must be accomplished with limited sensing capabilities. This thesis addresses these types of problems from a control-theoretic point-of-view, leveraging system nonlinearities to improve sensing performance. Using measures of observability as an information quality metric, guidance trajectories and sensor distributions are designed to improve the quality of sensor information. An observability-based sensor placement algorithm is developed to compute optimal sensor configurations for a general nonlinear system. The algorithm utilizes a simulation of the nonlinear system as the source of input data, and convex optimization provides a scalable solution method. The sensor placement algorithm is applied to a study of gyroscopic sensing in insect wings. The sensor placement algorithm reveals information-rich areas on flexible insect wings, and a comparison to biological data suggests that insect wings are capable of acting as gyroscopic sensors. An observability-based guidance framework is developed for robotic navigation with limited inertial sensing. Guidance trajectories and algorithms are developed for range-only and bearing-only navigation that improve navigation accuracy. Simulations and experiments with an underwater vehicle demonstrate that the observability measure allows tuning of the navigation uncertainty.

Interval Predictor Models for Data with Measurement Uncertainty

NASA Technical Reports Server (NTRS)

Lacerda, Marcio J.; Crespo, Luis G.

2017-01-01

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

How does locus of control and inflated sense of responsibility relate to obsessive-compulsive symptoms in Turkish adolescents?

PubMed

Altin, Müjgan; Karanci, A Nuray

2008-12-01

This study aimed to examine the effects of responsibility attitudes, locus of control and their interactions on the general obsessive-compulsive (OC) symptomatology and the dimensions of OC symptoms in a sample of Turkish adolescents (n=385), their ages varied from 16 to 20 with a mean of 17.23 (S.D.=.68). The results of the present study revealed a significantly positive relationship between responsibility attitudes and general OC symptomatology. However, locus of control did not appear as a significant predictor of general OC symptomatology. Furthermore, results revealed that there was a significant interaction effect of responsibility attitudes with locus of control on OC symptomatology. That is, an inflated sense of responsibility and the presence of an external locus of control produced the highest level of OC symptoms. Related to the dimensions of OC symptoms, responsibility was a weak predictor of obsessive thinking symptoms, and a moderate predictor of cleanliness and checking symptoms. Locus of control and its interaction with responsibility attitudes only significantly predicted obsessional thinking symptoms.

Experimental results for correlation-based wavefront sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poyneer, L A; Palmer, D W; LaFortune, K N

2005-07-01

Correlation wave-front sensing can improve Adaptive Optics (AO) system performance in two keys areas. For point-source-based AO systems, Correlation is more accurate, more robust to changing conditions and provides lower noise than a centroiding algorithm. Experimental results from the Lick AO system and the SSHCL laser AO system confirm this. For remote imaging, Correlation enables the use of extended objects for wave-front sensing. Results from short horizontal-path experiments will show algorithm properties and requirements.

Bayer Demosaicking with Polynomial Interpolation.

PubMed

Wu, Jiaji; Anisetti, Marco; Wu, Wei; Damiani, Ernesto; Jeon, Gwanggil

2016-08-30

Demosaicking is a digital image process to reconstruct full color digital images from incomplete color samples from an image sensor. It is an unavoidable process for many devices incorporating camera sensor (e.g. mobile phones, tablet, etc.). In this paper, we introduce a new demosaicking algorithm based on polynomial interpolation-based demosaicking (PID). Our method makes three contributions: calculation of error predictors, edge classification based on color differences, and a refinement stage using a weighted sum strategy. Our new predictors are generated on the basis of on the polynomial interpolation, and can be used as a sound alternative to other predictors obtained by bilinear or Laplacian interpolation. In this paper we show how our predictors can be combined according to the proposed edge classifier. After populating three color channels, a refinement stage is applied to enhance the image quality and reduce demosaicking artifacts. Our experimental results show that the proposed method substantially improves over existing demosaicking methods in terms of objective performance (CPSNR, S-CIELAB E, and FSIM), and visual performance.

Graphene-Based Long-Period Fiber Grating Surface Plasmon Resonance Sensor for High-Sensitivity Gas Sensing

PubMed Central

Wei, Wei; Nong, Jinpeng; Zhang, Guiwen; Tang, Linlong; Jiang, Xiao; Chen, Na; Luo, Suqin; Lan, Guilian; Zhu, Yong

2016-01-01

A graphene-based long-period fiber grating (LPFG) surface plasmon resonance (SPR) sensor is proposed. A monolayer of graphene is coated onto the Ag film surface of the LPFG SPR sensor, which increases the intensity of the evanescent field on the surface of the fiber and thereby enhances the interaction between the SPR wave and molecules. Such features significantly improve the sensitivity of the sensor. The experimental results demonstrate that the sensitivity of the graphene-based LPFG SPR sensor can reach 0.344 nm%−1 for methane, which is improved 2.96 and 1.31 times with respect to the traditional LPFG sensor and Ag-coated LPFG SPR sensor, respectively. Meanwhile, the graphene-based LPFG SPR sensor exhibits excellent response characteristics and repeatability. Such a SPR sensing scheme offers a promising platform to achieve high sensitivity for gas-sensing applications. PMID:28025483

Patient-reported outcomes as predictors of 10-year survival in women after acute myocardial infarction

PubMed Central

2010-01-01

Background Patient-reported outcomes are increasingly seen as complementary to biomedical measures. However, their prognostic importance has yet to be established, particularly in female long-term myocardial infarction (MI) survivors. We aimed to determine whether 10-year survival in older women after MI relates to patient-reported outcomes, and to compare their survival with that of the general female population. Methods We included all women aged 60-80 years suffering MI during 1992-1997, and treated at one university hospital in Norway. In 1998, 145 (60% of those alive) completed a questionnaire package including socio-demographics, the Sense of Coherence Scale (SOC-29), the World Health Organization Quality of Life Instrument Abbreviated (WHOQOL-BREF) and an item on positive effects of illness. Clinical information was based on self-reports and hospital medical records data. We obtained complete data on vital status. Results The all-cause mortality rate during the 1998-2008 follow-up of all patients was 41%. In adjusted analysis, the conventional predictors s-creatinine (HR 1.26 per 10% increase) and left ventricular ejection fraction below 30% (HR 27.38), as well as patient-reported outcomes like living alone (HR 6.24), dissatisfaction with self-rated health (HR 6.26), impaired psychological quality of life (HR 0.60 per 10 points difference), and experience of positive effects of illness (HR 6.30), predicted all-cause death. Major adverse cardiac and cerebral events were also significantly associated with both conventional predictors and patient-reported outcomes. Sense of coherence did not predict adverse events. Finally, 10-year survival was not significantly different from that of the general female population. Conclusion Patient-reported outcomes have long-term prognostic importance, and should be taken into account when planning aftercare of low-risk older female MI patients. PMID:21108810

Patient-reported outcomes as predictors of 10-year survival in women after acute myocardial infarction.

PubMed

Norekvål, Tone M; Fridlund, Bengt; Rokne, Berit; Segadal, Leidulf; Wentzel-Larsen, Tore; Nordrehaug, Jan Erik

2010-11-25

Patient-reported outcomes are increasingly seen as complementary to biomedical measures. However, their prognostic importance has yet to be established, particularly in female long-term myocardial infarction (MI) survivors. We aimed to determine whether 10-year survival in older women after MI relates to patient-reported outcomes, and to compare their survival with that of the general female population. We included all women aged 60-80 years suffering MI during 1992-1997, and treated at one university hospital in Norway. In 1998, 145 (60% of those alive) completed a questionnaire package including socio-demographics, the Sense of Coherence Scale (SOC-29), the World Health Organization Quality of Life Instrument Abbreviated (WHOQOL-BREF) and an item on positive effects of illness. Clinical information was based on self-reports and hospital medical records data. We obtained complete data on vital status. The all-cause mortality rate during the 1998-2008 follow-up of all patients was 41%. In adjusted analysis, the conventional predictors s-creatinine (HR 1.26 per 10% increase) and left ventricular ejection fraction below 30% (HR 27.38), as well as patient-reported outcomes like living alone (HR 6.24), dissatisfaction with self-rated health (HR 6.26), impaired psychological quality of life (HR 0.60 per 10 points difference), and experience of positive effects of illness (HR 6.30), predicted all-cause death. Major adverse cardiac and cerebral events were also significantly associated with both conventional predictors and patient-reported outcomes. Sense of coherence did not predict adverse events. Finally, 10-year survival was not significantly different from that of the general female population. Patient-reported outcomes have long-term prognostic importance, and should be taken into account when planning aftercare of low-risk older female MI patients.

Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing

USGS Publications Warehouse

Marshall, Michael T.; Thenkabail, Prasad S.

2015-01-01

Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.

Predicting Intra-Urban Population Densities in Africa using SAR and Optical Remote Sensing Data

NASA Astrophysics Data System (ADS)

Linard, C.; Steele, J.; Forget, Y.; Lopez, J.; Shimoni, M.

2017-12-01

The population of Africa is predicted to double over the next 40 years, driving profound social, environmental and epidemiological changes within rapidly growing cities. Estimations of within-city variations in population density must be improved in order to take urban heterogeneities into account and better help urban research and decision making, especially for vulnerability and health assessments. Satellite remote sensing offers an effective solution for mapping settlements and monitoring urbanization at different spatial and temporal scales. In Africa, the urban landscape is covered by slums and small houses, where the heterogeneity is high and where the man-made materials are natural. Innovative methods that combine optical and SAR data are therefore necessary for improving settlement mapping and population density predictions. An automatic method was developed to estimate built-up densities using recent and archived optical and SAR data and a multi-temporal database of built-up densities was produced for 48 African cities. Geo-statistical methods were then used to study the relationships between census-derived population densities and satellite-derived built-up attributes. Best predictors were combined in a Random Forest framework in order to predict intra-urban variations in population density in any large African city. Models show significant improvement of our spatial understanding of urbanization and urban population distribution in Africa in comparison to the state of the art.

TiO2 Nanotubes: Recent Advances in Synthesis and Gas Sensing Properties

PubMed Central

Galstyan, Vardan; Comini, Elisabetta; Faglia, Guido; Sberveglieri, Giorgio

2013-01-01

Synthesis—particularly by electrochemical anodization-, growth mechanism and chemical sensing properties of pure, doped and mixed titania tubular arrays are reviewed. The first part deals on how anodization parameters affect the size, shape and morphology of titania nanotubes. In the second part fabrication of sensing devices based on titania nanotubes is presented, together with their most notable gas sensing performances. Doping largely improves conductivity and enhances gas sensing performances of TiO2 nanotubes. PMID:24184919

Optical chaos and hybrid WDM/TDM based large capacity quasi-distributed sensing network with real-time fiber fault monitoring.

PubMed

Luo, Yiyang; Xia, Li; Xu, Zhilin; Yu, Can; Sun, Qizhen; Li, Wei; Huang, Di; Liu, Deming

2015-02-09

An optical chaos and hybrid wavelength division multiplexing/time division multiplexing (WDM/TDM) based large capacity quasi-distributed sensing network with real-time fiber fault monitoring is proposed. Chirped fiber Bragg grating (CFBG) intensity demodulation is adopted to improve the dynamic range of the measurements. Compared with the traditional sensing interrogation methods in time, radio frequency and optical wavelength domains, the measurand sensing and the precise locating of the proposed sensing network can be simultaneously interrogated by the relative amplitude change (RAC) and the time delay of the correlation peak in the cross-correlation spectrum. Assisted with the WDM/TDM technology, hundreds of sensing units could be potentially multiplexed in the multiple sensing fiber lines. Based on the proof-of-concept experiment for axial strain measurement with three sensing fiber lines, the strain sensitivity up to 0.14% RAC/με and the precise locating of the sensors are achieved. Significantly, real-time fiber fault monitoring in the three sensing fiber lines is also implemented with a spatial resolution of 2.8 cm.

TripSense: A Trust-Based Vehicular Platoon Crowdsensing Scheme with Privacy Preservation in VANETs

PubMed Central

Hu, Hao; Lu, Rongxing; Huang, Cheng; Zhang, Zonghua

2016-01-01

In this paper, we propose a trust-based vehicular platoon crowdsensing scheme, named TripSense, in VANET. The proposed TripSense scheme introduces a trust-based system to evaluate vehicles’ sensing abilities and then selects the more capable vehicles in order to improve sensing results accuracy. In addition, the sensing tasks are accomplished by platoon member vehicles and preprocessed by platoon head vehicles before the data are uploaded to server. Hence, it is less time-consuming and more efficient compared with the way where the data are submitted by individual platoon member vehicles. Hence it is more suitable in ephemeral networks like VANET. Moreover, our proposed TripSense scheme integrates unlinkable pseudo-ID techniques to achieve PM vehicle identity privacy, and employs a privacy-preserving sensing vehicle selection scheme without involving the PM vehicle’s trust score to keep its location privacy. Detailed security analysis shows that our proposed TripSense scheme not only achieves desirable privacy requirements but also resists against attacks launched by adversaries. In addition, extensive simulations are conducted to show the correctness and effectiveness of our proposed scheme. PMID:27258287

Extraordinary improvement of gas-sensing performances in SnO2 nanofibers due to creation of local p-n heterojunctions by loading reduced graphene oxide nanosheets.

PubMed

Lee, Jae-Hyoung; Katoch, Akash; Choi, Sun-Woo; Kim, Jae-Hun; Kim, Hyoun Woo; Kim, Sang Sub

2015-02-11

We propose a novel approach to improve the gas-sensing properties of n-type nanofibers (NFs) that involves creation of local p-n heterojunctions with p-type reduced graphene oxide (RGO) nanosheets (NSs). This work investigates the sensing behaviors of n-SnO2 NFs loaded with p-RGO NSs as a model system. n-SnO2 NFs demonstrated greatly improved gas-sensing performances when loaded with an optimized amount of p-RGO NSs. Loading an optimized amount of RGOs resulted in a 20-fold higher sensor response than that of pristine SnO2 NFs. The sensing mechanism of monolithic SnO2 NFs is based on the joint effects of modulation of the potential barrier at nanograin boundaries and radial modulation of the electron-depletion layer. In addition to the sensing mechanisms described above, enhanced sensing was obtained for p-RGO NS-loaded SnO2 NFs due to creation of local p-n heterojunctions, which not only provided a potential barrier, but also functioned as a local electron absorption reservoir. These mechanisms markedly increased the resistance of SnO2 NFs, and were the origin of intensified resistance modulation during interaction of analyte gases with preadsorbed oxygen species or with the surfaces and grain boundaries of NFs. The approach used in this work can be used to fabricate sensitive gas sensors based on n-type NFs.

CSmetaPred: a consensus method for prediction of catalytic residues.

PubMed

Choudhary, Preeti; Kumar, Shailesh; Bachhawat, Anand Kumar; Pandit, Shashi Bhushan

2017-12-22

Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc. Both meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization. The benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/ .

Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.

The improvement of gas-sensing properties of SnO2/zeolite-assembled composite

NASA Astrophysics Data System (ADS)

Sun, Yanhui; Wang, Jing; Li, Xiaogan; Du, Haiying; Huang, Qingpan

2018-05-01

SnO2-impregnated zeolite composites were used as gas-sensing materials to improve the sensitivity and selectivity of the metal oxide-based resistive-type gas sensors. Nanocrystalline MFI type zeolite (ZSM-5) was prepared by hydrothermal synthesis. Highly dispersive SnO2 nanoparticles were then successfully assembled on the surface of the ZSM-5 nanoparticles by using the impregnation methods. The SnO2 nanoparticles are nearly spherical with the particle size of 10 nm. An enhanced formaldehyde sensing of as-synthesized SnO2-ZSM-5-based sensor was observed whereas a suppression on the sensor response to other volatile organic vapors (VOCs) such as acetone, ethanol, and methanol was noticed. The possible reasons for this contrary observation were proposed to be related to the amount of the produced water vapor during the sensing reactions assisted by the ZSM-5 nanoparticles. This provides a possible new strategy to improve the selectivity of the gas sensors. The effect of the humidity on the sensor response to formaldehyde was investigated and it was found the higher humidity would decrease the sensor response. A coating layer of the ZSM-5 nanoparticles on top of the SnO2-ZSM-5-sensing film was thus applied to further improve the sensitivity and selectivity of the sensor through the strong adsorption ability to polar gases and the "filtering effect" by the pores of ZSM-5.

Does Sentinel multi sensor data offer synergy in Improving Accuracy of Aboveground Biomass Estimate of Dense Tropical Forest? - Utility of Decision Tree Based Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Ghosh, S. M.; Behera, M. D.

2017-12-01

Forest aboveground biomass (AGB) is an important factor for preparation of global policy making decisions to tackle the impact of climate change. Several previous studies has concluded that remote sensing methods are more suitable for estimating forest biomass on regional scale. Among all available remote sensing data and methods, Synthetic Aperture Radar (SAR) data in combination with decision tree based machine learning algorithms has shown better promise in estimating higher biomass values. There aren't many studies done for biomass estimation of dense Indian tropical forests with high biomass density. In this study aboveground biomass was estimated for two major tree species, Sal (Shorea robusta) and Teak (Tectona grandis), of Katerniaghat Wildlife Sanctuary, a tropical forest situated in northern India. Biomass was estimated by combining C-band SAR data from Sentinel-1A satellite, vegetation indices produced using Sentinel-2A data and ground inventory plots. Along with SAR backscatter value, SAR texture images were also used as input as earlier studies had found that image texture has a correlation with vegetation biomass. Decision tree based nonlinear machine learning algorithms were used in place of parametric regression models for establishing relationship between fields measured values and remotely sensed parameters. Using random forest model with a combination of vegetation indices with SAR backscatter as predictor variables shows best result for Sal forest, with a coefficient of determination value of 0.71 and a RMSE value of 105.027 t/ha. In teak forest also best result can be found in the same combination but for stochastic gradient boosted model with a coefficient of determination value of 0.6 and a RMSE value of 79.45 t/ha. These results are mostly better than the results of other studies done for similar kind of forests. This study shows that Sentinel series satellite data has exceptional capabilities in estimating dense forest AGB and machine learning algorithms are better means to do so than parametric regression models.

Blind compressed sensing image reconstruction based on alternating direction method

NASA Astrophysics Data System (ADS)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing.

PubMed

Hakkenberg, C R; Zhu, K; Peet, R K; Song, C

2018-02-01

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m 2 . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms. © 2017 by the Ecological Society of America.

Hardgrove grindability index and petrology used as an enhanced predictor of coal feed rate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hower, J.C.

1990-01-01

An improved predictor of coal pulverization behavior and coal feed rate is under development at the CAER based upon the interaction between Hardgrove Grindability Index (HGI) and coal petrology. With educated attention, this interaction may be a useful tool to enhance coal feed rates if cautiously extended to the mining environment where blends of coal lithotypes are produced.

Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

NASA Astrophysics Data System (ADS)

Liu, Ruian; Liu, Nan; Zeng, Beibei; Chen, Tingting; Yin, Ninghao

2018-03-01

Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.

The power of connections: Psychological sense of community as a predictor of volunteerism.

PubMed

Omoto, Allen M; Packard, Cody D

2016-01-01

Two studies explored psychological antecedents of volunteerism, including several dispositional constructs and psychological sense of community (PSOC). In Study 1, 140 retirees completed measures of empathy, self-esteem, generativity, and PSOC, as well as involvement in volunteer organizations and weekly volunteering hours at two points in time. PSOC predicted concurrent and future volunteerism even after controlling for the other predictors. In Study 2 (n = 427), PSOC and measures of environmental concern and connectedness were used to predict current environmental volunteerism and activism. PSOC was the only measure reliably and uniquely related to these behaviors. Across two different domains and operationalizations of PSOC, the findings support the validity and utility of PSOC for understanding general and issue-specific volunteerism. More generally, they highlight social relationships and psychological connections as potential pathways to volunteerism and social action.

Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland.

PubMed

Wilson, Chris H; Caughlin, T Trevor; Rifai, Sami W; Boughton, Elizabeth H; Mack, Michelle C; Flory, S Luke

2017-07-01

Soil carbon sequestration in agroecosystems could play a key role in climate change mitigation but will require accurate predictions of soil organic carbon (SOC) stocks over spatial scales relevant to land management. Spatial variation in underlying drivers of SOC, such as plant productivity and soil mineralogy, complicates these predictions. Recent advances in the availability of remotely sensed data make it practical to generate multidecadal time series of vegetation indices with high spatial resolution and coverage. However, the utility of such data largely is unknown, only having been tested with shorter (e.g., 1-2 yr) data summaries. Across a 2,000 ha subtropical grassland, we found that a long time series (28 yr) of a vegetation index (Enhanced Vegetation Index; EVI) derived from the Landsat 5 satellite significantly enhanced prediction of spatially varying SOC pools, while a short summary (2 yr) was an ineffective predictor. EVI was the best predictor for surface SOC (0-5 cm depth) and total measured SOC stocks (0-15 cm). The optimum models for SOC in the upper soil layer combined EVI records with elevation and calcium concentration, while deeper SOC was more strongly associated with calcium availability. We demonstrate how data from the open access Landsat archive can predict SOC stocks, a key ecosystem metric, and illustrate the rich variety of analytical approaches that can be applied to long time series of remotely sensed greenness. Overall, our results showed that SOC pools were closely coupled to EVI in this ecosystem, demonstrating that maintenance of higher average green leaf area is correlated with higher SOC. The strong associations of vegetation greenness and calcium concentration with SOC suggest that the ability to sequester additional SOC likely will rely on strategic management of pasture vegetation and soil fertility. © 2017 by the Ecological Society of America.

Double Threshold Energy Detection Based Cooperative Spectrum Sensing for Cognitive Radio Networks with QoS Guarantee

NASA Astrophysics Data System (ADS)

Hu, Hang; Yu, Hong; Zhang, Yongzhi

2013-03-01

Cooperative spectrum sensing, which can greatly improve the ability of discovering the spectrum opportunities, is regarded as an enabling mechanism for cognitive radio (CR) networks. In this paper, we employ a double threshold detection method in energy detector to perform spectrum sensing, only the CR users with reliable sensing information are allowed to transmit one bit local decision to the fusion center. Simulation results will show that our proposed double threshold detection method could not only improve the sensing performance but also save the bandwidth of the reporting channel compared with the conventional detection method with one threshold. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of CR users, it has been shown that the optimal number of CR users is related to the price of these Quality-of-Service (QoS) requirements.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

PubMed

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

A prototype wireless inertial-sensing device for measuring toe clearance.

PubMed

Lai, Daniel T H; Charry, E; Begg, R; Palaniswami, M

2008-01-01

Tripping and slipping are serious health concerns for the elderly because they result in life threatening injuries i.e., fractures and high medical costs. Our recent work in detection of tripping gait patterns has demonstrated that minimum toe clearance (MTC) is a sensitive falls risk predictor. MTC measurement has previously been done in gait laboratories and on treadmills which potentially imposes controlled walking conditions. In this paper, we describe a prototype design of a wireless device for monitoring vertical toe clearance. The sensors consists of a tri-axis accelerometer and dual-axis gyroscope connected to Crossbow sensor motes for wireless data transmission. Sensor data are transmitted to a laptop and displayed on a Matlab graphic user interface (GUI). We have performed zero base and treadmill experiments to investigate sensor performance to environmental variations and compared the calculated toe clearance against measurements made by an Optotrak motion system. It was found that device outputs were approximately independent of small ambient temperature variations, had a reliable range of 20m indoors and 50m outdoors and a maximum transmission rate of 20 packets/s. Toe clearance measurements were found to follow the Optotrak measurement trend but could be improved further by dealing with double integration errors and improving data transmission rates.

Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

PubMed

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

Remotely sensed predictors of conifer tree mortality during severe drought

NASA Astrophysics Data System (ADS)

Brodrick, P. G.; Asner, G. P.

2017-11-01

Widespread, drought-induced forest mortality has been documented on every forested continent over the last two decades, yet early pre-mortality indicators of tree death remain poorly understood. Remotely sensed physiological-based measures offer a means for large-scale analysis to understand and predict drought-induced mortality. Here, we use laser-guided imaging spectroscopy from multiple years of aerial surveys to assess the impact of sustained canopy water loss on tree mortality. We analyze both gross canopy mortality in 2016 and the change in mortality between 2015 and 2016 in millions of sampled conifer forest locations throughout the Sierra Nevada mountains in California. On average, sustained water loss and gross mortality are strongly related, and year-to-year water loss within the drought indicates subsequent mortality. Both relationships are consistent after controlling for location and tree community composition, suggesting that these metrics may serve as indicators of mortality during a drought.

The ASPRS Remote Sensing Industry Forecast: Phase II & III - Digital Sensor Compilation

NASA Technical Reports Server (NTRS)

Mondello, Charles

2007-01-01

In August 1999, ASPRS and NASA's (then) Commercial Remote Sensing Program (CRSP) entered into a 5-year Space Act Agreement (SAA), combining resources and expertise to: (a) Baseline the Remote Sensing Industry (RSI) based on GEIA Model; (b) Develop a 10-Year RSI market forecast and attendant processes; and (c) Provide improved information for decision makers.

Complex-Difference Constrained Compressed Sensing Reconstruction for Accelerated PRF Thermometry with Application to MRI Induced RF Heating

PubMed Central

Cao, Zhipeng; Oh, Sukhoon; Otazo, Ricardo; Sica, Christopher T.; Griswold, Mark A.; Collins, Christopher M.

2014-01-01

Purpose Introduce a novel compressed sensing reconstruction method to accelerate proton resonance frequency (PRF) shift temperature imaging for MRI induced radiofrequency (RF) heating evaluation. Methods A compressed sensing approach that exploits sparsity of the complex difference between post-heating and baseline images is proposed to accelerate PRF temperature mapping. The method exploits the intra- and inter-image correlations to promote sparsity and remove shared aliasing artifacts. Validations were performed on simulations and retrospectively undersampled data acquired in ex-vivo and in-vivo studies by comparing performance with previously proposed techniques. Results The proposed complex difference constrained compressed sensing reconstruction method improved the reconstruction of smooth and local PRF temperature change images compared to various available reconstruction methods in a simulation study, a retrospective study with heating of a human forearm in vivo, and a retrospective study with heating of a sample of beef ex vivo . Conclusion Complex difference based compressed sensing with utilization of a fully-sampled baseline image improves the reconstruction accuracy for accelerated PRF thermometry. It can be used to improve the volumetric coverage and temporal resolution in evaluation of RF heating due to MRI, and may help facilitate and validate temperature-based methods for safety assurance. PMID:24753099

Minimalist ensemble algorithms for genome-wide protein localization prediction.

PubMed

Lin, Jhih-Rong; Mondal, Ananda Mohan; Liu, Rong; Hu, Jianjun

2012-07-03

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi.

Minimalist ensemble algorithms for genome-wide protein localization prediction

PubMed Central

2012-01-01

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi. PMID:22759391

A Reasoning Hardware Platform for Real-Time Common-Sense Inference

PubMed Central

Barba, Jesús; Santofimia, Maria J.; Dondo, Julio; Rincón, Fernando; Sánchez, Francisco; López, Juan Carlos

2012-01-01

Enabling Ambient Intelligence systems to understand the activities that are taking place in a supervised context is a rather complicated task. Moreover, this task cannot be successfully addressed while overlooking the mechanisms (common-sense knowledge and reasoning) that entitle us, as humans beings, to successfully undertake it. This work is based on the premise that Ambient Intelligence systems will be able to understand and react to context events if common-sense capabilities are embodied in them. However, there are some difficulties that need to be resolved before common-sense capabilities can be fully deployed to Ambient Intelligence. This work presents a hardware accelerated implementation of a common-sense knowledge-base system intended to improve response time and efficiency. PMID:23012540

A temperature compensation methodology for piezoelectric based sensor devices

NASA Astrophysics Data System (ADS)

Wang, Dong F.; Lou, Xueqiao; Bao, Aijian; Yang, Xu; Zhao, Ji

2017-08-01

A temperature compensation methodology comprising a negative temperature coefficient thermistor with the temperature characteristics of a piezoelectric material is proposed to improve the measurement accuracy of piezoelectric sensing based devices. The piezoelectric disk is characterized by using a disk-shaped structure and is also used to verify the effectiveness of the proposed compensation method. The measured output voltage shows a nearly linear relationship with respect to the applied pressure by introducing the proposed temperature compensation method in a temperature range of 25-65 °C. As a result, the maximum measurement accuracy is observed to be improved by 40%, and the higher the temperature, the more effective the method. The effective temperature range of the proposed method is theoretically analyzed by introducing the constant coefficient of the thermistor (B), the resistance of initial temperature (R0), and the paralleled resistance (Rx). The proposed methodology can not only eliminate the influence of piezoelectric temperature dependent characteristics on the sensing accuracy but also decrease the power consumption of piezoelectric sensing based devices by the simplified sensing structure.

Accuracy improvement in the TDR-based localization of water leaks

NASA Astrophysics Data System (ADS)

Cataldo, Andrea; De Benedetto, Egidio; Cannazza, Giuseppe; Monti, Giuseppina; Demitri, Christian

A time domain reflectometry (TDR)-based system for the localization of water leaks has been recently developed by the authors. This system, which employs wire-like sensing elements to be installed along the underground pipes, has proven immune to the limitations that affect the traditional, acoustic leak-detection systems. Starting from the positive results obtained thus far, in this work, an improvement of this TDR-based system is proposed. More specifically, the possibility of employing a low-cost, water-absorbing sponge to be placed around the sensing element for enhancing the accuracy in the localization of the leak is addressed. To this purpose, laboratory experiments were carried out mimicking a water leakage condition, and two sensing elements (one embedded in a sponge and one without sponge) were comparatively used to identify the position of the leak through TDR measurements. Results showed that, thanks to the water retention capability of the sponge (which maintains the leaked water more localized), the sensing element embedded in the sponge leads to a higher accuracy in the evaluation of the position of the leak.

Branch classification: A new mechanism for improving branch predictor performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, P.Y.; Hao, E.; Patt, Y.

There is wide agreement that one of the most significant impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Speculative execution is one solution to the branch problem, but speculative work is discarded if a branch is mispredicted. For it to be effective, speculative work is discarded if a branch is mispredicted. For it to be effective, speculative execution requires a very accurate branch predictor; 95% accuracy is not good enough. This paper proposes branch classification, a methodology for building more accurate branch predictors. Branch classification allows anmore » individual branch instruction to be associated with the branch predictor best suited to predict its direction. Using this approach, a hybrid branch predictor can be constructed such that each component branch predictor predicts those branches for which it is best suited. To demonstrate the usefulness of branch classification, an example classification scheme is given and a new hybrid predictor is built based on this scheme which achieves a higher prediction accuracy than any branch predictor previously reported in the literature.« less

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape.

PubMed

Dahlin, Kyla M; Asner, Gregory P; Field, Christopher B

2012-01-01

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.

Theoretical analysis of metamaterial-gold auxiliary grating sensing structure for surface plasmon resonance sensing application based on polarization control method

NASA Astrophysics Data System (ADS)

Sun, Yi; Cai, Haoyuan; Wang, Xiaoping

2017-12-01

A metamaterial-gold multilayer sensing structure designed using the particle swarm optimization (PSO) algorithm with an auxiliary grating is proposed for using in a surface plasmon resonance (SPR) sensor system based on the polarization control method. After numerical calculations and simulation analysis, it was found that the metamaterial sensing structure significantly improves the sensitivity of the SPR signal with intensity singularity. The metamaterial sensing structure also increases the penetration depth of evanescent wave, making it possible to detect low-molecular-weight biomolecules and larger cells such as bacteria. The auxiliary grating structure was designed to identify the refractive index of the sensing region on both sides of intensity singularity. The stability of recognition and the electric field intensity of the visible light band were also studied.

The role of social support, family identification, and family constraints in predicting posttraumatic stress after cancer.

PubMed

Swartzman, Samantha; Sani, Fabio; Munro, Alastair J

2017-09-01

We compared social support with other potential psychosocial predictors of posttraumatic stress after cancer. These included family identification, or a sense of belonging to and commonality with family members, and family constraints, or the extent to which family members are closed, judgmental, or unreceptive in conversations about cancer. We also tested the hypothesis that family constraints mediate the relationship between family identification and cancer-related posttraumatic stress. We used a cross-sectional design. Surveys were collected from 205 colorectal cancer survivors in Tayside, Scotland. Both family identification and family constraints were stronger independent predictors of posttraumatic stress than social support. In multivariate analyses, social support was not a significant independent predictor of posttraumatic stress. In addition, there was a significant indirect effect of family identification on posttraumatic stress through family constraints. Numerous studies demonstrate a link between social support and posttraumatic stress. However, experiences within the family may be more important in predicting posttraumatic stress after cancer. Furthermore, a sense of belonging to and commonality with the family may reduce the extent to which cancer survivors experience constraints on conversations about cancer; this may, in turn, reduce posttraumatic stress. Copyright © 2016 John Wiley & Sons, Ltd.

Advanced Spatial-Division Multiplexed Measurement Systems Propositions—From Telecommunication to Sensing Applications: A Review

PubMed Central

Weng, Yi; Ip, Ezra; Pan, Zhongqi; Wang, Ting

2016-01-01

The concepts of spatial-division multiplexing (SDM) technology were first proposed in the telecommunications industry as an indispensable solution to reduce the cost-per-bit of optical fiber transmission. Recently, such spatial channels and modes have been applied in optical sensing applications where the returned echo is analyzed for the collection of essential environmental information. The key advantages of implementing SDM techniques in optical measurement systems include the multi-parameter discriminative capability and accuracy improvement. In this paper, to help readers without a telecommunication background better understand how the SDM-based sensing systems can be incorporated, the crucial components of SDM techniques, such as laser beam shaping, mode generation and conversion, multimode or multicore elements using special fibers and multiplexers are introduced, along with the recent developments in SDM amplifiers, opto-electronic sources and detection units of sensing systems. The examples of SDM-based sensing systems not only include Brillouin optical time-domain reflectometry or Brillouin optical time-domain analysis (BOTDR/BOTDA) using few-mode fibers (FMF) and the multicore fiber (MCF) based integrated fiber Bragg grating (FBG) sensors, but also involve the widely used components with their whole information used in the full multimode constructions, such as the whispering gallery modes for fiber profiling and chemical species measurements, the screw/twisted modes for examining water quality, as well as the optical beam shaping to improve cantilever deflection measurements. Besides, the various applications of SDM sensors, the cost efficiency issue, as well as how these complex mode multiplexing techniques might improve the standard fiber-optic sensor approaches using single-mode fibers (SMF) and photonic crystal fibers (PCF) have also been summarized. Finally, we conclude with a prospective outlook for the opportunities and challenges of SDM technologies in optical sensing industry. PMID:27589754

Advanced Spatial-Division Multiplexed Measurement Systems Propositions-From Telecommunication to Sensing Applications: A Review.

PubMed

Weng, Yi; Ip, Ezra; Pan, Zhongqi; Wang, Ting

2016-08-30

The concepts of spatial-division multiplexing (SDM) technology were first proposed in the telecommunications industry as an indispensable solution to reduce the cost-per-bit of optical fiber transmission. Recently, such spatial channels and modes have been applied in optical sensing applications where the returned echo is analyzed for the collection of essential environmental information. The key advantages of implementing SDM techniques in optical measurement systems include the multi-parameter discriminative capability and accuracy improvement. In this paper, to help readers without a telecommunication background better understand how the SDM-based sensing systems can be incorporated, the crucial components of SDM techniques, such as laser beam shaping, mode generation and conversion, multimode or multicore elements using special fibers and multiplexers are introduced, along with the recent developments in SDM amplifiers, opto-electronic sources and detection units of sensing systems. The examples of SDM-based sensing systems not only include Brillouin optical time-domain reflectometry or Brillouin optical time-domain analysis (BOTDR/BOTDA) using few-mode fibers (FMF) and the multicore fiber (MCF) based integrated fiber Bragg grating (FBG) sensors, but also involve the widely used components with their whole information used in the full multimode constructions, such as the whispering gallery modes for fiber profiling and chemical species measurements, the screw/twisted modes for examining water quality, as well as the optical beam shaping to improve cantilever deflection measurements. Besides, the various applications of SDM sensors, the cost efficiency issue, as well as how these complex mode multiplexing techniques might improve the standard fiber-optic sensor approaches using single-mode fibers (SMF) and photonic crystal fibers (PCF) have also been summarized. Finally, we conclude with a prospective outlook for the opportunities and challenges of SDM technologies in optical sensing industry.

Photon-limited Sensing and Surveillance

DTIC Science & Technology

2015-01-29

considerable time delay). More specifically, there were four main outcomes from this work: • Improved understanding of the fundmental limitations of...that we design novel cameras for photon-limited settings based on the principles of CS. Most prior theoretical results in compressed sensing and related...inverse problems apply to idealized settings where the noise is i.i.d., and do not account for signal-dependent noise and physical sensing

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Measuring sense of presence and user characteristics to predict effective training in an online simulated virtual environment.

PubMed

De Leo, Gianluca; Diggs, Leigh A; Radici, Elena; Mastaglio, Thomas W

2014-02-01

Virtual-reality solutions have successfully been used to train distributed teams. This study aimed to investigate the correlation between user characteristics and sense of presence in an online virtual-reality environment where distributed teams are trained. A greater sense of presence has the potential to make training in the virtual environment more effective, leading to the formation of teams that perform better in a real environment. Being able to identify, before starting online training, those user characteristics that are predictors of a greater sense of presence can lead to the selection of trainees who would benefit most from the online simulated training. This is an observational study with a retrospective postsurvey of participants' user characteristics and degree of sense of presence. Twenty-nine members from 3 Air Force National Guard Medical Service expeditionary medical support teams participated in an online virtual environment training exercise and completed the Independent Television Commission-Sense of Presence Inventory survey, which measures sense of presence and user characteristics. Nonparametric statistics were applied to determine the statistical significance of user characteristics to sense of presence. Comparing user characteristics to the 4 scales of the Independent Television Commission-Sense of Presence Inventory using Kendall τ test gave the following results: the user characteristics "how often you play video games" (τ(26)=-0.458, P<0.01) and "television/film production knowledge" (τ(27)=-0.516, P<0.01) were significantly related to negative effects. Negative effects refer to adverse physiologic reactions owing to the virtual environment experience such as dizziness, nausea, headache, and eyestrain. The user characteristic "knowledge of virtual reality" was significantly related to engagement (τ(26)=0.463, P<0.01) and negative effects (τ(26)=-0.404, P<0.05). Individuals who have knowledge about virtual environments and experience with gaming environments report a higher sense of presence that indicates that they will likely benefit more from online virtual training. Future research studies could include a larger population of expeditionary medical support, and the results obtained could be used to create a model that predicts the level of presence based on the user characteristics. To maximize results and minimize costs, only those individuals who, based on their characteristics, are supposed to have a higher sense of presence and less negative effects could be selected for online simulated virtual environment training.

Proposal and Implementation of a Robust Sensing Method for DVB-T Signal

NASA Astrophysics Data System (ADS)

Song, Chunyi; Rahman, Mohammad Azizur; Harada, Hiroshi

This paper proposes a sensing method for TV signals of DVB-T standard to realize effective TV White Space (TVWS) Communication. In the TVWS technology trial organized by the Infocomm Development Authority (iDA) of Singapore, with regard to the sensing level and sensing time, detecting DVB-T signal at the level of -120dBm over an 8MHz channel with a sensing time below 1 second is required. To fulfill such a strict sensing requirement, we propose a smart sensing method which combines feature detection and energy detection (CFED), and is also characterized by using dynamic threshold selection (DTS) based on a threshold table to improve sensing robustness to noise uncertainty. The DTS based CFED (DTS-CFED) is evaluated by computer simulations and is also implemented into a hardware sensing prototype. The results show that the DTS-CFED achieves a detection probability above 0.9 for a target false alarm probability of 0.1 for DVB-T signals at the level of -120dBm over an 8MHz channel with the sensing time equals to 0.1 second.

Shame and guilt in women with eating-disorder symptomatology.

PubMed

Burney, J; Irwin, H J

2000-01-01

The relationship of shame and guilt to eating-disorder symptomatology was investigated in a sample of 97 Australian women. In terms of the objective of predicting the severity of eating disturbance, the study explored the predictive utility of proneness to shame and guilt in a global sense, shame and guilt associated specifically with eating contexts, and shame associated with the body. The study also sought to determine if shame is a more prominent emotion than guilt among women who have eating difficulties. Shame associated with eating behavior was the strongest predictor of the severity of eating-disorder symptomatology. Other effective predictors were guilt associated with eating behavior and body shame. Eating disturbance was unrelated to proneness to shame and guilt in a global sense. Discussion of these findings focuses on the issue of determining whether self-conscious affects are best regarded as causes or as consequences of eating disturbance.

Fusion and quality analysis for remote sensing images using contourlet transform

NASA Astrophysics Data System (ADS)

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

2013-05-01

Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.

Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

PubMed Central

Genders, Tessa S S; Steyerberg, Ewout W; Nieman, Koen; Galema, Tjebbe W; Mollet, Nico R; de Feyter, Pim J; Krestin, Gabriel P; Alkadhi, Hatem; Leschka, Sebastian; Desbiolles, Lotus; Meijs, Matthijs F L; Cramer, Maarten J; Knuuti, Juhani; Kajander, Sami; Bogaert, Jan; Goetschalckx, Kaatje; Cademartiri, Filippo; Maffei, Erica; Martini, Chiara; Seitun, Sara; Aldrovandi, Annachiara; Wildermuth, Simon; Stinn, Björn; Fornaro, Jürgen; Feuchtner, Gudrun; De Zordo, Tobias; Auer, Thomas; Plank, Fabian; Friedrich, Guy; Pugliese, Francesca; Petersen, Steffen E; Davies, L Ceri; Schoepf, U Joseph; Rowe, Garrett W; van Mieghem, Carlos A G; van Driessche, Luc; Sinitsyn, Valentin; Gopalan, Deepa; Nikolaou, Konstantin; Bamberg, Fabian; Cury, Ricardo C; Battle, Juan; Maurovich-Horvat, Pál; Bartykowszki, Andrea; Merkely, Bela; Becker, Dávid; Hadamitzky, Martin; Hausleiter, Jörg; Dewey, Marc; Zimmermann, Elke; Laule, Michael

2012-01-01

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. Design Retrospective pooled analysis of individual patient data. Setting 18 hospitals in Europe and the United States. Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measures Obstructive coronary artery disease (≥50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined. Results We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory. Conclusions Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates. PMID:22692650

Searching for a sense of purpose: the role of parents and effects on self-esteem among female adolescents.

PubMed

Blattner, Meghan C C; Liang, Belle; Lund, Terese; Spencer, Renee

2013-10-01

Achieving a sense of purpose during adolescence is a developmental asset; however, searching for that purpose may be a developmental stressor. Supportive parent-child relationships may help youth during this stressful experience. The present study included 207 female students in the sixth, eighth, and tenth grades from two competitive private schools. Searching for purpose negatively predicted self-esteem. Hierarchical linear regression examined moderating effects of parental trust and alienation on searching for purpose as a predictor of self-esteem. Parental alienation significantly moderated the association between search for purpose and girls' self-esteem; conversely, parental trust did not moderate the association. Results suggest that parent-child relationships characterized by high levels of parental alienation may exacerbate the pernicious effects of search for purpose. Person-based analyses found four clusters corresponding to Foreclosed Purpose, Diffused Purpose, Uncommitted Purpose/Moratorium, and Achieved Purpose. We discuss implications for practice and research based on these results. Copyright © 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.

PubMed

Liu, Yu-Cheng; Yang, Meng-Han; Lin, Win-Li; Huang, Chien-Kang; Oyang, Yen-Jen

2009-12-03

Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research. In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode. Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

TiO2-Based Nanoheterostructures for Promoting Gas Sensitivity Performance: Designs, Developments, and Prospects

PubMed Central

Wang, Yuan; Wu, Tao; Zhou, Yun; Meng, Chuanmin; Zhu, Wenjun; Liu, Lixin

2017-01-01

Gas sensors based on titanium dioxide (TiO2) have attracted much public attention during the past decades due to their excellent potential for applications in environmental pollution remediation, transportation industries, personal safety, biology, and medicine. Numerous efforts have therefore been devoted to improving the sensing performance of TiO2. In those effects, the construct of nanoheterostructures is a promising tactic in gas sensing modification, which shows superior sensing performance to that of the single component-based sensors. In this review, we briefly summarize and highlight the development of TiO2-based heterostructure gas sensing materials with diverse models, including semiconductor/semiconductor nanoheterostructures, noble metal/semiconductor nanoheterostructures, carbon-group-materials/semiconductor nano- heterostructures, and organic/inorganic nanoheterostructures, which have been investigated for effective enhancement of gas sensing properties through the increase of sensitivity, selectivity, and stability, decrease of optimal work temperature and response/recovery time, and minimization of detectable levels. PMID:28846621

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

PubMed

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

NASA Astrophysics Data System (ADS)

Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi

2015-12-01

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Using Multitemporal Remote Sensing Imagery and Inundation Measures to Improve Land Change Estimates in Coastal Wetlands

USGS Publications Warehouse

Allen, Y.C.; Couvillion, B.R.; Barras, J.A.

2012-01-01

Remote sensing imagery can be an invaluable resource to quantify land change in coastal wetlands. Obtaining an accurate measure of land change can, however, be complicated by differences in fluvial and tidal inundation experienced when the imagery is captured. This study classified Landsat imagery from two wetland areas in coastal Louisiana from 1983 to 2010 into categories of land and water. Tide height, river level, and date were used as independent variables in a multiple regression model to predict land area in the Wax Lake Delta (WLD) and compare those estimates with an adjacent marsh area lacking direct fluvial inputs. Coefficients of determination from regressions using both measures of water level along with date as predictor variables of land extent in the WLD, were higher than those obtained using the current methodology which only uses date to predict land change. Land change trend estimates were also improved when the data were divided by time period. Water level corrected land gain in the WLD from 1983 to 2010 was 1 km 2 year -1, while rates in the adjacent marsh remained roughly constant. This approach of isolating environmental variability due to changing water levels improves estimates of actual land change in a dynamic system, so that other processes that may control delta development such as hurricanes, floods, and sediment delivery, may be further investigated. ?? 2011 Coastal and Estuarine Research Federation (outside the USA).

Capacitance-based damage detection sensing for aerospace structural composites

NASA Astrophysics Data System (ADS)

Bahrami, P.; Yamamoto, N.; Chen, Y.; Manohara, H.

2014-04-01

Damage detection technology needs improvement for aerospace engineering application because detection within complex composite structures is difficult yet critical to avoid catastrophic failure. Damage detection is challenging in aerospace structures because not all the damage detection technology can cover the various defect types (delamination, fiber fracture, matrix crack etc.), or conditions (visibility, crack length size, etc.). These defect states are expected to become even more complex with future introduction of novel composites including nano-/microparticle reinforcement. Currently, non-destructive evaluation (NDE) methods with X-ray, ultrasound, or eddy current have good resolutions (< 0.1 mm), but their detection capabilities is limited by defect locations and orientations and require massive inspection devices. System health monitoring (SHM) methods are often paired with NDE technologies to signal out sensed damage, but their data collection and analysis currently requires excessive wiring and complex signal analysis. Here, we present a capacitance sensor-based, structural defect detection technology with improved sensing capability. Thin dielectric polymer layer is integrated as part of the structure; the defect in the structure directly alters the sensing layer's capacitance, allowing full-coverage sensing capability independent of defect size, orientation or location. In this work, capacitance-based sensing capability was experimentally demonstrated with a 2D sensing layer consisting of a dielectric layer sandwiched by electrodes. These sensing layers were applied on substrate surfaces. Surface indentation damage (~1mm diameter) and its location were detected through measured capacitance changes: 1 to 250 % depending on the substrates. The damage detection sensors are light weight, and they can be conformably coated and can be part of the composite structure. Therefore it is suitable for aerospace structures such as cryogenic tanks and rocket fairings for example. The sensors can also be operating in space and harsh environment such as high temperature and vacuum.

Predicting Improvement in Social-Communication Symptoms of Autism Spectrum Disorders Using Retrospective Treatment Data

ERIC Educational Resources Information Center

Mazurek, Micah O.; Kanne, Stephen M.; Miles, Judith H.

2012-01-01

Data from 1433 children and adolescents with autism spectrum disorders (ASD) participating in the Simons Simplex Collection were examined to (1) investigate change in social-communication symptoms, and (2) examine predictors of improvement, particularly community-based treatments. Measures included the "Autism Diagnostic Interview--Revised"…

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

A Deep Machine Learning Algorithm to Optimize the Forecast of Atmospherics

NASA Astrophysics Data System (ADS)

Russell, A. M.; Alliss, R. J.; Felton, B. D.

Space-based applications from imaging to optical communications are significantly impacted by the atmosphere. Specifically, the occurrence of clouds and optical turbulence can determine whether a mission is a success or a failure. In the case of space-based imaging applications, clouds produce atmospheric transmission losses that can make it impossible for an electro-optical platform to image its target. Hence, accurate predictions of negative atmospheric effects are a high priority in order to facilitate the efficient scheduling of resources. This study seeks to revolutionize our understanding of and our ability to predict such atmospheric events through the mining of data from a high-resolution Numerical Weather Prediction (NWP) model. Specifically, output from the Weather Research and Forecasting (WRF) model is mined using a Random Forest (RF) ensemble classification and regression approach in order to improve the prediction of low cloud cover over the Haleakala summit of the Hawaiian island of Maui. RF techniques have a number of advantages including the ability to capture non-linear associations between the predictors (in this case physical variables from WRF such as temperature, relative humidity, wind speed and pressure) and the predictand (clouds), which becomes critical when dealing with the complex non-linear occurrence of clouds. In addition, RF techniques are capable of representing complex spatial-temporal dynamics to some extent. Input predictors to the WRF-based RF model are strategically selected based on expert knowledge and a series of sensitivity tests. Ultimately, three types of WRF predictors are chosen: local surface predictors, regional 3D moisture predictors and regional inversion predictors. A suite of RF experiments is performed using these predictors in order to evaluate the performance of the hybrid RF-WRF technique. The RF model is trained and tuned on approximately half of the input dataset and evaluated on the other half. The RF approach is validated using in-situ observations of clouds. All of the hybrid RF-WRF experiments demonstrated here significantly outperform the base WRF local low cloud cover forecasts in terms of the probability of detection and the overall bias. In particular, RF experiments that use only regional three-dimensional moisture predictors from the WRF model produce the highest accuracy when compared to RF experiments that use local surface predictors only or regional inversion predictors only. Furthermore, adding multiple types of WRF predictors and additional WRF predictors to the RF algorithm does not necessarily add more value in the resulting forecasts, indicating that it is better to have a small set of meaningful predictors than to have a vast set of indiscriminately-chosen predictors. This work also reveals that the WRF-based RF approach is highly sensitive to the time period over which the algorithm is trained and evaluated. Future work will focus on developing a similar WRF-based RF model for high cloud prediction and expanding the algorithm to two-dimensions horizontally.

3D Architectured Graphene/Metal Oxide Hybrids for Gas Sensors: A Review

PubMed Central

Xia, Yi; Li, Ran; Chen, Ruosong; Wang, Jing; Xiang, Lan

2018-01-01

Graphene/metal oxide-based materials have been demonstrated as promising candidates for gas sensing applications due to the enhanced sensing performance and synergetic effects of the two components. Plenty of metal oxides such as SnO2, ZnO, WO3, etc. have been hybridized with graphene to improve the gas sensing properties. However, graphene/metal oxide nanohybrid- based gas sensors still have several limitations in practical application such as the insufficient sensitivity and response rate, and long recovery time in some cases. To achieve higher sensing performances of graphene/metal oxides nanocomposites, many recent efforts have been devoted to the controllable synthesis of 3D graphene/metal oxides architectures owing to their large surface area and well-organized structure for the enhanced gas adsorption/diffusion on sensing films. This review summarizes recent advances in the synthesis, assembly, and applications of 3D architectured graphene/metal oxide hybrids for gas sensing. PMID:29735951

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

NASA Technical Reports Server (NTRS)

Kim, Kyu-Myong; Lau, William K. M.; Li, Guilong; Shen, Samuel S. P.; Lau, William K. M. (Technical Monitor)

2001-01-01

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Echo-Planar Imaging-Based, J-Resolved Spectroscopic Imaging for Improved Metabolite Detection in Prostate Cancer

DTIC Science & Technology

2016-12-01

tiple dimensions (20). Hu et al. employed pseudo-random phase-encoding blips during the EPSI readout to create nonuniform sampling along the spatial...resolved MRSI with Nonuniform Undersampling and Compressed Sensing 514 30.5 Prior-knowledge Fitting for Metabolite Quantitation 515 30.6 Future Directions... NONUNIFORM UNDERSAMPLING AND COMPRESSED SENSING Nonuniform undersampling (NUS) of k-space and subsequent reconstruction using compressed sensing (CS

Parametric amplification in a resonant sensing array

NASA Astrophysics Data System (ADS)

Yie, Zi; Miller, Nicholas J.; Shaw, Steven W.; Turner, Kimberly L.

2012-03-01

We demonstrate parametric amplification of a multidegree of freedom resonant mass sensing array via an applied base motion containing the appropriate frequency content and phases. Applying parametric forcing in this manner is simple and aligns naturally with the vibrational properties of the sensing structure. Using this technique, we observe an increase in the quality factors of the coupled array resonances, which provides an effective means of improving device sensitivity.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

Boylan, M.; Vlasin, R. D.

1976-01-01

Uses of remote sensing imagery were investigated based on exploring and evaluating the capability and reliability of all kinds of imagery for improving decision making on issues of land use at all scales of governmental administration. Emphasis was placed on applications to solving immediate problems confronting public agencies and private organizations. Resulting applications of remote sensing use by public agencies, public organizations, and related private corporations are described.

Axial strain and temperature sensing characteristics of the single-coreless-single mode fiber structure-based fiber ring laser

NASA Astrophysics Data System (ADS)

Liu, Zhi-bo; Yin, Bin; Liang, Xiao; Bai, Yunlong; Tan, Zhongwei; Liu, Shuo; Li, Yang; Liu, Yan; Jian, Shuisheng

2014-06-01

This paper experimentally demonstrated a singlemode-coreless-singlemode (SCS) fiber structure-based fiber ring cavity laser for strain and temperature measurement. The basis of the sensing system is the multimodal interference occurs in coreless fiber, and the transmission spectrum is sensitive to the ambient perturbation. In this sensing system, the SCS fiber structure not only acts as the sensing head of the sensor but also the band-pass filter of the ring laser. Blue shift with strain sensitivity of ˜ -2 pm/μɛ ranging from 0 to 730 μɛ and red shift with temperature sensitivity of ˜ 11 pm/°C ranging from 5 to 75 °C have been achieved. Experimental results also show the proposal has great potential in using long-distance operation. The fiber ring laser sensing system has a optical signal to noise ratio (OSNR) more than 50 and 3 dB bandwidth less than 0.05 nm. The result shows that the coreless fiber has no improvement of the temperature and axial strain sensitivity. However, compared to the common singlemode-multimode-singlemode fiber structure sensors, the laser sensing system has the additional advantages of high OSNR, high intensity and narrow 3 dB bandwidth, and thus improves the accuracy.

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

The Relationship between Ethnic Classroom Composition and Turkish-Origin and German Students' Reading Performance and Sense of Belonging

PubMed Central

Mok, Sog Yee; Martiny, Sarah E.; Gleibs, Ilka H.; Keller, Melanie M.; Froehlich, Laura

2016-01-01

Past research on ethnic composition effects on migrant and ethnic majority students' performance has reported inconclusive results: Some studies have found no relationship between the proportion of migrant students in school and students' performance, some revealed positive effects, whereas others showed negative effects of the proportion of migrant students. Most of the studies did not consider whether an increase in the proportion of migrant students in the classroom has different effects on migrant and ethnic majority students' performance. For this reason, the present study (N = 9215) extends previous research by investigating the cross-level interaction effect of the proportion of Turkish-origin students in classrooms on Turkish-origin and German students' reading performance with data based on the German National Assessment Study 2008/2009 in the school subject German. In addition, we examined the cross-level interaction effect of Turkish-origin students' proportion on sense of belonging to school for Turkish-origin and German students, as sense of belonging has been shown to be an important predictor of well-being and integration. No cross-level interaction effect on performance emerged. Only a small negative main effect of the Turkish-origin students' proportion on all students' performance was found. As predicted, we showed a cross-level interaction on sense of belonging. Only Turkish-origin students' sense of belonging was positively related to the proportion of Turkish-origin students: The more Turkish-origin students there were in a classroom, the higher Turkish-origin students' sense of belonging. German students' sense of belonging was not related to the ethnic classroom composition. Implications of the results in the educational context are discussed. PMID:27471484

The Relationship between Ethnic Classroom Composition and Turkish-Origin and German Students' Reading Performance and Sense of Belonging.

PubMed

Mok, Sog Yee; Martiny, Sarah E; Gleibs, Ilka H; Keller, Melanie M; Froehlich, Laura

2016-01-01

Past research on ethnic composition effects on migrant and ethnic majority students' performance has reported inconclusive results: Some studies have found no relationship between the proportion of migrant students in school and students' performance, some revealed positive effects, whereas others showed negative effects of the proportion of migrant students. Most of the studies did not consider whether an increase in the proportion of migrant students in the classroom has different effects on migrant and ethnic majority students' performance. For this reason, the present study (N = 9215) extends previous research by investigating the cross-level interaction effect of the proportion of Turkish-origin students in classrooms on Turkish-origin and German students' reading performance with data based on the German National Assessment Study 2008/2009 in the school subject German. In addition, we examined the cross-level interaction effect of Turkish-origin students' proportion on sense of belonging to school for Turkish-origin and German students, as sense of belonging has been shown to be an important predictor of well-being and integration. No cross-level interaction effect on performance emerged. Only a small negative main effect of the Turkish-origin students' proportion on all students' performance was found. As predicted, we showed a cross-level interaction on sense of belonging. Only Turkish-origin students' sense of belonging was positively related to the proportion of Turkish-origin students: The more Turkish-origin students there were in a classroom, the higher Turkish-origin students' sense of belonging. German students' sense of belonging was not related to the ethnic classroom composition. Implications of the results in the educational context are discussed.

Defect-engineered graphene chemical sensors with ultrahigh sensitivity.

PubMed

Lee, Geonyeop; Yang, Gwangseok; Cho, Ara; Han, Jeong Woo; Kim, Jihyun

2016-05-25

We report defect-engineered graphene chemical sensors with ultrahigh sensitivity (e.g., 33% improvement in NO2 sensing and 614% improvement in NH3 sensing). A conventional reactive ion etching system was used to introduce the defects in a controlled manner. The sensitivity of graphene-based chemical sensors increased with increasing defect density until the vacancy-dominant region was reached. In addition, the mechanism of gas sensing was systematically investigated via experiments and density functional theory calculations, which indicated that the vacancy defect is a major contributing factor to the enhanced sensitivity. This study revealed that defect engineering in graphene has significant potential for fabricating ultra-sensitive graphene chemical sensors.

Assimilation of Remotely-Sensed Snow information to improve streamflow predictions in the Southwestern US

NASA Astrophysics Data System (ADS)

López-Burgos, V.; Rajagopal, S.; Martinez Baquero, G. F.; Gupta, H. V.

2009-12-01

Rapidly growing population in the southwestern US is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial information of water fluxes from remote sensing platforms, and hydrological models coupled with ground based data is an important step towards this goal. This project is exploring the use of Snow Water Equivalent (SWE) estimates to update the snow component of the Variable Infiltration Capacity model (VIC). SWE estimates are obtained by combining SNOTEL data with MODIS Snow Cover Area (SCA) information. Because, cloud cover corrupts the estimates of SCA, a rule-based method is used to clean up the remotely sensed images. The rules include a time interpolation method, and the probability of a pixel for been covered with snow based on the relationships between elevation, temperature, lapse rate, aspect and topographic shading. The approach is used to improve streamflow predictions on two rivers managed by the Salt River Project, a water and energy supplier in central Arizona. This solution will help improve the management of reservoirs in the Salt and Verde River in Phoenix, Arizona (tributaries of the lower Colorado River basin), by incorporating physically based distributed models and remote sensing observations into their Decision Support Tools and planning tools. This research seeks to increase the knowledge base used to manage reservoirs and groundwater resources in a region affected by a long-term drought. It will be applicable and relevant for other water utility companies facing the challenges of climate change and decreasing water resources.

Nanomaterial-based electrochemical sensors for arsenic - A review.

PubMed

Kempahanumakkagari, Sureshkumar; Deep, Akash; Kim, Ki-Hyun; Kumar Kailasa, Suresh; Yoon, Hye-On

2017-09-15

The existence of arsenic in the environment poses severe global health threats. Considering its toxicity, the sensing of arsenic is extremely important. Due to the complexity of environmental and biological samples, many of the available detection methods for arsenic have serious limitations on selectivity and sensitivity. To improve sensitivity and selectivity and to circumvent interferences, different electrode systems have been developed based on surface modification with nanomaterials including carbonaceous nanomaterials, metallic nanoparticles (MNPs), metal nanotubes (MNTs), and even enzymes. Despite the progress made in electrochemical sensing of arsenic, some issues still need to be addressed to realize cost effective, portable, and flow-injection type sensor systems. The present review provides an in-depth evaluation of the nanoparticle-modified electrode (NME) based methods for the electrochemical sensing of arsenic. NME based sensing systems are projected to become an important option for monitoring hazardous pollutants in both environmental and biological media. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimation CODMN in Guangzhou Section of Pearl River Based on GF-1 Images

NASA Astrophysics Data System (ADS)

Feng, Y. B.; He, Y. Q.; Fu, Q. H.; Liu, C. Q.; Pan, H. Z.; Yin, B.

2018-04-01

Due to the way that remote sensing works, it has natural advantage to detect optical constituents in waters. And many kinds of inversion models were constructed based on the three main optical constituents, namely chlorophyll-a (Chl-a), suspended particulate matter (SPM), colored dissolved organic matter (CDOM). Except Chl-a used as an indicator of eutrophication, however, the public generally cares less about other two parameters and is more familiar with Grade I V scheme for utilization and protection purposes. Notice the three main optical constituents are also organic-related to some extent. It offers a possible way to estimate CODMn via remote sensing. According to field measurement conducted along the Guangzhou section of Pearl River (GPR for short), the spatial variation of CODMn in GPR shows some kinds of geographical feature, so does the correlation between CODMn and water color constituents. It indicated the complicated contribution of CODMn in GPR or some other urban rivers. Based on the band setting of GF-1 satellite, two kinds of inversion model of CODMn in GPR were finally constructed. One directly achieved CODMn from regression models of which predictors were different band combinations in different channels of GPR. To make the study more practical, the other one first provided empirical models of the three optical constituents, and then estimated CODMn of GPR based on its relationship with optical constituents. After all, Chl-a, SPM and CDOM could be distinguished optically, and remote sensing models of these three constituents in other studies may also be available.

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

Adherence predictors in an Internet-based Intervention program for depression.

PubMed

Castro, Adoración; López-Del-Hoyo, Yolanda; Peake, Christian; Mayoral, Fermín; Botella, Cristina; García-Campayo, Javier; Baños, Rosa María; Nogueira-Arjona, Raquel; Roca, Miquel; Gili, Margalida

2018-05-01

Internet-delivered psychotherapy has been demonstrated to be effective in the treatment of depression. Nevertheless, the study of the adherence in this type of the treatment reported divergent results. The main objective of this study is to analyze predictors of adherence in a primary care Internet-based intervention for depression in Spain. A multi-center, three arm, parallel, randomized controlled trial was conducted with 194 depressive patients, who were allocated in self-guided or supported-guided intervention. Sociodemographic and clinical characteristics were gathered using a case report form. The Mini international neuropsychiatric interview diagnoses major depression. Beck Depression Inventory was used to assess depression severity. The visual analogic scale assesses the respondent's self-rated health and Short Form Health Survey was used to measure the health-related quality of life. Age results a predictor variable for both intervention groups (with and without therapist support). Perceived health is a negative predictor of adherence for the self-guided intervention when change in depression severity was included in the model. Change in depression severity results a predictor of adherence in the support-guided intervention. Our findings demonstrate that in our sample, there are differences in sociodemographic and clinical variables between active and dropout participants and we provide adherence predictors in each intervention condition of this Internet-based program for depression (self-guided and support-guided). It is important to point that further research in this area is essential to improve tailored interventions and to know specific patients groups can benefit from these interventions.

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

NASA Astrophysics Data System (ADS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie

2014-03-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.

Toward Obtaining Reliable Particulate Air Quality Information from Satellites

NASA Astrophysics Data System (ADS)

Strawa, A. W.; Chatfield, R. B.; Legg, M.; Esswein, R.; Justice, E.

2009-12-01

Air quality agencies use ground sites to monitor air quality, providing accurate information at particular points. Using measurements from satellite imagery has the potential to provide air quality information in a timely manner with better spatial resolution and at a lower cost that can also useful for model validation. While previous studies show acceptable correlations between Aerosol Optical Depth (AOD) derived from MODIS and surface Particulate Matter (PM) measurements on the eastern US, the data do not correlate well in the western US (Al-Saadi et al., 2005; Engle-Cox et al., 2004) . This paper seeks to improve the AOD-PM correlations by using advanced statistical analysis techniques. Our study area is the San Joaquin Valley in California because air quality in this region has failed to meet state and federal attainment standards for PM for the past several years. A previous investigation found good correlation of the AOD values between MODIS, MISR and AERONET, but poor correlations (R2 ~ 0.02) between satellite-based AOD and surface PM2.5 measurements. PM2.5 measurements correlated somewhat better (R2 ~ 0.18) with MODIS-derived AOD using the Deep Blue surface reflectance algorithm (Hsu et al., 2006) rather than the standard MODIS algorithm. This level of correlation is not adequate for reliable air quality measurements. Pelletier et al. (2007) used generalized additive models (GAMs) and meteorological data to improve the correlation between PM and AERONET AOD in western Europe. Additive models are more flexible than linear models and the functional relationships can be plotted to give a sense of the relationship between the predictor and the response. In this paper we use GAMs to improve surface PM2.5 to MODIS-AOD correlations. For example, we achieve an R2 ~ 0.44 using a GAM that includes the Deep Blue AOD, and day of year as parameters. Including NOx observations, improves the R2 ~ 0.64. Surprisingly Ångström exponent did not prove to be a significant factor. The relationships between the predictor and the response are discussed. Al-Saadi, J., J. Szykman, R.B. Pierce, C. Kittaka, D. Neil, D.A. Chu, L. Remer, L. Gumley, E. Prins, L. Weinstock, C. MacDonald, R. Wayland, F. Dimmick, and J. Fishman, Imporving national air quality forecasts with satellite aerosol observations, Bull. Amer, Met. Soc. (Sept), 1249-1261, 2005. Engle-Cox, J.A., C.H. Holloman, B.W. Coutant, and R.M. Hoff, Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality, Atmos. En., 38, 2495-2509, 2004. Hsu, N.C., S.-C. Tsay, M.D. King, and J.R. Herman, Deep blue retrievals of Asian Aerosol properties during ACE-Asia, IEEE Trans. on Geosci.a nd Remote Sensing, 44 (11), 3180, 2006. Pelletier, B., R. Santer, and J. Vidot, Retrieving of particulate matter from optical measurements: A semi-parametric approach, J. Geophys. Res., 112 (D06208), 2007.

A New Adaptive Framework for Collaborative Filtering Prediction

PubMed Central

Almosallam, Ibrahim A.; Shang, Yi

2010-01-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix’s system. PMID:21572924

A New Adaptive Framework for Collaborative Filtering Prediction.

PubMed

Almosallam, Ibrahim A; Shang, Yi

2008-06-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Attenuation of Frontostriatal Connectivity During Reward Processing Predicts Response to Psychotherapy in Major Depressive Disorder.

PubMed

Walsh, Erin; Carl, Hannah; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Smoski, Moria J; Dichter, Gabriel S

2017-03-01

There are few reliable predictors of response to antidepressant treatments. In the present investigation, we examined pretreatment functional brain connectivity during reward processing as a potential predictor of response to Behavioral Activation Treatment for Depression (BATD), a validated psychotherapy that promotes engagement with rewarding stimuli and reduces avoidance behaviors. Thirty-three outpatients with major depressive disorder (MDD) and 20 matched controls completed two runs of the monetary incentive delay task during functional magnetic resonance imaging after which participants with MDD received up to 15 sessions of BATD. Seed-based generalized psychophysiological interaction analyses focused on task-based connectivity across task runs, as well as the attenuation of connectivity from the first to the second run of the task. The average change in Beck Depression Inventory-II scores due to treatment was 10.54 points, a clinically meaningful response. Groups differed in seed-based functional connectivity among multiple frontostriatal regions. Hierarchical linear modeling revealed that improved treatment response to BATD was predicted by greater connectivity between the left putamen and paracingulate gyrus during reward anticipation. In addition, MDD participants with greater attenuation of connectivity between several frontostriatal seeds, and midline subcallosal cortex and left paracingulate gyrus demonstrated improved response to BATD. These findings indicate that pretreatment frontostriatal functional connectivity during reward processing is predictive of response to a psychotherapy modality that promotes improving approach-related behaviors in MDD. Furthermore, connectivity attenuation among reward-processing regions may be a particularly powerful endophenotypic predictor of response to BATD in MDD.

Ultrathin Tungsten Oxide Nanowires/Reduced Graphene Oxide Composites for Toluene Sensing

PubMed Central

Hassan, Muhammad; Wang, Zhi-Hua; Huang, Wei-Ran; Li, Min-Qiang; Chen, Jia-Fu

2017-01-01

Graphene-based composites have gained great attention in the field of gas sensor fabrication due to their higher surface area with additional functional groups. Decorating one-dimensional (1D) semiconductor nanomaterials on graphene also show potential benefits in gas sensing applications. Here we demonstrate the one-pot and low cost synthesis of W18O49 NWs/rGO composites with different amount of reduced graphene oxide (rGO) which show excellent gas-sensing properties towards toluene and strong dependence on their chemical composition. As compared to pure W18O49 NWs, an improved gas sensing response (2.8 times higher) was achieved in case of W18O49 NWs composite with 0.5 wt. % rGO. Promisingly, this strategy can be extended to prepare other nanowire based composites with excellent gas-sensing performance. PMID:28961178

Integrated photonics for fiber optic based temperature sensing

NASA Astrophysics Data System (ADS)

Evenblij, R. S.; van Leest, T.; Haverdings, M. B.

2017-09-01

One of the promising space applications areas for fibre sensing is high reliable thermal mapping of metrology structures for effects as thermal deformation, focal plane distortion, etc. Subsequently, multi-point temperature sensing capability for payload panels and instrumentation instead of, or in addition to conventional thermo-couple technology will drastically reduce electrical wiring and sensor materials to minimize weight and costs. Current fiber sensing technologies based on solid state ASPIC (Application Specific Photonic Integrated Circuits) technology, allow significant miniaturization of instrumentation and improved reliability. These imperative aspects make the technology candidate for applications in harsh environments such as space. One of the major aspects in order to mature ASPIC technology for space is assessment on radiation hardness. This paper describes the results of radiation hardness experiments on ASPIC including typical multipoint temperature sensing and thermal mapping capabilities.

Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Fei; Piao, Yan

2018-04-01

In order to improve the subjective and objective quality of degraded images at low sampling rates effectively,save storage space and reduce computational complexity at the same time, this paper proposes a joint restoration algorithm of compressed sensing and two step iterative threshold shrinkage (TwIST). The algorithm applies the TwIST algorithm which used in image restoration to the compressed sensing theory. Then, a small amount of sparse high-frequency information is obtained in frequency domain. The TwIST algorithm based on compressed sensing theory is used to accurately reconstruct the high frequency image. The experimental results show that the proposed algorithm achieves better subjective visual effects and objective quality of degraded images while accurately restoring degraded images.

Predictors of Success for Community-Driven Water Quality Management--Lessons from Three Catchments in New Zealand

ERIC Educational Resources Information Center

Tyson, Ben; Unson, Christine; Edgar, Nick

2017-01-01

Three community engagement projects on the South Island of New Zealand are enacting education and communication initiatives to improve the uptake of best management practices on farms regarding nutrient management for improving water quality. Understanding the enablers and barriers to effective community-based catchment management is fundamental…

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

PubMed

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-11-07

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Distributed Long-Gauge Optical Fiber Sensors Based Self-Sensing FRP Bar for Concrete Structure

PubMed Central

Tang, Yongsheng; Wu, Zhishen

2016-01-01

Brillouin scattering-based distributed optical fiber (OF) sensing technique presents advantages for concrete structure monitoring. However, the existence of spatial resolution greatly decreases strain measurement accuracy especially around cracks. Meanwhile, the brittle feature of OF also hinders its further application. In this paper, the distributed OF sensor was firstly proposed as long-gauge sensor to improve strain measurement accuracy. Then, a new type of self-sensing fiber reinforced polymer (FRP) bar was developed by embedding the packaged long-gauge OF sensors into FRP bar, followed by experimental studies on strain sensing, temperature sensing and basic mechanical properties. The results confirmed the superior strain sensing properties, namely satisfied accuracy, repeatability and linearity, as well as excellent mechanical performance. At the same time, the temperature sensing property was not influenced by the long-gauge package, making temperature compensation easy. Furthermore, the bonding performance between self-sensing FRP bar and concrete was investigated to study its influence on the sensing. Lastly, the sensing performance was further verified with static experiments of concrete beam reinforced with the proposed self-sensing FRP bar. Therefore, the self-sensing FRP bar has potential applications for long-term structural health monitoring (SHM) as embedded sensors as well as reinforcing materials for concrete structures. PMID:26927110

Distributed Long-Gauge Optical Fiber Sensors Based Self-Sensing FRP Bar for Concrete Structure.

PubMed

Tang, Yongsheng; Wu, Zhishen

2016-02-25

Brillouin scattering-based distributed optical fiber (OF) sensing technique presents advantages for concrete structure monitoring. However, the existence of spatial resolution greatly decreases strain measurement accuracy especially around cracks. Meanwhile, the brittle feature of OF also hinders its further application. In this paper, the distributed OF sensor was firstly proposed as long-gauge sensor to improve strain measurement accuracy. Then, a new type of self-sensing fiber reinforced polymer (FRP) bar was developed by embedding the packaged long-gauge OF sensors into FRP bar, followed by experimental studies on strain sensing, temperature sensing and basic mechanical properties. The results confirmed the superior strain sensing properties, namely satisfied accuracy, repeatability and linearity, as well as excellent mechanical performance. At the same time, the temperature sensing property was not influenced by the long-gauge package, making temperature compensation easy. Furthermore, the bonding performance between self-sensing FRP bar and concrete was investigated to study its influence on the sensing. Lastly, the sensing performance was further verified with static experiments of concrete beam reinforced with the proposed self-sensing FRP bar. Therefore, the self-sensing FRP bar has potential applications for long-term structural health monitoring (SHM) as embedded sensors as well as reinforcing materials for concrete structures.

Automated railroad reconstruction from remote sensing image based on texture filter

NASA Astrophysics Data System (ADS)

Xiao, Jie; Lu, Kaixia

2018-03-01

Techniques of remote sensing have been improved incredibly in recent years and very accurate results and high resolution images can be acquired. There exist possible ways to use such data to reconstruct railroads. In this paper, an automated railroad reconstruction method from remote sensing images based on Gabor filter was proposed. The method is divided in three steps. Firstly, the edge-oriented railroad characteristics (such as line features) in a remote sensing image are detected using Gabor filter. Secondly, two response images with the filtering orientations perpendicular to each other are fused to suppress the noise and acquire a long stripe smooth region of railroads. Thirdly, a set of smooth regions can be extracted by firstly computing global threshold for the previous result image using Otsu's method and then converting it to a binary image based on the previous threshold. This workflow is tested on a set of remote sensing images and was found to deliver very accurate results in a quickly and highly automated manner.

Note: Durability analysis of optical fiber hydrogen sensor based on Pd-Y alloy film.

PubMed

Huang, Peng-cheng; Chen, You-ping; Zhang, Gang; Song, Han; Liu, Yi

2016-02-01

The Pd-Y alloy sensing film has an excellent property for hydrogen detection, but just for one month, the sensing film's property decreases seriously. To study the failure of the sensing film, the XPS spectra analysis was used to explore the chemical content of the Pd-Y alloy film, and analysis results demonstrate that the yttrium was oxidized. The paper presented that such an oxidized process was the potential reason of the failure of the sensing film. By understanding the reason of the failure of the sensing film better, we could improve the manufacturing process to enhance the property of hydrogen sensor.

Improving the efficiency of telephone-based disease management programs: getting the population and the timing right using hospital admission data.

PubMed

Watts, Jennifer J; Jolley, Damien; Wainer, Jo; Atchison, Rory

2012-12-01

Telephone-based disease management (DM) programs can improve health outcomes and provide a positive return on investment to funders. However, there is scant evidence about how to use hospital admission episode data to identify patients who are most likely to participate in a DM program. The objective of this study was to use hospital admission episode data held by health insurers to determine those factors that predict members with chronic disease joining and remaining in a DM program for at least 6 months. A multivariable logistic regression model was constructed to determine predictors of participating in a DM program for an insured population who had been admitted to hospital for congestive heart failure, coronary artery disease, or chronic obstructive pulmonary disease. The outcome variable was binary: did the member both opt into the DM program and remain in the program for at least 6 months? The study population included 9874 private health fund members. Time from a related hospital admission was a significant predictor, with those offered the program within 3 to 6 months being 71% more likely (95% confidence interval [CI]: 33%, 113%) to participate. The length of time from offer to commencement also was a significant predictor, with those commencing within 3 to 4 months being 75% (95% CI: 44%, 112%) as likely to remain in the program. It is possible to predict which individuals are most likely to participate in a telephone-based DM program using hospital admission episode data. Once individuals are identified, timely commencement of a DM program is an important predictor of success.

Platinum decorated carbon nanotubes for highly sensitive amperometric glucose sensing

NASA Astrophysics Data System (ADS)

Xie, Jining; Wang, Shouyan; Aryasomayajula, L.; Varadan, V. K.

2007-02-01

Fine platinum nanoparticles (1-5 nm in diameter) were deposited on functionalized multi-walled carbon nanotubes (MWNTs) through a decoration technique. A novel type of enzymatic Pt/MWNTs paste-based mediated glucose sensor was fabricated. Electrochemical measurements revealed a significantly improved sensitivity (around 52.7 µA mM-1 cm-2) for glucose sensing without using any picoampere booster or Faraday cage. In addition, the calibration curve exhibited a good linearity in the range of 1-28 mM of glucose concentration. Transmission electron microscopy (TEM) and x-ray photoelectron spectroscopy (XPS) were performed to investigate the nanoscale structure and the chemical bonding information of the Pt/MWNTs paste-based sensing material, respectively. The improved sensitivity of this novel glucose sensor could be ascribed to its higher electroactive surface area, enhanced electron transfer, efficient enzyme immobilization, unique interaction in nanoscale and a synergistic effect on the current signal from possible multi-redox reactions.

Photogrammetric Processing of Planetary Linear Pushbroom Images Based on Approximate Orthophotos

NASA Astrophysics Data System (ADS)

Geng, X.; Xu, Q.; Xing, S.; Hou, Y. F.; Lan, C. Z.; Zhang, J. J.

2018-04-01

It is still a great challenging task to efficiently produce planetary mapping products from orbital remote sensing images. There are many disadvantages in photogrammetric processing of planetary stereo images, such as lacking ground control information and informative features. Among which, image matching is the most difficult job in planetary photogrammetry. This paper designs a photogrammetric processing framework for planetary remote sensing images based on approximate orthophotos. Both tie points extraction for bundle adjustment and dense image matching for generating digital terrain model (DTM) are performed on approximate orthophotos. Since most of planetary remote sensing images are acquired by linear scanner cameras, we mainly deal with linear pushbroom images. In order to improve the computational efficiency of orthophotos generation and coordinates transformation, a fast back-projection algorithm of linear pushbroom images is introduced. Moreover, an iteratively refined DTM and orthophotos scheme was adopted in the DTM generation process, which is helpful to reduce search space of image matching and improve matching accuracy of conjugate points. With the advantages of approximate orthophotos, the matching results of planetary remote sensing images can be greatly improved. We tested the proposed approach with Mars Express (MEX) High Resolution Stereo Camera (HRSC) and Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images. The preliminary experimental results demonstrate the feasibility of the proposed approach.

Basic needs and their predictors for intubated patients in surgical intensive care units.

PubMed

Liu, Jin-Jen; Chou, Fan-Hao; Yeh, Shu-Hui

2009-01-01

This study was conducted to investigate the basic needs and communication difficulties of intubated patients in surgical intensive care units (ICUs) and to identify predictors of the basic needs from the patient characteristics and communication difficulties. In this descriptive correlational study, 80 surgical ICU patients were recruited and interviewed using 3 structured questionnaires: demographic information, scale of basic needs, and scale of communication difficulties. The intubated patients were found to have moderate communication difficulties. The sense of being loved and belonging was the most common need in the intubated patients studied (56.00 standardized scores). A significantly positive correlation was found between communication difficulties and general level of basic needs (r = .53, P < .01), and another positive correlation was found between the length of stay in ICUs and the need for love and belonging (r = .25, P < .05). The basic needs of intubated patients could be significantly predicted by communication difficulties (P = .002), use of physical restraints (P = .010), lack of intubation history (P = .005), and lower educational level (P = .005). These 4 predictors accounted for 47% of the total variance in basic needs. The intubated patients in surgical ICUs had moderate basic needs and communication difficulties. The fact that the basic needs could be predicted by communication difficulties, physical restraints, and educational level suggests that nurses in surgical ICUs need to improve skills of communication and limit the use of physical restraints, especially in patients with a lower educational level.

Challenges in paper-based fluorogenic optical sensing with smartphones

NASA Astrophysics Data System (ADS)

Ulep, Tiffany-Heather; Yoon, Jeong-Yeol

2018-05-01

Application of optically superior, tunable fluorescent nanotechnologies have long been demonstrated throughout many chemical and biological sensing applications. Combined with microfluidics technologies, i.e. on lab-on-a-chip platforms, such fluorescent nanotechnologies have often enabled extreme sensitivity, sometimes down to single molecule level. Within recent years there has been a peak interest in translating fluorescent nanotechnology onto paper-based platforms for chemical and biological sensing, as a simple, low-cost, disposable alternative to conventional silicone-based microfluidic substrates. On the other hand, smartphone integration as an optical detection system as well as user interface and data processing component has been widely attempted, serving as a gateway to on-board quantitative processing, enhanced mobility, and interconnectivity with informational networks. Smartphone sensing can be integrated to these paper-based fluorogenic assays towards demonstrating extreme sensitivity as well as ease-of-use and low-cost. However, with these emerging technologies there are always technical limitations that must be addressed; for example, paper's autofluorescence that perturbs fluorogenic sensing; smartphone flash's limitations in fluorescent excitation; smartphone camera's limitations in detecting narrow-band fluorescent emission, etc. In this review, physical optical setups, digital enhancement algorithms, and various fluorescent measurement techniques are discussed and pinpointed as areas of opportunities to further improve paper-based fluorogenic optical sensing with smartphones.

Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

PubMed

Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

2009-11-01

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

Factors supporting self-management in Parkinson's disease: implications for nursing practice.

PubMed

Chenoweth, Lynn; Gallagher, Robyn; Sheriff, June N; Donoghue, Judith; Stein-Parbury, Jane

2008-09-01

Aim.  To identify the factors associated with better self-management in people with moderate to high levels of Parkinson's disease following an acute illness event. Design and methods.  A prospective, descriptive study conducted with 75 persons with Parkinson's disease over the age of 55, collected twice: within a week of an acute event and 1 month later, after resuming usual life at home. Participants completed a questionnaire on self-rated health status, self-efficacy, sense of coherence, symptom monitoring and medication and general self-management. Background.  Parkinson's disease is a chronic neurological condition that affects many dimensions of life, including threats to self-identity and confidence in self-management. Self-management has the potential to reduce costs through decreased hospital admissions, disease progression and avoidance of complications. While evidence for the relationships between self-management and self-efficacy and sense of coherence has been demonstrated in some chronic illness groups, this has not previously been demonstrated in Parkinson's disease. Results.  The independent predictors of better self-management were not being hospitalized in the last 6 months, more frequent symptom checking and better self-efficacy for self-management. The influence of other factors on self-management, such as sense of coherence, was mediated through self-efficacy. Support of family and others was associated with better self-efficacy both directly and through an improved sense of coherence. Conclusions and relevance to nursing practice.  The presence of informal support plays an important role in sustaining self-efficacy and sense of coherence and hence self-management in persons with Parkinson's disease. Since these attributes are amenable to change, nurses are in a good position to encourage participation in Parkinson's support groups, teach self-management skills through regular symptom monitoring and to assess and promote self-efficacy and sense of coherence. © 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd.

Incidence and Predictors of Incontinence Associated Skin Damage in Nursing Home Residents with New Onset Incontinence

PubMed Central

Bliss, Donna Z.; Mathiason, Michelle A.; Gurvich, Olga; Savik, Kay; Eberly, Lynn E.; Fisher, Jessica; Wiltzen, Kjerstie R.; Akermark, Haley; Hildebrandt, Amanda; Jacobson, Megan; Funk, Taylor; Beckman, Amanda; Larson, Reed

2016-01-01

Purpose The purpose of this study was to determine the incidence and predictors of incontinence associated dermatitis (IAD) in nursing home residents. Methods Records of a cohort of 10,713 elderly (aged 65+) newly incontinent nursing home residents in 448 nursing homes in 28 states free of IAD were followed for IAD development. Potential multi-level predictors of IAD were identified in four national datasets containing information about the characteristics of individual nursing home residents, nursing home care environment, and communities in which the nursing homes were located. A unique set of health practitioner orders provided information about IAD and the predictors of IAD prevention and pressure injuries in the extended perineal area. Analysis was based on hierarchical logistical regression. Results The incidence of IAD was 5.5%. Significant predictors of IAD were not receiving preventive interventions for IAD, presence of a perineal pressure injury, having greater functional limitations in activities of daily living, more perfusion problems, and lesser cognitive deficits. Conclusion Findings highlight the importance of prevention of IAD and treatment/prevention of pressure injuries. A Wound Ostomy and Continence (WOC) nurse offers expertise in these interventions and can educate staff about IAD predictors which can improve resident outcomes. Other recommendations include implementing plans of care to improve functional status, treat perfusion problems, and provide assistance with incontinence and skin care to residents with milder as well as greater cognitive deficits. PMID:28267124

Lithographic VCSEL array multimode and single mode sources for sensing and 3D imaging

NASA Astrophysics Data System (ADS)

Leshin, J.; Li, M.; Beadsworth, J.; Yang, X.; Zhang, Y.; Tucker, F.; Eifert, L.; Deppe, D. G.

2016-05-01

Sensing applications along with free space data links can benefit from advanced laser sources that produce novel radiation patterns and tight spectral control for optical filtering. Vertical-cavity surface-emitting lasers (VCSELs) are being developed for these applications. While oxide VCSELs are being produced by most companies, a new type of oxide-free VCSEL is demonstrating many advantages in beam pattern, spectral control, and reliability. These lithographic VCSELs offer increased power density from a given aperture size, and enable dense integration of high efficiency and single mode elements that improve beam pattern. In this paper we present results for lithographic VCSELs and describes integration into military systems for very low cost pulsed applications, as well as continuouswave applications in novel sensing applications. The VCSELs are being developed for U.S. Army for soldier weapon engagement simulation training to improve beam pattern and spectral control. Wavelengths in the 904 nm to 990 nm ranges are being developed with the spectral control designed to eliminate unwanted water absorption bands from the data links. Multiple beams and radiation patterns based on highly compact packages are being investigated for improved target sensing and transmission fidelity in free space data links. These novel features based on the new VCSEL sources are also expected to find applications in 3-D imaging, proximity sensing and motion control, as well as single mode sensors such as atomic clocks and high speed data transmission.

Determinants of engagement in mental health consumer-run organizations.

PubMed

Brown, Louis Davis; Townley, Greg

2015-04-01

Mental health consumer-run organizations (CROs) are a low-cost, evidence-based strategy for promoting recovery. To increase CRO utilization, characteristics that promote engagement need to be identified and encouraged. The study examined individual and organizational characteristics that predict three types of engagement in CROs-attendance, leadership involvement, and socially supportive involvement. Surveys were administered to 250 CRO members attending 20 CROs. Leaders of each CRO reported organizational characteristics through a separate questionnaire. Multilevel regression models examined relationships between predictors and indicators of CRO engagement. Perceived sense of community was the only characteristic that predicted attendance, leadership involvement, and socially supportive involvement (p<.001). Perceived organizational empowerment, shared leadership, peer counseling, and several demographic characteristics also predicted some measures of engagement. CROs that can effectively promote sense of community, organizational empowerment, shared leadership, and peer counseling may be better able to engage participants. The discussion considers several strategies to enhance these characteristics, such as collectively establishing values and practicing shared decision making.

Determinants of Engagement in Mental Health Consumer-Run Organizations

PubMed Central

Brown, Louis Davis; Townley, Greg

2015-01-01

Objective Mental health consumer-run organizations (CROs) are a low-cost, evidence-based strategy for promoting recovery. To increase CRO utilization, characteristics that promote engagement need to be identified and encouraged. The study examined individual and organizational characteristics that predict three types of engagement in CROs—attendance, leadership involvement, and socially supportive involvement. Methods Surveys were administered to 250 CRO members attending 20 CROs. Leaders of each CRO reported organizational characteristics through a separate questionnaire. Multilevel regression models examined relationships between predictors and indicators of CRO engagement. Results Perceived sense of community was the only characteristic that predicted attendance as well as leadership involvement and socially supportive involvement. Perceived organizational empowerment, shared leadership, peer counseling, and several demographic characteristics also predicted some measures of engagement. Conclusions CROs that can effectively promote sense of community, organizational empowerment, shared leadership, and peer counseling may be better able to engage participants. The discussion considers several strategies to enhance these characteristics, such as collectively establishing values and practicing shared decision making. PMID:25554965

Is weight gain really a catalyst for broader recovery?: The impact of weight gain on psychological symptoms in the treatment of adolescent anorexia nervosa.

PubMed

Accurso, Erin C; Ciao, Anna C; Fitzsimmons-Craft, Ellen E; Lock, James D; Le Grange, Daniel

2014-05-01

The main aims of this study were to describe change in psychological outcomes for adolescents with anorexia nervosa across two treatments, and to explore predictors of change, including baseline demographic and clinical characteristics, as well as weight gain over time. Participants were 121 adolescents with anorexia nervosa from a two-site (Chicago and Stanford) randomized controlled trial who received either family-based treatment or individual adolescent supportive psychotherapy. Psychological symptoms (i.e., eating disorder psychopathology, depressive symptoms, and self-esteem) were assessed at baseline, end of treatment, 6-month, and 12-month follow-up. Conditional multilevel growth models were used to test for predictors of slope for each outcome. Most psychological symptoms improved significantly from baseline to 12 month follow-up, regardless of treatment type. Depressive symptoms and dietary restraint were most improved, weight and shape concerns were least improved, and self-esteem was not at all improved. Weight gain emerged as a significant predictor of improved eating disorder pathology, with earlier weight gain having a greater impact on symptom improvement than later weight gain. Adolescents who presented with more severe, complex, and enduring clinical presentations (i.e., longer duration of illness, greater eating disorder pathology, binge-eating/purging subtype) also appeared to benefit more psychologically from treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Association Between Bereaved Families' Sense of Security and Their Experience of Death in Cancer Patients: Cross-Sectional Population-Based Study.

PubMed

Igarashi, Ayumi; Miyashita, Mitsunori; Morita, Tatsuya; Akizuki, Nobuya; Akiyama, Miki; Shirahige, Yutaka; Sato, Kazuki; Yamamoto-Mitani, Noriko; Eguchi, Kenji

2016-05-01

The sense of security scale was developed to indicate care quality within the community. Bereaved families have perspective to evaluate the quality of the care system. The aim was to examine associations between end-of-life care and sense of security regarding regional cancer care among bereaved families. A cross-sectional population-based survey was conducted with families of cancer patients who died in regional areas of Japan. A total of 1046 family caregivers of patients responded to surveys (effective response rate of 65%). In multiple regression analyses, the families' higher age (P < 0.001), home death (P = 0.039), better health status of the family at patients' end of life (P = 0.016), lower caregiving burden (P < 0.001), and elements of perceived good patient death, including being free from physical distress (P < 0.001), trusting the physician (P < 0.001), living in calm circumstances (P = 0.042), and feeling that one's life was fulfilling (P = 0.035), were associated with a higher sense of security. Quality of death and lower burden on family caregivers were associated with families' sense of security. This suggests strategies for improving care quality for each patient to improve the sense of security. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

PubMed Central

Li, Jiayin; Guo, Wenzhong; Chen, Zhonghui; Xiong, Neal

2017-01-01

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs. PMID:29117152

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks.

PubMed

Zheng, Haifeng; Li, Jiayin; Feng, Xinxin; Guo, Wenzhong; Chen, Zhonghui; Xiong, Neal

2017-11-08

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .

Citrate-based fluorescent materials for low-cost chloride sensing in the diagnosis of Cystic Fibrosis.

PubMed

Kim, Jimin P; Xie, Zhiwei; Creer, Michael; Liu, Zhiwen; Yang, Jian

2017-01-01

Chloride is an essential electrolyte that maintains homeostasis within the body, where abnormal chloride levels in biological fluids may indicate various diseases such as Cystic Fibrosis. However, current analytical solutions for chloride detection fail to meet the clinical needs of both high performance and low material or labor costs, hindering translation into clinical settings. Here we present a new class of fluorescence chloride sensors derived from a facile citrate -based synthesis platform that utilize dynamic quenching mechanisms. Based on this low-cost platform, we demonstrate for the first time a selective sensing strategy that uses a single fluorophore to detect multiple halides simultaneously, promising both selectivity and automation to improve performance and reduce labor costs. We also demonstrate the clinical utility of citrate-based sensors as a new sweat chloride test method for the diagnosis of Cystic Fibrosis by performing analytical validation with sweat controls and clinical validation with sweat from individuals with or without Cystic Fibrosis. Lastly, molecular modeling studies reveal the structural mechanism behind chloride sensing, serving to expand this class of fluorescence sensors with improved chloride sensitivities. Thus citrate-based fluorescent materials may enable low-cost, automated multi-analysis systems for simpler, yet accurate, point-of-care diagnostics that can be readily translated into clinical settings. More broadly, a wide range of medical, industrial, and environmental applications can be achieved with such a facile synthesis platform, demonstrated in our citrate-based biodegradable polymers with intrinsic fluorescence sensing.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Filtering method of star control points for geometric correction of remote sensing image based on RANSAC algorithm

NASA Astrophysics Data System (ADS)

Tan, Xiangli; Yang, Jungang; Deng, Xinpu

2018-04-01

In the process of geometric correction of remote sensing image, occasionally, a large number of redundant control points may result in low correction accuracy. In order to solve this problem, a control points filtering algorithm based on RANdom SAmple Consensus (RANSAC) was proposed. The basic idea of the RANSAC algorithm is that using the smallest data set possible to estimate the model parameters and then enlarge this set with consistent data points. In this paper, unlike traditional methods of geometric correction using Ground Control Points (GCPs), the simulation experiments are carried out to correct remote sensing images, which using visible stars as control points. In addition, the accuracy of geometric correction without Star Control Points (SCPs) optimization is also shown. The experimental results show that the SCPs's filtering method based on RANSAC algorithm has a great improvement on the accuracy of remote sensing image correction.

Hydrogen sensing properties of nanocomposite graphene oxide/Co-based metal organic frameworks (Co-MOFs@GO)

NASA Astrophysics Data System (ADS)

Fardindoost, Somayeh; Hatamie, Shadie; Iraji Zad, Azam; Razi Astaraei, Fatemeh

2018-01-01

This paper reports on hydrogen sensing based graphene oxide hybrid with Co-based metal organic frameworks (Co-MOFs@GO) prepared by the hydrothermal process. The texture and morphology of the hybrid were characterized by powder x-ray diffraction, scanning electron microscopy and Brunauer-Emmett-Teller analysis. Porous flower like structures assembled from Co-MOFs and GO flakes with sufficient specific surface area are obtained, which are ideal for gas molecules diffusion and interactions. Sensing performance of Co-MOFs@GO were tested and also improved by sputtering platinum (Pt) as a catalyst. The Pt-sputtered Co-MOFs@GO show outstanding hydrogen resistive-sensing with response and recovery times below 12 s at 15 °C. Also, they show stable, repeatable and selective responses to the target gas which make it suitable for the development of a high performance hydrogen sensor.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

Pressure sensing element based on the BN-graphene-BN heterostructure

NASA Astrophysics Data System (ADS)

Li, Mengwei; Wu, Chenggen; Zhao, Shiliang; Deng, Tao; Wang, Junqiang; Liu, Zewen; Wang, Li; Wang, Gao

2018-04-01

In this letter, we report a pressure sensing element based on the graphene-boron nitride (BN) heterostructure. The heterostructure consists of monolayer graphene sandwiched between two layers of vertically stacked dielectric BN nanofilms. The BN layers were used to protect the graphene layer from oxidation and pollution. Pressure tests were performed to investigate the characteristics of the BN-graphene-BN pressure sensing element. A sensitivity of 24.85 μV/V/mmHg is achieved in the pressure range of 130-180 kPa. After exposing the BN-graphene-BN pressure sensing element to the ambient environment for 7 days, the relative resistance change in the pressure sensing element is only 3.1%, while that of the reference open-faced graphene device without the BN protection layers is 15.7%. Thus, this strategy is promising for fabricating practical graphene pressure sensors with improved performance and stability.

A clinical study of autogenic training-based behavioral treatment for panic disorder.

PubMed

Sakai, M

1996-03-01

The present study investigated the effect of autogenic training-based behavioral treatment for panic disorder and identified the predictors of treatment outcome. Thirty-four patients meeting DSM-III-R criteria for panic disorder received autogenic training-based behavioral treatment from October 1981 to December 1994. They were treated individually by the author. The medical records of the patients were investigated for the purpose of this study. The results showed that this autogenic training-based behavioral treatment had successful results. Fifteen patients were cured, nine much improved, five improved, and five unchanged at the end of the treatment. Improvement trends were found as for the severity of panic attack and the severity of agoraphobic avoidance. No consistent findings about predictors emerged when such pretreatment variables as demographics and severity of symptoms were used to predict the outcome. Also, three treatment variables showed useful predictive power. First, practicing the second standard autogenic training exercise satisfactorily predicted better outcomes. Second, application of in vivo exposure was found to be positively associated with the treatment outcome in patients with agoraphobic avoidance. Third, longer treatment periods were associated with better outcomes. These findings suggested that the autogenic training-based behavioral treatment could provide relief to the majority of panic disorder patients.

Structural and electronic engineering of 3DOM WO3 by alkali metal doping for improved NO2 sensing performance

NASA Astrophysics Data System (ADS)

Wang, Zhihua; Fan, Xiaoxiao; Han, Dongmei; Gu, Fubo

2016-05-01

Novel alkali metal doped 3DOM WO3 materials were prepared using a simple colloidal crystal template method. Raman, XRD, SEM, TEM, XPS, PL, Hall and UV-Vis techniques were used to characterize the structural and electronic properties of all the products, while the corresponding sensing performances targeting ppb level NO2 were determined at different working temperatures. For the overall goal of structural and electronic engineering, the co-effect of structural and electronic properties on the improved NO2 sensing performance of alkali metal doped 3DOM WO3 was studied. The test results showed that the gas sensing properties of 3DOM WO3/Li improved the most, with the fast response-recovery time and excellent selectivity. More importantly, the response of 3DOM WO3/Li to 500 ppb NO2 was up to 55 at room temperature (25 °C). The especially high response to ppb level NO2 at room temperature (25 °C) in this work has a very important practical significance. The best sensing performance of 3DOM WO3/Li could be ascribed to the most structure defects and the highest carrier mobility. And the possible gas sensing mechanism based on the model of the depletion layer was proposed to demonstrate that both structural and electronic properties are responsible for the NO2 sensing behavior.Novel alkali metal doped 3DOM WO3 materials were prepared using a simple colloidal crystal template method. Raman, XRD, SEM, TEM, XPS, PL, Hall and UV-Vis techniques were used to characterize the structural and electronic properties of all the products, while the corresponding sensing performances targeting ppb level NO2 were determined at different working temperatures. For the overall goal of structural and electronic engineering, the co-effect of structural and electronic properties on the improved NO2 sensing performance of alkali metal doped 3DOM WO3 was studied. The test results showed that the gas sensing properties of 3DOM WO3/Li improved the most, with the fast response-recovery time and excellent selectivity. More importantly, the response of 3DOM WO3/Li to 500 ppb NO2 was up to 55 at room temperature (25 °C). The especially high response to ppb level NO2 at room temperature (25 °C) in this work has a very important practical significance. The best sensing performance of 3DOM WO3/Li could be ascribed to the most structure defects and the highest carrier mobility. And the possible gas sensing mechanism based on the model of the depletion layer was proposed to demonstrate that both structural and electronic properties are responsible for the NO2 sensing behavior. Electronic supplementary information (ESI) available: Raman, SEM, TEM, mapping, XPS and PL images; transient plot; response of 3DOM WO3/Li to NO2 concentration, sensing stability and the corresponding log (Sg - 1) versus log Cg curves. See DOI: 10.1039/c6nr00858e

The decoration of Nb-doped TiO2 microspheres by reduced graphene oxide for enhanced CO gas sensing

NASA Astrophysics Data System (ADS)

Liang, Feng; Chen, Shimin; Xie, Wei; Zou, Changwei

2018-03-01

Reduced graphene oxide (rGO) was used to improve the CO sensing properties of Nb-doped TiO2 (TiO2:Nb) microspheres by an improved ultrasonic spray pyrolysis deposition method. The responses of the sensor dramatically increased as the CO gas concentration increased from 100 to 1000 ppm, which indicated that rGO/TiO2:Nb-based sensor had a wide detection range for CO gas. Moreover, the conductivity of sensor based on the rGO/TiO2:Nb would be greatly improved than that of sensors without decoration by rGO. The enhanced gas sensing performance of the rGO/TiO2:Nb can be attributed to two reasons. Firstly, rGO could facilitate charge transport from TiO2 to graphene which provided a preferential pathway for the charge currents. Secondly, the decorated TiO2 by rGO could provide more active sites such as oxygen vacancy (VO) which could capture electrons from the conductance band and form a space-charge region.

Predictors of retention among African American and Hispanic older adult research participants in the Well Elderly 2 randomized controlled trial.

PubMed

Carlson, Mike; Jackson, Jeanne; Mandel, Deborah; Blanchard, Jeanine; Holguin, Jess; Lai, Mei-Ying; Marterella, Abbey; Vigen, Cheryl; Gleason, Sarah; Lam, Claudia; Azen, Stan; Clark, Florence

2014-04-01

The purpose of this study was to document predictors of long-term retention among minority participants in the Well Elderly 2 Study, a randomized controlled trial of a lifestyle intervention for community-dwelling older adults. The primary sample included 149 African American and 92 Hispanic men and women aged 60 to 95 years, recruited at senior activity centers and senior residences. Chi-square and logistic regression procedures were undertaken to examine study-based, psychosocial and health-related predictors of retention at 18 months following study entry. For both African Americans and Hispanics, intervention adherence was the strongest predictor. Retention was also related to high active coping and average (vs. high or low) levels of activity participation among African Americans and high social network strength among Hispanics. The results suggest that improved knowledge of the predictors of retention among minority elders can spawn new retention strategies that can be applied at individual, subgroup, and sample-wide levels.

Predictors of outcome for cognitive behaviour therapy in binge eating disorder.

PubMed

Lammers, Mirjam W; Vroling, Maartje S; Ouwens, Machteld A; Engels, Rutger C M E; van Strien, Tatjana

2015-05-01

The aim of this naturalistic study was to identify pretreatment predictors of response to cognitive behaviour therapy in treatment-seeking patients with binge eating disorder (BED; N = 304). Furthermore, we examined end-of-treatment factors that predict treatment outcome 6 months later (N = 190). We assessed eating disorder psychopathology, general psychopathology, personality characteristics and demographic variables using self-report questionnaires. Treatment outcome was measured using the bulimia subscale of the Eating Disorder Inventory 1. Predictors were determined using hierarchical linear regression analyses. Several variables significantly predicted outcome, four of which were found to be both baseline predictors of treatment outcome and end-of-treatment predictors of follow-up: Higher levels of drive for thinness, higher levels of interoceptive awareness, lower levels of binge eating pathology and, in women, lower levels of body dissatisfaction predicted better outcome in the short and longer term. Based on these results, several suggestions are made to improve treatment outcome for BED patients. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

From sense of place to visualization of place: examining people-place relationships for insight on developing geovisualizations.

PubMed

Newell, Robert; Canessa, Rosaline

2018-02-01

Effective resource planning incorporates people-place relationships, allowing these efforts to be inclusive of the different local beliefs, interests, activities and needs. 'Geovisualizations' can serve as potentially powerful tools for facilitating 'place-conscious' resource planning, as they can be developed with high degrees of realism and accuracy, allowing people to recognize and relate to them as 'real places'. However, little research has been done on this potential, and the place-based applications of these visual tools are poorly understood. This study takes steps toward addressing this gap by exploring the relationship between sense of place and 'visualization of place'. Residents of the Capital Regional District of BC, Canada, were surveyed about their relationship with local coastal places, concerns for the coast, and how they mentally visualize these places. Factor analysis identified four sense of place dimensions - nature protection values, community and economic well-being values, place identity and place dependence, and four coastal concerns dimensions - ecological, private opportunities, public space and boating impacts. Visualization data were coded and treated as dependent variables in a series of logistic regressions that used sense of place and coastal concerns dimensions as predictors. Results indicated that different aspects of sense of place and (to a lesser degree) concerns for places influence the types of elements people include in their mental visualization of place. In addition, sense of place influenced the position and perspective people assume in these visualizations. These findings suggest that key visual elements and perspectives speak to different place relationships, which has implications for developing and using geovisualizations in terms of what elements should be included in tools and (if appropriate) depicted as affected by potential management or development scenarios.

Predictors of sense of coherence in typically developing adolescent siblings of individuals with autism spectrum disorder.

PubMed

Smith, L O; Elder, J H; Storch, E A; Rowe, M A

2015-01-01

Children with autism spectrum disorder (ASD) may be a stressor for family members yet there is little published research on the impact of having a child with ASD on their typically developing (TD) adolescent siblings. According to Antonovsky's salutogenic model, a strong sense of coherence leads to the view that the stressor is a manageable challenge rather than a burden and promotes healthier adaptation. This study examines the relationship between stress, TD sibling resources and the sense of coherence in TD siblings. This quantitative mail-based study uses a survey methodology, analysing the responses of TD adolescent siblings (n = 96) of individuals with autism, Asperger's syndrome, or pervasive developmental disorder - not otherwise specified to several rating scales. Adolescent siblings, ages 11 to 18 years, completed the Adolescent Coping Orientation for Problem Experience (ACOPE), Network of Relationship Inventory - Social Provision Version (NRI-SPV), Youth Self Report (YSR), and Sense of Coherence (SOC) instruments; parents completed the Child Autism Rating Scale - 2nd Edition (CARS-2). The salutogenesis model was used to guide and inform this research. Findings suggested the following: (a) the stress of ASD severity and resource of adjustment are related in TD adolescent siblings; (b) TD sibling adjustment has a strong relationship with sense of coherence levels; and (c) a greater number of positive coping strategies buffer TD sibling coherence levels when ASD severity scores are high. ASD severity and TD adolescent sibling resources influence sense of coherence in adolescent TD siblings of individuals with ASD. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat

NASA Astrophysics Data System (ADS)

Hilker, Thomas; Coops, Nicholas C.; Gaulton, Rachel; Wulder, Michael A.; Cranston, Jerome; Stenhouse, Gordon

2011-01-01

An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

Domain General Mediators of the Relation between Kindergarten Number Sense and First-Grade Mathematics Achievement

PubMed Central

Hassinger-Das, Brenna; Jordan, Nancy C.; Glutting, Joseph; Irwin, Casey; Dyson, Nancy

2013-01-01

Domain general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that attention problems and executive functioning both were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance while executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. PMID:24237789

Domain-general mediators of the relation between kindergarten number sense and first-grade mathematics achievement.

PubMed

Hassinger-Das, Brenna; Jordan, Nancy C; Glutting, Joseph; Irwin, Casey; Dyson, Nancy

2014-02-01

Domain-general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that both attention problems and executive functioning were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance, whereas executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. Copyright © 2013 Elsevier Inc. All rights reserved.

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Human-Centered Systems Analysis of Aircraft Separation from Adverse Weather: Implications for Icing Remote Sensing

NASA Technical Reports Server (NTRS)

Vigeant-Langlois, Laurence; Hansman, R. John, Jr.

2003-01-01

The objective of this project was to propose a means to improve aviation weather information, training procedures based on a human-centered systems approach. Methodology: cognitive analysis of pilot's tasks; trajectory-based approach to weather information; contingency planning support; and implications for improving weather information.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

PubMed Central

Li, Zhao-Liang

2018-01-01

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. PMID:29373548

Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

NASA Technical Reports Server (NTRS)

Miller, L. D.; Tom, C.; Nualchawee, K.

1977-01-01

A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

2001-01-01

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

The analysis of temperature effect and temperature compensation of MOEMS accelerometer based on a grating interferometric cavity

NASA Astrophysics Data System (ADS)

Han, Dandan; Bai, Jian; Lu, Qianbo; Lou, Shuqi; Jiao, Xufen; Yang, Guoguang

2016-08-01

There is a temperature drift of an accelerometer attributed to the temperature variation, which would adversely influence the output performance. In this paper, a quantitative analysis of the temperature effect and the temperature compensation of a MOEMS accelerometer, which is composed of a grating interferometric cavity and a micromachined sensing chip, are proposed. A finite-element-method (FEM) approach is applied in this work to simulate the deformation of the sensing chip of the MOEMS accelerometer at different temperature from -20°C to 70°C. The deformation results in the variation of the distance between the grating and the sensing chip of the MOEMS accelerometer, modulating the output intensities finally. A static temperature model is set up to describe the temperature characteristics of the accelerometer through the simulation results and the temperature compensation is put forward based on the temperature model, which can improve the output performance of the accelerometer. This model is permitted to estimate the temperature effect of this type accelerometer, which contains a micromachined sensing chip. Comparison of the output intensities with and without temperature compensation indicates that the temperature compensation can improve the stability of the output intensities of the MOEMS accelerometer based on a grating interferometric cavity.

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

NASA Technical Reports Server (NTRS)

Swain, P. H. (Principal Investigator)

1981-01-01

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented.

Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

PubMed Central

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-01-01

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

PubMed

Zinszer, Kate; Kigozi, Ruth; Charland, Katia; Dorsey, Grant; Brewer, Timothy F; Brownstein, John S; Kamya, Moses R; Buckeridge, David L

2015-06-18

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

Essential climatic variables estimation with satellite imagery

NASA Astrophysics Data System (ADS)

Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

Position and speed control of brushless DC motors using sensorless techniques and application trends.

PubMed

Gamazo-Real, José Carlos; Vázquez-Sánchez, Ernesto; Gómez-Gil, Jaime

2010-01-01

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.

Association of School-Based Physical Activity Opportunities, Socioeconomic Status, and Third-Grade Reading

ERIC Educational Resources Information Center

Kern, Ben D.; Graber, Kim C.; Shen, Sa; Hillman, Charles H.; McLoughlin, Gabriella

2018-01-01

Background: Socioeconomic status (SES) is the most accurate predictor of academic performance in US schools. Third-grade reading is highly predictive of high school graduation. Chronic physical activity (PA) is shown to improve cognition and academic performance. We hypothesized that school-based PA opportunities (recess and physical education)…

Attitudes and beliefs of emergency department staff regarding alcohol-related presentations.

PubMed

Indig, Devon; Copeland, Jan; Conigrave, Katherine M; Rotenko, Irene

2009-01-01

This study examined emergency department (ED) staff attitudes and beliefs about alcohol-related ED presentations in order to recommend improved detection and brief intervention strategies. The survey was conducted at two inner-Sydney hospital EDs in 2006 to explore ED clinical staff's attitudes, current practice and barriers for managing alcohol-related ED presentations. The sample included N=78 ED staff (54% nurses, 46% doctors), representing a 30% response rate. Management of alcohol-related problems was not routine among ED staff, with only 5% usually formally screening for alcohol problems, only 16% usually conducting brief interventions, and only 27% usually providing a referral to specialist treatment services. Over 85% of ED staff indicated that lack of patient motivation made providing alcohol interventions very difficult. Significant predictors of good self-reported practice among ED staff for patients with alcohol problems included: being a doctor, being confident and having a sense of responsibility towards managing patients with alcohol-related problems. This study reported that many staff lack the confidence or sense of clinical responsibility to fully and appropriately manage ED patients with alcohol-related problems. ED staff appear to require additional training, resources and support to enhance their management of patients with alcohol-related problems.

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.

Hospital management training and improvement in managerial skills: Serbian experience.

PubMed

Supic, Zorica Terzic; Bjegovic, Vesna; Marinkovic, Jelena; Milicevic, Milena Santric; Vasic, Vladimir

2010-06-01

The purpose of this study was to analyze the improvement of managerial skills of hospitals' top managers after a specific management training programme, and to explore possible predictors and relations. The study was conducted during the years 2006 and 2007 with cohort of 107 managers from 20 Serbian general hospitals. The managers self-assessed the improvement in their managerial skills before and after the training programme. After the training programme, all managers' skills had improved. The biggest improvement was in the following skills: organizing daily activities, motivating and guiding others, supervising the work of others, group discussion, and situation analysis. The least improved were: applying creative techniques, working well with peers, professional self-development, written communication, and operational planning. Identified predictors of improvement were: shorter years of managerial experience, type of manager, type of profession, and recognizing the importance of the managerial skills in oral communication, evidence-based decision making, and supervising the work of others. Specific training programme related to strategic management can increase managerial competencies, which are an important source of competitive advantage for organizations. Copyright (c) 2010 Elsevier Ireland Ltd. All rights reserved.

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles.

PubMed

Sharma, Ronesh; Bayarjargal, Maitsetseg; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-01-21

Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools. https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download. Copyright © 2017 Elsevier Ltd. All rights reserved.

Corticospinal excitability as a predictor of functional gains at the affected upper limb following robotic training in chronic stroke survivors

PubMed Central

Milot, Marie-Hélène; Spencer, Steven J.; Chan, Vicky; Allington, James P.; Klein, Julius; Chou, Cathy; Pearson-Fuhrhop, Kristin; Bobrow, James E.; Reinkensmeyer, David J.; Cramer, Steven C.

2014-01-01

Background Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective Determine the predictive ability of behavioral and brain measures to improve selection of individuals for robotic training. Methods Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, fMRI, diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Results Training was associated with an average gain of 6±5 blocks on the BBT (p<0.0001). Bivariate analysis revealed that lower baseline motor evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Conclusion Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains. PMID:24642382

Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder

PubMed Central

Reed, Margot O.; Jakubovski, Ewgeni; Johnson, Jessica A.

2017-01-01

Abstract Objective: To explore predictors of 8-year school-based behavioral outcomes in attention-deficit/hyperactivity disorder (ADHD). Methods: We examined potential baseline predictors of school-based behavioral outcomes in children who completed the 8-year follow-up in the multimodal treatment study of children with ADHD. Stepwise logistic regression and receiver operating characteristic (ROC) analysis identified baseline predictors that were associated with a higher risk of truancy, school discipline, and in-school fights. Results: Stepwise regression analysis explained between 8.1% (in-school fights) and 12.0% (school discipline) of the total variance in school-based behavioral outcomes. Logistic regression identified several baseline characteristics that were associated with school-based behavioral difficulties 8 years later, including being male (associated with truancy and school discipline), African American (school discipline, in-school fights), increased conduct disorder (CD) symptoms (truancy), decreased affection from parents (school discipline), ADHD severity (in-school fights), and study site (truancy and school discipline). ROC analyses identified the most discriminative predictors of truancy, school discipline, and in-school fights, which were Aggression and Conduct Problem Scale Total score, family income, and race, respectively. Conclusions: A modest, but nontrivial portion of school-based behavioral outcomes, was predicted by baseline childhood characteristics. Exploratory analyses identified modifiable (lack of paternal involvement, lower parental knowledge of behavioral principles, and parental use of physical punishment), somewhat modifiable (income and having comorbid CD), and nonmodifiable (African American and male) factors that were associated with school-based behavioral difficulties. Future research should confirm that the associations between earlier specific parenting behaviors and poor subsequent school-based behavioral outcomes are, indeed, causally related and independent cooccurring childhood psychopathology. Future research might target increasing paternal involvement and parental knowledge of behavioral principles and reducing use of physical punishment to improve school-based behavioral outcomes in children with ADHD. PMID:28253029

Predictors of Long-Term School-Based Behavioral Outcomes in the Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder.

PubMed

Reed, Margot O; Jakubovski, Ewgeni; Johnson, Jessica A; Bloch, Michael H

2017-05-01

To explore predictors of 8-year school-based behavioral outcomes in attention-deficit/hyperactivity disorder (ADHD). We examined potential baseline predictors of school-based behavioral outcomes in children who completed the 8-year follow-up in the multimodal treatment study of children with ADHD. Stepwise logistic regression and receiver operating characteristic (ROC) analysis identified baseline predictors that were associated with a higher risk of truancy, school discipline, and in-school fights. Stepwise regression analysis explained between 8.1% (in-school fights) and 12.0% (school discipline) of the total variance in school-based behavioral outcomes. Logistic regression identified several baseline characteristics that were associated with school-based behavioral difficulties 8 years later, including being male (associated with truancy and school discipline), African American (school discipline, in-school fights), increased conduct disorder (CD) symptoms (truancy), decreased affection from parents (school discipline), ADHD severity (in-school fights), and study site (truancy and school discipline). ROC analyses identified the most discriminative predictors of truancy, school discipline, and in-school fights, which were Aggression and Conduct Problem Scale Total score, family income, and race, respectively. A modest, but nontrivial portion of school-based behavioral outcomes, was predicted by baseline childhood characteristics. Exploratory analyses identified modifiable (lack of paternal involvement, lower parental knowledge of behavioral principles, and parental use of physical punishment), somewhat modifiable (income and having comorbid CD), and nonmodifiable (African American and male) factors that were associated with school-based behavioral difficulties. Future research should confirm that the associations between earlier specific parenting behaviors and poor subsequent school-based behavioral outcomes are, indeed, causally related and independent cooccurring childhood psychopathology. Future research might target increasing paternal involvement and parental knowledge of behavioral principles and reducing use of physical punishment to improve school-based behavioral outcomes in children with ADHD.

A Localized Surface Plasmon Resonance Sensor Using Double-Metal-Complex Nanostructures and a Review of Recent Approaches

PubMed Central

Ahn, Heesang; Song, Hyerin; Kim, Kyujung

2017-01-01

From active developments and applications of various devices to acquire outside and inside information and to operate based on feedback from that information, the sensor market is growing rapidly. In accordance to this trend, the surface plasmon resonance (SPR) sensor, an optical sensor, has been actively developed for high-sensitivity real-time detection. In this study, the fundamentals of SPR sensors and recent approaches for enhancing sensing performance are reported. In the section on the fundamentals of SPR sensors, a brief description of surface plasmon phenomena, SPR, SPR-based sensing applications, and several configuration types of SPR sensors are introduced. In addition, advanced nanotechnology- and nanofabrication-based techniques for improving the sensing performance of SPR sensors are proposed: (1) localized SPR (LSPR) using nanostructures or nanoparticles; (2) long-range SPR (LRSPR); and (3) double-metal-layer SPR sensors for additional performance improvements. Consequently, a high-sensitivity, high-biocompatibility SPR sensor method is suggested. Moreover, we briefly describe issues (miniaturization and communication technology integration) for future SPR sensors. PMID:29301238

Impact of correlation of predictors on discrimination of risk models in development and external populations.

PubMed

Kundu, Suman; Mazumdar, Madhu; Ferket, Bart

2017-04-19

The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations. We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity. For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects. Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.

A number sense intervention for low-income kindergartners at risk for mathematics difficulties.

PubMed

Dyson, Nancy I; Jordan, Nancy C; Glutting, Joseph

2013-01-01

Early number sense is a strong predictor of later success in school mathematics. A disproportionate number of children from low-income families come to first grade with weak number competencies, leaving them at risk for a cycle of failure. The present study examined the effects of an 8-week number sense intervention to develop number competencies of low-income kindergartners (N = 121). The intervention purposefully targeted whole number concepts related to counting, comparing, and manipulating sets. Children were randomly assigned to either a number sense intervention or a business as usual contrast group. The intervention was carried out in small-group, 30-min sessions, 3 days per week, for a total of 24 sessions. Controlling for number sense at pretest, the intervention group made meaningful gains relative to the control group at immediate as well delayed posttest on a measure of early numeracy. Intervention children also performed better than controls on a standardized test of mathematics calculation at immediate posttest.

A Number Sense Intervention for Low-Income Kindergartners at Risk for Mathematics Difficulties

PubMed Central

Dyson, Nancy I.; Jordan, Nancy C.; Glutting, Joseph

2012-01-01

Early number sense is a strong predictor of later success in school mathematics. A disproportionate number of children from low-income families come to first grade with weak number competencies, leaving them at-risk for a cycle of failure. The present study examined the effects of an 8-week number sense intervention to develop number competencies of low-income kindergartners (n = 121). The intervention purposefully targeted whole number concepts related to counting, comparing, and manipulating sets. Children were randomly assigned either to a number sense intervention or a business as usual contrast group. The intervention was carried out in small group, 30-minute sessions, 3 days per week for a total of 24 sessions. Controlling for number sense at pretest, the intervention group made meaningful gains relative to the control group at immediate as well delayed posttest on a measure of early numeracy. Intervention children also performed better than controls on a standardized test of mathematics calculation at immediate posttest. PMID:21685346

Preschool children's response to behavioural parent training and parental predictors of outcome in routine clinical care.

PubMed

van der Veen-Mulders, Lianne; Hoekstra, Pieter J; Nauta, Maaike H; van den Hoofdakker, Barbara J

2018-01-01

To investigate the effectiveness of behavioral parent training (BPT) for preschool children with disruptive behaviours and to explore parental predictors of response. Parents of 68 preschool children, aged between 2.7 and 5.9 years, participated in BPT. We evaluated the changes in children's behaviour after BPT with a one group pretest-posttest design, using a waiting period for a double pretest. Outcome was based on parents' reports of the intensity and number of behaviour problems on the Eyberg Child Behavior Inventory. Predictor variables included parents' attention-deficit/hyperactivity disorder symptoms, antisocial behaviours, and alcohol use, and maternal parenting self-efficacy and disciplining. Mother-reported child behaviour problems did not change in the waiting period but improved significantly after BPT (d = 0.63). High levels of alcohol use by fathers and low levels of maternal ineffective disciplining were each associated with somewhat worse outcome. BPT under routine care conditions clearly improves disruptive behaviours in preschool children. Mothers who consider themselves as inadequate in disciplining and mothers whose partners do not consume high levels of alcohol report the largest improvements. Copyright © 2017 John Wiley & Sons, Ltd.

Mapping SOC (Soil Organic Carbon) using LiDAR-derived vegetation indices in a random forest regression model

NASA Astrophysics Data System (ADS)

Will, R. M.; Glenn, N. F.; Benner, S. G.; Pierce, J. L.; Spaete, L.; Li, A.

2015-12-01

Quantifying SOC (Soil Organic Carbon) storage in complex terrain is challenging due to high spatial variability. Generally, the challenge is met by transforming point data to the entire landscape using surrogate, spatially-distributed, variables like elevation or precipitation. In many ecosystems, remotely sensed information on above-ground vegetation (e.g. NDVI) is a good predictor of below-ground carbon stocks. In this project, we are attempting to improve this predictive method by incorporating LiDAR-derived vegetation indices. LiDAR provides a mechanism for improved characterization of aboveground vegetation by providing structural parameters such as vegetation height and biomass. In this study, a random forest model is used to predict SOC using a suite of LiDAR-derived vegetation indices as predictor variables. The Reynolds Creek Experimental Watershed (RCEW) is an ideal location for a study of this type since it encompasses a strong elevation/precipitation gradient that supports lower biomass sagebrush ecosystems at low elevations and forests with more biomass at higher elevations. Sagebrush ecosystems composed of Wyoming, Low and Mountain Sagebrush have SOC values ranging from .4 to 1% (top 30 cm), while higher biomass ecosystems composed of aspen, juniper and fir have SOC values approaching 4% (top 30 cm). Large differences in SOC have been observed between canopy and interspace locations and high resolution vegetation information is likely to explain plot scale variability in SOC. Mapping of the SOC reservoir will help identify underlying controls on SOC distribution and provide insight into which processes are most important in determining SOC in semi-arid mountainous regions. In addition, airborne LiDAR has the potential to characterize vegetation communities at a high resolution and could be a tool for improving estimates of SOC at larger scales.

Telerobotic Surgery: An Intelligent Systems Approach to Mitigate the Adverse Effects of Communication Delay. Chapter 4

NASA Technical Reports Server (NTRS)

Cardullo, Frank M.; Lewis, Harold W., III; Panfilov, Peter B.

2007-01-01

An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.

Utilizing Computerized Cognitive Training to Improve Working Memory and Encoding: Piloting a School-Based Intervention

ERIC Educational Resources Information Center

Wiest, Dudley J.; Wong, Eugene H.; Minero, Laura P.; Pumaccahua, Tessy T.

2014-01-01

Working memory has been well documented as a significant predictor of academic outcomes (e.g., reading and math achievement as well as general life outcomes). The purpose of this study was to investigate the effectiveness of computerized cognitive training to improve both working memory and encoding abilities in a school setting. Thirty students…

OLED-based biosensing platform with ZnO nanoparticles for enzyme immobilization

NASA Astrophysics Data System (ADS)

Cai, Yuankun; Shinar, Ruth; Shinar, Joseph

2009-08-01

Organic light-emitting diode (OLED)-based sensing platforms are attractive for photoluminescence (PL)-based monitoring of a variety of analytes. Among the promising OLED attributes for sensing applications is the thin and flexible size and design of the OLED pixel array that is used for PL excitation. To generate a compact, fielddeployable sensor, other major sensor components, such as the sensing probe and the photodetector, in addition to the thin excitation source, should be compact. To this end, the OLED-based sensing platform was tested with composite thin biosensing films, where oxidase enzymes were immobilized on ZnO nanoparticles, rather than dissolved in solution, to generate a more compact device. The analytes tested, glucose, cholesterol, and lactate, were monitored by following their oxidation reactions in the presence of oxygen and their respective oxidase enzymes. During such reactions, oxygen is consumed and its residual concentration, which is determined by the initial concentration of the above-mentioned analytes, is monitored. The sensors utilized the oxygen-sensitive dye Pt octaethylporphyrin, embedded in polystyrene. The enzymes were sandwiched between two thin ZnO layers, an approach that was found to improve the stability of the sensing probes.

Severity, course, and predictors of sleep disruption following hematopoietic cell transplantation: a secondary data analysis from the BMT CTN 0902 trial.

PubMed

Jim, Heather S L; Sutton, Steven; Majhail, Navneet S; Wood, William A; Jacobsen, Paul B; Wingard, John R; Wu, Juan; Knight, Jennifer M; Syrjala, Karen L; Lee, Stephanie J

2018-03-07

Sleep disruption has received little attention in hematopoietic cell transplantation (HCT). The goal of this study was to describe severity, course, and predictors of sleep disruption following HCT. A secondary data analysis was conducted of the Blood and Marrow Transplantation Clinical Trials Network (BMT CTN) 0902 study. Participants completed a modified version of the Pittsburgh Sleep Quality Index prior to transplant and 100 and 180 days posttransplant. Growth mixture models were used to characterize subgroups of patients based on baseline sleep disruption and change over time. A total of 570 patients (mean age 55 years, 42% female) were included in the current analyses. Patients could be grouped into four distinct classes based on sleep disruption: (1) clinically significant sleep disruption at baseline that did not improve over time (20%); (2) clinically significant sleep disruption at baseline that improved over time (22%); (3) sleep disruption that did not reach clinical significance at baseline and did not improve over time (45%); and (4) no sleep disruption at baseline or over time (13%). These data provide a more comprehensive understanding of sleep disruption that can be used to develop interventions to improve sleep in HCT recipients.

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Predictors of Nursing Home Residents' Participation in Activity Programs.

ERIC Educational Resources Information Center

Voelkl, Judith E.; And Others

1995-01-01

Examines the relationship between resident characteristics and time participating in activities. For the 2,672 nursing home residents studied, measures of resource use, cognitive abilities, depression, sense of initiative/involvement, activity repertoire, location preferences, and gender were all found to be significant in explaining the amount of…

Motivation and Job Satisfaction of Catholic School Teachers

ERIC Educational Resources Information Center

Convey, John J.

2010-01-01

The study examined the relationship between Catholic school teachers' motivation and job satisfaction. The data came from a survey of 716 teachers in three dioceses (Atlanta, Biloxi, and Cheyenne). The school's academic philosophy and its environment were important predictors of the teachers' satisfaction with their sense of efficacy regarding…

Background Characteristics as Predictors of Greek Teachers' Self-Efficacy

ERIC Educational Resources Information Center

Gkolia, Aikaterini; Dimitrios, Belias A.; Koustelios, Athanasios

2016-01-01

Purpose: The purpose of this paper is to investigate the relationship between elementary and secondary teachers' background characteristics and constructs of self-efficacy, using the Teachers' Sense of Efficacy Scale--TSES, during a difficult economic period for Greece and other European countries. Design/methodology/approach Equation modeling…

Significance of Perceived Social Expectation and Implications to Conservation Education: Turtle Conservation as a Case Study

NASA Astrophysics Data System (ADS)

Lo, Alex Y.; Chow, Alex T.; Cheung, Sze Man

2012-11-01

The likelihood of participating in wildlife conservation programs is dependent on social influences and circumstances. This view is validated by a case study of behavioral intention to support conservation of Asian turtles. A total of 776 college students in China completed a questionnaire survey designed to identify factors associated with their intention to support conservation. A regression model explained 48 % of variance in the level of intention. Perceived social expectation was the strongest predictor, followed by attitudes toward turtle protection and perceived behavioral control, altogether explaining 44 %. Strong ethics and socio-economic variables had some statistical significant impacts and accounted for 3 % of the variance. The effects of general environmental awareness, trust and responsibility ascription were modest. Knowledge about turtles was a weak predictor. We conclude that perceived social expectation is a limiting factor of conservation behavior. Sustained interest and commitment to conservation can be created by enhancing positive social influences. Conservation educators should explore the potential of professionally supported, group-based actions that can nurture a sense of collective achievement as part of an educational campaign.

Integrating a Career Planning and Development Program into the Baccalaureate Nursing Curriculum: Part III. Impact on Faculty's Career Satisfaction and Confidence in Providing Student Career Coaching.

PubMed

Waddell, Janice; Spalding, Karen; Navarro, Justine; Gaitana, Gianina

2015-11-25

As career satisfaction has been identified as a predictor of retention of nurses across all sectors, it is important that career satisfaction of both new and experienced nursing faculty is recognized in academic settings. A study of a curriculum-based career planning and development (CPD) program was conducted to determine the program's effects on participating students, new graduate nurses, and faculty. This third in a series of three papers reports on how the CPD intervention affected faculty participants' sense of career satisfaction and confidence in their role as career educators and coaches. Faculty who participated in the intervention CPD intervention group reported an increase in confidence in their ability to provide career coaching and education to students. They further indicated that their own career development served to enhance career satisfaction; an outcome identified as a predictor of faculty career satisfaction. Study results suggest that interventions such as the one described in this paper can have a potentially positive impact in other settings as well.

Smith predictor with sliding mode control for processes with large dead times

NASA Astrophysics Data System (ADS)

Mehta, Utkal; Kaya, İbrahim

2017-11-01

The paper discusses the Smith Predictor scheme with Sliding Mode Controller (SP-SMC) for processes with large dead times. This technique gives improved load-disturbance rejection with optimum input control signal variations. A power rate reaching law is incorporated in the sporadic part of sliding mode control such that the overall performance recovers meaningfully. The proposed scheme obtains parameter values by satisfying a new performance index which is based on biobjective constraint. In simulation study, the efficiency of the method is evaluated for robustness and transient performance over reported techniques.

Predictors of improvement in sexual function of women with urinary incontinence after treatment with pelvic floor exercises: a secondary analysis.

PubMed

Sacomori, Cinara; Cardoso, Fernando Luiz

2015-03-01

Women with urinary incontinence (UI) frequently present with complaints of sexual problems. To evaluate the predictors of sexual function improvement after participating in three physical therapy sessions and performing home-based pelvic floor muscle exercises (PFME) for the treatment of female UI. This is a secondary analysis of a randomized trial with a 3-month follow-up in which the sexual function of 54 women with UI was evaluated. These women joined three supervised physiotherapy sessions that included PFME and health education during 1 month, with a 15-day interval between each session, and kept practicing home-based PFME for a further 2 months. Sexual function was assessed using the Female Sexual Quotient, the pelvic floor muscle strength was measured using the modified Oxford scale, and UI was assessed using the International Consultation on Incontinence Questionnaire. The mean of sexual quotient score improved after treatment (P = 0.001). With respect to specific domains of sexual function, improvement was observed only in the questions about sexual desire, arousal/excitement, and orgasm. Before treatment, 18 women (33.3%) were classified as having sexual dysfunction, and after treatment, eight remained with sexual dysfunction and two other joined this category (total of 18.5%). Those women who had sexual dysfunction at baseline experienced a higher level of improvement of the sexual quotient compared with those without sexual dysfunction (P = 0.001, 95% CI = 9.1-31.9). A multivariate linear regression with backward elimination revealed the following predictors of improvement of the sexual quotient: higher parity, higher adherence to PFME, improvement in the strength of PFM, and a decrease in the frequency of urine leakage (R(2)  = 0.497). PFME was more beneficial with regard to sexual function in those women who presented with sexual dysfunction at baseline. © 2015 International Society for Sexual Medicine.

Improving First-Year Intervention Strategies at Universities by Focusing on Meaning and Purpose in Life

ERIC Educational Resources Information Center

Shin, Joo Yeon

2013-01-01

Research has increasingly appreciated the potential benefits of having a higher sense of meaning in life for positive college student development. Drawing on Steger's (2009) meaning development model, this study investigated the effects of a 6-week web-based intervention designed to enhance a sense of meaning in life among college freshmen. The…

Twenty Weeks of Computer-Training Improves Sense of Agency in Children with Spastic Cerebral Palsy

ERIC Educational Resources Information Center

Ritterband-Rosenbaum, A.; Christensen, M. S.; Nielsen, J. B.

2012-01-01

Children with cerebral palsy (CP) show alteration of perceptual and cognitive abilities in addition to motor and sensory deficits, which may include altered sense of agency. The aim of this study was to evaluate whether 20 weeks of internet-based motor, perceptual and cognitive training enhances the ability of CP children to determine whether they…

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX): a multidisciplinary project to develop a robust remote sensing-based ET modeling tool for vineyards

USDA-ARS?s Scientific Manuscript database

The recent drought in much of California, particularly in the Central Valley region, has caused severe reduction in water reservoir levels and a major depletion of ground water by agriculture. Dramatic improvements in water and irrigation management practices are critical for agriculture to remain s...

Modulation aware cluster size optimisation in wireless sensor networks

NASA Astrophysics Data System (ADS)

Sriram Naik, M.; Kumar, Vinay

2017-07-01

Wireless sensor networks (WSNs) play a great role because of their numerous advantages to the mankind. The main challenge with WSNs is the energy efficiency. In this paper, we have focused on the energy minimisation with the help of cluster size optimisation along with consideration of modulation effect when the nodes are not able to communicate using baseband communication technique. Cluster size optimisations is important technique to improve the performance of WSNs. It provides improvement in energy efficiency, network scalability, network lifetime and latency. We have proposed analytical expression for cluster size optimisation using traditional sensing model of nodes for square sensing field with consideration of modulation effects. Energy minimisation can be achieved by changing the modulation schemes such as BPSK, 16-QAM, QPSK, 64-QAM, etc., so we are considering the effect of different modulation techniques in the cluster formation. The nodes in the sensing fields are random and uniformly deployed. It is also observed that placement of base station at centre of scenario enables very less number of modulation schemes to work in energy efficient manner but when base station placed at the corner of the sensing field, it enable large number of modulation schemes to work in energy efficient manner.

Integrated Remote Sensing Modalities for Classification at a Legacy Test Site

NASA Astrophysics Data System (ADS)

Lee, D. J.; Anderson, D.; Craven, J.

2016-12-01

Detecting, locating, and characterizing suspected underground nuclear test sites is of interest to the worldwide nonproliferation monitoring community. Remote sensing provides both cultural and surface geological information over a large search area in a non-intrusive manner. We have characterized a legacy nuclear test site at the Nevada National Security Site (NNSS) using an aerial system based on RGB imagery, light detection and ranging, and hyperspectral imaging. We integrate these different remote sensing modalities to perform pattern recognition and classification tasks on the test site. These tasks include detecting cultural artifacts and exotic materials. We evaluate if the integration of different remote sensing modalities improves classification performance.

Evaluating Response to High-Dose 13.3 mg/24 h Rivastigmine Patch in Patients with Severe Alzheimer's Disease.

PubMed

Farlow, Martin R; Sadowsky, Carl H; Velting, Drew M; Meng, Xiangyi; Islam, M Zahur

2015-06-01

To identify factors predicting improvement/stabilization on the Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change (ADCS-CGIC) and investigate whether early treatment responses can predict long-term outcomes, during a trial of 13.3 mg/24 h versus 4.6 mg/24 h rivastigmine patch in patients with severe Alzheimer's disease (AD). Logistic regression was used to relate Week 24 ADCS-CGIC score to potential baseline predictors. Additional analyses based on receiver-operating characteristic curves were performed using Week 8/16 ADCS-CGIC scores to predict response (13.3 mg/24 h patch) at Week 24. ADCS-CGIC score of (1) 1-3 = "improvement," (2) 1-4 = "improvement or no change". "Treatment" (13.3 mg/24 h patch) and increased age were significant predictors of "improvement" (P = 0.01 and P = 0.003, respectively), and "treatment" (P = 0.001), increased age (P = 0.002), and prior AD treatment (P = 0.03) for "improvement or no change". At Week 8 and 16, ADCS-CGIC scores of 4 and 5 were optimal thresholds in predicting "improvement," and "improvement or no change," respectively, at Week 24. A significant therapeutic effect of high-dose rivastigmine patch on ADCS-CGIC response was observed. The 13.3 mg/24 h patch was identified as a predictor of "improvement" or "improvement or no change". Patients with minimal worsening/improvement/no change after treatment initiation may be more likely to respond following long-term therapy. © 2015 John Wiley & Sons Ltd.

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

Validation plays the role of a "bridge" in connecting remote sensing research and applications

NASA Astrophysics Data System (ADS)

Wang, Zhiqiang; Deng, Ying; Fan, Yida

2018-07-01

Remote sensing products contribute to improving earth observations over space and time. Uncertainties exist in products of different levels; thus, validation of these products before and during their applications is critical. This study discusses the meaning of validation in depth and proposes a new definition of reliability for use with such products. In this context, validation should include three aspects: a description of the relevant uncertainties, quantitative measurement results and a qualitative judgment that considers the needs of users. A literature overview is then presented evidencing improvements in the concepts associated with validation. It shows that the root mean squared error (RMSE) is widely used to express accuracy; increasing numbers of remote sensing products have been validated; research institutes contribute most validation efforts; and sufficient validation studies encourage the application of remote sensing products. Validation plays a connecting role in the distribution and application of remote sensing products. Validation connects simple remote sensing subjects with other disciplines, and it connects primary research with practical applications. Based on the above findings, it is suggested that validation efforts that include wider cooperation among research institutes and full consideration of the needs of users should be promoted.

Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.

PubMed

Wang, Aosen; Lin, Feng; Jin, Zhanpeng; Xu, Wenyao

2016-06-01

Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.

On a stronger-than-best property for best prediction

NASA Astrophysics Data System (ADS)

Teunissen, P. J. G.

2008-03-01

The minimum mean squared error (MMSE) criterion is a popular criterion for devising best predictors. In case of linear predictors, it has the advantage that no further distributional assumptions need to be made, other then about the first- and second-order moments. In the spatial and Earth sciences, it is the best linear unbiased predictor (BLUP) that is used most often. Despite the fact that in this case only the first- and second-order moments need to be known, one often still makes statements about the complete distribution, in particular when statistical testing is involved. For such cases, one can do better than the BLUP, as shown in Teunissen (J Geod. doi: 10.1007/s00190-007-0140-6, 2006), and thus devise predictors that have a smaller MMSE than the BLUP. Hence, these predictors are to be preferred over the BLUP, if one really values the MMSE-criterion. In the present contribution, we will show, however, that the BLUP has another optimality property than the MMSE-property, provided that the distribution is Gaussian. It will be shown that in the Gaussian case, the prediction error of the BLUP has the highest possible probability of all linear unbiased predictors of being bounded in the weighted squared norm sense. This is a stronger property than the often advertised MMSE-property of the BLUP.

The effect of self-efficacy, depression and symptom distress on employment status and leisure activities of liver transplant recipients.

PubMed

Weng, Li-Chueh; Huang, Hsiu-Li; Wang, Yi-Wen; Lee, Wei-Chen; Chen, Kang-Hua; Yang, Tsui-Yun

2014-07-01

To examine the effect of self-efficacy, subjective work ability, depression and symptom distress on and to provide a description of, the employment and leisure activities of liver transplant recipients. Return to work and leisure activities have become an important aspect of life for liver transplant recipients worldwide. An investigation of the factors that influence the employment status and leisure activities has been recommended as a means to help transplant recipients restore their productivity. This was a cross-sectional, descriptive and correlational study in 2010. A convenience sampling method was used. Data were collected using a set of questionnaires that were administered retrospectively. A total of 106 liver transplant patients were included in this study. The post-transplantation employment rate was 45.2%. The positive predictors of employment were higher subjective work ability and higher symptom distress. Gender (female), monthly family income (

The use of indicators to improve the quality of intensive care: theoretical aspects and experiences from the Dutch intensive care registry.

PubMed

van der Voort, P H J; van der Veer, S N; de Vos, M L G

2012-10-01

In the concept of total quality management that was originally developed in industry, the use of quality indicators is essential. The implementation of quality indicators in the intensive care unit to improve the quality of care is a complex process. This process can be described in seven subsequent steps of an indicator-based quality improvement (IBQI) cycle. With this IBQI cycle, a continuous quality improvement can be achieved with the use of indicator data in a benchmark setting. After the development of evidence-based indicators, a sense of urgency has to be created, registration should start, raw data must be analysed, feedback must be given, and interpretation and conclusions must be made, followed by a quality improvement plan. The last step is the implementation of changes that needs a sense of urgency, and this completes the IBQI cycle. Barriers and facilitators are found in each step. They should be identified and addressed in a multifaceted quality improvement strategy. © 2012 The Authors. Acta Anaesthesiologica Scandinavica © 2012 The Acta Anaesthesiologica Scandinavica Foundation.

Risk Estimates and Risk Factors Related to Psychiatric Inpatient Suicide—An Overview

PubMed Central

Madsen, Trine; Erlangsen, Annette; Nordentoft, Merete

2017-01-01

People with mental illness have an increased risk of suicide. The aim of this paper is to provide an overview of suicide risk estimates among psychiatric inpatients based on the body of evidence found in scientific peer-reviewed literature; primarily focusing on the relative risks, rates, time trends, and socio-demographic and clinical risk factors of suicide in psychiatric inpatients. Psychiatric inpatients have a very high risk of suicide relative to the background population, but it remains challenging for clinicians to identify those patients that are most likely to die from suicide during admission. Most studies are based on low power, thus compromising quality and generalisability. The few studies with sufficient statistical power mainly identified non-modifiable risk predictors such as male gender, diagnosis, or recent deliberate self-harm. Also, the predictive value of these predictors is low. It would be of great benefit if future studies would be based on large samples while focusing on modifiable predictors over the course of an admission, such as hopelessness, depressive symptoms, and family/social situations. This would improve our chances of developing better risk assessment tools. PMID:28257103

Polarimetric Remote Sensing of Atmospheric Particulate Pollutants

NASA Astrophysics Data System (ADS)

Li, Z.; Zhang, Y.; Hong, J.

2018-04-01

Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF), whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

eFarm: A Tool for Better Observing Agricultural Land Systems

PubMed Central

Yu, Qiangyi; Shi, Yun; Tang, Huajun; Yang, Peng; Xie, Ankun; Liu, Bin; Wu, Wenbin

2017-01-01

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies. PMID:28245554

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

NASA Astrophysics Data System (ADS)

Vishnukumar, S.; Wilscy, M.

2017-12-01

In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

A new capacitive long-range displacement nanometer sensor with differential sensing structure based on time-grating

NASA Astrophysics Data System (ADS)

Yu, Zhicheng; Peng, Kai; Liu, Xiaokang; Pu, Hongji; Chen, Ziran

2018-05-01

High-precision displacement sensors, which can measure large displacements with nanometer resolution, are key components in many ultra-precision fabrication machines. In this paper, a new capacitive nanometer displacement sensor with differential sensing structure is proposed for long-range linear displacement measurements based on an approach denoted time grating. Analytical models established using electric field coupling theory and an area integral method indicate that common-mode interference will result in a first-harmonic error in the measurement results. To reduce the common-mode interference, the proposed sensor design employs a differential sensing structure, which adopts a second group of induction electrodes spatially separated from the first group of induction electrodes by a half-pitch length. Experimental results based on a prototype sensor demonstrate that the measurement accuracy and the stability of the sensor are substantially improved after adopting the differential sensing structure. Finally, a prototype sensor achieves a measurement accuracy of  ±200 nm over the full 200 mm measurement range of the sensor.

Research on distributed optical fiber sensing data processing method based on LabVIEW

NASA Astrophysics Data System (ADS)

Li, Zhonghu; Yang, Meifang; Wang, Luling; Wang, Jinming; Yan, Junhong; Zuo, Jing

2018-01-01

The pipeline leak detection and leak location problem have gotten extensive attention in the industry. In this paper, the distributed optical fiber sensing system is designed based on the heat supply pipeline. The data processing method of distributed optical fiber sensing based on LabVIEW is studied emphatically. The hardware system includes laser, sensing optical fiber, wavelength division multiplexer, photoelectric detector, data acquisition card and computer etc. The software system is developed using LabVIEW. The software system adopts wavelet denoising method to deal with the temperature information, which improved the SNR. By extracting the characteristic value of the fiber temperature information, the system can realize the functions of temperature measurement, leak location and measurement signal storage and inquiry etc. Compared with traditional negative pressure wave method or acoustic signal method, the distributed optical fiber temperature measuring system can measure several temperatures in one measurement and locate the leak point accurately. It has a broad application prospect.

Flexible hemispheric microarrays of highly pressure-sensitive sensors based on breath figure method.

PubMed

Wang, Zhihui; Zhang, Ling; Liu, Jin; Jiang, Hao; Li, Chunzhong

2018-05-30

Recently, flexible pressure sensors featuring high sensitivity, broad sensing range and real-time detection have aroused great attention owing to their crucial role in the development of artificial intelligent devices and healthcare systems. Herein, highly sensitive pressure sensors based on hemisphere-microarray flexible substrates are fabricated via inversely templating honeycomb structures deriving from a facile and static breath figure process. The interlocked and subtle microstructures greatly improve the sensing characteristics and compressibility of the as-prepared pressure sensor, endowing it a sensitivity as high as 196 kPa-1 and a wide pressure sensing range (0-100 kPa), as well as other superior performance, including a lower detection limit of 0.5 Pa, fast response time (<26 ms) and high reversibility (>10 000 cycles). Based on the outstanding sensing performance, the potential capability of our pressure sensor in capturing physiological information and recognizing speech signals has been demonstrated, indicating promising application in wearable and intelligent electronics.

Is questionnaire-based sitting time inaccurate and can it be improved? A cross-sectional investigation using accelerometer-based sitting time

PubMed Central

Gupta, Nidhi; Christiansen, Caroline Stordal; Hanisch, Christiana; Bay, Hans; Burr, Hermann; Holtermann, Andreas

2017-01-01

Objectives To investigate the differences between a questionnaire-based and accelerometer-based sitting time, and develop a model for improving the accuracy of questionnaire-based sitting time for predicting accelerometer-based sitting time. Methods 183 workers in a cross-sectional study reported sitting time per day using a single question during the measurement period, and wore 2 Actigraph GT3X+ accelerometers on the thigh and trunk for 1–4 working days to determine their actual sitting time per day using the validated Acti4 software. Least squares regression models were fitted with questionnaire-based siting time and other self-reported predictors to predict accelerometer-based sitting time. Results Questionnaire-based and accelerometer-based average sitting times were ≈272 and ≈476 min/day, respectively. A low Pearson correlation (r=0.32), high mean bias (204.1 min) and wide limits of agreement (549.8 to −139.7 min) between questionnaire-based and accelerometer-based sitting time were found. The prediction model based on questionnaire-based sitting explained 10% of the variance in accelerometer-based sitting time. Inclusion of 9 self-reported predictors in the model increased the explained variance to 41%, with 10% optimism using a resampling bootstrap validation. Based on a split validation analysis, the developed prediction model on ≈75% of the workers (n=132) reduced the mean and the SD of the difference between questionnaire-based and accelerometer-based sitting time by 64% and 42%, respectively, in the remaining 25% of the workers. Conclusions This study indicates that questionnaire-based sitting time has low validity and that a prediction model can be one solution to materially improve the precision of questionnaire-based sitting time. PMID:28093433

An improved flexible telemetry system to autonomously monitor sub-bandage pressure and wound moisture.

PubMed

Mehmood, Nasir; Hariz, Alex; Templeton, Sue; Voelcker, Nicolas H

2014-11-18

This paper presents the development of an improved mobile-based telemetric dual mode sensing system to monitor pressure and moisture levels in compression bandages and dressings used for chronic wound management. The system is fabricated on a 0.2 mm thick flexible printed circuit material, and is capable of sensing pressure and moisture at two locations simultaneously within a compression bandage and wound dressing. The sensors are calibrated to sense both parameters accurately, and the data are then transmitted wirelessly to a receiver connected to a mobile device. An error-correction algorithm is developed to compensate the degradation in measurement quality due to battery power drop over time. An Android application is also implemented to automatically receive, process, and display the sensed wound parameters. The performance of the sensing system is first validated on a mannequin limb using a compression bandage and wound dressings, and then tested on a healthy volunteer to acquire real-time performance parameters. The results obtained here suggest that this dual mode sensor can perform reliably when placed on a human limb.

An Improved Flexible Telemetry System to Autonomously Monitor Sub-Bandage Pressure and Wound Moisture

PubMed Central

Mehmood, Nasir; Hariz, Alex; Templeton, Sue; Voelcker, Nicolas H.

2014-01-01

This paper presents the development of an improved mobile-based telemetric dual mode sensing system to monitor pressure and moisture levels in compression bandages and dressings used for chronic wound management. The system is fabricated on a 0.2 mm thick flexible printed circuit material, and is capable of sensing pressure and moisture at two locations simultaneously within a compression bandage and wound dressing. The sensors are calibrated to sense both parameters accurately, and the data are then transmitted wirelessly to a receiver connected to a mobile device. An error-correction algorithm is developed to compensate the degradation in measurement quality due to battery power drop over time. An Android application is also implemented to automatically receive, process, and display the sensed wound parameters. The performance of the sensing system is first validated on a mannequin limb using a compression bandage and wound dressings, and then tested on a healthy volunteer to acquire real-time performance parameters. The results obtained here suggest that this dual mode sensor can perform reliably when placed on a human limb. PMID:25412216

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

PubMed

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

Improving the position control of a two degrees of freedom robotic sensing antenna using fractional-order controllers

NASA Astrophysics Data System (ADS)

Feliu-Talegon, D.; Feliu-Batlle, V.

2017-06-01

Flexible links combined with force and torque sensors can be used to detect obstacles in mobile robotics, as well as for surface and object recognition. These devices, called sensing antennae, perform an active sensing strategy in which a servomotor system moves the link back and forth until it hits an object. At this instant, information of the motor angles combined with force and torque measurements allow calculating the positions of the hitting points, which are valuable information about the object surface. In order to move the antenna fast and accurately, this article proposes a new closed-loop control for driving this flexible link-based sensor. The control strategy is based on combining a feedforward term and a feedback phase-lag compensator of fractional order. We demonstrate that some drawbacks of the control of these sensing devices like the apparition of spillover effects when a very fast positioning of the antenna tip is desired, and actuator saturation caused by high-frequency sensor noise, can be significantly reduced by using our newly proposed fractional-order controllers. We have applied these controllers to the position control of a prototype of sensing antenna and experiments have shown the improvements attained with this technique in the accurate and vibration free motion of its tip (the fractional-order controller reduced ten times the residual vibration obtained with the integer-order controller).

Recreational benefits of urban forests: explaining visitors' willingness to pay in the context of the theory of planned behavior.

PubMed

Bernath, Katrin; Roschewitz, Anna

2008-11-01

The extension of contingent valuation models with an attitude-behavior based framework has been proposed in order to improve the descriptive and predictive ability of the models. This study examines the potential of the theory of planned behavior to explain willingness to pay (WTP) in a contingent valuation survey of the recreational benefits of the Zurich city forests. Two aspects of WTP responses, protest votes and bid levels, were analyzed separately. In both steps, models with and without the psychological predictors proposed by the theory of planned behavior were compared. Whereas the inclusion of the psychological predictors significantly improved explanations of protest votes, their ability to improve the performance of the model explaining bid levels was limited. The results indicate that the interpretation of bid levels as behavioral intention may not be appropriate and that the potential of the theory of planned behavior to improve contingent valuation models depends on which aspect of WTP responses is examined.

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

PubMed Central

Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-01-01

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. PMID:29267252

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

PubMed

Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-12-21

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

PubMed

Takeshima, Hidenori; Saitoh, Kanako; Nitta, Shuhei; Shiodera, Taichiro; Takeguchi, Tomoyuki; Bannae, Shuhei; Kuhara, Shigehide

2018-03-13

Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k - t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements. The proposed method achieves a reduction in acquisition time by estimating the spatiotemporal (x - f) sensitivity without requiring the acquisition of the alias-free signals, typical of the k - t SENSE technique. The cost-effective reconstruction, in turn, is achieved by a computationally efficient estimation of the x - f sensitivity from the band-limited signals of the aliased inputs. Such band-limited signals are suitable for sensitivity estimation because the strongly aliased signals have been removed. For the same reduction factor 4, the net reduction factor 4 for the proposed method was significantly higher than the factor 2.29 achieved by k - t SENSE. The processing time is reduced from 4.1 s for k - t SENSE to 1.7 s for the proposed method. The image quality obtained using the proposed method proved to be superior (mean squared error [MSE] ± standard deviation [SD] = 6.85 ± 2.73) compared to the k - t SENSE case (MSE ± SD = 12.73 ± 3.60) for the vertical long-axis (VLA) view, as well as other views. In the present study, k - t SENSE was identified as a suitable base method to be improved achieving both short acquisition times and a cost-effective reconstruction. To enhance these characteristics of base method, a novel implementation is proposed, estimating the x - f sensitivity without the need for an explicit scan of the reference signals. Experimental results showed that the acquisition, computational times and image quality for the proposed method were improved compared to the standard k - t SENSE method.

Development of sensing techniques for weaponry health monitoring

NASA Astrophysics Data System (ADS)

Edwards, Eugene; Ruffin, Paul B.; Walker, Ebonee A.; Brantley, Christina L.

2013-04-01

Due to the costliness of destructive evaluation methods for assessing the aging and shelf-life of missile and rocket components, the identification of nondestructive evaluation methods has become increasingly important to the Army. Verifying that there is a sufficient concentration of stabilizer is a dependable indicator that the missile's double-based solid propellant is viable. The research outlined in this paper summarizes the Army Aviation and Missile Research, Development, and Engineering Center's (AMRDEC's) comparative use of nanoporous membranes, carbon nanotubes, and optical spectroscopic configured sensing techniques for detecting degradation in rocket motor propellant. The first sensing technique utilizes a gas collecting chamber consisting of nanoporous structures that trap the smaller solid propellant particles for measurement by a gas analysis device. In collaboration with NASA-Ames, sensing methods are developed that utilize functionalized single-walled carbon nanotubes as the key sensing element. The optical spectroscopic sensing method is based on a unique light collecting optical fiber system designed to detect the concentration of the propellant stabilizer. Experimental setups, laboratory results, and overall effectiveness of each technique are presented in this paper. Expectations are for the three sensing mechanisms to provide nondestructive evaluation methods that will offer cost-savings and improved weaponry health monitoring.

Enhancing intensity and refractive index sensing capability with infrared plasmonic perfect absorbers.

PubMed

Cheng, Fei; Yang, Xiaodong; Gao, Jie

2014-06-01

An infrared refractive index sensor based on plasmonic perfect absorbers for glucose concentration sensing is experimentally demonstrated. Utilizing substantial absorption contrast between a perfect absorber (∼98% at normal incidence) and a non-perfect absorber upon the refractive index change, a maximum value of figure of merit (FOM^*) about 55 and a bulk wavelength sensitivity about 590  nm/RIU are achieved. The demonstrated sensing platform provides great potential in improving the performance of plasmonic refractive index sensors and developing future surface enhanced infrared spectroscopy.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

Combined social cognitive and neurocognitive rehabilitation strategies in schizophrenia: neuropsychological and psychopathological influences on Theory of Mind improvement.

PubMed

Bechi, M; Bosia, M; Spangaro, M; Buonocore, M; Cocchi, F; Pigoni, A; Piantanida, M; Guglielmino, C; Bianchi, L; Smeraldi, E; Cavallaro, R

2015-11-01

Neurocognitive and social cognitive impairments represent important treatment targets in schizophrenia, as they are significant predictors of functional outcome. Different rehabilitative interventions have recently been developed, addressing both cognitive and psychosocial domains. Although promising, results are still heterogeneous and predictors of treatment outcome are not yet identified. In this study we evaluated the efficacy of two newly developed social cognitive interventions, respectively based on the use of videotaped material and comic strips, combined with domain-specific Cognitive Remediation Therapy (CRT). We also analysed possible predictors of training outcome, including basal neurocognitive performance, the degree of cognitive improvement after CRT and psychopathological variables. Seventy-five patients with schizophrenia treated with CRT, were randomly assigned to: social cognitive training (SCT) group, Theory of Mind Intervention (ToMI) group, and active control group (ACG). ANOVAs showed that SCT and ToMI groups improved significantly in ToM measures, whereas the ACG did not. We reported no influences of neuropsychological measures and improvement after CRT on changes in ToM. Both paranoid and non-paranoid subjects improved significantly after ToMI and SCT, without differences between groups, despite the better performance in basal ToM found among paranoid patients. In the ACG only non-paranoid patients showed an improvement in non-verbal ToM. Results showed that both ToMI and SCT are effective in improving ToM in schizophrenia with no influence of neuropsychological domains. Our data also suggest that paranoid symptoms may discriminate between different types of ToM difficulties in schizophrenia.

Interprofessional teamwork skills as predictors of clinical outcomes in a simulated healthcare setting.

PubMed

Shrader, Sarah; Kern, Donna; Zoller, James; Blue, Amy

2013-01-01

Teaching interprofessional (IP) teamwork skills is a goal of interprofessional education. The purpose of this study was to examine the relationship between IP teamwork skills, attitudes and clinical outcomes in a simulated clinical setting. One hundred-twenty health professions students (medicine, pharmacy, physician assistant) worked in interprofessional teams to manage a "patient" in a health care simulation setting. Students completed the Interdisciplinary Education Perception Scale (IEPS) attitudinal survey instrument. Students' responses were averaged by team to create an IEPS attitudes score. Teamwork skills for each team were rated by trained observers using a checklist to calculate a teamwork score (TWS). Clinical outcome scores (COS) were determined by summation of completed clinical tasks performed by the team based on an expert developed checklist. Regression analyses were conducted to determine the relationship of IEPS and TWS with COS. IEPS score was not a significant predictor of COS (p=0.054), but TWS was a significant predictor (p<0.001) of COS. Results suggest that in a simulated clinical setting, students' interprofessional teamwork skills are significant predictors of positive clinical outcomes. Interprofessional curricular models that produce effective teamwork skills can improve student performance in clinical environments and likely improve teamwork practice to positively affect patient care outcomes.

Gravimetric chemical sensors based on silica-based mesoporous organic-inorganic hybrids.

PubMed

Xu, Jiaqiang; Zheng, Qi; Zhu, Yongheng; Lou, Huihui; Xiang, Qun; Cheng, Zhixuan

2014-09-01

Silica-based mesoporous organic-inorganic hybrid material modified quartz crystal microbalance (QCM) sensors have been examined for their ability to achieve highly sensitive and selective detection. Mesoporous silica SBA-15 serves as an inorganic host with large specific surface area, facilitating gas adsorption, and thus leads to highly sensitive response; while the presence of organic functional groups contributes to the greatly improved specific sensing property. In this work, we summarize our efforts in the rational design and synthesis of novel sensing materials for the detection of hazardous substances, including simulant nerve agent, organic vapor, and heavy metal ion, and develop high-performance QCM-based chemical sensors.

Distributed optical fiber vibration sensing using phase-generated carrier demodulation algorithm

NASA Astrophysics Data System (ADS)

Yu, Zhihua; Zhang, Qi; Zhang, Mingyu; Dai, Haolong; Zhang, Jingjing; Liu, Li; Zhang, Lijun; Jin, Xing; Wang, Gaifang; Qi, Guang

2018-05-01

A novel optical fiber-distributed vibration-sensing system is proposed, which is based on self-interference of Rayleigh backscattering with phase-generated carrier (PGC) demodulation algorithm. Pulsed lights are sent into the sensing fiber and the Rayleigh backscattering light from a certain position along the sensing fiber would interfere through an unbalanced Michelson interferometry to generate the interference light. An improved PGC demodulation algorithm is carried out to recover the phase information of the interference signal, which carries the sensing information. Three vibration events were applied simultaneously to different positions over 2000 m sensing fiber and demodulated correctly. The spatial resolution is 10 m, and the noise level of the Φ-OTDR system we proposed is about 10-3 rad/\\surd {Hz}, and the signal-to-noise ratio is about 30.34 dB.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Quality of Life, Self-Esteem, and Future Expectations of Adolescent and Young Adult Cancer Survivors.

PubMed

Tonsing, Kareen N; Ow, Rosaleen

2018-02-01

Significant advancements in treatment modalities over the past few decades have significantly improved the survival rates of many types of childhood cancer, directing attention to the psychosocial consequences of successful treatment and subsequent survival. This study assesses quality of life (QoL) among survivors of childhood cancer. Data were collected by means of a survey questionnaire. Participants were assured of confidentiality and of the voluntary nature of participation. Participants ranged in age from 12 to 24 years (mean age = 17.2); 62 percent were male; 45.6 percent were in secondary grades (middle school or high school). Results showed that among the QoL domains, spiritual subscale ranked highest, and physical domain showed the lowest mean score. Self-esteem emerged as an important predictor for social domain of QoL. Cancer-specific worry emerged as a significant predictor for overall QoL. The findings suggest that survivors rated high on positive life changes and sense of purpose, which are associated with positive QoL. However, this was tempered by worries and uncertainty. This study provides seminal information on the psychosocial needs of childhood cancer survivors in an Asian context that can be used by health care professionals and providers to further promote support and health care following treatment. © 2017 National Association of Social Workers.

Improved Mapping of Carbon, Water and Energy Land-Surface Fluxes Using Remotely Sensed Indicators of Canopy Light Use Efficiency

NASA Astrophysics Data System (ADS)

Schull, M. A.; Anderson, M. C.; Kustas, W.; Cammalleri, C.; Houborg, R.

2012-12-01

A light-use-efficiency (LUE) based model of canopy resistance has been embedded into a thermal-based Two-Source Energy Balance (TSEB) model to facilitate coupled simulations of transpiration and carbon assimilation. The model assumes that deviations of the observed canopy LUE from a nominal stand-level value (LUEn - typically indexed by vegetation class) are due to varying conditions of light, humidity, CO2 concentration and leaf temperature. The deviations are accommodated by adjusting an effective LUE that responds to the varying conditions. The challenge to monitoring fluxes on a larger scale is to capture the physiological responses due to changing conditions. This challenge can be met using remotely sensed leaf chlorophyll (Cab). Since Cab is a vital pigment for absorbing light for use in photosynthesis, it has been recognized as a key parameter for quantifying photosynthetic functioning that are sensitive to these conditions. Recent studies have shown that it is sensitive to changes in LUE, which defines how efficiently a plant can assimilate carbon dioxide (CO2) given the absorbed Photosynthetically Active Radiation (PAR) and is therefore useful for monitoring carbon fluxes. We investigate the feasibility of leaf chlorophyll to capture these variations in LUEn using remotely sensed data. To retrieve Cab from remotely sensed data we use REGFLEC, a physically based tool that translates at-sensor radiances in the green, red and NIR spectral regions from multiple satellite sensors into realistic maps of LAI and Cab. Initial results show that Cab is exponentially correlated to light use efficiency. Incorporating nominal light use efficiency estimated from Cab is shown to improve fluxes of carbon, water and energy most notably in times of stressed vegetation. The result illustrates that Cab is sensitive to changes in plant physiology and can capture plant stress needed for improved estimation of fluxes. The observed relationship and initial results demonstrate the need for integrating remotely sensed Cab to facilitate improved mapping of coupled carbon, water, and energy fluxes across vegetated landscapes.

Compressed Sensing for Body MRI

PubMed Central

Feng, Li; Benkert, Thomas; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo; Chandarana, Hersh

2016-01-01

The introduction of compressed sensing for increasing imaging speed in MRI has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than that are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This paper presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and non-linear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the paper discusses current challenges and future opportunities. PMID:27981664

Wavelet-Based Processing for Fiber Optic Sensing Systems

NASA Technical Reports Server (NTRS)

Hamory, Philip J. (Inventor); Parker, Allen R., Jr. (Inventor)

2016-01-01

The present invention is an improved method of processing conglomerate data. The method employs a Triband Wavelet Transform that decomposes and decimates the conglomerate signal to obtain a final result. The invention may be employed to improve performance of Optical Frequency Domain Reflectometry systems.

Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

NASA Astrophysics Data System (ADS)

Elmasri, B.; Rahman, A. F.

2010-12-01

Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation will result in improved GPP predictions. Although there might be a room for improvements in our model outcomes through improved parameterization, our results suggest that such a methodology for running BIOME-BGC model based entirely on routinely available data can produce good predictions of GPP.

Glycemic Control Indices and Their Aggregation in the Prediction of Nocturnal Hypoglycemia From Intermittent Blood Glucose Measurements.

PubMed

Sampath, Sivananthan; Tkachenko, Pavlo; Renard, Eric; Pereverzev, Sergei V

2016-11-01

Despite the risk associated with nocturnal hypoglycemia (NH) there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data. One of the first methods that potentially can be used for NH prediction is based on the low blood glucose index (LBGI) and suggested, for example, in Accu-Chek® Connect as a hypoglycemia risk indicator. On the other hand, nowadays there are other glucose control indices (GCI), which could be used for NH prediction in the same spirit as LBGI. In the present study we propose a general approach of combining NH predictors constructed from different GCI. The approach is based on a recently developed strategy for aggregating ranking algorithms in machine learning. NH predictors have been calibrated and tested on data extracted from clinical trials, performed in EU FP7-funded project DIAdvisor. Then, to show a portability of the method we have tested it on another dataset that was received from EU Horizon 2020-funded project AMMODIT. We exemplify the proposed approach by aggregating NH predictors that have been constructed based on 4 GCI associated with hypoglycemia. Even though these predictors have been preliminary optimized to exhibit better performance on the considered dataset, our aggregation approach allows a further performance improvement. On the dataset, where a portability of the proposed approach has been demonstrated, the aggregating predictor has exhibited the following performance: sensitivity 77%, specificity 83.4%, positive predictive value 80.2%, negative predictive value 80.6%, which is higher than conventionally considered as acceptable. The proposed approach shows potential to be used in telemedicine systems for NH prediction. © 2016 Diabetes Technology Society.

Developing the remote sensing-based water environmental model for monitoring alpine river water environment over Plateau cold zone

NASA Astrophysics Data System (ADS)

You, Y.; Wang, S.; Yang, Q.; Shen, M.; Chen, G.

2017-12-01

Alpine river water environment on the Plateau (such as Tibetan Plateau, China) is a key indicator for water security and environmental security in China. Due to the complex terrain and various surface eco-environment, it is a very difficult to monitor the water environment over the complex land surface of the plateau. The increasing availability of remote sensing techniques with appropriate spatiotemporal resolutions, broad coverage and low costs allows for effective monitoring river water environment on the Plateau, particularly in remote and inaccessible areas where are lack of in situ observations. In this study, we propose a remote sense-based monitoring model by using multi-platform remote sensing data for monitoring alpine river environment. In this study some parameterization methodologies based on satellite remote sensing data and field observations have been proposed for monitoring the water environmental parameters (including chlorophyll-a concentration (Chl-a), water turbidity (WT) or water clarity (SD), total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC)) over the china's southwest highland rivers, such as the Brahmaputra. First, because most sensors do not collect multiple observations of a target in a single pass, data from multiple orbits or acquisition times may be used, and varying atmospheric and irradiance effects must be reconciled. So based on various types of satellite data, at first we developed the techniques of multi-sensor data correction, atmospheric correction. Second, we also built the inversion spectral database derived from long-term remote sensing data and field sampling data. Then we have studied and developed a high-precision inversion model over the southwest highland river backed by inversion spectral database through using the techniques of multi-sensor remote sensing information optimization and collaboration. Third, take the middle reaches of the Brahmaputra river as the study area, we validated the key water environmental parameters and further improved the inversion model. The results indicate that our proposed water environment inversion model can be a good inversion for alpine water environmental parameters, and can improve the monitoring and warning ability for the alpine river water environment in the future.

Transformer partial discharge monitoring based on optical fiber sensing

NASA Astrophysics Data System (ADS)

Wang, Kun; Tong, Xinglin; Zhu, Xiaolong

2014-06-01

The power transformer is the most important equipment of the high voltage power grid, however, some traditional methods of online partial discharge monitoring have some limitations. Based on many advantages of the optical fiber sensing technology, we have done some research on fiber optics Fabry-Perot (FP) sensing which can be useful for the transformer on online partial discharge monitoring. This research aimed at improving the reliability of power system safety monitoring. We have done some work as follows: designing a set for fiber optics FP sensor preparation, according to the fabrication procedure strictly making out the sensors, building a reasonable signal demodulation system for fiber optics FP sensing, doing a preliminary analysis about online partial discharge signal monitoring, including the research on different discharge intensities with the same measuring distance and different measuring distances with the same discharge intensity, and then making a detailed analysis of the experimental results.

Recent progress on gas sensor based on quantum cascade lasers and hollow fiber waveguides

NASA Astrophysics Data System (ADS)

Liu, Ningwu; Sun, Juan; Deng, Hao; Ding, Junya; Zhang, Lei; Li, Jingsong

2017-02-01

Mid-infrared laser spectroscopy provides an ideal platform for trace gas sensing applications. Despite this potential, early MIR sensing applications were limited due to the size of the involved optical components, e.g. light sources and sample cells. A potential solution to this demand is the integration of hollow fiber waveguide with novelty quantum cascade lasers.Recently QCLs had great improvements in power, efficiency and wavelength range, which made the miniaturized platforms for gas sensing maintaining or even enhancing the achievable sensitivity conceivable. So that the miniaturization of QCLs and HWGs can be evolved into a mini sensor, which may be tailored to a variety of real-time and in situ applications ranging from environmental monitoring to workplace safety surveillance. In this article, we introduce QCLs and HWGs, display the applications of HWG based on QCL gas sensing and discuss future strategies for hollow fiber coupled quantum cascade laser gas sensor technology.

A Prospective Study Investigating the Impact of School Belonging Factors on Negative Affect in Adolescents

ERIC Educational Resources Information Center

Shochet, Ian M.; Smith, Coral L.; Furlong, Michael J.; Homel, Ross

2011-01-01

School belonging, measured as a unidimensional construct, is an important predictor of negative affective problems in adolescents, including depression and anxiety symptoms. A recent study found that one such measure, the Psychological Sense of School Membership scale, actually comprises three factors: Caring Relations, Acceptance, and Rejection.…

Examining Relevant Influences on the Persistence of African-American College Students at a Diverse Urban University

ERIC Educational Resources Information Center

Thomas, Jackie C., Jr.; Wolters, Christopher; Horn, Catherine; Kennedy, Heidi

2014-01-01

In this study, campus involvement, faculty mentorship, motivational beliefs (self-efficacy and utility value), and sense of belonging were examined as potential predictors of African-American college student academic persistence. Participants (n = 139) in the study were African-American college students from a large-urban university. Separate…

Predictors of Aggression at School: The Effect of School-Related Alcohol Use.

ERIC Educational Resources Information Center

Finn, Kristin V.; Frone, Michael R.

2003-01-01

Examines factors related to aggression at school, particularly involving alcohol use. Finds school aggression higher among students who are male, rebellious, have a weak sense of school identification, low academic achievement, and drink alcohol during the school day. Schools that encourage school involvement and alcohol resistance may help…

Adolescents' Vulnerability to Peer Victimization: Interpersonal and Intrapersonal Predictors

ERIC Educational Resources Information Center

D'Esposito, Susan E.; Blake, Jamilia; Riccio, Cynthia A.

2011-01-01

This study explored how certain personality traits, behaviors, and social status may be associated with who is targeted as a victim of peer aggression. The sample consisted of 233 students in sixth through eighth grades from rural communities. Results indicate that symptoms of anxiety, a high sense of inadequacy, and elevated social stress are…

Stress and Fathers' Parental Competence: Implications for Family Life and Parent Educators.

ERIC Educational Resources Information Center

McBride, Brent A.

1989-01-01

Examined the relationship between fathers' parental stress and their perceived sense of competence in parenting roles. Regression analyses of data from 94 fathers suggest that fathers' depression in their parental role and perceptions of their children's demandingness are the best predictors of their perceived parental competence. (Author/TE)

Predictors of Tuition Worth: Psychological Sense of Community, Institutional Integrity, and Student Thriving

ERIC Educational Resources Information Center

Conn, Steven M.

2017-01-01

Using hierarchical multiple regression analysis, this study examined the factors that contribute to the variation in students' subjective perceptions of the value of their tuition dollars. This study utilized data on 6,322 undergraduate students from 11 institutions in the Council for Christian Colleges & Universities (CCCU) who completed the…

Reconceptualizing Teacher-Student Relationships to Foster School Success: Working Alliance within Classroom Contexts

ERIC Educational Resources Information Center

Toste, Jessica R.

2012-01-01

Teacher-student relationship has been shown to be a powerful predictor of students' classroom and school adjustment. Beyond the characteristics of warmth, trust, and bond that define an emotional connection, a positive working relationship also includes a sense of collaboration and partnership shared between the teacher and the student. Classroom…

Low emotion-oriented coping and informal help-seeking behaviour as major predictive factors for improvement in major depression at 5-year follow-up in the adult community.

PubMed

Rodgers, S; Vandeleur, C L; Strippoli, M-P F; Castelao, E; Tesic, A; Glaus, J; Lasserre, A M; Müller, M; Rössler, W; Ajdacic-Gross, V; Preisig, M

2017-09-01

Given the broad range of biopsychosocial difficulties resulting from major depressive disorder (MDD), reliable evidence for predictors of improved mental health is essential, particularly from unbiased prospective community samples. Consequently, a broad spectrum of potential clinical and non-clinical predictors of improved mental health, defined as an absence of current major depressive episode (MDE) at follow-up, were examined over a 5-year period in an adult community sample. The longitudinal population-based PsyCoLaus study from the city of Lausanne, Switzerland, was used. Subjects having a lifetime MDD with a current MDE at baseline assessment were selected, resulting in a subsample of 210 subjects. Logistic regressions were applied to the data. Coping styles were the most important predictive factors in the present study. More specifically, low emotion-oriented coping and informal help-seeking behaviour at baseline were associated with the absence of an MDD diagnosis at follow-up. Surprisingly, neither formal help-seeking behaviour, nor psychopharmacological treatment, nor childhood adversities, nor depression subtypes turned out to be relevant predictors in the current study. The paramount role of coping styles as predictors of improvement in depression found in the present study might be a valuable target for resource-oriented therapeutic models. On the one hand, the positive impact of low emotion-oriented coping highlights the utility of clinical interventions interrupting excessive mental ruminations during MDE. On the other hand, the importance of informal social networks raises questions regarding how to enlarge the personal network of affected subjects and on how to best support informal caregivers.

Mediation of the relationship of behavioural treatment type and changes in psychological predictors of healthy eating by body satisfaction changes in women with obesity.

PubMed

Annesi, James J

Psychological correlates of both short- and long-term weight loss are poorly understood. Changes in satisfaction with one's body might serve to motivate healthier eating by mediating treatments' effect on psychological factors previously suggested to be associated with weight loss. Women with obesity (age 48.6±7.1 years; BMI 35.4±3.3kg/m 2 ) were randomly assigned to social cognitive theory-based weight-management treatments that were either group sessions emphasizing physical activity-derived self-regulation (experimental; n=53) or review of a written manual and phone support (comparison; n=54). Changes in weight, physical activity, body satisfaction, negative mood, and self-efficacy and self-regulation for controlled eating were assessed over 3, 6, 12, and 24 months. The experimental treatment was associated with significantly more favourable changes across variables. Over 6, 12, and 24 months, body satisfaction change mediated relationships between treatment type and changes in each of the psychological predictors of healthier eating (mood, self-efficacy, self-regulation). Reciprocal, mutually reinforcing, relationships between changes in body satisfaction and those psychological predictors were also found. Increased physical activity was associated with improved body satisfaction, even after controlling for weight changes. Findings increased understandings of the role of body satisfaction in improving psychological predictors of healthier eating over both the short- and longer-term. Results also suggested that body satisfaction could be improved through increased physical activity, irrespective of change in weight. Although results were limited to women with class 1 and 2 obesity, findings on interactions of psychological factors associated with eating changes have implications for the architecture of improved behavioural treatments. Copyright © 2016 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Corticospinal excitability as a predictor of functional gains at the affected upper limb following robotic training in chronic stroke survivors.

PubMed

Milot, Marie-Hélène; Spencer, Steven J; Chan, Vicky; Allington, James P; Klein, Julius; Chou, Cathy; Pearson-Fuhrhop, Kristin; Bobrow, James E; Reinkensmeyer, David J; Cramer, Steven C

2014-01-01

Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot-based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective. To determine the predictive ability of behavioral and brain measures in order to improve selection of individuals for robotic training. Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, functional magnetic resonance imaging (fMRI), diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Training was associated with an average gain of 6 ± 5 blocks on the BBT (P < .0001). Bivariate analysis revealed that lower baseline motor-evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains. © The Author(s) 2014.

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

NASA Technical Reports Server (NTRS)

Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

2011-01-01

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Protein structure refinement using a quantum mechanics-based chemical shielding predictor.

PubMed

Bratholm, Lars A; Jensen, Jan H

2017-03-01

The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ , 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1-0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects.

PubMed

Raimondi, Daniele; Gazzo, Andrea M; Rooman, Marianne; Lenaerts, Tom; Vranken, Wim F

2016-06-15

There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants. We present DEOGEN, a novel variant effect predictor that can handle both missense SNVs and in-frame INDELs. By integrating information from different biological scales and mimicking the complex mixture of effects that lead from the variant to the phenotype, we obtain significant improvements in the variant-effect prediction results. Next to the typical variant-oriented features based on the evolutionary conservation of the mutated positions, we added a collection of protein-oriented features that are based on functional aspects of the gene affected. We cross-validated DEOGEN on 36 825 polymorphisms, 20 821 deleterious SNVs, and 1038 INDELs from SwissProt. The multilevel contextualization of each (variant, protein) pair in DEOGEN provides a 10% improvement of MCC with respect to current state-of-the-art tools. The software and the data presented here is publicly available at http://ibsquare.be/deogen : wvranken@vub.ac.be Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Perceived Vulnerability to Disease Predicts Environmental Attitudes

ERIC Educational Resources Information Center

Prokop, Pavol; Kubiatko, Milan

2014-01-01

Investigating predictors of environmental attitudes may bring valuable benefits in terms of improving public awareness about biodiversity degradation and increased pro-environmental behaviour. Here we used an evolutionary approach to study environmental attitudes based on disease-threat model. We hypothesized that people vulnerable to diseases may…

Photonic crystal resonances for sensing and imaging

NASA Astrophysics Data System (ADS)

Pitruzzello, Giampaolo; Krauss, Thomas F.

2018-07-01

This review provides an insight into the recent developments of photonic crystal (PhC)-based devices for sensing and imaging, with a particular emphasis on biosensors. We focus on two main classes of devices, namely sensors based on PhC cavities and those on guided mode resonances (GMRs). This distinction is able to capture the richness of possibilities that PhCs are able to offer in this space. We present recent examples highlighting applications where PhCs can offer new capabilities, open up new applications or enable improved performance, with a clear emphasis on the different types of structures and photonic functions. We provide a critical comparison between cavity-based devices and GMR devices by highlighting strengths and weaknesses. We also compare PhC technologies and their sensing mechanism to surface plasmon resonance, microring resonators and integrated interferometric sensors.

Utilization of services in a randomized trial testing phone- and web-based interventions for smoking cessation.

PubMed

Zbikowski, Susan M; Jack, Lisa M; McClure, Jennifer B; Deprey, Mona; Javitz, Harold S; McAfee, Timothy A; Catz, Sheryl L; Richards, Julie; Bush, Terry; Swan, Gary E

2011-05-01

Phone counseling has become standard for behavioral smoking cessation treatment. Newer options include Web and integrated phone-Web treatment. No prior research, to our knowledge, has systematically compared the effectiveness of these three treatment modalities in a randomized trial. Understanding how utilization varies by mode, the impact of utilization on outcomes, and predictors of utilization across each mode could lead to improved treatments. One thousand two hundred and two participants were randomized to phone, Web, or combined phone-Web cessation treatment. Services varied by modality and were tracked using automated systems. All participants received 12 weeks of varenicline, printed guides, an orientation call, and access to a phone supportline. Self-report data were collected at baseline and 6-month follow-up. Overall, participants utilized phone services more often than the Web-based services. Among treatment groups with Web access, a significant proportion logged in only once (37% phone-Web, 41% Web), and those in the phone-Web group logged in less often than those in the Web group (mean = 2.4 vs. 3.7, p = .0001). Use of the phone also was correlated with increased use of the Web. In multivariate analyses, greater use of the phone- or Web-based services was associated with higher cessation rates. Finally, older age and the belief that certain treatments could improve success were consistent predictors of greater utilization across groups. Other predictors varied by treatment group. Opportunities for enhancing treatment utilization exist, particularly for Web-based programs. Increasing utilization more broadly could result in better overall treatment effectiveness for all intervention modalities.

Evaluation of humidity sensing properties of TMBHPET thin film embedded with spinel cobalt ferrite nanoparticles

NASA Astrophysics Data System (ADS)

Zafar, Qayyum; Azmer, Mohamad Izzat; Al-Sehemi, Abdullah G.; Al-Assiri, Mohammad S.; Kalam, Abul; Sulaiman, Khaulah

2016-07-01

In this study, we report the enhanced sensing parameters of previously reported TMBHPET-based humidity sensor. Significant improved sensing performance has been demonstrated by coupling of TMBHPET moisture sensing thin film with cobalt ferrite nanoparticles (synthesized by eco-benign ultrasonic method). The mean size of CoFe2O4 nanoparticles has been estimated to be 6.5 nm. It is assumed that the thin film of organic-ceramic hybrid matrix (TMBHPET:CoFe2O4) is a potential candidate for humidity sensing utility by virtue of its high specific surface area and porous surface morphology (as evident from TEM, FESEM, and AFM images). The hybrid suspension has been drop-cast onto the glass substrate with preliminary deposited coplanar aluminum electrodes separated by 40 µm distance. The influence of humidity on the capacitance of the hybrid humidity sensor (Al/TMBHPET:CoFe2O4/Al) has been investigated at three different frequencies of the AC applied voltage ( V rms 1 V): 100 Hz, 1 kHz, and 10 kHz. It has been observed that at 100 Hz, under a humidity of 99 % RH, the capacitance of the sensor increased by 2.61 times, with respect to 30 % RH condition. The proposed sensor exhibits significantly improved sensitivity 560 fF/ % RH at 100 Hz, which is nearly 7.5 times as high as that of pristine TMBHPET-based humidity sensor. Further, the capacitive sensor exhibits improved dynamic range (30-99 % RH), small hysteresis ( 2.3 %), and relatively quicker response and recovery times ( 12 s, 14 s, respectively). It is assumed that the humidity response of the sensor is associated with the diffusion kinetics of water vapors and doping of the semiconductor nanocomposite by water molecules.

Improving simulations of snow water equivalent and total water storage changes over the Upper Yangtze River basin using multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Han, P.; Long, D.

2017-12-01

Snow water equivalent (SWE) and total water storage (TWS) changes are important hydrological state variables over cryospheric regions, such as China's Upper Yangtze River (UYR) basin. Accurate simulation of these two state variables plays a critical role in understanding hydrological processes over this region and, in turn, benefits water resource management, hydropower development, and ecological integrity over the lower reaches of the Yangtze River, one of the largest rivers globally. In this study, an improved CREST model coupled with a snow and glacier melting module was used to simulate SWE and TWS changes over the UYR, and to quantify contributions of snow and glacier meltwater to the total runoff. Forcing, calibration, and validation data are mainly from multi-source remote sensing observations, including satellite-based precipitation estimates, passive microwave remote sensing-based SWE, and GRACE-derived TWS changes, along with streamflow measurements at the Zhimenda gauging station. Results show that multi-source remote sensing information can be extremely valuable in model forcing, calibration, and validation over the poorly gauged region. The simulated SWE and TWS changes and the observed counterparts are highly consistent, showing NSE coefficients higher than 0.8. The results also show that the contributions of snow and glacier meltwater to the total runoff are 8% and 6%, respectively, during the period 2003‒2014, which is an important source of runoff. Moreover, from this study, the TWS is found to increase at a rate of 5 mm/a ( 0.72 Gt/a) for the period 2003‒2014. The snow melting module may overestimate SWE for high precipitation events and was improved in this study. Key words: CREST model; Remote Sensing; Melting model; Source Region of the Yangtze River

Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends

PubMed Central

Gamazo-Real, José Carlos; Vázquez-Sánchez, Ernesto; Gómez-Gil, Jaime

2010-01-01

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks. PMID:22163582

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

NASA Astrophysics Data System (ADS)

Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

2012-12-01

DARLA is 5-year collaborative project that couples state-of-the-art remote sensing and in situ measurements with advanced data assimilation (DA) modeling to (a) evaluate and improve remote sensing retrieval algorithms for environmental parameters, (b) determine the extent to which remote sensing data can be used in place of in situ data in models, and (c) infer bathymetry for littoral environments by combining remotely-sensed parameters and data assimilation models. The project uses microwave, electro-optical, and infrared techniques to characterize the littoral ocean with a focus on wave and current parameters required for DA modeling. In conjunction with the RIVET (River and Inlets) Project, extensive in situ measurements provide ground truth for both the remote sensing retrieval algorithms and the DA modeling. Our goal is to use remote sensing to constrain data assimilation models of wave and circulation dynamics in a tidal inlet and surrounding beaches. We seek to improve environmental parameter estimation via remote sensing fusion, determine the success of using remote sensing data to drive DA models, and produce a dynamically consistent representation of the wave, circulation, and bathymetry fields in complex environments. The objectives are to test the following three hypotheses: 1. Environmental parameter estimation using remote sensing techniques can be significantly improved by fusion of multiple sensor products. 2. Data assimilation models can be adequately constrained (i.e., forced or guided) with environmental parameters derived from remote sensing measurements. 3. Bathymetry on open beaches, river mouths, and at tidal inlets can be inferred from a combination of remotely-sensed parameters and data assimilation models. Our approach is to conduct a series of field experiments combining remote sensing and in situ measurements to investigate signature physics and to gather data for developing and testing DA models. A preliminary experiment conducted at the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

Criteria for successful government-industry-academic partnerships

NASA Astrophysics Data System (ADS)

Brannon, David P.

1996-03-01

The mission of the Commercial Remote Sensing Program (CRSP) Office at NASA's John C. Stennis Space Center is to maximize U.S. industry's commercial use of remote sensing and related space-based technologies and to develop advanced technical responses to spatial information requirements. The CRSP Office carries out this mission by offering several commercial partnership programs that help companies to apply remote sensing technologies in business applications and to buy down the risk of bringing new or improved products and services to market. Through its commercial partnerships, the CRSP seeks to increase the market demand for remote sensing products and related advanced technologies, thus increasing the use and reducing the cost of spatial information.

Apparatus for sensing volatile organic chemicals in fluids

DOEpatents

Hughes, Robert C.; Manginell, Ronald P.; Jenkins, Mark W.; Kottenstette, Richard; Patel, Sanjay V.

2005-06-07

A chemical-sensing apparatus is formed from the combination of a chemical preconcentrator which sorbs and concentrates particular volatile organic chemicals (VOCs) and one or more chemiresistors that sense the VOCs after the preconcentrator has been triggered to release them in concentrated form. Use of the preconcentrator and chemiresistor(s) in combination allows the VOCs to be detected at lower concentration than would be possible using the chemiresistor(s) alone and further allows measurements to be made in a variety of fluids, including liquids (e.g. groundwater). Additionally, the apparatus provides a new mode of operation for sensing VOCs based on the measurement of decay time constants, and a method for background correction to improve measurement precision.

Bio-sensing with butterfly wings: naturally occurring nano-structures for SERS-based malaria parasite detection.

PubMed

Garrett, Natalie L; Sekine, Ryo; Dixon, Matthew W A; Tilley, Leann; Bambery, Keith R; Wood, Bayden R

2015-09-07

Surface enhanced Raman scattering (SERS) is a powerful tool with great potential to provide improved bio-sensing capabilities. The current 'gold-standard' method for diagnosis of malaria involves visual inspection of blood smears using light microscopy, which is time consuming and can prevent early diagnosis of the disease. We present a novel surface-enhanced Raman spectroscopy substrate based on gold-coated butterfly wings, which enabled detection of malarial hemozoin pigment within lysed blood samples containing 0.005% and 0.0005% infected red blood cells.

Comparison of Uncalibrated Rgbvi with Spectrometer-Based Ndvi Derived from Uav Sensing Systems on Field Scale

NASA Astrophysics Data System (ADS)

Bareth, G.; Bolten, A.; Gnyp, M. L.; Reusch, S.; Jasper, J.

2016-06-01

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

NASA Astrophysics Data System (ADS)

Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

2016-06-01

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

Integrated remotely sensed datasets for disaster management

NASA Astrophysics Data System (ADS)

McCarthy, Timothy; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

2008-10-01

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remote sensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remote sensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotely sensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

a New Approach for Accuracy Improvement of Pulsed LIDAR Remote Sensing Data

NASA Astrophysics Data System (ADS)

Zhou, G.; Huang, W.; Zhou, X.; He, C.; Li, X.; Huang, Y.; Zhang, L.

2018-05-01

In remote sensing applications, the accuracy of time interval measurement is one of the most important parameters that affect the quality of pulsed lidar data. The traditional time interval measurement technique has the disadvantages of low measurement accuracy, complicated circuit structure and large error. A high-precision time interval data cannot be obtained in these traditional methods. In order to obtain higher quality of remote sensing cloud images based on the time interval measurement, a higher accuracy time interval measurement method is proposed. The method is based on charging the capacitance and sampling the change of capacitor voltage at the same time. Firstly, the approximate model of the capacitance voltage curve in the time of flight of pulse is fitted based on the sampled data. Then, the whole charging time is obtained with the fitting function. In this method, only a high-speed A/D sampler and capacitor are required in a single receiving channel, and the collected data is processed directly in the main control unit. The experimental results show that the proposed method can get error less than 3 ps. Compared with other methods, the proposed method improves the time interval accuracy by at least 20 %.

Patients' impression of change following treatment for chronic pain: global, specific, a single dimension, or many?

PubMed

Scott, Whitney; McCracken, Lance M

2015-06-01

The Patient Global Impression of Change (PGIC) measure has frequently been used as an indicator of meaningful change in treatments for chronic pain. However, limited research has examined the validity of PGIC items despite their wide adoption in clinical trials for pain. Additionally, research has not yet examined predictors of PGIC ratings following psychologically based treatment for pain. The purpose of the present study was to examine the validity, factor structure, and predictors of PGIC ratings following an interdisciplinary psychologically based treatment for chronic pain. Patients with chronic pain (N = 476) completed standard assessments of pain, daily functioning, and depression before and after a 4-week treatment program based on the principles of acceptance and commitment therapy. Following the program, patients rated 1 item assessing their impression of change overall and several items assessing their impression of more specific changes: physical and social functioning, work-related activities, mood, and pain. Results indicated that the global and specific impression of change items represent a single component. In the context of the acceptance and commitment therapy-based treatment studied here, overall PGIC ratings appeared to be influenced to a greater degree by patients' experienced improvements in physical activities and mood than by improvements in pain. The findings suggest that in addition to a single overall PGIC rating, domain-specific items may be relevant for some treatment trials. This article reports on the validity and predictors of patients' impression of change ratings following interdisciplinary psychologically based treatment for pain. In addition to a single overall PGIC rating, domain-specific items may be important for clinicians and researchers to consider depending on the focus of treatment. Copyright © 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

NASA Astrophysics Data System (ADS)

Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Vega, J.; Dormido-Canto, S.; Baruzzo, M.; Gelfusa, M.; Contributors, JET

2018-05-01

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

Predictors of adherence to treatment by patients with coronary heart disease after percutaneous coronary intervention.

PubMed

Kähkönen, Outi; Saaranen, Terhi; Kankkunen, Päivi; Lamidi, Marja-Leena; Kyngäs, Helvi; Miettinen, Heikki

2018-03-01

To identify the predictors of adherence in patients with coronary heart disease after a percutaneous coronary intervention. Adherence is a key factor in preventing the progression of coronary heart disease. An analytical multihospital survey study. A survey of 416 postpercutaneous coronary intervention patients was conducted in 2013, using the Adherence of People with Chronic Disease Instrument. The instrument consists of 37 items measuring adherence and 18 items comprising sociodemographic, health behavioural and disease-specific factors. Adherence consisted of two mean sum variables: adherence to medication and a healthy lifestyle. Based on earlier studies, nine mean sum variables known to explain adherence were responsibility, cooperation, support from next of kin, sense of normality, motivation, results of care, support from nurses and physicians, and fear of complications. Frequencies and percentages were used to describe the data, cross-tabulation to find statistically significant background variables and multivariate logistic regression to confirm standardised predictors of adherence. Patients reported good adherence. However, there was inconsistency between adherence to a healthy lifestyle and health behaviours. Gender, close personal relationship, length of education, physical activity, vegetable and alcohol consumption, LDL cholesterol and duration of coronary heart disease without previous percutaneous coronary intervention were predictors of adherence. The predictive factors known to explain adherence to treatment were male gender, close personal relationship, longer education, lower LDL cholesterol and longer duration of coronary heart disease without previous percutaneous coronary intervention. Because a healthy lifestyle predicted factors known to explain adherence, these issues should be emphasised particularly for female patients not in a close personal relationship, with low education and a shorter coronary heart disease duration with previous coronary intervention. © 2017 John Wiley & Sons Ltd.

Predictors of noninvasive ventilation tolerance in patients with amyotrophic lateral sclerosis.

PubMed

Gruis, K L; Brown, D L; Schoennemann, A; Zebarah, V A; Feldman, E L

2005-12-01

Noninvasive ventilation (NIV) appears to improve survival and quality of life in patients with amyotrophic lateral sclerosis (ALS), but little is known about predictors of NIV tolerance. NIV use was assessed and clinical predictors of tolerance were investigated, using predictive modeling, in ALS patients diagnosed and followed in our clinic until death over a 4-year time period. Patients were prescribed NIV based on current practice parameters when respiratory symptoms were present or forced vital capacity was less than 50%. We prescribed NIV in 52% (72) of patients. For those prescribed NIV, information regarding tolerance was available for 50 patients, with 72% (36) tolerant to its use. Tolerance was six times more likely in limb-onset than bulbar-onset ALS patients, with a trend toward reduced tolerance in those with lower forced vital capacity at NIV initiation. Age, gender, and duration of disease were not predictors of NIV tolerance. We conclude that a majority of ALS patients who are prescribed NIV can successfully become tolerant to its use.

Improving Adherence to Web-Based and Mobile Technologies for People With Psychosis: Systematic Review of New Potential Predictors of Adherence.

PubMed

Killikelly, Clare; He, Zhimin; Reeder, Clare; Wykes, Til

2017-07-20

Despite the boom in new technologically based interventions for people with psychosis, recent studies suggest medium to low rates of adherence to these types of interventions. The benefits will be limited if only a minority of service users adhere and engage; if specific predictors of adherence can be identified then technologies can be adapted to increase the service user benefits. The study aimed to present a systematic review of rates of adherence, dropout, and approaches to analyzing adherence to newly developed mobile and Web-based interventions for people with psychosis. Specific predictors of adherence were also explored. Using keywords (Internet or online or Web-based or website or mobile) AND (bipolar disorder or manic depression or manic depressive illness or manic-depressive psychosis or psychosis or schizophr* or psychotic), the following databases were searched: OVID including MedLine, EMBASE and PsychInfo, Pubmed and Web of Science. The objectives and inclusion criteria for suitable studies were defined following PICOS (population: people with psychosis; intervention: mobile or Internet-based technology; comparison group: no comparison group specified; outcomes: measures of adherence; study design: randomized controlled trials (RCT), feasibility studies, and observational studies) criteria. In addition to measurement and analysis of adherence, two theoretically proposed predictors of adherence were examined: (1) level of support from a clinician or researcher throughout the study, and (2) level of service user involvement in the app or intervention development. We provide a narrative synthesis of the findings and followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for reporting systematic reviews. Of the 20 studies that reported a measure of adherence and a rate of dropout, 5 of these conducted statistical analyses to determine predictors of dropout, 6 analyzed the effects of specific adherence predictors (eg, symptom severity or type of technological interface) on the effects of the intervention, 4 administered poststudy feedback questionnaires to assess continued use of the intervention, and 2 studies evaluated the effects of different types of interventions on adherence. Overall, the percentage of participants adhering to interventions ranged from 28-100% with a mean of 83%. Adherence was greater in studies with higher levels of social support and service user involvement in the development of the intervention. Studies of shorter duration also had higher rates of adherence. Adherence to mobile and Web-based interventions was robust across most studies. Although 2 studies found specific predictors of nonadherence (male gender and younger age), most did not specifically analyze predictors. The duration of the study may be an important predictor of adherence. Future studies should consider reporting a universal measure of adherence and aim to conduct complex analyses on predictors of adherence such as level of social presence and service user involvement. ©Clare Killikelly, Zhimin He, Clare Reeder, Til Wykes. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.07.2017.

A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes.

PubMed

Mushkudiani, Nino A; Hukkelhoven, Chantal W P M; Hernández, Adrián V; Murray, Gordon D; Choi, Sung C; Maas, Andrew I R; Steyerberg, Ewout W

2008-04-01

To describe the modeling techniques used for early prediction of outcome in traumatic brain injury (TBI) and to identify aspects for potential improvements. We reviewed key methodological aspects of studies published between 1970 and 2005 that proposed a prognostic model for the Glasgow Outcome Scale of TBI based on admission data. We included 31 papers. Twenty-four were single-center studies, and 22 reported on fewer than 500 patients. The median of the number of initially considered predictors was eight, and on average five of these were selected for the prognostic model, generally including age, Glasgow Coma Score (or only motor score), and pupillary reactivity. The most common statistical technique was logistic regression with stepwise selection of predictors. Model performance was often quantified by accuracy rate rather than by more appropriate measures such as the area under the receiver-operating characteristic curve. Model validity was addressed in 15 studies, but mostly used a simple split-sample approach, and external validation was performed in only four studies. Although most models agree on the three most important predictors, many were developed on small sample sizes within single centers and hence lack generalizability. Modeling strategies have to be improved, and include external validation.

Specialized inpatient treatment of adult anorexia nervosa: effectiveness and clinical significance of changes.

PubMed

Schlegl, Sandra; Quadflieg, Norbert; Löwe, Bernd; Cuntz, Ulrich; Voderholzer, Ulrich

2014-09-06

Previous studies have predominantly evaluated the effectiveness of inpatient treatment for anorexia nervosa at the group level. The aim of this study was to evaluate treatment outcomes at an individual level based on the clinical significance of improvement. Patients' treatment outcomes were classified into four groups: deteriorated, unchanged, reliably improved and clinically significantly improved. Furthermore, the study set out to explore predictors of clinically significant changes in eating disorder psychopathology. A total of 435 inpatients were assessed at admission and at discharge on the following measures: body-mass-index, eating disorder symptoms, general psychopathology, depression and motivation for change. 20.0-32.0% of patients showed reliable changes and 34.1-55.3% showed clinically significant changes in the various outcome measures. Between 23.0% and 34.5% remained unchanged and between 1.7% and 3.0% deteriorated. Motivation for change and depressive symptoms were identified as positive predictors of clinically significant changes in eating disorder psychopathology, whereas body dissatisfaction, impulse regulation, social insecurity and education were negative predictors. Despite high rates of reliable and clinically significant changes following intensive inpatient treatment, about one third of anorexia nervosa patients showed no significant response to treatment. Future studies should focus on the identification of non-responders as well as on the development of treatment strategies for these patients.

Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.

PubMed

Martín-Navarro, Antonio; Gaudioso-Simón, Andrés; Álvarez-Jarreta, Jorge; Montoya, Julio; Mayordomo, Elvira; Ruiz-Pesini, Eduardo

2017-03-07

Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset of neutral and damaging mtDNA missense variants to test the accuracy of predictors. Because mtDNA sequencing of patients suffering mitochondrial diseases is revealing many missense mutations, it is needed to prioritize candidate substitutions for further confirmation. Predictors can be useful as screening tools but their performance must be improved. We have developed a SVM classifier (Mitoclass.1) specific for mtDNA missense variants. Training and validation of the model was executed with 2,835 mtDNA damaging and neutral amino acid substitutions, previously curated by a set of rigorous pathogenicity criteria with high specificity. Each instance is described by a set of three attributes based on evolutionary conservation in Eukaryota of wildtype and mutant amino acids as well as coevolution and a novel evolutionary analysis of specific substitutions belonging to the same domain of mitochondrial polypeptides. Our classifier has performed better than other web-available tested predictors. We checked performance of three broadly used predictors with the total mutations of our curated dataset. PolyPhen-2 showed the best results for a screening proposal with a good sensitivity. Nevertheless, the number of false positive predictions was too high. Our method has an improved sensitivity and better specificity in relation to PolyPhen-2. We also publish predictions for the complete set of 24,201 possible missense variants in the 13 human mtDNA-encoded polypeptides. Mitoclass.1 allows a better selection of candidate damaging missense variants from mtDNA. A careful search of discriminatory attributes and a training step based on a curated dataset of amino acid substitutions belonging exclusively to human mtDNA genes allows an improved performance. Mitoclass.1 accuracy could be improved in the future when more mtDNA missense substitutions will be available for updating the attributes and retraining the model.

A highly efficient CMOS nanoplasmonic crystal enhanced slow-wave thermal emitter improves infrared gas-sensing devices

PubMed Central

Pusch, Andreas; De Luca, Andrea; Oh, Sang S.; Wuestner, Sebastian; Roschuk, Tyler; Chen, Yiguo; Boual, Sophie; Ali, Zeeshan; Phillips, Chris C.; Hong, Minghui; Maier, Stefan A.; Udrea, Florin; Hopper, Richard H.; Hess, Ortwin

2015-01-01

The application of plasmonics to thermal emitters is generally assisted by absorptive losses in the metal because Kirchhoff’s law prescribes that only good absorbers make good thermal emitters. Based on a designed plasmonic crystal and exploiting a slow-wave lattice resonance and spontaneous thermal plasmon emission, we engineer a tungsten-based thermal emitter, fabricated in an industrial CMOS process, and demonstrate its markedly improved practical use in a prototype non-dispersive infrared (NDIR) gas-sensing device. We show that the emission intensity of the thermal emitter at the CO2 absorption wavelength is enhanced almost 4-fold compared to a standard non-plasmonic emitter, which enables a proportionate increase in the signal-to-noise ratio of the CO2 gas sensor. PMID:26639902

Tuning Fluorescence Direction with Plasmonic Metal–Dielectric– Metal Substrates

PubMed Central

Choudhury, Sharmistha Dutta; Badugu, Ramachandram; Nowaczyk, Kazimierz; Ray, Krishanu; Lakowicz, Joseph R.

2013-01-01

Controlling the emission properties of fluorophores is essential for improving the performance of fluorescence-based techniques in modern biochemical research, medical diagnosis, and sensing. Fluorescence emission is isotropic in nature, which makes it difficult to capture more than a small fraction of the total emission. Metal– dielectric–metal (MDM) substrates, discussed in this Letter, convert isotropic fluorescence into beaming emission normal to the substrate. This improves fluorescence collection efficiency and also opens up new avenues for a wide range of fluorescence-based applications. We suggest that MDM substrates can be readily adapted for multiple uses, such as in microarray formats, for directional fluorescence studies of multiple probes or for molecule-specific sensing with a high degree of spatial control over the fluorescence emission. SECTION: Physical Processes in Nanomaterials and Nanostructures PMID:24013521

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

PubMed

Hoogendoorn, Mark; Szolovits, Peter; Moons, Leon M G; Numans, Mattijs E

2016-05-01

Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploiting the rich content of the EMRs. In this paper, we explore the usage of a range of natural language processing (NLP) techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance. We study a number of existing techniques for the extraction of predictors from the consultation notes, namely a bag of words based approach and topic modeling. In addition, we develop a dedicated technique to match the uncoded consultation notes with a medical ontology. We apply these techniques as an extension to an existing pipeline to extract predictors from EMRs. We evaluate them in the context of predictive modeling for colorectal cancer (CRC), a disease known to be difficult to diagnose before performing an endoscopy. Our results show that we are able to extract useful information from the consultation notes. The predictive performance of the ontology-based extraction method moves significantly beyond the benchmark of age and gender alone (area under the receiver operating characteristic curve (AUC) of 0.870 versus 0.831). We also observe more accurate predictive models by adding features derived from processing the consultation notes compared to solely using coded data (AUC of 0.896 versus 0.882) although the difference is not significant. The extracted features from the notes are shown be equally predictive (i.e. there is no significant difference in performance) compared to the coded data of the consultations. It is possible to extract useful predictors from uncoded consultation notes that improve predictive performance. Techniques linking text to concepts in medical ontologies to derive these predictors are shown to perform best for predicting CRC in our EMR dataset. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of molecularly imprinted polymers to block quorum sensing and inhibit bacterial biofilm formation.

PubMed

Ma, Luyao; Feng, Shaolong; de la Fuente-Nunez, Cesar; Hancock, Robert E W; Lu, Xiaonan

2018-05-16

Bacterial biofilms are responsible for most clinical infections and show increased antimicrobial resistance. In this study, molecularly imprinted polymers (MIPs) were developed to specifically capture prototypical quorum sensing autoinducers [i.e., N-(3-oxododecanoyl)-L-homoserine lactone (3-oxo-C12AHL)], interrupt quorum sensing, and subsequently inhibit biofilm formation of Pseudomonas aeruginosa, an important human nosocomial pathogen. The synthesis of MIPs was optimized by considering the amount and type of the functional monomers itaconic acid (IA) and 2-hydroxyethyl methacrylate (HEMA). IA-based MIPs showed high adsorption affinity towards 3-oxo-C12AHL with an imprinting factor of 1.68. Compared to IA-based MIPs, the adsorption capacity of HEMA-based MIPs was improved 5-fold. HEMA-based MIPs significantly reduced biofilm formation (by ~65%), while biofilm suppression by IA-based MIPs was neutralized due to increased bacterial attachment. The developed MIPs represent promising alternative biofilm intervention agents that can be applied to surfaces relevant to clinical settings and food processing equipment.

Smart architecture for stable multipoint fiber Bragg grating sensor system

NASA Astrophysics Data System (ADS)

Yeh, Chien-Hung; Tsai, Ning; Zhuang, Yuan-Hong; Huang, Tzu-Jung; Chow, Chi-Wai; Chen, Jing-Heng; Liu, Wen-Fung

2017-12-01

In this work, we propose and investigate an intelligent fiber Bragg grating (FBG)-based sensor system in which the proposed stabilized and wavelength-tunable single-longitudinal-mode erbium-doped fiber laser can improve the sensing accuracy of wavelength-division-multiplexing multiple FBG sensors in a longer fiber transmission distance. Moreover, we also demonstrate the proposed sensor architecture to enhance the FBG capacity for sensing strain and temperature, simultaneously.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

Chemical and Physical Sensing in the Petroleum Industry

NASA Astrophysics Data System (ADS)

Disko, Mark

2008-03-01

World-scale oil, gas and petrochemical production relies on a myriad of advanced technologies for discovering, producing, transporting, processing and distributing hydrocarbons. Sensing systems provide rapid and targeted information that can be used for expanding resources, improving product quality, and assuring environmentally sound operations. For example, equipment such as reactors and pipelines can be operated with high efficiency and safety with improved chemical and physical sensors for corrosion and hydrocarbon detection. At the interface between chemical engineering and multiphase flow physics, ``multi-scale'' phenomena such as catalysis and heat flow benefit from new approaches to sensing and data modeling. We are combining chemically selective micro-cantilevers, fiber optic sensing, and acoustic monitoring with statistical data fusion approaches to maximize control information. Miniaturized analyzers represent a special opportunity, including the nanotech-based quantum cascade laser systems for mid-infrared spectroscopy. Specific examples for use of these new micro-systems include rapid monocyclic aromatic molecule identification and measurement under ambient conditions at weight ppb levels. We see promise from emerging materials and devices based on nanotechnology, which can one day be available at modest cost for impact in existing operations. Controlled surface energies and emerging chemical probes hold the promise for reduction in greenhouse gas emissions for current fuels and future transportation and energy technologies.

The relationship between performance and flow state in tennis competition.

PubMed

Koehn, S; Morris, T

2012-08-01

The study aimed to examine 1) the validity of the nine-factor flow model in tennis competition; 2) differences in flow state between athletes who won or lost their competition match; 3) the link between flow and subjective performance; and 4) flow dimensions as predictors of performance outcome The sample consisted of 188 junior tennis players (115 male, 73 female) between 12 and 18 years of age. Participants' performance was recorded during junior ranking-list tournaments. Following the completion of a tennis competition match, participants completed the Flow State Scale-2 and a subjective performance outcome measure. Acceptable flow model fit indices of CFI, TLI, SRMR, and RMSEA were only found for winning athletes. The group of winning athletes scored significantly higher on all nine flow dimensions, except time transformation, than losing athletes, showing statistically significant differences for challenge-skills balance, clear goals, sense of control, and autotelic experience. Significant correlation coefficients were found between flow state and subjective performance assessments. The binary logistic regression revealed concentration on the task and sense of control to be significant predictors of performance outcome. The predictor variables explained 13% of the variance in games won. The study showed that athletes who win or lose perceived flow state differently. Studies using retrospective assessments need to be aware that subjective experience could be biased by performance outcomes. Pinpointing psychological variables and their impact on ecologically valid measures, such as performance results, would support the development of effective intervention studies to increase performance in sport competition.

Improving the prediction of African savanna vegetation variables using time series of MODIS products

NASA Astrophysics Data System (ADS)

Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

2017-09-01

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Using Sap Flow Monitoring for Improved Process-based Ecohydrologic Understanding 2022

USDA-ARS?s Scientific Manuscript database

Sap flow measurements can be an important tool for unraveling the complex web of ecosystem fluxes, especially when it is combined with other measurements like eddy covariance, isotopes, remote sensing, etc. In this talk, we will demonstrate how sap flow measurements have improved our process-level u...

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

PubMed

Vilar, Santiago; Hripcsak, George

2016-01-01

Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Wanders, N.; Bierkens, M. F. P.; de Jong, S. M.; de Roo, A.; Karssenberg, D.

2014-08-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. This article was corrected on 15 SEP 2014. See the end of the full text for details.

Mobile Phone Based Participatory Sensing in Hydrology

NASA Astrophysics Data System (ADS)

Lowry, C.; Fienen, M. N.; Böhlen, M.

2014-12-01

Although many observations in the hydrologic sciences are easy to obtain, requiring very little training or equipment, spatial and temporally-distributed data collection is hindered by associated personnel and telemetry costs. Lack of data increases the uncertainty and can limit applications of both field and modeling studies. However, modern society is much more digitally connected than the past, which presents new opportunities to collect real-time hydrologic data through the use of participatory sensing. Participatory sensing in this usage refers to citizens contributing distributed observations of physical phenomena. Real-time data streams are possible as a direct result of the growth of mobile phone networks and high adoption rates of mobile users. In this research, we describe an example of the development, methodology, barriers to entry, data uncertainty, and results of mobile phone based participatory sensing applied to groundwater and surface water characterization. Results are presented from three participatory sensing experiments that focused on stream stage, surface water temperature, and water quality. Results demonstrate variability in the consistency and reliability across the type of data collected and the challenges of collecting research grade data. These studies also point to needed improvements and future developments for widespread use of low cost techniques for participatory sensing.

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Predictors of a favourable outcome in patients with fibromyalgia: results of 1-year follow-up.

PubMed

Kim, Ji-Eun; Park, Dong-Jin; Choi, Sung-Eun; Kang, Ji-Hyoun; Yim, Yi-Rang; Lee, Jeong-Won; Lee, Kyung-Eun; Wen, Lihui; Kim, Seong-Kyu; Choe, Jung-Yoon; Lee, Shin-Seok

2016-01-01

To determine the outcomes of Korean patients with fibromyalgia (FM) and to identify prognostic factors associated with improvement at 1-year follow-up. Forty-eight patients with FM were enrolled and examined every 3 months for 1 year. At the time of enrollment, we interviewed all patients using a structured questionnaire that recorded socio-demographic data, current or past FM symptoms, and current use of relevant medications. Tender point counts and scores were assessed by thumb palpation. Patients were asked to complete the Korean versions of the Fibromyalgia Impact Questionnaire (FIQ), the Brief Fatigue Inventory, the SF-36, the Beck Depression Inventory, the State-Trait Anxiety Inventory (STAI), the Self-Efficacy Scale, and the Social Support Scale. Tender points, FIQ scores, and the use of relevant medications were recorded during one year of follow-up. Of the 48 patients, 32 (66.7%) had improved FIQ scores 1 year after enrollment. Improved patients had higher baseline FIQ scores (68.4±13.9 vs. 48.4±20.8, p=0.001) and STAI-II scores (55.8±10.9 vs. 11.5±11.5, p=0.022). Patients treated with pregabalin were more likely to improve after 1 year, based on the FIQ scores (71.9% vs. 37.5%, p=0.031). On multivariate logistic regression analysis, a higher STAI-II score at the time of enrollment and pregabalin treatment during one year of follow-up were the predictors of improvement. Two-thirds of our Korean FM patients experienced some clinical improvement by 1-year follow-up. A high baseline STAI-II score and treatment with pregabalin were the important predictor of improved FM.

Understanding work contextual factors: a short-cut to evidence-based practice?

PubMed

Wallin, Lars; Ewald, Uwe; Wikblad, Karin; Scott-Findlay, Shannon; Arnetz, Bengt B

2006-01-01

It has become increasingly clear that workplace contextual factors make an important contribution to provider and patient outcomes. The potential for health care professionals of using research in practice is also linked to such factors, although the exact factors or mechanisms for enhancing this potential are not understood. From a perspective of implementing evidence-based nursing practice, the authors of this article report on a study examining contextual factors. The objective of this study was to identify predictors of organizational improvement by measuring staff perceptions of work contextual factors. The Quality Work Competence questionnaire was used in a repeated measurement survey with a 1-year break between the two periods of data collection. The sample consisted of 134 employees from four neonatal units in Sweden. Over the study period significant changes occurred among staff perceptions, both within and between units, on various factors. Changes in staff perceptions on skills development and participatory management were the major predictors of enhanced potential of overall organizational improvement. Perceived improvement in skills development and performance feedback predicted improvement in leadership. Change in commitment was predicted by perceived decreases in work tempo and work-related exhaustion. These findings indicate the potential for organizational improvement by developing a learning and supportive professional environment as well as by involving staff in decision-making at the unit level. Such initiatives are also likely to be of importance for enhanced use of research in practice and evidence-based nursing. On the other hand, high levels of work tempo and burnout appear to have negative consequences on staff commitment for improving care and the work environment. A better understanding of workplace contextual factors is necessary for improving the organizational potential of getting research into practice and should be considered in future implementation projects.

A novel classification of prostate specific antigen (PSA) biosensors based on transducing elements.

PubMed

Najeeb, Mansoor Ani; Ahmad, Zubair; Shakoor, R A; Mohamed, A M A; Kahraman, Ramazan

2017-06-01

During the last few decades, there has been a tremendous rise in the number of research studies dedicated towards the development of diagnostic tools based on bio-sensing technology for the early detection of various diseases like cardiovascular diseases (CVD), many types of cancer, diabetes mellitus (DM) and many infectious diseases. Many breakthroughs have been developed in the areas of improving specificity, selectivity and repeatability of the biosensor devices. Innovations in the interdisciplinary areas like biotechnology, genetics, organic electronics and nanotechnology also had a great positive impact on the growth of bio-sensing technology. As a product of these improvements, fast and consistent sensing policies have been productively created for precise and ultrasensitive biomarker-based disease diagnostics. Prostate-specific antigen (PSA) is widely considered as an important biomarker used for diagnosing prostate cancer. There have been many publications based on various biosensors used for PSA detection, but a limited review was available for the classification of these biosensors used for the detection of PSA. This review highlights the various biosensors used for PSA detection and proposes a novel classification for PSA biosensors based on the transducer type used. We also highlight the advantages, disadvantages and limitations of each technique used for PSA biosensing which will make this article a complete reference tool for the future researches in PSA biosensing. Copyright © 2017 Elsevier B.V. All rights reserved.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

On the Identification of Associations between Five World Health Organization Water, Sanitation and Hygiene Phenotypes and Six Predictors in Low and Middle-Income Countries.

PubMed

Ellis, Hugh; Schoenberger, Erica

2017-01-01

According to the most recent estimates, 842,000 deaths in low- to middle-income countries were attributable to inadequate water, sanitation and hygiene in 2012. Despite billions of dollars and decades of effort, we still lack a sound understanding of which kinds of WASH interventions are most effective in improving public health outcomes, and an important corollary-whether the right things are being measured. The World Health Organization (WHO) has made a concerted effort to compile comprehensive data on drinking water quality and sanitation in the developing world. A recent 2014 report provides information on three phenotypes (responses): Unsafe Water Deaths, Unsafe Sanitation Deaths, Unsafe Hygiene Deaths; two grouped phenotypes: Unsafe Water and Sanitation Deaths and Unsafe Water, Sanitation and Hygiene Deaths; and six explanatory variables (predictors): Improved Sanitation, Unimproved Water Source, Piped Water To Premises, Other Improved Water Source, Filtered and Bottled Water in the Household and Handwashing. Regression analyses were performed to identify statistically significant associations between these mortality responses and predictors. Good fitted-model performance required: (1) the use of population-normalized death fractions as opposed to number of deaths; (2) transformed response (logit or power); and (3) square-root predictor transformation. Given the complexity and heterogeneity of the relationships and countries being studied, these models exhibited remarkable performance and explained, for example, about 85% of the observed variance in population-normalized Unsafe Sanitation Death fraction, with a high F-statistic and highly statistically significant predictor p-values. Similar performance was found for all other responses, which was an unexpected result (the expected associations between responses and predictors-i.e., water-related with water-related, etc. did not occur). The set of statistically significant predictors remains the same across all responses. That is, Unsafe Water Source (UWS), Improved Sanitation (IS) and Filtered and Bottled Water in the Household (FBH) were the only statistically significant predictors whether the response was Unsafe Sanitation Death Fraction, Unsafe Hygiene Death Fraction or Unsafe Water Death Fraction. Moreover, the fraction of variance explained for all fitted models remained relatively high (adjusted R2 ranges from 0.7605 to 0.8533). We find that two of the statistically significant predictors-Improved Sanitation and Unimproved Water Sources-are particularly influential. We also find that some predictors (Piped Water to Premises, Other Improved Water Sources) have very little explanatory power for predicting mortality and one (Other Improved Water Sources) has a counterintuitive effect on response (Unsafe Sanitary Death Fraction increases with increases in OIWS) and one predictor (Hand Washing) to have essentially no explanatory usefulness. Our results suggest that a higher priority may need to be given to improved sanitation than has been the case. Nevertheless, while our focus in this paper is mortality, morbidity is a staggering consequence of inadequate water, sanitation and hygiene, and lower impact on mortality may not mean a similarly low impact on morbidity. More specifically, those predictors that we found uninfluential for predicting mortality-related responses may indeed be important when morbidity is the response.

Perceived Burdensomeness, Familism, and Suicidal Ideation among Mexican Women: Enhancing Understanding of Risk and Protective Factors

ERIC Educational Resources Information Center

Garza, Monica J.; Pettit, Jeremy W.

2010-01-01

The interpersonal-psychological theory of suicide and a culturally-relevant construct, familism, was used to examine predictors of suicidal ideation among Mexican and Mexican American women in the United States. A sense of perceived burdensomeness toward others was expected to significantly predict suicidal ideation, especially among women who…

Social Phobia as a Predictor of Social Competence Perceived by Teenagers

ERIC Educational Resources Information Center

Ates, Bünyamin

2016-01-01

In this research, it was analyzed to what extent the variables of social avoidance, concern for being criticized and sense of individual worthlessness as sub-dimensions of social phobia predicted the perceived social competence levels of teenagers. The study group of this study included totally 648 students including 301 (46.5%) female and 347…

The Relationship of Childhood Upbringing and University Degree Program to Environmental Identity: Experience in Nature Matters

ERIC Educational Resources Information Center

Prévot, Anne-Caroline; Clayton, Susan; Mathevet, Raphael

2018-01-01

Education has been proposed as an important way to increase environmental concern. Beyond providing information, education could also encourage a stable sense of oneself as connected to the natural world, or environmental identity (EID), which is a predictor of environmental concern and behavior. This study explored the relative roles of…

Investigation of Parental Involvement Tasks as Predictors of Primary Students' Turkish, Math, and Science & Technology Achievement

ERIC Educational Resources Information Center

Sad, Suleyman Nihat

2012-01-01

Problem statement: Parental involvement is used as an umbrella term to imply parents' efforts to take an active role in their children's education. In this sense it takes many forms ranging from parent-child communication to participating/volunteering in school activities. Although parental involvement is one condition for students' success, the…

Hyperspectral canopy reflectance as a predictor for root concentrations of nitrogen and carbon in native and non native grass species

USDA-ARS?s Scientific Manuscript database

Land managers, scientists, and crop professionals need real-time, inexpensive, and labor-saving methods to determine below-ground biomass and potential carbon (C) and nitrogen (N) inputs of that biomass. Remote sensing is a non-destructive tool that monitors vigor of vegetation and has been used t...

Hyperspectral canopy reflectance as a predictor for root concentrations of nitrogen and carbon in native and non native grass species

USDA-ARS?s Scientific Manuscript database

Land managers, scientists, and crop professionals need real-time, inexpensive, and labor-saving methods to determine below-ground biomass and potential carbon (C) and nitrogen (N) inputs of that biomass. Remote sensing is a non-destructive tool that monitors vigor of vegetation and has been used ...

The Nature of Relationships between Mental Rotation, Math, and Language in Deaf Signers

ERIC Educational Resources Information Center

Halper, Elizabeth Blaisdell

2009-01-01

Three mental rotation tasks, the Card Rotation Task (CRT), the Vandenberg Mental Rotation Test (VMRT), and the Money Road-Map of Direction Sense (MRM), were administered to 60 deaf students from Gallaudet University to determine if mental rotation was predictive of scores on the ACT English or Math subtests. Other predictor variables, such as…

Remote sensing and GIS techniques for assessment of the soil water content in order to improve agricultural practice and reduce the negative impact on groundwater: case study, agricultural area Ştefan cel Mare, Călăraşi County.

PubMed

Tevi, Giuliano; Tevi, Anca

2012-01-01

Traditional agricultural practices based on non-customized irrigation and soil fertilization are harmful for the environment, and may pose a risk for human health. By continuing the use of these practices, it is not possible to ensure effective land management, which might be acquired by using advanced satellite technology configured for modern agricultural development. The paper presents a methodology based on the correlation between remote sensing data and field observations, aiming to identify the key features and to establish an interpretation pattern for the inhomogeneity highlighted by the remote sensing data. Instead of using classical methods for the evaluation of land features (field analysis, measurements and mapping), the approach is to use high resolution multispectral and hyperspectral methods, in correlation with data processing and geographic information systems (GIS), in order to improve the agricultural practices and mitigate their environmental impact (soil and shallow aquifer).

Improved Airborne System for Sensing Wildfires

NASA Technical Reports Server (NTRS)

McKeown, Donald; Richardson, Michael

2008-01-01

The Wildfire Airborne Sensing Program (WASP) is engaged in a continuing effort to develop an improved airborne instrumentation system for sensing wildfires. The system could also be used for other aerial-imaging applications, including mapping and military surveillance. Unlike prior airborne fire-detection instrumentation systems, the WASP system would not be based on custom-made multispectral line scanners and associated custom- made complex optomechanical servomechanisms, sensors, readout circuitry, and packaging. Instead, the WASP system would be based on commercial off-the-shelf (COTS) equipment that would include (1) three or four electronic cameras (one for each of three or four wavelength bands) instead of a multispectral line scanner; (2) all associated drive and readout electronics; (3) a camera-pointing gimbal; (4) an inertial measurement unit (IMU) and a Global Positioning System (GPS) receiver for measuring the position, velocity, and orientation of the aircraft; and (5) a data-acquisition subsystem. It would be necessary to custom-develop an integrated sensor optical-bench assembly, a sensor-management subsystem, and software. The use of mostly COTS equipment is intended to reduce development time and cost, relative to those of prior systems.

Parental perceptions of neighborhood processes, stress, personal control, and risk for physical child abuse and neglect.

PubMed

Guterman, Neil B; Lee, Shawna J; Taylor, Catherine A; Rathouz, Paul J

2009-12-01

This study set out to examine whether mothers' individual perceptions of their neighborhood social processes predict their risk for physical child abuse and neglect directly and/or indirectly via pathways involving parents' reported stress and sense of personal control in the parenting role. In-home and phone interview data were examined cross-sectionally from a national birth cohort sample of 3,356 mothers across 20 US cities when the index child was 3 years of age. Mothers' perceptions of neighborhood social processes, parenting stress, and personal control were examined as predictors, and three subscales of the Parent-To-Child Conflict Tactics Scale (CTS-PC) were employed as proxies of physical child abuse and neglect risk. Structural equation modeling (SEM) was employed to test direct and indirect pathways (via parenting stress and control) from perceived neighborhood processes to proxy measures of physical child abuse and neglect. Multiple group SEM was conducted to test for differences across major ethnic groups: African American, Hispanic, and White. Although perceived negative neighborhood processes had only a mild direct role in predicting risk for physical child abuse, and no direct role on child neglect, these perceptions had a discernable indirect role in predicting risk via parenting stress and personal control pathways. Parenting stress exerted the clearest direct role on both physical abuse and neglect risk. This predictor model did not significantly differ across ethnic groups. Although neighborhood conditions may not play a clear directly observable role on physical child abuse and neglect risk, the indirect role they play underscores the importance of parents' perceptions of their neighborhoods, and especially the role they play via parents' reported stress and personal control. Such findings suggest that targeting parents' sense of control and stress in relation to their immediate social environment holds particular potential to reduce physical child abuse and neglect risk. Addressing parents' perceptions of their neighborhood challenges may serve to reduce parenting risk via improving parents' felt control and stress.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

PubMed Central

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

PubMed

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Biocompatible Pressure Sensing Skins for Minimally Invasive Surgical Instruments

PubMed Central

Arabagi, Veaceslav; Felfoul, Ouajdi; Gosline, Andrew H.; Wood, Robert J.; Dupont, Pierre E.

2016-01-01

This paper presents 800-μm thick, biocompatible sensing skins composed of arrays of pressure sensors. The arrays can be configured to conform to the surface of medical instruments so as to act as disposable sensing skins. In particular, the fabrication of cylindrical geometries is considered here for use on endoscopes. The sensing technology is based on polydimethylsiloxane synthetic silicone encapsulated microchannels filled with a biocompatible salt-saturated glycerol solution, functioning as the conductive medium. A multi-layer manufacturing approach is introduced that enables stacking sensing microchannels, mechanical stress concentration features, and electrical routing via flexcircuits in a thickness of less than 1 mm. The proposed approach is inexpensive and does not require clean room tools or techniques. The mechanical stress concentration features are implemented using a patterned copper layer that serves to improve sensing range and sensitivity. Sensor performance is demonstrated experimentally using a sensing skin mounted on a neuroendoscope insertion cannula and is shown to outperform previously developed non-biocompatible sensors. PMID:27642266

Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling.

PubMed

Walz, Yvonne; Wegmann, Martin; Leutner, Benjamin; Dech, Stefan; Vounatsou, Penelope; N'Goran, Eliézer K; Raso, Giovanna; Utzinger, Jürg

2015-11-30

Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d'Ivoire using high- and moderate-resolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixel-based modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

The construction, characterization, Hg(II)-sensing and removal behavior of magnetic core-shell nanospheres loaded with fluorescence "Off-On" probe

NASA Astrophysics Data System (ADS)

Tan, Jun; Wei, Xiaoyan; Chen, Jie; Sun, Ping; Ouyang, Yuxia; Fan, Juhong; Liu, Rui

2014-12-01

The present paper constructed and discussed core-shell structured nanospheres grafted with rhodamine based probe for Hg(II) sensing and removal. Electron microscopy images, XRD curves, thermogravimetric analysis and N2 adsorption/desorption isotherms were used to identify the core-shell structure. The inner core consisted of superparamagnetic Fe3O4 nanoparticles, which made the nanocomposite magnetically removable. The outer shell was constructed with silica molecular sieve which provided large surface area and ordered tunnels for the sensing probe, accelerating analyte adsorption and transportation. The rhodamine based sensing probe emission increased with the increasing Hg(II) concentration, showing emission "Off-On" effect, which could be explained by the structural transformation from a non-emissive one to a highly emissive one. The influence from various metal ions and pH values was also investigated, which suggested this structural transformation could only be triggered by Hg(II), showing high selectivity and linear response. The Hg(II) sensing nanocomposite could be regenerated after usage. The response time was slightly compromised and could be further improved.

Study on additional carrier sensing for IEEE 802.15.4 wireless sensor networks.

PubMed

Lee, Bih-Hwang; Lai, Ruei-Lung; Wu, Huai-Kuei; Wong, Chi-Ming

2010-01-01

Wireless sensor networks based on the IEEE 802.15.4 standard are able to achieve low-power transmissions in the guise of low-rate and short-distance wireless personal area networks (WPANs). The slotted carrier sense multiple access with collision avoidance (CSMA/CA) is used for contention mechanism. Sensor nodes perform a backoff process as soon as the clear channel assessment (CCA) detects a busy channel. In doing so they may neglect the implicit information of the failed CCA detection and further cause the redundant sensing. The blind backoff process in the slotted CSMA/CA will cause lower channel utilization. This paper proposes an additional carrier sensing (ACS) algorithm based on IEEE 802.15.4 to enhance the carrier sensing mechanism for the original slotted CSMA/CA. An analytical Markov chain model is developed to evaluate the performance of the ACS algorithm. Both analytical and simulation results show that the proposed algorithm performs better than IEEE 802.15.4, which in turn significantly improves throughput, average medium access control (MAC) delay and power consumption of CCA detection.

Interpersonal relatedness and psychological functioning following traumatic brain injury: implications for marital and family therapists

PubMed Central

Bay, EH; Blow, AJ; Yan, XE

2015-01-01

Recovery from a mild to moderate traumatic brain injury (TBI) is a challenging process for injured persons and their families. Guided by attachment theory, we investigated whether relationship conflict, social support, or sense of belonging were associated with psychological functioning. Community-dwelling persons with TBI (N=75) and their relatives/significant others (N=74) were surveyed on relationship variables, functional status, and TBI symptom severity. Results from this cross-sectional study revealed that only sense of belonging was a significant predictor of post-injury psychological functioning, although interpersonal conflict approached significance. No relevant pre-injury or injury-related variables impacted these relationships, except marital status. Our findings suggest that interventions targeting strengthening the injured persons' sense of belonging and lowering interpersonal conflict may benefit those living with TBI. PMID:22804472

Interpersonal relatedness and psychological functioning following traumatic brain injury: implications for marital and family therapists.

PubMed

Bay, Esther H; Blow, Adrian J; Yan, Xie Emily

2012-07-01

Recovery from a mild-to-moderate traumatic brain injury (TBI) is a challenging process for injured persons and their families. Guided by attachment theory, we investigated whether relationship conflict, social support, or sense of belonging were associated with psychological functioning. Community-dwelling persons with TBI (N = 75) and their relatives/significant others (N = 74) were surveyed on relationship variables, functional status, and TBI symptom severity. Results from this cross-sectional study revealed that only sense of belonging was a significant predictor of postinjury psychological functioning, although interpersonal conflict approached significance. No relevant preinjury or injury-related variables impacted these relationships, except marital status. Our findings suggest that interventions targeting strengthening the injured persons' sense of belonging and lowering interpersonal conflict may benefit those living with TBI. © 2011 American Association for Marriage and Family Therapy.

Changes and Predictors of the Sense of Meaning in Life in Polish University Students Participating in Psychological Workshops Communication-Forgiveness-Love.

PubMed

Kuk, Anna; Guszkowska, Monika

2018-04-28

The aim of this study was to determine changes in the sense of meaning in life of university students who participated in psychological workshops "Communication-Forgiveness-Love". The study evaluated 33 university students from first-cycle and second-cycle studies in physical education in the Józef Piłsudski University of Physical Education in Warsaw. The Reker's Life Attitude Profile-Revised Questionnaire, Social Competencies Questionnaire (KKS) by Matczak, Emotional Intelligence Questionnaire (INTE) by Schutte et al. and the Goldberg's General Health Questionnaire GHQ-28 were used. The study found that psychological workshops can be effective in instilling the sense of meaning in life in university students, especially those from first-cycle studies. The workshops can produce more benefits to students with worse mental status and with lower social competencies.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

MOF-Based Membrane Encapsulated ZnO Nanowires for Enhanced Gas Sensor Selectivity.

PubMed

Drobek, Martin; Kim, Jae-Hun; Bechelany, Mikhael; Vallicari, Cyril; Julbe, Anne; Kim, Sang Sub

2016-04-06

Gas sensors are of a great interest for applications including toxic or explosive gases detection in both in-house and industrial environments, air quality monitoring, medical diagnostics, or control of food/cosmetic properties. In the area of semiconductor metal oxides (SMOs)-based sensors, a lot of effort has been devoted to improve the sensing characteristics. In this work, we report on a general methodology for improving the selectivity of SMOx nanowires sensors, based on the coverage of ZnO nanowires with a thin ZIF-8 molecular sieve membrane. The optimized ZnO@ZIF-8-based nanocomposite sensor shows markedly selective response to H2 in comparison with the pristine ZnO nanowires sensor, while showing the negligible sensing response to C7H8 and C6H6. This original MOF-membrane encapsulation strategy applied to nanowires sensor architecture pave the way for other complex 3D architectures and various types of applications requiring either gas or ion selectivity, such as biosensors, photo(catalysts), and electrodes.

The influence inquiry-based science has on elementary teachers' perception of instruction and self-efficacy

NASA Astrophysics Data System (ADS)

Lewis, Felecia J.

The nature and purpose of this study was to examine the self-efficacy of teachers who use an inquiry-based science program to provide authentic experiences within the elementary school setting. It is essential to explore necessary improvements to bring about effective science education. Using a mixed methods study, the researcher conducted interviews with elementary teachers from five elementary schools within the same school district. The interviews focused on the teachers' experiences with inquiry-based science and their perceptions of quality science instruction. The Teachers' Sense of Efficacy Scale was used to collect quantitative data regarding the teachers' perception of instructional practice and student engagement. The study revealed that limited science content knowledge, inadequate professional development, and a low sense of self-efficacy have a substantial effect on teacher outcomes, instructional planning, and ability to motivate students to participate in inquiry-based learning. It will take a collective effort from administrators, teachers, parents, and students to discover ways to improve elementary science education.

Numerical study on refractive index sensor based on hybrid-plasmonic mode

NASA Astrophysics Data System (ADS)

Yun, Jeong-Geun; Kim, Joonsoo; Lee, Kyookeun; Lee, Yohan; Lee, Byoungho

2017-04-01

We propose a highly sensitive hybrid-plasmonic sensor based on thin-gold nanoslit arrays. The transmission characteristics of gold nanoslit arrays are analyzed as changing the thickness of gold layer. The surface plasmon polariton mode excited on the sensing medium, which is sensitive to refractive index change of the sensing medium, is strengthened by reducing the thickness of the gold layer. A design rule is suggested that steeper dispersion curve of the surface plasmon polariton mode leads to higher sensitivity. For the dispersion engineering, hybrid-plasmonic structure, which consists of thin-gold nanoslit arrays, sensing region and high refractive index dielectric space is introduced. The proposed sensor structure with period of 700 nm shows the improved sensitivity up to 1080 nm/RIU (refractive index unit), and the surface sensitivity is extremely enhanced.

Predicting positive mental health in internally displaced persons in Indonesia: the roles of economic improvement and exposure to violent conflict.

PubMed

Saragih Turnip, Sherly; Sörbom, Dag; Hauff, Edvard

2016-01-01

Positive mental health, rather than just the absence of mental illness, is rarely investigated among the internally displaced persons (IDPs) affected by violent conflict in low-income countries. The purpose of this study was to investigate a model that could explain the interrelationship between factors contributing to positive mental health in displaced populations. In a longitudinal study we examine poverty, exposure to traumatic events and the change of material well-being after one year. We collected data in two consecutive years (2005 and 2006) from a community-based sample of IDPs in Ambon, Indonesia, through face-to-face structured interviews with consenting adults. Participants of this study were IDPs lived in Ambon during the violent conflict period. We interviewed 471 IDPs in the first year and reinterviewed 399 (85%) of the same subjects in the second year. The IDPs possessed good sense of coherence and subjective well-being. Our final model, which was generated by the use of structural equation modeling, fits the data well (χ(2) = 52.51, df = 45, p = .21, CFI = .99, RMSEA = .019). Exposure to violent conflict had a negative impact on IDPs' mental health initially and better economic conditions improved it (r = -.30 and .29 respectively). Mental health status one year previously was a strong predictor of future mental health, followed by individual economic growth in the past year (r = .43 and .29 respectively). On a group level the IDPs were resilient and adaptive to survive in adverse living conditions after devastating violent conflict, and the economic improvement contributed to it.

Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.

PubMed

Pareschi, Fabio; Mangia, Mauro; Bortolotti, Daniele; Bartolini, Andrea; Benini, Luca; Rovatti, Riccardo; Setti, Gianluca

2017-12-01

In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.

Estimating Gross Primary Production in Cropland with High Spatial and Temporal Scale Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lin, S.; Li, J.; Liu, Q.

2018-04-01

Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R2 = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE < 3 gC/m2/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

Remote sensing based crop type mapping and evapotranspiration estimates at the farm level in arid regions of the globe

NASA Astrophysics Data System (ADS)

Ozdogan, M.; Serrat-Capdevila, A.; Anderson, M. C.

2017-12-01

Despite increasing scarcity of freshwater resources, there is dearth of spatially explicit information on irrigation water consumption through evapotranspiration, particularly in semi-arid and arid geographies. Remote sensing, either alone or in combination with ground surveys, is increasingly being used for irrigation water management by quantifying evaporative losses at the farm level. Increased availability of observations, sophisticated algorithms, and access to cloud-based computing is also helping this effort. This presentation will focus on crop-specific evapotranspiration estimates at the farm level derived from remote sensing in a number of water-scarce regions of the world. The work is part of a larger effort to quantify irrigation water use and improve use efficiencies associated with several World Bank projects. Examples will be drawn from India, where groundwater based irrigation withdrawals are monitored with the help of crop type mapping and evapotranspiration estimates from remote sensing. Another example will be provided from a northern irrigation district in Mexico, where remote sensing is used for detailed water accounting at the farm level. These locations exemplify the success stories in irrigation water management with the help of remote sensing with the hope that spatially disaggregated information on evapotranspiration can be used as inputs for various water management decisions as well as for better water allocation strategies in many other water scarce regions.

An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks.

PubMed

Yan, Jun; Yu, Kegen; Chen, Ruizhi; Chen, Liang

2017-05-30

In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared.

Exploiting domain information for Word Sense Disambiguation of medical documents.

PubMed

Stevenson, Mark; Agirre, Eneko; Soroa, Aitor

2012-01-01

Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the domain of the target documents. The authors' goal is to improve these methods by using information about the topic of the document in which the ambiguous term appears. The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. The authors report a significant improvement when combining those key terms with local context, showing that domain information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency.

Exploiting domain information for Word Sense Disambiguation of medical documents

PubMed Central

Agirre, Eneko; Soroa, Aitor

2011-01-01

Objective Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the domain of the target documents. The authors' goal is to improve these methods by using information about the topic of the document in which the ambiguous term appears. Design The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. Measurements A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. Results and discussion The authors report a significant improvement when combining those key terms with local context, showing that domain information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency. PMID:21900701

An Improved Image Matching Method Based on Surf Algorithm

NASA Astrophysics Data System (ADS)

Chen, S. J.; Zheng, S. Z.; Xu, Z. G.; Guo, C. C.; Ma, X. L.

2018-04-01

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

Development and validation of an educational program to enhance sense of coherence in patients with diabetes mellitus type 2.

PubMed

Odajima, Yuki; Kawaharada, Mariko; Wada, Norio

2017-08-01

This study aimed to develop a group education program that facilitates a sense of coherence among patients with type 2 diabetes mellitus, which was provided four times, and to validate the effect of the program among the patients. Researchers allocated 40 patients with type 2 diabetes, who had been admitted to a general hospital in Japan for diabetes education for two weeks. Twenty-one patients were allocated to the intervention group and 19 to the control group. The control group undertook a lecture-based educational program that the facility offered. The intervention group received the program, in addition to the facility's educational program. The sense of coherence scale and the Problem Areas in Diabetes Survey were used as evaluation indices. The average age of the intervention group was 59.1 years and that of the control group was 59.5 years. The intervention group showed a between-group effect of improvement in the sense of coherence score. Additionally, the intervention group showed a within-group effect of improvement in the sense of coherence score, as well as the comprehensibility and manageability scores, which are subdomains, and the Problem Areas in Diabetes Survey score. The within-group comparison showed a significant decrease in the early-morning FPG at both groups by an effect of treatment. The program suggested the possibility of improving the sense of coherence and the Problem Areas in Diabetes Survey. In order to enhance general use of the program, it is necessary to reach out to participating facilities and verify the effect of the program.

Mothers' intentions to vaccinate their teenaged children against human papillomavirus, as predicted by sex in South Korea: An application of the theory of planned behavior.

PubMed

Kim, Kyung Mi; Choi, Jeong Sil

2017-10-01

This study was conducted in order to examine the intention of mothers to vaccinate their teenaged children against human papillomavirus (HPV) infection, according to the children's sex. Based on the theory of planned behavior, the study identified the sex-specific predictors of mothers' intention to vaccinate their teenaged children against HPV. This was a descriptive survey study that included, as participants, 200 mothers whose teenaged children were not vaccinated against HPV. The mothers' experience with HPV vaccination was a significant predictor of their childrens' HPV vaccination status. For the mothers of sons, subjective norms, attitudes, and perceived behavioral control were found to be significant predictors of intention of HPV vaccination, with an explanatory power of 69.5%. For those with daughters, only attitudes and subjective norms were significant predictors, with an explanatory power of 79.6%. The application of the theory of planned behavior is an effective method to determine the predictors of children's HPV vaccination status. In order to improve the HPV vaccination rate of teenaged children, strategies for education and effective promotion that involve mothers should be developed. © 2016 Japan Academy of Nursing Science.

Role of Contact Force Sensing in Catheter Ablation of Cardiac Arrhythmias: Evolution or History Repeating Itself?

PubMed

Ariyarathna, Nilshan; Kumar, Saurabh; Thomas, Stuart P; Stevenson, William G; Michaud, Gregory F

2018-06-01

Adequate catheter-tissue contact facilitates efficient heat energy transfer to target tissue. Tissue contact is thus critical to achieving lesion transmurality and success of radiofrequency (RF) ablation procedures, a fact recognized more than 2 decades ago. The availability of real-time contact force (CF)-sensing catheters has reinvigorated the field of ablation biophysics and optimized lesion formation. The ability to measure and display CF came with the promise of dramatic improvement in safety and efficacy; however, CF quality was noted to have just as important an influence on lesion formation as absolute CF quantity. Multiple other factors have emerged as key elements influencing effective lesion formation, including catheter stability, lesion contiguity and continuity, lesion density, contact homogeneity across a line of ablation, spatiotemporal dynamics of contact governed by cardiac and respiratory motion, contact directionality, and anatomic wall thickness, in addition to traditional ablation indices of power and RF duration. There is greater appreciation of surrogate markers as a guide to lesion formation, such as impedance fall, loss of pace capture, and change in unipolar electrogram morphology. In contrast, other surrogates such as tactile feedback, catheter motion, and electrogram amplitude are notably poor predictors of actual contact and lesion formation. This review aims to contextualize the role of CF sensing in lesion formation with respect of the fundamental principles of biophysics of RF ablation and summarize the state-of-the-art evidence behind the role of CF in optimizing lesion formation. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Feminists wrestle with testosterone: hormones, socialization and cultural interactionism as predictors of women's gendered selves.

PubMed

Davis, Shannon N; Risman, Barbara J

2015-01-01

Sociology of gender has developed beyond a personality-centered idea of "sex-roles" to an approach that stresses interaction and social structure. At the same time, there has been a concurrent development in the psychological sex-differences and medical literatures toward including the biological bases of sex-typed behavior and gender identities. In this paper, while we conceptualize gender as a social structure, we focus only on the individual level of analysis: testing the relative strength of (maternal circulating) prenatal hormones, childhood socialization, and the power of expectations attached to adult social roles (cultural interactionist) as explanations for women's self-reported feminine and masculine selves. Our findings are complex, and support some importance of each theory. Prenatal hormones, childhood socialization, and cultural interactionism were all influential factors for gendered selves. While cultural expectations predicted only feminine selves, prenatal hormones were more robust predictors of masculine sense of self. While personality may be a relatively stable characteristic influenced by the body and childhood socialization, our results reinforce the importance of studying how the social world responds to and reinforces gendered personality. Copyright © 2014 Elsevier Inc. All rights reserved.

Ego and spiritual transcendence: relevance to psychological resilience and the role of age.

PubMed

Hanfstingl, Barbara

2013-01-01

The paper investigates different approaches of transcendence in the sense of spiritual experience as predictors for general psychological resilience. This issue is based on the theoretical assumption that resilience does play a role for physical health. Furthermore, there is a lack of empirical evidence about the extent to which spirituality does play a role for resilience. As potential predictors for resilience, ego transcendence, spiritual transcendence, and meaning in life were measured in a sample of 265 people. The main result of a multiple regression analysis is that, in the subsample with people below 29 years, only one rather secular scale that is associated with ego transcendence predicts resilience, whereas for the older subsample of 29 years and above, spiritual transcendence gains both a positive (oneness and timelessness) and a negative (spiritual insight) relevance to psychological resilience. On the one hand, these results concur with previous studies that also found age-related differences. On the other hand, it is surprising that the MOS spiritual insight predicts psychological resilience negatively, the effect is increasing with age. One possible explanation concerns wisdom research. Here, an adaptive way of dealing with the age-related loss of control is assumed to be relevant to successful aging.

Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

2016-03-01

Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

Compressive Sensing of Foot Gait Signals and Its Application for the Estimation of Clinically Relevant Time Series.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2016-07-01

A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher-Reeves' conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from 88% to 94%, the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as 94% indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Utilization of Services in a Randomized Trial Testing Phone- and Web-Based Interventions for Smoking Cessation

PubMed Central

Jack, Lisa M.; McClure, Jennifer B.; Deprey, Mona; Javitz, Harold S.; McAfee, Timothy A.; Catz, Sheryl L.; Richards, Julie; Bush, Terry; Swan, Gary E.

2011-01-01

Introduction: Phone counseling has become standard for behavioral smoking cessation treatment. Newer options include Web and integrated phone–Web treatment. No prior research, to our knowledge, has systematically compared the effectiveness of these three treatment modalities in a randomized trial. Understanding how utilization varies by mode, the impact of utilization on outcomes, and predictors of utilization across each mode could lead to improved treatments. Methods: One thousand two hundred and two participants were randomized to phone, Web, or combined phone–Web cessation treatment. Services varied by modality and were tracked using automated systems. All participants received 12 weeks of varenicline, printed guides, an orientation call, and access to a phone supportline. Self-report data were collected at baseline and 6-month follow-up. Results: Overall, participants utilized phone services more often than the Web-based services. Among treatment groups with Web access, a significant proportion logged in only once (37% phone–Web, 41% Web), and those in the phone–Web group logged in less often than those in the Web group (mean = 2.4 vs. 3.7, p = .0001). Use of the phone also was correlated with increased use of the Web. In multivariate analyses, greater use of the phone- or Web-based services was associated with higher cessation rates. Finally, older age and the belief that certain treatments could improve success were consistent predictors of greater utilization across groups. Other predictors varied by treatment group. Conclusions: Opportunities for enhancing treatment utilization exist, particularly for Web-based programs. Increasing utilization more broadly could result in better overall treatment effectiveness for all intervention modalities. PMID:21330267

Simultaneous piston position and tilt angle sensing for large vertical displacement micromirrors by frequency detection inductive sensing

NASA Astrophysics Data System (ADS)

Tseng, V. F.-G.; Xie, H.

2015-11-01

This paper presents a frequency detection based inductive eddy current sensing mechanism to simultaneously sense the piston position and tilt angle of the mirror plate of large vertical displacement micromirrors that exhibit piston scan ranges above 100 μm. This is accomplished by sensing the inductance change, and thus resonant frequency shift, of two microfabricated sensing coils packaged underneath the mirror plate. For demonstration purpose, the coils were paired with discrete circuit components to oscillate at 11.9 MHz and 12.5 MHz, respectively. The piston position and tilt angle of the mirror plate could be simultaneously monitored over a 500 μm piston scan range, achieving a maximum piston sensitivity of 4.15 kHz/μm with a piston sensing resolution of 96 nm and a maximum tilt angle sensitivity of 60.5 kHz/° with a tilt angle sensing resolution of 0.0013°. Analytical modeling of the coil inductance change via image theory was also conducted, showing that the sensor sensitivity and resolution could be improved by increasing the coil oscillation frequency and decreasing the coil size.

Evaluating the Impact of Spatial Resolution of Landsat Predictors on the Accuracy of Biomass Models for Large-area Estimation Across the Eastern USA

NASA Astrophysics Data System (ADS)

Deo, R. K.; Domke, G. M.; Russell, M.; Woodall, C. W.

2017-12-01

Landsat data have been widely used to support strategic forest inventory and management decisions despite the limited success of passive optical remote sensing for accurate estimation of aboveground biomass (AGB). The archive of publicly available Landsat data, available at 30-m spatial resolutions since 1984, has been a valuable resource for cost-effective large-area estimation of AGB to inform national requirements such as for the US national greenhouse gas inventory (NGHGI). In addition, other optical satellite data such as MODIS imagery of wider spatial coverage and higher temporal resolution are enriching the domain of spatial predictors for regional scale mapping of AGB. Because NGHGIs require national scale AGB information and there are tradeoffs in the prediction accuracy versus operational efficiency of Landsat, this study evaluated the impact of various resolutions of Landsat predictors on the accuracy of regional AGB models across three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We used recent national forest inventory (NFI) data with numerous Landsat-derived predictors at ten different spatial resolutions ranging from 30 to 1000 m to understand the optimal spatial resolution of the optical data for enhanced spatial inventory of AGB for NGHGI reporting. Ten generic spatial models at different spatial resolutions were developed for all sites and large-area estimates were evaluated (i) at the county-level against the independent designed-based estimates via the US NFI Evalidator tool and (ii) within a large number of strips ( 1 km wide) predicted via LiDAR metrics at a high spatial resolution. The county-level estimates by the Evalidator and Landsat models were statistically equivalent and produced coefficients of determination (R2) above 0.85 that varied with sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of decreasing resolutions. The Landsat-based total AGB estimates within the strips against the total AGB obtained using LiDAR metrics did not differ significantly and were within ±15 Mg/ha for each of the sites. We conclude that the optical satellite data at resolutions up to 1000 m provide acceptable accuracy for the US' NGHGI.

Small-displacement sensing system based on multiple total internal reflections in heterodyne interferometry.

PubMed

Wang, Shinn-Fwu; Chiu, Ming-Hung; Chen, Wei-Wu; Kao, Fu-Hsi; Chang, Rong-Seng

2009-05-01

A small-displacement sensing system based on multiple total internal reflections in heterodyne interferometry is proposed. In this paper, a small displacement can be obtained only by measuring the variation in phase difference between s- and p-polarization states for the total internal reflection effect. In order to improve the sensitivity, we increase the number of total internal reflections by using a parallelogram prism. The theoretical resolution of the method is better than 0.417 nm. The method has some merits, e.g., high resolution, high sensitivity, and real-time measurement. Also, its feasibility is demonstrated.

Photoinduced Electron Transfer Based Ion Sensing within an Optical Fiber

PubMed Central

Englich, Florian V.; Foo, Tze Cheung; Richardson, Andrew C.; Ebendorff-Heidepriem, Heike; Sumby, Christopher J.; Monro, Tanya M.

2011-01-01

We combine suspended-core microstructured optical fibers with the photoinduced electron transfer (PET) effect to demonstrate a new type of fluorescent optical fiber-dip sensing platform for small volume ion detection. A sensor design based on a simple model PET-fluoroionophore system and small core microstructured optical fiber capable of detecting sodium ions is demonstrated. The performance of the dip sensor operating in a high sodium concentration regime (925 ppm Na+) and for lower sodium concentration environments (18.4 ppm Na+) is explored and future approaches to improving the sensor’s signal stability, sensitivity and selectivity are discussed. PMID:22163712

Infrared Detector Activities at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Abedin, M. N.; Refaat, T. F.; Sulima, O. V.; Amzajerdian, F.

2008-01-01

Infrared detector development and characterization at NASA Langley Research Center will be reviewed. These detectors were intended for ground, airborne, and space borne remote sensing applications. Discussion will be focused on recently developed single-element infrared detector and future development of near-infrared focal plane arrays (FPA). The FPA will be applied to next generation space-based instruments. These activities are based on phototransistor and avalanche photodiode technologies, which offer high internal gain and relatively low noise-equivalent-power. These novel devices will improve the sensitivity of active remote sensing instruments while eliminating the need for a high power laser transmitter.

Building a framework to manage trust in automation

NASA Astrophysics Data System (ADS)

Metcalfe, J. S.; Marathe, A. R.; Haynes, B.; Paul, V. J.; Gremillion, G. M.; Drnec, K.; Atwater, C.; Estepp, J. R.; Lukos, J. R.; Carter, E. C.; Nothwang, W. D.

2017-05-01

All automations must, at some point in their lifecycle, interface with one or more humans. Whether operators, end-users, or bystanders, human responses can determine the perceived utility and acceptance of an automation. It has been long believed that human trust is a primary determinant of human-automation interactions and further presumed that calibrating trust can lead to appropriate choices regarding automation use. However, attempts to improve joint system performance by calibrating trust have not yet provided a generalizable solution. To address this, we identified several factors limiting the direct integration of trust, or metrics thereof, into an active mitigation strategy. The present paper outlines our approach to addressing this important issue, its conceptual underpinnings, and practical challenges encountered in execution. Among the most critical outcomes has been a shift in focus from trust to basic interaction behaviors and their antecedent decisions. This change in focus inspired the development of a testbed and paradigm that was deployed in two experiments of human interactions with driving automation that were executed in an immersive, full-motion simulation environment. Moreover, by integrating a behavior and physiology-based predictor within a novel consequence-based control system, we demonstrated that it is possible to anticipate particular interaction behaviors and influence humans towards more optimal choices about automation use in real time. Importantly, this research provides a fertile foundation for the development and integration of advanced, wearable technologies for sensing and inferring critical state variables for better integration of human elements into otherwise fully autonomous systems.

Distributed acoustic sensing: how to make the best out of the Rayleigh-backscattered energy?

NASA Astrophysics Data System (ADS)

Eyal, A.; Gabai, H.; Shpatz, I.

2017-04-01

Coherent fading noise (also known as speckle noise) affects the SNR and sensitivity of Distributed Acoustic Sensing (DAS) systems and makes them random processes of position and time. As in speckle noise, the statistical distribution of DAS SNR is particularly wide and its standard deviation (STD) roughly equals its mean (σSNR/ ≍ 0.89). Trading resolution for SNR may improve the mean SNR but not necessarily narrow its distribution. Here a new approach to achieve both SNR improvement (by sacrificing resolution) and narrowing of the distribution is introduced. The method is based on acquiring high resolution complex backscatter profiles of the sensing fiber, using them to compute complex power profiles of the fiber which retain phase variation information and filtering of the power profiles. The approach is tested via a computer simulation and demonstrates distribution narrowing up to σSNR/ < 0.2.

Deep neural network-based domain adaptation for classification of remote sensing images

NASA Astrophysics Data System (ADS)

Ma, Li; Song, Jiazhen

2017-10-01

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

School and System Improvement: A Narrative State-of-the-Art Review

ERIC Educational Resources Information Center

Hopkins, David; Stringfield, Sam; Harris, Alma; Stoll, Louise; Mackay, Tony

2014-01-01

Over the last 4 decades, the school effectiveness and school improvement research bases have gained prominence and recognition on the international stage. In both a theoretical and empirical sense, they have matured through a wide range of well-documented projects, interventions, and innovations across a range of countries, describing how efforts…

Is There Anybody There? A Psychodynamic View of Panic Attack.

ERIC Educational Resources Information Center

Rizq, Rosemary

2002-01-01

Presents a process analysis of a psychodynamic intervention for a client with panic attacks. Discusses how a psychodynamic understanding of the complex etiology of the client's panic attacks that ultimately produced improved coping skills and a subjective sense of improvement for her. Process analysis is used to illustrate the theoretical base,…

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Ultra-short FBG based distributed sensing using shifted optical Gaussian filters and microwave-network analysis.

PubMed

Cheng, Rui; Xia, Li; Sima, Chaotan; Ran, Yanli; Rohollahnejad, Jalal; Zhou, Jiaao; Wen, Yongqiang; Yu, Can

2016-02-08

Ultrashort fiber Bragg gratings (US-FBGs) have significant potential as weak grating sensors for distributed sensing, but the exploitation have been limited by their inherent broad spectra that are undesirable for most traditional wavelength measurements. To address this, we have recently introduced a new interrogation concept using shifted optical Gaussian filters (SOGF) which is well suitable for US-FBG measurements. Here, we apply it to demonstrate, for the first time, an US-FBG-based self-referencing distributed optical sensing technique, with the advantages of adjustable sensitivity and range, high-speed and wide-range (potentially >14000 με) intensity-based detection, and resistance to disturbance by nonuniform parameter distribution. The entire system is essentially based on a microwave network, which incorporates the SOGF with a fiber delay-line between the two arms. Differential detections of the cascaded US-FBGs are performed individually in the network time-domain response which can be obtained by analyzing its complex frequency response. Experimental results are presented and discussed using eight cascaded US-FBGs. A comprehensive numerical analysis is also conducted to assess the system performance, which shows that the use of US-FBGs instead of conventional weak FBGs could significantly improve the power budget and capacity of the distributed sensing system while maintaining the crosstalk level and intensity decay rate, providing a promising route for future sensing applications.

Sensing Performance Analysis on Quartz Tuning Fork-Probe at the High Order Vibration Mode for Multi-Frequency Scanning Probe Microscopy

PubMed Central

Gao, Fengli; Li, Xide

2018-01-01

Multi-frequency scanning near-field optical microscopy, based on a quartz tuning fork-probe (QTF-p) sensor using the first two orders of in-plane bending symmetrical vibration modes, has recently been developed. This method can simultaneously achieve positional feedback (based on the 1st in-plane mode called the low mode) and detect near-field optically induced forces (based on the 2nd in-plane mode called the high mode). Particularly, the high mode sensing performance of the QTF-p is an important issue for characterizing the tip-sample interactions and achieving higher resolution microscopic imaging but the related researches are insufficient. Here, we investigate the vibration performance of QTF-p at high mode based on the experiment and finite element method. The frequency spectrum characteristics are obtained by our homemade laser Doppler vibrometer system. The effects of the properties of the connecting glue layer and the probe features on the dynamic response of the QTF-p sensor at the high mode are investigated for optimization design. Finally, compared with the low mode, an obvious improvement of quality factor, of almost 50%, is obtained at the high mode. Meanwhile, the QTF-p sensor has a high force sensing sensitivity and a large sensing range at the high mode, indicating a broad application prospect for force sensing. PMID:29364847

Sense of agency in continuous action: Assistance-induced performance improvement is self-attributed even with knowledge of assistance.

PubMed

Inoue, Kazuya; Takeda, Yuji; Kimura, Motohiro

2017-02-01

In a task involving continuous action to achieve a goal, the sense of agency increases with an improvement in task performance that is induced by unnoticed computer assistance. This study investigated how explicit instruction about the existence of computer assistance affects the increase of sense of agency that accompanies performance improvement. Participants performed a continuous action task in which they controlled the direction of motion of a dot to a goal by pressing keys. When instructions indicated the absence of assistance, the sense of agency increased with performance improvement induced by computer assistance, replicating previous findings. Interestingly, this increase of sense of agency was also observed even when instructions indicated the presence of assistance. These results suggest that even when a plausible cause of performance improvement other than one's own action exists, the improvement can be misattributed to one's own control of action, resulting in an increased sense of agency. Copyright © 2016 Elsevier Inc. All rights reserved.

Intolerance for withdrawal discomfort and motivation predict voucher-based smoking treatment outcomes for smokers with substance use disorders.

PubMed

Rohsenow, Damaris J; Tidey, Jennifer W; Kahler, Christopher W; Martin, Rosemarie A; Colby, Suzanne M; Sirota, Alan D

2015-04-01

Identifying predictors of abstinence with voucher-based treatment is important for improving its efficacy. Smokers with substance use disorders have very low smoking cessation rates so identifying predictors of smoking treatment response is particularly important for these difficult-to-treat smokers. Intolerance for Smoking Abstinence Discomfort (IDQ-S), motivation to quit smoking, nicotine dependence severity (FTND), and cigarettes per day were examined as predictors of smoking abstinence during and after voucher-based smoking treatment with motivational counseling. We also investigated the relationship between IDQ-S and motivation to quit smoking. Smokers in residential substance treatment (n=184) were provided 14days of vouchers for complete smoking abstinence (CV) after a 5-day smoking reduction lead-in period or vouchers not contingent on abstinence. Carbon monoxide readings indicated about 25% of days abstinent during the 14days of vouchers for abstinence in the CV group; only 3-4% of all participants were abstinent at follow-ups. The IDQ-S Withdrawal Intolerance scale and FTND each significantly predicted fewer abstinent days during voucher treatment; FTND was nonsignificant when controlling for variance shared with withdrawal intolerance. The one significant predictor of 1-month abstinence was pretreatment motivation to quit smoking, becoming marginal (p<.06) when controlling for FTND. Lower withdrawal intolerance significantly predicted 3month abstinence when controlling for FTND. Higher withdrawal intolerance pretreatment correlated with less motivation to quit smoking. Implications for voucher-based treatment include the importance of focusing on reducing these expectancies of anticipated smoking withdrawal discomfort, increasing tolerance for abstinence discomfort, and increasing motivation. Published by Elsevier Ltd.

Comparison of ISS, NISS, and RTS score as predictor of mortality in pediatric fall.

PubMed

Soni, Kapil Dev; Mahindrakar, Santosh; Gupta, Amit; Kumar, Subodh; Sagar, Sushma; Jhakal, Ashish

2017-01-01

Studies to identify an ideal trauma score tool representing prediction of outcomes of the pediatric fall patient remains elusive. Our study was undertaken to identify better predictor of mortality in the pediatric fall patients. Data was retrieved from prospectively maintained trauma registry project at level 1 trauma center developed as part of Multicentric Project-Towards Improving Trauma Care Outcomes (TITCO) in India. Single center data retrieved from a prospectively maintained trauma registry at a level 1 trauma center, New Delhi, for a period ranging from 1 October 2013 to 17 February 2015 was evaluated. Standard anatomic scores Injury Severity Score (ISS) and New Injury Severity Score (NISS) were compared with physiologic score Revised Trauma Score (RTS) using receiver operating curve (ROC). Heart rate and RTS had a statistical difference among the survivors to nonsurvivors. ISS, NISS, and RTS were having 50, 50, and 86% of area under the curve on ROCs, and RTS was statistically significant among them. Physiologically based trauma score systems (RTS) are much better predictors of inhospital mortality in comparison to anatomical based scoring systems (ISS and NISS) for unintentional pediatric falls.

Dipole-modified graphene with ultrahigh gas sensibility

NASA Astrophysics Data System (ADS)

Jia, Ruokun; Xie, Peng; Feng, Yancong; Chen, Zhuo; Umar, Ahmad; Wang, Yao

2018-05-01

This study reports the supramolecular assembly of functional graphene-based materials with ultrahigh gas sensing performances which are induced by charge transfer enhancement. Two typical Donor-π-Accepter (D-π-A) structure molecules 4-aminoquinoline (4AQ, μ = 3.17 Debye) and 4-hydroxyquinoline (4HQ, μ = 1.98 Debye), with different charge transfer enhancing effects, were selected to modify reduce oxide graphene (rGO) via supramolecular assembly. Notably, compared to the 4HQ-rGO, the 4AQ-rGO exhibits more significant increase of gas response (Ra/Rg = 3.79) toward 10 ppm NO2, which is ascribed to the larger dipole moment (μ) of 4AQ and hence the more intensive enhancing effect of charge transfer on the interface of rGO. Meanwhile, 4AQ-rGO sensors also reveal superior comprehensive gas sensing performances, including excellent gas sensing selectivity, linearity, repeatability and stability. It is believed that the present work demonstrates an effective supramolecular approach of modifying rGO with strong dipoles to significantly improve gas sensing properties of graphene-based materials.

[Review of estimation on oceanic primary productivity by using remote sensing methods.

PubMed

Xu, Hong Yun; Zhou, Wei Feng; Ji, Shi Jian

2016-09-01

Accuracy estimation of oceanic primary productivity is of great significance in the assessment and management of fisheries resources, marine ecology systems, global change and other fields. The traditional measurement and estimation of oceanic primary productivity has to rely on in situ sample data by vessels. Satellite remote sensing has advantages of providing dynamic and eco-environmental parameters of ocean surface at large scale in real time. Thus, satellite remote sensing has increasingly become an important means for oceanic primary productivity estimation on large spatio-temporal scale. Combining with the development of ocean color sensors, the models to estimate the oceanic primary productivity by satellite remote sensing have been developed that could be mainly summarized as chlorophyll-based, carbon-based and phytoplankton absorption-based approach. The flexibility and complexity of the three kinds of models were presented in the paper. On this basis, the current research status for global estimation of oceanic primary productivity was analyzed and evaluated. In view of these, four research fields needed to be strengthened in further stu-dy: 1) Global oceanic primary productivity estimation should be segmented and studied, 2) to dee-pen the research on absorption coefficient of phytoplankton, 3) to enhance the technology of ocea-nic remote sensing, 4) to improve the in situ measurement of primary productivity.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

PubMed

Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

2017-09-19

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

A patch-based convolutional neural network for remote sensing image classification.

PubMed

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

Patient safety education to change medical students' attitudes and sense of responsibility.

PubMed

Roh, Hyerin; Park, Seok Ju; Kim, Taekjoong

2015-01-01

This study examined changes in the perceptions and attitudes as well as the sense of individual and collective responsibility in medical students after they received patient safety education. A three-day patient safety curriculum was implemented for third-year medical students shortly before entering their clerkship. Before and after training, we administered a questionnaire, which was analysed quantitatively. Additionally, we asked students to answer questions about their expected behaviours in response to two case vignettes. Their answers were analysed qualitatively. There was improvement in students' concepts of patient safety after training. Before training, they showed good comprehension of the inevitability of error, but most students blamed individuals for errors and expressed a strong sense of individual responsibility. After training, students increasingly attributed errors to system dysfunction and reported more self-confidence in speaking up about colleagues' errors. However, due to the hierarchical culture, students still described difficulties communicating with senior doctors. Patient safety education effectively shifted students' attitudes towards systems-based thinking and increased their sense of collective responsibility. Strategies for improving superior-subordinate communication within a hierarchical culture should be added to the patient safety curriculum.

Securing Collaborative Spectrum Sensing against Untrustworthy Secondary Users in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Wang, Wenkai; Li, Husheng; Sun, Yan(Lindsay); Han, Zhu

2009-12-01

Cognitive radio is a revolutionary paradigm to migrate the spectrum scarcity problem in wireless networks. In cognitive radio networks, collaborative spectrum sensing is considered as an effective method to improve the performance of primary user detection. For current collaborative spectrum sensing schemes, secondary users are usually assumed to report their sensing information honestly. However, compromised nodes can send false sensing information to mislead the system. In this paper, we study the detection of untrustworthy secondary users in cognitive radio networks. We first analyze the case when there is only one compromised node in collaborative spectrum sensing schemes. Then we investigate the scenario that there are multiple compromised nodes. Defense schemes are proposed to detect malicious nodes according to their reporting histories. We calculate the suspicious level of all nodes based on their reports. The reports from nodes with high suspicious levels will be excluded in decision-making. Compared with existing defense methods, the proposed scheme can effectively differentiate malicious nodes and honest nodes. As a result, it can significantly improve the performance of collaborative sensing. For example, when there are 10 secondary users, with the primary user detection rate being equal to 0.99, one malicious user can make the false alarm rate [InlineEquation not available: see fulltext.] increase to 72%. The proposed scheme can reduce it to 5%. Two malicious users can make [InlineEquation not available: see fulltext.] increase to 85% and the proposed scheme reduces it to 8%.

Number sense across the lifespan as revealed by a massive Internet-based sample

PubMed Central

Halberda, Justin; Ly, Ryan; Wilmer, Jeremy B.; Naiman, Daniel Q.; Germine, Laura

2012-01-01

It has been difficult to determine how cognitive systems change over the grand time scale of an entire life, as few cognitive systems are well enough understood; observable in infants, adolescents, and adults; and simple enough to measure to empower comparisons across vastly different ages. Here we address this challenge with data from more than 10,000 participants ranging from 11 to 85 years of age and investigate the precision of basic numerical intuitions and their relation to students’ performance in school mathematics across the lifespan. We all share a foundational number sense that has been observed in adults, infants, and nonhuman animals, and that, in humans, is generated by neurons in the intraparietal sulcus. Individual differences in the precision of this evolutionarily ancient number sense may impact school mathematics performance in children; however, we know little of its role beyond childhood. Here we find that population trends suggest that the precision of one’s number sense improves throughout the school-age years, peaking quite late at ∼30 y. Despite this gradual developmental improvement, we find very large individual differences in number sense precision among people of the same age, and these differences relate to school mathematical performance throughout adolescence and the adult years. The large individual differences and prolonged development of number sense, paired with its consistent and specific link to mathematics ability across the age span, hold promise for the impact of educational interventions that target the number sense. PMID:22733748

Pathways to Success for Students of Color in Christian Colleges: The Role of Institutional Integrity and Sense of Community

ERIC Educational Resources Information Center

Ash, Allison N.; Schreiner, Laurie A.

2016-01-01

This study explored the predictors of success among 1,536 students of color from 12 Council for Christian Colleges & Universities (CCCU) member institutions. Student success was measured by examining students' intent to graduate along with the degree to which they were thriving intellectually, socially, and psychologically. The study utilized…

From Social Class to Self-Efficacy: Internalization of Low Social Status Pupils' School Performance

ERIC Educational Resources Information Center

Wiederkehr, Virginie; Darnon, Céline; Chazal, Sébastien; Guimond, Serge; Martinot, Delphine

2015-01-01

Previous research has largely documented that socioeconomic status (SES) is a strong and consistent predictor of pupils' school performance in several countries. In this research, we argue that children internalize the SES achievement gap in the form of a lower/higher sense of school self-efficacy. In two studies, teenaged students' (Study 1) and…