Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
39,410
result(s) for
"Ceilings"
Sort by:
The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part II: Forecast Performance
The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.
Journal Article
Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms
Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost Regressor (CatBoost). Leveraging a dataset of 21045 samples, factors like Humidity, Ambient temperature, Wind speed, Visibility, Cloud ceiling and Pressure serve as inputs for constructing these machine learning models in forecasting solar energy. Model accuracy is meticulously assessed and juxtaposed using metrics such as coefficient of determination (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the CatBoost model emerges as the frontrunner in predicting solar energy, with training values of R 2 value of 0.608, RMSE of 4.478 W and MAE of 3.367 W and the testing value is R 2 of 0.46, RMSE of 4.748 W and MAE of 3.583 W. SHAP analysis reveal that ambient temperature and humidity have the greatest influences on the value solar energy generated from photovoltaic panel.
Journal Article
Tip-over stability analysis and enhancement for a wall-perching quadcopter based on negative-pressure effect
A suction force model for a noncontact negative-pressure suction cup designed for multi-rotor aircraft’s wall-perching is established. To address the negative stiffness phase in the suction model, a model to determine tip-over instability conditions during wall-landing and take-off is developed. Based on the ceiling-perching tip-over instability model, an onboard parallel posture-adjustment platform actively regulates the suction-cup-to-wall clearance and tilt angles, thereby enhancing tip-over stability during wall-landing and take-off. Results demonstrate that the quadrotor with negative-pressure suction effectively extends the ceiling effect range of ducted rotors, enabling low-power-consumption adhesion to ceilings. The parallel platform improves disturbance rejection by actively modulating the wall-cup clearance during wall-landing and take-off, effectively preventing quadcopter tip-over.
Journal Article
Scalable aesthetic transparent wood for energy efficient buildings
Nowadays, energy-saving building materials are important for reducing indoor energy consumption by enabling better thermal insulation, promoting effective sunlight harvesting and offering comfortable indoor lighting. Here, we demonstrate a novel scalable aesthetic transparent wood (called aesthetic wood hereafter) with combined aesthetic features (e.g. intact wood patterns), excellent optical properties (an average transmittance of ~ 80% and a haze of ~ 93%), good UV-blocking ability, and low thermal conductivity (0.24 W m
−1
K
−1
) based on a process of spatially selective delignification and epoxy infiltration. Moreover, the rapid fabrication process and mechanical robustness (a high longitudinal tensile strength of 91.95 MPa and toughness of 2.73 MJ m
−3
) of the aesthetic wood facilitate good scale-up capability (320 mm × 170 mm × 0.6 mm) while saving large amounts of time and energy. The aesthetic wood holds great potential in energy-efficient building applications, such as glass ceilings, rooftops, transparent decorations, and indoor panels.
Transparent wood composites are promising engineered materials for green energy-efficient building. Here, authors demonstrate novel aesthetic wood with integrated functions of optical transparency, UV-blocking, thermal insulation, and mechanical strength for this sustainable application.
Journal Article
Paleoindian settlement of the high-altitude Peruvian Andes
Study of human adaptation to extreme environments is important for understanding our cultural and genetic capacity for survival. The Pucuncho Basin in the southern Peruvian Andes contains the highest-altitude Pleistocene archaeological sites yet identified in the world, about 900 meters above confidently dated contemporary sites. The Pucuncho workshop site [4355 meters above sea level (masl)] includes two fishtail projectile points, which date to about 12.8 to 11.5 thousand years ago (ka). Cuncaicha rock shelter (4480 masl) has a robust, well-preserved, and well-dated occupation sequence spanning the past 12.4 thousand years (ky), with 21 dates older than 11.5 ka. Our results demonstrate that despite cold temperatures and low-oxygen conditions, hunter-gatherers colonized extremehigh-altitudeAndean environments in the Terminal Pleistocene, within about 2 ky of the initial entry of humans to South America.
Journal Article
Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning
To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914 images collected by the Kawaguchi Lab since 1995 with over 7000 learnable damages in the ceilings and is categorized into four typical damage forms (peelings, cracks, distortions, and fall-offs). Twelve detection models are established, trained, and compared by variable hyperparameter analysis. The best performing model reaches a mean average precision (mAP) of 75.28%, which is considerably high for object detection. A comparative study indicates that the model is generally robust to the challenges in ceiling damage detection, including partial occlusion by visual obstructions, the extremely varied aspect ratios, small object detection, and multi-object detection. Another comparative study in the F1 score performance, which combines the precision and recall in to one single metric, shows that the model outperforms the CNN (convolutional neural networks) model using the Saliency-MAP method in our previous research to a remarkable extent. In the case of a large-area ratio with a non-ceiling region, the F1 score of these two models are 0.83 and 0.28, respectively. The findings of this study push automatic ceiling damage detection in large-span structures one step further.
Journal Article
The NHS workforce crisis is a retention crisis
Pay cuts, worsening conditions, and an inability to provide high quality care all threaten the retention of doctors, writes Malinga Ratwatte
Journal Article
An Analysis of Glass Ceiling Perceptions in the Accounting Profession
Access to a deep pool of talent is essential to the success of every professional services firm. The supply of that talent is contingent upon the available rewards for the exercise of that talent, and both the existence of the potential rewards and the beliefs that individuals hold about the existence of the rewards affect the decision to remain in the field. One structural factor that may affect the judgment about whether to remain in a profession concerns promotions based on the gender of the employee (i.e., the \"glass ceiling\"). In this study, we examine the \"glass ceiling\" within the context of the accounting profession. While advances have been made within the accounting profession to address the glass ceiling, the continued existence—and perceptions about the continued existence—of the issue exert adverse effects upon the available talent pool and may create long-term problems for the profession. In this study, we investigate glass ceiling perceptions among a large sample of female accounting professionals employed in accounting; the sample includes both public accountants, and those employed in industry accounting. Our study yields the finding of beliefs in bias-driven effects (e.g., a bias against female promotions to the top level), structural effects (e.g., a lack of mentoring opportunities, networking opportunities, and high-profile job assignments), and cultural effects (e.g., a lack of social support from the male leaders within the organization) among these accounting professionals. Glass ceiling perceptions are also influenced by several demographic factors. Furthermore, accounting professionals employed by industry are more likely to report a glass ceiling within their firms than accounting professionals employed by public accounting firms. The findings are of interest to researchers who explore gender-related issues in professional service firms such as the field of accounting, and to senior members of practice who are tasked with ensuring the integrity and quality of the talent pool and the equitable distribution of rewards to employees.
Journal Article
Stratiform Cloud-Hydrometeor Assimilation for HRRR and RAP Model Short-Range Weather Prediction
Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spin-up problems, a non-variational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1-9h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing.
Journal Article