INVESTIGATION OF THE INFLUENCE OF CORRELATED COLOR TEMPERATURE OF LED illuminatorS AS A FACTOR OF SAFETY LIGHTING OF HIGHWAYS FOR DIFFICULT WEATHER CONDITIONS

A investigation of the dependence of relative horizontal illumination level and brightness of the most common types of road surface in Ukraine when using LED illuminators with different spectral distribution of radiation in the presence of aerial aerosols of different optical densities (strong, moderate, weak fog and haze) is performed. Mathematical models of LED illuminators with correlated color temperature Tcc = (2200÷8400) K in the visible range of the spectrum λ = (380÷780) nm have been created. Their use together with the selected model of the spectral dependence of the scattering index β (λ) (Ferdinandоv’s model) of the atmospheric environment with the presence of aqueous aerosols, which cause a limited meteorological range of visibility MOR = (0.1÷3) km, showed a slight (1%) advantage in creation of illumination from lamps of a warm spectrum of radiation (TCC = 2200 K) in comparison with cold (Tcc = 8400 K) for cases of strong fogs with MOR = 100 m. Calculations involving the analysis of mathematical modeling of the spectral distribution of the reflection coefficients for two types of road surface (new and worn asphalt) proved that when using warm spectrum illuminators the brightness of the most common in Ukraine old asphalt pavement in the conditions of MOR = 100 m by 5% exceeds the corresponding value of the cold spectrum emitters. This difference gradually decreases as visibility is improved due to the weaker spectral dependence of β (λ) and at MV = 3 km is 4.5%. The advantage of using warm spectrum illuminators will be significantly enhanced for the case of estimating the brightness of the coverage over long distances, for example, airfield runways, which will significantly improve flight safety.

Polarization Measurements and Evaluation Based on Multidimensional Polarization Indices Applied in Analyzing Atmospheric Particulates

Online identification and characterization of suspended aerosols can provide a scientific basis for understanding aerosol transformations, quantitatively evaluating the impacts on air quality, public health, and the source apportionment of different atmospheric particulate matters. In this study, we confirm the validity of our developed high-throughput multi-angle polarized scattering vector detection of aerosols and multidimensional polarization scattering index systems. By observation of the mean values, variance, and Wilk’s Lambda of multidimensional polarization indices for different aerosol types, the polarization index shows unique characterization abilities for aerosol properties, and the optimal combination of polarization indices can always be found for a specific aerosol category with a high resolution and discrimination. Clearly, the multidimensional polarization indices of individual aerosols are more suitable for online and real-time aerosol identification and even help to explain the in situ microphysical characteristics of aerosols or evaluate the dynamic evolution of aerosols.

Can machine learning correct microwave humidity radiances for the influence of clouds?

A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.

A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

Utility of the optical quality analysis system for decision-making in Nd: YAG laser posterior capsulotomy in patients with light posterior capsule opacity

Background A prospective cohort study was performed to evaluate whether the Optical Quality Analysis System (OQAS) can serve as a valuable additional indicator for appropriate posterior capsulotomy referral. Methods One hundred and five eyes from 96 patients undergoing capsulotomy were divided into precapsulotomy logMAR CDVA ≤0.1 group and logMAR CDVA > 0.1 group. CDVA, and the Visual Function 14 index (VF-14) score were estimated before and 1 month after capsulotomy. The objective scattering index (OSI) value was measured by using the OQAS. Posterior capsule opacification (PCO) severity was assessed with Evaluation of PCO 2000 (EPCO 2000) software. Results In logMAR CDVA > 0.1 group, the correlations of OSI, logMAR CDVA, EPCO score and VF-14 score were very strong preoperatively. In logMAR CDVA ≤0.1 group, preoperatively, OSI was correlated with logMAR CDVA (r = 0.451), EPCO score (r = 0.789), and VF-14 score (r = 0.852). LogMAR CDVA has weak correlation with VF-14 score (r = − 0.384) and EPCO score (r = 0.566). VF-14 score was correlated with EPCO score (r = − 0.669). In the logMAR CDVA ≤0.1 group, there was no significant difference in logMAR CDVA between precapsulotomy and postcapsulotomy (P > 0.05). In the two groups, all the other optical quality parameters were significantly improved after capsulotomy (P < 0.05). In logMAR CDVA > 0.1 group, the area under the curve of the ROC of the OSI was 0.996 (P = 0.000). In logMAR CDVA ≤0.1 group, the area under the curve of the ROC of the OSI was 0.943 (P = 0.000). Conclusions The OSI was useful for evaluating of PCO and prediction of beneficial capsulotomy. Especially for patients with slight PCO and better visual acuity, OSI is more valuable than CDVA and completely objective examination. Trial registration The study protocol was registered at the Chinese Clinical Trial Registry. Register: ChiCTR1800018842 (Registered Date: October 13th, 2018).

Can machine learning correct microwave humidity radiances for the influence of clouds?

A methodology based on quantile regression neural networks (QRNN) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder-2 (MWHS-2), then the applicability to sub-millimeter (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve, but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.

Utility of the optical quality analysis system for decision-making in Nd: YAG laser posterior capsulotomy in patients with light posterior capsule opacity

Background: A prospective cohort study was performed to evaluate whether the Optical Quality Analysis System (OQAS) can serve as a valuable additional indicator for appropriate posterior capsulotomy referral. Methods: One hundred and five eyes from 96 patients undergoing capsulotomy were divided into precapsulotomy logMAR CDVA ≤ 0.1 group and logMAR CDVA > 0.1 group. CDVA， and the Visual Function 14 index (VF-14) score were estimated before and one month after capsulotomy. The objective scattering index (OSI) value was measured by using the OQAS. Posterior capsule opacification (PCO) severity was assessed with Evaluation of PCO 2000 (EPCO 2000) software. Results：In logMAR CDVA > 0.1 group, the correlations of OSI, logMAR CDVA, EPCO score and VF-14 score were very strong preoperatively. In logMAR CDVA ≤ 0.1 group, preoperatively, OSI was correlated with logMAR CDVA (r = 0.451), EPCO score (r = 0.789), and VF-14 score (r = 0.852). LogMAR CDVA has weak correlation with VF-14 score (r = -0.384) and EPCO score (r = 0.566). VF-14 score was correlated with EPCO score (r = -0.669). In the logMAR CDVA ≤ 0.1 group, there was no significant difference in logMAR CDVA between precapsulotomy and postcapsulotomy (P > 0.05). In the two groups, all the other optical quality parameters were significantly improved after capsulotomy (P < 0.05). In logMAR CDVA > 0.1 group, the area under the curve of the ROC of the OSI was 0.996 (P = 0.000). In logMAR CDVA ≤ 0.1 group, the area under the curve of the ROC of the OSI was 0.943 (P = 0.000).Conclusions：The OSI was useful for evaluating of PCO and prediction of beneficial capsulotomy. Especially for patients with slight PCO and better visual acuity, OSI is more valuable than CDVA and completely objective examination. Trial registration: The study protocol was registered at the Chinese Clinical Trial Registry. Register: ChiCTR1800018842 (Registered Date: October 13th, 2018).

Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.

Utility of the optical quality analysis system for decision-making in Nd: YAG laser posterior capsulotomy in patients with light posterior capsule opacity

Background: A prospective cohort study was performed to evaluate whether the Optical Quality Analysis System (OQAS) can serve as a valuable additional indicator for appropriate posterior capsulotomy referral. Methods: One hundred and five eyes from 96 patients undergoing capsulotomy were divided into precapsulotomy logMAR CDVA ≤ 0.1 group and logMAR CDVA > 0.1 group. CDVA， and the Visual Function 14 index (VF-14) score were estimated before and one month after capsulotomy. The objective scattering index (OSI) value was measured by using the OQAS. Posterior capsule opacification (PCO) severity was assessed with Evaluation of PCO 2000 (EPCO 2000) software. Results：In logMAR CDVA > 0.1 group, the correlations of OSI, logMAR CDVA, EPCO score and VF-14 score were very strong preoperatively. In logMAR CDVA ≤ 0.1 group, preoperatively, OSI was correlated with logMAR CDVA (r = 0.451), EPCO score (r = 0.789), and VF-14 score (r = 0.852). LogMAR CDVA has weak correlation with VF-14 score (r = -0.384) and EPCO score (r = 0.566). VF-14 score was correlated with EPCO score (r = -0.669). In the logMAR CDVA ≤ 0.1 group, there was no significant difference in logMAR CDVA between precapsulotomy and postcapsulotomy (P > 0.05). In the two groups, all the other optical quality parameters were significantly improved after capsulotomy (P < 0.05). In logMAR CDVA > 0.1 group, the area under the curve of the ROC of the OSI was 0.996 (P = 0.000). In logMAR CDVA ≤ 0.1 group, the area under the curve of the ROC of the OSI was 0.943 (P = 0.000).Conclusions：The OSI was useful for evaluating of PCO and prediction of beneficial capsulotomy. Especially for patients with slight PCO and better visual acuity, OSI is more valuable than CDVA and completely objective examination. Trial registration: The study protocol was registered at the Chinese Clinical Trial Registry. Register: ChiCTR1800018842 (Registered Date: October 13th, 2018).

Utility of the optical quality analysis system for decision-making in Nd: YAG laser posterior capsulotomy in patients with light posterior capsule opacity

Background: To evaluate whether the Optical Quality Analysis System (OQAS) can serve as a valuable additional indicator for appropriate posterior capsulotomy referral. Methods: One hundred and five eyes from 96 patients undergoing capsulotomy were divided into precapsulotomy logMAR CDVA ≤ 0.1 group and logMAR CDVA > 0.1 group. CDVA， and the Visual Function 14 index (VF-14) score were estimated before and 1 month after capsulotomy. Objective scattering index (OSI) value was measured by using the OQAS. Posterior capsule opacification (PCO) severity was assessed with Evaluation of PCO 2000 (EPCO 2000) software. Results：In logMAR CDVA > 0.1 group, the correlations of OSI, logMAR CDVA, EPCO score and VF-14 score were very strong preoperatively. In logMAR CDVA ≤ 0.1 group, preoperatively, OSI was correlated with logMAR CDVA (r = 0.451), EPCO score (r = 0.789), and VF-14 score (r = 0.852). LogMAR CDVA has weak correlation with VF-14 score (r = -0.384) and EPCO score (r = 0.566). VF-14 score was correlated with EPCO score (r = -0.669). In the logMAR CDVA ≤ 0.1 group, there was no significant difference in logMAR CDVA between precapsulotomy and postcapsulotomy (P > 0.05). In the two groups, all the other optical quality parameters were significantly improved after capsulotomy (P < 0.05). Conclusions：The OSI was useful for the evaluation of PCO and prediction of beneficial capsulotomy. Especially for patients with slight PCO and better visual acuity, OSI is more valuable than CDVA and a complete objectivity examination. Trial registration: The study protocol was registered at the Chinese Clinical Trial Registry. Register: ChiCTR1800018842 (Registered Date: October 13th, 2018). Keywords: Posterior capsule opacification, Objective scattering index, Capsulotomy, Visual Function 14 index, Visual outcomes