Application of genetic algorithm in atmospheric carbon dioxide concentration retrieval

Clew Bay is an important aquaculture production area in Ireland. In this study, we focused on a high-resolution simulation of the Clew Bay region based on a regional ocean modeling system (ROMS). Freshwater discharges from eight rivers are included in the model and a wetting–drying scheme has been implemented. The Clew Bay model simulation was validated and calibrated with available observations (e.g., acoustic Doppler current profiler (ADCP), vertical salinity and temperature profiles, and a tide gauge) in the geographic area of the model domain. High correlations were found between the model data and observed temperature, salinity and water levels, along with small root mean square errors. This indicates that the model is able to reproduce the oceanographic phenomena in the study area. The Taylor diagram analysis showed a high correlation coefficient (R = 0.99) between the observed bottom temperature in the Inner Bay and Clew Bay model, along with a small centered root mean square error (RMSD = 0.5 °C). High correlation coefficients (R > 0.80) were found between the model and the two ADCPs for the zonal current component. There was a resemblance in structure between the model and the observed salinity profiles, indicating that freshwater was correctly implemented in the model. Moreover, the correlation coefficient between the model and the tidal sea surface height (SSH) was 0.99, with an RMSD of 0.09 m. We discovered that wind direction and speed had a significant impact on the bay’s water inflow rate. The model outputs can be used to provide scientists, fishermen, and decision-makers with hydrodynamic information on ocean conditions in the bay.

The physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder (AIRS) radiances is presented. The retrieval algorithm is applied to AIRS clearsky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations (RAOBs) comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement (ARM) Program Cloud And Radiation Testbed (CART) in Oklahoma, U. S. A. Comparisons show that the physicallybased AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error (RMSE) of 1 K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite (GOES) sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.

Abstract. Space-borne observations of atmospheric methane (CH4) have been made using the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua satellite since August 2002 and the Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT) since April 2009. This study compared the GOSAT TANSO-FTS thermal infrared (TIR) version 1.0 CH4 product with the collocated AIRS version 6 CH4 product using data from 1 August 2010 to 30 June 2012, including the CH4 mixing ratios and the total column amounts. The results show that at 300–600 hPa, where both AIRS and GOSAT-TIR CH4 have peak sensitivities, they agree very well, but GOSAT-TIR retrievals tend to be higher than AIRS in layer 200–300 hPa. At 300 hPa the CH4 mixing ratio from GOSAT-TIR is, on average, 10.3 ± 31.8 ppbv higher than that from AIRS, and at 600 hPa GOSAT-TIR retrieved CH4 is −16.2 ± 25.7 ppbv lower than AIRS CH4. Comparison of the total column amount of CH4 shows that GOSAT-TIR agrees with AIRS to within 1 % in the mid-latitude regions of the Southern Hemisphere and in the tropics. In the mid to high latitudes in the Northern Hemisphere, comparison shows that GOSAT-TIR is ∼ 1–2 % lower than AIRS, and in the high-latitude regions of the Southern Hemisphere the difference of GOSAT from AIRS varies from −3 % in October to +2 % in July. The difference between AIRS and GOSAT TANSO-FTS retrievals is mainly due to the difference in retrieval algorithms and instruments themselves, and the larger difference in the high-latitude regions is associated with the low information content and small degrees of freedom of the retrieval. The degrees of freedom of GOSAT-TIR retrievals are lower than that of AIRS, which also indicates that the constraint in GOSAT-TIR retrievals may be too strong. From the good correlation between AIRS and GOSAT-TIR retrievals and the seasonal variation they observed, we are confident that the thermal infrared measurements from AIRS and GOSAT-TIR can provide valuable information to capture the spatial and temporal variation of CH4, especially in the mid-upper troposphere, in most periods and regions.

A method is proposed for the estimation of absolute binding free energy of interaction between proteins and ligands. The linear interaction energy method is combined with atom-bond electronegativity equalization method at σπ level Force field (fused into molecular mechanics) and generalized Born continuum model calculation of electrostatic solvation for the estimation of the absolute free energy of binding. The parameters of this method are calibrated by using a training set of 24 HIV-1 protease-inhibitor complexes (PDB entry 1AAQ). A correlation coefficient of 0.93 was obtained with a root mean square deviation of 0.70 kcal mol(-1) . This approach is further tested on seven inhibitor and protease complexes, and it provides small root mean square deviation between the calculated binding free energy and experimental binding free energy without reparametrization. By comparing the radii of gyration and the hydrogen bond distances between ligand and protein of three training model molecules, the consistent comparison result of binding free energy is obtained. It proves that this method of calculating the binding free energy with appropriate structural analysis can be applied to quickly assess new inhibitors of HIV-1 proteases. To test whether the parameters of this method can apply to other drug targets, we have validated this method for the drug target cyclooxygenase-2.

Pose estimation towards providing the assessments of animal health and welfare monitoring has strongly gained interest in the last few years. However, it is a challenging computer vision problem as the frequent interaction causes occlusions the association of detected key-points to the correct individuals. Deep Learning (DL) offers major advances in the field of pose estimation. In this paper, we investigated the possibility of using a famous open-source DL-based toolbox, DeepLabCut [1], for the specific pig pose estimation task. We predicted the body part of each individual pig from only input images or video sequences directly with no adaptations to the application setting. We used a real dataset which contains 2000 annotated images with 24,842 individually annotated pigs from 17 different locations and light conditions. The experimental results demonstrated that we can achieve a small root mean square error between the manual and predicted labels (10.1) when detecting pigs in environments previously seen by a DL model during training. To evaluate the robustness of the trained model, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 12.0 root mean square error.

Improved binary parameters using GA for multi-component aromatic extraction: NRTL model without and with closure equations

Improved aerosol retrieval algorithm using Landsat images and its application for PM10 monitoring over urban areas

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.

<strong class="journal-contentHeaderColor">Abstract.</strong> Multiyear measurements from Joss-Waldvogel disdrometer (5 years) and X-band dual-polarization radar (2 years) made at Gadanki (13.5 N, 79.18 E), a low latitude station, are used to i) retrieve appropriate raindrop size distribution (DSD) relations for monsoonal rain, ii) understand their dependency on temperature, raindrop size-shape model and season and iii) assess polarimetric radar DSD retrievals by various popular techniques (Exponential-Exp, Constrained Gamma &ndash; CG, Normalized Gamma &ndash; N-Gamma and <em>&beta;</em> methods). The coefficients obtained for different DSD relations for monsoonal rain are found to be different from that of existing relations elsewhere. The seasonal variation in DSD is quite large and significant and as a result the coefficients also vary considerably between the seasons. The slope of the drop size - shape relation, assumed to be constant in several studies, vary considerably between the seasons with warmer seasons showing smaller slope value than cold season. It is found that the constant (0.062) used in linear drop shape models is valid only for cold season. The derived coefficients for CG method for different seasons coupled with those available in the literature reveals that the warm seasons/regions typically have larger curvature and slope values than in cold seasons/regions. The coefficients of mass weighted mean diameter (<em>D_m</em>) &ndash; differential reflectivity (<em>Z_DR</em>) exhibit strong dependency on drop shape model, while those for the derivation intercept parameter exhibit strong seasonal dependency. Using the retrieved relations and X-band polarimetric radar at Gadanki, four popular DSD methods are evaluated against disdrometer measurements collected over 12 events. All the methods estimated <em>D_m</em> reasonably well with small root mean square error, however failed to estimate intercept parameter accurately. Only N-gamma method estimated the normalized intercept parameter reasonably. Problems associated with specific differential phase (<em>K_DP</em>)-based estimates close to the radar location, particularly during overhead convection, are also discussed.

Machine learning and mathematical modeling techniques have been conducted to model the thin layer drying kinetics of pea pods, under either microwave or conventional air drying,. The effect of nine different microwave output powers (200–1000 W) and five different ventilated oven temperatures (40, 60, 80, 100, and 120°C) on drying kinetics was studied. The experimental drying rates were fitted to 11 literature semi‐empirical models to determine the kinetic parameters, finding the higher goodness‐of‐fit for the Midilli et al. model (average R2 = 0.999 for both drying methods). Moreover, the data were modeled using support vector machine (SVM) for regression which was optimized with dragonfly algorithm (DA) technique. The best result was obtained by Gaussian kernel with the optimal parameters σ, C, and ε values estimated as 0.2871, 78.45, and 0, respectively. The small root mean square error (RMSE = 0.0132) and the high determination coefficient (R2 = 0.9983) values proved how robust the SVM model is. DA‐SVM techniques can reliably be utilized to describe the thin layer drying kinetics of pea pods. It is useful to provide models that can assist in the development of food process control algorithms, and provided insights into complex processes, for the technological design of microwave or convective drying for pea pods preservation.Practical applicationsDrying of by‐products from pea processing industry was investigated as a critical step prior to their valorization. The drying of pea pods has never been investigated before which is the case of the present study whose objective was to study and model the microwave and convective drying kinetics of pea pods. Our research work reported that the Midilli et al. model was the most appropriate to describe the thin layer drying kinetics of pea pods for both drying methods, but mathematical drying models, although a useful tool, remains empirical in nature and product specific. Because of these limitations the new model DA‐SVM, developed using artificial intelligence techniques, can reliably be used to describe the nonlinear behavior of pea pods drying. These results could be further used for scale up calculation, which would further allow industrial scale preservation by microwave or convective drying of pea pods.

Powder Cd of 99.999% purity was prepared at room temperature (25 °C) and x-ray diffraction patterns were obtained using CuKaα radiation with Ni-filter. The line broadening was analyzed after incorporating the appropriate correction factors. At room temperature Cd was found to have large particle size (653 A), small root mean square strain (.001), small deformation fault probability a (.003). and negligible growth fault probability β(0). Compared to other hep metals which have been studied earlier and which have higher melting temperatures, metal Cd is much less affected by mechanical deformation at room temperature.

Radiation portal monitors (RPMs) are globally employed in ports to decrease the illicit trafficking of nuclear materials. Most RPMs are generally considered as threshold devices because they utilize large plastic scintillators, which have limited energy resolution and no position sensitivity, to count the gamma-rays and neutrons emitted by nuclear materials. Hence, we designed an RPM detector system with NaI(Tl) detectors inserted into the plastic scintillators to realize gamma-ray source identification and positioning. In this work, a 2D gamma-ray source positioning algorithm based on the attenuation law and solidangle variation was studied with the simulation results, and the variation of the positioning deviations with the real positions was spline interpolated to calibrate the calculated position. The testing experiment showed a small root mean square error (RMSE) for both x (0.86 cm) and y (1.52 cm) after calibration.

The structure of native alpha1-antitrypsin, the most abundant protease inhibitor in human plasma, is characterised primarily by a reactive loop containing the centre of proteinase inhibition, and a beta-sheet composed of five strands. Mobility of the reactive loop is confined as a result of electrostatic interactions between side chains of Glu342 and Lys290, both located at the junction of the reactive loop and the beta structure. The most common mutation in the protein, resulting in its inactivation, is Glu342-->Lys, named the Z mutation. The main goal of this work was to investigate the influence of the Z mutation on the structure of alpha1-antitrypsin. Commonly used molecular modelling methods have been applied in a comparative study of two protein models: the wild type and the Z mutant. The results indicate that the Z mutation introduces local instabilities in the region of the reactive loop. Moreover, even parts of the protein located far apart from the mutation region are affected. The Z mutation causes a relative change in the total energy of about 3%. Relatively small root mean square differences between the optimised structures of the wild type and the Z mutant, together with detailed analysis of 'conformational searching' process, lead to the hypothesis that the Z mutation principally induces a change in the dynamics of alpha1-antitrypsin.

Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data. Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted using expression data. We further confirmed that a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) whose expression levels alone were predictive of tumor purity regardless of tumor type. We tested the predicative power of the ten-gene set on an independent dataset that was not from TCGA and showed that the expression levels of the ten genes can accurately predict the cancer cell proportions in those independent samples. The correlation coefficient between the predicted and experimentally obtained tumor purity was 0.88. Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data. Citation Format: Yuanyuan Li. Putative biomarkers for tumor sample purity prediction based on gene expression data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5107.

Predictive models to describe the growth of Bacillus cereus in tteokgalbi were developed during storage under various temperatures (5–25C) and inoculation levels (2 and 3 log) to estimate the shelf life. To develop primary models, the observed data were applied to the Baranyi and Gompertz equations. The growth rate was dependent on temperature, but the effect of inoculation level on growth rate was not significant (P &gt; 0.05). Thus, the secondary polynomial model was developed as a function of temperature. At 5C, the predicted shelf life of tteokgalbi was approximately 65.5–73.2 h. The shelf life at 15 or 25C decreased sharply during storage, and was less than 20 h during storage. All primary and polynomial secondary models were characterized by high R2 values, small root mean square error values and very ideal values of Bf and Af. These results present reliable predictive growth models describing the growth of B. cereus under various temperature conditions in tteokgalbi.Practical ApplicationsMany traditional homemade foods have been industrialized at recent days. Tteokgalbi is the most popular processed pork product in Korea, and the acceptability of consumers is high because of its good flavor and nutritional value. During industrial production, the possibility of contamination of pathogens including Bacillus cereus can be high through manufacture and distribution. The predictive model in this study can be used to control the possibility of contamination for safety of tteokgalbi as basic information.