Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6)

Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute ~7 and 20%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.

VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.

Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks

Visual information processing requires an efficient visual attention system. The neural theory of visual attention (TVA) proposes that visual processing speed depends on the coordinated activity between frontoparietal and occipital brain areas. Previous research has shown that the coordinated activity between (i.e., functional connectivity, ‘inter-FC’) cingulo-opercular (COn) and right-frontoparietal (RFPn) networks is linked to visual processing speed. However, evidence for how inter-FC of COn and RFPn with visual networks links to visual processing speed is still missing. Forty-eight healthy human adult participants (27 females) underwent resting-state (rs-)fMRI and performed a whole-report psychophysical task. To obtain inter-FC, we analyzed the entire frequency range available in our rs-fMRI data (i.e., 0.01-0.4 Hz) to avoid discarding neural information. Following previous approaches, we analyzed the data across frequency bins (Hz): Slow-5 (0.01-0.027), Slow-4 (0.027-0.073), Slow-3 (0.073-0.198), and Slow-2 (0.198-0.4). We used the mathematical TVA framework to estimate an individual, latent-level visual processing speed parameter. We found that visual processing speed was negatively associated with inter-FC between RFPn and visual networks in Slow-5 and Slow-2, with no corresponding significant association for inter-FC between COn and visual networks. These results provide first empirical evidence that links inter-FC between RFPn and visual networks with the visual processing speed parameter. These findings suggest a direct connectivity between occipital and right frontoparietal, but not frontoinsular, regions, to support visual processing speed.Significance statementAn efficient visual processing is at the core of visual cognition. Here, we provide evidence for a brain correlate of how fast individuals process visual stimuli. We used mathematical modeling of performance in a visual report task to estimate visual processing speed. A frequency-based analysis of resting-state fMRI signals revealed that functional connectivity between the right frontoparietal network and primary and dorsal occipital networks is linked to visual processing speed. This link was present in the slowest, typical frequency of the fMRI signal but also in the higher frequencies that are routinely discarded. These findings imply that the coordinated spontaneous activity between right frontoparietal and occipital regions supports the individual potential of the visual system for efficient processing.

The Effect of Low Friction in Pavement Due to Floods and High-speed Vehicles in Increasing the Number of Rescue Vehicles' Driving Accidents

Background: The rescue operation is dangerous during floods when the street surface is wet and the rescue vehicles moving fast. Rural roads are known with a high percentage of run-off-the-road crashes and accidents, mostly affected by the unsuitable and inappropriate speed of the car and failure to keep a safe side distance from the highway alignment. Determining the length of the vertical curve and the stopping distance depends on the speed parameter in the geometric design. This parameter depends on the traffic flow and the vehicle driver. Materials and Methods: The calculation of distance visibility, especially in the vertical and horizontal curves, has a significant role in the geometric design of roads and the safety of roads and drivers. This study investigates changes in two critical parameters of friction surface of pavement and vehicles’ speed on the probability of accidents. In the present study, by changing the speed parameter, namely, increasing speed and decreasing friction, and reducing the distance of vision, the required vertical curve is calculated and compared with the existing curve length. Thus, when the curve length does not match the required arch length, the accident’s probability increases. Results: One of the study’s findings indicates that with a 5% decrease in the friction coefficient of the vertical curve, the probability of accident increases by about 20%. The results show that an approximately 10% increase in speed can raise the likelihood of a crash by 25% on average. Conclusion: The use of accident reduction-related strategies for rescue vehicles and ambulances will help the victims affected by natural disasters. The results showed that choosing the right speed is very important in flooded wet streets. Driving fast to speed up the relief operation not only increases the risk of traffic accidents but also slows down the relief of natural disasters. The results indicate the proper performance of the model created by the probabilistic method. Besides, this model’s use has the advantage of correctly identifying the factors affecting the increase in risk. Also, it helps to take the right measures to increase safety in the relief of natural disasters, especially floods.

Toxicity of β-(1→3,1→6)-ᴅ-glucans produced by Diaporthe sp. endophytes on Metarhizium anisopliae (Metschnikoff) Sorokin assessed by conidia germination speed parameter

Previously, our study reported that β-(1→3,1→6)-ᴅ-glucans produced by endophytes Diaporthe sp. G27-60 and G65-65 (GenBank accession codes JF766998 and JF767007, respectively) are promising antiproliferative agents against human breast carcinoma (MCF-7) and hepatocellular carcinoma (HepG2-C3A) cells. However, effects of Diaporthe exopolysaccharides on eukaryotic healthy cells are not described in current literature. The fungus Metarhizium anisopliae has been employed as teste-system to evaluate the toxicity of pharmaceutical and agricultural-interest substances, considering, among other parameters, conidia germination speed. The current study aimed to verify the effect of different concentrations of Diaporthe β-glucans on the germination speed of M. anisopliae. Conidia were incubated with β-glucans treatments (50, 200 and 400 μg/mL) at 28 ºC, sampled throughout 24 h and analyzed by light microscopy. At the end of 24 h of incubation, the amount of germinated conidia was ≈99% for controls and ranged from 97.7 to 98.6% for treatments. Bayesian analysis indicated that Diaporthe glucans had no toxicity on M. anisopliae and the curve of germination occurred as expected for this fungal strain. Considering the validity of filamentous fungi as model systems, these results are important data about the toxicity of these endophytic EPS on healthy cells and may be associated with previous results obtained for these polymers against tumor cells. Keywords: bayesian analysis, conidia germination, endophytic fungi, exopolysaccharide, fungal model-system.

ASSESSMENT OF VELOCITY ACCURACY OF AIRCRAFT IN THE DYNAMIC TESTS USING GNSS SENSORS

The paper presents a new model for determining the accurate and reliable flight speed of an aircraft based on navigation data from the three independent Global Navigation Satellite System (GNSS) receivers. The GNSS devices were mounted on-board of a Cessna 172 aircraft during a training flight in south-eastern Poland. The speed parameter was determined as the resultant value based on individual components from 3 independent solutions of the motion model. In addition, the standard deviation of the determined flight speed values for the Cessna 172 aircraft was determined in the paper. The resultant on-ground and flight speed of the Cessna 172 aircraft ranged from 0.23 m/s to 74.81 m/s, while the standard deviation of the determined speed values varied from 0.01 m/s to 1.07 m/s. In addition, the accuracy of research method equals to -0.46 m/s to +0.61 m/s, in respect to the RTK-OTF solution. The RMS parameter as an accuracy term amounts to 0.07 m/s for the presented research method.

PENGARUH VARIASI PARAMETER KECEPATAN TORCH, TEKANAN OKSIGEN DAN JARAK NOZZLE TERHADAP MATERIAL REMOVAL RATE PADA PROSES OTOMASI GAS CUTTING

The number of industries that make building, bridge and machine construction are inseparable from the metal cutting process, to produce optimal output, the process is done automatically. Gas Cutting is a metal cutting technique that uses a combination of oxygen gas and fuel. The purpose of this study was to determine the most optimal combination of low carbon cutting fabrication processes with variations in the Torch Speed ​​parameter (mm / min), Oxygen Pressure (Kg / cm2), Nozzle Distance (mm) to produce the greatest MRR using the taguchi method. From the research, the most optimal combination of parameters on MRR is level 3 torch speed of 475 mm / minute, oxygen pressure at level 3 of 7.5 kg / cm2 and nozzle distance at level 2 of 6 mm. with the contribution of torch speed contribution of 87.39%, oxygen pressure of 10.35%, the effect of the nozzle distance parameter of 1.17%.