Windtech – A Sensory Device for ‘Cold’ Sensation Measurements

Windtech device is a novel tool for measuring the sensation of the ‘cold’. Cold poses numerous challenges for industrial operations, human survival, and living convenience. The impact of the cold is not possible to be quantified just based on temperatures; however other factors such as wind speed, humidity, irradiance have to be taken into consideration. Efforts have been made to develop combined indices such as wind chill temperature (WCT), AccuWeather RealFeel®, and others. The presented article discusses these along with the industrial standards that emphasize on the quantification of the ‘cold’. The following article introduces the Windtech device and its operating principle involving ‘heated temperature’, where the ‘heated temperature’ is affected by environmental parameters including ambient temperature, humidity, wind velocity, and irradiance. The discussed Windtech device is calibrated for operation according to the ISO 11079:2007 standard.

High-resolution Late-holocene Biomarkers Records in Southern Continental Shelf of the Korean Peninsula: Implications for Climate Variations Over the Last 4 Kyr

High-resolution records for carbon isotopes of organic matter and n-alkane compounds were investigated in two gravity cores (SJP15-2 and SJP15-4) taken from the southern continental shelf of the Korean peninsula to evaluate the influxes of terrestrial biomarkers and their linkage to paleoclimate and marine environmental changes since the last 4 kyr. The total organic carbon contents were < 1%, and the carbon isotope ratio of organic matters (d13Corg) ranged from approximately −21‰ to -22‰ and, they did not highly fluctuate throughout the two cores. However, the vertical distributions of total terrestrial biomarkers, long-chain n-alkanes (nC25-35), and individual n-alkane compounds exhibited distinctive fluctuations. There are two switching points that discriminate patterns of excursion and distribution at ca. 2.5 ka, and 0.5 ka. Several n-alkane combined indices such as average chain length (ACL), carbon preference index (CPI), and paleovegetation index (Paq) were coincident with these switching points, implying that the supply of terrestrial biomarkers was strongly associated with environmental changes at the source area. In particular, variations of compound-specific n-alkanes isotope and the ratios of nC31/nC27 and nC31/nC29 follow those of n-alkanes indices, implying that this millennium records were associated with wetter climate conditions, and thus paleovegetation and paleoclimate variation. Comparison with previous data of the detrital quartz from the East China Sea and aeolian dust in the Cheju (Jeju) Island, South Korea, and Dongge cave oxygen isotope records indicates strong synchronicity with gradual paleoclimate degradation between 2.5 ka and 0.5 ka. Therefore, our high-resolution n-alkane data are very useful for reconstructing past climatic variability, suggesting that paleoclimate system of the East Asian region may have influenced the sediment records of study area since the last 4 kyr.

Agricultural drought monitoring in Tamil Nadu in India using Satellite-based multi vegetation indices

Drought being an insidious hazard, is considered to have one of the most complex phenomenons. The proposed study identifies remote sensing-based indices that could act as a proxy indicator in monitoring agricultural drought over Tamil Nadu's region India. The satellite data products were downloaded from 2000 to 2013 from MODIS, GLDAS – NOAH, and TRMM. The intensity of agricultural drought was studied using indices viz., NDVI, NDWI, NMDI, and NDDI. The satellite-derived spectral indices include raw, scaled, and combined indices. Comparing satellite-derived indices with in-situ rainfall data and 1-month SPI data was performed to identify exceptional drought to no drought conditions for September month. The additive combination of NDDI showed a positive correlation of 0.25 with rainfall and 0.23 with SPI, while the scaled NDDI and raw NDDI were negatively correlated with rainfall and SPI. Similar cases were noticed with raw LST and raw NMDI. Indices viz., LST, NDVI, and NDWI performed well; however, it was clear that NDWI performed better than NDVI while LST was crucial in deciding NDVI coverage over the study area. These results showed that no single index could be put forward to detect agricultural drought accurately; however, an additive combination of indices could be a successful proxy to vegetation stress identification.

Impact of a formula combining local impedance and conventional parameters on lesion size prediction

Background: Although ablation energy (AE) and force-time integral (FTI) are well-known active predictors of lesion characteristics, these parameters do not reflect passive tissue reactions during ablation, which may instead be represented by drops in local impedance (LI). This study aimed to investigate if additional LI-data improves predicting lesion characteristics and steam-pops. Methods: RF applications at a range of powers (30W, 40W, and 50W), contact forces (8g, 15g, 25g, and 35g), and durations (10-180s) using perpendicular/parallel catheter orientations, were performed in excised porcine hearts (N=30). The correlation between AE, FTI and lesion characteristics was examined and the impact of LI (%LI-drop [%LID] defined by the ΔLI/Initial LI) was additionally assessed. Results: 375 lesions without steam-pops were examined. Ablation energy (W*s) and FTI (g*s) showed a positive correlation with lesion depth (ρ=0.824:P<0.0001 and ρ=0.708:P<0.0001), surface area (ρ=0.507:P<0.0001 and ρ=0.562:P<0.0001) and volume (ρ=0.807:P<0.0001 and ρ=0.685:P<0.0001). %LID also showed positive correlation individually with lesion depth (ρ=0.643:P<0.0001), surface area (ρ=0.547:P<0.0001) and volume (ρ=0.733, P<0.0001). However, the combined indices of AE*%LID and FTI*%LID provided significantly stronger correlation with lesion depth (ρ=0.834:P<0.0001 and ρ=0.809P<0.0001), surface area (ρ=0.529:P<0.0001 and ρ=0.656:P<0.0001) and volume (ρ=0.864:P<0.0001 and ρ=0.838:P<0.0001). This tendency was observed regardless of the catheter placement (parallel/perpendicular). AE (P=0.02) and %LID (P=0.002) independently remained as significant predictors to predict steam-pops (N=27). However, the AE*%LID did not increase the predictive power of steam-pops compared to the AE alone. Conclusion: LI, when combined with conventional parameters (AE and FTI), may provide stronger correlation with lesion characteristics.

Assessing Hydrological Vulnerability to Future Droughts in a Mediterranean Watershed: Combined Indices-Based and Distributed Modeling Approaches

Understanding the spatiotemporal distribution of future droughts is essential for effective water resource management, especially in the Mediterranean region where water resources are expected to be scarcer in the future. In this study, we combined meteorological and hydrological drought indices with the Soil and Water Assessment Tool (SWAT) model to predict future dry years during two periods (2035–2050and 2085–2100) in a typical Mediterranean watershed in Northern Morocco, namely, Bouregreg watershed. The developed methodology was then used to evaluate drought impact on annual water yields and to identify the most vulnerable sub-basins within the study watershed. Two emission scenarios (RCP4.5 and RCP8.5) of a downscaled global circulation model were used to force the calibrated SWAT model. Results indicated that Bouregreg watershed will experience several dry years with higher frequency especially at the end of current century. Significant decreases of annual water yields were simulated during dry years, ranging from −45.6% to −76.7% under RCP4.5, and from −66.7% to −95.6% under RCP8.5, compared to baseline. Overall, hydrologic systems in sub-basins under the ocean or high-altitude influence appear to be more resilient to drought. The combination of drought indices and the semi-distributed model offer a comprehensive tool to understand potential future droughts in Bouregreg watershed.

Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking

Crop biomass is a critical variable to make sound decisions about field crop monitoring activities (fertilizers and irrigation) and crop productivity forecasts. More importantly, crop biomass estimations by components are essential for crop growth monitoring as the yield formation of crops results from the accumulation and transportation of substances between different organs. Retrieval of crop biomass from synthetic aperture radar SAR or optical imagery is of paramount importance for in-season monitoring of crop growth. A combination of optical and SAR imagery can compensate for their limitations and has exhibited comparative advantages in biomass estimation. Notably, the joint estimations of biophysical parameters might be more accurate than that of an individual parameter. Previous studies have attempted to use satellite imagery to estimate aboveground biomass, but the estimation of biomass for individual organs remains a challenge. Multi-target Gaussian process regressor stacking (MGPRS), as a new machine learning method, can be suitably utilized to estimate biomass components jointly from satellite imagery data, as the model does not require a large amount of data for training and can be adjusted to the required degrees of relationship exhibited by the given data. Thus, the aim of this study was to estimate the biomass of individual organs by using MGPRS in conjunction with optical (Sentinel-2A) and SAR (Sentinel-1A) imagery. Two hybrid indices, SAR and optical multiplication vegetation index (SOMVI) and SAR and optical difference vegetation index (SODVI), have been constructed to examine their estimation performance. The hybrid vegetation indices were used as input for the MGPRS and single-target Gaussian process regression (SGPR). The accuracy of the estimation methods was analyzed by in situ measurements of aboveground biomass (AGB) and organ biomass conducted in 2018 and 2019 over the paddy rice fields of Xinghua in Jiangsu Province, China. The results showed that the combined indices (SOMVI and SODVI) performed better than those derived from either the optical or SAR data only. The best predictive accuracy was achieved by the MGPRS using SODVI as input (r2 = 0.84, RMSE = 0.4 kg/m2 for stem biomass; r2 = 0.87, RMSE = 0.16 kg/m2 for AGB). This was higher than using SOMVI as input for the MGPRS (r2 = 0.71, RMSE = 1.12 kg/m2 for stem biomass; r2 = 0.71, RMSE = 0.56 kg/m2 for AGB) or SGPR (r2 = 0.63, RMSE = 1.08 kg/m2 for stem biomass; r2 = 0.67, RMSE = 1.08 kg/m2 for AGB). Relatively, higher accuracy for leaf biomass was achieved using SOMVI (r2 = 0.83) than using SODVI (r2 = 0.73) as input for MGPRS. Our results demonstrate that the combined indices are effective by integrating SAR and optical imagery and MGPRS outperformed SGPR with the same input variable for estimating rice crop biomass. The presented workflow will improve the estimation of crops biomass components from satellite data for effective crop growth monitoring.

Machine learning model for estimating agricultural crop insurance payout based on air temperature, rainfall, and relative humidity

In Agriculture, the weather-based variations are deliberated to estimate the crop insurance payout. This research model includes linear regression technique (LR) for air temperature payout prediction and fuzzy based choquistic regression (FCR) technique for rainfall payout prediction of agricultural blocks. Then the combined indices of rainfall, relative humidity and air temperature are considered as input to the proposed model named fuzzy based Quasi Poisson Regression technique (FQPR) implementing the multi-indices evaluation function that performs the total payout prediction per hectare of the specified block. The deviations in weather indices determine the insurance payout value with the threshold parameter specified as per policy makers. Thus, the proposed techniques can support the prediction of the total insurance payout with additional weather parameters for the seasonal period of the selected crop for selected five districts with reduced error rate. The results show that the proposed work is appropriate for combining weather indices and predicting the total insurance payout of the groundnut crop of the selected districts.

How to detect a positive response to a fluid bolus when cardiac output is not measured?

Background Volume expansion is aimed at increasing cardiac output (CO), but this variable is not always directly measured. We assessed the ability of changes in arterial pressure, pulse pressure variation (PPV) and heart rate (HR) or of a combination of them to detect a positive response of cardiac output (CO) to fluid administration. Methods We retrospectively included 491 patients with circulatory failure. Before and after a 500-mL normal saline infusion, we measured CO (PiCCO device), HR, systolic (SAP), diastolic (DAP), mean (MAP) and pulse (PP) arterial pressure, PPV, shock index (HR/SAP) and the PP/HR ratio. Results The fluid-induced changes in HR were not correlated with the fluid-induced changes in CO. The area under the receiver operating characteristic curve (AUROC) for changes in HR as detectors of a positive fluid response (CO increase ≥ 15%) was not different from 0.5. The fluid-induced changes in SAP, MAP, PP, PPV, shock index (HR/SAP) and the PP/HR ratio were correlated with the fluid-induced changes in CO, but with r < 0.4. The best detection was provided by increases in PP, but it was rough (AUROC = 0.719 ± 0.023, best threshold: increase ≥ 10%, sensitivity = 72 [66–77]%, specificity = 64 [57–70]%). Neither the decrease in shock index nor the changes in other indices combining changes in HR, shock index, PPV and PP provided a better detection of a positive fluid response than changes in PP. Conclusion A positive response to fluid was roughly detected by changes in PP and not detected by changes in HR. Changes in combined indices including the shock index and the PP/HR ratio did not provide a better diagnostic accuracy.