Empirical Modelling of Household Oils in UV-Vis-NIR Spectrum through Developed Low-Cost Spectroscopy Setup (LCSS)

In this research work, a low-cost \(UV-Vis-NIR\) spectroscopy setup (\(LCSS\)) is developed and presented to analyze transmission (%) and absorption (\(Au\)) from household oils samples. The sensing potential of the developed setup is examined using four different oil samples. These oil samples consist of olive oil, mustard oil, amla oil, and red palm oil. The transmission (%) obtained for olive oil, mustard oil, amla oil, and red palm oil is \(75.66\%\), \(71.10\%\), \(69.87\%\), and \(68.12 \%\) at \(923.2 nm\), \(924.5 nm\), \(925.9 nm,\) and \(927.8 nm\) respectively. Similarly, the absorbance (\(Au\)) for olive oil, mustard oil, amla oil, and red palm oil is \(0.121 Au\), \(0.141 Au\), \(0.153 Au,\) and \(0.163 Au\) at \(920.0 nm\), \(923.0 nm\), \(925.8 nm\), and \(930.2 nm\) respectively. A linear relationship in the wavelength range of \(920 nm\) to \(935 nm\) between transmission (%) and wavelength produce \({R}^{2}=0.9717\) corresponding to a degree (2). Similarly, the linear relation between absorbance (\(Au\)) and wavelength produce \({R}^{2}=0.9997\) corresponding to a degree (2). Finally, an 8th order empirical sinusoidal model is developed for transmission (\(\%\)) and absorbance (\(Au\)) corresponding to the olive oil, mustard oil, amla oil, and red palm oil. The maximum value of \(R-\)square corresponding to the transmission (\(\%\)) for the developed empirical model is obtained for amla oil. Similarly, the full value of the absorbance (\(Au\)) from the developed empirical model is obtained for red palm oil, which indicates a great response towards the empirical sinusoidal model.

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R2 = 0.710) when contrasted with Landsat, followed by the Sin model (R2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d−1) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET.

Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning

Voice conversion (VC) transforms the speaking style of a source speaker to the speaking style of a target speaker by keeping linguistic information unchanged. Traditional VC techniques rely on parallel recordings of multiple speakers uttering the same sentences. Earlier approaches mainly find a mapping between the given source–target speakers, which contain pairs of similar utterances spoken by different speakers. However, parallel data are computationally expensive and difficult to collect. Non-parallel VC remains an interesting but challenging speech processing task. To address this limitation, we propose a method that allows a non-parallel many-to-many voice conversion by using a generative adversarial network. To the best of the authors’ knowledge, our study is the first one that employs a sinusoidal model with continuous parameters to generate converted speech signals. Our method involves only several minutes of training examples without parallel utterances or time alignment procedures, where the source–target speakers are entirely unseen by the training dataset. Moreover, empirical study is carried out on the publicly available CSTR VCTK corpus. Our conclusions indicate that the proposed method reached the state-of-the-art results in speaker similarity to the utterance produced by the target speaker, while suggesting important structural ones to be further analyzed by experts.

Matrix stiffness and shear stresses modulate hepatocyte functions in a fibrotic liver sinusoidal model

Extracellular matrix rigidity has important effects on cell behaviors and is increased sharply during liver fibrosis and cirrhosis. Hepatic blood flow is essential in maintaining hepatocytes (HC) functions. However, it is still unclear how matrix stiffness and shear stresses orchestrate HC phenotype in concert. A fibrotic 3D liver sinusoidal model is constructed using a porous membrane sandwiched between two PDMS layers with respective flow channels. The HC are cultured in collagen gels of various stiffness in the lower channel, while the upper channel is pre-seeded with liver sinusoidal endothelial cells (LSEC) and accessible to shear flow. The results reveal that HC cultured within stiffer matrices exhibit less albumin production and cytochrome P450 (CYP450) reductase expression. Low shear stresses enhance synthetic and metabolic functions of HC, while high shear stresses lead to the loss of HC phenotype. Furthermore, both two mechanical factors regulate HC functions in a cooperative way by complementing with each other. These observations are likely attributed to mechanically-induced mass transport or key signaling molecule of hepatocyte nuclear factor 4 alpha (HNF4α). Present results provide an insight in understanding the mechanisms of HC dysfunction in liver fibrosis and cirrhosis especially from viewpoint of matrix stiffness and blood flow.