Single-photon dehazing imaging method based on density clustering-guided Gaussian model fitting.

To improve control over the drying process and the quality of the dried products, it is essential to comprehend the drying kinetics model. This study aim to find the best thin-layer drying kinetics model for box-shaped candied mangoes dried in a rotary oven. The source of the data is Rozana’s (2016) research into the moisture content and drying times of candied mangoes at 45 and 50°C in a rotating oven. The Lewis, Page, Henderson and Pabis, Logarithmic, Two Term, Two Term Exponential, Diffusion Approximation, Midilli et al., and Modified Henderson and Perry models are among the kinetic models used. The moisture ratios of mangoes collected during drying at various time intervals were used to fit drying models in order to predict the moisture ratio. The model with the highest coefficient of determination (R2), the lowest root mean square error (χ2), and the lowest root mean square error (RMSE) is the best drying kinetics model. The Midilli et al. model was determined to be the most appropriate drying model for box-shaped candied mangoes dried in a rotating oven with R2 of 0,9989, χ2 of 0,0001, and an RMSE value of 0,0074 for a temperature of 45°C; and R2 is 0,9973, χ2 is 0,0002, and RMSE is 0,0137 at 50°C. The Midilli et al. model equations obtained is MR=1,026 exp(-0,0007t1,0706)+0,0001t at 45°C and MR=1,0129exp(-0,0003t1,3568)+0,0002t at 50°C.

ABSTRACTThis paper deals with seasonal climate forecasting using as many as 13 coupled ocean—atmosphere models. An analysis of the individual model, multimodel ensemble, and FSU synthetic superensemble (FSUSSE) climate forecasts was performed for monthly and seasonal forecasts of precipitation, sea surface temperature (SST) and surface air temperature over the Euro-Mediterranean region including the land areas of Europe, North Africa and the Near East, during the period 1989–2001. In the FSUSSE methodology, forecasts are obtained by a weighted combination of the individual coupled ocean—atmosphere model forecasts based on a training period. The set of 13 individual climate forecast models utilized in this research is comprised of the seven models in the European suite of DEMETER models, a suite of four Florida State University models, the Australian POAMA model and the NCAR CCM3 model.The FSUSSE forecasts of seasonal precipitation anomalies were found to have the lowest root mean square (RMS) errors in comparison to the models in the multimodel ensemble, and their ensemble mean. However, the anomaly correlation (AC) coefficient results for seasonal precipitation anomaly forecasts by the FSUSSE were less impressive. The equitable threat scores for the FSUSSE forecasts of seasonal precipitation were found to be better than the various models in the multimodel ensemble, but those scores for the forecasts of positive seasonal anomalies were found to be worse than most of the models in the multimodel ensemble.The FSUSSE seasonal forecasts for SST and surface air temperature for the season considered (winter) were found to be excellent for both AC coefficients and RMS errors in the forecast anomalies.

Electric power grids are increasingly penetrated by generation from photovoltaic (PV) systems. The volatility of solar irradiance can therefore cause problematic fluctuations of grid voltage and frequency. For local low voltage power grid sections highly accurate nowcasts of the short-term local PV-power generation may enable higher PV penetration by employing short-term flexibility options while saving investments in grid-infrastructure. Systems of up to four All-Sky-Imagers (ASI) have been used successfully to nowcast direct and global irradiance as well as the production of solar power plants. In Oldenburg and the surrounding area these systems are enhanced by interconnecting 34 All-Sky-Imagers in a measurement network called Eye2Sky. Intermediate results can be combined and exchanged between systems with overlapping observation areas but also between systems up to 100 km apart. To demonstrate the potential of the approach three state-of-the-art 2-ASI-systems from the network are selected. Nowcasts of the individual systems are averaged weighting each prediction by its expected accuracy in the single points of the forecast domain. In a validation period of 86 days the network outperforms a single 2-ASI-system. For all lead times greater than zero, significantly lower root mean square errors are found compared to the 2-ASI system. For highly variable time intervals the root mean square error is reduced by up to 50 %. The network´s combined nowcast is found to reduce forecast errors related to the often uncertain cloud edge positions and further systematic errors found in single 2-ASI-systems.

Refining the global branched glycerol dialkyl glycerol tetraether (brGDGT) soil temperature calibration

Predictive maintenance has gained importance across various industries, including the automotive sector. It is very challenging to detect vehicle failures in advance due to the intricate composition of various components and sensors. The vehicle’s reliability is of utmost importance for ensuring the absence of fatalities or malfunctions to foster economic development. This study introduces an innovative method for developing a predictive framework for vehicle engines with faster and higher decision accuracy. The framework is specifically designed to recognize patterns and abnormalities that may suggest prospective engine problems in real-time and allow proactive maintenance. We assessed the performance of the developed vehicular engine health monitoring systems using a deep learning model based on essential measures like root mean square error, root mean square deviation, mean absolute error, accuracy, confusion matrix, and area under the curve. In this case, the deep learning models are developed by following ensemble techniques using the most prominently used machine learning techniques. Significantly, Stacked Model 1 outperformed other stacked models (Models 2 and 3) and achieved an impressive AUC value of 0.9702 with a low root mean square error (RMSE) of 0.3355, a high accuracy rate of 0.9470, and a precision of 0.9486. It happens due to the effective incorporation of different approaches into Stacked Model 1, which signifies a significant advancement in predicting vehicular engine failures. The model can be used in real-time monitoring systems to continuously monitor the health of vehicular engines and provide early warnings of potential failures, thereby reducing maintenance costs and improving safety.

Rainfalls are one of the most important climate variables that today impact significantly different sectors like agriculture, energy, industry, and so on. Agriculture is one of the most sensitive sectors to climate change because rainfalls in this case, directly affect the positive progress of corps activity. In this case, forecasting rainfalls would help farmers to effectively survive the increasing occurrence of extreme weather events, plan their farming activities, and reduce costs. On the other hand, circular economy (CE) promises a strategy to support sustainable and regenerative agriculture by supporting the sustainable management of water based on water resources. This paper aims to determine the best method for forecasting a natural phenomenon such as the rainfall, that today in Albania, as a result of the unpredictable flows that it often has, is a major problem in the field of agriculture. In this study, the rainfall model based on statistical methods, Auto-Regressive Integrated Moving Average (ARIMA), Error, Trend & Seasonal (ETS) and deep learning models, Long Short-Term Memory Network (LSTM), and Deep Forward Neural Network (DFNN) was developed. The study area that will be used for rainfall forecasting is Albania with a time interval between January 1901 and December 2022. The period that will be used for prediction will be January 2023- December 2024. The performance of each of the models used has been evaluated by using Root Mean Square Error (RMSE) where we also used the comparison of training and validation loss curves to analyze and avoid the model overfitting in the training phase. The results showed that from the comparison between ARIMA and ETS, ETS has the minimum prediction error value while between LSTM and DFNN, DFNN has the best performance in the evaluation metrics (RMSE) and with the best training and validation loss curves. From the final comparison, ETS was better than the DFNN model with the lowest root mean square error (RMSE). ETS was the best model and provided higher accuracy in precipitation forecast.

Assessment of gridded datasets of various near surface temperature variables over Heihe River Basin: Uncertainties, spatial heterogeneity and clear-sky bias

Total viable count (TVC) is often used as an important indicator for chicken freshness evaluation. In this study, 112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR (900-1700 nm) range. The full LW-NIR spectra (486 wavebands) within the images were extracted and applied to related to reference TVC values measured in different storage period, using partial least squares regression (PLSR) algorithm, resulting in high correlation coefficients (R) and low root mean square errors (RMSE), for either raw spectra or pretreatment spectra. By using regression coefficients (RC) method, 20, 18, 17 and 20 optimal wavebands were respectively selected from raw spectra, baseline correction (BC) spectra, Savitzky-Golay convolution smoothing (SGCS) spectra and standard normal variate (SNV) spectra and applied for the optimization of original full waveband PLSR model. By comparison, RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with RC of 0.98 and RMSEC of 0.35 log10 CFU/g in calibration set, and RP of 0.98 and RMSEP of 0.44 log10 CFU/g in prediction set. At last, by transferring the best RC-PLSR model, the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree. The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh. Keywords: hyperspectral imaging, chicken, TVC, partial least square regression (PLSR) DOI: 10.25165/j.ijabe.20191203.4444 Citation: H Wang, H J He, H J Ma, F S Chen, Z L Kang, M M Zhu, et al. LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh. Int J Agric & Biol Eng, 2019; 12(3): 180–186.

Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the literature may not always yield conclusive results, as the choice could be influenced by biases or misleading evaluations stemming from different methodologies, datasets, and preprocessing techniques. This study aims to compare and comprehensively analyze the performance of various deep learning models across diverse datasets to assess their applicability and generalizability across a broader spectrum of scenarios. Commonly used deep learning models for blood glucose level forecasting, such as feed-forward neural network, convolutional neural network, long short-term memory network (LSTM), temporal convolutional neural network, and self-attention network (SAN), are considered in this study. To evaluate the generalization capabilities of each model, four datasets of varying sizes, encompassing samples from different age groups and conditions, are utilized. Performance metrics include Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Coefficient of Determination (CoD) for analytical asssessment, Clarke Error Grid (CEG) for clinical assessments, Kolmogorov-Smirnov (KS) test for statistical analysis, and generalization ability evaluations to obtain both coarse and granular insights. The experimental findings indicate that the LSTM model demonstrates superior performance with the lowest root mean square error and highest generalization capability among all other models, closely followed by SAN. The ability of LSTM and SAN to capture long-term dependencies in blood glucose data and their correlations with various influencing factors and events contribute to their enhanced performance. Despite the lower predictive performance, the FFN was able to capture patterns and trends in the data, suggesting its applicability in forecasting future direction. Moreover, this study helps in identifying the optimal model based on specific objectives, whether prioritizing generalization or accuracy.

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

This paper presents the application of an artificial neural network (ANN) model for predicting the penetration depth under projectile impact in ultra-high-performance concrete (UHPC) targets containing steel fibres. Despite the availability of a large number of existing empirical models, the prediction of penetration depth remained inconclusive, partly due to the phenomenon's complexity and partly due to the limitation of statistical regression. From the results of this study, it is evident that the ANN model is capable of predicting the penetration depth of UHPC more accurately than other machine-learning (ML) models (linear regression, decision tree regression and random forest regression) and empirical formulae. The ANN model achieved a lower root mean square error (RMSE) of 11.68 compared to the other ML models (RMSE: 16.66–19.74) and empirical equations (RMSE: 25.17–53.42), when applied to the test data set. The velocity, impact energy, diameter of the projectile and thickness of the UHPC targets are the most significant parameters (P-value <5%) for predicting the penetration depth using ANN and multiple linear regression models.

Prediction of Sitophilus granarius infestation in stored wheat grain using multivariate chemometrics & fuzzy logic-based electronic nose analysis

Crops monitoring is a challengeable subject that radar images can help it. The applicability of Sentinel-1 SAR data with dual polarization provided a splendid opportunity to develop a method for estimating rice parameters. Heights of cereal and biomass are two significant characteristics of rice that can be estimated with assessing satellite data and field measurements by classical regression methods [multiple linear regression (MLR), relevance vector regression (RVR), and support vector regression (SVR)]. In this study, Sentinel-1 SAR data from April 2018 to September 2018 in Astaneh-ye Ashrafiyeh region in the north of Iran were used. To evaluate and analyze validation of regression methods, field measurements (gathered from 15 plots) were utilized. The efficiency of nonparametric methods (SVR and RVR) is much better than that of the parametric regression (MLR) for rice parameter estimations. Among nonparametric approaches, RVR method has better results than SVR, because of the highest correlation coefficient (R2) and lowest root mean square error (RMSE). R2 = 0.92, RMSE = 162.1, and MAE = 971.9 and R2 = 0.92, RMSE = 10.9, and MAE = 70.71 are the results of height and biomass, respectively.

This study employs Cantera code to investigate the laminar burning velocity of different ammonia–hydrogen mixtures. Suitable models were selected from recent literature, and the one with the lowest root mean square error (RMSE) against experimental data was identified through the error function method. Bao mechanism shows an RMSE value of 4.71 at atmospheric pressure for ammonia–hydrogen mixtures, while the Otomo mechanism exhibits an RMSE of 2.11 under high‐pressure conditions. Additionally, sensitivity analysis was conducted to highlight critical reactions within each mechanism, emphasizing distinctions between different pressures. This approach aims to choose the proper mechanism to reduce computational and experimental costs in the early stages of ammonia–hydrogen research.

Quantitative analysis of polycyclic aromatic hydrocarbons in soil by infrared spectroscopy combined with hybrid variable selection strategy and partial least squares