Development of biochar/HDPE composites and characterization of the effects of carbon loadings on the electromagnetic shielding properties.

The aim of this research is to develop high carbon-yielding biochar from pinewood, coffee husk, sugarcane bagasse, and maize cob and to characterize the biochar/HDPE composites for electromagnetic (EM) shielding application. During the biochar/HDPE composites fabrication, slow pyrolysis and compression molding manufacturing were used. The enhanced properties characterizations were conducted by using thermogravimetric analysis (TGA), scanning electron microscopy (SEM), differential thermal analysis (DTA), Fourier transform spectrometry (FTIR), Brunauer-Emmet-Teller (BET) analysis, digital multi-meter, and proximity analysis. The results of biochar pyrolysis showed the maximum carbon yield of 74.6 %, 68.9 %, 68.4 %, and 40 % for pine wood, maize cob, sugarcane bagasse, and coffee husk respectively. The BET analysis showed the maximum specific surface area (734.5 m2/g), pore volume (0.2364 cm3/g), and pore radius (9.897 Å) from the pine wood biochar. The biochar loading analysis results showed that the 30 % and 40 % pine wood biochar significantly enhanced the electrical conductivity, thermal conductivity, thermal stability, crystallinity, and EM shielding effectiveness (SE) of the biochar/HDPE composites. In particular, the biochar/HDPE composite with 30 wt% pine wood biochar showed the highest thermal conductivity of 2.219 W/mK, and the 40 wt% pine wood biochar/HDPE composite achieved the highest electrical conductivity of 4.67 × 10-7 S/cm and EM SE of 44.03 dB at 2.1 GHz.

Assessment of harvest and postharvest losses of teff (Eragrostistef (Zucc.)) and methods of loss reduction: A review.

Teff [Eragrostistef (Zucc.)] is one of the most important cereal crops in Ethiopia which is part of the traditional dish of the people in the form of Injera. Interest in Teff has increased noticeably due to its very attractive nutritional profile and the gluten-free nature of the grain. It is a gluten-free cereal, among the major cereal crops, Teff accounts for the largest average annual acreage in the country. It also accounts for the second-largest average annual production, next to maize. The study endeavored to review the harvest and postharvest losses, the causes of these losses, and possible solutions to reduce the postharvest losses of the Teff crop in Ethiopia. There are inadequate postharvest research works conducted in Ethiopia, and most of the limited studies are focused on cereals other than Teff. Teff farming in Ethiopia is dominated by traditional methods of harvesting and postharvest handling. The application of modernharvest and postharvest technologies during Teff production and handling is critically low. As a result, a considerable loss of Teffgrain (16-30 %) in the harvest and postharvest stages has been recorded. The largest share of this loss is observed during the harvesting stage due to shattering, scattering, animal feeding, and contamination with unwanted parts. Lack of awareness of postharvest losses, limited access and availability of postharvest technologies, and low attention given to postharvest research, extension, and infrastructure have also contributed their share to these losses. Transforming the traditional practice into mechanized farming, modern postharvest technologies, and supporting the system by further research works and pieces of training on loss prevention methods could potentially minimize the harvest and postharvest losses of Teff in the country.

Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.

As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various types of application equipment. Nevertheless, mechanistic models are quite often intricate and complex with a large number of input parameters required. Quite often, the detailed data needed for such models are not readily available. In this study, two advanced machine learning models (artificial neural network (ANN) and support vector regression (SVR)) were developed for pesticide drift prediction and compared with three conventional regression-based models: multiple linear regression (MLR), generalized linear model (GLM), and generalized nonlinear least squares (GNLS). The models were evaluated in fivefold cross-validation and by external validation using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute bias (MAB). From regression-based models, GLM and GNLS models performed very well when evaluated by cross-validation with R2 = 0.96 and 0.95 and RMSE = 0.70 and 0.82 respectively, while MLR performed less with R2 of 0.65 and RMSE of 2.25. Simultaneously, ANN and SVR models performed very well with R2 = 0.98 and 0.97 and RMSE = 0.58 and 0.71 respectively. Overall, ANN model performed best compared to the other four models followed by SVR. A comparison was also made between the high-performing model, ANN, and two previously published empirical models. The ANN model outperformed the two previously published empirical models and can be used to predict pesticide drift. Therefore, the ANN model is a potentially promising new approach for predicting ground drift that merits further study. In conclusion, our work demonstrated that the new approach, ANN and SVR-based models, for pesticide drift modeling has better predictive power than conventional regression models. Their ability to model complex relationships is a clear benefit in pesticide drift modeling where the variability in pesticide drift is often affected by a number of variables and the relationships between drift and predictors are very complicated. We believe such insights will pave better way for the application of machine learning towards spray drift modeling.