This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions.
Natural fibres are attractive as the raw material for developing sound absorber, as they are green, eco-friendly, and health friendly. In this paper, pineapple leaf fibre/epoxy composite is considered in sound absorber development where several values of mechanical pressures were introduced during the fabrication of absorber composite. The results show that the composite can absorb incoming sound wave, where sound absorption coefficients α _n > 0.5 are pronounced at mid and high frequencies. It is also found that 23.15 kN/m^2 mechanical pressure in composite fabrication is preferred, while higher pressure leads to solid panel rather than sound absorber so that the absorption capability reduces. To extend the absorption towards lower frequency, the composite absorber requires thickness higher than 3 cm, while a thinner absorber is only effective at 1 kHz and above. Additionally, it is confirmed that the Delany-Bazley formulation fails to predict associated absorption behavior of pineapple leaf fibre-based absorber. Meanwhile, a modified Delany-Bazley model discussed in this paper is more useful. It is expected that the model can assist further development of the pineapple leaf composite sound absorber.