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Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

The quality detection of alfalfa hay is crucial for the development of animal husbandry. In this study, a method for quality detection of alfalfa hay based on the fusion of multisource information including near-infrared spectroscopy, image processing techniques, and electronic nose is proposed. After SG convolution smoothing, feature wavelengths were extracted using Competitive Adaptive Re-weighting Scheme and Successive Projections Algorithm from the spectral data. The image data were denoised using adaptive wavelet thresholding, and color and texture features were extracted using color histograms and random forest algorithms, respectively. Electronic nose data using principal component analysis was used for data dimensionality reduction. Support Vector Machine, Extreme Learning Machine, and Multi-Layer Perceptron were employed to establish quality detection models of alfalfa hay based on spectroscopy, image, gas information, and their combination, respectively. Experimental results demonstrate that the fusion of near-infrared spectroscopy, image data, and gas information effectively enhances the classification accuracy of the model. The accuracy of the test set reaches 100%, with root mean square error and determination coefficient values of 0.1728 and 0.9239, respectively, surpassing prediction models established solely on individual information. This study provides new insights into alfalfa hay quality detection.

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Modeling of mechanical properties of wood-polymer composites with Artificial Neural Networks

Mechanical properties (tensile strength (TS), modulus of elasticity in tensile (MET), flexural strength (FS), modulus of elasticity (MOE)) of the material to be obtained depending on the production parameters in the production of high-density polyethylene (HDPE) wood-polymer composites with Scots pine wood flour additive were predicted using Artificial Neural Networks (ANN) model and without destructive testing. In the first stage of the study, an ANN model was developed using data from 56 different studies in the literature on the mechanical properties of wood polymer composites. In the second stage, in order to determine the reliability of the model, output values were estimated using input parameters that had not been used in training and testing of the model. Based on the same input parameters, test specimens were produced and mechanical tests were performed. The results obtained from the experiments and ANN model were compared by considering the mean absolute percentage error (MAPE) value. The coefficient of determination (R2) values obtained in the training and testing phase of the ANN models were all higher than 0.90. In this way, the mechanical properties of the wood polymer composite were successfully predicted by the ANN model. Because most of the MAPE values obtained from the mechanical tests were below 10%, the model was considered a reliable model.

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Morpho-physiological traces of exogenous biogenic iron oxide nanoparticles in basil seedlings

Chemical fertilizers used in plant development and differentiation have become a global problem affecting the entire ecosystem, especially soil pollution. Food production demand with the increasing population has encouraged scientists to use biogenic nanoparticles in the agricultural field. Evaluation of growth, development, and differentiation processes of sweet basil (Ocimum basilicum L.) seedlings at gradually increasing concentrations of biogenic iron oxide nanoparticles (BIO-NPs) were identified by morphological and physiological parameters in this study. The results showed that growth parameters reached the maximum value at 100 mg/L but were less at other concentrations. At similar concentration, the stomatal density of the leaf was the maximum, while the stomatal area showed the lowest value. The levels of H2O2 and malondialdehyde (MDA) decreased in the treated seedlings. BIO-NPs increased the antioxidant defense and supported its growth by changing the antioxidant enzyme activities, H2O2, and MDA contents. The BIO-NP treatment provided positive improvements in phytochemical content in parallel with the growth and development of sweet basil seedlings. Different growth parameters, physiological results, supporting enzyme activities, and biochemical data revealed the contribution of the BIO-NP treatments to the growth and development of sweet basil seedlings. BIO-NPs improved higher phytochemical production of sweet basil, which may be suitable for its propagation on a commercial scale.

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Green synthesis of silver nanoparticles using leaf extract of Calluna vulgaris: Characterizations, properties, and photocatalytic activities

Green synthesis of silver nanoparticles was carried out using leaf extract from Calluna vulgaris. The formation of nanoparticles was confirmed through the emergence of a surface plasmon resonance band in ultraviolet-visible spectroscopy. The characterization conducted using various microscopic techniques revealed that the nanoparticles mostly ranged in size from approximately 20 to 70 nm. Analysis, including Fourier transform infrared spectrometry, X-ray diffraction, and energy-dispersive X-ray spectroscopy, confirmed the chemical, crystalline structure, and presence of silver, respectively. The synthesized nanoparticles exhibited notable stability with an average zeta potential of -23.1 ± 0.6 mV. Evaluation of their antibacterial activity against Staphylococcus aureus and Escherichia coli demonstrated significant efficacy with diameters of inhibition zones measuring 10.23 ± 0.54 mm and 15.38 ± 0.32 mm, respectively. Additionally, the nanoparticles displayed a remarkable inhibition of approximately 88% against E. coli biofilm formation at a concentration of 100 μg/mL. They also exhibited unique photocatalytic performances. This research contributes to the literature in this field by producing new silver nanoparticles with cost-effectiveness, stability, antibacterial, antioxidant, antibiofilm, and photocatalytic properties, while using a previously untapped plant extract for this purpose.

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