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The current research aims to predict and optimize process conditions to produce germinated VD20 with a high rate of germination and antioxidant properties. Box-Behnken design (BBD) was used to build models with three factors [soaking time (ST: 4–6 h), germination time (GT: 18–22 h), and germination temperature (33–37 °C)] and three replications. The data set from the BBD experiment was used to create an artificial neural network (ANN) model that estimated the change in responses by variable factors. The ANN model was extremely accurate, with an overall correlation coefficient (R) of 0.9997 and showed the best fit with actual and predicted data. The germination conditions were further optimized using multi-objective RSM, which revealed that the optimal conditions were ST of 5.34 h, GT of 20.78 h, and germination temperature of 35.6 °C. The experimental validation revealed a high level of agreement between the results of the BBD models forecasted and the actual experimental values. The ANN-coupled BBD methodology is a promising hybrid method for modeling, forecasting, and optimizing the impact of process conditions on the quality of germinated grain. In addition, the raw and germinated VD20 rice were analyzed for their techno-functional properties, estimated glycemic index (eGI), and FTIR. Lower peak viscosity, values of breakdown and setback, and phytic acid were found after rice was germinated. The result revealed that high antioxidant content and activity, which were confirmed by the FTIR pattern, and low digestion behaviors (eGI = 64.23) were the attributes of the germinated product. Furthermore, the results of pasting, thermal, swelling power, and solubility showed the wide range of further application of this material, which should receive more consideration in future research.

Cam mechanisms are used as the control system in several industrial fields such as filling and canning, textiles, automotive. This paper introduces the development of a software for designing planar cam mechanism adapting the dynamic and kinematic requirements. The designed cam profile is then changed into numerical data used to generate the machine codes served for CNC-based fabrication. Traditional cam design is very complicated and has accumulated errors. The envelope theory and inverse method are utilized to perform constraint in pressure angle and motion. The smallest cam radius is then determined in a non-linear optimization adapting the constraints. The paper also presents the design of a CNC machine for cam profile fabrication. The design includes the control system and the circuit board. The CNC control program is built to receive numerical data from cam design section, to simulate the cam profile fabrication and then to fabricate. The optimal design and fabrication are applied to the cam mechanisms with knife-edge/roller reciprocating/oscilating followers. The verification shows that the designed and fabricated cam adapts the requirement of accuracy.

To study the boundedness of the acoustic field in Lp(ℝ+;dx) $L_{p}(\mathbb {R}_{+} ; dx)$and its asymptotic behavior, we introduce a new generalized convolution for the Kontorovich-Lebedev ( Kℒ $\mathcal {K}\mathcal {L}$) transform and the Fourier transforms, and a new representation of the acoustic field via this generalized convolution. Some properties of the new generalized convolution are obtained. Moreover, an analog of Watson’s theorem is established, in which we obtain the necessary and sufficient conditions for a class of generalized convolution transforms to be isomorphism-isometric.

This study clarifies the reaction pathway for biomass pyrolysis to phenolic compounds and aromatic hydrocarbons by analyzing 20 biomass samples using a central composite design and response surface plot. The pyrolysis reaction was performed using two integrated fixed-bed reactors. The product yield was significantly affected by pyrolysis temperature when utilizing the thermal cracking, repolymerization, and Boudouard reaction pathways. The bio-oil used in the study was obtained from the volatile matter fraction, where the fixed carbon content strongly affected biochar yield. Regarding bio-oil composition, both pyrolysis temperature and biomass constituents, including hemicellulose and lignin, significantly affected the yield of phenolics and aromatic hydrocarbons. In addition, biomass pyrolysis reaction pathways toward phenolic compounds and aromatic hydrocarbons were proposed.