The main objective of this work was to estimate the usefulness of response surface methodology (RSM) and genetic algorithm (GA) in modeling and predicting the strength of jute/poly(lactic acid) (PLA) composite laminates. Firstly, the impact of the hot water treatment on the thermal, molecular structure, and morphological characterizations as well as kinetic analysis of jute fibers were performed by Thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM). The FTIR results showed that the hot water treatments reduced the content of lignin, hemicellulose, and impurities of the fibers. Hot water treated fibers exhibited higher thermal stabilities compared with untreated fibers as shown with TGA results. The SEM results showed effect contact surface area improved after the hot water treatment, leading to better mechanical properties of bio‐composites. Secondly, the influences of the ply orientation, plies and fiber content on the tensile strength and flexural strength of the hot water treated fibers composites were evaluated by RSM. RSM with the Box Behnken Design was utilized to establish the quadratic models of two objectives in response to input parameters. Analysis of variance (ANOVA) was used to determine percentage contribution of various parameters on two quality objectives. According to the ANOVA results, ply orientation had the most important impact on tensile strength while the ply orientation‐fiber content interaction had the most important impact on flexural strength. Finally, multi‐objective optimization was carried out to maximize tensile strength and flexural strength using the desirability function and GA. The optimized results both showed an acceptable relative error between experimental and optimized values.

Fusarium graminearum is the main pathogen of Fusarium head blight (FHB), which causes huge economic losses every year. In this study, an attempt was made to control FHB from the point of view of the physiological behavior of the pathogen itself. Autophagic inhibitors and activators were used, and the pathogenicity-related indices of F. graminearum were measured. The results showed that under nitrogen-rich conditions, macroautophagy inhibition and activation greatly reduced the mycelium weight to 0.28 and 0.25 g/mL at 24 h, which were 17.82% and 24.77% lower than that of the control treatment, respectively. Mitophagy inhibition also significantly decreased the mycelium weight (P<0.05). Conidial yield was found to be affected by factors related to autophagy occurrence. It was found that both autophagy inhibition and activation could reduce the conidiation of F. graminearum. The toxin contents in wheat medium of macroautophagy activation treatments were 0.678, 0.190, 0.402 and 0.195 μg/g when cultured for 8 and 24 h under 0% N and 100% N conditions, respectively, which were significantly higher than those of the control treatments (P<0.05). The infection length was measured to characterize the infectivity of F. graminearum, and we found that the length was short under macroautophagy activation conditions. However, mitophagy did not seem to affect the infectivity of F. graminearum. In summary, the above results indicated that macroautophagy and mitophagy inhibition could reduce the pathogenicity of F. graminearum, which would provide a new perspective for management of plant fungal diseases.

Seeds from Cassia obtusifolia L., i.e. Semen cassiae (SC) were evaluated as environmentally friendly filler particles for artificial turf. The goal was to avoid unwanted germination problems of SC under wet conditions. This work evaluated the influence of different pretreatments on the germination rate and performance of SC. After the combination of polishing and dry heat (90 °C, 72 h) treatment, the germination rate of SC decreased to 0% and the activation energy increased to 216 kJ/mol. Compared with the untreated SC, the thermal stability of SC improved, with an initial degradation temperature of 214 °C and a pyrolysis residue of 31.9%. Additionally, the resilience and the water absorption of SC as a filler material increased to 4.13% and 169%, respectively. This study provides an effective pretreatment method for the solution of germination problem and the performance improvement of SC. This makes the pretreated SC a prospective candidate as an environmentally protective granular filling material.

Wood plastic composites (WPCs) were prepared by extrusion molding with eucalyptus powder, polyvinyl chloride (PVC), and silica as additives. The mechanical properties, creep behavior, thermal properties, and cross-section microstructure of the composites were analyzed by universal testing machine, thermogravimetric analyzer, and scanning electron microscope. The results show that with the increase of silica content, the tensile strength, bending strength, and impact strength of the WPCs first increased and then decreased. When the silica content was 3.0%, the tensile strength, bending strength, and impact strength of WPC reached the maximum values of 27.5 MPa, 48.8 MPa, and 4.18 KJ·m-2, respectively, which represented increases of 12.6%, 9.4%, and 20.1%, respectively, compared with those without silica. When the stress was 13.4 MPa, the strain value of 3.0% SiO2-eucalyptus/PVC wood plastic composite was 3.3 times that of 4.46 MPa and 1.7 times that of 8.92 MPa. The pyrolysis process of eucalyptus/PVC WPCs showed a similar trend with different silica content.

With the exponential growth of the computing power, machine learning techniques have been successfully used in various applications. This paper intended to predict and optimize the shear strength of single lap cassava starchbased adhesive joints for comparison with the application of artificial intelligence (AI) methods. The shear strength was firstly determined by the experiment with three independent experimental variables (starch content, NaOH concentration and reaction temperature). The analysis of range (ANORA) and analysis of variance (ANOVA) were applied to investigate the optimal combination and the significance of each factor for the shear strength based on the orthogonal experiment. The performance of all AI models was characterized by mean absolute error (MAE), root mean square error (RMSE) and regression coefficient (R 2 ) compared with the experimental ones. The GA-optimized ANN model was combined with the genetic algorithm (GA) to find the optimal combination of factors for the finalized optimized cassava starch adhesives (CSA-OP). The physicochemical properties of the cassava starch and CSA-OP were determined by the FTIR, TGA and SEM-EDS, respectively. The results showed that the numerical optimized condition of the GA-optimized ANN model was superior to the orthogonal experimental optimized condition. The sensitivity analysis revealed that the relative importance of variables was consistent with the results from ANOVA. FTIR results showed that there were high hydroxyl groups in cassava starch. TGA results showed that the residue of CSA-OP was higher than the cassava starch. SEM-EDS results showed that both the cassava starch and CSA-OP had abundant carbon and oxygen functional groups. Consequently, the obtained results revealed that the use of AI methods was an adequate approach to model and optimize the experimental variables of the shear strength of single lap cassava starch-based adhesive joints.