In this study, the influence of sewage sludge ash (SSA) waste particle contents on the mechanical properties and interlaminar fracture toughness for mode I and mode II delamination of S-glass fiber-reinforced epoxy composites was investigated. Composite laminate specimens for tensile, flexural double-cantilever beam (DCB), and end-notched fracture (ENF) tests were prepared and tested according to ASTM standards with 5, 10, 15, and 20 wt% SSA-filled S-glass/epoxy composites. Property improvement reasons were explained based on optical and scanning electron microscopy. The highest improvement in tensile and flexural strength was obtained with a 10 wt% content of SSA. The highest mode I and mode II interlaminar fracture toughness’s were obtained with 15 wt% content of SSA. The mode I and mode II interlaminar fracture toughness improved by 33% and 63.6%, respectively, compared to the composite without SSA.

The nylon 66/nano-CaCO3 composites were prepared by melt blending on a twin-screw extruder. Scanning electron microscopy (SEM), polarized light microscopy (PLM), thermal loss (TGA) and differential scanning calorimetry (DSC) The effects of nanometer calcium carbonate on the polycrystalline behavior and thermal properties of nylon 66/nano CaCO3 composites were investigated. The results show that the nanometer calcium carbonate particles are dispersed in the nylon 66 matrix and exist in the form of aggregates. The nanometer calcium carbonate has the effect of heterogeneous nucleation, which can reduce the size of the spherules. The decomposition temperature of the nylon 66 is 400 ℃, the addition of nano-CaCO3 to reduce the decomposition temperature. At the same time, DSC test showed that the β-crystalline form in the material reduced the melting temperature of the material. The addition of nano-CaCO3 in the nylon 66 matrix resulted in the decrease of the crystallization temperature and the increase of the half-height width of the endothermic peak. The lower the crystallization temperature, the wider the crystallization temperature range.

Yunnan is rich in cultural heritage, with its primitive pottery techniques coexisting with modern pottery techniques, and is known as the “Museum of Ceramic History”. Due to regional and socio-economic development factors, some folk pottery and craftsmen have faded out of sight or only circulated in a few small areas and specific environments. The study analyzes and summarizes the characteristics of Yunnan folk pottery and industry and evaluates the Yunnan folk pottery value based on the conditional valuation method. The study takes the folk pottery of the Bai nationality in Dali, Yunnan as an example and obtains the evaluation results of the purchasing motivation value of the pottery through a questionnaire survey. 45.26% of people pay for their existence value, 26.03% pay for their choice value, and 28.71% pay for their legacy value. Based on the evaluation results, the study proposes targeted activation paths for Yunnan folk pottery, including innovative development combined with new technologies, highlighting the functional characteristics of pottery, and brand building. This study will help Yunnan folk pottery find more suitable ways of protection and inheritance in the rapid development of materials and technology. This study can help inheritors gain the possibility of sustainable development and provide reference value for the activation path of other traditional folk.

This research focused on the design and implementation of the flipped classroom approach for higher mathematics courses in medical colleges. Out of 120 students, 60 were assigned to the experimental group and 60 to the control group. In the continuous assessment, which included homework and quizzes, the average score of the experimental group was 85.5 ± 5.5, while that of the control group was 75.2 ± 8.1 (P < 0.05). For the final examination, the average score in the experimental group was 88.3 ± 6.2, compared to 78.1 ± 7.3 in the control group (P < 0.01). The participation rate of students in the experimental group was 80.5%, significantly higher than the 50.3% in the control group (P < 0.001). Regarding autonomous learning ability, the experimental group spent an average of 3.2 hours per week on self-study, compared to 1.5 hours in the control group (P < 0.005). Other potential evaluation indicators could involve the percentage of students achieving high scores (90% or above) in problem-solving tasks (25.8% in the experimental group vs. 10.3% in the control group, P < 0.05), and the improvement in retention of key concepts after one month (70.2% in the experimental group vs. 40.5% in the control group, P < 0.01). In conclusion, the flipped classroom approach holds substantial promise in elevating the learning efficacy of higher mathematics courses within medical colleges, offering valuable insights for educational innovation and improvement.

The objective of this work was to evaluate the effect of potassium concentrations applied via fertigation on the growth, yield and chemical composition of eggplant ‘Ciça’ in a distroferric red Latosol. The treatments were composed of five concentrations of K₂O (0, 36, 72, 108 and 144 kg ha^-1 supplied via fertigation), using potassium chloride as a source, divided into six applications. The irrigation system was of the drip type and irrigation management was done via a “Class A” evaporometer tank. Harvest started at 62 days after transplanting (DAT) and lasted for five months. The variables evaluated were: plant height, number of leaves, fresh fruit mass, number of fruits per plant, yield per plant, productivity and classification of the fruits according to their length and diameter. At 85 DAT, fruit were collected for characterization as to the percentage of lipids, proteins and fibers. Although the potassium fertigation in cover provided a reduction in the production and productivity, the concentrations of 36 kg ha^-1 and 72 kg ha^-1 of K₂O applied via fertigation, increased the physical-chemical characteristics of the fruits.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.