Low temperature chilling damage is one of the most serious disasters in maize production, which is a typical non-linear complex issue with numerous influencing factors and strong uncertainty. How to predict it is not only a hot theoretical research topic, but also an urgent practical problem to be solved. However, most of the current researches are focusing on post-disaster static descriptive assessment rather than pre-disaster dynamic predictive analysis, resulting in the problems such as no indicative result and low accuracy. In this study, the satisfaction rate of environmental accumulated temperature for maize production was used to measure the chilling damage risk, and a model for maize chilling damage risk prediction based on probabilistic neural network was constructed. The model was composed of input layer, pattern layer, summation layer and output layer. The obtained results showed that the prediction accuracy for the most serious risk level was as high as 0.91, and the rates of the Type I Error and Type II Error made by the model were 0.1 and 0.09, respectively. This indicated that the model employed was promising with good performance. The results of this research are of both theoretical significance for providing a new reference method of pre-disaster prediction to study maize chilling disaster risk and practical significance for reducing maize production risk and ensuring yield safety.

The quality of course teaching is directly related to education quality. Many scholars have attempted to identify the associations between course-teaching quality and teachers' characteristics, such as educational background, degree, professional title, age, teaching age, job burnout, and academic research. However, because these characteristics are mostly evolvable, research findings are inconsistent. Therefore, we attempted to identify the association between teaching styles that reflect teachers' stable psychological quality, Technological Pedagogical Content Knowledge (TPACK), and teaching quality. To this end, we first collected data from three different disciplines at a university using the constructed teaching quality, TPACK, and course difficulty questionnaires, together with the TSTI scale proposed by Grigorenko and Sternberg. We constructed three matrices with different sparsities as experimental datasets using teachers with the teaching style and PTACK attributes, courses with the course difficulty attribute, and teaching quality. We then constructed a weighted bipartite graph with the teachers and courses in the matrix as nodes and the teaching quality divided by course difficulty as the weights of the edges. We proposed an improved Slope One algorithm based on a weighted bipartite graph to scientifically predict teachers' teaching quality in untaught courses. Finally, we constructed a TOP-N recommendation model for course teachers that combined teaching style and TPACK features to achieve accurate recommendations for course teachers. The experiments show that our proposed solution is feasible and that the algorithmic model is effective. Therefore, we developed a scientific method to improve the quality of university course teaching.

Accurate traffic speed prediction is necessary to promote the development of intelligent transportation systems. The construction of consummate models is challenging owing to nonlinearity, nonstationarity, and long-term dependence in traffic speed prediction. This study proposed an ensemble long short-term memory (LSTM) model that was based on adaptive weighting, in which ensemble learning was the main solution. First, a data preprocessing model based on a seasonal statistical model was introduced to reconcile the long- and short-term dependence of the data. Second, the LSTM time step was considered during training, and a classification-type loss was designed to calculate the error rate in the ensemble system. Last, an adaptive weighting strategy was constructed to integrate a series of LSTMs generated in the system to obtain an ensemble model for traffic speed prediction. The experimental results showed that the proposed method was more stable and accurate than individual methods and existing ensemble learning methods.

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