The aim of the study was to provide a comprehensive overview of global long-distance road transportation of industrial roundwood. The study focused on the maximum gross vehicle weight (GVW) limits allowed with different timber truck configurations, typical payloads in timber trucking, the road transportation share of the total industrial roundwood long-distance transportation volume, and the average long-distance transportation distances and costs of industrial roundwood. The study was carried out as a questionnaire survey. The questionnaire was sent to timber transportation logistics experts and research scientists in the 30 countries with the largest industrial roundwood removals in Europe, as well as selected major forestry countries in the world (Argentina, Australia, Brazil, Canada, Chile, China, Japan, New Zealand, South Africa, Türkiye, the United States of America and Uruguay) in February 2022, and closed in May 2022. A total of 31 countries took part in the survey. The survey illustrated that timber trucking was the main long-distance transportation method of industrial roundwood in almost every country surveyed. Road transportation averaged 89% of the total industrial roundwood long-distance transportation volume. Timber truck configurations of 4 to 9 axles with GVW limits of around 30 tonnes to over 70 tonnes were most commonly used. The results indicated that higher GVW limits allowed significantly higher payloads in timber trucking, with the lowest payloads at less than 25 tonnes, and the highest payloads more than 45 tonnes. The average road transportation distance with industrial roundwood was 128 km, and the average long-distance transportation cost in timber trucking was €11.1 per tonne of timber transported. In the entire survey material, there was a direct relationship between transportation distance and transportation costs and an inverse relationship between maximum GVW limits and transportation costs. Consequently, in order to reduce transportation costs, it is essential to maximise payloads (within legal limits) and minimise haul distances. Several measures to increase cost- and energy-efficiency, and to reduce greenhouse gas emissions in road transportation logistics, are discussed in the paper. On the basis of the survey, it is recommended that up-to-date statistical data and novel research studies on the long-distance transportation of industrial roundwood be conducted in some countries in the future.

Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.

The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, students, and classrooms. Although various algorithms have been applied to solve the UCSP, most of the existing methods are limited to scheduling independent courses, neglecting the impact of joint courses on the overall scheduling results. To address this limitation, this paper proposed an innovative mixed-integer linear programming model capable of handling the complex constraints of both joint and independent courses simultaneously. To improve the computational efficiency and solution quality, a hybrid method combining a genetic algorithm and dynamic programming, named POGA-DP, was designed. Compared to the traditional algorithms, POGA-DP introduced exchange operations based on a judgment mechanism and mutation operations with a forced repair mechanism to effectively avoid local optima. Additionally, by incorporating a greedy algorithm for classroom allocation, the utilization of classroom resources was further enhanced. To verify the performance of the new method, this study not only tested it on real UCSP instances at Beijing Forestry University but also conducted comparative experiments with several classic algorithms, including a traditional GA, Ant Colony Optimization (ACO), the Producer–Scrounger Method (PSM), and particle swarm optimization (PSO). The results showed that POGA-DP improved the scheduling quality by 46.99% compared to that of the traditional GA and reduced classroom usage by up to 29.27%. Furthermore, POGA-DP increased the classroom utilization by 0.989% compared to that with the traditional GA and demonstrated an outstanding performance in solving joint course scheduling problems. This study also analyzed the stability of the scheduling results, revealing that POGA-DP maintained a high level of consistency in scheduling across adjacent weeks, proving its feasibility and stability in practical applications. In conclusion, POGA-DP outperformed the existing algorithms in the UCSP, making it particularly suitable for efficient scheduling under complex constraints.

The double V-wing honeycomb can be applied in many fields because of its lower mass and higher performance. In this study, the volume, in-plane elastic modulus and unit cell area of the double V-wing honeycomb were analytically derived, which became parts of the theoretical basis of the novel equivalent method. Based on mass, plateau load, in-plane elastic modulus, compression strain and energy absorption of the double V-wing honeycomb, a novel equivalent method mapping relationship between the thickness–width ratio and the basic parameters was established. The various size factor of the equivalent honeycomb model was denoted as n and constructed by the explicit finite element analysis method. The mechanical properties and energy absorption performance for equivalent honeycombs were investigated and compared with hexagonal honeycombs under dynamic impact. Numerical results showed a well coincidence for each honeycomb under dynamic impact before 0.009 s. Honeycombs with the same thickness–width ratio had similar mechanical properties and energy absorption characteristics. The equivalent method was verified by theoretical analysis, finite element analysis and experimental testing. Equivalent honeycombs exceeded the initial honeycomb in performance efficiency. Improvement of performance and weight loss reached 173.9% and 13.3% to the initial honeycomb. The double V-wing honeycomb possessed stronger impact resistance and better load-bearing capacity than the hexagonal honeycomb under impact in this study. The equivalent method could be applied to select the optimum honeycomb based on requirements and improve the efficiency of the double V-wing honeycomb.

The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.