Previous studies on visual effects primarily focus on evaluating the overall urban environment, lacking specific research on historical districts within cities. In order to evaluate the visual effects of plantscapes in historic districts, street view images and machine learning methods were used. The ResNeSt model was selected to assess the coordination and health of plantscapes. The results show that the ResNeSt model performs best in classification and regression tasks. Its scores are consistent with expert evaluations and moderately to highly correlated with public evaluations. Additionally, the visual effects of plantscapes are significantly influenced by economic factors, with the visual effect scores of streets outside the historic districts generally higher than those inside. It is concluded that machine learning models are highly effective in evaluating the visual effects of plantscapes in historic districts. This provides a scientific basis for their protection and optimization, with important implications for urban planning and tourism.

A hybrid algorithm (IWOA-BP) combining the improved whale optimization algorithm (IWOA) and backpropagation neural network (BP) was proposed to offer theoretical support for the formulation of grain strategies in the agriculture sector and its related industries. By introducing an improved convergence factor, nonlinear inertia weight, and optimal neighborhood disturbance strategy into the modified whale optimization algorithm, the optimal solution of the algorithm was obtained. This solution was then utilized as the initial weights and thresholds of the BP neural network, thereby enhancing the convergence speed and accuracy of the IWOA-BP hybrid algorithm. Subsequently, a grain yield prediction model based on the improved whale optimization algorithm was established using data from China’s grain yield over 45 years and seven influencing factors including effective irrigation area, chemical fertilizer application, rural electricity consumption, total power of agricultural machinery, sowing area of grain crops, disaster-affected area, and per capita consumption expenditure in rural areas. Through extensive experiments on a test set, it was found that the IWOA-BP model consistently outperformed other prediction models such as long short-term memory (LSTM), extreme learning machine (ELM), BP neural network with whale optimization algorithm (WOA-BP), and BP neural network with particle swarm optimization (PSO-BP). Compared to the ELM model, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the IWOA-BP model were reduced by 77.12% and 88.18% respectively. When compared to the LSTM model, the RMSE and MAPE of the IWOA-BP model were reduced by 69.11% and 47.36% respectively. Furthermore, in comparison to the WOA-BP model, the mean absolute error (MAE), RMSE, and MAPE of the IWOA-BP model were reduced by 43.78%, 43.22% and 45.96% respectively. Additionally, when compared to the PSO-BP model, the MAE, RMSE, and MAPE of the IWOA-BP model were reduced by 89.67%, 90.61% and 90.82% respectively. Therefore, the proposed IWOA-BP prediction model can be effectively used to predict grain yield due to its higher coefficient of determination, smaller prediction error, and faster convergence speed. It has important technical reference value for agricultural departments and relevant policymakers.

Wall-mounted furnace heating is one of the main ways of winter heating in northern rural areas. Due to the pressure fluctuation of the gas pipeline, the wall-mounted furnace is prone to combustion instability, and even CO poisoning accidents. In order to reveal the variation law of temperature field and combustion products in gas-fired wall-mounted furnace under the change of pipeline pressure, taking the fire row burner as the research object, the distribution of gas composition at the outlet of the ejector device, the temperature in the combustion chamber, and the concentration of CO and NO in the flue gas at the outlet of the combustion chamber under different pipeline pressures were studied by numerical simulation and experiment. The results show that. ① With the decrease of the gas inlet pressure, the methane concentration at the outlet of the fire increases, and the non-uniformity of methane concentration increases. ② As the gas inlet pressure decreases, the CO mass concentration at the outlet of the combustion chamber gradually increases. When the gas inlet pressure is 500 Pa, the CO mass concentration reaches a peak of 25.2 mg/m³, which is higher than the human body CO poisoning accident limit of 23 mg/m³. The mass concentration of NO at the outlet of the combustion chamber increases first and then decreases, reaching a peak of 18.99 mg/m³ at 1 500 Pa. ③ As the gas inlet pressure decreases, the maximum temperature in the combustion chamber increases first and then decreases. The minimum temperature is 1 840 K at 500 Pa. The combustion is not sufficient and the heat generated by combustion is less. It can be seen that the decrease of the pipeline pressure increases the instability of the wall-mounted furnace combustion and the CO concentration also increases significantly. The results can provide some theoretical support for the manufacturers of gas wall-mounted furnaces in enhancing the safety of equipment.

Research on the factors affecting soil organic carbon density is of great significance for regulating climate change and sustainable agricultural development. Previous studies have mainly explored the relationship between various factors (e.g., climate, altitude, soil physicochemical properties, etc.) and the influence of soil organic carbon density, but less involved in the interaction relationship between factors. Typical soil profiles were collected in Anhui Province to estimate the soil organic carbon density (SOCD) in the 0~10 cm, 10~20 cm, 20~30 cm and 30~100 cm soil horizons. The structural equation model was used to analyze the effects of climate, elevation, vegetation, soil water content, human activities and other environmental factors on SOCD. The results are as follows. In the 0~30 cm soil layer, SOCD show a gradually decreasing trend, and the average SOCD in the 0~10 cm, 10~20 cm and 20~30 cm soil layers were 2.09, 1.63 and 1.10 kg/m², respectively. The average SOCD of 30~100 cm soil layer is 4.46 kg/m². The spatial distribution of SOCD in the province gradually increased from north to south. The SOCD of 0~10 cm and 10~20 cm soil layer is higher than 5.00 kg/m^-2, mainly distributed in the Jianghuai hilly downland and the Riverine Plain. The areas with SOCD higher than 3.00 kg/m² in the 20~30 cm soil layer were distributed in the South Anhui hilly region. The high SOCD values of 30~100 cm are mainly distributed in the South Anhui hilly region. In the structural equation model of 0~10 cm, 10~20 cm and 20~30 cm soil layer, land use has the largest positive influence on SOCD, and the influence coefficients are 0.22, 0.20 and 0.22, respectively. The average annual temperature has the largest negative influence on SOCD, and the influence coefficients are -0.04 and -0.03. Annual rainfall was the most significant in 30~100 cm soil layer, but land use and NDVI were not significantly affected (p>0.05). Topography affects SOCD through four paths: land use, NDVI, annual precipitation and average annual temperature. Human footprint affected SOCD through NDVI, and the effect on NDVI reached a very significant level (p<0.001). The structural equation model established in this study initially explained the relationship between different environmental factors, and provid a theoretical basis for SOCD regulation and agricultural sustainable development.

Due to the increasing scarcity of precious woods and the severe environmental issues caused by overexploitation, it is necessary to mimic the appearance of precious woods by dyeing ordinary wood. Computer-assisted dyeing technology was utilized to achieve high-precision dyeing of ordinary wood, thus creating substitutes that resemble precious woods and reducing dependence on them. Initially, based on the concept of gene expression programming (GEP), a multi-expression programming (MEP) algorithm was proposed to predict dye ratios. Considering the complex interactions among various dyes, multi-gene expression was employed. The MEP algorithm can handle these complex interactions between multiple dyes, resulting in more intuitive functional expressions. To enhance the function mining accuracy of MEP, the probabilities of mutation and recombination operators ware adaptively adjusted, and parallel programming was employed to boost function mining efficiency. Compared to gene expression programming results, MEP delves deeper into functional relationships and achieves a relative deviation of 0.113 in color prediction.

Since 2019, the Jiubaoyan landslide has exhibited continuous and gradual deformation. On September 17, 2021, during the rainy season, the landslide was obviously deformed and slipped due to the continuous heavy rainfall. On the basis of traditional engineering geological exploration methods such as on-site investigation, drilling and displacement monitoring, the finite element simulation method Midas GTS was utilized to simulate and calculate the seepage and displacement field of the slope under different working conditions, the landslide formation mechanism was comprehensively analyzed. Furthermore, the Fast GPU Matrix computing of discrete element method (MatDEM) was introduced to forecast the trend of the landslide sliding evolution under rainstorm working conditions. The results indicate these as follows. ① The finite element numerical simulation results are consistent with the drilling results, revealing that the sliding zone of Jiubaoyan landslide is located at the interface between the quaternary landslide accumulation layer gravel soil (${Q}_{4}^{del}$) and the mudstone of the Jurassic Suining Formation (${J}_{2}^{sn}$); ② Both finite element numerical simulation and on-site investigation suggest that Jiubaoyan landslide is a multi-level shallow soil landslide mainly driven by push forces and secondarily propelled by traction forces. The sliding mass is divided into upper and lower parts, which are closely interconnected. The loose soil structure in the landslide area as well as the abundant water storage ahead and behind the slope serves as material source for the landslide formation and annual heavy rainfall during the rainy season acts as critical external triggers for landslides.③ The finite element numerical simulation results reveal that Jiubaoyan landslide remains relatively stable under the natural conditions but transitions to an unstable state under the rainfall conditions. It is possible to lead to large displacement landslide under the persistent extreme rainstorms. ④ The discrete element simulation results suggest that the slope is in an destabilized state under extreme rainfall conditions, with two parts of sliding mass are penetrated through two sliding zones. This further landslide instability may culminate in significant displacement landslide, resulting in considerable economic and human losses. ⑤ The research method combining finite element analysis with discrete element analysis not only corroborates on-site investigation, drilling, and monitoring, but also enables a quantitatively analysis of the formation mechanism and prediction of potential working conditions in the future. It is hoped that such a study can offer some useful references for studying similar multi-level landslide disasters in mountainous regions.

The selection of variables affecting fuel consumption in the existing studies usually has no clear criteria, and it is difficult to combine the research results with actual flight. The flight training data of a Cessna 172 was used to predict the fuel consumption during the airborne phase of general aviation trainer aircraft. Firstly, based on the authors’ flight experience as well as correlation analysis, the features that influence fuel flow rate were selected from the pilot’s operational perspective. Secondly, a regression tree model was used to predict fuel flow rate under different flight conditions, correlating the aircraft’s actual flight status with the predicted fuel flow rate, in order to facilitate subsequent research on specific fuel-saving strategies from the flight technique perspective. Finally, a random forest model optimized with hyperparameter tuning was used to predict the fuel flow rate. The experimental results show that the accuracy of the model used is better than that of the existing research results, with a mean absolute error of 0.286 gallon/h, a root mean squared error of 0.496 gallon/h, a residual sum of squares of 0.968 4, and a mean absolute percentage error of 4.00%.

To achieve safe and efficient replacement of stay cables that exceeded their designed service life or suffer serious damage, a stay-cable replacement system (SRS) was proposed which used the existing cable-stayed anchor plate (ECAP) as a reaction force system. The scaled model of the typical main girder segment and the full-scale model of the SRS were designed and produced. The axial compression and eccentric compression static tests were carried out under the conditions of dry contact and adhesive contact between the steel anchor barrel (SAB) of SRS and the ECAP. The results show that, under dry contact conditions, the failure mode of the SAB is the buckling of the contact point between the eccentric compression side of the SAB end and the ECAP. Under adhesive contact conditions, the failure mode of the SAB is the buckling at the interface between the eccentric compression side of the SAB and the epoxy resin glue. After pouring of the epoxy resin glue, the displacement and maximum compressive stress of the SAB are smaller, and the compressive safety and stability of the SAB are better. The proposed SRS can be applied in stay-cable replacement.

With the continuous development of container cloud technology, it is of great significance to predict and analyze the overall trend and peak of cloud resource requests for efficient utilization and reasonable allocation of container cloud resources. Deep learning technology for load prediction has become a key technology to solve the unbalanced utilization of container cloud resources. Aiming at the problems of low prediction accuracy and insufficient capture sequence features existing in the current single model and combination model of load prediction, a cloud resource combination prediction model based on temporal convolutional network-long short-term memory(TCN-LSTM)was proposed. The hollow convolution in the combination model increased the sensitivity field without reducing the feature size to obtain longer time series features. The residual network could transfer information across layers to accelerate the convergence of the network, and the obtained time series features could effectively improve the prediction accuracy of LSTM. Useing Alibaba’s publicly available dataset to make predictions, the experiment shows that the proposed model is compared with the single prediction model and other combined models, and the error index-mean absolute error(MAE) is reduced by 8%~13.7% and root mean squared error (RMSE) by 9.8%~13.1%, which proves the effectiveness of the proposed model.

Controlled-source audio-frequency magnetotellurics (CSAMT) uses artificial sources, providing strong anti-interference capabilities. It is widely used in oil exploration, mineral surveys and other areas. Traditional 2D inversion technology is mature, and deep learning has recently made some research advancements in geophysical exploration. There is still a research gap in applying deep learning to CSAMT inversion. Therefore, developing a 2D inversion algorithm for CSAMT based on deep learning is highly significant for advancing the use of deep learning in electromagnetic exploration. The characteristics of deep learning components such as convolutional layers, pooling layers, fully connected layers, and the UNet network were introduced. An explanation was provided on how to construct the training dataset, the UNet network used in this study, and how to set various training parameters. The network was saved after training. When the inversion was needed, the net was loaded and the algorithm could predict the result. Several theoretical models were designed for inversion, and the experiment results verified the reliability and effectiveness of the algorithm. The time of the deep learning inversion and the tranditional inversion was recorded. Building training set needed much time, but the time of deep learning inverison was much less than the tranditional inversion. The deep learning inversion is more efficient than the traditional inversion.