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The detection and counting of Camellia oleifera trees are important parts of the yield estimation of Camellia oleifera. The ability to identify and count Camellia oleifera trees quickly has always been important in the context of research on the yield estimation of Camellia oleifera. Because of their specific growing environment, it is a difficult task to identify and count Camellia oleifera trees with high efficiency. In this paper, based on a UAV RGB image, three different types of datasets, i.e., a DOM dataset, an original image dataset, and a cropped original image dataset, were designed. Combined with the YOLOv8 model, the detection and counting of Camellia oleifera trees were carried out. By comparing YOLOv9 and YOLOv10 in four evaluation indexes, including precision, recall, mAP, and F1 score, Camellia oleifera trees in two areas were selected for prediction and compared with the real values. The experimental results show that the cropped original image dataset was better for the recognition and counting of Camellia oleifera, and the mAP values were 8% and 11% higher than those of the DOM dataset and the original image dataset, respectively. Compared to YOLOv5, YOLOv7, YOLOv9, and YOLOv10, YOLOv8 performed better in terms of the accuracy and recall rate, and the mAP improved by 3–8%, reaching 0.82. Regression analysis was performed on the predicted and measured values, and the average R2 reached 0.94. This research shows that a UAV RGB image combined with YOLOv8 provides an effective solution for the detection and counting of Camellia oleifera trees, which is of great significance for Camellia oleifera yield estimation and orchard management. [ABSTRACT FROM AUTHOR]

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses. [ABSTRACT FROM AUTHOR]

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops. [ABSTRACT FROM AUTHOR]

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands. [ABSTRACT FROM AUTHOR]

Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R2 greater than 0.6, with validation accuracy R2 values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm−2, indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making. [ABSTRACT FROM AUTHOR]

The intelligent transformation of logistics plays a significant role in meeting the diverse needs of customers, improving operational efficiency, and reducing carbon emissions in logistics activities. Therefore, to achieve sustainable development, logistics enterprises need to face the decision-making problem of intelligent logistics transformation. In this paper, we construct a Stackelberg game model between a financially constrained logistics-service provider (LSP) and a well-funded logistics-service integrator (LSI) and discuss the impact of the wholesale price contract, the cost-sharing contract, the revenue-sharing contract, the two-part tariff contract, and the hybrid cost-sharing and revenue-sharing contract on the intelligence level of logistics services, the profits of supply-chain members, and the channel for logistics-service demand. We found that the cost-sharing contract and the revenue-sharing contract cannot achieve Pareto improvement in the profits of supply-chain members. In addition, the increase in bank-loan interest rates would seriously weaken the level of intelligence and market demand for the entire logistics service. However, when consumers do not have high requirements for the intelligence of logistics services, the two-part power–price contract can create a win–win situation for supply-chain members and increase market demand within a certain range; on the contrary, a hybrid contract of cost sharing and revenue sharing is the best choice. Moreover, in the process of contract design for the intelligent transformation of logistics services, it is necessary to pay attention to the influence of the price-sensitivity coefficient on decision-making. [ABSTRACT FROM AUTHOR]

Using the MTS816 rock mechanics servo tester, an acoustic emission monitoring system and high-speed digital photographic equipment, uniaxial compression tests were conducted on granite specimens containing single fracture slabs after repeated treatment (treatment times 1, 5, 10, 15 and 20) with three types of high temperature (250, 350 and 450 °C) water cooling, respectively, to analyze the basic mechanical parameters, acoustic emission change characteristics and fracture evolution of the specimens during the uniaxial compression process. It is shown that the heating temperature and the number of treatments not only have a deteriorating effect on the basic mechanical parameters of the specimens but also have an important effect on the changes in the basic parameters of acoustic emission at different compression stages. At 250 °C, the acoustic emission characteristics of the specimens at the initial tightening stage tended to decrease (N = 1 and 5 times) then, increase (N = 10 and 15 times) and then decrease (N = 20 times) as the number of treatments increased. At the same set temperature, the percentage of the bottom amplitude value of the acoustic emission of the specimen gradually decreases, and the percentage of the high amplitude value gradually increases as the number of treatments increases. After the specimen undergoes one and five treatments at 250 °C, the maximum acoustic emission energy value changes less, the maximum acoustic emission energy value decreases with the increase of treatment times in an approximately exponential function, the specimen is transformed from the brittle damage mode to the plastic damage mode and the effect of the prefabricated fracture on the damage of the specimen gradually disappears. [ABSTRACT FROM AUTHOR]

In order to investigate the effect of a high temperature-water cooling cycle on the acoustic emission characteristics of the granite splitting process, Brazilian splitting tests were conducted on granite disc specimens treated with high temperature–water cooling (cycle times 1, 5, 10, 15, 20) from 250 to 650 °C. The relationship between the acoustic emission count, cumulative acoustic emission number, amplitude distribution, and the maximum energy of the specimens and temperature as well as the number of hot and cold cycles were investigated, and the relationship between the acoustic emission changes and specimen damage during the splitting of the granite specimens after the high temperature-water cooling cycle was discussed and analyzed. The test results show that the acoustic emission changes in the splitting process of granite disc specimens have obvious hot and cold shock effects, and that the acoustic emission α value and amplitude density of the specimens at the initial stage of splitting show an increasing trend with an increasing number of hot and cold cycles, and the amplitude distribution is more obviously affected by temperature. When the temperature is low and the number of hot and cold cycles is small, the maximum energy value at the peak stress point is larger, and the maximum energy value tends to decrease gradually as the temperature increases and the number of cycles increases. [ABSTRACT FROM AUTHOR]

A hybrid finite–discrete element method (FDEM) is proposed to investigate dynamic pure-mode-II fracture behaviors. The transition of continuum to discontinuum was applied to the FDEM through the use of three fracture modes, so that the whole fracture process could be modeled naturally. The FDEM was then employed to model the dynamic pure-mode-II fracture behavior of rock during a four-point bending test with a prefabricated notch. The results showed that the fracture initiated from the tip of the prefabricated notch under a relatively lower loading rate, i.e., 1 m/s and 5 m/s. However, when the loading rate reached higher levels, i.e., 10 m/s and 50 m/s, the prefabricated notch played a small role in the fracture patterns. Under these conditions, the fracture initiated from the center of the beam bottom or the stress concentration vicinity, instead of the tip of the prefabricated notch. Regardless of the loading rate, the obtained force-loading displacement curves showed a typical brittle material failure process. Additionally, by incorporating the empirical correlation between the static and dynamic strengths obtained from the dynamic rock fracture tests, the hybrid finite–discrete element method could effectively reflect the impact of the loading rate on the strength of the rock. To conclude, the hybrid finite–discrete element method is an effective instrument to investigate the fracture initiation and propagation of rock, since it can both naturally simulate the process of rock fracture and capture the effect of the loading rate on the rock behaviors. [ABSTRACT FROM AUTHOR]

The stability of rock slopes is of significance, as even the slightest slope failure can result in damage to infrastructure and catastrophes for human beings. Thus, this article focuses on the review of the current techniques available for rock slope stability analysis. The rock slope stability techniques can be classified as conventional methods and numerical methods. The advantages and limitations of the conventional method are briefly reviewed. The numerical methods mainly included three types, i.e., continuum methods, discontinuum methods, and the combined/hybrid continuum–discontinuum methods. This article pays more attention to the last type. The combined/hybrid finite–discrete element method (FDEM), which might be the most widely used continuum–discontinuum method, is introduced and we illustrated its abilities in modelling the entire rock slope failure process. The fundamental principles of FDEM, i.e., the contact interaction of the discrete bodies and the transition from continuum to discontinuum, are introduced in detail. The abilities of the FDEM in modelling the rock slope failure process are calibrated by modelling the entire typical rock slope failure process. Then, the application of the FDEM in the analysis of slope stability is introduced and discussed. Finally, the authors give insight into the GPGUP-parallelized FDEM modelling of the high rock slope failure process by the implementation of the strength reduction method (SRM). It is concluded that the FDEM can effectively model the entire rock slope failure process, even without the implantation of any slope modes, and the GPGUP-parallelized FDEM is a promising tool in the study and application of rock slope stabilities. [ABSTRACT FROM AUTHOR]

The wide use of high-performance concrete (HPC) makes it essential to study its dynamic and thermal behavior. In this study, polypropylene fiber-reinforced high-performance concrete was developed and a series of tests were carried out to obtain its mechanical and thermal properties. Since high-strength HPC has previously been studied intensively, only low-strength HPC—i.e., C30, C40, and C50—was studied in this research. The split Hopkinson pressure bar (SHPB) was employed to carry out the dynamic tests of the HPC under various loading rates and the principles of the SHPB were introduced in detail. Then, the polypropylene fiber-reinforced HPCs were heated to various high temperatures and measures were taken to keep the temperatures relatively constant. It was found that at temperatures lower than 100 °C, the specimen could still be kept in its entirety, although many fractures were produced in the HPC specimen under dynamic loading conditions. However, it was found that at temperatures higher than 200 °C, all the HPC samples were smashed into fragments. In addition, the HPC's compressive strength was found to be significantly influenced by the temperature. At temperatures lower than 300 °C, the HPC's compressive strength was found to increase with increases in temperature. At temperatures higher than 300 °C, the HPC's compressive strength was found to decrease with increases in temperature. [ABSTRACT FROM AUTHOR]