ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases

Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under a complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing the Mobilenet-V3s basic block, which integrates the coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depth-wise convolution, and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared with yolov5s) on the test set and reducing the floating point of operations (FLOPs) to 6.1 G (a decrease of 9.7 G compared with yolov5s). In summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields

Dynamic state of ecosystem carrying capacity under island urbanization: a case study of Pingtan Island in the Southeastern coast of China

The assessment of ecological environment during the large-scale development of islands is a major topic in the study of current coastal islands. Choosing the appropriate assessment method to evaluate the suitability of carrying capacity of islands and making relevant suggestions are significant to the sustainable development of islands. Ecological footprint method is used to analyze the ecological carrying capacity of Pingtan Island (PI) from 2005 to 2016 for promoting the coordinated rational development and construction and ecological environment of the island. Although PI is in rapid urban development and construction, the island maintains secure and stable ecological conditions. PI is used as a research case to analyze the sustainable development of the ecological environment through the carrying capacity of the island ecosystem

Arrhythmia Classification Algorithm Based on Multi-Feature and Multi-type Optimized SVM

The electrocardiogram (ECG) signal feature extraction and classification diagnosis algorithm is proposed to address the high incidence of heart disease and difficulty in self-detection. First, the collected ECG signals are preprocessed to remove the noise of the ECG signals. Next, wavelet packet decomposition is used to perform a four-layer transformation on the denoised ECG signal and the 16 obtained wavelet packet coefficients analyzed statistically. Next, the slope threshold method is used to extract the R-peak of the denoised ECG signal. The RR interval can be calculated according to the extracted R peak. The extracted statistical features and time domain RR interval features are combined into a multi-domain feature space. Finally, the particle swarm optimization algorithm (PSO), genetic algorithm (GA), and grid search (GS) algorithms are applied to optimize the support vector machine (SVM). The optimized SVM is utilized to classify the extracted multi-domain features. Classification results show the proposed algorithm can classify six types of ECG beats accurately. The classification efficiency achieved by PSO, GA, and GS are 97.78%, 98.33%, and 98.89%, respectively

Evaluation of an identification method for the SARS-CoV-2 Delta variant based on the amplification-refractory mutation system

The Delta variant of SARS-CoV-2 dominated the COVID-19 pandemic due to its high viral replication capacity and immune evasion, causing massive outbreaks of cases, hospitalizations, and deaths. Currently, variant identification is performed mainly by sequencing. However, the high requirements for equipment and operators as well as its high cost have limited its application in underdeveloped regions. To achieve an economical and rapid method of variant identification suitable for undeveloped areas, we applied an amplification-refractory mutation system (ARMS) based on PCR for the detection of novel coronavirus variants. The results showed that this method could be finished in 90 min and detect as few as 500 copies/mL and not react with SARS-Coronavirus, influenza A H1N1(2009), and other cross-pathogens or be influenced by fresh human blood, α- interferon, and other interfering substances. In a set of double-blind trials, tests of 262 samples obtained from patients confirmed with Delta variant infection revealed that our method was able to accurately identify the Delta variant with high sensitivity and specificity. In conclusion, the ARMS-PCR method applied in Delta variant identification is rapid, sensitive, specific, economical, and suitable for undeveloped areas. In our future study, ARMS-PCR will be further applied in the identification of other variants, such as Omicron

ESO-Based Fuzzy Sliding-Mode Control for a 3-DOF Serial-Parallel Hybrid Humanoid Arm

This paper presents a unique ESO-based fuzzy sliding-mode controller (FSMC-ESO) for a 3-DOF serial-parallel hybrid humanoid arm (HHA) for the trajectory tracking control problem. The dynamic model of the HHA is obtained by Lagrange method and is nonlinear in dynamics with inertia uncertainty and external disturbance. The FSMC-ESO is based on the combination of the sliding-mode control (SMC), extended state observer (ESO) theory, and fuzzy control (FC). The SMC is insensitive to both internal parameter uncertainties and external disturbances. The motivation for using ESO is to estimate the disturbance in real-time. The fuzzy parameter self-tuning strategy is proposed to adjust the switching gain on line according to the running state of the system. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. The effectiveness and robustness of the designed FSMC-ESO are illustrated by simulations

A Most-Unfavorable-Condition Method for Bridge-Damage Detection and Analysis Using PSP-InSAR

The main contribution of this study is to provide a new idea to detect bridge damage by using PSP-InSAR technology. A most-unfavorable-condition method is proposed for bridge-damage detection and analysis. The method can determine the specific damaged location and occurrence time by using the differential deformation values of persistent scatterer (PS) points on bridge piers. Taking Beijing Suzhou Bridge as an experimental area, 96 COSMO-SkyMed time-series SAR images were used from September 2011 to November 2017. Deformation values of PS points around Suzhou Bridge were acquired and analyzed. Experimental results show that in July 2017, the unusual maximum differential deformation value was 25.73 mm. It occurred between piers D3 and D4 of Suzhou Bridge, and it was deduced that the main girder between piers D3 and D4 may have been damaged in July 2017. As a validation, taking the differential deformation value between piers D3 and D4 as an input, the maximum tensile stress, and the maximum compressive stress were calculated as 2.1 MPa and 8.4 MPa, respectively, through a finite element model. The tensile stress exceeded the design value of the concrete, further confirming the damage of the girder between piers D3 and D4. Moreover, all results are consistent with the Suzhou Bridge damage information shown in existing records, which verify the accuracy and reliability of the proposed method

Decisions of E-Commerce Supply Chain under Consumer Returns and Different Power Structures

Considering the growing phenomenon of consumer returns and channel power struggles in e-commerce supply chains (ESCs), the ESC model is constructed and its equilibrium solutions are calculated and compared. Further, the consumer utility function is constructed to explore the impact of returns and dominant enterprises on consumer utility. Based on this, the “return cost-sharing and commission readjusting” contract is designed to maximize both ESC and consumer utility. Finally, the paper validates and further analyzes conclusions through numerical simulation. The main conclusions are as follows: higher return rates and return handling costs will reduce market demand and ESC profits, while higher salvage value of returned products will have a positive impact on ESC, but the above factors will not affect the online service level under decentralized decisions. The impact of consumer’s service quality preferences on manufacturer’s profits and e-commerce platform’s profit is determined by channel power structure. The impact of return rate on consumer utility depends on two factors: the decision-making model and the hidden cost of consumer returns