Real-Time Detection Algorithm for Kiwifruit Canker Based on a Lightweight and Efficient Generative Adversarial Network.

Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision (mAP@0.5) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms.

Nonlinear dwell-time algorithm for freeform surface generation by atmospheric-pressure plasma processing.

Based on deterministic chemical etching, atmospheric pressure plasma processing (APPP) with a high material removal rate and spatial machining resolution, is a promising computer-controlled optical surfacing (CCOS) technique for freeform surface generation. However, the time-variant removal characteristics of APPP induce nonlinearity in the CCOS process, which requires more consideration in the dwell-time calculation. In this paper, the nonlinear dwell-time algorithm based on the concept of controlling volumetric removal is studied. The freeform surface generation by controlling volumetric removal is modeled to provide the theoretical basis for the algorithm. The applicability of the algorithm in freeform generation by APPP with time-varying characteristics is explored through numerical simulations. Finally, a freeform surface is successfully created based on the algorithm and relevant analysis results, which validates the applicability of the algorithm in freeform generation using time-variant tool influence functions.

Freeform surface generation by atmospheric pressure plasma processing using a time-variant influence function.

Based on a controllable chemical reaction, atmospheric pressure plasma processing (APPP) can achieve efficient material removal even when the tool influence function (TIF) size is reduced to several millimeters, resulting in its great application potential for generating freeform surfaces. However, the TIF changes with the local dwell time, introducing nonlinearity into processing, because of the influence of the plasma thermal effect on chemical reactions. In this paper, a freeform generation method using a time-variant TIF is presented and validated. First, the time-variant removal characteristics of APPP and its nonlinear influence on freeform surface generation are analyzed. Then, the freeform surface generation concept is proposed based on controlling the local volumetric removal. Consequently, the dwell time calculation method is developed to suppress the nonlinearity induced by the time-variant TIF. Finally, the developed method is evaluated by the simulation and experimental analysis of the complex structure generation process. Results show that the proposed method can reduce the nonlinear influence of the time-variant TIF by reasonably calculating dwell time, promoting the application of APPP in freeform surface generation.

The Magnetorheological Finishing (MRF) of Potassium Dihydrogen Phosphate (KDP) Crystal with Fe3O4 Nanoparticles.

The cubic Fe3O4 nanoparticles with sharp horns that display the size distribution between 100 and 200 nm are utilized to substitute the magnetic sensitive medium (carbonyl iron powders, CIPs) and abrasives (CeO2/diamond) simultaneously which are widely employed in conventional magnetorheological finishing fluid. The removal rate of this novel fluid is extremely low compared with the value of conventional one even though the spot of the former is much bigger. This surprising phenomenon is generated due to the small size and low saturation magnetization (M s) of Fe3O4 and corresponding weak shear stress under external magnetic field according to material removal rate model of magnetorheological finishing (MRF). Different from conventional D-shaped finishing spot, the low M s also results in a shuttle-like spot because the magnetic controllability is weak and particles in the fringe of spot are loose. The surface texture as well as figure accuracy and PSD1 (power spectrum density) of potassium dihydrogen phosphate (KDP) is greatly improved after MRF, which clearly prove the feasibility of substituting CIP and abrasive with Fe3O4 in our novel MRF design.

Exploratory quantum resonance spectrometer as a discriminator for psychiatric affective disorders.

The aim of this study was to evaluate reliability and psychiatric clinical value of quantum resonance spectrometer (QRS) in detection of affective disorders. We studied 1014 patients with schizophrenia and 248 patients with mood disorders (including 93 patients with major depression). Affective disorder symptoms of the same subjects obtained from the QRS test and psychiatrists' diagnoses were compared. In addition, three affective disorder symptoms of renumbered 93 patients with major depression were discriminated using QRS. Kappa values of affective disorder detection and diagnosis were more than 0.69 in all three symptoms of schizophrenia and more than 0.65 in six of seven symptoms of mood disorder. The same consistency could also be seen in receiver operating characteristic curve area under the curve. In the discriminated analysis, sensitivity, specificity, positive predictive value, and negative predictive value of hypothymia, anxiety, and irritability detected using QRS are more than 0.66 compared with psychiatrists' diagnoses. QRS could be an objective identification and diagnosis instrument and might promote psychiatric clinical diagnosis.