Search

The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human–computer interaction, sports rehabilitation, and virtual reality.

In a survey of mycoviruses in Fusarium species that cause sugarcane Pokkah boeng disease, twelve Fusarium strains from three Fusarium species (F. sacchari, F. andiyazi, and F. solani) were found to contain Fusarium sacchari hypovirus 1 (FsHV1), which we reported previously. The genomes of these variants range from 13,966 to 13,983 nucleotides, with 98.6% to 99.9% nucleotide sequence identity and 98.70% to 99.9% protein sequence similarity. Phylogenetic analysis placed these FsHV1 variants within the Alphahypovirus cluster of Hypoviridae. Intriguingly, no clear correlation was found between the geographic origin and host specificity of these viral variants. Additionally, six out of the twelve variants displayed segmental deletions of 1.5 to 1.8 kilobases, suggesting the existence of defective viral dsRNA. The presence of defective viral dsRNA led to a two-thirds reduction in the dsRNA of the wild-type viral genome, yet a tenfold increase in the total viral dsRNA content. To standardize virulence across natural strains, all FsHV1 strains were transferred into a single, virus-free Fusarium recipient strain, FZ06-VF, via mycelial fusion. Strains of Fusarium carrying FsHV1 exhibited suppressed pigment synthesis, diminished microspore production, and a marked decrease in virulence. Inoculation tests revealed varying capacities among different FsHV1 variants to modulate fungal virulence, with the strain harboring the FsHV1-FSA1 showing the lowest virulence, with a disease severity index (DSI) of 3.33, and the FsHV1-FS1 the highest (DSI = 17.66). The identification of highly virulent FsHV1 variants holds promise for the development of biocontrol agents for Pokkah boeng management.

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions.

Electric vehicles can effectively make use of the time-of-use electricity price to reduce the charging cost. Additionally, using grid power to preheat the battery before departure is particularly important for improving the vehicle mileage and reducing the use cost. In this paper, a dynamic programming algorithm is used to optimize the battery AC (Alternating Current) charging–preheating strategy to minimize the total cost of battery charging and preheating, with the charging current and battery preheating power consumption as the control variables. The cost difference between the optimized control strategy and the conventional preheating strategy was analyzed under different ambient temperatures (−20~0 °C) and different target travel times (7:00~12:00). The simulation results show that the optimized control strategy makes the state of charge (SOC) and temperature of the battery reach the set value at the user’s target departure time, and the total cost of the grid is the lowest. Compared with the conventional preheating strategy, the optimized control strategy can utilize the power grid energy in the valley price area and reduce the opening time of the positive temperature coefficient (PTC) heater in the flat and the peak price zones. Furthermore, the cost utilization rate can reach 18.41~73.96%, and the cost-saving effect is significant.

With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

Fusarium sacchari, a key pathogen of sugarcane, is responsible for the Pokkah boeng disease (PBD) in China. The 14-3-3 proteins have been implicated in critical developmental processes, including dimorphic transition, signal transduction, and carbon metabolism in various phytopathogenic fungi. However, their roles are poorly understood in F. sacchari. This study focused on the characterization of two 14-3-3 protein-encoding genes, FsBmh1 and FsBmh2, within F. sacchari. Both genes were found to be expressed during the vegetative growth stage, yet FsBmh1 was repressed at the sporulation stage in vitro. To elucidate the functions of these genes, the deletion mutants ΔFsBmh1 and ΔFsBmh2 were generated. The ΔFsBmh2 exhibited more pronounced phenotypic defects, such as impaired hyphal branching, septation, conidiation, spore germination, and colony growth, compared to the ΔFsBmh1. Notably, both knockout mutants showed a reduction in virulence, with transcriptome analysis revealing changes associated with the observed phenotypes. To further investigate the functional interplay between FsBmh1 and FsBmh2, we constructed and analyzed mutants with combined deletion and silencing (ΔFsBmh/siFsBmh) as well as overexpression (O-FsBmh). The combinations of ΔFsBmh1/siFsBmh2 or ΔFsBmh2/siFsBmh1 displayed more severe phenotypes than those with single allele deletions, suggesting a functional redundancy between the two 14-3-3 proteins. Yeast two-hybrid (Y2H) assays identified 20 proteins with pivotal roles in primary metabolism or diverse biological functions, 12 of which interacted with both FsBmh1 and FsBmh2. Three proteins were specifically associated with FsBmh1, while five interacted exclusively with FsBmh2. In summary, this research provides novel insights into the roles of FsBmh1 and FsBmh2 in F. sacchari and highlights potential targets for PBD management through the modulation of FsBmh functions.