Chlorpyrifos is a widely used organophosphorus insecticide. The acute oral 24 h median lethal dose (LD50) value of chlorpyrifos in Apis mellifera and in Apis cerana was estimated to assess differential acute chlorpyrifos toxicity in the two bee species. The LD50 values of chlorpyrifos in A. mellifera and in A. cerana are 103.4 ng/bee and 81.8 ng/bee, respectively, which suggests A. cerana bees are slightly more sensitive than A. mellifera bees to the toxicity of chlorpyrifos. Doses half the acute LD50 of chlorpyrifos were selected to study behavioral changes in the two bee species using proboscis extension response assay. A. mellifera foragers treated with chlorpyrifos showed significantly lower response to the 10% sucrose solution compared to control bees after 2, 24 and 48 h. Chlorpyrifos significantly impaired the olfactory learning abilities and 2 h memory retention of forager bees regardless of honey bee species, which may affect the foraging success of bees exposed to chlorpyrifos.

This article proposes an automatic titration algorithm for organic matter content detection based on machine vision, which addresses the disadvantages of high risk factor, strong odour, significant pollution to laboratory environment and slow efficiency of manual titration in organic matter detection. First, by analysing the colour change characteristics during the titration process, machine learning techniques are used to classify the titration speed, and a titration experiment state recognition model is constructed to divide the titration speed into four categories and improve titration efficiency; Second, through a large number of titration experiments to collect relevant data and extract key feature parameters, an efficient titration algorithm based on histogram similarity was designed to accurately identify titration endpoints and improve detection accuracy. This study not only solves the limitations of manual operation in traditional titration methods, but also provides new ideas and methods for the automation and intelligence of chemical titration. The test results showed that the device had a titration error of less than 0.2 ml and was more efficient than manual titration. When comparing the results with manual titration, no statistically significant difference was observed when paired t -test was applied at a 95% confidence level. Therefore, it has been confirmed that it has good recognition rate and control accuracy.

Electrical stimulation (ES) has been widely applied in sports science, primarily in long-term training assistance and postexercise recovery. However, its role during exercise, particularly in high-intensity running, remains underexplored. This study introduces a wearable Acupoint ES (AES) system to modulate systemic physiological states during exercise. Complementary animal experiments validated the mechanism, showing that the stimulation protocol significantly modulates the balance of inhibitory and excitatory neurotransmitters [e.g., gamma-aminobutyric acid (GABA) and 5-hydroxytryptamine (5-HT)] in the hypothalamus. Twenty-four healthy participants completed a 400-m sprint trial with continuous monitoring of running speed, stride frequency, heart rate, and recovery dynamics. Results demonstrated that AES applied during running significantly reduced heart rate and enhanced postrun recovery efficiency, without disrupting natural running mechanics. These findings highlight AES as a promising integrative strategy for improved physiological efficiency, representing a departure from conventional muscle-targeted paradigms and offering practical implications for sports training and rehabilitation engineering.