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Constructing an intelligent management platform for laboratory safety informationization for mining majors in colleges and universities is an important way to achieve campus safety in colleges and universities, and safety assessment is the core of this work. This study proposes to establish a laboratory Tripod-Delta model to establish the factors that affect laboratory safety and analyze their weights through a C-OWA operator to provide directions for the design of an intelligent platform. The fuzzy set qualitative comparative analysis method is employed to analyze the conditional grouping of laboratory accidents, given multiple factors that cause laboratory safety accidents. Among the established factors affecting the safety of mining professional laboratories in colleges and universities, according to the weight analysis, it can be seen that colleges and universities need to pay more attention to the influence of safety facilities (15.88%), instrument materials (14.03%), management system (12.35%) and experimental habits (10.6%). And there is a need to pay more attention to the human aspect. The sum of the factors under this aspect is the largest, which is 29.49%. Using the fuzzy set qualitative comparative analysis method for grouping analysis, we get five groupings leading to laboratory accidents, starting from the causal relationship leading to the frequent occurrence of laboratory accidents in colleges and universities, to provide a reference for the construction of laboratory safety management systems in colleges and universities.

It is the most effective way to miniaturize and compact the seeker at present by using small MEMS gyroscopes sensitive seeker platform and the semi-strapdown technology resolving together with the gyro information and frame angular velocity information of the navigation IMU. However, compared with the traditional seeker control methods, there is still a problem of large noise when using these two methods to control the seeker stability platform and generate guidance information.In this paper, the "virtual gyro" technology is used to fuse the two signals to improve the signal accuracy and reduce the noise. Based on the analysis of the generation principle and signal characteristics of the semi-strapdown signal, the noise model is established. Using this model and the noise model of MEMS gyroscope, the state equation and measurement equation of MEMS gyroscope and semi-strapdown signal fusion system are established, and the Kalman filter method is used to estimate the real signal. The influence of key parameters in the model on the filtering effect and bandwidth under steady-state conditions is analyzed by using continuous Kalman theory. Simulation and experimental results show that the fusion system can greatly reduce the noise of the original signal.

The problem of marine noise pollution has a long history. Strong noise (>120 dB re 1 µPa) will affects the growth, development, physiological responses, and behaviors of fish, and also can induce the stress response, posing a mortal threat. Although many studies have reported that underwater noise may affect the survival of fish by disturbing their nervous system and endocrine system, the underlying causes of death due to noise stimulation remain unknown. Therefore, in this study, we used the underwater noise stress models to conduct underwater strong noise (50–125 dB re 1 µPa, 10–22,000 Hz) stress experiments on small yellow croaker for 10 min (short-term noise stress) and 6 days (long-term noise stress). A total of 150 fishes (body weight: 40–60 g; body length: 12–14 cm) were used in this study. Omics (metabolomics and transcriptomics) studies and quantitative analyses of important genes (HPA (hypothalamic–pituitary–adrenal)-axis functional genes) were performed to reveal genetic and metabolic changes in the important tissues associated with the HPA axis (brain, heart, and adrenal gland). Finally, we found that the strong noise pollution can significantly interfere with the expression of HPA-axis functional genes (including corticotropin releasing hormone (CRH), corticotropin releasing hormone receptor 2 (CRHR2), and arginine vasotocin (AVT)), and long-term stimulation can further induce metabolic disorders of the functional tissues (brain, heart, and adrenal gland), posing a lethal threat. Meanwhile, we also found that there were two kinds of death processes, direct death and chronic death, and both were closely related to the duration of stimulation and the regulation of the HPA axis. [ABSTRACT FROM AUTHOR]

Seafloor landing research is crucial for underwater robots tasked with long-term fixed-point observation missions, particularly those requiring prolonged operations on the seabed. Developing safe and stable landing technology is essential for the success of these missions. This paper introduces a novel underwater robot by establishing its dynamic model and using the arbitrary Lagrangian–Eulerian (ALE) method to conduct a comprehensive study of its deep-sea landing process. Prior to detailed parameter analysis, the ALE algorithm's effectiveness and accuracy were validated through experimental data and simulation results from previous studies. This study examines the penetration depth and force conditions during the landing process by varying factors such as seabed soil properties, landing velocity, and the robot's mass. Findings indicate that seabed soil properties significantly influence penetration depth and pressure, with variations in soil density and strength affecting the robot's landing behavior. In contrast, the robot's mass has a relatively minor effect, suggesting that the choice of structural materials has limited impact on penetration depth and pressure during landing. Additionally, landing velocity was found to significantly affect penetration depth and pressure; higher velocities result in greater penetration depths and pressures. This highlights the importance of controlling landing velocity to minimize impact forces and protect the robot. The results of this study provide theoretical support and data for developing deep-sea landing technology for novel underwater robots and inform the selection of structural materials, ensuring the successful execution of long-term fixed-point observation missions in deep-sea environments. [ABSTRACT FROM AUTHOR]