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Traditional indicators such as equivalent salt deposit density, surface pollution layer conductance and leakage current are mainly used to measure the contamination grades of ceramic insulators, and the detection process is time-consuming and inefficient. Hyperspectral technology can obtain rich information of target spectrum non-contact, and has great application potential in insulator pollution detection. Therefore, a method of ceramic insulator pollution level detection based on hyperspectral technology is proposed in this paper. Firstly, there is a reflection phenomenon when collecting hyperspectral image due to the material of ceramic insulator, so histogram equalization is used to process the hyperspectral image to remove the reflective interference. Then, the hyperspectral image is preprocessed to remove the noise interference. And the successive projections algorithm (SPA) is used to extract the feature of the sample spectral line for removing the redundant information. Finally, the classification model of support vector machine (SVM) is established according to the characteristic spectral lines, and the classification accuracy of ceramic samples is 95%.

According to the difficulties of complex business process, scattered experimental data and corresponding file storage, difficult to retrieve and process experimental data, low experimental data security in the gas disaster prevention laboratory, this study carried out demand analysis and business process combing, associated the experimental data of existing experimental projects with their corresponding sample information, designed functional modules and system architecture, realized basic data management, mandatory management, report management and comprehensive query functions, developed a laboratory information management system(LIMS) based on B/S structure. Compared with traditional manual management, LIMS provided a more convenient management method, improved data management capabilities and work efficiency of testing and inspection personnel, reduced laboratory operating costs and realized functions such as fast traceability and report output.

Numerous landslides occur in waste dumps due to unscientific placement. The behavior of waste dumps with different material layouts has yet to be understood when it comes to deformation and failure. In this research, several tests of base friction were carried out on mine waste dumps by considering various material layouts under self-weight loading conditions. The waste dump's displacement was detected by the system composed with image-capture equipment. The findings indicate that the layout of waste materials greatly affected the way a waste dump fails and could be categorized into three types: crest-arced slip, bottom-arced slip, and composite overall slip. Sliding failure caused a gradual development of the slip surface from the dominant fine-gravel material stage to the adjacent waste dump stage. Three phases can be assigned to the waste dump's failure process: the constant deformation phase, strain localization phase, and failed phase. The layout of waste materials exerts a significant influence on the moment that the waste dump's deformation enters the three phases. The waste dump's failure mechanism was explained by describing how maximum shear and volumetric strain developed and transmitted. To determine the most dominant impact during the tests by analyzing the relative degree of volumetric and shear influences on the waste dump, an effect coefficient was proposed. The stability of the waste dump was illustrated and comparatively analyzed based on a simplified approach, in which the moments of initial cracking and local failure were used as a failure index for the model tests. The design and stability analysis of mine waste dumps is strengthened by this study's strong support, thereby minimizing the risk of landslides and promoting the sustainability of the mining industry. [ABSTRACT FROM AUTHOR]

To investigate the effects of environmental enrichment on the growth and welfare level of the Chinese hooksnout carp (Opsariichthys bidens), an experiment was carried out for 56 d with four types of enrichment environments: low-density physical enrichment, high-density physical enrichment, low-density social enrichment, and high-density social enrichment. The experimental results revealed that both physical and social enrichment treatments positively affected the growth and gonadal development of O. bidens. The physical enrichment treatment was found to increase the antioxidants' content, such as CAT, SOD, and GSH, in the liver of O. bidens. Moreover, this treatment was able to lead to a reduction in MDA content and enhancement of the fish's antioxidant capabilities. Behavioral observations, cortisol level analysis, and pcna gene expression analysis indicated that the physical enrichment treatment redused the clustering, aggression, and stress behaviors of O. bidens, maintaining them in a low-stress state. In most respects, the high-density physical enrichment has more positive effects than the low-density one. In contrast, the social enrichment treatments resulted in higher stress levels for O. bidens. [ABSTRACT FROM AUTHOR]

Opsariichthys bidens (O. bidens) is a fish species native to China and sensitive to temperature changes. In this study, the effects of acute temperature stress on brain gene expression in O. bidens were investigated by sampling brain tissues from specimens exposed to three different temperatures (15 °C, 25 °C, and 35 °C) for varying durations of 2 h, 4 h, 6 h, and 8 h. The study focused on analyzing the expression patterns of key genes implicated in neural function and stress response, including brain-derived neurotrophic factor (BDNF), c-FOS, heat shock proteins (HSP70, HSP90), endoplasmic reticulum stress markers (IRE1, GRP78), oxidative stress enzymes (CAT, SOD), and apoptotic regulators (caspase3, Bax). The findings revealed that upon exposure to acute heat stress, the expression levels of the aforementioned genes in the brain of O. bidens were up-regulated within 2 h, peaking at the 4-h mark. Conversely, following acute cold stress, the expression of c-FOS, BDNF, HSP70, HSP90, SOD, and CAT genes increased significantly after 4 h, while caspase3 expression was notably elevated at the 6-h mark, with no significant impact observed on Bax, IRE1, or GRP78 gene expression levels. The study suggested that the brain of O. bidens responds to high temperatures through mechanisms involving neural activation, heat shock proteins, endoplasmic reticulum stress, oxidative stress, and apoptosis. Similarly, adaptation to low temperatures by O. bidens' brain was associated with neural activation, regulation of heat shock proteins, oxidative stress responses, and apoptotic processes. Overall, this research aimed to elucidate the impact of temperature stress on brain physiology and the adaptive mechanisms of O. bidens at the genetic level, providing a foundational understanding of its temperature adaptation strategies. [ABSTRACT FROM AUTHOR]

The 3-D TLC NAND flash memory significantly increases the storage capacity but makes data more prone to errors. To address the reliability problem, Polar Code is one coding method that can reach the channel capacity (Arikan 2009). However, the 3-D NAND flash memory channel is uncertain due to the variation of retention time and program/erase(P/E) cycle. So it is difficult to construct a Polar Code in the NAND flash memory. This letter proposes a new Polar Code construction method called dynamic merge Monte Carlo (DMMC). DMMC uses the Monte Carlo method to obtain the optimal Polar Code construction and then reduces the space overhead by combining similar construction so that it can be used in flash memory systems. Compared with the conventional method, the uncorrectable bit error rate (UBER) of the proposed method can reduce by up to 90.5%, and the read latency can reduce by more than 50%. [ABSTRACT FROM AUTHOR]

To solve the problem of traditional pollution detection methods on the prevention and control of pollution flashover of transmission line insulators,the non-contact and high-resolution hyperspectral technology is used to study the on-line pollution detection technology. At the same time,an insulator pollution level identification technology based on wavelet packet energy spectrum feature optimization is proposed to effectively extract the spectral features reflecting the pollution degree and weaken the influence of redundancy and interference information. Firstly,the spectral images of insulator samples with different pollution levels are segmented to extract the spectral mean curve of pixels in uniform pollution area. Secondly,the difference of light intensity uniformity and environmental noise of different images are preprocessed,and the differentiability among different pollution levels is improved by logarithmic transformation. Thirdly,the feature extraction of wavelet packet energy spectrum is performed on the preprocessed spectral lines. Finally,a pollution level recognition model based on the proposed features and support vector machines (SVM) is established. The experimental results show that the SVM pollution level recognition model based on wavelet energy spectrum features achieves 99.8%,and it has higher recognition accuracy than full band data or principal component analysis (PCA) feature data does.