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During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

In coal and rock recognition technology, the acquisition of sound signals is affected by background noise. It is challenging to extract cutting features and accurately identify cutting patterns effectively. Therefore, this paper proposes an approach for combined noise reduction of the cutting sound signal based on the improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN) and a singular value decomposition (SVD). First, the method used the ICEEMDAN method to decompose the noisy signal into several intrinsic mode functions (IMF). It calculated the correlation coefficient between the IMF component and the noisy signal and then selected the noisy IMF components based on the threshold formula. Meanwhile, this method constructed a Hankel matrix of the noisy IMF component signals. It used SVD technology to obtain the singular values. According to the singular value standard energy spectrum curve, the paper determined the order of the effective singular value and removed the noise component in the signal. Then, the denoised IMF and noiseless IMF components are superimposed and reconstructed to obtain the noise-reduced cutting sound signal. Finally, it applied simulation signal and simulated shearer cutting experiment to verify the performance of the method. The results show that the proposed method can effectively remove the influence of background noise in the signal and retain the characteristic frequencies of the original cutting sound signal. Compared with traditional noise reduction methods, the ICEEMDAN-SVD combined noise reduction method performs better in noise reduction evaluation standards of signal-noise ratio and root mean square error. It achieved a better noise reduction effect, which could help coal and rock recognition technology based on sound signals.

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

Sun glint is commonly observed as interference in the imaging process of ocean color satellite sensors, making the extraction of water color information in sun glint-affected areas challenging and often leading to significant data gaps. The remote sensing baseline indices, calculated based on Rayleigh-corrected reflectance (Rrc), are recognized as effective in reflecting water color variability in sun glint-affected regions. However, the accurate extraction of the Rrc baseline indices requires sun glint correction. The determination of sun glint correction coefficients for different bands lacks a clear methodology, and the currently available correction coefficients are not applicable to different sea regions. Therefore, this study focuses on the South China Sea, where VIIRS imagery is significantly affected by sun glint. Based on paired datasets comprising sun glint-affected and -unaffected images acquired over the same region on adjacent dates, sun glint correction coefficients for each spectral band were derived by maximizing the cosine similarity of histograms constructed from three baseline indices: SS486 (Spectral Shape index at 486 nm), CI551 (Color Index at 551 nm), and SS671 (Spectral Shape index at 671 nm). To further evaluate the effectiveness of the proposed correction, chlorophyll-a concentrations were retrieved using a Random Forest regression model trained with baseline indices derived from sun glint-free Rrc data and subsequently applied to baseline indices after sun glint correction. Comparative analyses of both baseline index extraction and chlorophyll-a retrieval demonstrate that the proposed optimal-value and mean-value correction approaches effectively mitigate sun glint effects. The mean sun glint correction coefficients α(443), α(486), α(551), α(671) and α(745) were determined to be 0.75, 0.83, 0.89, 0.95 and 0.94, respectively. These coefficients can be applied as sun glint correction coefficients for the VIIRS Rrc data in the South China Sea region. Furthermore, the proposed method for determining sun glint correction coefficients offers a transferable framework that can be extended to other sea areas.

When the shearer cuts coal or rock with different hardness, it will produce corresponding cutting state information. This paper develops a simulation cutting experiment system for the drum shearer based on similarity theory. It took the spiral cutting drum of a shearer as the research target and derived the principal similarity coefficients through the dimensional analysis method. Meanwhile, this paper designed the structure of the cutting power system and hydraulic system. Then, it chose a certain amount of coal powder as an aggregate, cement 325# as cementing material, sand, and water as auxiliary materials to prepare simulated coal samples. The paper adopted the orthogonal experiment method and used a proportion of cement, sand, and water as the influencing factors in designing a simulated coal sample preparation plan. In addition, it utilized the range analysis method to research the influence of various factors on the density and compressive strength of simulated coal samples. Finally, it conducted simulated coal sample cutting tests. The results show that the density of the simulated coal samples is between 1192.59 Kg/m3–1483.51 Kg/m3, and the compressive strength range reaches 0.16 MPa–3.94 MPa. The density of the simulated coal sample is related to the mass proportion of cement and sand. When the ratio gradually increases, the influence of sand increases. Furthermore, the compressive strength is linearly proportional to the proportion of cement. The self-designed simulation cutting experiment system could effectively carry out the relevant experiments and obtain the corresponding cutting condition signals through the sensors. There are differences in vibration signals generated by cutting different strength materials. Extracting the kurtosis value as the characteristic value can distinguish various cutting modes, which can provide a reliable experimental solution for the research of coal-rock identification.

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%.

Colorectal Cancer (CRC) progression is a complex and dynamic process closely linked to TET2-mediated DNA demethylation. Distinct from our previous study on TET2 nuclear loss, which can be observed in the whole tumor progression process, the nuclear increase of TET2 was only observed in tumors at the beginning of metastasis. In addition, cells with nuclear TET2 were located at the bottom of the mucosa, which is the invasion front of CRC. All of these results suggested crucial roles of TET2 nuclear increase during tumor progression. Mechanistically, epithelial-mesenchymal transition (EMT) and the activation of the WNT pathway, which is normally recognized as tumor promotion events, were shown to correlate with the cytoplasm-nucleus shuttling of TET2, which is associated with tumor suppression. Nuclear TET2, in turn, mitigated further EMT and WNT activation, suggesting a negative feedback loop between TET2 and the EMT/WNT pathway. Such a negative feedback loop was further supported by single-cell RNA sequencing (scRNA-seq) analysis of both the CRC progression models and the clinical CRC samples. Together, these findings indicate that the tumor inhibition role of EMT/WNT pathway and TET2 is an intrinsic brake on cancer progression, which represents a potential therapeutic target for CRC.

Mutation-induced malfunction of ten-eleven translocation methylcytosine dioxygenase 2 (TET2) is widely reported in haematological malignancies. However, the role of TET2 in solid cancers, including colorectal cancer (CRC), is unclear. Here, we found that TET2 malfunction in CRC is mostly due to decreased nuclear localization and that nuclear localization of TET2 is correlated with better survival of patients. To explore the underlying mechanisms, 14 immortalized solid tumour cell lines and 12 primary CRC cell lines were used. TET2 was mostly detected in the nucleus, and it induced significant DNA demethylation and suppressed cell growth by demethylating RORA and SPARC in cell lines like SW480. While in cell lines like SW620, TET2 was observed in the cytosol and did not affect DNA methylation or cell growth. Further examination with immunoprecipitation–mass spectrometry illustrated that β-catenin activation was indispensable for the nuclear localization and tumour suppression effects of TET2. In addition, the β-catenin pathway activator IM12 and the TET2 activator vitamin C were used simultaneously to enhance the effects of TET2 under low-expression conditions, and synergistic inhibitory effects on the growth of cancer were observed both in vitro and in vivo. Collectively, these data suggest that β-catenin-mediated nuclear localization of TET2 is an important therapeutic target for solid tumours.