Epidemiological survey of Blastocystis hominis in Huainan City, Anhui Province, China

AIM: To investigate the infection of Cyclospore cayetanensis in Anhui Province. METHODS: Identification of Cyclospore cayetanensis was made microscopically by finding the oocysts of Cyclospore cayetanensis in fecal smears taken from the infants, pupils and adults with obstinate diarrhea, and immunocompromised individuals by using a auramine-phenol stain and modified acid-fast stain. Cellular immune function was detected with biotin-streptavidin (BSA), and the specific antibody against Cyclospore cayetanensis was detected with method of ELISA. RESULTS: (1) The positive rates of Cyclospore cayetanensis infection in infants, pupils, infants and adults with obstinate diarrhea and with immunocompromised individuals were significantly different (P < 0.01), with the rates of 0%, 0.50% (1/200), 5.62% (10/178), and 9.38% (3/32) respectively. (2) The infection rates of males and females were 2.61% (10/383) and 1.44% (4/227) respectively, with no significant difference (P < 0.05). (3) The positive rates of population with oocysts in urban and rural areas were 0.92% (3/325) and 3.86% (11/285) respectively. (4) The positive rates of CD₃⁺, CD₄⁺, CD₈⁺ and the ratio of CD₄⁺/CD₈⁺ of individuals with and without oocysts were significantly different (P < 0.05, P < 0.01), and their values were (64.28% ± 6.55%), (43.55% ± 5.80%), (28.23% ± 4.32%), 1.52 ± 0.32 and (58.97% ± 5.23%), (39.26% ± 4.93%), (30.54% ± 5.17%), 1.26 ± 0.21, respectively. (5) Specific IgG, IgM and IgG+IgM in serum of the patients with oocyst were significantly different (P < 0.01) with the positive rates of 63.41% (9/14), 17.07% (1/14) and 19.51% (4/14) respectively. CONCLUSION: Cyclospore cayetanensis infection is present in Anhui, China and it was confirmed to be a new pathogen associated with children diarrhea, adults obstinate diarrhea and diarrhea in immunocompromised individuals. Among all the infected individuals, adult obstinate diarrhea patients and immunocompromised individuals are common. Feces examination of oocysts and serological examination of the specific antibody will be of much help in the diagnosis of Cyclospore cayetanensis infection.

Clinical and epidemiological data of patients with clonorchiasis

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.

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.