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The integrated analysis of host metabolome and intestinal microbiome is an opportunity to explore the complex therapeutic mechanisms of traditional Chinese medicines. Currently, researchers mainly employ various statistical correlation analytical methods to investigate metabolome-microbiome correlations. However, these conventional correlation techniques often focus on statistical correlations and their biological meanings are always ignored, especially the functional relevance between them. Here, we developed a novel enzyme-based functional correlation (EBFC) algorithm to further improve the interpretability and the identified scope of microbe-metabolite correlations based on the conventional Spearman's analysis. The proposed EBFC algorithm is successfully utilized to reveal the therapeutic mechanisms of Jian-Pi-Yi-Shen (JPYS) formula on the treatment of adenine-induced chronic kidney disease (CKD) rats. JPYS, a TCM formula for treating CKD, has beneficial clinical effects. We tentatively revealed the potential mechanism of JPYS for treating CKD rats from the perspective of the serum metabolome, gut microbiome, and their interactions. Specifically, 11 metabolites and 19 bacterial genera in the CKD rats were significantly regulated to approaching normal status after JPYS treatment, suggesting that JPYS could ameliorate the pathological symptoms of CKD rats by reshaping the disturbed metabolome and gut microbiota. Further correlation analysis between the significantly perturbed metabolites, microbiota, and the related enzymes provided more strong evidence for the study of host metabolism-microbiota interactions and the therapeutic mechanism of JPYS on CKD rats. In conclusion, these findings will help us to deeply understand the pathogenesis of CKD and provide new insights into the therapeutic mechanism of JPYS.

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With the development of underwater navigation and underwater communication, it remains difficult to obtain time delay measurements after propagating long distance. This paper proposes an improved high-accuracy time delay measuring method for long distance underwater channel propagation. First, by sending an encoded signal, the signal acquisition is carried out at the receiving end. Then, to improve signal to noise ratio (SNR), bandpass filtering is carried out on the receiving end. Next, considering the random changes in the underwater sound propagation channel, a strategy is proposed to select the optimal time window for cross-correlation. Then, new regulations are proposed to calculate the cross-correlation results. To verify the effectiveness of the algorithm, we compared it with other algorithms under low SNR conditions using Bellhop simulation data. Finally, the accurate time delay is obtained. With underwater experiments over different distances, the method proposed by the paper achieves high accuracy. The error is about 10-3 s. The proposed method makes a contribution to underwater navigation and communication.

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The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability.

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The exit velocity of the armature is an important indicator in measuring the launching performance of the electromagnetic gun. The non-contact photoelectric detection technology with the use of a laser screen was applied to the measurement of the armature velocity of the electromagnetic gun. By means of taking the signals that pass through the laser screen obtained by the velocity measurement system as the research object, we solved problems such as the harsh test environment of the launch armature velocity of the electromagnetic gun, the interferences on the armature signal passing through the laser screen unavoidably caused by various factors such as vibration, electromagnetic interference, shock wave, flare, smoke and fragments, and even the non-recognition of the signal passing through the laser screen in severe cases. A data-processing algorithm that combines the Ensemble Empirical Mode Decomposition (EEMD) with Correlation Algorithm (CA) was proposed, with the aim of processing the signals passing through the laser screen, while using the maximum slope point as the time passing through the laser screen so as to calculate the velocity of the armature passing the laser screen. This method can effectively reduce the influence of interference on the test results, and the test results from two sets of velocity measuring systems show that the velocity obtained by the proposed approach is highly consistent.

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Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient's tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.

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BACKGROUND & OBJECTIVE: Thermal ablation is the predominant methodology to treat liver tumors for segregating patients who are not permitted to have surgical intervention. However, noticing or predicting the size of the thermal strategies is a challenging endeavor. We aim to analyze the effects of ablation district volume following radiofrequency ablation (RFA) of ex-vivo liver exploiting a custom Hyperspectral Imaging (HSI) system. MATERIALS AND METHODS: RFA was conducted on the ex-vivo bovine liver at focal and peripheral blood vessel sites and observed by Custom HSI system, which has been designed to assess the exactness and proficiency using visible and near-infrared wavelengths region for tissue thermal effect. The experiment comprised up to ten trials with RFA. The experiment was carried out in two stages to assess the percentage of the thermal effect on the investigated sample superficially and for the side penetration effect. Measuring the diffuse reflectance (Rd) of the sample to identify the spectral reflectance shift which could differentiate between normal and ablated tissue exploiting the designed cross-correlation algorithm for monitoring of thermal ablation. RESULTS: Determination of the diffuse reflection (Rd) spectral signature responses from normal, thermal effected, and thermal ablation regions of the investigated liver sample. Where the ideal wavelength range at (600-640 nm) could discriminate between these different regions. Then, exploited the converted RGB image of the HS liver tissue after RFA for more validations which shows that the optimum wavelength for differentiation at (530-560 nm and 600-640 nm). Finally, applying statistical analysis to validate our results presenting that wavelength 600 nm had the highest standard deviation (δ) to differentiate between various thermally affected regions regarding the normal tissue and wavelength 640 nm shows the highest (δ) to differentiate between the ablated and normal regions. CONCLUSION: The designed and implemented medical imaging system incorporated the hyperspectral camera capabilities with the associate cross-correlation algorithm that could successfully distinguish between the ablated and thermally affected regions to assist the surgery during the tumor therapy.

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In this paper, we proposed ultrasound homodyned K (HK) imaging based on the noise-assisted correlation algorithm (NCA) for monitoring microwave ablation of porcine liver ex vivo. The NCA-based HK (αNCA and kNCA) imaging was compared with NCA-based Nakagami (mNCA) imaging and NCA-based cumulative echo decorrelation (CEDNCA) imaging. Backscattered ultrasound radiofrequency signals of porcine liver ex vivo during and after the heating of microwave ablation were collected (n = 15), which were processed for constructing B-mode imaging, NCA-based HK imaging, NCA-based Nakagami imaging, and NCA-based CED imaging. To quantitatively evaluate the final coagulation zone, the polynomial approximation (PAX) technique was applied. The accuracy of detecting coagulation area with αNCA, kNCA, mNCA, and CEDNCA parametric imaging was evaluated by comparing the PAX imaging with the gross pathology. The receiver operating characteristic (ROC) curve was used to further evaluate the performance of the three quantitative ultrasound imaging methods for detecting the coagulation zone. Experimental results showed that the average accuracies of αNCA, kNCA, mNCA, and CEDNCA parametric imaging combined with PAX imaging were 89.6%, 83.25%, 89.23%, and 91.6%, respectively. The average areas under the ROC curve (AUROCs) of αNCA, kNCA, mNCA, and CEDNCA parametric imaging were 0.83, 0.77, 0.83, and 0.86, respectively. The proposed NCA-based HK imaging may be used as a new method for monitoring microwave ablation.

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Based on the growing interest in encephalography to enhance human-computer interaction (HCI) and develop brain-computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band-electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions-valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20-8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.

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The SMS phishing is another method where the phisher operates the SMS as a medium to communicate with the victims and this method is identified as smishing (SMS + phishing). Researchers promoted several anti-phishing methods where the correlation algorithm is applied to explore the relevancy of the features since there are numerous features in the features corpus. The correlation algorithm assesses the rank of the features that is the highest rank leads to the more relevant to the appropriate assignment. Therefore, this paper analyses four rank correlation algorithms particularly Pearson rank correlation, Spearman's rank correlation, Kendall rank correlation, and Point biserial rank correlation with a machine-learning algorithm to determine the best features set for detecting Smishing messages. The result of the investigation reveals that the AdaBoost classifier offered better accuracy. Further analysis shows that the classifier with the ranking algorithm that is Kendall rank correlation appeared superior accuracy than the other correlation algorithms. The inferred of this experiment confirms that the ranking algorithm was able to reduce the dimension of features with 61.53% and presented an accuracy of 98.40%.

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An inductive debris sensor can monitor a mechanical system's debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles' aliasing behavior. Then, a cross-correlation-based method was proposed to deal with this aliasing. Afterwards, taking advantage of the processed signal along with the original signal, an optimization strategy was proposed to make the evaluation of the aliasing debris more accurate than that merely using initial signals. Compared to other methods, the proposed method has fewer limitations in practical applications. The simulation and experimental results also verified the advantage of the proposed method.

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Early decision-making and the prevention of construction safety risks are very important for the safety, quality, and cost of construction projects. In the field of construction safety risk management, in the face of a loose, chaotic, and huge information environments, how to design an efficient construction safety risk management decision support method has long been the focus of academic research. An effective approach to safety management is to structuralize safety risk knowledge, then identify and reuse it, and establish a scientific and systematic construction safety risk management decision system. Based on ontology and improved case-based reasoning (CBR) methods, this paper proposes a decision-making approach for construction safety risk management in which the reasoning process is improved by integrating a similarity algorithm and correlation algorithm. Compared to the traditional CBR approach in which only the similarity of information is considered, this method can avoid missing important correlated information by making inferences from multiple sources of information. Finally, the method is applied to the safety risks of subway construction for verification to show that the method is effective and easy to implement.

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In industrial production processes, rotational speed is a key parameter for equipment condition monitoring and fault diagnosis. To achieve rotational speed measurement of rotational equipment under a condition of high temperature and heavy dust, this article proposes a digital approach using an electrostatic sensor. The proposed method utilizes a strip of a predetermined material stuck on the rotational shaft which will accumulate a charge because of the relative motion with the air. Then an electrostatic sensor mounted near the strip is employed to obtain the fluctuating signal related to the rotation of the charged strip. Via a signal conversion circuit, a square wave, the frequency of which equals that of the rotation shaft can be obtained. Having the square wave, the M/T method and T method are adopted to work out the rotational speed. Experiments were conducted on a laboratory-scale test rig to compare the proposed method with the auto-correlation method. The largest relative errors of the auto-correlation method with the sampling rate of 2 ksps, 5 ksps are 3.2% and 1.3%, respectively. The relative errors using digital approaches are both within ±4‰. The linearity of the digital approach combined with the M/T method or T method is also superior to that of the auto-correlation method. The performance of the standard deviations and response speed was also compared and analyzed to show the priority of the digital approach.

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Viscosity is an important property of liquids. A viscosity change of aqueous substances that deviates from their normal levels usually implies a compromise in quality due to degradation or microorganism proliferation. Monitoring of macro-scale viscosity can be simply realized by various conventional tools, such as rotational viscometers, capillary tubes, falling bodies, and so forth. Nevertheless, today, micro-volume viscosity measurement remains a challenging endeavor, resulting in rare, expensive, or difficult-to-obtain samples not very well studied. For this reason, a novel technique for micro-viscosity based on rotational Brownian motion is presented in this paper. Janus microbeads were made by coating fluorescent polystyrene beads with gold film. Taking advantage of the bead configuration of half gold/half fluorescence, the rotational Brownian signal was expressed in terms of blinking fluorescent intensity. The characteristic correlation time was derived from the blinking intensity of trace amounts of a selected medium over a certain time period, and results were correlated with viscosity. Given a volume of only 2 µL for each measurement, calibration of a series of glycerol⁻water mixtures (100%⁻1% (v/v) water content) yielded good agreement with the expected viscosity predictions over the range of 0.8⁻574.8 cP. Five common oil products, including lubricant oil, baby oil, food oil, olive oil, and motor oil, were further investigated to demonstrate the feasibility and practicability of the proposed technique. Data measured by the rotational Brownian motion-based diffusometer were comparable with those measured by a commercial rotational viscometer. The method also explicitly showed viscosity degradation after the oils were heated at a high temperature of over 100 °C for 10 min. Evaluation proved the proposed Janus microbead-enabled rotational diffusometric technique to be a promising approach for rapid and micro-scale viscosity measurement.

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Partial discharge (PD) localization in substations based on the ultra-high frequency (UHF) method can be used to efficiently assess insulation conditions. Localization accuracy is affected by the accuracy of the time delay (TD) estimation, which is critical for PD localization in substations. A review of existing TD estimation methods indicates that there is a need to develop methods that are both accurate and computationally efficient. In this paper, a novel TD estimation method is proposed to improve both accuracy and efficiency. The TD is calculated using an improved cross-correlation algorithm based on full-wavefronts of array UHF signals, which are extracted using the minimum cumulative energy method and zero-crossing points searching methods. The cross-correlation algorithm effectively suppresses the TD error caused by differences between full-wavefronts. To verify the method, a simulated PD source test in a laboratory and a field test in a 220 kV substation were carried out. The results show that the proposed method is accurate even in case of low signal-to-noise ratio, but with greatly improved computational efficiency.

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Objective To explore the application of de-correlation algorithm to laser speckle blood flow imaging technique and study the blood flow velocity.Methods The principle and algorithm were analyzed for de-correlation laser speckle blood flow imaging.A laser speckle imaging platform was established,and de-correlation laser speckle blood flow imaging algorithm was used to execute microfluidic target experiment and animal experiment.De-correlation algorithm was applied to obtaining blood flow image and velocity curve of microfluidic target.Cross-correlation curve on the original speckle images were got with cross-correlation algorithm,and the original images corresponding to the peak points were selected to undergo computation with de-correlation algorithm,so that the minute information could be augmented on the blood vessel.Results The de-correlation laser speckle blood flow imaging algorithm could be used to obtain the blood flow information and velocity information during microfluidic target experiment and the animal experiment.Conclusion The de-correlation laser speckle blood flow imaging algorithm is feasible in laser speckle imaging,and has good application prospects.

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The internet -based home tele -monitoring system for fetus has the functions of monitoring fetal heart and movement, analysis and alarm. With this system, data related to fetuses can be shared and real -time analyses and suggestions from doctors can be available. Current network system is involved in, and thus it's inexpensive and practical.