Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings.

Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings' polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = -5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.

Reliable Denoising Strategy to Enhance the Accuracy of Arrival Time Picking of Noisy Microseismic Recordings.

We propose a method to enhance the accuracy of arrival time picking of noisy microseismic recordings. A series of intrinsic mode functions (IMFs) of the microseismic signal are initially decomposed by employing the ensemble empirical mode decomposition. Subsequently, the sample entropy values of the obtained IMFs are calculated and applied to set an appropriate threshold for selecting IMFs. These are then reconstructed to distinguish between noise and useful signals. Ultimately, the Akaike information criterion picker is used to determine the arrival time of the denoised signal. Test results using synthetic noisy microseismic recordings demonstrate that the proposed approach can significantly reduce picking errors, with errors within the range of 1-3 sample intervals. The proposed method can also give a more stable picking result when applied to different microseismic recordings with different signal-to-noise ratios. Further application in real microseismic recordings confirms that the developed method can estimate an accurate arrival time of noisy microseismic recordings.

Start-Up's Road to Disruptive Innovation in the Digital Era: The Interplay Between Dynamic Capabilities and Business Model Innovation.

The emergence and infusion of digital technologies bring greater chances for start-ups to conduct disruptive innovation through digital entrepreneurship. Despite the existed business practices, the happening mechanism of start-up's disruptive innovation in the digital economy context remains unclear. This study aims to understand the evolutionary mechanism and fulfillment path start-ups' disruptive innovation in the digital era. The longitudinal case study is conducted for a Chinese Internet start-up that successfully launched disruptive innovation under the digital economy background. Adopting a process perspective, this study analyzes the evolutionary phases of digital disruptive innovation. Moreover, this study identifies the digital technologies adoption, dynamic capabilities deployment, and business model innovation as the key pillars, and their interactions. Finally, this study induces and proposes its evolution mechanism and fulfillment path models. This study enriches the research scope of disruptive innovation and digital entrepreneurship. This study can offer theoretical guidance for the start-ups' disruptive innovation in the digital era, and practical implications for implementing a digital catching-up strategy.

Development of Duramycin-Based Molecular Probes for Cell Death Imaging.

Cell death is involved in numerous pathological conditions such as cardiovascular disorders, ischemic stroke and organ transplant rejection, and plays a critical role in the treatment of cancer. Cell death imaging can serve as a noninvasive means to detect the severity of tissue damage, monitor the progression of diseases, and evaluate the effectiveness of treatments, which help to provide prognostic information and guide the formulation of individualized treatment plans. The high abundance of phosphatidylethanolamine (PE), which is predominantly confined to the inner leaflet of the lipid bilayer membrane in healthy mammalian cells, becomes exposed on the cell surface in the early stages of apoptosis or accessible to the extracellular milieu when the cell suffers from necrosis, thus representing an attractive target for cell death imaging. Duramycin is a tetracyclic polypeptide that contains 19 amino acids and can bind to PE with excellent affinity and specificity. Additionally, this peptide has several favorable structural traits including relatively low molecular weight, stability to enzymatic hydrolysis, and ease of conjugation and labeling. All these highlight the potential of duramycin as a candidate ligand for developing PE-specific molecular probes. By far, a couple of duramycin-based molecular probes such as Tc-99 m-, F-18-, or Ga-68-labeled duramycin have been developed to target exposed PE for in vivo noninvasive imaging of cell death in different animal models. In this review article, we describe the state of the art with respect to in vivo imaging of cell death using duramycin-based molecular probes, as validated by immunohistopathology.

Clinical and prognostic significance of CC chemokine receptor type 8 protein expression in gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract. Surgical resection and tyrosine kinase inhibitors are defined as the main treatments but cannot cure patients with advanced GIST, which eventually develops into recurrence and acquired drug resistance. Therefore, it is necessary to identify prognostic biomarkers and new therapeutic targets for GISTs. CC chemokine receptor type 8 (CCR8) protein participates in regulation of immune responses. Recent studies on CCR8 in non-small cell lung cancer and colorectal cancer showed that it was highly expressed in tumor-infiltrating regulatory T cells and correlated with a poor prognosis. AIM: To detect CCR8 expression in GIST tissues and analyze its relationships with clinicopathological features and prognosis in patients with GISTs. METHODS: Tissue samples were used for the tissue microarrays construction. The microarrays were then subjected to immunohistochemical analyses to detect CCR8 expression. Next, Kaplan-Meier analysis was utilized to calculate the survival rate of patients with complete follow-up data, and the potential prognostic value of CCR8 was evaluated by Cox regression analysis. Finally, a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes single-gene enrichment chart of CCR8 was constructed using the STRING database. RESULTS: CCR8-positive signals were detected as brown or brown-yellow particles by immunohistochemistry located in the cytoplasm. Among 125 tissue samples, 74 had CCR8 high expression and 51 had low or negative expression. Statistical analyses suggested CCR8 was significantly correlated with tumor size, mitotic index, AFIP-Miettinen risk classification and tumor location. Kaplan-Meier and multivariate analyses showed that patients with low or negative CCR8 expression, mitotic index < 5/high-power fields (HPF) and tumor diameter < 5 cm had a better prognosis. Based on the STRING database, CCR8 was significantly enriched in biological processes such as tumor immunity, T lymphocyte chemotaxis, migration and pathways like the nuclear factor-κB and tumor necrosis factor pathways as well as intestinal immune regulation networks. CONCLUSION: CCR8 is a prognostic biomarker for malignant potential of GISTs, with high expression correlated with malignancy and poor prognosis.

Janus multi-responsive superparamagnetic nanoparticles functionalized with two on-demand and independently cleavable ligands for Actinide separation.

HYPOTHESIS: Nanoparticles functionalized with ligands which can on-demand and remotely be detached have recently attracted interest as stimuli-responsive materials. Research is now focused on multi-responsive systems, with applications in environmental science and biomedicine. The possibility to covalently couple two different ligands on a single nanoparticles, and to release them independently is investigated. This concept of nanoparticles functionalized with dual on-demand cleavable ligands is exploited in ground water decontamination and radionuclides separation. Efficient separation of contaminants in a single step is expected, simplifying partitioning process and decreasing generation of secondary waste by nuclear industry. EXPERIMENTS: Sub-10 nm Janus superparamagnetic nanoparticles are functionalized by click-chemistry (thiol and Diels-Alder) with two different Actinide-specific chelators. The reversible covalent bonds allow to detach chelators independently by either pH- or thermo-stimulation. The nanoparticles decorated with diethylenetriamine-pentaacetic acid (DTPA) and [(2-furan-2-yl-2-hydroxy-ethylcarbamoyl)-methoxy]-acetic acid (FHECMAA) are incubated with UO22+ and La3+ (as substitute for Pu3+) at pH = 3 and 7 before chelator-metal complexes are released. Metal contents are measured to determine separation efficiency. FINDINGS: Chelators can be detached from Janus nanoparticles with perfect selectivity. The nanoparticles are highly efficient for extraction of metals in acidic medium and show good ability for separation of U and La at neutral pH.

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis.

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data.

Fast adaptive particle spectrum fitting algorithm based on moment-estimated initial parameters.

An algorithm based on moment estimation is presented to determine the initial parameters of the particle spectrum peak shape function for the iteration fitting procedure. The algorithm calculates the mean value, variance, and third-order central moment by using the spectrum peak data, solves the parameters of the fitting function, and then provides them as the initial values to the Levenberg-Marquardt algorithm to ensure convergence and optimized fitting. The effectiveness of the proposed algorithm was tested by gamma and alpha spectra. The algorithm can be used in automated peak curve fitting and spectral analysis.

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks.

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.

DOUBLE-EXPONENTIAL FITTING FUNCTION FOR EVALUATION OF COSMIC-RAY-INDUCED NEUTRON FLUENCE RATE IN ARBITRARY LOCATIONS.

The fluence rate of cosmic-ray-induced neutrons (CRINs) varies with many environmental factors. While many current simulation and experimental studies have focused mainly on the altitude variation, the specific rule that the CRINs vary with geomagnetic cutoff rigidity (which is related to latitude and longitude) was not well considered. In this article, a double-exponential fitting function F=(A1e-A2CR+A3)eB1Al, is proposed to evaluate the CRINs' fluence rate varying with geomagnetic cutoff rigidity and altitude. The fitting R2 can have a value up to 0.9954, and, moreover, the CRINs' fluence rate in an arbitrary location (latitude, longitude and altitude) can be easily evaluated by the proposed function. The field measurements of the CRINs' fluence rate and H*(10) rate in Mt. Emei and Mt. Bowa were carried out using a FHT-762 and LB 6411 neutron prober, respectively, and the evaluation results show that the fitting function agrees well with the measurement results.