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  • 5 Most Exciting New Running Shoes in 2026 – RUN

    5 Most Exciting New Running Shoes in 2026 – RUN

    Published December 26, 2025 04:00AM

    At a recent trade show, I had the opportunity to sit down with product managers from close to 30 running shoe brands to get inside information on what is coming in 2026. Amid lots of exciting models, a few stood…

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  • Wu, X., Liang, L., Shi, Y. & Fomel, S. FaultSeg3D: using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. GEOPHYSICS 84, IM35–IM45 (2019).

    Google Scholar 

  • Suping Peng. Current status and prospects of research on geological assurance system for coal mine safe and high efficient mining. J. China Coal Soc. 45, 2331–2345 (2020).

    Google Scholar 

  • Mousavi, S. M. & Beroza, G. C. Deep-learning seismology. Science 377, eabm4470 (2022).

    Google Scholar 

  • Lin, P., Peng, S., Zhao, J., Cui, X. & Du, W. Accurate diffraction imaging for detecting small-scale geologic discontinuities. GEOPHYSICS 83, S447–S457 (2018).

    Google Scholar 

  • Zou, G., Ren, K., Sun, Z., Peng, S. & Tang, Y. Fault interpretation using a support vector machine: A study based on 3D seismic mapping of the Zhaozhuang coal mine in the Qinshui Basin, China. J. Appl. Geophys. 171, 103870 (2019).

    Google Scholar 

  • Ren, K. et al. Fault identification based on the KPCA-GPSO-SVM algorithm for seismic attributes in the Sihe Coal Mine, Qinshui Basin, China. Interpretation 1–58 (2022). https://doi.org/10.1190/int-2022-0039.1

  • Yang, Y. et al. Feature Extraction, Selection, and K-Nearest neighbors algorithm for shark behavior classification based on imbalanced dataset. IEEE Sens. J. 21, 6429–6439 (2021).

    Google Scholar 

  • Ren, K. et al. Fault identification and reliability evaluation using an SVM model based on 3-D seismic data volume. Geophys. J. Int. 234, 755–768 (2023).

    Google Scholar 

  • Zuo, R. & Carranza, E. J. M. Support vector machine: A tool for mapping mineral prospectivity. Comput. Geosci. 37, 1967–1975 (2011).

    Google Scholar 

  • Han, C. et al. Intelligent fault prediction with wavelet-SVM fusion in coal mine. Comput. Geosci. 194, 105744 (2025).

    Google Scholar 

  • Xiong, W. et al. Seismic fault detection with convolutional neural network. GEOPHYSICS 83, O97–O103 (2018).

    Google Scholar 

  • Pochet, A., Diniz, P. H. B., Lopes, H. & Gattass, M. Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geosci. Remote Sens. Lett. 16, 352–356 (2019).

    Google Scholar 

  • Di, H., Li, Z., Maniar, H. & Abubakar, A. Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow. GEOPHYSICS 85, WA77–WA86 (2020).

    Google Scholar 

  • Geng, Z. & Wang, Y. Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification. Nat. Commun. 11, 3311 (2020).

    Google Scholar 

  • Zhang, G., Lin, C. & Chen, Y. Convolutional neural networks for microseismic waveform classification and arrival picking. GEOPHYSICS 85, WA227–WA240 (2020).

    Google Scholar 

  • Yu, S. & Ma, J. Deep learning for geophysics: current and future trends. Rev. Geophys. 59, e2021RG000742 (2021).

    Google Scholar 

  • Zou, G., Liu, H., Ren, K., Deng, B. & Xue, J. Automatic recognition of faults in mining areas based on convolutional neural network. Energies 15, 3758 (2022).

    Google Scholar 

  • Deng, B. et al. An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking. Geophys. Prospect. 72, 3350–3370 (2024).

    Google Scholar 

  • An, Y. et al. Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review. Earth-Sci. Rev. 243, 104509 (2023).

    Google Scholar 

  • An, Y. et al. Deep convolutional neural network for automatic fault recognition from 3D seismic datasets. Comput. Geosci. 153, 104776 (2021).

    Google Scholar 

  • Kay, S. M. & Marple, S. L. Spectrum analysis—A modern perspective. Proc. IEEE 69, 1380–1419 (1981).

  • Hubral, P., Tygel, M. & Schleicher, J. Seismic image waves. Geophys. J. Int. 125, 431–442 (1996).

    Google Scholar 

  • Cao, S. & Chen, X. The second-generation wavelet transform and its application in denoising of seismic data. Appl. Geophys. 2, 70–74 (2005).

    Google Scholar 

  • Neut, J. V. D., Sen, M. K. & Wapenaar, K. Seismic reflection coefficients of faults at low frequencies: a model study. Geophys. Prospect. 56, 287–292 (2008).

    Google Scholar 

  • Liu, W., Wang, Z. & Cao, S. Stratigraphic interfaces identification based on wavelet transform. SEG Tech. Program. Expanded Abstracts. 2012, 1–5. https://doi.org/10.1190/segam2012-0247.1 (2012). Society of Exploration Geophysicists.

    Google Scholar 

  • Botter, C., Cardozo, N., Qu, D., Tveranger, J. & Kolyukhin, D. Seismic characterization of fault facies models. Interpretation 5, SP9–SP26 (2017).

    Google Scholar 

  • Yeh, H. G., Sim, S. & Bravo, R. J. Wavelet and denoising techniques for Real-Time HIF detection in 12-kV distribution circuits. IEEE Syst. J. 13, 4365–4373 (2019).

    Google Scholar 

  • Li, X., Chen, S., Wang, E. & Li, Z. Rockburst mechanism in coal rock with structural surface and the microseismic (MS) and electromagnetic radiation (EMR) response. Eng. Fail. Anal. 124, 105396 (2021).

    Google Scholar 

  • Li, X., Chen, S., Liu, S. & Li, Z. AE waveform characteristics of rock mass under uniaxial loading based on Hilbert-Huang transform. J. Cent. South. Univ. 28, 1843–1856 (2021).

    Google Scholar 

  • Li, X. et al. Rock burst monitoring by integrated microseismic and electromagnetic radiation methods. Rock. Mech. Rock. Eng. 49, 4393–4406 (2016).

    Google Scholar 

  • Sang, E. F. & Yeh, H. G. The use of transform domain LMS algorithm to adaptive equalization. in Proceedings of IECON ’93–19th Annual Conference of IEEE Industrial Electronics –2064 vol.3 2061. https://doi.org/10.1109/IECON.1993.339393 (1993).

  • Daubechies, I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41, 909–996 (1988).

    Google Scholar 

  • Larsonneur, J. L. & Morlet, J. Wavelets and seismic interpretation. In Wavelets (eds Combes, J. M. et al.) 126–131 (Springer, 1990). https://doi.org/10.1007/978-3-642-75988-8_7.

    Google Scholar 

  • Chakraborty, A. & Okaya, D. Frequency-time decomposition of seismic data using wavelet-based methods. GEOPHYSICS 60, 1906–1916 (1995).

    Google Scholar 

  • Graps, A. An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995).

    Google Scholar 

  • Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).

    Google Scholar 

  • Singh, G. K. & Sa’ad Ahmed, S. A. K. Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine. Electr. Power Syst. Res. 68, 119–136 (2004).

    Google Scholar 

  • Osowski, S. & Garanty, K. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng. Appl. Artif. Intell. 20, 745–755 (2007).

    Google Scholar 

  • Gumus, E., Kilic, N., Sertbas, A. & Ucan, O. N. Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Syst. Appl. 37, 6404–6408 (2010).

    Google Scholar 

  • Liu, W., Cao, S. & Chen, Y. Seismic Time–Frequency analysis via empirical wavelet transform. IEEE Geosci. Remote Sens. Lett. 13, 28–32 (2016).

    Google Scholar 

  • Wang, Z., Zhang, B., Gao, J., Wang, Q. & Liu, Q. H. Wavelet transform with generalized beta wavelets for seismic time-frequency analysis. Geophysics 82, O47–O56 (2017).

    Google Scholar 

  • Liu, S. et al. Experimental study of effect of liquid nitrogen cold soaking on coal pore structure and fractal characteristics. Energy 275, 127470 (2023).

    Google Scholar 

  • Li, H. et al. Experimental study on compressive behavior and failure characteristics of imitation steel fiber concrete under uniaxial load. Constr. Build. Mater. 399, 132599 (2023).

    Google Scholar 

  • Liu, N. et al. Seismic data reconstruction via Wavelet-Based residual deep learning. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022).

    Google Scholar 

  • Shen, S., Li, H., Chen, W., Wang, X. & Huang, B. Seismic fault interpretation using 3-D scattering wavelet transform CNN. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022).

    Google Scholar 

  • Jiang, J., Stankovic, V., Stankovic, L., Parastatidis, E. & Pytharouli, S. Microseismic event classification with Time-, Frequency-, and Wavelet-Domain convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 61, 1–14 (2023).

    Google Scholar 

  • Fujieda, S., Takayama, K. & Hachisuka, T. Wavelet Convolutional Neural Networks. Preprint at https://doi.org/10.48550/arXiv.1805.08620 (2018).

  • Yeh, H. G., Corona, A. & Ramirez, T. Data-Driven Adaptive Modulation Classification Systems. in IEEE International systems Conference (SysCon) 1–7 (2025). https://doi.org/10.1109/SysCon64521.2025.11014808 (2025).

  • Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708 (2017).

  • Springenberg, J. T., Dosovitskiy, A., Brox, T. & Riedmiller, M. Striving for Simplicity: The All Convolutional Net. Preprint at (2015). https://doi.org/10.48550/arXiv.1412.6806

  • Zhang, L. et al. Signal modulation classification based on deep learning and Software-Defined radio. IEEE Commun. Lett. 25, 2988–2992 (2021).

    Google Scholar 

  • Huang, G., Liu, Z., Pleiss, G., van der Maaten, L. & Weinberger, K. Q. Convolutional networks with dense connectivity. IEEE Trans. Pattern Anal. Mach. Intell. 44, 8704–8716 (2022).

    Google Scholar 

  • Hsiao, T. Y., Chang, Y. C., Chou, H. H. & Chiu, C. T. Filter-based deep-compression with global average pooling for convolutional networks. J. Syst. Archit. 95, 9–18 (2019).

    Google Scholar 

  • Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE. 86, 2278–2324 (1998).

    Google Scholar 

  • Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM. 60, 84–90 (2017).

    Google Scholar 

  • Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. Preprint at (2015). https://doi.org/10.48550/arXiv.1409.1556

  • Mallat, S. A Wavelet Tour of Signal Processing.

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  • Turkey arrests suspected ISIS member linked to planning attacks on new year celebrations

    Turkey arrests suspected ISIS member linked to planning attacks on new year celebrations

    Arts

    Turkish authorities said Friday they’ve apprehended a suspected ISIS member who was planning attacks on celebrations ushering in the new year. It comes a day after the prosecutor’s office said authorities carried out raids and then detained…

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  • Head ‘Back to Broadway’ with the Pembroke Community Choir 

    Article content

    The glow of Christmas might still be shining in our homes and in our hearts, but the Pembroke Community Choir already has  its sights set on the neon lights of Broadway!  Choir Director Gerald LaRonde has sifted through a…

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  • Effectiveness of Empagliflozin-Linagliptin Fixed-Dose Combination on Chronic Kidney Disease Outcomes in Patients With Type 2 Diabetes in a Real-World Setting

    Effectiveness of Empagliflozin-Linagliptin Fixed-Dose Combination on Chronic Kidney Disease Outcomes in Patients With Type 2 Diabetes in a Real-World Setting

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  • Austin middle school delivers gifts to refugee family from Afghanistan

    Austin middle school delivers gifts to refugee family from Afghanistan

    Habibullah Babrakzai, 10, and his younger sister, Medina, 5, high-five as students from Grisham Middle School deliver collected gifts.

    Sara Diggins/Austin American-Statesman

    “We have to get her the Barbie Dreamhouse,” Lucy Tarno told her mother.

    But…

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  • The Warminster Thing: 60 years since town’s UFO fascination began

    The Warminster Thing: 60 years since town’s UFO fascination began

    Back in 1965, The Warminster Thing was reported by local media and then by national media, including the BBC.

    Local paper the Warminster Journal reported on it and also had people writing in letters about it.

    One reporter who became especially…

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  • Harrison Howard Dyna-Sorb Full Shock Absorbing Memory Half Saddle Pad for Horse

    Jane


    Good buy. Very flexible and breathable. Comfortable. Does the job.

    Amber






    Reviewed in the United States on February 24, 2025


    I love this pad and so do my horses. The horse pictured has very low withers and used to be nervous and uptight under saddle but not anymore after using this pad. My other horse is very high withered and is much more comfortable with this pad also.

    ParkS12






    Reviewed in the United States on June 11, 2024


    This pad is great, it wasn’t quite big enough for my saddle. I knew this was a risk as I have a fairly small western endurance saddle, I was hoping it would sit where the saddle touches. I have a really short backed Arabian. It’s probably perfect for English saddles or true endurance saddles.

    Customer






    Reviewed in the United States on September 19, 2023


    No es lo q espere . Ojala dure ,viene como un cojin interior con forro negro . Medio raro … no lo recomiendo…

    C.N.M.






    Reviewed in the United States on December 20, 2022


    I was looking for an impact gel type of pad to go under an already well fitted saddle, just for a little added shock absorption because my horse may stay to be used for some beginner lessons here or there, and that means sometimes a kid will be struggling to learn balance and will have a little extra bounce on the horses back at first.The pad is thin enough that it doesn’t alter the way the saddle fits, but at the same time it offers that little extra shock absorption and I can definitely see him loosen his back and move more comfortably with the pad versus without. I only use a thin pad liner under this pad and it works well for my purpose.Definitely recommended and IMO it was worth the price. If I happen to get a picture next time I’m at the barn, I’ll update my review.

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  • How to Fight ‘Middle-Age Spread’ | Health

    How to Fight ‘Middle-Age Spread’ | Health



























    How to Fight ‘Middle-Age Spread’ | Health | nbcrightnow.com


    We recognize you are attempting to access this website from a country belonging to the European Economic Area…

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  • Curcumin Supplementation Lowers Blood Pressure in Diabetes

    Curcumin Supplementation Lowers Blood Pressure in Diabetes

    CURCUMIN or turmeric supplementation was associated with a modest but significant reduction in systolic blood pressure in adults with prediabetes or Type 2 diabetes (T2D), according to a new meta-analysis of randomised trials.

    Curcumin and…

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