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  • Maity, D. & Ciezobka, J. Diagnostic assessment of reservoir response to fracturing: a case study from hydraulic fracturing test site (HFTS) in Midland basin. J. Petrol. Explor. Prod. Technol. 11, 3177–3192 (2021).

    Google Scholar 

  • Sahai, R. & Moghanloo, R. G. Proppant transport in complex fracture networks–A review. J. Petrol. Sci. Eng. 182, 106199 (2019).

    Google Scholar 

  • Maity, D., Ciezobka, J. & Eisenlord, S. Assessment of in-situ proppant placement in SRV using through-fracture core sampling at HFTS. in SPE/AAPG/SEG Unconventional Resources Technology Conference. D023S023R004 (URTeC, 2018).

  • Zhang, X., Zhang, S., Zou, Y. & Li, J. Effects of laminar structure on fracture propagation and proppant transportation in continental shale oil reservoirs with multiple lithological-combination. Int. J. Fract. 249, 3 (2025).

    Google Scholar 

  • Ciezobka, J. & Reeves, S. Overview of Hydraulic Fracturing Test Sites (HFTS) in the Permian Basin and Summary of Selected Results (HFTS-I in Midland and HFTS-II in Delaware). In: Proceedings of the 2020 Latin America Unconventional Resources Technology ConferenceUnconventional Resources Technology Conference. https://doi.org/10.15530/urtec-2020-1544 (2020).

  • Ciezobka, J., Courtier, J. & Wicker, J. Hydraulic Fracturing Test Site (HFTS) – Project Overview and Summary of Results. in Proceedings of the 6th Unconventional Resources Technology Conference. https://doi.org/10.15530/urtec-2018-2937168 (American Association of Petroleum Geologists, 2018).

  • Pudugramam, S. et al. American Association of Petroleum Geologists, Colorado Convention Center, Denver, Colorado, US,. A Comprehensive Simulation Study of Hydraulic Fracturing Test Site 2 (HFTS-2): Part I – Modeling Pressure Dependent and Time Dependent Fracture Conductivity in Fully Calibrated Fracture and Reservoir Models. In: Proceedings of the 11th Unconventional Resources Technology Conference. https://doi.org/10.15530/urtec-2023-3864710 (2023).

  • Bessa, F. et al. American Association of Petroleum Geologists, Colorado Convention Center, Denver, Colorado, US,. A Comprehensive Simulation Study of Hydraulic Fracturing Test Site 2 (HFTS-2): Part II – Development Optimization in the Delaware Basin Using an Integrated Modeling Workflow. In: Proceedings of the 11th Unconventional Resources Technology Conference. https://doi.org/10.15530/urtec-2023-3851681 (2023).

  • Rongli, X. et al. SPE,. Analysis and Understanding of Interwell Communication in Multiple Fracture Monitoring Technology: A Case Study of the Qingcheng Shale Oil Hydraulic Fracturing Field Lab. in SPE Gas & Oil Technology Showcase and Conference D022S002R001 (2025).

  • Maity, D. & Ciezobka, J. A systematic interpretation of subsurface proppant concentration from drilling mud returns: case study from hydraulic fracturing test site (HFTS-2) in Delaware basin. in SPE/AAPG/SEG Unconventional Resources Technology Conference D021S031R003. (URTEC, 2021).

  • Li, S. et al. Study on automatic lithology identification based on convolutional neural network and deep transfer learning. Discov Appl. Sci. 6, (2024).

  • Xiao, J. Lithology identification method of cuttings based on improved VGG16. in Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE). 12787 87–92. (SPIE, 2023).

  • Chawshin, K., Berg, C. F., Varagnolo, D. & Lopez, O. Lithology classification of whole core CT scans using convolutional neural networks. SN Appl. Sci. 3, (2021).

  • Zhang, Y., Li, M. & Han, S. Automatic identification and classification in lithology based on deep learning in rock images. Yanshi Xuebao/Acta Petrologica Sinica. 34, 333–342 (2018).

    Google Scholar 

  • Abdullah, M. A., Mohammed, A. A., Awad, S. R. & RockDNet Deep learning approach for lithology classification. Appl. Sci. 14, 5511 (2024).

    Google Scholar 

  • Zedong, M. A. et al. Multi-scale lithology recognition based on deep learning of rock images. Bull. Geol. Sci. Technol. 41, 316–322 (2022).

    Google Scholar 

  • Lin, N., Fu, J., Jiang, R., Li, G. & Yang, Q. Lithological classification by hyperspectral images based on a two-layer XGBoost model, combined with a greedy algorithm. Remote Sens. 15, 3764 (2023).

    Google Scholar 

  • Alzubaidi, F., Mostaghimi, P., Swietojanski, P., Clark, S. R. & Armstrong, R. T. Automated lithology classification from drill core images using convolutional neural networks. J. Petrol. Sci. Eng. 197, 107933 (2021).

    Google Scholar 

  • Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intelligence. 679–698 (2009).

  • Maragos, P. & Schafer, R. Morphological skeleton representation and coding of binary images. IEEE Trans. Acoust. Speech Signal Process. 34, 1228–1244 (2003).

    Google Scholar 

  • Kornilov, A. S. & Safonov, I. V. An overview of watershed algorithm implementations in open source libraries. J. Imaging. 4, 123 (2018).

    Google Scholar 

  • Soille, P. Morphological Image Analysis (Springer Berlin Heidelberg, 2004). https://doi.org/10.1007/978-3-662-05088-0.

    Google Scholar 

  • He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778. (2016).

  • Liu, Z. et al. KAN: Kolmogorov-Arnold Networks. arXiv:2404.19756 [cs.LG]. https://doi.org/10.48550/arXiv.2404.19756 (2025).

  • Zhang, X. et al. LDConv: linear deformable Convolution for improving Convolutional neural networks. Image Vis. Comput. 149, 105190 (2024).

    Google Scholar 

  • Ma, X., Dai, X., Bai, Y., Wang, Y. & Fu, Y. Rewrite the stars. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5694–5703. (2024).

  • Hu, Q. et al. Damage location and area measurement of aviation functional surface via neural radiance field and improved Yolov8 network. Artif Intell. Rev. 58, (2024).

  • Zhang, X. et al. Starnet: an efficient Spatiotemporal feature sharing reconstructing network for automatic modulation classification. IEEE Trans. Wireless Commun. 23, 13300–13312 (2024).

    Google Scholar 

  • Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 7132–7141 (2018).

  • Han, D. et al. Demystify mamba in vision: a linear attention perspective. Adv. Neural Informat. Process. Syst. 37, 127181–127203 (2024).

    Google Scholar 

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  • No. 5 Red Rocks Packed Jon M. Huntsman Center to Open 2026 Season

    No. 5 Red Rocks Packed Jon M. Huntsman Center to Open 2026 Season

    SALT LAKE CITY—The No. 5 Red Rocks open their 2026 season in front of 11,191 fans at the Jon M. Huntsman Center Friday night. Utah had an overall winning score of 196.625 besting both No.
    15 Minnesota (195.475) and No.22 Iowa (194.825) in a…

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  • Rushden cafe for homeless charity ready to expand operations

    Rushden cafe for homeless charity ready to expand operations

    “We were in profit in our first month and we haven’t looked back really,” said Mr Robertson.

    He said all money raised from the cafe, in Hamblin Court, goes back into the charity which has meant it has funded additional volunteers to do welfare and…

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  • East of England news quiz of the week January 3 2026

    East of England news quiz of the week January 3 2026

    Hello, we are back for another year, and this edition features news from the last two weeks [we will be back to normal next week], including the unusual thing about a pretty village and a runner defying the odds during a race.

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  • 2025 Football Postgame Notes Mississippi State (Duke’s Mayo Bowl)

    2025 Football Postgame Notes Mississippi State (Duke’s Mayo Bowl)

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  • Dar heads to Beijing to co-chair strategic dialogue with China – Dawn

    1. Dar heads to Beijing to co-chair strategic dialogue with China  Dawn
    2. Islamabad and Beijing open strategic dialogue as Pakistan’s top diplomat begins China visit  TRT World
    3. One-China policy: Islamabad reaffirms support for Beijing on Taiwan issue  

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  • Comeback Cougs Rally for Victory Over LMU

    Comeback Cougs Rally for Victory Over LMU

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  • SAT 3RD JAN PREVIEW: PANTHERS HOST FLAMES

    SAT 3RD JAN PREVIEW: PANTHERS HOST FLAMES

    Sat 3 Jan 2026 – 06:00AM

    🎟 CLICK HERE TO BUY TICKETS FOR PANTHERS HOME GAMES 🎟

    It’s gameday at the Motorpoint Arena as Nottingham Panthers host Guildford Flames in the Elite League (19:00 face-off).

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  • With 72% ownership of the shares, Nedbank Group Limited (JSE:NED) is heavily dominated by institutional owners

    With 72% ownership of the shares, Nedbank Group Limited (JSE:NED) is heavily dominated by institutional owners

    • Significantly high institutional ownership implies Nedbank Group’s stock price is sensitive to their trading actions

    • The top 8 shareholders own 52% of the company

    • Analyst forecasts along with ownership data serve to give a strong idea about prospects for a business

    AI is about to change healthcare. These 20 stocks are working on everything from early diagnostics to drug discovery. The best part – they are all under $10bn in marketcap – there is still time to get in early.

    Every investor in Nedbank Group Limited (JSE:NED) should be aware of the most powerful shareholder groups. With 72% stake, institutions possess the maximum shares in the company. Put another way, the group faces the maximum upside potential (or downside risk).

    Since institutional have access to huge amounts of capital, their market moves tend to receive a lot of scrutiny by retail or individual investors. Therefore, a good portion of institutional money invested in the company is usually a huge vote of confidence on its future.

    Let’s take a closer look to see what the different types of shareholders can tell us about Nedbank Group.

    Check out our latest analysis for Nedbank Group

    JSE:NED Ownership Breakdown January 3rd 2026

    Many institutions measure their performance against an index that approximates the local market. So they usually pay more attention to companies that are included in major indices.

    Nedbank Group already has institutions on the share registry. Indeed, they own a respectable stake in the company. This implies the analysts working for those institutions have looked at the stock and they like it. But just like anyone else, they could be wrong. When multiple institutions own a stock, there’s always a risk that they are in a ‘crowded trade’. When such a trade goes wrong, multiple parties may compete to sell stock fast. This risk is higher in a company without a history of growth. You can see Nedbank Group’s historic earnings and revenue below, but keep in mind there’s always more to the story.

    earnings-and-revenue-growth
    JSE:NED Earnings and Revenue Growth January 3rd 2026

    Investors should note that institutions actually own more than half the company, so they can collectively wield significant power. Nedbank Group is not owned by hedge funds. The company’s largest shareholder is Public Investment Corporation Limited, with ownership of 17%. Allan Gray Proprietary Ltd. is the second largest shareholder owning 8.4% of common stock, and Coronation Fund Managers Limited holds about 5.4% of the company stock.

    On further inspection, we found that more than half the company’s shares are owned by the top 8 shareholders, suggesting that the interests of the larger shareholders are balanced out to an extent by the smaller ones.

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  • Ladies Fall 79-66 To YellowJackets On Friday Night

    Shreveport – The Centenary women’s basketball team fell 79-66 to the LeTourneau University YellowJackets on Friday night in a Southern Collegiate Athletic Conference contest inside the Gold Dome.

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