Category: 3. Business

  • Westbury welcomes permanent banking hub after campaign

    Westbury welcomes permanent banking hub after campaign

    While the new hub was being refurbished, a temporary one was set up at the Laverton building in Westbury.

    It is hoped the permanent location will complement Westbury’s rotunda project, which will see a space near the town’s library used to host more events.

    Ms Russ said: “The temporary banking hub was a little out of town and up a hill which meant it wasn’t useful for everyone. I’m delighted this new permanent hub has been sorted out.

    “By the time we’ve got the rotunda sorted, this will all suddenly alter the whole feel of the high street in Westbury. If a town looks loved, it will be.”

    The official opening of the banking hub will take place on 9 January at 11:00 GMT, with campaigner Val Jarvis – who launched a petition to reinstate banking services to Westbury – set to cut the ribbon.

    Continue Reading

  • Drone rules ‘will protect Lincolnshire military airspace’

    Drone rules ‘will protect Lincolnshire military airspace’

    Mr Kheng told BBC Radio Lincolnshire: “With these mini drones… before yesterday, you didn’t need to know anything about airspace or aviation.

    “By taking the test, you will have that basic knowledge of airspace and airspace use.”

    Before the new rule came into place, RAF Waddington and the Red Arrows posted on social media warning people to not fly drones in Flight Restriction Zones.

    The base posted a map of the Flight Restriction Zone around RAF Waddington, which includes areas like Bracebridge Heath, North Hykeham, and Boothby Graffoe.

    It said flying without permission “poses a serious safety and security risk” and advised users to check airspace restrictions before take-off.

    Jonathan Nicholson, head of special projects at the CAA, said people who took the test felt “much more confident” flying their drones because they understood where they could operate.

    Users who fail to take the test before flying will be breaking the law and liable to potential fines or even imprisonment.

    Mr Nicholson said: “We want people to fly drones, we want people to enjoy drones, but they must do it safely and responsibly and that will ensure the future with drones in this country and worldwide.”

    Continue Reading

  • Practical tips to save on energy bills this winter

    Practical tips to save on energy bills this winter

    “Some other low-cost wins include reflective panels,” said Mr Pearson.

    “You can put them behind radiators and they can bounce the heat back into the space, so you’re not losing some of that heat generated into the actual wall itself.”

    Mr Pearson also suggests bleeding radiators, external to remove trapped air and maintain even distribution of heat.

    Although there are lots of plug-in heaters on the market, Mr Trapp warned that these can often be more expensive than using central heating.

    “People get tempted by them because they look like they’re smaller, so you expect them to use less energy, but they’re actually a lot less efficient,” he said.

    Changing your energy tariff can save you money by switching to a cheaper fixed deal, a discounted variable tariff or a time-of-use tariff like economy, which offers cheaper electricity at night.

    Continue Reading

  • Luangcharoenrat, C., Intrachooto, S., Peansupap, V. & Sutthinarakorn, W. Factors Influencing Construction Waste Generation in Building Construction: Thailand’s Perspective, Sustainability 2019, Vol. 11, Page 3638 11 3638. (2019). https://doi.org/10.3390/SU11133638

  • Tafesse, S., Girma, Y. E. & Dessalegn, E. Analysis of the socio-economic and environmental impacts of construction waste and management practices. Heliyon 8, e09169. https://doi.org/10.1016/J.HELIYON.2022.E09169 (2022).

    Google Scholar 

  • Solís-Guzmán, J., Marrero, M., Montes-Delgado, M. V. & Ramírez-de-Arellano, A. A Spanish model for quantification and management of construction waste. Waste Manage. 29, 2542–2548. https://doi.org/10.1016/J.WASMAN.2009.05.009 (2009).

    Google Scholar 

  • Yuan, H., Chini, A. R., Lu, Y. & Shen, L. A dynamic model for assessing the effects of management strategies on the reduction of construction and demolition waste. Waste Manage. 32, 521–531. https://doi.org/10.1016/J.WASMAN.2011.11.006 (2012).

    Google Scholar 

  • Jaradat, H., Alshboul, O. A. M., Obeidat, I. M. & Zoubi, M. K. Green building, carbon emission, and environmental sustainability of construction industry in jordan: Awareness, actions and barriers. Ain Shams Eng. J. 15, 102441. https://doi.org/10.1016/J.ASEJ.2023.102441 (2024).

    Google Scholar 

  • Olabi, A. G. et al. The role of green buildings in achieving the sustainable development goals. Int. J. Thermofluids. 25, 101002. https://doi.org/10.1016/J.IJFT.2024.101002 (2025).

    Google Scholar 

  • Chang, D., Lee, C. K. M. & Chen, C. H. Review of life cycle assessment towards sustainable product development. J. Clean. Prod. 83, 48–60. https://doi.org/10.1016/J.JCLEPRO.2014.07.050 (2014).

    Google Scholar 

  • Campo Gay, I., Hvam, L., Haug, A., Huang, G. Q. & Larsson, R. A digital tool for life cycle assessment in construction projects. Developments Built Environ. 20, 100535. https://doi.org/10.1016/J.DIBE.2024.100535 (2024).

    Google Scholar 

  • Yin, Q. et al. Sustainability, A, Vol. 16, Page 7805 16 (2024) 7805. (2024). https://doi.org/10.3390/SU16177805

  • Cha, G. W., Choi, S. H., Hong, W. H. & Park, C. W. Developing a prediction model of Demolition-Waste Generation-Rate via principal component analysis. Int. J. Environ. Res. Public. Health. 20, 3159. https://doi.org/10.3390/IJERPH20043159 (2023).

    Google Scholar 

  • Samal, C. G., Biswal, D. R., Udgata, G. & Pradhan, S. K. Estimation, Classification, and prediction of construction and demolition waste using machine learning for sustainable waste management: A critical review. Constr. Mater. 2025. 5 (Page 10 5), 10. https://doi.org/10.3390/CONSTRMATER5010010 (2025).

    Google Scholar 

  • Zheng, L., Mueller, M., Luo, C. & Yan, X. Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK. Appl. Energy. 357, 122472. https://doi.org/10.1016/J.APENERGY.2023.122472 (2024).

    Google Scholar 

  • El-Kenawy, E. S. M. et al. Smart City Electricity Load Forecasting Using Greylag Goose Optimization-Enhanced Time Series Analysis, Arab. J. Sci. Eng. https://doi.org/10.1007/S13369-025-10647-3. (2025).

  • Saqr, A. E. S., Saraya, M. S. & El-Kenawy, E. S. M. Enhancing CO2 emissions prediction for electric vehicles using Greylag Goose optimization and machine learning. Sci. Rep. 2025. 15, 1. https://doi.org/10.1038/s41598-025-99472-0 (2025).

    Google Scholar 

  • How Saudi Arabia is making the construction industry greener. and more sustainable, (n.d.). accessed April 29, (2025). https://www.arabnews.com/node/2587731/saudi-arabia

  • Cha, G. W. & Park, C. W. Development of an optimal machine learning model to predict CO2 emissions at the Building demolition Stage, buildings 2025, 15, Page 526 15 526. (2025). https://doi.org/10.3390/BUILDINGS15040526

  • Maged, A., Elshaboury, N. & Akanbi, L. Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach. Environ. Dev. Sustain. 1–25. https://doi.org/10.1007/S10668-024-04814-Z/METRICS (2024).

  • Lu, W., Long, W. & Yuan, L. A machine learning regression approach for pre-renovation construction waste auditing. J. Clean. Prod. 397, 136596. https://doi.org/10.1016/J.JCLEPRO.2023.136596 (2023).

    Google Scholar 

  • Hu, R., Chen, K., Chen, W., Wang, Q. & Luo, H. Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China. Waste Manage. 126, 791–799. https://doi.org/10.1016/J.WASMAN.2021.04.012 (2021).

    Google Scholar 

  • Gulghane, A., Sharma, R. L. & Borkar, P. A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach. Asian J. Civil Eng. 25, 265–280. https://doi.org/10.1007/S42107-023-00772-5/METRICS (2024).

    Google Scholar 

  • Guerra, B. C., Koo, H. J., Caldas, C. & Leite, F. Prediction of waste diversion and identification of trends in construction and demolition waste data using data mining. Int. J. Constr. Manage. 24, 374–383. https://doi.org/10.1080/15623599.2023.2235106 (2024). ;PAGE:STRING:ARTICLE/CHAPTER.

    Google Scholar 

  • Cha, G. W., Moon, H. J. & Kim, Y. C. A hybrid machine-learning model for predicting the waste generation rate of Building demolition projects. J. Clean. Prod. 375, 134096. https://doi.org/10.1016/J.JCLEPRO.2022.134096 (2022).

    Google Scholar 

  • Akanbi, L. A., Oyedele, A. O., Oyedele, L. O. & Salami, R. O. Deep learning model for demolition waste prediction in a circular economy. J. Clean. Prod. 274, 122843. https://doi.org/10.1016/J.JCLEPRO.2020.122843 (2020).

    Google Scholar 

  • Lu, W. et al. Estimating construction waste generation in the greater Bay Area, China using machine learning. Waste Manage. 134, 78–88. https://doi.org/10.1016/J.WASMAN.2021.08.012 (2021).

    Google Scholar 

  • Cha, G. W., Moon, H. J. & Kim, Y. C. Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. Int. J. Environ. Res. Public. Health 2021. 18, 8530. https://doi.org/10.3390/IJERPH18168530 (2021).

    Google Scholar 

  • Fang, Y., Lu, X. & Li, H. A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage. J. Clean. Prod. 328, 129657. https://doi.org/10.1016/J.JCLEPRO.2021.129657 (2021).

    Google Scholar 

  • Razi, N. & Ansari, R. A prediction-based model to optimize construction programs: considering time, cost, energy consumption, and CO2 emissions trade-off. J. Clean. Prod. 445, 141164. https://doi.org/10.1016/J.JCLEPRO.2024.141164 (2024).

    Google Scholar 

  • Hou, Y. & Liu, S. Predictive modeling and validation of carbon emissions from china’s coastal construction industry: A BO-XGBoost ensemble Approach, sustainability 2024, 16, Page 4215 16 4215. (2024). https://doi.org/10.3390/SU16104215

  • Cha, G., Moon, H. & Kim, J. A method to improve the performance of support vector machine regression model for predicting demolition waste generation using categorical principal components analysis. Int. J. Sustainable Building Technol. Urban Dev. 2021. 12 (12), 3. https://doi.org/10.22712/SUSB.20210023 (2021).

    Google Scholar 

  • Jafari, M. & Mousavi, E. Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study. Environ. Sci. Pollut. Res. 1–12. https://doi.org/10.1007/S11356-024-34527-9/METRICS (2024).

  • Li, Q., Meng, Q., Cai, J., Yoshino, H. & Mochida, A. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Convers. Manag. 50, 90–96. https://doi.org/10.1016/J.ENCONMAN.2008.08.033 (2009).

    Google Scholar 

  • Amasyali, K. & El-Gohary, N. M. A review of data-driven Building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095 (2018).

    Google Scholar 

  • Olu-Ajayi, R., Alaka, H., Owolabi, H., Akanbi, L. & Ganiyu, S. Data-Driven Tools for Building Energy Consumption Prediction: A Review, Energies 2023, Vol. 16, Page 2574 16 2574. (2023). https://doi.org/10.3390/EN16062574

  • Ardabili, S., Abdolalizadeh, L., Mako, C., Torok, B. & Mosavi, A. Systematic review of deep learning and machine learning for Building energy. Front. Energy Res. 10, 786027. https://doi.org/10.3389/FENRG.2022.786027/BIBTEX (2022).

    Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478. https://doi.org/10.2307/30036540 (2003).

    Google Scholar 

  • Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–339. https://doi.org/10.2307/249008 (1989).

    Google Scholar 

  • Jeurissen, R. & Elkington, J. Cannibals With Forks: The Triple Bottom Line of 21st Century Business, Journal of Business Ethics 2000 23:2 23 229–231. (2000). https://doi.org/10.1023/A:1006129603978

  • Rogers, E. M. Diffusion of Innovations, 5th Edition (Google eBook), 576. (2003). https://books.google.com/books/about/Diffusion_of_Innovations_5th_Edition.html?id=9U1K5LjUOwEC (accessed August 18, 2025).

  • Darko, A., Chan, A. P. C., Owusu-Manu, D. G. & Ameyaw, E. E. Drivers for implementing green Building technologies: an international survey of experts. J. Clean. Prod. 145, 386–394. https://doi.org/10.1016/J.JCLEPRO.2017.01.043 (2017).

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324/METRICS (2001).

    Google Scholar 

  • Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13-17-August-2016 785–794. (2016). https://doi.org/10.1145/2939672.2939785/SUPPL_FILE/KDD2016_CHEN_BOOSTING_SYSTEM_01-ACM.MP4

  • Ding, G. K. C. Sustainable construction—The role of environmental assessment tools. J. Environ. Manage. 86, 451–464. https://doi.org/10.1016/J.JENVMAN.2006.12.025 (2008).

    Google Scholar 

  • Darko, A. et al. Review of application of analytic hierarchy process (AHP) in construction. Int. J. Constr. Manage. 19, 436–452. https://doi.org/10.1080/15623599.2018.1452098;SUBPAGE:STRING:ACCESS (2019).

    Google Scholar 

  • Chan, A. P. C., Darko, A., Olanipekun, A. O. & Ameyaw, E. E. Critical barriers to green Building technologies adoption in developing countries: the case of Ghana. J. Clean. Prod. 172, 1067–1079. https://doi.org/10.1016/J.JCLEPRO.2017.10.235 (2018).

    Google Scholar 

  • Alsanad, S. Awareness, Drivers, Actions, and barriers of sustainable construction in Kuwait. Procedia Eng. 118, 969–983. https://doi.org/10.1016/J.PROENG.2015.08.538 (2015).

    Google Scholar 

  • Akadiri, P. O., Olomolaiye, P. O. & Chinyio, E. A. Multi-criteria evaluation model for the selection of sustainable materials for Building projects. Autom. Constr. 30, 113–125. https://doi.org/10.1016/J.AUTCON.2012.10.004 (2013).

    Google Scholar 

  • Ding, Z., Zuo, J., Wu, J. & Wang, J. Y. Key factors for the BIM adoption by architects: A China study. Eng. Constr. Architectural Manage. 22, 732–748. https://doi.org/10.1108/ECAM-04-2015-0053/FULL/XML (2015).

    Google Scholar 

  • Ochieng, E. G., Price, A. & Moore, D. Management of global construction projects. SSRN Electron. J. https://doi.org/10.2139/SSRN.3105473 (2018).

    Google Scholar 

  • Richard & Fellows Anita. Liu, Research methods for construction, (2015). https://www.wiley.com/en-us/Research+Methods+for+Construction%2C+4th+Edition-p-9781118915738 (accessed April 29, 2025).

  • Tate, R., Beauregard, F., Peter, C. & Marotta, L. Pilot testing as a strategy to develop interview and questionnaire skills for scholar practitioners: A selection of education doctorate students’ reflective Vignettes, impacting education. J. Transforming Prof. Pract. 8, 20–25. https://doi.org/10.5195/IE.2023.333 (2023).

    Google Scholar 

  • Kirchner, K., Zec, J. & Delibašić, B. Facilitating data preprocessing by a generic framework: a proposal for clustering. Artif. Intell. Rev. 45, 271–297. https://doi.org/10.1007/S10462-015-9446-6 (2016).

    Google Scholar 

  • Prakash, A., Navya, N. & Natarajan, J. Big data preprocessing for modern world: opportunities and challenges. Lecture Notes Data Eng. Commun. Technol. 26, 335–343. https://doi.org/10.1007/978-3-030-03146-6_37 (2019).

    Google Scholar 

  • Golazad, S. Z., Mohammadi, A., Rashidi, A. & Ilbeigi, M. From Raw to refined: data preprocessing for construction machine learning (ML), deep learning (DL), and reinforcement learning (RL) models. Autom. Constr. 168, 105844. https://doi.org/10.1016/J.AUTCON.2024.105844 (2024).

    Google Scholar 

  • G.E.A.P.A. Batista, M. C. & Monard An analysis of four missing data treatment methods for supervised learning, Appl. Artif. Intell. 17 519–533. https://doi.org/10.1080/713827181;JOURNAL. (2003). :JOURNAL:UAAI20;REQUESTEDJOURNAL:JOURNAL:UAAI20;WGROUP:STRING:PUBLICATION.

  • Data Mining. Concepts and Techniques | ScienceDirect, (n.d.). accessed April 29, (2025). https://www.sciencedirect.com/book/9780123814791/data-mining-concepts-and-techniques

  • Opoku, A. & Fortune, C. Implementation of sustainable practices in UK construction organizations. Int. J. Sustain. Policy Pract. 8, 121–132. https://doi.org/10.18848/2325-1166/CGP/V08I01/55360 (2013).

    Google Scholar 

  • Kernbach, J. M. & Staartjes, V. E. Foundations of machine Learning-Based clinical prediction modeling: part II—Generalization and overfitting. Acta Neurochir. Suppl. (Wien). 134, 15–21. https://doi.org/10.1007/978-3-030-85292-4_3 (2022).

    Google Scholar 

  • Lipkovich, I., Ratitch, B. & Ivanescu, C. Statistical data mining of clinical data. Quant. Methods Pharm. Res. Development: Concepts Appl. 225–315. https://doi.org/10.1007/978-3-030-48555-9_6 (2020).

  • Kuhn, M. & Johnson, K. Applied predictive modeling. Appl. Predictive Model. 1–600. https://doi.org/10.1007/978-1-4614-6849-3/COVER (2013).

  • Mahmood, S. et al. Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability. Sci. Rep. 14, 1–17. https://doi.org/10.1038/S41598-024-70519- (2024). Y;SUBJMETA=4066,685,704,844;KWRD=ENERGY+AND+SOCIETY,SUSTAINABILITY.

    Google Scholar 

  • Rashid, K. et al. Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials. Case Stud. Constr. Mater. 21, e04080. https://doi.org/10.1016/J.CSCM.2024.E04080 (2024).

    Google Scholar 

  • Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/J.1600-0587.2012.07348 (2013). .X;PAGE:STRING:ARTICLE/CHAPTER.

    Google Scholar 

  • Awad, M. & Khanna, R. Support vector machines for classification. Efficient Learn. Machines. 39–66. https://doi.org/10.1007/978-1-4302-5990-9_3 (2015).

  • Liu, Y. & Song, H. Study on constructing support vector machine with granular computing. Procedia Eng. 15, 3098–3102. https://doi.org/10.1016/J.PROENG.2011.08.581 (2011).

    Google Scholar 

  • , A. J., Smola, B. & Schölkopf A tutorial on support vector regression. Stat. Comput. 14, 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88 (2004).

    Google Scholar 

  • Tayefeh Hashemi, S., Ebadati, O. M. & Kaur, H. Cost Estimation and prediction in construction projects: a systematic review on machine learning techniques. SN Appl. Sci. 2, 1–27. https://doi.org/10.1007/S42452-020-03497-1/FIGURES/11 (2020).

    Google Scholar 

  • Mai, H. V. T., Nguyen, T. A., Ly, H. B. & Tran, V. Q. Prediction compressive strength of concrete containing GGBFS using random forest model. Adv. Civil Eng. 2021, 6671448. https://doi.org/10.1155/2021/6671448 (2021).

    Google Scholar 

  • Asadi, E., Da Silva, M. G., Antunes, C. H. & Dias, L. Multi-objective optimization for Building retrofit strategies: A model and an application. Energy Build. 44, 81–87. https://doi.org/10.1016/J.ENBUILD.2011.10.016 (2012).

    Google Scholar 

  • Kiangala, S. K. & Wang, Z. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an industry 4.0 environment. Mach. Learn. Appl. 4, 100024. https://doi.org/10.1016/J.MLWA.2021.100024 (2021).

    Google Scholar 

  • Al-Fakih, A., Al-wajih, E., Saleh, R. A. A. & Muhit, I. B. Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete. J. Clean. Prod. 472, 143463. https://doi.org/10.1016/J.JCLEPRO.2024.143463 (2024).

    Google Scholar 

  • Yaseen, Z. M., Ali, Z. H., Salih, S. Q. & Al-Ansari, N. Prediction of risk delay in construction projects using a hybrid artificial intelligence Model, sustainability 2020, 12, Page 1514 12 1514. (2020). https://doi.org/10.3390/SU12041514

  • ForouzeshNejad, A. A., Arabikhan, F. & Aheleroff, S. Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm, Machines 2024, Vol. 12, Page 867 12 867. (2024). https://doi.org/10.3390/MACHINES12120867

  • Ji, S., Lee, B. & Yi, M. Y. Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach. Build. Environ. 205, 108267. https://doi.org/10.1016/J.BUILDENV.2021.108267 (2021).

    Google Scholar 

  • Fan, J. et al. Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Convers. Manag. 164, 102–111. https://doi.org/10.1016/J.ENCONMAN.2018.02.087 (2018).

    Google Scholar 

  • Koo, B., La, S., Cho, N. W. & Yu, Y. Using support vector machines to classify Building elements for checking the semantic integrity of Building information models. Autom. Constr. 98, 183–194. https://doi.org/10.1016/J.AUTCON.2018.11.015 (2019).

    Google Scholar 

  • Kumar, M. et al. Soft computing-based prediction models for compressive strength of concrete. Case Stud. Constr. Mater. 19, e02321. https://doi.org/10.1016/J.CSCM.2023.E02321 (2023).

    Google Scholar 

  • Khan, A. A., Chaudhari, O. & Chandra, R. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Syst. Appl. 244, 122778. https://doi.org/10.1016/J.ESWA.2023.122778 (2024).

    Google Scholar 

  • Xia, Y., Jiang, S., Meng, L. & Ju, X. XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring, Systems 2024, Vol. 12, Page 254 12 254. (2024). https://doi.org/10.3390/SYSTEMS12070254

  • Sathiparan, N., Jeyananthan, P. & Subramaniam, D. N. A comparative study of machine learning techniques and data processing for predicting the compressive strength of pervious concrete with supplementary cementitious materials and chemical composition influence, Next Mater. 9 100947. https://doi.org/10.1016/J.NXMATE.2025.100947. (2025).

  • Alkaissy, M. et al. Enhancing construction safety: machine learning-based classification of injury types. Saf. Sci. 162, 106102. https://doi.org/10.1016/J.SSCI.2023.106102 (2023).

    Google Scholar 

  • Uddin, M. N., Ye, J., Deng, B., Li, L. & Yu, K. Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC). J. Building Eng. 72, 106648. https://doi.org/10.1016/J.JOBE.2023.106648 (2023).

    Google Scholar 

  • Malakouti, S. M. From accurate to actionable: interpretable PM2.5 forecasting with feature engineering and SHAP for the Liverpool–Wirral region. Environ. Challenges. 21, 101290. https://doi.org/10.1016/J.ENVC.2025.101290 (2025).

    Google Scholar 

  • Zhao, Q. et al. The influencing factors and future development of energy consumption and carbon emissions in urban households: A review of china’s experience. Appl. Sci. (Switzerland). 15, 2961. https://doi.org/10.3390/APP15062961/S1 (2025).

    Google Scholar 

  • Zaman, K. Urban governance and power consumption dynamics in china’s carbon-intensive sectors: insights for sustainable development. Urban Gov. 4, 313–328. https://doi.org/10.1016/J.UGJ.2024.12.001 (2024).

    Google Scholar 

Continue Reading

  • Nottingham food waste trial reduced after ‘disappointing’ uptake

    Nottingham food waste trial reduced after ‘disappointing’ uptake

    Green Party councillor for Berridge, Shuguftah Quddoos, said it had been difficult to get people involved.

    “Overall, it’s been disappointing, take-up has been low,” she said.

    “It’s a challenging neighbourhood because we have a really mixed community here of all ages and all backgrounds, so it’s been a real challenge to raise awareness.”

    She added she is optimistic, however, that more people can be convinced.

    “A generation ago, none of us had a brown [recycling] bin, none of us recycled at all, it was a new concept, so changing behaviour and changing your routine to do things differently is always going to be a challenge,” she said.

    “It was a challenge for me, and once I understood that this food waste is going to power buses and heat homes, I was like – ‘this is great’.”

    Local resident Mark Shotter said taking part had been very straightforward.

    “The peelings and other bits of food that can’t be used for whatever reason simply go into the little food waste bin which I’ve got next to my general waste bin,” he said.

    “When it gets full enough, I take it outside and put it into the larger food waste bin provided by the council. There’s no real extra work involved as far as I’m concerned, it’s just a case of which bin you put it in.”

    Continue Reading

  • BYD Surpasses Tesla as the World’s Top Electric Vehicle Seller

    BYD Surpasses Tesla as the World’s Top Electric Vehicle Seller

    Beijing (TDI): More than a decade after Elon Musk publicly brushed off China’s BYD, the electric vehicle maker has achieved what once seemed unlikely: overtaking Tesla to become the world’s largest seller of fully electric vehicles.

    BYD announced on Thursday that it sold 2.26 million battery-electric vehicles in 2025, marking a year-on-year increase of nearly 28 percent. Tesla, by contrast, reported 1.64 million vehicle deliveries, an 8 percent decline from the previous year and its second straight annual drop. Tesla’s fourth-quarter performance was particularly weak, with deliveries falling about 16 percent compared with the same period in 2024.

    The moment is symbolic. In a 2011 interview, Musk had dismissed BYD outright, questioning the quality of its cars and saying he did not view the company as competition. Fourteen years later, BYD’s rapid rise has reshaped the global EV market.

    Tesla’s struggles in 2025 stemmed from multiple pressures. Intensifying competition from Chinese automakers squeezed market share, while the company also faced reputational challenges linked to Musk’s political comments. According to media reports, Tesla sales weakened in several key regions as consumer sentiment shifted. The situation worsened after the United States ended its $7,500 EV tax credit in late September, dampening demand more than analysts had anticipated.

    Read More: Elon Musk Stays in the Driver Seat as Tesla Denies Leadership Change

    Founded in 1995 as a battery producer, BYD, short for “Build Your Dreams”, has steadily transformed into a dominant force in China’s new-energy vehicle industry. Unlike Tesla, BYD sells both fully electric and plug-in hybrid models, allowing it to reach a wider customer base. Its focus on affordable, high-volume vehicles has paid off, particularly in China, the world’s largest EV market.

    Read More: Tesla Sales in the Netherlands Plummet by Nearly 50% in Q1

    Despite facing steep tariffs in the US, BYD has aggressively expanded abroad. In 2025 alone, the company exported more than one million vehicles, a 150 percent jump from the year before. December set a record with 133,000 vehicles shipped overseas, and new factories in Brazil and Hungary are expected to come online soon to strengthen its global footprint.

    Industry analysts point to BYD’s vertical integration as a key advantage. By manufacturing its own batteries and key components, the company has been able to control costs and protect profit margins at a time when many rivals are struggling.

    Continue Reading

  • Probabilistic slope stability assessment of variably saturated overburden dump slopes

  • Bhatt, A. et al. Physical, chemical, and geotechnical properties of coal fly ash: A global review. Case Stud. Constr. Mater. 11, e00263 (2019).

    Google Scholar 

  • CEA Report. (2023). https://www.eai.in/ref/fe/coa/coa.html

  • Kumar, A., Das, S. K., Nainegali, L., Raviteja, K. V. N. S. & Reddy, K. R. Probabilistic slope stability analysis of coal mine waste rock dump. Geotech. Geol. Eng. 41, 4707–4724 (2023).

    Google Scholar 

  • Golder, A., Dandapat, S. P. & Roy, I. Instability of topsoil benches of a pit caused by dumping of waste rock outside an opencast coal mine. J. South. Afr. Inst. Min. Metall. 124, 703–710 (2024).

    Google Scholar 

  • Poulsen, B., Khanal, M., Rao, A. M., Adhikary, D. & Balusu, R. Mine overburden dump failure: A case study. Geotech. Geol. Eng. 32, 297–309 (2014).

    Google Scholar 

  • Bowman, P. M. & Gilchrist, H. G. Waste dump instability and its operational impact for a Canadian Plains lignite mine. in Proc. Internat. Symp. on stability in coal mining, Vancouver 381–394 (1978).

  • Kasmer, O., Ulusay, R. & Gokceoglu, C. Spoil pile instabilities with reference to a strip coal mine in turkey: mechanisms and assessment of deformations. Environ. Geol. 49, 570–585 (2006).

    Google Scholar 

  • Pradhan, S. P., Vishal, V., Singh, T. N. & Singh, V. K. Optimisation of dump slope geometry vis-à-vis flyash utilisation using numerical simulation. Amer Jour Min. Metall. 2, 1–7 (2014).

    Google Scholar 

  • Singh, T. N., Pradhan, S. P. & Vishal, V. Stability of slopes in a fire-prone mine in Jharia Coalfield, India. Arab. J. Geosci. 6, 419–427 (2013).

    Google Scholar 

  • Mohanty, M., Sarkar, R. & Das, S. K. Probabilistic assessment of effects of heterogeneity on the stability of coal mine overburden dump slopes through discrete element framework. Bull. Eng. Geol. Environ. 81, 228 (2022).

    Google Scholar 

  • Mohanty, M., Sarkar, R. & Das, S. K. Effects of Spatial heterogeneity on pseudo-static stability of coal mine overburden dump slope, using random limit equilibrium and random finite element methods: A comparative study. Earthq. Eng. Eng. Vib. 24, 83–99 (2025).

    Google Scholar 

  • Reddy, S. K. Analysis of fault’s effect on the highwall stability of Medapalli open pit coal mine. Geotech. Geol. Eng. 41, 2969–2986 (2023).

    Google Scholar 

  • Chaulya, S. K. Quantification of stability improvement of a dump through biological reclamation. Geotech. Geol. Eng. 18, 193–207 (2000).

    Google Scholar 

  • Rajak, T. K., Yadu, L., Chouksey, S. K. & Dewangan, P. K. Stability analysis of mine overburden dump stabilized with fly Ash. Int. J. Geotech. Eng. 15, 587–597 (2021).

    Google Scholar 

  • Rahman, T., Sarkar, K. & Singh, A. K. Correlation of Geomechanical and dynamic elastic properties with the P-Wave velocity of lower Gondwana coal measure rocks of India. International J. Geomechanics 20, (2020).

  • Rajak, T. K., Yadu, L. & Chouksey, S. K. Effect of fly Ash on geotechnical properties and stability of coal mine overburden dump: an overview. SN Appl. Sci. 2, 973 (2020).

    Google Scholar 

  • Dwinagara, B. & BACK ANALYSIS OF RAINFALL-INDUCED WASTE DUMP FAILURE USING COUPLED HYDRO-MECHANICAL ANALYSIS – A CASE STUDY IN COAL MINE. International J. GEOMATE 27, (2024).

  • Akram, M. S., Mirza, K., Ali, U. & Zeeshan, M. Geotechnical and hydrological characterization of subsurface for metallic minerals mining operations in Punjab, Pakistan. Open. J. Geol. 09, 752–767 (2019).

    Google Scholar 

  • Wang, L. et al. Pseudo-static analysis of 3D unsaturated bench slopes stabilized by multiple rows of piles. Transp. Geotechnics. 46, 101255 (2024).

    Google Scholar 

  • Huang, A., Zhu, Y., Ye, S., Wang, L. & Fang, G. Three-dimensional seismic stability of unsaturated soil slopes with cracks reinforced by frame beam anchor plates. Structures 73, 108430 (2025).

    Google Scholar 

  • Zhang, J., Wang, Z. P., Zhang, G. D. & Xue, Y. D. Probabilistic prediction of slope failure time. Eng. Geol. 271, 105586 (2020).

    Google Scholar 

  • Amoushahi, S., Grenon, M., Locat, J. & Turmel, D. Deterministic and probabilistic stability analysis of a mining rock slope in the vicinity of a major public road — case study of the LAB Chrysotile mine in Canada. Can. Geotech. J. 55, 1391–1404 (2018).

    Google Scholar 

  • Wang, J. & Ji, H. G. Analysis of rock slope stability on the basis of limit equilibrium method. Adv. Mat. Res. 711, 333–337 (2013).

    Google Scholar 

  • Hsein Juang, C., Zhang, J. & Gong, W. Reliability-based assessment of stability of slopes. IOP Conf. Ser. Earth Environ. Sci. 26, 012006 (2015).

    Google Scholar 

  • Hamedifar, H., Bea, R. G., Pestana-Nascimento, J. M. & Roe, E. M. Role of probabilistic methods in sustainable geotechnical slope stability analysis. Procedia Earth Planet. Sci. 9, 132–142 (2014).

    Google Scholar 

  • Dash, A. K. Analysis of accidents due to slope failure in Indian opencast coal mines. Curr. Sci. 117, 304 (2019).

    Google Scholar 

  • Directorate General of Mines Safety (DGMS). Accident alerts. (2021).

  • Mohanty, M., Sarkar, R. & Das, S. K. A critical review on static and dynamic performance of coal mine overburden dump slopes: present status and way forward. J. Geol. Soc. India. 100, 1271–1286 (2024).

    Google Scholar 

  • Wu, S., Chen, L., Wang, N. & Assouline, S. Modeling rainfall-infiltration-runoff processes on sloping surfaces subject to rapidly changing soil properties during seal formation. J. Hydrol. (Amst). 619, 129318 (2023).

    Google Scholar 

  • Wu, W., Yang, Y., Jiao, Y. & Wang, S. Stability analysis of unsaturated slopes under rainfall and drainage using the vector-sum-based numerical manifold model. Comput. Geotech. 179, 106992 (2025).

    Google Scholar 

  • Bishop, A. W. The principal of effective stress. Teknisk Ukeblad. 39, 859–863 (1959).

    Google Scholar 

  • Terzaghi, K. Theoretical Soil Mechanics. (1943).

  • Fredlund, D. G. Volume change behavior of unsaturated soils. (1973).

  • Lu, N. & Likos, W. J. Unsaturated Soil Mechanics (Wiley, 2004).

    Google Scholar 

  • van Genuchten, M. Th. A Closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).

    Google Scholar 

  • GARDNER, W. R. & SOME STEADY-STATE SOLUTIONS OF THE UNSATURATED MOISTURE FLOW EQUATION WITH APPLICATION TO EVAPORATION FROM A WATER TABLE. Soil. Sci. 85, 228–232 (1958).

    Google Scholar 

  • Vahedifard, F. & Robinson, J. D. Unified method for estimating the ultimate bearing capacity of shallow foundations in variably saturated soils under steady flow. Journal Geotech. Geoenvironmental Engineering 142, (2016).

  • Wang, L., Sun, D., Chen, B. & Li, J. Three-dimensional seismic stability of unsaturated soil slopes using a semi-analytical method. Comput. Geotech. 110, 296–307 (2019).

    Google Scholar 

  • Wang, L., Tang, L., Wang, Z., Liu, H. & Zhang, W. Probabilistic characterization of the soil-water retention curve and hydraulic conductivity and its application to slope reliability analysis. Comput Geotech 121, (2020).

  • Tschuchnigg, F., Schweiger, H. F. & Sloan, S. W. Slope stability analysis by means of finite element limit analysis and finite element strength reduction techniques. Part II: back analyses of a case history. Comput. Geotech. 70, 178–189 (2015).

    Google Scholar 

  • Raj, D., Singh, Y. & Shukla, S. K. Seismic bearing capacity of strip foundation embedded in c – ϕ soil slope. International J. Geomechanics 18, (2018).

  • MARTIN, C. M. The use of adaptive finite-element limit analysis to reveal slip-line fields. Géotechnique Lett. 1, 23–29 (2011).

    Google Scholar 

  • Anand, A. & Sarkar, R. A comprehensive investigation on bearing capacity of shallow foundations on unsaturated fly Ash slopes adopting finite element limit analysis. Eur. J. Environ. Civil Eng. 26, 6914–6940 (2022).

    Google Scholar 

  • Environmental Systems Research Institute (ESRI), R. C. ESRI ArcGIS Release 10.8. (2016).

  • Kumar, A., Anand, A., Singh, R. V., Kumar, R. & Gohil, M. Vegetation hydrology and slope interaction under variable infiltration: a state-of-the-art review. J. Infrastructure Preservation Resil. 6, 33 (2025).

    Google Scholar 

  • Zhou, T. et al. Assessing the rainfall infiltration on FOS via a new NSRM for a case study at high rock slope stability. Sci. Rep. 12, 11917 (2022).

    Google Scholar 

  • Khan, M. & Wang, S. Slope stability analysis to develop correlations between different soil parameters and factor of safety using regression analysis. Pol. J. Environ. Stud. 30, 4021–4030 (2021).

    Google Scholar 

  • Prakash, A., Hazra, B. & Sreedeep, S. Probabilistic analysis of unsaturated fly Ash slope. J Hazard. Toxic. Radioact Waste 23, (2019).

  • Anand, A. & Sarkar, R. Seismic bearing capacity of strip footing on partially saturated soil using modal response analysis. Earthq. Eng. Eng. Vib. 21, 641–662 (2022).

    Google Scholar 

  • Li, D. Q., Wang, L., Cao, Z. J. & Qi, X. H. Reliability analysis of unsaturated slope stability considering SWCC model selection and parameter uncertainties. Eng. Geol. 260, 105207 (2019).

    Google Scholar 

  • GHANBARIAN-ALAVIJEH, B. & HUANG, L. I. A. G. H. A. T. A. VAN GENUCHTEN, M. Th. Estimation of the Van Genuchten soil water retention properties from soil textural data. Pedosphere 20, 456–465 (2010).

    Google Scholar 

  • PADAMWAR, M. & POKHARKAR, V. Development of vitamin loaded topical liposomal formulation using factorial design approach: drug deposition and stability. Int. J. Pharm. 320, 37–44 (2006).

    Google Scholar 

  • Roy, N., Sarkar, R. & Bharti, S. D. Relative influence of strength and geometric parameters on the behavior of jointed rock slopes. Arabian J. Geosciences 12, (2019).

  • Baecher, G. B. & Christian, J. T. Reliability and Statistics in Geotechnical Engineering (Wiley, 2005).

  • Continue Reading

  • Core-based recognition of well proppant particles using an enhanced ResNet model

  • 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 

  • Continue Reading

  • 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.

    Continue Reading

  • Autologous cell therapy with CD133+ bone marrow-derived stem cells for Asherman Syndrome: a phase 1/2 trial

  • Dmowski, W. P. & Greenblatt, R. B. Asherman’s syndrome and risk of placenta accreta. Obstet Gynecol 34, 288–299 (1969).

    Google Scholar 

  • Santamaria, X., Isaacson, K. & Simón, C. Asherman’s Syndrome: it may not be all our fault. Hum Reprod 33, 1374–1380 (2018).

    Google Scholar 

  • Santamaria, X. et al. Decoding the endometrial niche of Asherman’s Syndrome at single-cell resolution. Nat Commun 14, 5890 (2023).

    Google Scholar 

  • Hooker, A. B. et al. Systematic review and meta-analysis of intrauterine adhesions after miscarriage: prevalence, risk factors and long-term reproductive outcome. Hum Reprod Update 20, 262–278 (2014).

    Google Scholar 

  • Pabuccu, R. et al. Efficiency and pregnancy outcome of serial intrauterine device-guided hysteroscopic adhesiolysis of intrauterine synechiae. Fertil Steril 90, 1973–1977 (2008).

    Google Scholar 

  • Pistofidis, G. A., Dimitropoulos, K. & Mastrominas, M. Comparison of Operative and Fertility Outcome Between Groups of Women with Intrauterine Adhesions after Adhesiolysis. J Am Assoc Gynecol Laparosc 3, S40 (1996).

    Google Scholar 

  • Santamaria, X., Mas, A., Cervelló, I., Taylor, H. & Simon, C. Uterine stem cells: from basic research to advanced cell therapies. Hum Reprod Update 24, 673–693 (2018).

    Google Scholar 

  • Alawadhi, F., Du, H., Cakmak, H. & Taylor, H. S. Bone Marrow-Derived Stem Cell (BMDSC) transplantation improves fertility in a murine model of Asherman’s syndrome. PLoS ONE 9, e96662 (2014).

    Google Scholar 

  • Cervello, I. et al. Human CD133(+) bone marrow-derived stem cells promote endometrial proliferation in a murine model of Asherman syndrome. Fertil Steril 104, 1552–1553 (2015).

    Google Scholar 

  • Santamaria, X. et al. Autologous cell therapy with CD133+ bone marrow-derived stem cells for refractory Asherman’s syndrome and endometrial atrophy: a pilot cohort study. Hum Reprod 31, 1087–1096 (2016).

    Google Scholar 

  • (COMP), C. O. M. P. Public summary of opinion on orphan designation. Committee Orphan Medicinal Products (COMP). EMA/206895/2017 (2017).

  • The American Fertility Society. The American Fertility Society classifications of adnexal adhesions, distal tubal occlusion, tubal occlusion secondary to tubal ligation, tubal pregnancies, mullerian anomalies and intrauterine adhesions. Fertil Steril 49, 944–955 (1988).

  • Wyatt, K. M., Dimmock, P. W., Walker, T. J. & O’Brien, P. M. Determination of total menstrual blood loss. Fertil Steril 76, 125–131 (2001).

    Google Scholar 

  • Miller, T. E. et al. Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat Biotechnol 40, 1030–1034 (2022).

    Google Scholar 

  • Xiao, S. et al. Etiology, treatment, and reproductive prognosis of women with moderate-to-severe intrauterine adhesions. Int J Gynaecol Obstet 125, 121–124 (2014).

    Google Scholar 

  • Yu, D., Wong, Y. M., Cheong, Y., Xia, E. & Li, T. C. Asherman syndrome-one century later. Fertil Steril 89, 759–779 (2008).

    Google Scholar 

  • Pittenger, M. F. et al. Multilineage potential of adult human mesenchymal stem cells. Science 284, 143–147 (1999).

  • Davies, L. C., Jenkins, S. J., Allen, J. E. & Taylor, P. R. Tissue-resident macrophages. Nat Immunol 14, 986–995 (2013).

    Google Scholar 

  • Li, Z. et al. Transplantation of human endometrial perivascular cells with elevated CYR61 expression induces angiogenesis and promotes repair of a full-thickness uterine injury in rat. Stem Cell Res Ther 10, 179 (2019).

    Google Scholar 

  • Slater, T., Haywood, N. J., Matthews, C., Cheema, H. & Wheatcroft, S. B. Insulin-like growth factor binding proteins and angiogenesis: from cancer to cardiovascular disease. Cytokine Growth Factor Rev 46, 28–35 (2019).

    Google Scholar 

  • Milingos, D. S. et al. Insulinlike growth factor-1Ec (MGF) expression in eutopic and ectopic endometrium: characterization of the MGF E-peptide actions in vitro. Mol Med 17, 21–28 (2011).

    Google Scholar 

  • Horikawa, S. et al. PDGFRα plays a crucial role in connective tissue remodeling. Sci Rep 5, 1–14 (2015).

    Google Scholar 

  • Balko, J. M. et al. The receptor tyrosine kinase ErbB3 maintains the balance between luminal and basal breast epithelium. Proc Natl Acad Sci USA 109, 221–226 (2012).

    Google Scholar 

  • Edeling, M., Ragi, G., Huang, S., Pavenstädt, H. & Susztak, K. Developmental signalling pathways in renal fibrosis: the roles of Notch, Wnt and Hedgehog. Nat Rev Nephrol 12, 426–439 (2016).

    Google Scholar 

  • Bachelier, K., Bergholz, C. & Friedrich, E. B. Differentiation potential and functional properties of a CD34‑CD133+ subpopulation of endothelial progenitor cells. Mol Med Rep 21, 501–507 (2020).

    Google Scholar 

  • Díaz-Gimeno, P. et al. A genomic diagnostic tool for human endometrial receptivity based on the transcriptomic signature. Fertil Steril 95, 50–60 (2011). 60.e1.

    Google Scholar 

  • Moreno, I. et al. Evidence that the endometrial microbiota has an effect on implantation success or failure. Am J Obstet Gynecol 215, 684–703 (2016).

    Google Scholar 

  • SELDINGER, S. I. Catheter replacement of the needle in percutaneous arteriography; a new technique. Acta Radio 39, 368–376 (1953).

    Google Scholar 

  • Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017).

    Google Scholar 

  • Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Google Scholar 

  • Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).

    Google Scholar 

  • Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Google Scholar 

  • Wilm, A. et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res 40, 11189–11201 (2012).

    Google Scholar 

  • DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43, 491–498 (2011).

    Google Scholar 

  • Robinson, J. T. et al. Integrative genomics viewer. Nat Biotechnol 29, 24–26 (2011).

    Google Scholar 

  • Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol 42, 293–304 (2024).

    Google Scholar 

  • Borcherding, N. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun Biol 4, 1–11 (2021).

    Google Scholar 

  • Wang, W. et al. Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. Nat Med 26, 1644–1653 (2020).

    Google Scholar 

  • Boretto, M. et al. Development of organoids from mouse and human endometrium showing endometrial epithelium physiology and long-term expandability. Development 144, 1775–1786 (2017).

    Google Scholar 

  • Boretto, M. et al. Patient-derived organoids from endometrial disease capture clinical heterogeneity and are amenable to drug screening. Nat Cell Biol 21, 1041–1051 (2019).

    Google Scholar 

  • European IVF-Monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology, E. S. H. R. E. et al. Assisted reproductive technology in Europe, 2012: results generated from European registers by. Eshre Hum Reprod 31, 1638–1652 (2016).

    Google Scholar 

  • Continue Reading