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Pakistan rejects Jaishankar remarks, defends Indus Waters Treaty stance

FO accuses India of deflecting blame, reiterates position on Kashmir and water sharing

Foreign Office Spokesperson Tahir Hussain Andrabi. PHOTO: Radio Pakistan

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January 3, 2026
See Northern Lights, ‘Shooting Stars’ And A Full Moon This Weekend – Forbes
1. See Northern Lights, ‘Shooting Stars’ And A Full Moon This Weekend Forbes
2. Northern lights may be visible in 18 states tonight Space
3. The Aurora Borealis Is Back Tonight, and It May Hit Up to 20 States CNET
4. A New Year’s toast: Here’s…
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January 3, 2026
Why Erasing A Bit Generates Heat

The digital world thrives on the ability to manipulate information, to write, read, and, crucially, erase. But what if erasing information wasn’t merely a computational step, but a physical process with a fundamental energy cost? The…

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January 3, 2026
Automated water demand forecasting for national-scale deployment: a prophet-based framework for Palestinian municipal water management
UN-Water. United Nations World Water Development Report 2023: Partnerships and cooperation for water (2023). https://www.unwater.org/publications/un-world-water-development-report-2023. Accessed July 2025.
Fang, X., Liu, J., Zhang, M., Zhang, H. & Zhao, J. Review of the mechanism and methodology of water demand forecasting in the socio-economic system. Water 16, 1631 (2024).

Google Scholar
Ristow, D. C. M., Henning, E., Kalbusch, A. & Petersen, C. E. Models for forecasting water demand using time series analysis: A case study in Southern Brazil. J. Water Sanit. Hygiene Dev. 11, 231–240. https://doi.org/10.2166/washdev.2021.208 (2021).

Google Scholar
Almanjahie, I. M., Elmezouar, Z. C., Baig, M. B. & Ahmad, I. Modeling of water consumption in Saudi Arabia using classical and modern time series methods. Arab. J.Geosci. 14, 522 (2021).

Google Scholar
Stefaniak, A. K., Jaskowiak, P. A. & Weihmann, L. A case study on water demand forecasting in a coastal tourist city. In Intelligent Systems (eds Paes, A. & Verri, F. A. N.) (Springer Nature Switzerland, Cham, 2025).

Google Scholar
Shuang, Q. & Zhao, R. T. Water demand prediction using machine learning methods: A case study of the Beijing-Tianjin-Hebei region in China. Water 13, 310 (2021).

Google Scholar
Banda, P. C., Bhuiyan, M. A. R., Zhang, K. & Song, A. Multivariate monthly water demand prediction using ensemble and gradient boosting machine learning techniques. In Proceedings of the International Conference on Evolving Cities (ICEC2021), 29–36 (2021), Southampton, UK. https://publications.evolvingcities.org/proc-icec/article/download/14/7.
Görenekli, K. & Gülbaug, A. Comparative analysis of machine learning techniques for water consumption prediction: A case study from Kocaeli Province. Sensors 24, 5846 (2024).

Google Scholar
Jiang, Q. et al. Forecasting regional water demand using multi-fidelity data and Harris Hawks Optimization of generalized regression neural network models – A case study of Heilongjiang Province China. J. Hydrol. 634, 131084 (2024).

Google Scholar
Shu, J. et al. Long-term water demand forecasting using artificial intelligence models in the Tuojiang River Basin, China. PLOS ONE 19 (2024). https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0302558&type=printable.
Liu, G., Savic, D. & Fu, G. Short-term water demand forecasting using data-centric machine learning approaches. Journal of Hydroinformatics 25, 895 – 911 (2023). https://iwaponline.com/jh/article-pdf/doi/10.2166/hydro.2023.163/1186085/jh2023163.pdf.
Niazkar, M. et al. Applications of XGBoost in water resources engineering: A systematic literature review Dec 2018-May 2023. Environ. Model. Softw. 174, 105971 (2024).

Google Scholar
Papacharalampous, G. & Langousis, A. Probabilistic water demand forecasting using quantile regression algorithms. Water Resources Research 58, e2021WR030216 (2022). https://doi.org/10.1029/2021WR030216.https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR030216.
Shan, S., Ni, H., Chen, G., Lin, X. & Li, J. A machine learning framework for enhancing short-term water demand forecasting using attention-BiLSTM networks integrated with XGBoost residual correction. Water 15(20), 3605 (2023).

Google Scholar
Du, B., Zhou, Q., Guo, J., Guo, S. & Wang, L. Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting. Expert Syst. Appl. 171, 114571. https://doi.org/10.1016/j.eswa.2021.114571 (2021).

Google Scholar
Wang, K., Ye, Z., Wang, Z., Liu, B. & Feng, T. MACLA-LSTM: A novel approach for forecasting water demand. Sustainability 15, 3628 (2023).

Google Scholar
Gil-Gamboa, A., Paneque, P., Trull, O. & Troncoso, A. Medium-term water consumption forecasting based on deep neural networks. Expert Syst. Appl. 247, 123234 (2024).

Google Scholar
Que, Q., Gao, J. & Qian, Y. Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture. Water Res. X 25, 100269 (2024).

Google Scholar
Zanfei, A. Graph convolutional recurrent neural networks for water demand forecasting. Water Resour. Res. 58, e2022WR032299 (2022).

Google Scholar
Wang, K., Xie, X., Liu, B., Yu, J. & Wang, Z. Reliable multi-horizon water demand forecasting model: A temporal deep learning approach. Sustain. Cities Soc. 112, 105595 (2024).

Google Scholar
Xu, J. Forecasting water demand with the long short-term memory deep learning mode. Int. J. Inform. Technol. Syst. Approach 17(1), 1–18. https://doi.org/10.4018/IJITSA.338910 (2024).

Google Scholar
Liu, J., Zhou, X., Zhang, L. & Xu, Y.-P. Forecasting short-term water demands with an ensemble deep learning model for a water supply system. Water Resour. Manag. 37, 1–22. https://doi.org/10.1007/s11269-022-03409-0 (2023).

Google Scholar
Liu, C., Liu, Z., Yuan, J., Wang, D. & Liu, X. Urban water demand prediction based on attention mechanism graph convolutional network-long short-term memory. Water 16(6), 831. https://doi.org/10.3390/w16060831 (2024).

Google Scholar
Taylor, S. J. & Letham, B. Forecasting at scale. Am. Stat. 72(1), 37–45 (2018).

Google Scholar
Guo, L., Fang, W., Zhao, Q. & Wang, X. The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality. Comput. Ind. Eng. 161, 107598 (2021).

Google Scholar
Liu, H., Xing, R. & Davies, E. G. R. Forecasting municipal water demands: Evaluating the impacts of population growth, climate change, and conservation policies on water end-use. Sustain. Cities Soc. 130, 106581 (2025).

Google Scholar
Zhou, S., Guo, S., Du, B., Huang, S. & Guo, J. A hybrid framework for multivariate time series forecasting of daily urban water demand using attention-based convolutional neural network and long short-term memory network. Sustainability 14, 11086 (2022).

Google Scholar
Ghannam, S. & Hussain, F. Comparison of deep learning approaches for forecasting urban short-term water demand: A Greater Sydney Region case study. Knowl. Based Syst. 275, 110660. https://doi.org/10.1016/j.knosys.2023.110660 (2023).

Google Scholar
Al-Ghamdi, A.-B., Kamel, S. & Khayyat, M. A hybrid neural network-based approach for forecasting water demand. Comput. Mater. Continua 73, 1365–1383 (2022).

Google Scholar
Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2623–2631 (2019).
Kutner, M. H., Nachtsheim, C. J., Neter, J. & Li, W. Applied Linear Statistical Models 5th edn. (McGraw-Hill/Irwin, New York, NY, 2005).

Google Scholar
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, New York, NY, 2013).

Google Scholar