Agastya Nanda, the grandson of Bollywood’s living legend Amitabh Bachchan, celebrated the New Year with the release of his debut film ‘Ikkis.’ The war drama, which also marks the debut of Akhshay Kumar’s niece Simar Bhatia, is the last…
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CCP penalises Mezan Beverages Rs150m for misleading branding
January 03, 2026 (MLN): The Competition Commission of Pakistan (CCP) has taken a major enforcement action against deceptive marketing practices.
The Commission found that Mezan’s “Storm” energy drink
imitated the packaging and trade dress of PepsiCo’s Sting energy drink.It noted that the overall look, colour scheme, bottle
design, and branding elements were closely replicated, creating a strong
likelihood of consumer confusion at the point of sale.The CCP concluded that the conduct amounted to parasitic
copying and constituted deceptive marketing under Pakistan’s competition law,
according to the press release.The case dates back to 2018, when PepsiCo Inc. filed a
complaint alleging that Mezan had deliberately designed Storm to benefit from
the established goodwill of Sting in Pakistan’s energy drink market.Instead of responding to the allegations on merit, Mezan
repeatedly challenged the CCP’s jurisdiction and pursued prolonged litigation,
obtaining stay orders from the Lahore High Court in 2018 and again in 2021.These legal challenges delayed the inquiry for several years
and prevented the Commission from concluding the matter in a timely manner.In June 2024, the Lahore High Court dismissed Mezan’s
petition, upheld the CCP’s authority to proceed with the case, and ruled that
early challenges to show-cause notices were not maintainable.The Court also clarified that regulatory proceedings are
independent of trademark disputes and observed that Mezan had used litigation
tactics to delay the process, allowing the inquiry to resume after years of
suspension.In its detailed order, the CCP held that Mezan’s Storm
energy drink adopted a red-dominant colour scheme, bold slanted white
lettering, aggressive visual motifs, and a bottle shape and presentation
closely resembling Sting.The Commission emphasized that deception is assessed based
on the overall commercial impression rather than minor differences examined
side by side.It noted that an ordinary consumer with imperfect
recollection was likely to be misled.The Commission further ruled that Mezan’s registered
trademark for “Storm” did not grant immunity from regulatory action.It stated that trademark registration cannot shield conduct
that results in consumer deception or passing-off.While imposing the Rs150 million fine, the CCP reiterated
that copycat branding and misleading packaging will not be tolerated.Such practices would face strict action regardless of the
size or local status of the company, reinforcing its commitment to protecting
consumers and ensuring fair competition in Pakistan’s market.Copyright Mettis Link News
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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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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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Dhurandhar North America Box Office: Only 3.87 Crores Away From Becoming 2nd Highest Indian Grosser Ever! – Koimoi
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Scientists tested intermittent fasting without eating less and found no metabolic benefit
A new study from the German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) and Charité — Universitätsmedizin Berlin challenges a widely held belief about intermittent fasting. The research shows that time-restricted eating does not…
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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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Wang, K., Ye, Z., Wang, Z., Liu, B. & Feng, T. MACLA-LSTM: A novel approach for forecasting water demand. Sustainability 15, 3628 (2023).
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).
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).
Zanfei, A. Graph convolutional recurrent neural networks for water demand forecasting. Water Resour. Res. 58, e2022WR032299 (2022).
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).
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).
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).
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).
Taylor, S. J. & Letham, B. Forecasting at scale. Am. Stat. 72(1), 37–45 (2018).
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).
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).
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).
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).
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).
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).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, New York, NY, 2013).
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London’s wildlife captured by the camera lens
London’s creatures great and small have been captured on camera by the capital’s residents.
The photographs have been sent to BBC London following the new BBC documentary Wild London, in which Sir David Attenborough explores the wildlife of the…
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