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  • 2025 a ‘dreadful’ year for Atlantic salmon farming, says Mowi managing director

    2025 a ‘dreadful’ year for Atlantic salmon farming, says Mowi managing director

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    Mowi Canada East’s managing director says 2025 was a “dreadful” year for Atlantic salmon farming.

    “I’ve been in this industry 40 years, and 2025 has been my worst experience ever,” Gideon Pringle told CBC News in a telephone interview on Friday.

    He said the year’s environmental conditions made aquaculture a difficult business.  Although not a direct link, Pringle pointed to the wildfires experienced in Newfoundland and Labrador as an example.

    “It’s just the environment we live in. We have good years and bad years, and I think that probably goes back, in farming terms, to the dawn of time.”

    Mowi says recent deaths not alarming

    In August,  the company reported that about 400,000 salmon died at three Mowi sites in the province. 

    And in July, thousands of fish died at their Little Burdock Cove site, due to increased water temperatures. 

    Boats are seen ini front of a large blue building
    In August, the fish plant in Harbour Breton had to process surviving fish from the Mowi Canada East incident, which saw the deaths of about 400,000 salmon. (Troy Turner/CBC)

    Meanwhile, Mowi reported on Dec. 20 that 24,696 salmon died at its Friar Cove site, near Francois on the south coast of Newfoundland. That number makes up more than 10 per cent of the farm’s population, which is why the company was required to report it publicly.

    In the report, the company said there was no single cause for the deaths, and that it was “likely due to the residual effects from a sea lice infestation experienced during Fall 2025.”

    Mowi said sea lice are naturally occurring parasites that live on many fish species, and do not pose human health or food safety risks. 

    Pringle noted that this report was a result of a culmination of deaths that added up over multiple weeks.

    The company said prolonged storm conditions over three weeks at the end of November caused the number of deaths to accumulate.

    “Really what’s happened here is the numbers have added up…[over] four weeks of not being able to harvest and empty that [pen],” said Pringle. 

    So despite the high number of salmon mortalities this year, Pringle said the recently-reported deaths are still normal. 

    “There’s no issue here for us. There’s no die-off,” he said. 

    “We’ve just really had a combination of slightly higher than normal farming mortality combined with bad weather.” 

    Province’s reporting system criticized

    Pringle said the company had to report the deaths in December due to what he called “very inefficient” provincial regulations. 

    He said the government requires that anytime a unit reaches 10 percent mortality, the company must make a public report. 

    Pringle said this reporting system is “sometimes distressing” as it “portrays Newfoundland as a very poor place to farm salmon.” 

    “[It] takes away all sorts of investment opportunities,” he said. “The reporting systems that we have is doing a lot of harm for our industry.”

    Download our free CBC News app to sign up for push alerts for CBC Newfoundland and Labrador. Sign up for our daily headlines newsletter here. Click here to visit our landing page.

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  • CCP penalises Mezan Beverages Rs150m for misleading branding

    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 regulator has imposed a penalty of Rs150 million on Mezan Beverages (Private) Limited for engaging in 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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  • Imminent Rupture of an Infected Aortic Aneurysm Presenting as Lower Back Pain in an Elderly Patient

    Imminent Rupture of an Infected Aortic Aneurysm Presenting as Lower Back Pain in an Elderly Patient

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  • Pakistan rejects Jaishankar remarks, defends Indus Waters Treaty stance

    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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  • 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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  • Why Erasing A Bit Generates Heat

    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

    1. Dhurandhar North America Box Office: Only 3.87 Crores Away From Becoming 2nd Highest Indian Grosser Ever!  Koimoi
    2. Dhurandhar becomes highest-grossing Hindi film in India — giving Akshaye Khanna a Shah Rukh-sized record  Dawn
    3. Dhurandhar Box Office…

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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).

    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 

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  • Scientists tested intermittent fasting without eating less and found no metabolic benefit

    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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  • London’s wildlife captured by the camera lens

    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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