Qaasid News

Download Our App

Author: admin

  • Bangladesh will not play T20 World Cup matches in India, decision taken in context of ‘extreme communal policy’ of BCCI – Dawn

    1. Bangladesh will not play T20 World Cup matches in India, decision taken in context of ‘extreme communal policy’ of BCCI  Dawn
    2. Bangladesh look to move T20 World Cup matches from India amid Mustafizur row  ESPNcricinfo
    3. T20 World Cup 2026:…

    Continue Reading

  • Type 2 diabetes physically changes the human heart, study finds

    Type 2 diabetes physically changes the human heart, study finds

    Researchers at the University of Sydney have uncovered new evidence showing that type 2 diabetes directly changes the heart’s structure and how it produces energy. These findings help explain why people living with diabetes face a much higher…

    Continue Reading

  • 2025 Winners and losers: OnePlus

    2025 Winners and losers: OnePlus

    2025 has been a busy year for OnePlus, with close to a dozen different launches across the globe in various categories. However, it was also one of the more controversial years for the brand, and signaled a change in direction for the…

    Continue Reading

  • Men’s Basketball Win Streak Ends At UC San Diego

    Men’s Basketball Win Streak Ends At UC San Diego

    Continue Reading

  • A big data approach to artificial intelligence driven predictive modelling for optimizing material properties in additive manufacturing

  • Joshua, R. J. N. et al. Powder bed fusion 3D printing in precision manufacturing for biomedical applications. Compr. Rev. Materials. 17 (3), 769 (2024).

    Google Scholar 

  • Zhou, L. et al. Additive manufacturing: A comprehensive review. Sensors 24 (9), 2668 (2024).

    Google Scholar 

  • Onuike, B. & Bandyopadhyay, A. Additive manufacturing of inconel 718–Ti6Al4V bimetallic structures. Additive Manuf. 22, 844–851 (2018).

    Google Scholar 

  • Ji, S. et al. Microstructural evolution and high-temperature resistance of functionally graded material Ti-6Al-4V/Inconel 718 coated by directed energy deposition-laser. J. Alloys Compd. 848, 156255 (2020).

    Google Scholar 

  • Park, C. W., Hajra, R. N., Kim, S. H., Lee, S. H. & Kim, J. H. Optimizing multi-interlayered additive manufacturing for high-strength robust joints in inconel 718 and Ti–6Al–4V alloys. J. Mater. Res. Technol. 25, 855–872 (2023).

    Google Scholar 

  • Hashemi, S. M. et al. Computational modeling of process–structure–property–performance relationships in metal additive manufacturing: a review. Int. Mater. Rev. 67 (1), 1–46 (2022).

    Google Scholar 

  • Sharma, A. S., Yadav, S., Biswas, K. & Basu, B. High-entropy alloys and metallic nanocomposites: processing challenges, microstructure development and property enhancement. Mater. Sci. Engineering: R: Rep. 131, 1–42 (2018).

    Google Scholar 

  • Kouraytem, N., Li, X., Tan, W., Kappes, B. & Spear, A. D. Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches. J. Physics: Mater. 4 (3), 032002 (2021).

    Google Scholar 

  • Yigezu, B. S., Mahapatra, M. M. & Jha, P. K. Influence of reinforcement type on microstructure, hardness, and tensile properties of an aluminum alloy metal matrix composite. J. Minerals Mater. Charact. Eng. 1 (4), 124–130 (2013).

  • Shahzad, K., Mardare, A. I. & Hassel, A. W. Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances. Sci. Technol. Adv. Materials: Methods. 4 (1), 2292486 (2024).

    Google Scholar 

  • Shen, S. C. et al. Computational design and manufacturing of sustainable materials through first principles and materiomics. Chem. Rev. 123 (5), 2242–2275 (2023).

    Google Scholar 

  • Herriott, C. & Spear, A. D. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine and deep-learning methods. Comput. Mater. Sci. 175, 109599 (2020).

    Google Scholar 

  • Lee, K. H., Lim, H. J. & Yun, G. J. A data-driven framework for designing microstructure of multifunctional composites with deep-learned diffusion-based generative models. Eng. Appl. Artif. Intell. 129, 107590 (2024).

    Google Scholar 

  • Guo, S. et al. Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm. J. Manuf. Syst. 62, 145–163 (2022).

    Google Scholar 

  • Nasiri, S. & Khosravani, M. R. Machine learning in predicting mechanical behavior of additively manufactured parts. J. Mater. Res. Technol. 14, 1137–1153 (2021).

    Google Scholar 

  • Min, Y. I. et al. Machine learning for predicting fatigue properties of additively manufactured materials. Chin. J. Aeronaut. 37 (4), 1–22 (2024).

    Google Scholar 

  • Valizadeh, M. & Wolff, S. J. Convolutional neural network applications in additive manufacturing: A review. Adv. Industrial Manuf. Eng. 4, 100072 (2022).

    Google Scholar 

  • Battula, S., Alla, S. N., Ramana, E. V., Kumar, N. K. & Murthy, S. B. Uncertainty quantification for digital twins in smart manufacturing and robotics: A review. In Journal of Physics: Conference Series (Vol. 2837, No. 1, p. 012059). IOP Publishing. (2024), October.

  • Akbari, P., Zamani, M. & Mostafaei, A. Machine learning prediction of mechanical properties in metal additive manufacturing. Additive Manuf. 91, 104320 (2024).

    Google Scholar 

  • Pouchard, L., Reyes, K. G., Alexander, F. J. & Yoon, B. J. A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digit. Discovery. 2 (5), 1251–1258 (2023).

    Google Scholar 

  • Sanyal, S. et al. Mt-cgcnn: integrating crystal graph convolutional neural network with multitask learning for material property prediction. arXiv Preprint arXiv:1811.05660. (2018).

  • Kong, S., Guevarra, D., Gomes, C. P. & Gregoire, J. M. Materials representation and transfer learning for multi-property prediction. Applied Phys. Reviews, 8(2). (2021).

  • Massa, D., Chmielna, I. N., Kaszuba, G., Papanikolaou, S. & Sankowski, P. Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input.

  • Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120 (14), 145301 (2018).

    Google Scholar 

  • Karamad, M. et al. Orbital graph convolutional neural network for material property prediction. Phys. Rev. Mater. 4 (9), 093801 (2020).

    Google Scholar 

  • Shin, S. J. et al. Material-Adaptive anomaly detection using Property-Concatenated transfer learning in wire Arc additive manufacturing. Int. J. Precis Eng. Manuf. 25, 383–408 (2024).

    Google Scholar 

  • Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59 (8), 3370–3388 (2019).

    Google Scholar 

  • Luo, Q., Shimanek, J. D., Simpson, T. W. & Beese, A. M. An image-based transfer learning approach for using in situ processing data to predict laser powder bed fusion additively manufactured Ti-6Al-4V mechanical properties: 3D printing and additive manufacturing. 12(1), 48–60 (2024).

  • Li, L. et al. Uncertainty quantification in multivariable regression for material property prediction with bayesian neural networks. Sci. Rep. 14 (1), 10543 (2024).

    Google Scholar 

  • Tang, W. et al. Modeling and prediction of fatigue properties of additively manufactured metals. Acta Mech. Solida Sin. 36, 181–213 (2023).

    Google Scholar 

  • Battula, S., Alla, S. N., Ramana, E. V., Kumar, N. K. & Murthy, S. B. Uncertainty quantification for digital twins in smart manufacturing and robotics: A review. Journal of Physics: Conference Series, 2837(1), 012059. (2024), October.

  • Toprak, C. B. & Dogruer, C. U. Optimal process parameter determination in selective laser melting via machine learning-guided sequential quadratic programing. Proc. Institution Mech. Eng. Part. C: J. Mech. Eng. Sci. 239 (3), 807–820 (2025).

    Google Scholar 

  • ASTM F2924-14. Standard Specification for Additive Manufacturing Titanium-6 Aluminium-4 Vanadium with Powder Bed Fusion (ASTM International, 2014).

  • ASTM B213-13. Standard Test Methods for Flow Rate of Metal Powders Using the Hall Flowmeter Funnel (ASTM International, 2013).

  • Dadbakhsh, S., Speirs, M., Kruth, J. P. & Van Humbeeck, J. Influence of SLM parameters on mechanical properties of austenitic stainless steel 316L. Rapid Prototyp. J. 22 (4), 654–664 (2016).

    Google Scholar 

  • ASTM E11-20. Standard Specification for Woven Wire Test Sieve Cloth and Test Sieves (ASTM International, 2020).

  • ASTM E8/E8M-21. Standard Test Methods for Tension Testing of Metallic Materials (ASTM International, 2021).

  • Vrancken, B., Thijs, L., Kruth, J. P. & Van Humbeeck, J. Heat treatment of Ti6Al4V produced by selective laser melting: microstructure and mechanical properties. J. Alloys Compd. 541, 177–185 (2012).

    Google Scholar 

  • ASTM E112-13. Standard Test Methods for Determining Average Grain Size (ASTM International, 2013).

  • Savitzky, A. & Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36 (8), 1627–1639 (1964).

    Google Scholar 

  • Li, R. et al. Application of unsupervised learning methods based on video data for real-time anomaly detection in wire Arc additive manufacturing. J. Manuf. Process. 143, 37–55 (2025).

    Google Scholar 

  • Zheng, Y. et al. A thermal history-based approach to predict mechanical properties of plasma Arc additively manufactured IN625 thin-wall. J. Manuf. Process. 140, 91–107 (2025).

    Google Scholar 

  • Zuo, X. et al. 3D printed All-Natural hydrogels: Flame-Retardant materials toward attaining green sustainability. Adv. Sci. 11 (3), 2306360 (2024).

    Google Scholar 

  • Wang, L. et al. Multi-unit global-local registration for 3D bent tube based on implicit structural feature compatibility. Adv. Eng. Inform. 65, 103120. https://doi.org/10.1016/j.aei.2025.103120 (2025).

    Google Scholar 

  • Yao, S., Chen, F., Wang, Y., Zhou, H. & Liu, K. Manufacturing defect-induced multiscale weakening mechanisms in carbon fiber reinforced polymers captured by 3D CT-based machine learning and high-fidelity modeling. Compos. Part A: Appl. Sci. Manufac. 197, 109052 (2025).

    Google Scholar 

  • Zhang, Z. et al. Robotic wire-based friction stir additive manufacturing. Additive Manuf. 88, 104261 (2024).

    Google Scholar 

  • Niu, S. et al. Breaking the Trade-Off between complexity and absorbing performance in metamaterials through intelligent design. Small 21 (24), 2502828 (2025).

    Google Scholar 

  • Ren, D. et al. Harmonizing physical and deep learning modeling: A computationally efficient and interpretable approach for property prediction. Scripta Mater. 255, 116350 (2025).

    Google Scholar 

  • Jain, A. et al. Commentary: the materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1 (1), 011002. https://doi.org/10.1063/1.4812323 (2013).

    Google Scholar 

  • Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with High-Throughput density functional theory: the open quantum materials database (OQMD). JOM 65 (11), 1501–1509 (2013).

    Google Scholar 

  • Shen, C. et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta Mater. 179, 201–214 (2019).

    Google Scholar 

  • Sun, X. et al. Wire-based friction stir additive manufacturing of AZ31B magnesium alloy: precipitate behavior and mechanical properties. J. Magnesium Alloys. https://doi.org/10.1016/j.jma.2025.04.025 (2025).

    Google Scholar 

  • Chen, S., Khan, S. B., Li, N. & Xiao, C. In-depth analysis of sintering, exposure time, and layer height (µm) in LRS 3D printed devices with DLP. J. Manuf. Process. 133, 576–591 (2025).

    Google Scholar 

  • Ding, C., Li, C., Xiong, Z. & Li, Z. Computer-based big data technology in additive manufacturing and product design in sustainable manufacturing. Int. J. Adv. Manuf. Technol. 135 (9), 4855–4863 (2024).

    Google Scholar 

  • Long, J. et al. Printing dense and low-resistance copper microstructures via highly directional laser-induced forward transfer. Additive Manuf. 103, 104755 (2025).

    Google Scholar 

  • Xu, K. et al. Data-Driven materials research and development for functional coatings. Adv. Sci. 11 (42), 2405262 (2024).

    Google Scholar 

  • Shi, S. et al. Significance of α-Al cellular matrix in tensile behavior and work-hardening of additive manufactured AlSi10Mg alloy. Virtual Phys. Prototyp., 20(1), e2449189. (2025).

  • Zheng, Y. et al. A thermal history-based approach to predict mechanical properties of plasma Arc additively manufactured IN625 thin-wall. J. Manuf. Process. 140, 91–107. https://doi.org/10.1016/j.jmapro.2025.02.043 (2025).

    Google Scholar 

  • Wang, Z. et al. Digital-twin-enabled online wrinkling monitoring of metal tube bending manufacturing: A multi-fidelity approach using forward-convolution-GAN. Appl. Soft Comput. 171, 112684. https://doi.org/10.1016/j.asoc.2024.112684 (2025).

    Google Scholar 

  • Sun, S. et al. Wire-based friction stir additive manufacturing enables enhanced interlayer bonding in aluminum-matrix composites. J. Manuf. Process. 153, 1–15. https://doi.org/10.1016/j.jmapro.2025.08.078 (2025).

    Google Scholar 

  • Luo, J., Cheng, Z., Yu, N., Tian, Y. & Meng, J. A flexible skin material with switchable wettability for trans-medium vehicles. Int. J. Smart Nano Mater. 16 (2), 419–442. https://doi.org/10.1080/19475411.2025.2504442 (2025).

    Google Scholar 

  • Sun, F. et al. A versatile microporous design toward toughened yet softened Self-Healing materials. Adv. Mater. 36 (50), 2410650. https://doi.org/10.1002/adma.202410650 (2024).

    Google Scholar 

  • Zhang, Z. et al. Robotic wire-based friction stir additive manufacturing. Additive Manuf. 88, 104261. https://doi.org/10.1016/j.addma.2024.104261 (2024).

    Google Scholar 

  • Yang, G. et al. Prediction of restrained stress for UHPC: considering relationship between long-term and in-situ creep. Constr. Build. Mater. 484, 141722. https://doi.org/10.1016/j.conbuildmat.2025.141722 (2025).

    Google Scholar 

  • Zhang, J. et al. Modeling sediment flux in river confluences: A comprehensive momentum-based study. Water Resour. Res. 61, e2024WR039154. https://doi.org/10.1029/2024WR039154 (2025).

    Google Scholar 

  • Ren, D. et al. Harmonizing physical and deep learning modeling: A computationally efficient and interpretable approach for property prediction. Scripta Mater. 255, 116350. https://doi.org/10.1016/j.scriptamat.2024.116350 (2025).

    Google Scholar 

  • Shen, C. et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta Mater. 179, 201–214. https://doi.org/10.1016/j.actamat.2019.08.033 (2019).

    Google Scholar 

  • Zhang, Z. et al. Optimization of low-power femtosecond laser Trepan drilling by machine learning and a high-throughput multi-objective genetic algorithm. Opt. Laser Technol. 148, 107688. https://doi.org/10.1016/j.optlastec.2021.107688 (2022).

    Google Scholar 

  • Wang, H., Li, Y., Men, T. & Li, L. Physically interpretable Wavelet-Guided networks with dynamic frequency decomposition for machine intelligence fault prediction. IEEE Trans. Syst. Man. Cybernetics: Syst. 54 (8), 4863–4875. https://doi.org/10.1109/TSMC.2024.3389068 (2024).

    Google Scholar 

  • Liu, H., Zhang, D. & Geng, D. Design of a self-excited vibration tool bar for cutting difficult-to-machine alloys. Int. J. Mech. Sci. 300, 110456. https://doi.org/10.1016/j.ijmecsci.2025.110456 (2025).

    Google Scholar 

  • Xu, H. et al. An innovative normal self-positioning method with gravity and friction compensation for wall-climbing drilling robot in aircraft assembly. Int. J. Adv. Manuf. Technol. 138 (7), 3687–3704. https://doi.org/10.1007/s00170-025-15677-7 (2025).

    Google Scholar 

  • Cao, Y. et al. Structural-Functional-Integrated Ultra-Wideband Microwave-Absorbing composites based on in Situ-Grown graphene Meta-nanointerface. Adv. Funct. Mater. 34 (52), 2411271. https://doi.org/10.1002/adfm.202411271 (2024).

    Google Scholar 

  • Peng, J. et al. Numerical simulation and process optimization of laser welding in 6056 aluminum alloy T-Joints. Crystals 15 (1), 35. https://doi.org/10.3390/cryst15010035 (2025).

    Google Scholar 

  • Cao, Y. et al. Multi-Functional Self-Sensing electronic gasket for structural health monitoring of transportation pipelines. Adv. Funct. Mater. 35 (20), 2412634. https://doi.org/10.1002/adfm.202412634 (2025).

    Google Scholar 

  • Shi, S. et al. Significance of α-Al cellular matrix in tensile behavior and work-hardening of additive-manufactured AlSi10Mg alloy. Virtual Phys. Prototyp. 20 (1), e2449189. https://doi.org/10.1080/17452759.2024.2449189 (2025).

    Google Scholar 

Continue Reading

  • Bangladesh to demand T20 World Cup matches be moved outside India – France 24

    1. Bangladesh to demand T20 World Cup matches be moved outside India  France 24
    2. Bangladesh look to move T20 World Cup matches from India amid Mustafizur row  ESPNcricinfo
    3. ‘Days of slavery are over’: Bangladesh to demand its T20 World Cup matches…

    Continue Reading

  • These 1980s Home Décor Ideas Are Suddenly Trending Again

    These 1980s Home Décor Ideas Are Suddenly Trending Again

    There is a particular pleasure in leafing through old magazine archives. You notice which interiors still feel timeless, which collectables did people value and what recipes appeared again and again.

    There are, of course, certain ideas best…

    Continue Reading

  • the productivity hack for 2026

    the productivity hack for 2026

    Stay informed with free updates

    Time (to mangle the Rolling Stones lyric) is definitely not on my side. Like many Financial Times readers I’m sure, life for most of last year involved juggling a sheaf of to-do lists, with the clock as a permanent reminder that such a volume and variety of tasks would not and could not be completed.

    Procrastination is not one of my psychological quirks, so that’s not it. And I’ve become quite good at “eating the frog” — doing one of the most unpleasant things on the list first, to give yourself a boost from getting it over with. No, the problem is quite simple: there is too much to do. This may be particularly the case for those with both young and old people to look after, as well as work responsibilities.

    Luckily (if not happily), it seems that for many middle-aged women, large chunks of extra time open up in your diary around the same time as the tasks and responsibilities proliferate. But there is a catch — those hours are from about 3am to maybe five or six in the morning.

    Yes, that’s right. Insomnia — it’s my tried and tested productivity hack for 2026. We’ve all been bludgeoned by the competitiveness of “the 5am club”, the go-getters who boast of starting their day super early to steal a march on the losers who need eight hours’ sleep a night. Well, this year I’m already planning the 3am club — think less business elite, more frazzled sandwich generation.

    Here’s how it goes. Strange mid-life biological changes start to interrupt your sleep patterns, leading to some sort of internal alarm going off at approximately 3am, regular as clockwork. It’s not that you are sleeping badly (although a newfound sympathy for friends who have suffered with life-long insomnia is perhaps a moral gain from the experience). You are just plain awake. And judging by the number of times colleagues and friends have described the same phenomenon, joking that we could have sorted out our work questions in the small hours when we were both up, this is widespread.

    You then have a choice: either worry about how bad the next day will feel, thereby worsening your anxious state; or embrace the bonus of a couple of extra hours to get on top of things. Personally, I found, following advice from a counsellor, that getting up to put on a load of laundry or mopping the floors has the benefit of a physical task that will eventually summon sleep again. Then I rationalise my to-do lists for the following days, and often get my physio exercises done. If I’m incurably alert, I might do some work, but using screens is not conducive to winding down again (though many are the columns I have written after midnight).

    You could also get creative — but be warned the output may not be a gift to the rest of humanity. One of the moments of 2025 was surely Reform UK’s sequin-clad Andrea Jenkyns, on stage at the party’s conference belting out lines from her own composition — a bombastic rock anthem entitled “Insomniac”. “I’m an insomniac!/ Staring at the ceiling, waiting for my thoughts to switch off.”

    Unforgettable, for sure, but not in a good way — and you have to assume she wrote it at night. So make sure you take a good hard look at your own masterpiece after you have managed to get some sleep.

    Reading, somehow, doesn’t work for me — the mind stays too active and I’m still awake as the rest of the household gets up. Last year I took up Japanese visible mending for our socks and sweaters, with mixed but useful (and therapeutic) results. The aim is to eventually fall back into an emergency top-up sleep before the day really has to start.

    Clearly, none of this is ideal. The mental and physical health effects of a lack of sleep can be severe. And on those nights when the internal alarm fails to go off, I slumber on blissfully and wake refreshed, much like my old (for which read young) self. But since at least for now it seems to be inevitable, better to accept and adjust. Chances are that this productivity hack will not solve the insomnia. But hey, we can beat those layabout 5am-ers at their own game and carry the day. And the night.

    miranda.green@ft.com

    Continue Reading

  • Europe’s renewables push slowed by waits for links to grid, operator warns

    Europe’s renewables push slowed by waits for links to grid, operator warns

    Stay informed with free updates

    The boss of one of Europe’s largest grid operators has warned that too many speculative and unprepared projects are holding up grid connections for critical energy projects and causing years-long queues.

    Bernard Gustin, chief executive of Elia Group, which operates the Belgian and parts of the German grid, said that operators of network infrastructure should be able to allocate connections to projects that are ready, rather than those that applied first.

    ‘‘I think in Belgium we have 10 times more projects [than] needed until 2030,” he said, referring to battery storage projects. “If you change from first come, first served to first ready, first served, then you will focus on the ones who are really serious because they have everything [ready].”

    Grid connections have become a huge issue for European countries. Many are trying to manage a rapid increase in demand for grid access as more industrial plants and households install wind and solar power that can go into the grid, as well as an increasing number of applications from data centres to use energy from it.

    In some countries, such as the Netherlands, queues to be connected to the grid stretch more than seven years. In Slovakia, about 50 per cent of capacity reserved for connection remains unused, according to commission figures. In Germany, there are twice as many requests to add battery storage to the grid as is planned in the country’s grid development plan, an Elia Group report found.

    The rollout of renewables in the EU has outpaced the infrastructure needed to support it, as countries race to meet renewable energy targets set by Brussels and move away from imported fossil fuels. The European Commission has estimated that €1.2tn needs to be spent on the EU’s grids by 2040 in order to support the transition.

    Gustin said that grid operators are competing for funding to rapidly build out networks and upgrade infrastructure to balance the volatility of wind and solar energy.

    After years of stagnant investment levels, “we all have huge capex plans, so big that you need to be able to finance them, which is a challenge”, he said.

    Bernard Gustin: ‘If you change from first come, first served to first ready, first served, then you will focus on the ones who are really serious because they have everything [ready]’ © Jonas Roosens/Belga/AFP/Getty Images

    Costs from grid congestion — where cheap electricity cannot flow to where demand is so people have to pay for higher cost sources — are rising. Acer, the EU energy regulator, has said that they reached €5.2bn in 2022 and could rise to €26bn by 2030.

    In a document published in December, Brussels set out recommendations to prioritise connections to the grid. The commission also said that it would take a more top-down approach to energy infrastructure planning in order to accelerate the build-out and ensure costs were shared between EU countries.

    “In Europe it’s a huge problem and we lose billions every year in lost value because of curtailment and bottlenecks,” EU energy commissioner Dan Jørgensen told the Financial Times.

    In a report on energy storage, Elia Group found that the first 100GW of installed batteries in Europe would reduce the curtailment of renewable power by 13 per cent, meaning that 13 per cent more power would be available.

    People observe a large control room screen displaying Belgium’s electricity grid data.
    A control room screen displaying Belgium’s electricity grid data © Jonas Roosens/BELGA MAG/AFP/Getty Images

    Elia plans to spend €31.6bn on grid upgrades until 2028, split roughly one-third to Belgium and two-thirds to its German arm. To deal with connection demands from batteries, data centres and renewable energy installations could cost an additional €10bn, Gustin estimated.

    “These are not small amounts . . . you have a lot of people saying we don’t want tariffs to go up on energy, electricity is not competitive. And so we have a first challenge [which] is how do we make sure, given the amounts we need to raise, that we have a competitive return on equity?”

    Gustin, who was formerly chief executive of Brussels Airlines, oversaw a €2.2bn capital raise earlier this year, bringing in investors such as BlackRock and the Canadian pension fund CPP.

    Often the length of time it takes to grant permits for infrastructure projects is seen as a risk factor by investors, he said, with permitting times in Belgium running up to eight years.

    “By [that] time inflation and the price have increased and some investors are telling you these were not the conditions we had at the start, we cannot continue,” he said.

    The EU’s recent legislation aims to speed up permitting times by setting time limits for permit deliberations and proposing that energy projects should be seen as having an overriding interest.

    Continue Reading

  • Investor urges corporate Japan to get over bubble-era ‘trauma’

    Investor urges corporate Japan to get over bubble-era ‘trauma’

    Stay informed with free updates

    Japan’s executives have to change their mindset and exert more pricing power as the country moves on from an era of deflation, one of its biggest independent asset managers has said.

    Shuhei Abe, founder of asset manager Sparx, said he was looking to invest in companies and managers willing to emerge from a defensive crouch and raise prices in Japan’s changed economic landscape.

    “Investors in this country have waited for years for inflation to return and now the time has come,” he said in an interview in Tokyo. “One of the biggest catalysts for the coming years will be changes of management attitude.”

    His comments underline the change of mood among Japanese investors, who for years sought to pick stock market winners in an economy with barely any growth and entrenched deflation following the end of its asset price bubble in 1989.

    However, in 2025 Japan’s stock market index has climbed decisively beyond its previous peaks while rising inflation has allowed the central bank to raise interest rates to the highest level in 30 years.

    Managers of the previous era “suffered from the trauma of the past bubble” and had it instilled in them to cut debt and hoard capital rather than raise prices, Abe said.

    “Most of the top management guys who joined [Japanese companies] during this time were trained . . . to reduce the debt, to not waste capital,” said Abe, a former employee of George Soros. “But finally, now, they have started to understand they cannot continue like they have over the past 30 years.”

    Sparx has ¥2tn ($12.7bn) of assets under management. Among its investments Abe cites Morinaga, a confectioner benefiting from Japan’s boom in inbound tourism, and Shoei, a maker of premium motorcycle helmets, as benefiting from pricing power.

    Abe is also invested in Pilot, one of the largest pen companies in the world, which has recently moved to satisfy some of Abe’s demands, raising the price of its best-selling pen in Japan by 10 per cent.

    Most of Japan’s asset managers are riding the wave of stock price records over the past 18 months. Sparx, which was founded in 1989 just before the end of the bubble, managed in August to exceed its previous peak for assets under management, set 19 years earlier.

    Its funds have recorded, over their lifetimes, annualised returns of between 4.7 per cent for its long-short fund and 11.4 per cent for its active long-only strategy. The Topix returned about 4.6 per cent over roughly the same period.

    Japan’s average annual growth was less than 1 per cent for more than 30 years, Abe pointed out. “In this environment, it’s not easy to invest in any equity asset. So Sparx did very well in that sense. But, at the same time, no one else could do it, thus there was room for us.”

    Before founding Sparx, Abe was funded by Soros in 1985 to invest in Japanese railroad stock, in a bet that the market would start to apply more value to the sector’s vast real estate holdings — a variant on a strategy that some activists and private equity groups are using in Japan today.

    It is not just the end of a long period of stagnation that has put Japan back into investors’ sights. Regulators, the government and the stock exchange are pushing companies to pay more attention to shareholders.

    The government is also pushing to improve the quality and quantity of asset managers, convinced that they are crucial to improving corporate performance and getting capital flowing.

    Abe expects that a wave of retail investors will come into the market, with the side-effect that companies will have a powerful new constituency pushing them to perform.

    “Individuals will move the market. Individuals will eventually be . . . a most powerful activist,” he said.

    Continue Reading