- Australia imposes sanctions, travel bans on four Taliban officials RADIO PAKISTAN
- Australia sanctions Afghan Taliban officials over women’s rights abuses Al Jazeera
- Australia imposes sanctions on three Taliban ministers, chief justice Dialogue…
Blog
-
Australia imposes sanctions, travel bans on four Taliban officials – RADIO PAKISTAN
-
Vietnam’s trade turnover earns nearly 840 bln USD in first 11 months
HANOI, Dec. 6 (Xinhua) — Vietnam’s total import-export turnover reached 839.75 billion U.S. dollars in the first 11 months of 2025, up 17.2 percent year on year, the National Statistics Office reported Saturday.
During the period, exports totaled 430.14 billion U.S. dollars, rising 16.1 percent, while imports climbed 18.4 percent to 409.61 billion dollars.
The Southeast Asian country posted a trade surplus of 20.53 billion U.S. dollars, the data showed. Enditem
Continue Reading
-
NASA launches tech demo to make spacecraft cheaper to design
The satellite described (Image source: NASA’s Kennedy Space Center on Facebook) Continue Reading
-
Australia sanctions Taliban officials over women’s rights
The Australian government has imposed financial sanctions and travel bans on four Taliban officials, citing severe oppression of women and girls in Afghanistan and concerns over the erosion of good governance and…
Continue Reading
-
A novel two-stage deep learning approach for lung cancer using enhanced ResNet50 segmentation and LungSwarmNet classification
Šarić, M., Russo, M., Stella, M. & Sikora, M. (2019). CNN-based method for lung cancer detection in whole slide histopathology images. In 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1–4). IEEE.
Pang, S., Zhang, Y., Ding, M., Wang, X. & Xie, X. A deep model for lung cancer type identification by densely connected convolutional networks and adaptive boosting. IEEE Access 8, 4799–4805 (2019).
Damayanti, N. P., Ananda, M. N. D. & Nugraha, F. W. Lung cancer classification using convolutional neural network and DenseNet. J. Soft Comput. Explor. 4(3), 133–141 (2023).
Kanakaraddi, S. G., Handur, V. S., Jalannavar, A., Chikaraddi, A. & Giraddi, S. Segmentation and classification of lung cancer using deep learning techniques. Procedia Comput. Sci. 235, 3226–3235 (2024).
Chen, W. et al. HSN: Hybrid segmentation network for small cell lung cancer segmentation. IEEE Access 7, 75591–75603 (2019).
Da Silva, G. L. F., Valente, T. L. A., Silva, A. C., De Paiva, A. C. & Gattass, M. Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput. Methods Programs Biomed. 162, 109–118 (2018).
Sandag, G. A. & Kabo, D. T. Comparative analysis of lung cancer classification models using EfficientNet and ResNet on CT-scan lung images. CogITo Smart J. 10(1), 259–270 (2024).
Cao, H. et al. Dual-branch residual network for lung nodule segmentation. Appl. Soft Comput. 86, 105934 (2020).
Mohite, A. Application of transfer learning technique for detection and classification of lung cancer using CT images. Int. J. Sci. Res. Manag. 9(11), 621–634 (2021).
Liu, X., Li, K. W., Yang, R. & Geng, L. S. Review of deep learning based automatic segmentation for lung cancer radiotherapy. Front. Oncol. 11, 717039 (2021).
Uddin, J. Attention-based DenseNet for lung cancer classification using CT scan and histopathological images. Designs 8(2), 27 (2024).
Chen, Y. et al. Classification of lungs infected COVID-19 images based on inception-ResNet. Comput. Methods Prog. Biomed. 225, 107053 (2022).
Abedalla, A., Abdullah, M., Al-Ayyoub, M. & Benkhelifa, E. Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures. PeerJ Comput. Sci. 7, e607 (2021).
WahabSait, A. R. Lung cancer detection model using deep learning technique. Appl. Sci. 13(22), 12510 (2023).
Qayyum, A., Ang, C. K., Sridevi, S., Khan, M. A., Hong, L. W., Mazher, M. & Chung, T. D. (2020). Hybrid 3D-ResNet deep learning model for automatic segmentation of thoracic organs at risk in CT images. In 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) (pp. 1–5). IEEE.
Arthi, T., Premkumar, S., Sivakumar, S. & Partheeban, N. (2023). Leveraging DenseNet and Genetic Algorithms for Lung Cancer Severity Classification. In 2023 1st International Conference on Optimization Techniques for Learning (ICOTL) (pp. 1–6). IEEE.
Mienye, I. D., Sun, Y. & Wang, Z. Improved predictive sparse decomposition method with densenet for prediction of lung cancer. Int. J. Comput. 1, 533–541 (2020).
Raghuvanshi, S. S., Arya, K. V. & Patel, V. PSbBO-Net: A hybrid particle swarm and bayesian optimization-based DenseNet for lung cancer detection using histopathological and CT images. Int. J. Electr. Electron. Res. 12(3), 1074–1086 (2024).
Kalaivani, N., Manimaran, N., Sophia, S. & Devi, D. D. (2020). Deep learning based lung cancer detection and classification. In IOP conference series: materials science and engineering (Vol. 994, No. 1, p. 012026). IOP Publishing.
Biyyala, V. (2023). Lung image segmentation: Custom U-Net with ResNet encoder architecture.
Asuntha, A., Singh, N. & Srinivasan, A. PSO, genetic optimization and SVM algorithm used for lung cancer detection. J. Chem. Pharm. Res. 8(6), 351–359 (2016).
Lyu, J., Bi, X. & Ling, S. H. Multi-level cross residual network for lung nodule classification. Sensors 20(10), 2837 (2020).
Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V. & Anisi, M. H. ResBCDU-Net: A deep learning framework for lung CT image segmentation. Sensors 21(1), 268 (2021).
Xiao, Z., Liu, B., Geng, L., Zhang, F. & Liu, Y. Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry 12(11), 1787 (2020).
Zhang, F., Wang, Q. & Li, H. Automatic segmentation of the gross target volume in non-small cell lung cancer using a modified version of ResNet. Technol. Cancer Res. Treat. 19, 1533033820947484 (2020).
Chavan, M., Varadarajan, V., Gite, S. & Kotecha, K. Deep neural network for lung image segmentation on chest x-ray. Technologies 10(5), 105 (2022).
Zhang, G., Lin, L. & Wang, J. (2021). Lung nodule classification in CT images using 3D DenseNet. In Journal of Physics: Conference Series (Vol. 1827, No. 1, p. 012155). IOP Publishing.
Continue Reading
-
The Rhythm of Blues – Colours of the Year 2026
AkzoNobel announced The Rhythm of Blues as the Colours of the Year 2026 on December 5.
On this occasion, Dulux by AkzoNobel continues its partnership with ELLE Decoration for the Pop-Up…
Continue Reading
-
George Russell beats Lando Norris to top spot as Lewis Hamilton crashes out of final practice
George Russell stormed to the top of the timesheets in the final practice session for the Abu Dhabi Grand Prix, marginally leading Lando Norris and Max Verstappen ahead of an all-important Qualifying hour.
The Mercedes driver found a small…
Continue Reading
-
Don’t get hung up on investment trust discounts
Unlock the Editor’s Digest for free
Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
In the first 10 months of 2025, investment trusts bought back more than £8.6bn worth of shares — 35 per cent more than in the same period last year. The aim was to close the discount between the share price of the trust and the value of the assets it contains per share. Did it work? Not very well; discounts remain stubbornly high for many trusts.
To explain why, we need to understand why there are discounts in the first place. This is a question I get asked a lot.
The big difference between an investment trust and a unit trust or “fund” comes when you want your money back. In an investment fund when lots of holders decide to sell units, the manager must dispose of underlying assets — and quickly. Fine if they’re liquid. Not so easy if those assets are something like property or a wind farm.
In a closed-ended investment trust, if you want your money back the manager doesn’t have to sell. The onus is on you to find a buyer for your shares. When the shares are less popular, buyers may offer less than the value of the underlying assets — a discounted price.
A discount, then, is the price you pay for liquidity, but it’s also an incentive for being patient. And that makes investment trusts an excellent vehicle for buying less liquid assets, like smaller companies, which can earn an illiquidity premium. This illiquidity reward is why over 25 years, for example, the smaller company equity index has outperformed the main market.
The discounts available today on income-generating assets can boost future returns significantly, too. If you buy £1 of assets for 90p you have the full £1 working to generate dividends, and that should compound over time. The closed-ended structure also allows gearing to be safely deployed, which can apply even more turbo to returns over time.
Today’s discounts — typically 14 per cent — are higher than average. We might expect them to close more than widen over the long term. But how? This year’s extraordinary level of share buybacks and windups has had little impact.
For a discount to close there needs to be a belief that the trust isn’t just a collection of assets. There needs to be a secret sauce — human expertise in managing the assets to enhance their value over time.
This has always been the case in the quoted property sector. A portfolio of properties will normally trade at a discount unless the managers can squeeze more out of the assets either by smart trading or by revitalising them.
The same was the case when the investment trust sector was used to inject capital into Lloyd’s of London. In 1992, the historic insurance market was on its knees and virtually bust. It needed a new supply of capital, which came with its plan of reconstruction and renewal.
Investment trusts were launched that pledged some of their capital to sit behind certain underwriting syndicates. The effect in the good insurance years was to boost the earnings of the investment trust, but a short-term earnings boost isn’t proof of a long-term earnings flow. The trusts went to discounts. The source of real value was the underwriting skills in some of the syndicates. And the trusts didn’t own that.
The answer in time was for the trusts — simply the providers of capital — to be folded into the managing agents that did the underwriting. Today, Lloyd’s insurer Hiscox trades at around 1.8 times its asset value, and many of the others that were originally investment trusts have been taken over at large multiples of book value. That experience is why I say it’s management skill that brings in discounts.
An area where there are currently large discounts is infrastructure and renewables. Too many managers have done little but buy assets. The dividends they generated when rates were low looked great, and the sector shot to a big premium. Rising rates have scuppered that. Those premiums are now deep discounts.
Many believe the answer is mergers and buybacks. But what is needed, in my view, is more proactive asset management. In fact, buybacks arguably only make the teams that want to be active despondent, because they have to be funded through disposals of the assets they would like to sweat.
What does this mean for the investor saving for the long term? Don’t get too hung up on discounts — they can work to your favour. Focus on the trusts that play to the structure’s strengths — that make the most of the illiquidity premium and of gearing, that give you access to assets you can’t buy easily, and where you can see the managers’ skill adding long-term value.
James Henderson is co-manager of the Lowland and Law Debenture investment trusts
Continue Reading
-
Hans-Jörg Rudloff, banker, 1940-2025
Unlock the Editor’s Digest for free
Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
Few bankers are as closely associated with a single financial product as Hans-Jörg Rudloff. The Swiss-German banker might not have invented the eurobond, but he presided over its greatest period of growth — the 1980s — and dominated the hard-charging entrepreneurial firm that most embodied its spirit: Credit Suisse First Boston.
In some ways Rudloff, who has died at the age of 85, was an enigma. Despite spending his entire career in an industry often derided as cynical and greedy, he was also a visionary, believing sincerely in the power of finance to drive growth and improve people’s lives. “Capital markets have steered capital to all corners of this world and lifted billions of people out of poverty,” he said. It was a credo first formed on Wall Street in the late 1960s when, as a young bond salesman at Kidder Peabody, he witnessed the vigour and efficiency of New York’s financial markets, which he associated with the prosperity of American life.
This was a time when the US government, amid concerns about widening deficits during the Vietnam war, had slapped restrictions on exports of dollars. One unintended consequence was to spark into life a market centred in London but outside any national regulatory or tax control that shuffled offshore dollar deposits from investors into the hands of international corporations.
Rudloff moved back to Europe, keen to participate in this development, and in 1980 was recruited to a senior role at CSFB, then a new joint venture between Credit Suisse and the US investment bank First Boston. It was a propitious moment: the market was exploding, propelled by ever faster communications and the Thatcher-Reagan era rolling-back of currency controls.
Rudloff thrived in this highly competitive environment. For much of the 1980s, CSFB topped the league tables for eurobond issues. He acquired the sobriquet “king of the Euromarkets” for the invention of the “bought deal”. Instead of surveying the appetite of prospective investors before launching an issue, the underwriter would buy it outright, hoping to resell the bonds for a profit. Rudloff’s advantage, he once said, “was that I had permission to underwrite at 10 o’clock at night, whereas every other firm had to go to ten bloody committees to get any permission to underwrite anything”. It was a freedom he freely indulged, reportedly signing contracts over champagne at Annabel’s — a London nightclub of which he was an enthusiastic patron.
The one certainty in banking is that advantages conferred by innovation get whittled away, and by the end of the 1980s CSFB lost its lead to giant Japanese banks. The firm descended into infighting, some of it blamed on Rudloff’s confrontational style (he cheerfully described himself as “a bit more ruthless than other people”). Rudloff responded by leaving Credit Suisse and reinventing himself as an emerging markets banker, setting out to bring the benefits of capital markets to the newly freed countries beyond the Berlin Wall at a time when few investment banks dared to go there.
It was an adventure that catapulted him into the cockpit of Russian business in the era of Vladimir Putin, sitting on the board of the oil company Rosneft right until the outbreak of the Ukraine war. Rudloff continued to believe that in financing the reconstruction of eastern Europe, investment banking had “proved its worth, just as it did in 19th-century America”. But by the end of his life, he was dismayed to see the liberal, open world in which he believed was strongly in retreat.
Rudloff was born in wartime Cologne in 1940 to a German industrialist father and a Swiss mother. His dual nationality made him an outsider in the conservative world of Swiss finance, and perhaps impelled him to look beyond its borders. Small, bustling and intense, he always competed to the utmost. Unable to ski until later in life, Rudloff set out to rectify the situation, engaging a “crazy but enormously talented ski coach”, according to friend Bob Loverd, and systematically acquiring the skills of a pro. “If he decided to do something, he put everything he had into it,” Loverd recalled.
Rudloff could be polarising: his manner was often abrupt — even if the barbs were delivered with a twinkle. But to those he gave his friendship he was enormously loyal, and liked nothing more than to help those in whom he saw flashes of his younger self. “While he pushed you hard, he could be very kind,” said Charles Harman, a colleague at CSFB. “He would come round the floor at 8.30 in the evening and say: ‘Who wants dinner?’ to the juniors who were there. How many City bosses do that?” Rudloff’s last wish was to throw a party for his many friends.
Continue Reading
-
Glencore’s copper pitch: buy in or buy me – Financial Times
- Glencore’s copper pitch: buy in or buy me Financial Times
- Glencore’s Copper Promises Demand Blind Faith From Investors Bloomberg.com
- Key facts: Glencore’s stock hits 10-month high; 1,000 roles eliminated; layoffs in chrome venture TradingView
- Swiss commodities trader Glencore cuts 1,000 jobs SWI swissinfo.ch
- Glencore to restart production at Alumbrera copper mine in Argentina WKZO
Continue Reading