Qaasid News

Download Our App

Category: 3. Business

  • Exclusive: China's ZTE may pay more than $1 billion to the US over foreign bribery allegations, sources say – Reuters

    1. Exclusive: China’s ZTE may pay more than $1 billion to the US over foreign bribery allegations, sources say  Reuters
    2. ZTE May Pay US Govt USD1B+ to Settle Overseas Bribery Allegations: Report  AASTOCKS.com
    3. Justice Department has moved ahead with a U.S. investigation into ZTE for allegedly violating Foreign Corrupt Practices Act in South America and other regions -sources  marketscreener.com
    4. ZTE Corp shares slide on report of over $1 bln fine to US govt  Investing.com
    5. ZTE Communicating with US Department of Justice Over Compliance Probe Related to Foreign Corrupt Practices Act, Will Protect Rights Through Legal Means  AASTOCKS.com

    Continue Reading

  • Graph-augmented transformer ensemble framework for robust and scalable fake news detection in social media ecosystems

  • Almandouh, M., Alrahmawy, M. F., Eisa, M., Elhoseny, M. & Tolba, A. S. Ensemble based high performance deep learning models for fake news detection. Sci. Rep. 14, 26591. https://doi.org/10.1038/s41598-024-26591-5 (2024).

    Google Scholar 

  • Praseed, A., Rodrigues, J. & Santhi, T. P. Disinformation detection using graph neural networks: A survey. Artif. Intell. Rev. 57 (2024).

  • Liu, J., Wu, F., Jin, H., Zhu, X. & Jing, X. Y. Inter-modal fusion network with graph structure preserving for fake news detection. In: Neural Information Processing. Springer, 1–9 https://doi.org/10.1007/978-981-99-8076-5_20 (2024).

  • Jiang, Y. & Liu, X. Deep learning for fake news detection: A survey. Artif. Intell. 303, 101235. https://doi.org/10.1016/j.artint.2022.101235 (2022).

    Google Scholar 

  • Harris, S., Hadi, H. J., Ahmad, N. & Alshara, M. A. Fake news detection revisited: an extensive review of theoretical frameworks, dataset assessments, model constraints, and forward-looking research agendas. Technologies 12, 222. https://doi.org/10.3390/technologies12110222 (2024).

    Google Scholar 

  • Wang, B., Feng, Y., Xiong, X. C., Wang, Y. H. & Qiang, B. H. Multi-modal transformer using two-level visual features for fake news detection. Appl. Intell. 53, 10429–10443. https://doi.org/10.1007/s10489-022-04055-5 (2022).

    Google Scholar 

  • Lee, D. & Kim, S. Detecting fake news using social media and graph neural networks. J. Comput. Social Sci. 5, 125–137. https://doi.org/10.1007/s42001-021-00110-3 (2022).

    Google Scholar 

  • Song, C., Teng, Y., Zhu, Y., Wei, S. & Bin, B. Dynamic graph neural network for fake news detection. Neurocomputing 505, 362–374. https://doi.org/10.1016/j.neucom.2022.07.013 (2022).

    Google Scholar 

  • Xu, W., Wang, X. & Chen, Q. Leveraging attention-based networks for fake news detection in social media. IEEE Access. 10, 47115–47127. https://doi.org/10.1109/ACCESS.2022.3181273 (2022).

    Google Scholar 

  • Roumeliotis, K. I., Tselikas, N. D. & Nasiopoulos, D. K. Fake news detection and classification: A comparative study of convolutional neural networks, large Language models, and natural Language processing models. Future Internet. 17, 28. https://doi.org/10.3390/fi17010028 (2025).

    Google Scholar 

  • Folino, F., Folino, G., Guarascio, M. & Tagarelli, A. Towards data- and compute-efficient fake news detection: an approach combining active learning and pre-trained Language models. SN Comput. Sci. 5, 470. https://doi.org/10.1007/s42979-024-02809-1 (2024).

    Google Scholar 

  • Sudhakar, M. & Kaliyamurthie, K. P. Efficient prediction of fake news using novel ensemble technique based on machine learning algorithm. In: Information and Communication Technology for Competitive Strategies. Springer, 1–10 https://doi.org/10.1007/978-981-19-0098-3_1 (2023).

  • Luo, P. & Xie, Y. Fake news detection via multi-task learning on graph networks. Inf. Sci. 613, 533–550. https://doi.org/10.1016/j.ins.2022.09.040 (2023).

    Google Scholar 

  • Xu, X., Sun, C. & Wang, S. Fake news detection via multi-view graph convolutional network. ACM Trans. Inform. Syst. 41, 47. https://doi.org/10.1145/3458986.3458991 (2023).

    Google Scholar 

  • Zhang, X. & Zhao, Q. Fake news detection using graph neural networks: A comprehensive survey. Neural Comput. Appl. https://doi.org/10.1007/s00542-023-08257-w (2023).

    Google Scholar 

  • Rani, S. & Kumar, M. Multi-modal topic modeling from social media data using deep transfer learning. Appl. Soft Comput. 160, 111706. https://doi.org/10.1016/j.asoc.2024.111706 (2024).

    Google Scholar 

  • Kumar, Y., Bhardwaj, P., Shrivastav, S. & Mehta, K. Predicting paediatric brain disorders from MRI images using advanced deep learning techniques. Neuroinformatics 23 (2), 9. https://doi.org/10.1007/s12021-024-09707-0 (2025).

    Google Scholar 

  • Modi, N. et al. Physiological signal-based mental stress detection using hybrid deep learning models. Discover Artif. Intell. 5, 166. https://doi.org/10.1007/s44163-025-00412-8 (2025).

    Google Scholar 

  • Alzahrani, M. A. & Aljuhani, M. A. Enhancing fake news detection with word embedding: A machine learning and deep learning approach. Computers 13, 239. https://doi.org/10.3390/computers13090239 (2024).

    Google Scholar 

  • Zamani, A. S., Hashim, A. H. A., Mohamed, S. S. I. & Alam, M. N. Optimized deep learning techniques to identify rumors and fake news in online social networks. J. Comput. Cogn. Eng. 2, 1–12. https://doi.org/10.47852/bonviewJCCE52023348 (2023).

    Google Scholar 

  • Wei, L. & Zhang, T. Fake news detection using deep learning-based fusion of graph convolutional networks and transformer models. Inform. Fusion. 81, 146–159. https://doi.org/10.1016/j.inffus.2022.11.004 (2023).

    Google Scholar 

  • Abduljaleel, I. Q. & Ali, I. H. Deep learning and fusion mechanism-based multimodal fake news detection methodologies: A review. Eng. Technol. Appl. Sci. Res. 14, 15665–15675. https://doi.org/10.48084/etasr.7907 (2024).

    Google Scholar 

  • Li, H., Liu, L. & Wang, Y. Fake news detection using a transformer-based framework with attention mechanisms. Expert Syst. Appl. 182, 115129. https://doi.org/10.1016/j.eswa.2021.115129 (2022).

    Google Scholar 

  • Jing, J., Wu, H., Sun, J., Fang, X. & Zhang, H. Multimodal fake news detection via progressive fusion networks. Inf. Process. Manag. 60, 103120. https://doi.org/10.1016/j.ipm.2022.103120 (2023).

    Google Scholar 

  • Dixit, D. K., Bhagat, A. & Dangi, D. An accurate fake news detection approach based on a levy flight honey Badger optimized convolutional neural network model. Concurrency Computation: Pract. Experience. 35, e7382. https://doi.org/10.1002/cpe.7382 (2023).

    Google Scholar 

  • Yang, L. & Lee, K. Fake news detection in social media using a hybrid model of deep neural networks. Neural Netw. 145, 202–213. https://doi.org/10.1016/j.neunet.2021.09.003 (2022).

    Google Scholar 

  • Singhania, S., Fernandez, N. & Rao, S. 3HAN: A deep neural network for fake news detection. ArXiv Preprint (2023). ArXiv:2306.12014

  • Kikon, J. M. & Bania, R. K. Towards development of machine learning models for fake news detection and sentiment analysis. In: Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technology (NICEDT 2024). pp. 99–107 https://doi.org/10.1007/978-981-97-3601-0_8 (Springer, Singapore, 2024).

  • Fu, X., Guo, C. & Yuan, Z. A survey of fake news detection in social media: Methods, trends, and challenges. Computers 12, 80. https://doi.org/10.3390/computers12050080 (2023).

    Google Scholar 

  • Patel, R. & Gupta, P. Fake news detection via text and graph-based fusion learning. Comput. Intell. Neurosci. https://doi.org/10.1155/2022/6574754 (2022).

    Google Scholar 

  • Truică, C. O., Apostol, E. S., Marogel, M., Paschke, A. & GETAE Graph information enhanced deep neural network ensemble architecture for fake news detection. Expert Syst. Appl. 275, 126984. https://doi.org/10.1016/j.eswa.2025.126984 (2025).

    Google Scholar 

  • Zhang, L. & Chen, X. An improved method for fake news detection using attention-based neural networks. Appl. Soft Comput. 115, 108273. https://doi.org/10.1016/j.asoc.2021.108273 (2022).

    Google Scholar 

  • Papageorgiou, E., Varlamis, I. & Chronis, C. Harnessing large Language models and deep neural networks for fake news detection. Information 16, 297. https://doi.org/10.3390/info16040297 (2025).

    Google Scholar 

  • Jin, W. et al. Veracity-oriented context-aware large Language models–based prompting optimization for fake news detection. Int. J. Intell. Syst. 40, 5920142. https://doi.org/10.1002/int.5920142 (2025).

    Google Scholar 

  • Jin, W. et al. -FND: A multi-role fake news detection method based on argument switching-based courtroom debate. J. King Saud Univ. – Comput. Inform. Sci. 37, 33. https://doi.org/10.1016/j.jksuci.2024.101033 (2025).

    Google Scholar 

  • Khattar, D., Goud, J. S., Gupta, M. & Mvae, V. V. Multimodal variational autoencoder for fake news detection. In: The World Wide Web Conference, pp. 2915–2921 (2019).

  • Jin, W. et al. A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning. Sci. Rep. 14, 19470. https://doi.org/10.1038/s41598-024-19470-0 (2024).

    Google Scholar 

  • Jin, W. et al. A prompting multi-task learning-based veracity dissemination consistency reasoning augmentation for few-shot fake news detection. Eng. Appl. Artif. Intell. 144, 110122. https://doi.org/10.1016/j.engappai.2025.110122 (2025).

    Google Scholar 

  • Abulaish, M., Kamal, A. & Zaki, M. J. A survey of figurative Language and its computational detection in online social networks. ACM Trans. Web. 14, 1–52. https://doi.org/10.1145/3383212 (2020).

    Google Scholar 

  • Kamal, A. & Abulaish, M. Contextualized satire detection in short texts using deep learning techniques. J. Web Eng. 23, 27–52 (2024).

    Google Scholar 

  • Abulaish, M. & Kamal, A. Self-deprecating sarcasm detection: An amalgamation of rule-based and machine learning approach. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). pp. 415–422 https://doi.org/10.1109/WI.2018.00-25 (IEEE, 2018).

  • Kamal, A., Anwar, T., Sejwal, V. K., Fazil, M. & BiCapsHate Attention to the linguistic context of hate via bidirectional capsules and hatebase. IEEE Trans. Comput. Social Syst. 11, 1781–1792. https://doi.org/10.1109/TCSS.2022.3228775 (2023).

    Google Scholar 

  • Mohankumar, P., Kamal, A., Singh, V. K. & Satish, A. Financial fake news detection via context-aware embedding and sequential representation using cross-joint networks. In: 2023 15th International Conference on Communication Systems & Networks (COMSNETS), pp. 780–784 https://doi.org/10.1109/COMSNETS56262.2023.10041125 (IEEE, 2023).

  • Kamal, A., Mohankumar, P. & Singh, V. K. Financial misinformation detection via RoBERTa and multi-channel networks. In: International Conference on Pattern Recognition and Machine Intelligence. pp. 646–653 https://doi.org/10.1007/978-3-031-40375-8_53 (Springer, Cham, 2023).

  • Ilie, V. I., Truică, C. O., Apostol, E. S. & Paschke, A. Context-aware misinformation detection: A benchmark of deep learning architectures using word embeddings. IEEE Access. 9, 162122–162146. https://doi.org/10.1109/ACCESS.2021.3132502 (2021).

    Google Scholar 

  • Truică, C. O., Apostol, E. S. & MisRoBÆRTa Transformers versus misinformation. Mathematics 10, 569. https://doi.org/10.3390/math10040569 (2022).

    Google Scholar 

  • Truică, C. O. & Apostol, E. S. Fake news detection using sentence Transformers. CEUR Workshop Proc. 3180, paper–61 (2022). https://ceur-ws.org/Vol-3180/paper-61.pdf

    Google Scholar 

  • Truică, C. O. & Apostol, E. S. It’s all in the embedding! Fake news detection using document embeddings. Mathematics 11, 508. https://doi.org/10.3390/math11030508 (2023).

    Google Scholar 

  • Petrescu, A., Truică, C. O. & Apostol, E. S. Language-based mixture of Transformers for EXIST2024. CEUR Workshop Proc. 3740, paper–108 (2024). https://ceur-ws.org/Vol-3740/paper-108.pdf

    Google Scholar 

  • Truică, C. O., Apostol, E. S. & Karras, P. Deep neural network ensemble architecture for social and textual context-aware fake news detection. Knowl. Based Syst. 294, 111715. https://doi.org/10.1016/j.knosys.2024.111715 (2024).

    Google Scholar 

  • E Almandouh M, Alrahmawy MF, Eisa M, Elhoseny M, Tolba AS. Ensemble based highperformance deep learning models for fake news detection. Scientific Reports 14 (1), 26591 (2024).

  • Ghosh, A. et al. Proactive network immunization for misinformation control. In: Proceedings of the ACM Conference on Computer and Communications Security (CCS), pp. xxx–xxx https://doi.org/10.1145/3459637.3482481 (2021).

  • Truică, C. O., Apostol, E. S., Nicolescu, R. C. & Karras, P. M. C. W. D. S. T. A minimum-cost weighted directed spanning tree algorithm for real-time fake news mitigation in social media. IEEE Access. 11, 125861–125873. https://doi.org/10.1109/ACCESS.2023.3331220 (2023).

    Google Scholar 

  • Apostol, E. S., Coban, Ö. & Truică, C. O. CONTAIN: A community-based algorithm for network immunization. Eng. Sci. Technol. Int. J. 55, 101728. https://doi.org/10.1016/j.jestch.2024.101728 (2024).

    Google Scholar 

  • Apostol, E. S., Truică, C. O., Paschke, A. & ContCommRTD A distributed content-based misinformation-aware community detection system for real-time disaster reporting. IEEE Trans. Knowl. Data Eng. 36, 5811–5822. https://doi.org/10.1109/TKDE.2024.3417232 (2024).

    Google Scholar 

  • Truică, C. O., Constantinescu, A. T. & Apostol, E. S. StopHC: A harmful content detection and mitigation architecture for social media platforms. In: Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. xxx–xxx https://doi.org/10.1109/ICCP63557.2024.10793051 (2024).

  • Almeida, F. et al. Virality detection in social media. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. xxx–xxx https://doi.org/10.5441/002/edbt.2021.69 (2021).

Continue Reading

  • Why Big Tech is doubling down on investing in India

    Why Big Tech is doubling down on investing in India

    A slogan related to Artificial Intelligence (AI) is displayed on a screen in Intel pavilion, during the 54th annual meeting of the World Economic Forum in Davos, Switzerland, January 16, 2024. 

    Denis Balibouse | Reuters

    Big Tech is doubling down on investing billions in India, drawn by its abundance of resources for building data centers, a large talent and digital user pool, and market opportunity.

    In under 24 hours, Microsoft and Amazon pledged more than $50 billion toward India’s cloud and AI infrastructure, while Intel on Monday announced plans to make chips in the country to capitalize on its growing PC demand and speedy AI adoption.

    While India trails the U.S. and China in the race to develop a native AI foundational model, and lacks a large domestic AI infrastructure company, it wants to leverage its expertise in the information technology sector to create and deploy AI applications at enterprise level, also offering Big Tech companies a huge opportunity.

    Having a model or computing is not enough for any enterprise to use AI effectively, and it requires companies making application layer and a large talent pool to deploy them, S. Krishnan, secretary at India’s Ministry of Electronics and Information Technology, told CNBC.

    Stanford University ranks India among the top four countries along with the U.S., China and the UK in the global and national AI vibrancy ranking. GitHub, a community of developers, has ranked India at the top with the global share of 24% of all projects.

    India’s opportunity lies more in “developing applications” which will be used to drive revenues for AI companies, Krishnan said.

    On Tuesday, Microsoft announced $17.5 billion in investment in the country, spread over 4 years, aimed at expanding hyperscale infrastructure, embedding AI into national platforms, and advancing workforce readiness.

    “This scale of capex gives Microsoft first‑mover advantage in GPU‑rich data centers while making Azure the preferred platform for India’s AI workloads, as well as deepening alignment with the government’s AI public infrastructure push,” said Tarun Pathak, research Director at Counterpoint Research. 

    Amazon on Wednesday announced plans to invest over $35 billion, on top of the $40 billion it has already invested in the country.

    Over the past few months, AI and tech majors such as OpenAI, Google, and Perplexity have offered their tools for free to millions in India, with Google also firming up its plans to invest $15 billion toward building data center capacity for a new AI hub in southern India.

    “India combines a huge digital user base, rapidly growing cloud and AI demand, and a high-talent IT ecosystem that can build and consume AI at scale, making it more than just a market for users and instead a core engineering and deployment hub,” Pathak said.

    Data center opportunity

    India has several advantages when it comes to building data centers. Markets such as Japan, Australia, China and Singapore in the Asia Pacific region have matured. Singapore, one of the oldest data center hubs in the region, has limited room to deploy large-scale data centers due to land availability issues.

    India has abundant space for large-scale data center developments. When compared with data center hubs in Europe, power costs in India are relatively low. Coupled with India’s growing renewable energy capacity — critical for power-hungry data centers — and the economics begin to look compelling.

    Local demand, fueled by the rise of e-commerce — a major driver of data center growth in recent years — and potential new rules for storing social media data, strengthens the case.

    Put simply: India is entering a sweet spot where global cloud providers, AI players, and domestic digitalization all converge to create one of the world’s hottest data center markets.

    “India is a pivotal market and one of the fastest‑growing regions for AI spending in Asia Pacific,” said Deepika Giri, associate vice president and head of research, big data & AI, at International Data Corporation.

    “A major gap, and therefore a significant opportunity, lies in the shortage of suitable compute infrastructure for running AI models,” she added. Big Tech is looking to capitalize on the infrastructure opportunity in India by investing heavily in the cloud and data center space.

    Global companies are expanding capacities closer to service bases in IT cities such as Bangalore, Hyderabad and Pune from traditional centers like Mumbai and Chennai which are closer to landing cables, as they build data centers in India for the world, Krishnan said.

    — CNBC’s Dylan Butts, Amitoj Singh contributed to this report. 

    Continue Reading

  • Stocks Slide as Oracle Spoils Mood After Fed Cut: Markets Wrap

    Stocks Slide as Oracle Spoils Mood After Fed Cut: Markets Wrap


    This content was published on

    (Bloomberg) — A global equities rally spurred by the Federal Reserve’s interest-rate cut evaporated as disappointing results from Oracle Corp. weighed on tech shares and focus turned to the US central bank’s outlook for further easing next year.

    Futures on Nasdaq 100 slumped more than 1.5%, while a selloff in technology stocks in Asia caused the regional equity gauge to reverse earlier gains. S&P 500 futures were down 0.8%. Shares of Oracle, whose fate is deeply tied to the artificial intelligence boom, plunged more than 10% in extended US trading after second-quarter cloud sales fell just short of analysts’ estimates. In a sign of waning risk appetite, Bitcoin lost more than 2%.

    The moves came after the S&P 500 rose 0.7% on Wednesday, ending just short of all-time highs, as the Fed cut rates for a third consecutive time and Chair Jerome Powell voiced optimism that the economy will strengthen as the inflationary impact from tariffs fades away. The result marked the first time since 2019 that three officials voted against a policy decision, with dissents on both ends of the policy spectrum. Officials maintained their outlook for a single cut in 2026.

    “While most of the focus was on the FOMC, a key risk for markets overnight was Oracle,” said Billy Leung, investment strategist at Global X Management. Its result was a key test for the AI infrastructure trade given Oracle’s role as a bellwether for hyperscale data center spending and has added to broader tech pressure, he said.

    Oracle reported a jump in spending on AI data centers and other equipment, rising outlays that are taking longer to translate into cloud revenue than investors wanted. Powered by an AI-driven rally, MSCI Inc.’s index of global stocks has surged about 20% this year and is on course for its best annual advance since 2019.

    A gauge of Asian technology stocks was down more than 1%, versus a 0.5% decline in the broader regional benchmark. Shares of SoftBank Group lost more than 8% in Tokyo.

    Traders in Asia were also assessing the impact of Mexican lawmakers’ final approval for new tariffs on the region’s imports. Also in focus was Thursday’s interest-rate decision in the Philippines, where the central bank is predicted to cut its key interest rate for a fifth straight meeting.

    In Japan, bonds gained after an auction of 20-year government debt drew its strongest demand since 2020. Yields across the curve have recently climbed to multi-year highs on renewed fiscal concerns as well as rising expectations for a Bank of Japan rate hike at its meeting next week.

    Elsewhere in markets, a gauge of the dollar edged higher after falling 0.4% on Wednesday. In commodities, oil prices were in focus after the US seized a sanctioned tanker off Venezuela, deterring more shipments from the South American producer and raising the risk of a conflict.

    ‘Fairly Choppy’

    US Treasuries rallied on Wednesday, with the policy-sensitive two-year yield sliding eight basis points, as the Fed’s quarter-point rate reduction was accompanied by the authorization of fresh Treasury bill purchases to rebuild bank reserves. The yield fell one more basis point on Thursday.

    The 10-year yield, which dropped around four basis points in the previous session, was two basis points lower on Thursday. The declines have stalled a prior run up in yields that had driven one global gauge to its highest since 2009.

    Powell pushed through the quarter percentage point cut not only over the objection of a few voters. A much larger group of regional Fed bank presidents who participated in the debate but weren’t among this year’s voting roster also signaled they opposed the cut.

    The fractures could foreshadow what’s to come in 2026, when a new chair may struggle even more than Powell to marshal consensus at the Fed.

    The Fed chair suggested the central bank had now acted sufficiently to help stabilize the labor market while leaving rates high enough to continue weighing on price pressures. He also underscored the importance of upcoming economic reports while advising caution on assessing household jobs readouts, given technical distortions after a government shutdown caused a data blackout.

    Nick Twidale, chief analyst at AT Global Markets in Sydney, said he is “hesitant” on how much momentum the Fed’s cut will bring to global markets.

    “The forward guidance was probably less dovish than most investors were hoping for,” he said. “We may see some fairly choppy markets in the sessions ahead as the market digests what Jerome Powell had to say.”

    Corporate News

    Private equity firm TPG Inc. is considering options for APM Monaco, including a possible stake sale or an initial public offering of the jeweler, according to people familiar with the matter. SK Hynix Inc. fell after South Korea’s main bourse issued a higher-level warning on investing in the stock following strong gains sparked by expectations of a listing in New York. Investment banks are on track to take home their smallest slice of underwriting fees from Hong Kong listings in years, even as share sales in the city have staged a blistering rebound. President Donald Trump signaled he’ll oppose a Warner Bros. Discovery Inc. deal that doesn’t include new ownership of CNN, a potential wrinkle for the bid from Netflix Inc. Shares of Jingdong Industrials Inc., the supply-chain unit of Chinese e-commerce giant JD.com Inc., fell in their Hong Kong trading debut after a HK$2.98 billion ($383 million) initial public offering. Japan’s stock market is witnessing a record wave of large private transactions known as block trades, stemming from companies reducing cross-shareholdings to improve corporate governance. Chinese artificial intelligence startup DeepSeek has relied on Nvidia Corp. chips that are banned in the country to develop an upcoming AI model, according to a new report in The Information. Coca-Cola Co. said Chief Executive Officer James Quincey is stepping down and will be replaced at the end of March by Henrique Braun, the company’s chief operating officer. Some of the main moves in markets:

    Stocks

    S&P 500 futures fell 0.8% as of 2:16 p.m. Tokyo time Japan’s Topix fell 0.7% Australia’s S&P/ASX 200 rose 0.1% Hong Kong’s Hang Seng was little changed The Shanghai Composite fell 0.6% Euro Stoxx 50 futures fell 0.1% Currencies

    The Bloomberg Dollar Spot Index rose 0.1% The euro was little changed at $1.1690 The Japanese yen was little changed at 155.91 per dollar The offshore yuan was little changed at 7.0611 per dollar The Australian dollar fell 0.6% to $0.6637 Cryptocurrencies

    Bitcoin fell 2.4% to $90,140.14 Ether fell 4.3% to $3,197.85 Bonds

    The yield on 10-year Treasuries declined two basis points to 4.12% Japan’s 10-year yield declined three basis points to 1.925% Australia’s 10-year yield declined nine basis points to 4.72% Commodities

    West Texas Intermediate crude was little changed Spot gold fell 0.4% to $4,212.65 an ounce This story was produced with the assistance of Bloomberg Automation.

    –With assistance from Richard Henderson.

    ©2025 Bloomberg L.P.

    Continue Reading

  • Policy levers to support viability and increase sustainable finance – United Nations Environment – Finance Initiative

    Policy levers to support viability and increase sustainable finance – United Nations Environment – Finance Initiative

    In this new policy brief, UNEP FI and the European Banking Federation (EBF) share seven potential policy levers to help strengthen the investment case for the EU chemical sector and help it attract finance for its sustainable transition.

    The brief discusses the sector’s market and decarbonization challenges and how new policy packages anchored in the European Green Deal—including the 2025 EU Chemicals Industry Package and the 2025 Clean Industrial Deal (CID), supported by the Industrial Decarbonization Bank—present opportunities by aiming to create the policy certainty and financing tools needed for large-scale sectoral transformation.

    This is the first in a new EU sectoral policy brief series developed by UNEP FI and EBF, intended to strengthen the connection between financial institutions, policymakers, and the real economy.

    Continue Reading

  • Starling eyes UK acquisition to boost corporate lending

    Starling eyes UK acquisition to boost corporate lending

    Stay informed with free updates

    Starling Bank is exploring plans to buy another UK lender, an overhaul to its business model that would allow the digital bank to put its £12bn worth of customer deposits to more profitable use. 

    The fintech is actively looking at several acquisition options that would allow it to expand its lending capacity and deploy deposits into areas that could generate higher returns, such as corporate lending, according to two people familiar with the discussions. One said the acquisition could be “substantial” in scale.  

    The acquisition is part of an effort by the fintech to diversify its income. Currently, Starling customer deposits are either held at the Bank of England collecting interest, invested in securities such as bonds, or channelled into mortgage lending through Fleet, a small lender that Starling bought in 2021.

    John Cronin, an independent bank analyst, said Starling “has struggled to grow its loan book commensurate with the size of its deposit book”. He said Starling’s latest set of accounts showed that £4.7bn worth of its £12.1bn deposits were net loans, which was lower than peers.

    Starling has thought about growing its capacity organically but has decided that an acquisition would be faster, according to the people familiar with the situation.

    Starling declined to comment. 

    The neobank, which was founded in 2014 by former Allied Irish Banks executive Anne Boden, has been tied to an acquisition before. In April, Sky News reported that Shawbrook, the SME lender, had approached Starling about a potential combination, but no deal emerged. 

    One person familiar with the business said Starling was not eyeing up mergers but acquisitions of a mixture of cash or cash and shares.

    Starling was previously criticised by the government’s former anti-fraud minister, Lord Theodore Agnew, for expanding its lending during Covid-19. The neobank built out a large portfolio of loans from the government-backed “bounce back loans”, which were issued by banks to support struggling businesses quickly during the pandemic.

    This grew to become a significant chunk of Starling’s lending book, but it announced in May that it was setting aside £28mn to cover loans that may not have complied with the lending scheme’s requirements. The UK financial regulator fined Starling in 2024 for “shockingly lax controls” against financial crime during its expansion from 2017 to 2023. The bank said it had since invested heavily in strengthening its controls.

    Starling has also built software called Engine, which allows lenders to design and build their own digital capabilities. The software, which Starling believes will be key to growth, is already being used by a bank in Australia and another in Romania. Last month, Starling announced that it had signed Canada’s Tangerine Bank as its latest Engine customer. 

    Starling is also weighing up buying a bank in the US to accelerate its global expansion, the Financial Times has previously reported. The plans could also include a US initial public offering. Part of the ambition is to plug Engine into an American bank and attract more local customers.

    Raman Bhatia, the group’s chief executive officer, told the FT’s Global Banking Summit last week that Starling was also open to buying a European business in the next three to five years to further boost expansion.

    Starling acquired in August the accounting software start-up Ember, which it hopes to provide to its small-business customers. “We have focused on high-quality acquisitions but now we want to turbocharge that engine in the UK so there is so much to do here in terms of market share and growth expansion,” said Bhatia at the FT’s banking summit.

    Continue Reading

  • New materials could boost the energy efficiency of microelectronics | MIT News

    New materials could boost the energy efficiency of microelectronics | MIT News

    MIT researchers have developed a new fabrication method that could enable the production of more energy efficient electronics by stacking multiple functional components on top of one existing circuit.

    In traditional circuits, logic devices that perform computation, like transistors, and memory devices that store data are built as separate components, forcing data to travel back and forth between them, which wastes energy.

    This new electronics integration platform allows scientists to fabricate transistors and memory devices in one compact stack on a semiconductor chip. This eliminates much of that wasted energy while boosting the speed of computation.

    Key to this advance is a newly developed material with unique properties and a more precise fabrication approach that reduces the number of defects in the material. This allows the researchers to make extremely tiny transistors with built-in memory that can perform faster than state-of-the-art devices while consuming less electricity than similar transistors.

    By improving the energy efficiency of electronic devices, this new approach could help reduce the burgeoning electricity consumption of computation, especially for demanding applications like generative AI, deep learning, and computer vision tasks.

    “We have to minimize the amount of energy we use for AI and other data-centric computation in the future because it is simply not sustainable. We will need new technology like this integration platform to continue that progress,” says Yanjie Shao, an MIT postdoc and lead author of two papers on these new transistors.

    The new technique is described in two papers (one invited) that were presented at the IEEE International Electron Devices Meeting. Shao is joined on the papers by senior authors Jesús del Alamo, the Donner Professor of Engineering in the MIT Department of Electrical Engineering and Computer Science (EECS); Dimitri Antoniadis, the Ray and Maria Stata Professor of Electrical Engineering and Computer Science at MIT; as well as others at MIT, the University of Waterloo, and Samsung Electronics.

    Flipping the problem

    Standard CMOS (complementary metal-oxide semiconductor) chips traditionally have a front end, where the active components like transistors and capacitors are fabricated, and a back end that includes wires called interconnects and other metal bonds that connect components of the chip.

    But some energy is lost when data travel between these bonds, and slight misalignments can hamper performance. Stacking active components would reduce the distance data must travel and improve a chip’s energy efficiency.

    Typically, it is difficult to stack silicon transistors on a CMOS chip because the high temperature required to fabricate additional devices on the front end would destroy the existing transistors underneath.

    The MIT researchers turned this problem on its head, developing an integration technique to stack active components on the back end of the chip instead.

    “If we can use this back-end platform to put in additional active layers of transistors, not just interconnects, that would make the integration density of the chip much higher and improve its energy efficiency,” Shao explains.

    The researchers accomplished this using a new material, amorphous indium oxide, as the active channel layer of their back-end transistor. The active channel layer is where the transistor’s essential functions take place.

    Due to the unique properties of indium oxide, they can “grow” an extremely thin layer of this material at a temperature of only about 150 degrees Celsius on the back end of an existing circuit without damaging the device on the front end.

    Perfecting the process

    They carefully optimized the fabrication process, which minimizes the number of defects in a layer of indium oxide material that is only about 2 nanometers thick.

    A few defects, known as oxygen vacancies, are necessary for the transistor to switch on, but with too many defects it won’t work properly. This optimized fabrication process allows the researchers to produce an extremely tiny transistor that operates rapidly and cleanly, eliminating much of the additional energy required to switch a transistor between off and on.

    Building on this approach, they also fabricated back-end transistors with integrated memory that are only about 20 nanometers in size. To do this, they added a layer of material called ferroelectric hafnium-zirconium-oxide as the memory component.

    These compact memory transistors demonstrated switching speeds of only 10 nanoseconds, hitting the limit of the team’s measurement instruments. This switching also requires much lower voltage than similar devices, reducing electricity consumption.

    And because the memory transistors are so tiny, the researchers can use them as a platform to study the fundamental physics of individual units of ferroelectric hafnium-zirconium-oxide.

    “If we can better understand the physics, we can use this material for many new applications. The energy it uses is very minimal, and it gives us a lot of flexibility in how we can design devices. It really could open up many new avenues for the future,” Shao says.

    The researchers also worked with a team at the University of Waterloo to develop a model of the performance of the back-end transistors, which is an important step before the devices can be integrated into larger circuits and electronic systems.

    In the future, they want to build upon these demonstrations by integrating back-end memory transistors onto a single circuit. They also want to enhance the performance of the transistors and study how to more finely control the properties of ferroelectric hafnium-zirconium-oxide.

    “Now, we can build a platform of versatile electronics on the back end of a chip that enable us to achieve high energy efficiency and many different functionalities in very small devices. We have a good device architecture and material to work with, but we need to keep innovating to uncover the ultimate performance limits,” Shao says.

    This work is supported, in part, by Semiconductor Research Corporation (SRC) and Intel. Fabrication was carried out at the MIT Microsystems Technology Laboratories and MIT.nano facilities. 

    Continue Reading

  • Taiwan probes leaks of vital chip technology

    Taiwan probes leaks of vital chip technology

    Taiwan has begun trade secrets investigations in its critical chipmaking sector under newly broadened national security laws, but the probes have raised eyebrows for who they are targeting: not companies from China but from the island nation’s closest allies.

    Last week, prosecutors charged the local subsidiary of Japanese chip equipment maker Tokyo Electron with failing to prevent alleged theft of trade secrets from Taiwan Semiconductor Manufacturing Company.

    A week earlier, prosecutors raided two homes of former TSMC executive Lo Wei-jen, who joined Intel after he left the Taiwanese company in July, as part of a probe into whether the 75-year-old was sharing “national core critical technology” with his new employer.

    The probe came after TSMC sued Lo for violating his non-compete agreement, saying it was highly probable he “uses, leaks, discloses, delivers or transfers TSMC’s trade secrets and confidential information to Intel”.

    Legal and industry experts in Taiwan said they were glad to finally see investigators getting serious about protecting a technology that had made Taiwan indispensable to the global economy. TSMC is the world’s largest chipmaker and dominates the market for cutting-edge semiconductors.

    But, unexpectedly, the first trade secrets cases under national security laws are not implicating companies from China — long seen as the main culprit in technology theft and the biggest threat to Taiwan’s security — but Tokyo Electron, a supplier, and Intel, a customer and rival. Both companies are from countries viewed as Taiwan’s closest partners.

    The cases have emerged amid concern in Taipei over the reliability of its main security backer given US President Donald Trump’s desire to make a “deal” with China, as well as his remarks about Taiwan “stealing” America’s chip business and allegedly freeriding on defence support.

    An executive at a Taiwanese chip company in the US likened the investigations to a “man bites dog” scenario, saying the probes went against both the narrative that Beijing was poaching Taiwanese talent and Trump’s position that Taiwan had stolen the US’s technological leadership.

    A US executive at a fund invested in semiconductor companies warned that Taipei’s more aggressive protection of its economic security could create risks for its geopolitical security by offending the US.

    “This is not a good look for Taiwan right now,” said the executive. “Do they really think they can afford to go after US efforts to revive its chip manufacturing industry?”

    Under pressure from the Trump administration, TSMC raised its US investment commitment by $100bn to $165bn in March. But Washington has made clear this is not enough. Trump administration officials have said they want 50 per cent of chip manufacturing to happen onshore, far more than TSMC’s expanded capacity can deliver.

    In August, Washington agreed to take a 10 per cent stake in Intel as it aimed to resurrect the struggling company as a national champion of semiconductor manufacturing.

    Neither TSMC nor the prosecution has targeted Intel directly or suggested its involvement in alleged technology theft. Prosecutors are only investigating Lo and have not brought charges against him. But observers suggested Washington could apply political leverage on any Taiwanese legal case.

    “Taiwan has very limited options to refuse US requests and pressure” because it was pursuing a trade deal to lower Washington’s 20 per cent tariff on Taiwanese exports, said James Chen, a professor at Tamkang University in Taipei. It is also seeking US support for President Lai Ching-te’s tough approach towards China.

    Provisions introduced in 2022 made the unauthorised transfer of “national core critical technology” to a foreign entity a national security offence for the first time, with a clear focus on China, to which Taiwan has been losing chip engineers for years.

    In one of the most prominent controversies, Liang Mong-song, a former TSMC research and development executive, joined Semiconductor Manufacturing International Company, China’s largest chipmaker, in 2017 and is now its co-chief executive. He and the many TSMC engineers who followed him are credited with helping SMIC narrow its technology gap with the Taiwanese chipmaker.

    The national security law amendments have raised the risk of such moves and stipulate much higher fines for leaking trade secrets to China than to allies such as Japan and the US.

    But experts said the law still fell short. Tsai Ing-wen, Lai’s predecessor, initially aimed for a government role in initiating trade secrets cases through broad national security powers. The law adopted by parliament only allows prosecutors to move when a Taiwanese company makes a complaint, mirroring the US Economic Espionage Act and similar laws in Japan.

    Investigators are now under pressure to build solid cases. In the instance of Tokyo Electron’s subsidiary, prosecutors have charged former TSMC staff with technology theft, but the indictment of the company only lists a failure to prevent such behaviour, not an accusation of theft itself.

    “They have established the precedent that companies are responsible for building strong internal compliance mechanisms to protect against trade secrets theft,” said Jeremy Chang, chief executive of the Research Institute for Democracy, Society and Emerging Technology under Taiwan’s technology ministry.

    “That could become a key task for everyone in the semiconductor supply chain,” he said, especially as more countries try to onshore chip manufacturing.

    The former TSMC staff who have been charged declined to comment.

    Tokyo Electron said the indictment of its subsidiary did not allege it had directed or encouraged its employee to improperly obtain TSMC technology. The company added that it had measures in place to prevent such behaviour and would strengthen its compliance systems.

    Lo declined to comment. Intel stressed its commitment to internal controls that prohibit the use of third-party technology and said it had no reason to believe there was merit to the allegations involving Lo.

    Observers cautioned that as Taipei worries about securing continued support from Washington, politics might play into prosecutors’ decisions.

    “The government might have some thoughts of intervening or using leverage, but they cannot directly intervene in the judicial system,” said Chen. “This is a very politicised and sensitive issue.”

    Continue Reading

  • Oracle shares slide as earnings fail to ease AI bubble fears

    Oracle shares slide as earnings fail to ease AI bubble fears

    Shares of cloud computing giant Oracle plunged more than 10% in after-hours trading on Wednesday after the company’s revenues fell short of Wall Street expectations.

    The company reported revenue of $16.06bn (£11.99bn) for the three months that ended in November, compared with the $16.21bn projected by analysts.

    Revenue growth was up 14%, with a 68% surge in sales at its AI business, Oracle Cloud Infrastructure (OCI), the company said.

    OCI services major AI technology developers whose demand for Oracle’s AI infrastructure helped the company’s shares reach new highs this fall but Wednesday’s results failed to quell fears about a potential AI bubble.

    In September, Oracle agreed a highly sought-after contract with ChatGPT-maker OpenAI, which agreed to purchase $300bn in computing power from Oracle over five years.

    Oracle chairman and chief technology officer Larry Ellison briefly became the world’s richest man in after the announcement.

    But the firm’s shares have lost 40% of their value since peaking three months ago. Still, they are up by more than a third since the start of the year.

    In a statement issued on Wednesday, Mr Ellison struck a cautious tone.

    “There are going to be a lot of changes in AI technology over the next few years and we must remain agile in response to those changes,” he wrote.

    Mr Ellison also appeared to snub Nvidia, the designer of highly-sophisticated AI chips, saying Oracle would buy chips from any maker in order to serve clients.

    “We will continue to buy the latest GPUs from Nvidia, but we need to be prepared and able to deploy whatever chips our customers want to buy,” Mr Ellison declared in a policy he called “chip neutrality”.

    Oracle is involved in multiple AI infrastructure arrangements that have raised the prospect that major players in the sector are participating in ‘circular financing’ deals whereby companies finance purchases of their own products and services.

    “Oracle’s earnings arrive as investors weigh whether its massive OpenAI partnership might mean overexposure with a customer currently in the spotlight over profitability concerns,” said Emarketer analyst Jacob Bourne following the release of the company’s quarterly report.

    Mr Bourne said Oracle faced mounting scrutiny over the increased debt the company has amassed to fund building data centres.

    But others said Wall Street’s negative reaction was unfounded.

    “This was nothing but a great quarter for Oracle,” said Cory Johnson, Chief Market Strategist at Epistrophy Capital Research. “Revenue growth of 14% is accelerating.”

    Including the OpenAI deal from September, Mr Johnson noted, Oracle has signed $385bn in contracts over six months, and “those new clients are the likes of Meta and Nvidia.”

    “But AI sentiment is so bad right now, that’s seen as a bad thing for Oracle,” he added.

    Oracle raised a record $18bn in a massive bond sale in September, one of the largest debt issuances ever in the tech sector.

    “Although Oracle’s shares are buoyed by its September surge, this revenue miss will likely exacerbate concerns among already cautious investors about its OpenAI deal and its aggressive AI spending,” Mr Bourne said.

    The Ellison family, supporters of US President Donald Trump, also recently purchased Paramount and have spearheaded a bid to take over another major Hollywood studio, Warner Brothers Discovery.

    Continue Reading

  • Ranking the Leaders and Laggards on Circular Plastics: From Pledges to Pullback

    Ranking the Leaders and Laggards on Circular Plastics: From Pledges to Pullback

    With mounting pressure to tackle plastic pollution, large users and producers of plastic packaging have promised to take action by switching to use or make more sustainable plastics by 2025 and 2030. However, as target deadlines approach fast, not all companies are pulling their weight equally on the journey to achieve a circular economy for packaging.

    BloombergNEF has once again assessed 40 firms – 20 brand owners and 20 plastic producers – to reveal those that are leading the charge to use or develop more sustainable packaging and those that are falling behind. Our analysis reveals patchy progress from both brand owners and plastic producers toward achieving circular economy targets in 2024.

    BNEF’s Circular Economy Company Ranking measures selected companies’ circular economy ambition based on publicly announced targets and commitments from their 2024 company reports. Both brand owners and plastic producers are split into three tiers – leaders, followers and laggards – based on BNEF’s ranking methodology.

    As sustainable packaging target deadlines draw close, 2025 is a pivotal year reflecting the feasibility of achieving the circular economy goals set by companies a few years back. The shifts in the ranking reflect how companies making steady progress continue to remain on top, while others tapping the brakes on their circular economy ambitions are scoring lower and sliding down.

    The companies that moved up the most in the latest ranking are:

    • Asahi Breweries moved up six places after announcing a new target to achieve 100% conversion to recycled or bio-based polyethylene terephthalate (PET) bottles by 2030, from 37% in 2024. Asahi also benefits from rigid packaging such as glass and aluminum that are easier to recycle.
    • Alpek moved up four places. The company has announced plans to expand PET bottle recycling capacity to 300,000 metric tons annually by 2025. Alpek was also among the few producers that did not announce any new virgin plastic expansion plans.

    Companies that moved down in the ranking were:

    • PepsiCo lost its leading position in the ranking, sliding down to 12th place this year. Despite being a circular economy pioneer, PepsiCo dropped its goal to reduce virgin resin use by 50% compared to 2020. The company also lowered its target for recycled content from 50% by 2030 to 40% by 2035.
    • LyondellBasell lost its top spot this year. It performed well overall, achieving a 65% increase in recycled/renewable polymer production to 200,000 tons in 2024. The company has a target to produce 2 million tons of recycled and renewable polymers by 2030. However, the company lost points after announcing virgin plastics production expansion plans in 2025.

    Brands make patchy progress toward recycled content targets

    PepsiCo, Unilever and Coca-Cola were among the brand owners that tapped the brakes on their circular economy ambition, setting new, less ambitious targets with deadlines further out in the future. However, these new targets are likely more achievable, given the underdeveloped supply chains for recycling in many markets.

    Coca-Cola had previously set a goal to achieve 50% recycled content in all packaging by 2030. However, it reduced this target to 35-40% recycled content by 2035. Coca-Cola achieved 28% recycled content in 2024 and benefits from the fact that majority of its packaging comes from easier to recycle materials like PET bottles and aluminum cans, compared to flexible film packaging. Sourcing rigid packaging with recycled content can be easier in markets with mature waste sorting and recycling in place. For example, water bottle brands like Danone’s Evian and Nestle’s Vittel have achieved 100% recycled content for plastic water bottles in Europe.

    In contrast, companies that are dependent on food-contact grade, flexible film-based packaging are falling behind or are stagnant. This is reflected in the ranking with a large set of companies categorized as “followers,” scoring in the range of 50-55%, including Mondelez, Kraft Heinz and PepsiCo. These companies have set circular packaging targets but are failing to source the suitable plastics they require at a reasonable price.

    Despite the slowing ambition, some brand owners are making progress. Colgate-Palmolive increased the recycled content in its plastic packaging from 18% in 2023 to 21% in 2024 and is on track to meet its 25% target for 2025.

    Weak demand and challenging economics hamper producers’ progress

    Of the 20 plastic producers, 13 have set clear targets to produce sustainable (recycled or bio-based) materials by 2030 or earlier. If these 13 companies meet their targets, they alone would supply around 13 million tons of sustainable plastics per year by 2030. Production targets hold the greatest weight in BNEF’s ranking methodology, as this reflects the upper limit of how circular a producer’s polymers will be this decade.

    Indorama, Alpek and Braskem have made the strongest progress toward their production targets in 2024. However, most plastic producers still have a long way to go. Insufficient demand for recycled products is one of the biggest challenges that producers face.

    Nonetheless, plastic producers are seeking out sectors where the demand for high-grade recycled plastics is on the rise and buyers are willing to pay a premium over virgin plastics. For example, Borealis launched a glass-fiber reinforced polypropylene with a 65% post-consumer recycled content for the automotive industry. Sabic introduced a new resin with a 30% post-consumer recycled content used in MSI’s gaming laptops.

    Globally, the chemicals market is facing a supply glut, exacerbated by new primary production capacity coming online in Asia. As a result, several plastic recyclers closed operations in recent years. With record low utilization rates and low or even negative margins, companies have halted or delayed investments in sustainable chemicals production.

    However, some producers have signed offtake agreements or acquired recycling companies, instead of building their own plants. For example, Dow formed a supply agreement with Freepoint Eco‑Systems for 65,000 tons of pyrolysis oil, which is used as feedstock to make plastics with a recycled content. In 2024, Borealis increased its circular production capacity by 18% compared to the previous year, processing 221,200 metric tons of circular feedstock. The company acquired plastic recycler, Integra Plastics in 2024 and acquired a minority stake in Renasci in 2021.

    Continue Reading