Category: 3. Business

  • Govt introduces changes to solar net metering policy, launches new billing system – Hum News English

    1. Govt introduces changes to solar net metering policy, launches new billing system  Hum News English
    2. NEPRA introduces new regulations for net metering users  The Express Tribune
    3. Axe may fall on prosumers to save power utilities  Dawn
    4. Pakistan revises net metering policy for solar consumers  Daily Times
    5. Hebei Juhang Energy Technology plans solar panel factory in Pakistan  pv magazine International

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  • Food truck plan for Oxenhope’s Millennium Green

    Food truck plan for Oxenhope’s Millennium Green

    The proposal to the authority, by applicant Rachel Coe, said: “The proposal introduces a small-scale, modern feature into the green.

    “The introduction of a catering trailer will deliver a clear community facility, enhancing the social and economic vitality of Oxenhope, increasing local activity and visitor engagement with the Millennium Green and thereby outweighing any limited adverse impact.

    “The applicants intend this to be a short-term measure to start their business. The long-term goal is to find premises within the village and have a cafe.”

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  • ‘Iconic’ Nottingham city centre vintage shop to close

    ‘Iconic’ Nottingham city centre vintage shop to close

    Mr Hague, from Hertfordshire, has worked in the vintage clothing business on and off since the 90s, and took on the store in 2023 as he “couldn’t bear” to see it close.

    He added: “I have been aware of Nottingham coming up here in the 90s to get stock from Robin. I sort of got to love the city through that.

    “Clothes and music have always been part of my life, it was very much led by the heart.

    “Robin and Mary worked really hard for 37 years. They rarely took holidays and dedicated their lives to it.

    “When it became available I did not think it was going to be the best idea financially, but I really wanted to give it a go.

    “We have always had a great team. That is the saddest thing, that we have had to give notice [to staff] and that is a real tragedy.”

    The last day of trading is set to be New Year’s Eve.

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  • Ipswich Copdock Toys R Us: What will happen to M&S in town?

    Ipswich Copdock Toys R Us: What will happen to M&S in town?

    Helen Davies, Babergh councillor for Sproughton & Pinewood, which encompasses the site, told the meeting that ever since she had moved to the ward in 2011, the site had always been “a little bit run down”.

    “It’s lain derelict for ages, and I think we are all well aware that the nature of shopping has changed,” she said.

    “I try and make an effort to do my shopping physically, but I fail dismally at times, especially now, and it is a lot easier for us to go to an outside retail area because generally you can park pretty easily, so you can do your bigger purchases there.

    “The site is rundown, it’s fenced off, it would be great to have it back into use, but I also feel we need to make certain that the Ipswich store is protected to a degree.”

    She said she had felt some promise of this from M&S, but said there was still a risk.

    Ipswich Borough Council, prior to the decision, said it was in support of the proposal.

    In a letter within the planning documents, its head of planning and development acknowledged that while the new store would have an impact on the town centre, “there are reasons to be optimistic about the direct of travel in the town centre that will keep footfall and investor sentiment high”.

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  • Federated two-edge graph attention network with weighted global aggregation for electricity consumption demand forecasting

    Accurate electricity demand forecasting is crucial for the stable operation of smart grids, as it enables proactive resource allocation and prevents grid failures caused by demand–supply mismatches. However, achieving precise predictions requires modeling both temporal consumption patterns and peak variations in electricity usage data. Regional power consumption data may contain sensitive commercial information, while federated learning (FL) offers a privacy-preserving approach to address data scarcity. Nevertheless, existing FL approaches struggle with two critical limitations: (1) the inherent risk of overfitting when modeling peak demand variations with sparse client-side data, and (2) the loss of client-specific features during the aggregation process, which can result in over-smoothing of predictions for some clients due to parameter inconsistencies across local models. To overcome these challenges, this paper proposes a Federated Two-Edge Graph Attention Network with Weighted Global Aggregation (FapDGN) for electricity demand forecasting. The FapDGN framework initiates by constructing a hybrid feature representation that simultaneously encapsulates both temporal dynamics and numerical fluctuations in electricity consumption patterns. Recognizing that temporal characteristics are crucial for prediction accuracy while peak variations pose higher overfitting risks, the system employs two-edge graph structures to process these elements independently. Specifically, it utilizes temporal edges in graphs coupled with a multi-scale attention mechanism to capture consumption trends over time, while implementing dynamic covariance through numerical structure edges in graphs to represent peak variations as parameterized Gaussian distributions, an approach that mitigates overfitting. The model subsequently combines these extracted temporal and peak variation features to produce its final predictive outputs. Furthermore, to combat potential over-smoothing issues, FapDGN integrates a similarity-based adaptive dynamic fusion mechanism for parameter aggregation at the server level when building the global model. Experimental results show that FapDGN outperforms commonly used FL methods in forecasting electricity demand.

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  • Carter, M. J. et al. Evaluation of Acute and Convalescent Antibody Concentration Against Pneumococcal Capsular Polysaccharides for the Diagnosis of Pneumococcal Infection in Children with Community-Acquired Pneumonia. Pediatr. Infect. Dis. J. 43(2), E67–E70. https://doi.org/10.1097/INF.0000000000004185.

  • Duan, B. Advances in Pneumonia Detection: A Comprehensive Investigation of Federated Learning and Deep Learning-Based Approaches, in scitepress.org, pp. 714–718. (2024). https://doi.org/10.5220/0012969400004508

  • Khan, R. et al. Advanced federated ensemble internet of learning approach for cloud based medical healthcare monitoring system. Sci. Rep. 14(1), https://doi.org/10.1038/s41598-024-77196-x (2024).

  • Hassan, E., Saber, A., El-Kenawy, E. S. M., Bhatnagar, R. & Shams, M. Y. Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis, in Proceedings of the International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024, 2024, pp. 426–431., 2024, pp. 426–431. (2024). https://doi.org/10.1109/ICETCI62771.2024.10704103

  • Hassan, E., Saber, A., El-Sappagh, S. & El-Rashidy, N. Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization, Evol. Syst., vol. 16, no. 2, pp. 1–17, Jun. (2025). https://doi.org/10.1007/s12530-025-09686-w

  • Khan, S. H. & Alam, M. G. R. A Federated Learning Approach to Pneumonia Detection, in 7th International Conference on Engineering and Emerging Technologies, ICEET 2021, (2021). https://doi.org/10.1109/ICEET53442.2021.9659591

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  • Feki, I., Ammar, S., Kessentini, Y. & Muhammad, K. Federated learning for COVID-19 screening from chest X-ray images. Appl. Soft Comput. 106, 107330. https://doi.org/10.1016/j.asoc.2021.107330 (Jul. 2021).

  • Hu, E. et al. Jun., LoRA: Low-Rank Adaptation of Large Language Models, ICLR 2022–10th Int. Conf. Learn. Represent., Accessed: Jul. 08, 2025. [Online]. (2021). Available: https://arxiv.org/abs/2106.09685v2

  • Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2 (6), 305–311. https://doi.org/10.1038/s42256-020-0186-1 (2020).

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  • Pardamean, B., Cenggoro, T. W., Rahutomo, R., Budiarto, A. & Karuppiah, E. K. Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data, in Procedia Computer Science, Jan. vol. 135, pp. 400–407. (2018). https://doi.org/10.1016/j.procs.2018.08.190

  • Brendan McMahan, H. et al. y Communication-efficient learning of deep networks from decentralized data, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS, Feb. 2017. Accessed: Jul. 08, 2025. [Online]. Available: https://arxiv.org/abs/1602.05629v4, Feb. 2017. Accessed: Jul. 08, 2025. [Online]. Available: https://arxiv.org/abs/1602.05629v4 (2017).

  • Asokan, M., Benjamin, J. G., Yaqub, M. & Nandakumar, K. A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Jul. 15274 LNCS, 226–235 https://doi.org/10.1007/978-3-031-77610-6_21 (2025).

  • C. Mathew and P. Asha, “FedProx: FedSplit Algorithm based Federated Learning for Statistical and System Heterogeneity in Medical Data Communication,” J. Internet Serv. Inf. Secur., vol. 14, no. 3, pp. 353–370, Aug. 2024, https://doi.org/10.58346/JISIS.2024.I3.021 .

  • Mabrouk, A., Díaz, R. P., Redondo, M., Abd Elaziz & Kayed, M. Ensemble federated learning: an approach for collaborative pneumonia diagnosis. Appl. Soft Comput. 144, 110500. https://doi.org/10.1016/j.asoc.2023.110500 (Sep. 2023).

  • Sun, Y., Li, Z., Li, Y. & Ding, B. IMPROVING LORA IN PRIVACY-PRESERVING FEDERATED LEARNING, in 12th International Conference on Learning Representations, ICLR, 2024., 2024. (2024).

  • Ulku, I., Tanriover, O. O. & Akagunduz, E. Low-Rank Adaptation of Vision Transformers for Remote Sensing With Near-Infrared Imagery. IEEE Geosci. Remote Sens. Lett. 21, https://doi.org/10.1109/LGRS.2024.3449372 (2024).

  • Dai, Y., Gao, Y. & Liu, F. Transmed: Transformers advance multi-modal medical image classification. Diagnostics 11(8), https://doi.org/10.3390/diagnostics11081384 (2021).

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  • Slika, B., Dornaika, F., Merdji, H. & Hammoudi, K. Lung pneumonia severity scoring in chest X-ray images using transformers, Med. Biol. Eng. Comput., vol. 62, no. 8, pp. 2389–2407, Aug. (2024). https://doi.org/10.1007/s11517-024-03066-3

  • Liu, J. et al. Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices, IEEE Trans. Mob. Comput., no. 01, pp. 1–18, Jul. (2024). https://doi.org/10.1109/TMC.2025.3586644

  • Wu, P. et al. FedFMSL: federated learning of foundation models with sparsely activated LoRA. IEEE Trans. Mob. Comput. 23 (12), 15167–15181. https://doi.org/10.1109/TMC.2024.3454634 (2024).

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  • Al Jazeera launches new integrative AI model, ‘The Core’ | Media News

    Al Jazeera launches new integrative AI model, ‘The Core’ | Media News

    Al Jazeera Media Network says initiative will shift role of AI ‘from passive tool to active partner in journalism’.

    Al Jazeera Media Network is launching a new integrative artificial intelligence (AI) model in collaboration with Google Cloud.

    Al Jazeera said on Sunday that it was expanding its collaboration with Google Cloud on the network’s new initiative, dubbed “The Core”, that will integrate AI into its news operations.

    “The Core” aims to shift the role of AI “from a passive tool to an active partner in journalism”, Al Jazeera said.

    Relying on six pillars, the initiative will integrate AI systems to help Al Jazeera journalists process complex data, produce immersive content, gain access to analytical context and automate internal workflows, among other things.

    “Al Jazeera is committed to establishing a global technological ecosystem that cements our leadership in the AI era,” said Sheikh Nasser bin Faisal Al Thani, director general of Al Jazeera Media Network.

    “‘The Core’ is the embodiment of this vision – an integrated model where human expertise and artificial intelligence work in tandem to modernize journalism,” Al Thani said.

    “Google Cloud’s proven expertise in AI make it the ideal partner to help us execute this ambitious transformation, ensuring our journalism remains agile, accurate, and deeply engaging for our global audience.”

    Alex Rutter, AI managing director for Europe, the Middle East and Africa at Google Cloud, welcomed Al Jazeera’s decision to build “The Core” platform as a “pivotal step in developing the next generation of intelligent media”.

    “This transformational program leverages our advanced AI tools to reshape how journalists report and create news, and how audiences consume it. Together, Google Cloud and Al Jazeera are setting a new future direction for digital journalism,” Rutter said.

    Ahmad Al-Fahad, executive director of technology and network operations at Al Jazeera, added: “Al Jazeera is committed to keeping pace with the technological advances shaping the media industry. We consistently strive to integrate the latest tools and best practices into content production across our channels and platforms.”

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  • Elon Musk becomes world’s first $700 billion person after court restores Tesla pay deal

    Elon Musk becomes world’s first $700 billion person after court restores Tesla pay deal

    By Reuters  &nbspDecember 20, 2025 | 09:28 pm PT

    Tesla CEO Elon Musk boards Air Force One with U.S. President Donald Trump (not pictured) as they depart for Philadelphia, Pennsylvania, from Morristown Municipal Airport in Morristown, New Jersey, U.S., March 22, 2025. Photo by Reuters


    Tesla CEO Elon Musk’s net worth surged to $749 billion late Friday after the Delaware Supreme Court reinstated Tesla stock options worth $139 billion that were voided last year, according to Forbes’ billionaires index.

    Musk’s 2018 pay package, once worth $56 billion, was restored by the Delaware Supreme Court on Friday, two years after a lower court struck down the compensation deal as “unfathomable.”

    The Supreme Court said that a 2024 ruling that rescinded the pay package had been improper and inequitable to Musk.

    Earlier this week, Musk became the first person ever to surpass $600 billion in net worth on the heels of reports that his aerospace startup SpaceX was likely to go public.

    In November, Tesla shareholders separately approved a $1 trillion pay plan for Musk, the largest corporate pay package in history, as investors endorsed his vision of morphing the EV maker into an AI and robotics juggernaut.

    Musk’s fortune now exceeds that of Google co-founder Larry Page, the world’s second-richest person, by nearly $500 billion, according to Forbes’ billionaires list.



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  • Prediction of bearing capacity of ring footings on cohesive frictional soils using Terzaghi stability factors and Kolmogorov Arnold networks

  • Prasad, S. D. & Chakraborty, M. Bearing capacity of ring footing resting on two layered soil. Comput. Geotech. 134, 104088. https://doi.org/10.1016/j.compgeo.2021.104088 (2021).

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  • What is WhatsApp ghost pairing? The new silent way scammers use to secretly access your messages and photos

    What is WhatsApp ghost pairing? The new silent way scammers use to secretly access your messages and photos

    A new WhatsApp threat, ‘ghost pairing,’ is silently compromising user privacy. Scammers trick individuals into linking their accounts via fake verification codes or QR scans. This allows attackers to view conversations in real-time without the victim’s knowledge. Users must remain vigilant, regularly check linked devices, and never share sensitive codes to prevent data misuse and privacy breaches.

    WhatsApp has become like our companion, next ‘click’ that helps us communicate in personal and professional settings. The green white interface integrated with various features and generative Meta AI is probably also one of the most recognised by children who use the app for chatting with friends and be connected in school groups.But with the platform’s growing popularity every day, the methods used by cybercriminals exploit trust and familiarity. One such emerging threat is WhatsApp ghost pairing. It is not something that becomes immediately obvious. Instead, it works quietly in the background, often going unnoticed until real damage is done.The problem has become pretty alarming as the victims may unknowingly continue using WhatsApp normally. They might remain unaware that someone else is watching their conversations in real time. Unlike traditional hacking, this does not always involve stealing passwords or breaking security systems directly.

    WhatsApp ghost pairing

    WhatsApp ghost pairing: What does this mean and how to protect yourself from the scam

    How WhatsApp ghost pairing works

    WhatsApp ghost pairing uses the “Linked Devices” feature. A scammer tricks a user into sharing a verification code or scanning a QR code, mostly through fake messages pretending to be support or known contacts. Once linked, the attacker’s device silently syncs with the victim’s WhatsApp. The victim continues chatting as usual, unaware that messages, media, and sometimes even contacts are being mirrored in some other unwanted device.

    Why is it hard to detect

    Ghost pairing is dangerous because it leaves very few visible signs. There is no sudden logout or app crash. Notifications and chats work normally, which never let’s theuser doubt what malicious is going on behind their suspicion. Many users rarely check their linked devices, allowing attackers prolonged access. Since the pairing uses a legitimate WhatsApp feature, victims often realise something is wrong only after data misuse, fraud, or leaked private conversations.

    Representative Image

    Representative Image

    What are some common tactics used by scammers

    Scammers depend mostly on human psychology, posting situations of utmost urgency. They create a necessity by claiming account verification issues, job offers, or urgent requests from “friends.” Some hackers might even impersonate WhatsApp officials or company HR teams. The goal is to make users act instantly without giving them much time for deliberation. Then, once the user trusts the scenario and scans the code, the devices get synced.

    How to stay safe from ghost pairing

    Prevention starts with just being aware. Never share WhatsApp verification codes or scan QR codes sent by others. Regularly check the “Linked Devices” section and remove devices that might look fishy and problematic. Enable two-step verification for increased protection. Most importantly, it is necessary to rethink when messages create panic or urgency. A few seconds of verification and cross-checking can prevent weeks of damage and loss of privacy.

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