Category: 3. Business

Good morning from London, here are the opening calls

What to look out for Tuesday

So far, so ordinary. BA343 is just one of about 650 planes that land at Europe’s busiest airport each day. But the cool thing here is that it’s the first time a BA turnaround has been performed using only zero-emission equipment: buses that run on vegetable oil; baggage tugs, which look like little golf buggies, running on lithium batteries; and electric-powered steps to get passengers off. It might not sound like much, and if you’re disembarking chances are you won’t even notice. But the goal of this new hardware is to make the whole process more efficient and reduce delays for passengers. It’s part of a wider multibillion-pound transformation of the airline.

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This summer is predicted to be exceptionally busy, according to figures from the aviation data agency Cirium, with a record number of passengers forecast to go on holiday. Almost 52 million of us will travel between June 1 and August 31, up from 51 million last summer. There is also some nervousness about air-traffic control delays due to hot weather or strikes. Eurocontrol, which runs European air traffic control, said in April that in the first four months of the year European air traffic was up by 5 per cent compared with the same period in 2024, with delays also up by the same amount. Add to that a pile-on over British Airways’ revamped loyalty scheme, the Club, and you can see why the flag carrier might be nervous.

Last year BA announced a £7 billion transformation programme, with money funnelled into new cabins in state-of-the-art aircraft such as the Airbus A320neo and sustainable initiatives, such as the carbon-neutral hardware out on the airfield. The airline has also invested £100m in “gamechanging’ and “integral” AI forecasting tools — essentially what the BA boss Sean Doyle calls “devising a better way of working on the ground at Heathrow” — which, when put into practice, will ultimately mean fewer delays and cancellations for passengers. The results so far are promising: in the first three months of 2025, 86 per cent of BA flights left on time from London Heathrow, the highest on record; in 2008 it was 46 per cent.

I went to Heathrow to find out more and see how BA is using machine learning to improve the passenger experience. I’m getting an exclusive look at the airport’s revamped Air Operations Control Centre (AOCC), the eyes and ears of BA’s operation at Heathrow; as well BA’s Integrated Operations Control (IOC) at its Waterside headquarters, to the northwest of the airport.

One of the new tools at the AOCC is Mission Control, a giant interface beamed onto a giant screen in the control centre. It shows real-time tracking of each BA plane on the ground at Heathrow (blue shows arriving aircraft, flashing yellow shows planes about to depart) and how many connecting passengers are on board. I can see flight BA453 arrive from Ibiza and BA115 depart to New York. A screen tracking New York JFK has been configured too. London-New York is the busiest international route in the world; after the British capital it’s BA’s largest international destination.

Mission Control is also a big part of BA’s IOC at its Waterside headquarters, where staff monitor up to 900 daily BA flights across the network. The real-time data from the interface ensures that staff can track the aircraft and make on-the-go decisions about where aircraft need to go.

I sit down with Ben Lang, who looks after BA’s schedule, planning where to use its more than 280 jets. He showed me the Pathfinder planning tool, which uses thousands of pieces of historical data to make the flight plans, pulling in information about delays, air-traffic control restrictions and aircraft capacity. For example, Lang explains, if flights from Paris are always delayed by ten minutes, Pathfinder will allocate an extra ten minutes to the turnaround process; and if there’s a big sporting event happening, bigger jets will be deployed to cope with demand.

I also get a look at Runway, another AI forecasting tool that kicks in when disruptive events such as storms, strikes and — particularly relevant at the moment — blocked airspaces threaten to throw passengers off course. Using masses of data, it can allocate the most efficient aircraft for a particular flight, making what it calls a “swap”. For example, during Storm Eowyn in January, Runway swapped out smaller aircraft that would typically fly to Glasgow and Edinburgh for larger planes to stop passengers getting stranded in Scotland. At the top of Lang’s screen, it says the tool has improved the number of on-time flights by 1.1 per cent over the past five days by making 233 of these swaps.

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Elsewhere in the IOC there’s AI-powered Flight Watch, which shows flight routes, closed sections of airspace and particularly nasty bouts of turbulence as well as other weather events; it can also communicate directly with air-traffic control towers, which helps teams to reroute flights through less-delayed airspace. More than 3,500 minutes of flight time were recently saved in one day, Richard Treeves, head of the IOC, told me. A new AI crew app launched earlier this month, automating the manual task of rostering the right staff onto the right aircraft. Everything is designed to make the operation run more smoothly and crucially reduce delays and cancellations for passengers.

“We’re now entering one of our busiest periods of the year and will be flying millions of customers around the world throughout July and August,” René de Groot, BA’s chief operating officer, says. “The new technology we’ve introduced has been a real gamechanger, allowing us to make more informed decisions based on vast amounts of data. We have even more in the pipeline — including new apps for our operational colleagues — and we’re in a much better place to deliver a smooth travel experience this summer and beyond.”

Back on the tarmac, I can see for myself how Mission Control has alerted flight teams, showing them that it was better to change the aircraft to quash the potential delay. The late arrival of BA343 means it’s too late to be turned around for its planned 2pm departure to Milan. It’s instead bound for Amsterdam, now departing at 2.30pm — not even 45 minutes after it arrived. I watch as bags are loaded and passengers arrive to board. No one will notice the work that went on behind the scenes — but that’s the point; the important thing is that everyone gets from A to B on time.

Do you think the changes will make a difference this summer? Let us know in the comments below

The Courts (Remote Hearing) Ordinance (Cap. 654) came into effect on 28 March 2025, authorising and broadening the use of remote hearings, particularly for shorter and non-trial criminal proceedings, by establishing a comprehensive legal framework for conducting remote hearings across all levels of courts and tribunals.

Jennifer Wu, an expert in TMT and commercial disputes at Pinsent Masons, said: “As remote hearings become more common, adaptability and technological proficiencies are no longer optional — they are essential skills for modern legal practice. Legal practitioners must be prepared to operate flexibility on digital platforms and be comfortable with all virtual courtroom etiquette in order to meet the tech-driven advancements in the legal system.”

A remote hearing is a court proceeding ordered by the court to be conducted via a remote medium, such as telephone or live audio-visual link. The ordinance allows remote hearings for civil cases and non-trial criminal proceedings, such as pre-trial reviews and case management conferences.

When determining whether to order a remote hearing, the court must take into account several factors, including the nature, complexity and urgency of the case, the type of evidence involved, the views of the parties, the ability of the parties to engage with and follow the proceeding, any impairments or special circumstances affecting the parties, the fair and efficient resolution of the matter and other relevant factors.

“These considerations ensure that remote hearings are used appropriately and do not compromise the fairness or integrity of the judicial process,” Wu said.

Courts will ensure public access to public remote hearings through, for example, issuing directions on live broadcast. The ordinance also introduces new offences of unauthorised recording, publishing and broadcasting of both physical and remote hearings to safeguard the integrity of proceedings.

If there are concerns about the reliability of any remote testimony, the court may amend or revoke the remote hearing order and require in-person attendance.

Wu said: “Effective preparation is crucial to ensure smooth and efficient remote hearings. Parties are encouraged to liaise with the court on logistics, address technical questions, and do equipment test runs prior to the hearing.”

“Legal practitioners and participants preparing for remote hearings should ensure that all required hardware such as video conferencing (VC) units, laptops, display monitors, cameras, speakers and microphone function properly and that they connect to the remote website of the court’s VC system well before the scheduled hearing time to address any connectivity or technical issues,” she said.

“Choose a quiet, well-lit and private location free from interruptions to create a professional and distraction-free setting, and do not share any VC links on public forums or with individuals who do not need to be present. Avoid using public Wi-Fi to ensure secure connections.”

Educating those needing to attend the remote hearings on the process, providing clear instructions on how to log in, explaining the technology involved and preparing them for what to expect during the hearing and how to give evidence virtually are also important steps to ensure smooth running of hearings in the virtual space, according to Wu.

Artificial intelligence (AI) has potential to improve the diagnosis of melanoma, but the road to its widespread adoption requires overcoming various challenges, according to a new review.¹

The promise of AI has been anticipated as traditional diagnostic methods, including physical examination and nodal assessments, have lacked reliability and face variability in interpretation based on who is reviewing.

“Given the growing incidence of melanoma in the world, applications based on AI can help reduce the burden on doctors, simplify the diagnostic algorithm, and provide populations with equal access to adequate treatment,” the researchers explained.

The review, published in International Journal of Intelligent Systems, explored the current landscape of AI models being used to aid in the diagnosis of melanoma. These approaches include machine learning, deep learning, and mixed-approach methods.

Newer approaches like convolutional neural networks (CNNs) have gained steam for their ability to detect features in images and videos, and in turn have been explored in health care, including for melanoma.² In one study of more than 2600 images, the deep learning approach had an 88% accuracy rate in classifying skin lesions as malignant or nonmalignant. Another study has demonstrated a 96% accuracy rate, showing accuracy improvements compared with earlier AI methods like Support Vector Machines (SVMs).

SVMs have demonstrated the ability to detect melanoma early, with accuracy of up to and more than 87%. Data have shown that the model is able to use just 6 identifiable factors to make a diagnosis.

Some models have approached AI with a hybrid of CNN and SVM, combining CNN’s ability to automatically pinpoint important features of images without the need for manual input with SVM’s ability to classify data.

“According to the research, various AI models could perform equally well or even better than experts in dermatology in specific situations, since most of them reported accuracy, sensitivity, and specificity levels higher than 85%. Nevertheless, several difficulties make it hard to put these findings into practice,” explained the researchers, noting that methods like CNNs struggle with widespread uptake.

In addition to resourcing issues, with many health care organizations facing structural and financial challenges for implementation, the researchers highlighted inconsistencies in the performance of current AI models. Inconsistent results are driven by differences in how these models are built and the data that they are trained to assess. Current models often leave an absence of explanation of their results, highlighting an opportunity for improved models to offer a basis for their diagnosis.

Improvements in the data used for AI approaches were highlighted by the group. It’s well documented that clinical trials, including those for melanoma, are often not representative of true patient populations in the real world.³ As a result, lesion images included in studies remain limited for analysis. The researchers emphasized the need for data on a variety of patient types, including those with different skin types and with different stages of disease.

Outside of patient diversity, the researchers call for variety in data types to strengthen AI models, with a compilation of patient records with genomic data and images. Improvements in AI-based models also require health care organizations to work closely with researchers and developers of such technology to share data.

At the same time, as patient populations and cases evolve, AI models must adapt with it, balancing both flexibility and accuracy.

“AI should be applied in health care after being tested in studies that follow its performance and effects in various settings,” the authors concluded. “Improved user interfaces and immediate support for making decisions must be available for practical use by doctors. It will be vital for AI developers, doctors, and healthcare policymakers to cooperate in order to develop rules that allow safe, ethical, and effective implementation of AI technology for early diagnostics of melanoma.”

References

1. Alam F, Ullah A, Shah D, Ali S, Tahir M. Artificial intelligence in melanoma detection: A review of current technologies and future direction. Int J Intell Syst. Published online June 13, 2025. doi:10.1155/int/3164952

2. Kwiatkowska D, Kluska P, Reich A. Convolutional neural networks for the detection of malignant melanoma in dermoscopy images. Postepy Dermatol Alergol. 2021;38(3):412-420. doi:10.5114/ada.2021.107927

3. Donia M, Kimper-Karl ML, Hoyer KL, Bastholt L, Schmidt H, Svane IM. The majority of patients with metastatic melanoma are not represented in pivotal phase III immunotherapy trials. Eur J Cancer. 2017;74:89-95. doi:10.1016/j.ejca.2016.12.017

The prices of 19kg commercial LPG cylinders dropped today by 58 rupees 50 paise in Delhi. The Indian Oil Corporation Limited (IOCL) announced that the retail price of a commercial LPG gas cylinder is now 1 thousand 665 rupees in Delhi. Meanwhile, the commercial LPG price has dropped by 57 rupees in Kolkata, 58 rupees in Mumbai and 57 rupees 50 paise in Chennai. There is no change in the prices of domestic cylinders.