Category: 3. Business

  • National Lottery operator Allwyn to merge with Greece’s OPAP; European markets lifted as US tariff fears ease – business live | Business

    National Lottery operator Allwyn to merge with Greece’s OPAP; European markets lifted as US tariff fears ease – business live | Business

    National Lottery operator Allwyn to merge with Greece’s OPAP in £14bn deal

    The National Lottery operator Allwyn is to merge with Greece’s leading gambling company OPAP to create a global listed gaming giant worth about €16bn (£13.9bn).

    Allwyn, which owns a near-52% controlling stake in Athens-headquartered OPAP, has agreed an all-share tie-up with OPAP that will see the combined group renamed Allwyn.

    It will give Allwyn a presence on the stock market, with plans to retain OPAP’s Athens listing for the merged group, and to launch an additional stock market listing in either London or New York.

    For many years OPAP was a state-owned gambling monopoly. The company holds the exclusive rights to run lotteries and sports betting in Greece.

    In 2022, Camelot lost the licence to run the lottery in the UK to rival Allwyn. The billionaire media mogul Richard Desmond had also been bidding for the contract, and is suing the gambling regulator in a bitter dispute that opened at the high court last Thursday. He has brought a £1.3bn damages claim against the Gambling Commission.

    Last year, the Guardian revealed that Allwyn was borrowing millions from Kremlin-owned banks when it won the UK’s largest public-sector contract. Allwyn is ultimately owned by the Czech billionaire Karel Komárek.

    A National Lottery sign. Photograph: Andrew Milligan/PA
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    Key events

    Nobel prize for economics goes to Joel Mokyr, Philippe Aghion and Peter Howitt

    This year’s Nobel prize for economics is about creation and destruction.

    It has gone to Joel Mokyr, economics professor at Northwestern University, the French economist Philippe Aghion and Peter Howitt, economics professor at Brown University in Rhode Island.

    The Sveriges Riksbank prize in economic aciences in memory of Alfred Nobel was awarded “for having explained innovation-driven economic growth,” with one half to Mokyr “for having identified the prerequisites for sustained growth through technological progress” and the other half jointly to Aghion and Howitt “for the theory of sustained growth through creative destruction.”

    Mokyr is a Dutch-born American-Israeli economic historian, professor of economics and history and the Robert H. Strotz Professor of Arts and Sciences at Northwestern University.

    Aghion is a professor at the Collège de France, at INSEAD, at the London School of Economics, and at the Paris School of Economics.

    Howitt is a Canadian economist.

    The Riksbank said:

    Joel Mokyr used historical observations to identify the factors necessary for sustained growth based on technological innovations, Philippe Aghion and Peter Howitt have produced a mathematical model of creative destruction, an endless process in which new and better products replace the old. With the understanding of the mechanisms of creative destruction provided by the laureates and the follow up research, we have a better chance to make sure growth can continue and be guided in the direction that benefits humankind.

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  • Feasibility and safety evaluation of remimazolam in geriatric patients during bronchoscopy: a single-centre randomized controlled trial | BMC Anesthesiology

    Feasibility and safety evaluation of remimazolam in geriatric patients during bronchoscopy: a single-centre randomized controlled trial | BMC Anesthesiology

    Human ethics and consent and trail registration

    The trial was performed and reported accordance with the CONSORT guidelines for interventional trials at the Hangzhou First People’s Hospital, School of Medicine, Westlake University, following approval by the Ethics Committee (2020YLSD003- 01).We provided written forms to all the participants involved in the research which illustrated the research purpose, process, the risks and benefits, and obtained their individual or guardian participants written informed consent from July 2021 to December 2021. And the trail was registered at the Chinese clinical trial registry (2021/06/19,ChiCTR2100047459).

    Patients inclusion and exclusion criteria

    The inclusion criteria were as follows: (1) patients aged 60 ~ 80 years old with an American Society of Anesthesiologists (ASA) class I or II; (2)Geriatric patients with respiratory diseases undergoing elective FB treatment (routine bronchoscopy and some simple FB treatments including transbronchial needle aspiration biopsy, transbronchial brushing, and bronchoalveolar lavage); (3)body mass index < 30 kg/m2. The exclusion criteria were as follows: (1)operation time > 30 min; (2)neuropsychiatric diseases and taking related medications; (3) target organ damage such as liver, kidney, heart and brain; (4)allergies to anesthetic drugs; (5)cannot cooperate with doctors; (6)uncontrolled hypertension, heart disease, diabetes and other diseases.

    Randomization and grouping

    A total of 105 elderly patients who underwent bronchofiberoscopy were enrolled in this study. By using a list of numbers generated by the QuickCalcs (GraphpadPrism 7),the patients were randomly assigned to three groups (n = 35 each).

    Grouping: Group A (remimazolam + fentanyl + local anesthesia): intravenous injection of remimazolam 0.2 mg/kg and fentanyl 0.5 µg/kg for sedation,10 ml 2% lidocaine spraying pharynx and tracheal mucosa for local anesthesia; Group B (midazolam + fentanyl + local anesthesia): intravenous injection of midazolam 0.075 mg/kg and fentanyl 0.5 µg/kg for sedation, 10 ml 2% lidocaine spraying pharynx and tracheal mucosa for local anesthesia; and an additional dose with fentanyl 25 µg every 5 to 10 min for the either group if necessary; Group C (local anesthesia): 10 ml 2% lidocaine spraying pharynx and tracheal mucosa for local anesthesia.

    Experimental protocols

    The patients were instructed to fast before the operation. Electrocardiogram (ECG), heart rate (HR), saturation of pulse oximetry (SpO2),non-invasive blood pressure (NBP), respiratory rate(RR), end-tidal carbon dioxide (PetCO2) and bispectral index (BIS) were monitored in the operating room with the patient in supine position. Group A was inducted by intravenous injections of remimazolam 0.2 mg/kg and fentanyl 0.5 µg/kg for sedation/analgesia. Group B was inducted by intravenous injections of midazolam 0.075 mg/kg and fentanyl 0.5 µg/kg for sedation/analgesia. Group C was performed by spraying 2% lidocaine on the surface of tracheal mucosa without any other special treatment.

    The endoscopic operation process was performed by an experienced respiratory physician. Each patient in Gruop A, Group B and Group C was assisted with local anesthesia by spraying 10 ml 2% lidocaine when the fiberoptic bronchoscope first entered the glottis, carina, and left and right bronchus, and then fiberoptic bronchoscopy was performed.

    When the Modified Observer’s Alertness/Sedation (MOAA/S) score was ≤ 3, bronchoscopy was started. If a persistent cough occurs during the operation and body movement affects the operator’s operation, 25 µg of fentanyl can be administered every 5 to 10 min until a maximum of 200 µg is achieved. It was designated a treatment failure if the degree of sedation was still insufficient for the operation. Every patient was sent to the post-anesthesia care unit (PACU) after operation. Patients can exit PACU until regained consciousness and completed command actions, recovered orientation ability, and raised the head while supine for more than 10 s.

    Measurement of endpoints

    Primary endpoints

    The vital signs including HR, BP, MAP, SpO2,RR, PetCO2, BIS were recorded at four times. T0: the time patient was awake while breathing room air pre-anesthesia, T1: the post-anesthesia induction state under nasal cannula oxygen inhalation (oxygen flow rate 3–5 L/min), T2: the time when the bronchoscopy was inserted into the glottis, T3: the postoperative at the time of completion, T4: when the patient wakes up.

    The rate of adverse events was recorded. Including intraoperative coughing, hypoxemia, and other adverse events (including blood pressure fluctuation > 20% before anesthesia, bradycardia or tachycardia, arrhythmia, larynx or bronchospasm, etc.).

    Record the MOAA/S scores of groups A and B at each time point from T1 to T4: 5 points – Responds readily to name spoken in normal tone; 4 points – Lethargic response to name spoken in normal tone; 3 points – Responds only after name is called loudly and/or repeatedly; 2 points – Responds only after mild prodding or shaking; 1 point – Responds only after painful trapezius squeeze; 0 point – Does not respond to painful trapezius squeeze.

    Secondary endpoints

    Record the onset time of the drugs, operation time (the time from the beginning of insertion of the bronchoscopy to the completion of the insertion), and recovery time (have command action, recovery of orientation ability, time to raise the head in supine position >10 s). Assessment of surgeon satisfaction (excellent: the opening of the glottis is excellent; good: the opening of the glottis is good, the mirror placement is smooth, and the patient’s cooperation is good; poor: the opening of the glottis is not opening properly, there is obvious body movement or coughing, which leads to mirror withdrawal affecting the operator’s operation), patients satisfaction (satisfactory: there was no discomfort during the bronchoscopy; moderate satisfactory: there was mild discomfort during the bronchoscopy and willing to accept a re-examination; unsatisfactory: patient felt extremely uncomfortable during the bronchoscopy and was unwilling to go through it again).

    Statistical analysis

    The sample size for calculating the difference in recovery time between the two groups was determined using G-power (version 3.1.9.2, α = 0.05, power = 0.8) depending on our preliminary study results (intervention group: mean = 2.26, SD = 2.08; control group: mean = 4.25, SD = 3). A total of 28 patients were required per group, and considering an expulsion rate of 0.2, the sample size was increased to 35 patients per group.

    All variables were expressed as numbers, mean (SD) or median (interquartile range). Statistical analysis was performed using the SPSS25.0 Software and the measurement data was normally distributed using the Shapiro-Wilk test. The normally distributed measurement data were expressed as mean ± standard deviation (X ± s), and the comparison between groups was analyzed by one-way ANOVA, and the Bonferroni multiple comparison test was performed, and the Welch test was used for heterogeneity of variance. Non-normally distributed continuous variables were expressed as medians (interquartile ranges), and analyzed using nonparametric tests (Kruskal-Wallis H test). Counting data were compared using chi-square test or Fisher test, and Bonferroni correction test was used. Bilateral P < 0.05 was considered statistically significant.

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  • Lessons from AI for data integration in neuroscience

    Lessons from AI for data integration in neuroscience

    In the previous article, I argued that advancing data integration in neuroscience requires incorporating resting-state spontaneous activity into each experiment, framing it as ‘adhesive dots.’ Here, I extend that discussion by drawing strategic lessons from the success of large language models (LLMs) and by concretizing the earlier claims from the perspective of data

    What LLMs can teach us about data integration?

    The worldwide construction of data centers illustrates how AI development has advanced through scaling – expanding data volume, model size, and computational resources. LLM performance improves according to power-law scaling when all three expand together. (1) Furthermore, scaling model size and dataset size in tandem has been shown to be near-optimal. (2)

    Yet progress has required more than scale: cleaning and curation have been equally crucial. GPT-3 demonstrated the power of large-scale training with filtered Common Crawl, (3) and T5 achieved major improvements by building the C4 dataset after aggressively removing duplicates and low-quality text. (4) PaLM 2 also reported the significant impact of data quality. (5) On the other hand, concerns have been raised about the potential exhaustion of high-quality web text, (6) and partly motivated by applications such as edge AI (the concept of running AI on devices or chips), efficiency efforts such as Mixture-of-Experts (MoE) and compact models are also being pursued in parallel. (7,8) In short, AI has advanced through scaling and curation, while efficiency has also evolved along a complementary path.

    Current landscape and challenges in neuroscience

    By contrast, neuroscience has yet to fully address the ‘limits of data accumulation’ or the ‘optimization of modeling.’ Advances in optical methods, such as two-photon calcium imaging, now enable large-scale simultaneous measurements at single-neuron resolution. Recently, functional data spanning multiple fields of view have been integrated with EM connectomics, yielding analyses on the order of 75,000 neurons in total – marking major progress. (9)

    The greater challenge, however, lies in behavioral and environmental diversity. Laboratory experiments still focus largely on ‘screen-based stimuli’ and ‘controlled tasks.’ Although natural scene stimuli are increasingly employed, (10) real-world contexts are far harder to reproduce. Consider sudden crowd surges in a train station, unexpected issues at immigration control, or nighttime evacuation after a major earthquake with power outages and aftershocks. Such scenarios are common in life, but even if reproduced and recorded, the resulting datasets would be rare and highly specialized. Thus, it becomes essential to examine how such data – naturally incorporating individual differences – can be meaningfully connected to others.

    This contextual diversity makes integration particularly difficult. Unlike web text, which is relatively static and independent at scale, neural time-series data are strongly influenced by arousal, attention, individuality, apparatus, and surrounding environment. Therefore, standardized and shareable frameworks (NWB, BIDS, DANDI, OpenNeuro), (11) together with detailed metadata such as illumination, arousal state, and behavioral logs, are indispensable.

    Figure 1. Relationship between spontaneous activity and task-related activity
    Spontaneous activity states (Spon.1, Spon.2) represent the baseline states before the task. For simplicity, they are depicted as points in this figure, but in reality they are temporally fluctuating dynamics. Conventionally, analyses have been limited to quantifying the changes Δ1 and Δ2 in post-task activity (Aft.Task1, Aft.Task2) relative to each spontaneous state, without considering the relationship between Spon.1 and Spon.2. If the two differ substantially, comparing only Δ1 and Δ2 is insufficient to properly discuss task effects. Therefore, understanding the relative relationship between spontaneous states is essential, and this figure illustrates the necessity of comparing baseline states in addition to observing differences.

    The Idea of a ‘ten-minute spontaneous activity’ baseline

    As a realistic step, I have proposed adding a ‘ten-minute spontaneous activity’ segment to each experiment. Spontaneous activity provides a statistical foundation less constrained by specific tasks or environments, reflecting arousal, attention, and individuality while serving as the substrate for task-evoked activity. This has been supported by findings from both human fMRI and mouse research. (12)

    Moreover, resting brain activity exhibits scale-free long-range correlations lasting minutes to tens of minutes. (13) A ten-minute window thus captures the key temporal scales while remaining feasible as a unifying standard across laboratories. Longer recordings are, of course, preferable, but a two-step strategy – first establishing a ten-minute baseline and then extending it for refinement – is the most pragmatic approach.

    Attaching this “ten-minute spontaneous activity” baseline forms the “adhesive dots” (as described in a previous article), enabling cross-comparison across studies (Fig.1).

    This is not an abstract ideal: the role of resting-state structure as a foundation for interpreting and predicting task responses has been empirically demonstrated. (12)

    Definitions and non-stationarity of spontaneous activity

    The definition of spontaneous activity differs across species and paradigms. In humans, it is typically defined as an ‘eyes-open, fixation-rest state,’ whereas in animals it is categorized as ‘head-fixed, task-free’ or ‘freely moving without tasks.’ Importantly, spontaneous activity is not a static point, but a fluctuating dynamic influenced by arousal and microenvironmental factors. Thus, detailed metadata are indispensable. Notably, the diversity within spontaneous activity is far smaller than the vast diversity of tasks and environments.

    This – the “Principle of External Complexity” – highlights that in situations like crowded trains or large gatherings, where one brain is surrounded by dozens or even thousands of other brains, environmental complexity can easily exceed an individual’s internal complexity, making neural data integration difficult. Focusing first on the limited variability of spontaneous activity provides AI with a practical intermediate target for translation and alignment.

    A bridge to the next article

    The key lesson from LLMs is that breakthroughs emerged not from scaling alone but at the convergence of scaling, curation, and efficiency. In neuroscience, progress likewise requires addressing not only the expansion of neuron counts but also the challenge of behavioral and environmental diversity. As a preparatory step, standardizing the inclusion of a “ten-minute spontaneous activity” segment in each experiment – curated and shared as adhesive dots – would provide a common foundation for integration. This article has emphasized data-side strategies; the next will examine how AI can serve as the glue, through representational mapping and transformation learning, to connect fragmented datasets into a coherent understanding.

    CLICK HERE for references

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  • Cyber security warning for UK operational technology sector

    Cyber security warning for UK operational technology sector

    The NCSC’s guidance is aimed at operational technology organisations, particularly those deploying equipment across greenfield and brownfield sites

    Operational technology – the infrastructure that controls and manages industrial infrastructure – is seen as highly vulnerable to being targeted, and loss of that infrastructure can be catastrophic to businesses and the wider landscape.

    The new guidance encourages those working in OT roles to not only securely map all digital and physical components of the system to establish where risks may arise, but to limit that mapping to essential viewers only.

    This definitive record should cover key factors that could put companies at risk, including components, connectivity, system architecture and third-party access to infrastructure, and the potential impact to the business if something was to happen.

    Laura Gillespie, a cyber readiness expert with Pinsent Masons, said the advice was essential for companies who might find themselves at risk.

    “Operational technology can be a prime target to threat actors, who understand that business operation disruption can escalate very quickly, putting victims under pressure to closely consider the need for threat actor engagement,” she added.

    Stuart Davey, a cyber expert at Pinsent Masons with a particular focus on critical national infrastructure (CNI) warned: “While many manufacturing processes increasingly rely on technology-based processes, other sectors and critical national infrastructure are equally at risk.  It is crucial potential victims understand how operational technology is mapped, to ensure they are prepared in the event of an attack”.

    “With the Cyber Security and Resilience Bill expected by the end of 2025, which will bring greater protections to CNI, the UK is doubling down on efforts to ensure businesses take a proactive approach to cyber security.”

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  • Nomad Foods names Dominic Brisby CEO – SeafoodSource

    1. Nomad Foods names Dominic Brisby CEO  SeafoodSource
    2. Could Leadership Change at Nomad Foods (NOMD) Reshape Its Innovation and Cost Management Agenda?  simplywall.st
    3. Evaluating Nomad Foods (NYSE:NOMD) Valuation Following CEO Transition Announcement  Yahoo Finance
    4. Flora Foods’ Brisby named Nomad Foods CEO  Global Food Industry News
    5. Nomad Foods names Dominic Brisby as new CEO, Descheemaeker to retire  Investing.com

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  • Siemens to support Airbus in decarbonizing major industrial locations | Press | Company

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    Press Release13 October 2025Siemens AGMunich

    The decarbonization roadmaps for the individual
    Airbus locations include scalable solutions to reduce energy demand and CO₂
    emissions. For this purpose, digital twins of the energy system will be used to
    simulate energy consumption in order to identify optimal packages of measures
    for the locations. These measures include on-site power supply from renewable
    energy sources, heat pumps for heat generation, improvements in energy efficiency, intelligent metering systems
    and smart energy management systems for monitoring, controlling and optimizing
    consumption at the locations. The
    expansion of the infrastructure is scheduled to begin in 2026. Siemens will
    also operate and maintain the new systems to ensure long-term efficiency and
    resilience.

    Siemens and Airbus have been collaborating
    successfully for more than half a century. Key initiatives to date include
    factory automation, industrial software, safety and security, and building
    automation.

    Press Folders
    Siemens AG (Berlin and Munich) is a leading technology company focused on industry, infrastructure, mobility, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably.
    In fiscal 2024, which ended on September 30, 2024, the Siemens Group generated revenue of €75.9 billion and net income of €9.0 billion. As of September 30, 2024, the company employed around 312,000 people worldwide on the basis of continuing operations. Further information is available on the Internet at www.siemens.com.

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  • Investcorp Announces Final Close of Golden Horizon Cooperation Fund at $750 Million

    13 Oct 2025

    Investcorp, a leading global alternative investment firm, is pleased to announce the successful final close of its Golden Horizon Cooperation Fund (the “Platform”), launched in collaboration with China Investment Corporation (CIC), one of the world’s largest sovereign wealth funds. The Platform secured commitments from a diverse group of global institutional investors and LPs, including Jada Fund of Funds (a subsidiary of PIF), Saudi Venture Capital, Silk Road Fund, and Bank of China, among others spanning the GCC, Asia, and China.

    The Platform is focused on investing in high-growth, profitable companies across Consumer, Healthcare, Transportation & Logistics, and Business Services sectors in the GCC and China. Its objective is to foster cross-border expansion and value creation, with Investcorp and our Chinese partners providing strategic support to help portfolio companies scale and deepen commercial ties between the two regions.

    To date, the Platform has completed three investments in the GCC: NourNet, a leading ICT services provider in Saudi Arabia, serving over 1,500 clients across more than 20 industries; Trukker, MENA’s largest on-demand digital trucking aggregator, operating across the Middle East and Europe with over 70,000 trucks and 1,200 B2B enterprise clients; and Salla, Saudi Arabia’s leading SaaS e-commerce enablement platform, which has facilitated over $9 billion in e-commerce GMV since 2020 and currently supports more than 100,000 active merchants.

    This milestone underscores Investcorp’s commitment to building strategic investment bridges between the GCC and China, seeking to unlock long-term value for investors and portfolio companies alike.

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  • Siemens partners with Airbus to decarbonize major industrial sites in the U.S. and U.K. | Press | Company

    Siemens partners with Airbus to decarbonize major industrial sites in the U.S. and U.K. | Press | Company

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    Press Release13 October 2025Siemens AGZug,
    Switzerland

    Left to right: Sabrina Herrmann – Head of Buildings Germany,
    Siemens Smart Infrastructure; Benoit Shultz – Chief Procurement Officer,
    Airbus; Herbert Klipp – Corporate Account Manager, Siemens; Carl Ennis – CEO
    Siemens UK & Ireland; Jan Schwarz – Vice President Real Estate &
    Facility Management Procurement, Airbus; Florent Massou dit Labaquère, EVP
    Operations Commercial Aircraft, Airbus; Carsten Liesener – Global Head Sales
    & Operations, Siemens Smart Infrastructure; Charles Huguet – Senior Vice
    President General Procurement, Airbus 

    As part of the
    framework agreement, Siemens will deploy its scalable and proven
    decarbonization solutions, tailored to the selected sites, to support Airbus in
    reaching the target of 80 kt CO2e abated annually from 2030.

    The project will be led by Siemens’ Buildings
    business and supported by Capgemini, combining the companies’ vast expertise in
    sustainability and digitalization to ensure the success and timely delivery of
    the project. 


    Decarbonization at
    scale: Scalable solutions for measurable impact


    Siemens will evaluate the sites and create
    and implement an overall decarbonization masterplan for the awarded sites,
    resulting in scalable solutions to reduce energy demand and carbon emissions.
    To accelerate the selection of the measures, Energy System Twins will simulate
    and help determine the best decarbonization roadmaps for the sites. The key
    elements that will enable this include decarbonization of heat production via
    heat pumps, energy efficiency upgrades, smart metering systems, on-site smart integration
    of renewable energy, and smart energy management systems to monitor, control,
    and optimize usage across the sites. These measures are designed to help Airbus
    achieve its targets of reducing energy consumption by 20 percent and Scope 1
    and 2 greenhouse gas emissions by 85 percent through 2030, compared to 2015
    levels.

    Capgemini has been appointed by Siemens for
    the program to complete first phase consulting activities, governance
    definition, and support for project management and planning. Capgemini also brings
    its expertise in the digitalization and automation of the
    energy monitoring and measurement systems.

    “Our collaboration with Airbus is built on
    years of mutual trust and shared ambition. It highlights Siemens’ capability to
    deliver smart and scalable technologies and services to reduce the carbon
    footprint of Airbus. At Siemens, we’re committed to making the energy
    transition not only sustainable, but also achievable and scalable, so that
    Airbus and other industrial leaders can confront climate challenges while
    boosting operational resilience and long-term competitiveness,” said Susanne
    Seitz, CEO of Buildings at Siemens Smart Infrastructure.

    “We’re proud to be taking this important step
    toward making our operations more energy-efficient and future-ready.
    Collaborating with trusted partners is key in building a more resilient
    industrial footprint. The site-specific expertise from our Airbus colleagues in
    the U.K. and the U.S. combined with Siemens’ technical know-how will keep us on
    the path toward meeting our energy use and emission reduction targets,” said
    Florent Massou dit Labaquere, EVP Operations of Airbus Commercial Aircraft. 


    Roadmap to 2030

    The initial phase began in summer 2025 with the
    development of decarbonization roadmaps for each site. Engineering studies will
    guide the implementation, with infrastructure rollout starting in 2026. Siemens
    can also operate and maintain the new infrastructure, ensuring long-term
    efficiency and resilience.

    Siemens and Airbus have been working together
    for more than half a century. Key initiatives have included factory automation,
    industrial software, safety and security, building automation technology, and
    beyond. This new agreement builds on successful collaboration to support
    Airbus’ sustainability ambitions and achieve its targets for minimizing its
    operational environmental footprint.

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    Siemens Smart Infrastructure (SI) is shaping the market for intelligent, adaptive infrastructure for today and the future. It addresses the pressing challenges of urbanization and climate change by connecting energy systems, buildings, and industries. SI provides customers with a comprehensive end-to-end portfolio from a single source – with products, systems, solutions, and services from the point of power generation all the way to consumption. With an increasingly digitalized ecosystem, it helps customers thrive and communities progress while contributing toward protecting the planet. To protect this journey, we foster holistic cybersecurity to ensure secure and reliable operations. Siemens Smart Infrastructure has its global headquarters in Zug, Switzerland. As of September 30, 2024, the business had around 78,500 employees worldwide.

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    Siemens AG (Berlin and Munich) is a leading technology company focused on industry, infrastructure, mobility, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably.
    In fiscal 2024, which ended on September 30, 2024, the Siemens Group generated revenue of €75.9 billion and net income of €9.0 billion. As of September 30, 2024, the company employed around 312,000 people worldwide on the basis of continuing operations. Further information is available on the Internet at www.siemens.com.

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    Transcriptomic profiles of endocrine-resistant breast cancer | BMC Cancer

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