Scientist Challenges CERN and Industry to Public ‘Known Dataset Test’ to Prove 3D-Flow Can Save Billions and Millions of Lives
DALLAS, March 20, 2026 (GLOBE NEWSWIRE) -- In PDF (https://bit.ly/4bQwVCe). The Crosetto Foundation for the Reduction of Cancer Deaths, a registered nonprofit organization, urgently calls on the global community to disseminate information regarding the upcoming Total Body PET (TBPET) Conference in Valencia, Spain, May 11-14, 2026.
This event will host a critical forum where Italian-American scientist Dario Crosetto has submitted a 500-word abstract and 2-page summary detailing a breakthrough in radiation signal detection.
A Media Snippet accompanying this announcement is available by clicking on this link.
Crosetto’s 3D-Flow architecture, validated by Fermilab as a breakthrough in 1993, offers a paradigm shift in both High Energy Physics and medical imaging.
By performing thousands of operations per dataset where current systems perform fewer than 100, the 3D-Flow can identify ‘Good Events’ currently lost to electronic ‘dead time.’
The Foundation is calling for a public ‘Known Dataset Test’—a definitive, laboratory-based verification that can prove these claims in seconds, potentially saving over $12 billion in research waste and millions of lives through early cancer detection.
Abstract (500-word in PDF: https://bit.ly/4lH1zBt)
Title:
PET Cost-Effectiveness in Reducing Premature Mortality
Depends on Accurate Detection of Radiation Signals with 3D-Flow
The cost-effectiveness of Positron Emission Tomography (PET) in reducing premature mortality depends fundamentally on accurate radiation signal detection.
In 1992, Italian-American scientist Dario Crosetto invented the 3D-Flow architecture, recognized as a breakthrough for identifying signals in ultra-high-speed data streams during Fermilab’s 1993 international scientific review.
Consequently, in 1994, the U.S. Government granted Crosetto a Green Card for ‘Exceptional Ability’ within 24 hours, recognizing expertise that brought tangible benefits to the nation’s scientific advancement and economy.
In 1995, the U.S. Department of Energy awarded Crosetto a $1 million grant to validate his invention, completed in 1999 with FPGA simulations, a 300 nm CMOS ASIC design, and a full 1,024-channel system described in a 45-page, peer-reviewed article.
Despite proven feasibility, Non-Recurring Engineering (NRE) funding for ASIC fabrication was never provided, delaying scientific progress and life-saving applications like the 3D-CBS for three decades.
This prevented deployment of systems meeting CERN LHC Level-1 Trigger requirements (1.2 billion events/second) through 2026.
In 2003, Crosetto personally funded a 144-processor 3D-Flow FPGA demonstrator (16 channels), and in 2015 designed a 4,096-channel system in a single crate, supported by 59 industrial quotations.
Crosetto’s updated 2025 designs show a 20 nm 3D-Flow ASIC (128-PEs) enables modular, scalable systems of any size and performance: a 6U-VME64 PET board (1,536-PEs) executes ~2,015 operations on each datasets arriving every 25ns on 128 channels at ~$15/channel, while an ATCA HEP board (8,448-PEs) executes ~51,000 operations/dataset on 32 channels at ~$218/channel, meeting HL-LHC requirements of 8 billion events/second beyond 2042.
These systems provide orders-of-magnitude improvements in processing capability per dataset at low cost and without dead time.
In contrast, current CERN-FPGA-based Level-1 triggers built for 2026-2036, using ~20 trillion transistors and ~650 kW, perform fewer than 100 operations/dataset. This bottleneck forces ‘discovery by luck’—randomly capturing rare ‘Good Events’ due to insufficient processing capability per dataset, while costing taxpayers ~$4 million daily to operate the HL-LHC.
To resolve this, Crosetto proposes the ‘Known Dataset Test,’ a laboratory test taking seconds—never performed by CERN and prevented in 2015. It involves inserting 1,000 manually identified ‘Good Events’ into a 2-terabyte dataset to compare a 6-kW 3D-Flow system executing 2,800 operations against the 650-kW CERN-CMS system executing <100 operations.
This would prove current CERN architecture mathematically incapable of detecting hidden events and could prevent over $12 billion in projected waste.
A corresponding test in medical imaging would compare the 3D-CBS against existing systems. While 3D-CBS is projected at $3.5M ($200/test), systems like EXPLORER cost $22.5M ($4,500–$9,000/test). Critically, current protocols using traditional PET devices have failed to improve two-year survival rates among 100,000 patients who received 1 to 8 exams over two decades.
Scientific progress requires objective verification of ideas. In the absence of a written scientific refutation, institutional silence constitutes a breach of duty compromising public health and finances.
Allow Crosetto to present at conferences and CERN; counter his ideas with scientific evidence, not exclusion, and endorse funding the inventor to prove his idea if no more cost-effective alternative can be cited.
Summary (two-page in PDF: https://bit.ly/4cYhUzv)
Title:
PET Cost-Effectiveness in Reducing Premature Mortality
Depends on Accurate Detection of Radiation Signals with 3D-Flow
I. The Core Problem: Inadequate Signal Detection — Accurate Detection Saves Lives
The cost-effectiveness of Positron Emission Technology (PET) depends fundamentally on the accurate detection of radiation signals.
Current industrial paradigms fail because their electronics cannot process signals without dead time, leaving devices ‘blind’ to minimal biophysiological anomalies. While billions are spent on infrastructure, the failure to optimize processors prevents seeing disease in its infancy.
Accurate detection of signals from radiation is not just a technical requirement; it is the essential requirement for reducing premature mortality and healthcare expenditures.
II. 1993: Fermilab Validates the 3D-Flow Breakthrough
In 1992, Italian-American scientist Dario Crosetto invented the 3D-Flow processor and system architecture. This innovation was officially recognized as a breakthrough for identifying specific signals or objects in an ultra-high-speed data stream during a scientific review held at Fermilab in 1993 [1]. It established a new standard for parallel data processing that remains unrivaled in speed and efficiency.
III. Exceptional Ability and Validated Feasibility
In 1994, for this and other inventions, the U.S. Government granted Crosetto a Green Card for ‘Exceptional Ability’ within 24 hours of submission, recognizing expertise that brings tangible benefits to the nation’s scientific advancement and economy [2].
In 1995, the U.S. DOE awarded him a $1 million grant [3] for a feasibility study, successfully completed in 1999 and described in a 45-page peer-reviewed article [4].
This study—demonstrating the 3D-Flow architecture is technology-independent through simulations on FPGAs from three different manufacturers—also proved the 3D-Flow ASIC design in 300 nm CMOS. The file called ‘tape-out’ to be sent to the silicon foundry TSMC for fabrication of the ASIC was prepared by Synopsys, a world leader in ASIC design. Crosetto also designed a 1,024-channel 3D-Flow system in a 9U-VME crate.
Despite proven success, the NRE funding required to manufacture the 3D-Flow ASIC was never provided. This system would have satisfied Level-1 Trigger requirements of 1.2 billion events per second for all LHC experiments until 2026, offering a massive potential for cost-effective early cancer detection capable of saving millions of lives.
IV. A Working Hardware Demonstration Shows What 3D-Flow Can Do
In 2003, Crosetto built a 144-processor 3D-Flow demonstrator system [5], at his own expense, implemented in FPGAs. The system was designed to demonstrate the feasibility and functionality of the architecture and justify funding the NRE required to fabricate the ASIC.
In 2015, Crosetto designed a complete 3D-Flow system with 4,096 channels in a single crate [6], supported by 59 industry quotations from manufacturers. He further upgraded the design in 2025 [7] with new electronic boards in different form factors [8].
These developments were described in two articles [7], [8] and related references showing a 20nm 3D-Flow ASIC (128-PEs) enables modular systems of any size. A 6U-VME64 PET board (1,536 PEs) executes 2,015 programmable operations on 128 channels on datasets arriving every 25ns at $15/channel.
This scales to an ATCA HEP board (8,448 PEs) executing up to 51,000 operations per dataset on 32 channels at $218/channel, for HL-LHC beyond 2042. These modular implementations enable an eight-board ATCA, with 4,096-channel 3D-Flow Level-1 Trigger system consuming ~6kW, performing 2,800 operations per dataset for $85,500 total.
The 3D-Flow invention drastically outperforms industry standards providing orders-of-magnitude improvements in processing capability per dataset at low cost. It accurately detects all tumor marker signals in PET applications—from fast LSO to slow BGO crystals—and is compatible with any detector type.
V. Funding Requests Were Submitted – Evidence Ignored
Crosetto submitted multiple funding proposals to government and private agencies in the United States and Europe, including CERN.
These proposals were supported by letters of collaboration from universities and PET centers in San Antonio and Dallas, Texas, universities in Italy, and a team of qualified collaborators [9].
Despite these collaborations and working demonstrator justifying NRE funding, agencies instead funded several ineffective FPGA-based trigger systems.
Furthermore, CERN used EU funds for the Wearable PET (WPET) project [10]—an impractical 350 kg garment for 24-hour cancer screening triggering a European Parliamentary Question [11] comparing WPET’s failure to the 3D-CBS [15].
VI. Device and Test Costs: 3D-CBS ($3.5M, $200/Test) vs. EXPLORER ($22.5M, $4,500–$9,000/Test)
The $3.5M projected 3D-CBS sale price derives from a $2M component cost supported by 59 industrial quotes (see pp. 300-304 of [6]), with 155-page quotes detail available—plus a 75% margin for assembly, software, and overhead. Detector crystals are the primary cost; electronics are negligible, with 25,344 3D-Flow processors ($0.50/unit) and total hardware (A/D boards, VME64 crates) costing ~$100,000 [8].
The $200 per-test cost is detailed in page 5 of [12].
Conversely, the $22.5M EXPLORER cost reflects the price of units purchased in Italy (June 2024) for €21M each, referenced in slide 70 of [13]. The cost per exam on the EXPLORER is listed at UCDavis as $9,000 [14] (in Italy patients do not pay directly because universal healthcare covers the cost).
VII. Clinical Failure: EXPLORER’s Inefficiency and Lack of Utility
The EXPLORER is limited by 18 mm thin, expensive radioactive LSO crystals (58% stopping power), whereas the 3D-CBS uses 30 mm thick, economical BGO crystals (98% stopping power). To test machine sensitivity, Crosetto underwent an EXPLORER exam on 1 December 2023 for a biopsy-confirmed basal cell carcinoma, seeking metabolic activity measurements relative to surrounding tissue.
Despite receiving 1.63 GB of data, neither the hospital nor UIH provided these essential results. This failure to report vital data—not even a null result—proves the €21M device is not designed to provide precise, lifesaving information required for early cancer management.
VIII. Industry Recognition: Siemens, GE and UIH Evidence
Industry giants have effectively validated Crosetto's work by adopting his patented ideas. He shared his 2000 book ‘400+ Time Improved PET Efficiency for Lower-Dose Radiation, Lower-Cost Cancer Screening’ [15] and related articles 2015 [6], 2020 [18], 2025 [7-8] with GE, Siemens, and United Imaging Healthcare (UIH) during extensive meetings where his patented ideas were discussed.
Regarding Siemens: On 06/11/2002, their President of Nuclear Medicine and PET Director, with the consent of all participants, held a recorded full-day technical meeting with Crosetto, followed by additional conference calls. Despite initially claiming electronics did not limit PET efficiency [16], Siemens later announced a 70% efficiency increase attributed specifically to electronic improvements.
Similarly, Crosetto maintained long-term communication with GE through technical discussions at conferences. At the 2018 IEEE-MIC, he met with GE leadership and an engineer interested in the 3D-Flow architecture capable of executing complex algorithms. GE later reported enhanced 511-keV photon signal filtering via improved electronics capable of executing complex algorithms.
After corresponding with UIH leadership, Crosetto gave a two-hour seminar on 19/11/2018 at their Shanghai headquarters. UIH engineers admitted to using ‘neighboring data exchange’—a core 3D-Flow feature—in ASICs that later they presented at the 2023 IEEE-MIC Conference (Slide 86 of [13]). These interactions demonstrate the high industrial value of the 3D-Flow architecture, despite the inventor lacking resources for legal patent protection.
IX. The Known Dataset Test: A Test of Scientific Honesty
The public is unaware of the staggering inefficiency behind celebrated discoveries like the Higgs Boson. Current CERN-FPGA-based triggers [17] act as bottlenecks, ‘grabbing’ events by chance because they are limited to fewer than 100 operations per dataset. This ‘discovery by luck’ costs taxpayers $4 million daily to operate the HL-LHC. Without a Known Dataset Test, billions are spent analyzing ‘garbage’ data in hopes of a random success.
This laboratory test takes only seconds. CERN has refused to conduct it and blocked Crosetto from doing so in 2015 (see [6], pp. 158-200).
This test is the definitive measure of scientific honesty. It involves inserting 1,000 ‘Good Events’ into a 2-terabyte dataset (3 seconds of LHC data). A 6-kW 3D-Flow system executing 2,800 operations per dataset would be tested against the 650-kW CERN-CMS FPGA system, which uses 20 trillion transistors but performs fewer than 100 operations.
This would prove current FPGA architecture is mathematically incapable of detecting hidden events within data arriving at 8 billion events per second.
Performing this test is essential to prevent wasting $12 billion over the next decade and shift physics from a game of chance to deterministic precision.
Similarly, a Known Dataset Test for medical imaging would compare 3D-Complete Body Screening (3D-CBS) [18] with GE, Siemens, and UIH systems. Current protocols using traditional PET devices have failed to improve two-year survival rates among 100,000 patients receiving 1 to 8 exams over two decades (slide 89 of [13]).
The proposed test would screen a defined population and measure mortality reduction after 2-10 years against historical averages. Only such transparent, outcome-based evaluation can determine which technology truly saves lives and prevents further waste of public resources.
X. More Money Does Not Solve Scientific Problems Without Better Ideas
The key paradigm shift required is to recognize that simply injecting more funding into research conducted by large institutions, corporations, or major universities does not necessarily solve complex scientific problems. Real progress depends on innovative ideas that demonstrate measurable advantages through objective verification.
This is the purpose of the proposed ‘Known Dataset Test’, performed in a controlled environment before extending new technologies to large populations or global deployment.
Reductions in cancer mortality in the United States, Europe, and worldwide will not become significant simply by investing more money into existing screening technologies if controlled testing within a defined territory does not demonstrate a measurable reduction in premature mortality [12].
Scientific progress therefore requires experimental verification of ideas, not only larger budgets.
The broader problem is not limited to this case. Leading scientific publications have documented structural problems in research funding systems, including the emergence of ‘oligopolies’ in knowledge and funding. Examples include analyses published in the Journal of the Royal Society of Medicine (2006) [19], Nature (2010) [20], Vox (2016) [21], and Scientific American (2018) [22].
For decades, Crosetto has pursued scientific validation by demonstrating the 3D-Flow hardware feasibility—a powerful tool designed to help colleagues discover new particles and save lives.
In 2000, he distributed 200 free copies of his 3D-CBS book [15]. Since 2014, he has personally distributed ~1,000 copies of his work to colleagues at each IEEE-NSS-MIC-RTSD conference. In 2025, he distributed 1,200 double-sided handouts: one side proving mathematically that FPGAs cannot sustain billion-event-per-second speeds, with a summary of his 30-year research on the reverse [23].
By issuing press releases in 2025—republished by over 6,000 news outlets with a potential audience of ~800 million readers [24], Crosetto fulfilled his responsibility to the public despite 30 years of institutional silencing.
XI. Scientific Responsibility: Evidence Must Be Evaluated and Comparative ‘Known Dataset Test’ Must Be Funded
Crosetto has been criticized for not ‘convincing’ reviewers and funding agencies over the past 30 years to fund him to develop his invention and build a commercial enterprise. However, a scientist’s duty is to demonstrate truth—not to lobby for funding or wealth.
A scientist committed to scientific integrity must present evidence, compare alternative approaches, and propose experiments capable of verifying which solution performs better.
Crosetto’s objective has therefore been to demonstrate the value of the 3D-Flow invention and to build two 3D-CBS devices which make use of the 3D-Flow, capable of proving a measurable reduction in cancer mortality within a defined population.
The historical facts reported here are a reminder that scientific progress depends on scientific integrity, open evaluation of evidence, and collaboration among colleagues. We must not compete by excluding colleagues with superior ideas from the decision table, nor by silencing, plagiarizing or appropriating their work to secure a share of the $2 trillion annual R&D funding. What endures in history is the intellectual honesty of those involved.
Science should unite researchers in understanding the laws of nature and overcoming barriers that limit benefits to humanity. The true competition is against disease and unresolved scientific challenges—not against each other.
Those entrusted with allocating taxpayer resources have a duty to evaluate documented evidence.
Refusing to assess a breakthrough without providing scientific reasons is a failure of duty—especially when the stakes include saving over $12 billion at CERN and millions of lives through the 3D-CBS. Both claims can be definitively verified through the ‘Known Dataset Test’.
In the absence of a written scientific refutation of Crosetto’s work [15], [6], [18], [7], [8], institutional silence constitutes a breach of duty that compromises both public health and public finances.
Crosetto’s aim is to serve his colleagues by providing a powerful tool capable of delivering 2,015 to 51,000 operations per dataset.
A transparent public evaluation at CERN will allow the community to determine which technological approach offers the greatest benefit for fundamental physics discovery and the reduction of cancer mortality.
Allow Crosetto to present his ideas at conferences and at CERN, counter them with scientific evidence rather than exclusion, and endorse funding the inventor to prove his ideas if no more cost-effective alternative can be cited.
#### End of two-page Summary submitted to the TBPET Conference in Valencia, Spain, 11-14 May 2026 ###
CALL TO ACTION
The 3D-CBS (3-D Complete Body Screening) is an advanced PET (Positron Emission Tomography) system whose operating principle is based on the cost-effective filtering of tumor-marker signals from radiation. This is conceptually similar to the essential task performed at CERN, where systems must filter ‘good events’ from large amounts of radiation background.
By improving the detection of relevant signals while filtering noise, this approach offers the potential to detect tumors at a very early stage — with fewer than 100 cancer cells — using very low radiation doses and at low cost. These advantages depend on a breakthrough innovation in identifying and processing meaningful signals within large radiation data streams.
For this reason, it is necessary to organize a panel of multidisciplinary experts for an international public comparative scientific review, similar to the one held at Fermilab in 1993 on Crosetto’s 3D-Flow invention.
Because signal detection, extraction, and analysis are central to PET imaging — and also fundamental to the research conducted at CERN, which is funded primarily by European, U.S., and international taxpayers — it is important that citizens ask their representatives to request that CERN appoint qualified experts to participate in such a review panel and ensure accountability for public funds invested in these research areas.
For these reasons, citizens in both the United States and Europe are encouraged to write to their elected representatives requesting a public comparative scientific evaluation.
Institutional Obligation and Final Call
Parliamentarians and public administrators entrusted with taxpayer resources are not required to resolve technical disputes. They are, however, obligated to demand transparency, public procedures, and measurable accountability. Closed-door evaluations, anonymous rejections, and the absence of public technical comparisons are incompatible with democratic governance when scientific, medical and economic stakes are significant.
The only legitimate path forward is the organization of public, comparative scientific reviews—in both particle physics and medical imaging—where competing technologies can be evaluated openly using quantified metrics, and where conclusions are fully documented and publicly disclosed.
This is not a conflict between individuals or institutions. It is a test of whether science serves truth, humanity, and the public interest.
History will judge this moment not by intentions, but by actions taken when the evidence was already available.
How You Can Help
1. Spread the Word
- Share this information with your personal and professional networks.
- Forward this to scientists, journalists, policymakers, and advocacy groups.
- Use social media to demand a public, evidence-based comparison of current institutional technologies versus Crosetto’s 3D-Fow and 3D-CBS.
2. Write to Your Representative: Demand transparency and public comparative review of these life- and money-saving innovations.
In the United States:
- Find and contact your U.S. Representative here: https://www.house.gov/representatives/find-your-representative [25]
In Europe:
- Contact your national parliamentarians: Find their addresses at this link [26].
- Contact your EU representatives: View the full list of all 720 Members of the European Parliament (MEPs) is available at this link [27].
3. A template letter addressed ‘To Whom It May Concern’ is available for download here [28].
Contact:
Jennifer Colburn
Crosetto Foundation for the Reduction of Cancer Deaths
DeSoto, Texas
jcolburn@crosettofoundation.org
https://crosettofoundation.org/
Blog: https://crosettofoundation.org/blog/
Facebook: https://www.facebook.com/profile.php?id=100064846172129
Instagram: https://www.instagram.com/dariocrosetto/
Linkedin: https://www.linkedin.com/in/dario-crosetto-4b69a1227/
X: https://x.com/crosettodario
About the Crosetto Foundation: The Crosetto Foundation for the Reduction of Cancer Deaths is a nonprofit organization dedicated to advancing scientific transparency and implementing ultra-high-speed data processing technology to detect cancer at its earliest, most treatable stages. For more information, visit crosettofoundation.org
References
[1] Fermilab review of 3D-Flow architecture (https://bit.ly/41i4ace)
[2] Crosetto Green Card awarded for ‘Exceptional Ability’ (https://bit.ly/4c6q9cn)
[3] U.S. DOE awarded $1 million grant to Crosetto (https://bit.ly/3Pszu1y)
[4] Crosetto 45-page peer-reviewed NIM article (https://bit.ly/45Mw6pM)
[5] 144 3D-Flow processor hardware demonstrator (https://bit.ly/43Rlk0s)
[6] Crosetto 2015 proposal supported by 59 industry quotes (https://bit.ly/4myTwpY)
[7] Crosetto article, 14 April 2025: 3D-Flow breakthrough (https://bit.ly/4e1uURA)
[8] Crosetto article, 20 December 2025: Universal boards (https://bit.ly/437YX7H)
[9] Testimonials: https://crosettofoundation.org/testimonials/
[10] 2018 Wearable PET (WPET) funded by CERN (https://bit.ly/3iydDp3)
[11] 2019 European Parliamentary Question: 3D-CBS vs. WPET (https://bit.ly/3HKjreL),
[12] Roadmap to save lives with 3D-CBS (https://bit.ly/47eqiIh),
[13] 102 technical slides presented by Crosetto at 2024 IEEE Conf. (https://bit.ly/45uaZtz)
[14] Cost of exam on the EXPLORER (https://bit.ly/3554QnH).
[15] 3D-CBS book 2000 ( https://bit.ly/4fZTzZC); video (https://bit.ly/4oN7Xbx).
[16] 6 November 2002: Crosetto-Siemens meeting (https://bit.ly/3hp68z3).
[17] CERN-CMS FPGAs. (https://cds.cern.ch/record/2759072/files/CMS-TDR-022.pdf)
[18] Crosetto 3D-CBS article 2020 (http://bit.ly/2QdgdTx)
[19] JRSM 2006: (https://bit.ly/3P3X0VB).
[20] Nature 2010: (http://go.nature.com/3qMovS7)
[21] Vox 2016: (http://bit.ly/3iGbiaN)
[22] Scientific American 2018: (https://bit.ly/3KWqoWD)
[23] Hand-out to 1,200 scientists, 2025 IEEE-NSS-MIC-RTSD (https://bit.ly/3NIWU5l).
[24] Global Press Releases 2025 (https://bit.ly/3HtisQv).
[25] United States Representatives contacts: Write to your representative in U.S.: https://www.house.gov/representatives/find-your-representative
[26] European States Parliamentarians contacts: Write to your national parliamentarians of all European states: https://secure.ipex.eu/IPEXL-WEB/parliaments/list_parliaments
[27] European Parliamentarians contacts: Write to the 720 members of the European Parliament. https://www.europarl.europa.eu/meps/en/full-list/all
[28] Template letter to assist you in drafting a message to your representative. (https://bit.ly/4j4J74s), (https://drive.google.com/file/d/1zSgLZin69ZaSFcuzc_HjITkbQ8iTe1Pa/view?usp=sharing)
APPENDIXES
Callouts:
- “A 6 kW 3D-Flow system vs 650 kW CERN-FPGAs—30 years, no scientific refutation. Can science afford not to test it?”
- “3D-Flow handles 8 billion events/sec with zero dead time. A breakthrough validated in 1993—still ignored.”
- “The ‘Known Dataset Test’ can prove Crosetto’s 3D-Flow's superiority in seconds.”
- “Why has CERN never performed this test? Discovery by luck or by design?”
- “A test that takes seconds: The 3D-Flow could save over $12 billion at CERN by capturing hidden data.”
- “3D-CBS detects early cancer signals missed by current ‘blind’ devices.”
- “Scientist Dario Crosetto challenges CERN to a public hardware comparison.”
- “Current PET protocols failed to improve 2-year survival for 100,000 patients.”
- “Institutional silence for 30 years compromises public health and finance.”
- “TBPET 2026 in Valencia: A forum for scientific truth and transparency”
Scientific/Technical demonstration of the advantages and benefits of Crosetto’s 3D-Flow breakthrough invention: (https://bit.ly/4aX5R4b).
Summary of Press Releases with Reach and Media Outlets:
- Lang.: Language (EN = English, FR = French, DE = German, IT = Italian)
- MEPs: Members of the European Parliament
- Sci.: Scientists, IEEE, CERN, Leaders
- Pub.: General Public, Media, Journalists (Total Potential Reach: M = million, K = thousand)
- To: Recipients (Total Potential Reach / Known Unique Readers) + unknown readers
- Media: Number of media outlets publishing (see thousands of links at https://bit.ly/3HtisQv).
| Date | Lang. | Link | To | Media |
| 02/15/2026 | EN | https://bit.ly/4sfxR97 | Pub (150/21k) | 946 |
| 12/20/2025 | EN | https://bit.ly/4aX5R4b | Tech/Sci. Dem. Pub (150M/22k) | 1,000 |
| 11/07/2025 | EN | https://bit.ly/43idsFY | 300 | |
| 10/28/2025 | EN | https://bit.ly/4qKVar8 | Pub (148M/13.8K) | 940 |
| 09/15/2025 | EN | https://bit.ly/41TMUKF | Cancer Pub (145M/11K) | 804 |
| 09/06/2025 | EN | https://bit.ly/3HYBePY | Pub (145M/23K) | 876 |
| 08/28/2025 | EN | https://bit.ly/4p0DneC | Tech/Sci. Dem. Pub (116M/22K), MEPs (720/420), Sci (40/27) | 597 |
| 07/15/2025 | EN | https://bit.ly/4m57FKZ | Pub (87M/10K), MEPs (720/41), Sci (40/14) | N/A |
| 07/04/2025 | FR | https://bit.ly/4lfjnTe | Pub (8.3M/2.5K) | 421 |
| 07/04/2005 | DE | https://bit.ly/3TTV0yb | Pub (11.3M/2.4K) | 487 |
| 07/04/2025 | IT | https://bit.ly/4loi7go | N/A | <5 |
| 07/03/2025 | EN | https://bit.ly/44cIbVQ | Pub (63.7M/2K), MEPs (720/448) | 441 |
| 06/30/2025 | EN | https://bit.ly/3TMnDNI | N/A | N/A |
| 06/30/2025 | IT | https://bit.ly/4nsvk9E | N/A | <5 |
| 06/23/2025 | EN | https://bit.ly/4era28b | MEPs (720/423) | N/A |
| 06/23/2025 | IT | https://bit.ly/3T7G1R8 | N/A | <5 |
| 04/14/2025 | EN | https://bit.ly/4oNUOyT | Technical Scientific Demonstration to Scientists |
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