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Ethical Barriers to Artificial Intelligence Adoption in Vaccine Distribution: A Systematic Scoping Review
No full textThe rapid advancement of AI has opened new avenues for improving healthcare systems, particularly in a pandemic response. AI technologies can potentially affect the equitable distribution of vaccines. However, there are ethical concerns such as privacy, governance, data security, acceptance, access, affordability, prioritization among others that arise from such implementation. This article synthesizes literature to identify the ethical implications of utilizing AI in vaccine distribution, planning and scheduling during a pandemic, with a focus on ensuring equitable access to vaccines in LMICs using a combination of 20 search string-words. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guideline for scoping review was used. A full-text open access peer review journals in English addressing the research interest from PubMed, ScienceDirect, and the Directory of Open Access Journals (DOAJ) was included in the study. These search engines were chosen based on their comprehensive coverage, advanced search capabilities, reputation for academic quality, and efficient retrieval of relevant and diverse literature. Data from each search engine was screened for inclusion criteria and charted from 2019 to 2023 to cover the COVID-19 pandemic period. Bibliometric analysis was done on the Web of Science search engine using R-studio and Biblioshiny to identify trends. Out of 1,555 records, 358 articles relevant to the search query were found; after careful consideration, 28 articles met the inclusion criteria for analysis. Thematic analysis was done to identify the ethical considerations associated with using AI in planning and scheduling vaccine distribution, particularly in the context of a pandemic. The article emphasized the importance of integrating lessons learned from the COVID-19 pandemic into future actions to strengthen a fair and equitable pandemic preparedness plan ensuring the ethical compliance of AI-support system responses in LMICs during pandemics. © 2025 Elsevier B.V., All rights reserved
Multilingual Domain Adaptation for Speech Recognition Using LLMs
No full textSiemens Healthineers AGWe present a practical pipeline for multilingual domain adaptation in automatic speech recognition (ASR) that combines the Whisper model with large language models (LLMs). Using Aya-23-8B, Common Voice transcripts in 22 languages are automatically classified into the Law and Healthcare domains, producing high-quality domain labels at a fraction of the manual cost. These labels drive parameter-efficient (LoRA) fine-tuning of Whisper and deliver consistent relative Word Error Rate (WER) reductions of up to 14.3% for languages that contribute at least 800 in-domain utterances. A data-volume analysis reveals a clear breakpoint: gains become reliably large once that 800-utterance threshold is crossed, while monolingual tuning still rescues performance in truly low-resource settings. The workflow therefore shifts the key success factor from expensive hand labelling to scalable data acquisition, and can be replicated in new domains with minimal human intervention. © 2025 Elsevier B.V., All rights reserved
Improved Approximation via Hybrid Shepard-Lagrange Operators: Linear and Nonlinear Perspectives
No full textThis paper introduces a hybrid operator that combines Shepard operators with Lagrange polynomials, proving that the new operator exhibits superior approximation properties compared to the classical Shepard operator. In the linear case, our approach advances known results in the literature, providing a more effective framework for approximation. Building on this foundation, the method is also extended to nonlinear scenarios by employing max-product operations, demonstrating that the nonlinear operator achieves even better approximation characteristics than its linear counterpart. The theoretical findings are validated through numerical computations and graphical representations, strongly supporting the effectiveness of the hybrid operator in both linear and nonlinear contexts
Estimation of the Mean Radiant Temperature in Office Buildings Using an Artificial Neural Network Developed in a Phyton Environment
No full textThermal comfort describes an occupant's state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, T ; uuml;rkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R-2 value of 0.94 compared to the globe thermometer measurement method. The model's advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT
Selective Electrochemical Conversion of Co2 To Formate Via Redox-Modulated Porous Metal Electrodes Coupled With Efficient Oxygen Evolution
No full textThe electrochemical conversion of carbon dioxide (CO2) to formate holds significant promise for CO2 mitigation and as a foundational process for various crucial chemicals. However, the efficiency of this conversion process is hindered by the sluggish kinetics of the counter oxygen evolution reaction (OER). In this study, we explore the impact of redox modulation in dendritic lead (Pb) doped tin (Sn) catalysts to enhance the Faradaic efficiency of CO2 reduction to formate, achieving an impressive Faradaic efficiency of 92.5% and a cathodic energy efficiency of 75%. Moreover, Iron cobalt layered double hydroxide (CoFeLDH) grown on hierarchically porous nickel acts as a standout performer for the OER, demonstrating a remarkably low overpotential of 90 mV at 50 mA cm-2, accompanied by a high electrochemical surface area of 684.25 cm2. Integration of these cost-effective catalysts into a two-electrode electrolyzer enables simultaneous reduction of CO2 to formate and water oxidation to oxygen, exhibiting exceptional activity, stability, and efficiency, with an overall bias as low as 2.56 V required to achieve a current density of 25 mA cm-2. This study represents a significant advancement in sustainable CO2 conversion technologies, offering promising avenues for carbon utilization and renewable energy generation. © 2025 American Chemical Society.Pakistan Science Foundation, PSF; Persian Scholarship Foundation, PSF; National Natural Science Foundation of China, NSFC; Lahore University of Management Sciences, LUM
Capacity Maximization for MIMO Channels Assisted by Beyond-Diagonal RIS
No full textReconfigurable intelligent surfaces (RISs) can improve the capacity of wireless communication links by passively beamforming the impinging signals in desired directions. This feature has been demonstrated both analytically and experimentally for conventional RISs, consisting of independently reflecting elements. To further enhance reconfigurability, a new architecture called beyond-diagonal RIS (BD-RIS) has been proposed. It allows for controllable signal flows between RIS elements, resulting in a non-diagonal reflection matrix, unlike the conventional RIS architecture. Previous studies on BD-RIS-assisted communications have predominantly considered single-antenna transmitters/receivers. One recent work provides an iterative capacity-improving algorithm for multiple-input multiple-output (MIMO) setups but without providing geometrical insights. In this paper, we derive the first closed-form capacity-maximizing BD-RIS reflection matrix for a MIMO channel. We describe how this solution pairs together propagation paths, how it behaves when the signal-to-noise ratio is high, and what capacity is achievable with ideal semi-unitary channel matrices. The analytical results are corroborated numerically.E. Bjornson was supported by the FFL18-0277 grant from the Swedish Foundation for Strategic Research. O. T. Demir was supported by 2232-B International Fellowship for Early Stage Researchers Programme funded by the Scientific and Technological Research Council of Turkiye.Swedish Foundation for Strategic Research [FFL18-0277]; Scientific and Technological Research Council of Turkiye [2232-B]Stiftelsen för Strategisk Forskning, SSF; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITA
Collective Bargaining With Pre-Donation May Lead To Tax Evasion in the Labor Market
No full textWe consider a collective bargaining model in the presence of a government that can tax corporate income. Using this model, we investigate whether workers or the firm can manipulate the bargaining equilibrium, with the help of Sertel's (1992a) pre-donation idea, by committing to transfer a part of their would-be payoffs to the other party. We show that making pre-donation is beneficial for workers but harmful to the firm. Moreover, the optimal pre-donation of workers enables them to fully extract the tax revenue that the government could obtain in the absence of pre-donation while keeping the welfare of the firm unchanged
Search for Dark Matter Produced in Association With a Dark Higgs Boson in the (Formula Presented) Final State Using (Formula Presented) Collisions at (Formula Presented) With the Atlas Detector
No full textA search is performed for dark matter particles produced in association with a resonantly produced pair of (Formula presented)-quarks with (Formula presented) using (Formula presented) of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the LHC. This signature is expected in extensions of the standard model predicting the production of dark matter particles, in particular those containing a dark Higgs boson (Formula presented) that decays into (Formula presented). The highly boosted (Formula presented) topology is reconstructed using jet reclustering and a new identification algorithm. This search places stringent constraints across regions of the dark Higgs model parameter space that satisfy the observed relic density, excluding dark Higgs bosons with masses between 30 and 150 GeV in benchmark scenarios with (Formula presented) mediator masses up to 4.8 TeV at 95% confidence level. © 2025 CERN, for the ATLAS Collaboration.Ministerio de Ciencia, Innovación y Universidades, MCIU; BSF-NSF; Australian Research Council, ARC; DRAC; La Caixa Banking Foundation; BMWFW; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; Cooperative Research Centres, Australian Government Department of Industry, CRCs; Center for Advancing Research Impact in Society, ARIS; National Science Foundation, NSF; CEA-DRF; Science and Technology Facilities Council, STFC; Horizon 2020, ICSC-NextGenerationEU; H2020 Marie Skłodowska-Curie Actions, MSCA; INFN-CNAF; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Technology, Taiwan, MOST; Israel Science Foundation, ISF; Wallenberg Foundation; Leverhulme Trust; Baden-Württemberg Stiftung, BWS; MVZI; PROMETEO; Neubauer Family Foundation, NFF; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; IDUB AGH; Generalitat de Catalunya; Instituto Nazionale di Fisica Nucleare, INFN; Austrian Science Fund, FWF; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Helmholtz-Gemeinschaft, HGF; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Forskningsrådet för hälsa, arbetsliv och välfärd, FORTE; Karlsruhe Institute of Technology, KIT; Canarie; GridKA; Horizon 2020 Framework Programme, H2020; Göran Gustafssons Stiftelser; European Commission, EC; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; European Cooperation in Science and Technology, COST; EU-ESF; International Council of Shopping Centers, ICSC; RGC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Institutul de Fizică Atomică, IFA; Natural Sciences and Engineering Research Council of Canada, NSERC; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; GenT Programmes Generalitat Valenciana, Spain; National Science and Technology Council, NSTC; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Royal Society; Minerva Foundation; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; Generalitat Valenciana, GVA; CERN; National Research Council Canada, NRC; Brookhaven National Laboratory, BNL; Alexander von Humboldt-Stiftung, AvH; Multiple Sclerosis Scientific Research Foundation, MSSRF; Caring Futures Institute, Flinders University, CFI; British Columbia Knowledge Development Fund, BCKDF; Ministry of Education, Culture, Sports, Science and Technology, MEXT; UK Research and Innovation, UKRI; Australian Education International, Australian Government, AEI; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (PRIN—20223N7F8K—PNRR M4.C2.1.1); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; European Research Council, ERC, (101089007, 948254); European Research Council, ERC; Deutsche Forschungsgemeinschaft, DFG, (DFG—CR 312/5-2, DFG—469666862); Deutsche Forschungsgemeinschaft, DFG; GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027, CIDEGENT/2019/023); Ministerio de Ciencia e Innovación, MCIN, (PID2021-125273NB, RYC2020-030254-I, PCI2022-135018-2, RYC2021-031273-I, RYC2019-028510-I, RYC2022-038164-I); Ministerio de Ciencia e Innovación, MCIN; Narodowe Centrum Nauki, NCN, (UMO-2019/34/E/ST2/00393, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, 2022/47/B/ST2/03059, UMO-2023/49/B/ST2/04085, UMO-2020/37/B/ST2/01043, H2020 MSCA 945339, 2021/42/E/ST2/00350); Narodowe Centrum Nauki, NCN; Ministry of Science and Technology of the People's Republic of China, MOST, (MOST-2023YFA1609300, MOST-2023YFA1605700); Ministry of Science and Technology of the People's Republic of China, MOST; The Slovenian Research and Innovation Agency, ARRS, (J1-3010); The Slovenian Research and Innovation Agency, ARRS; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT, (1230987, 1190886, 1230812); Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT; Carl Tryggers Stiftelse för Vetenskaplig Forskning, (CTS 22∶2312); Carl Tryggers Stiftelse för Vetenskaplig Forskning; Grantová Agentura České Republiky, GAČR, (GACR—24-11373S); Grantová Agentura České Republiky, GAČR; European Regional Development Fund, ERDF, (IDIFEDER/2018/048); European Regional Development Fund, ERDF; H2020 European Research Council, ERC, (ERC—101002463); H2020 European Research Council, ERC; DNSRC, (IN2P3-CNRS); Japan Society for the Promotion of Science, JSPS, (JP21H05085, JP22KK0227, JP22H04944, JP22H01227); Japan Society for the Promotion of Science, JSPS; MUCCA, (CHIST-ERA-19-XAI-00); U.S. Department of Energy, USDOE, (ECA DE-AC02-76SF00515); U.S. Department of Energy, USDOE; Norwegian Financial Mechanism, (2014-2021); Agence Nationale de la Recherche, ANR, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-22-EDIR-0002, ANR-21-CE31-0022); Agence Nationale de la Recherche, ANR; Norges Forskningsråd, (RCN-314472); Norges Forskningsråd; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (RPG-2020-004, NIF-R1-231091, SNSF—PCEFP2_194658); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Knut och Alice Wallenbergs Stiftelse, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Knut och Alice Wallenbergs Stiftelse; Vetenskapsrådet, VR, (VR 2018-00482, 2021-03651, VR 2023-03403, VR 2022-04683, 2023-04654, VR 2022-03845); Vetenskapsrådet, VR; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; National Natural Science Foundation of China, NSFC, (12275265, NSFC—12175119, NSFC-12075060); National Natural Science Foundation of China, NSFC; FAIR-NextGenerationEU, (PE00000013); Narodowa Agencja Wymiany Akademickiej, NAWA, (PPN/PPO/2020/1/00002/U/00001); Narodowa Agencja Wymiany Akademickiej, NAWA; Investissements d’Avenir Labex, (ANR-11-LABX-0012); North Dakota Game and Fish Department, (CC-IN2P3); North Dakota Game and Fish Departmen
Combination of Searches for Singly Produced Vectorlike Top Quarks in Pp Collisions at √sp=13 Tev With the Atlas Detector
No full textLeeuw, Lerothodi/0000-0002-3365-6781; Stanislaus, Beojan/0000-0001-9007-7658; Bortoletto, Daniela/0000-0002-1287-4712; Carbone, Antonio/0000-0002-4117-3800; Nitika, Nitika/0000-0003-0576-3122; Yang, Siqi/0000-0002-0204-984X; Grinstein, Sebastian/0000-0002-6460-8694; Introzzi, Gianluca/0000-0002-1314-2580; D'Uffizi, Matteo/0000-0003-2499-1649; Das, Sruthy Jyothi/0000-0003-2693-3389; Konstantinidis, Nikolaos/0000-0002-4140-6360; Qian, Jianming/0000-0003-4813-8167; Lewicki, Maciej Piotr/0000-0002-8972-3066; Nasri, Salah/0000-0002-5985-4567; Tu, Yanjun/0000-0002-5865-183X; Haas, Andrew/0000-0002-4832-0455; Panwar, Lata/0000-0003-2461-4907; Stupak Iii, John/0000-0001-9610-0783; Romain, Madar/0000-0002-6875-6408; Chapon, Emilien/0000-0001-6968-9828; Saibel, Andrej/0000-0002-9932-7622; Yorita, Kohei/0000-0003-1988-8401; Li, Liang/0000-0001-6411-6107; Alvarez Fernandez, Adrian/0000-0003-1525-4620; Mazzeo, Elena/0000-0002-8406-0195; Gonski, Julia/0000-0003-2037-6315; De Almeida Dias, Flavia/0000-0001-6882-5402; Filthaut, Frank/0000-0003-3338-2247; Brandt, Oleg/0000-0001-5219-1417; Mondal, Santu/0000-0002-6965-7380; Franklin, Melissa/0000-0002-6595-883X; Sciandra, Andrea/0000-0001-7163-501X; Volkotrub, Yuriy/0000-0002-3114-3798; Elsing, Markus/0000-0002-1213-0545; Shabalina, Elizaveta/0000-0003-4849-556X; Lopez Solis, Alvaro/0000-0002-0511-4766; Sato, Koji/0000-0001-8988-4065; Gaudio, Gabriella/0000-0002-6833-0933; Shah, Aashaq/0000-0002-6157-2016; Fox, Harald/0000-0003-3089-6090; Canbay, Ali Can/0000-0003-4602-473X; Zamora-Saa, Jilberto/0000-0002-5030-7516; Affolder, Anthony/0000-0002-9058-7217; Zenz, Seth/0000-0002-9720-1794; Carmignani, Joseph (Joe)/0000-0002-1705-1061; Ragusa, Francesco/0000-0002-4064-0489; Escalier, Marc/0000-0003-4270-2775; Nasella, Laura/0000-0002-4871-784X; Redlinger, George/0000-0002-6437-9991; Deliot, Frederic/0000-0003-0777-6031; Calafiura, Paolo/0000-0002-1692-1678; Farrington, Sinead/0000-0001-5350-9271; Camarda, Stefano/0000-0003-0479-7689; Mckee, Shawn/0000-0002-4551-4502; Burdin, Sergey/0000-0003-4831-4132; De La Torre Perez, Hector/0000-0002-4516-5269; Kretzschmar, Jan/0000-0002-8515-1355; Mitsou, Vasiliki A./0000-0002-1533-8886; Cadamuro, Luca/0000-0001-8789-610X; Gonzalez Suarez, Rebeca/0000-0002-6126-7230; Alderweireldt, Sara/0000-0002-8224-7036; Snyder, Scott/0000-0001-8610-8423; Keaveney, James/0000-0003-0766-5307; Rompotis, Nikolaos/0000-0003-2577-1875; Pianori, Elisabetta/0000-0001-9233-5892; Robson, Aidan/0000-0002-1659-8284; Castro, Nuno/0000-0001-8491-4376; Cepaitis, Vilius/0000-0002-4809-4056; Grivaz, Jean-Francois/0000-0003-4793-7995; Lister, Alison/0000-0002-1552-3651; Aad, Georges/0000-0002-6665-4934; Tishelman-Charny, Abraham/0000-0002-7332-5098; Zhang, Zhicai/0000-0002-1630-0986; Khwaira, Yahya/0000-0001-8538-1647; Ulloa Poblete, Pablo Augusto/0000-0002-0789-7581; Duperrin, Arnaud/0000-0002-5789-9825; Petersen, Troels/0000-0003-0221-3037; Wu, Xin/0000-0001-7655-389X; Dong, Qichen/0000-0002-0117-7831; Gwilliam, Carl/0000-0002-9401-5304; Hopkins, Walter/0000-0001-7814-8740; Thomson, Evelyn/0000-0001-6031-2768; Vormwald, Benedikt/0000-0003-2607-7287; Hays, Chris/0000-0003-2371-9723; D'Auria, Saverio/0000-0003-3393-6318; Escobar Ibanez, Carlos/0000-0003-4442-4537; Cerri, Alessandro/0000-0002-1904-6661; Munoz Sanchez, Francisca/0000-0002-6374-458X; Islam, Wasikul/0000-0002-5624-5934; Kourlitis, Vangelis/0000-0001-6568-2047; Worm, Steven/0000-0002-3865-4996; Leblanc, Matt/0000-0001-5977-6418; Butterworth, Jonathan/0000-0002-5905-5394A combination of searches for the single production of vectorlike top quarks (T) is presented. These analyses are based on proton-proton collisions at root sp = 13 TeV recorded in 2015-2018 with the ATLAS detector at the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb(-1). The T decay modes considered in this combination are into a top quark and either a Standard Model Higgs boson or a Z boson (T -> Ht and T -> Zt). The individual searches used in the combination are differentiated by the number of leptons (e, mu) in the final state. The observed data are found to be in good agreement with the Standard Model background prediction. Interpretations are provided for a range of masses and couplings of the vectorlike top quark for benchmark models and generalized representations in terms of 95% confidence level limits. For a benchmark signal prediction of a vectorlike top quark SU(2) singlet with electroweak coupling, kappa, of 0.5, masses below 2.1 TeV are excluded, resulting in the most restrictive limits to date.We thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [114]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSTDI, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, U.S. Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSCNextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1230812, FONDECYT 1230987, FONDECYT 1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), National Natural Science Foundation of China (NSFC12175119, NSFC 12275265, NSFC-12075060); Czech Republic: Czech Science Foundation (GACR-2411373S), Ministry of Education Youth and Sports (FORTE CZ.02.01. 01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC-101002463); European Union: European Research Council (ERC-948254, ERC 101089007), Horizon 2020 Framework Programme (MUCCA-CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research (FAIRNextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR0002), Investissements d'Avenir Labex (ANR-11LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-469666862, DFG-CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universit`a e della Ricerca (PRIN-20223N7F8K-PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAKENHI JP23KK0245); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020-VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 no 9722), Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, NCN ; H2020 MSCA 945339, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920); Slovenia: Slovenian Research Agency (ARIS grant J1-3010); Spain: Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN ; NextGenEU PCI2022-135018-2, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/027); Sweden: Carl Trygger Foundation (Carl Trygger Foundation CTS 22:2312), Swedish Research Council (Swedish Research Council 2023-04654, VR2018-00482, VR2022-03845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF -PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIFR1-231091); U.S.: U.S. Department of Energy (ECA DEAC02-76SF00515), Neubauer Family Foundation.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA) [114]; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF; Benoziyo Center, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSSR, Slovakia; SRC; Wallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; STFC/UKRI, United Kingdom; DOE; NSF; BCKDF; CANARIE; DRAC, Canada; FORTE; PRIMUS, Czech Republic; ERC; Marie Sklodowska-Curie Actions; European Union; Investissements d'Avenir Labex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN [UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920]; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO [CIDEGENT/2019/027]; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society [NIFR1-231091]; Leverhulme Trust; Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research; Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1230812]; FONDECYT [1240864]; China: Chinese Ministry of Science and Technology [MOST-2023YFA1605700]; National Natural Science Foundation of China [NSFC12175119]; NSFC [12275265, NSFC-12075060]; Czech Republic: Czech Science Foundation [GACR-2411373S]; Ministry of Education Youth and Sports [FORTE CZ.02.01.01/00/22_008/0004632]; PRIMUS Research Programme [PRIMUS/21/SCI/017]; EU [ERC-101002463]; European Union: European Research Council [ERC-948254, ERC 101089007, MUCCA-CHIST-ERA-19-XAI-00]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR0002]; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-469666862, DFG-CR 312/5-2]; Ministero dell'Universit; Japan Society for the Promotion of Science (JSPS KAKENHI) [JP22H01227, JP22H04944, JP22KK0227, JP23KK0245]; Netherlands Organisation for Scientific Research (NWO Veni) [2020-VI]; Norway: Research Council of Norway [RCN-314472]; Ministry of Science and Higher Education [9722]; Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Polish National Science Centre (NCN) [2021/42/E/ST2/00350]; NCN OPUS [2022/47/B/ST2/03059]; Slovenian Research Agency [J1-3010]; Spain: Generalitat Valenciana; FEDER [IDIFEDER/2018/048]; Ministry of Science and Innovation [NextGenEU PCI2022-135018-2]; MICIN FEDER [PID2021-125273NB, RYC2019-028510-I, RYC2021-031273-I, RYC2022-038164-I]; Carl Trygger Foundation; Swedish Research Council (Swedish Research Council) [2023-04654, VR2018-00482, VR2022-03845, VR 2022-04683, VR 2023-03403]; VR [2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, SNSF -PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; U.S.: U.S. Department of Energy [ECA DEAC02-76SF00515]; Neubauer Family Foundatio
Constraint on the Total Width of the Higgs Boson From Higgs Boson and Four-Top Measurements in Pp Collisions at √s=13 Tev With the Atlas Detector
No full textMckee, Shawn/0000-0002-4551-4502; Filthaut, Frank/0000-0003-3338-2247; Alvarez Fernandez, Adrian/0000-0003-1525-4620; Keaveney, James/0000-0003-0766-5307; Franklin, Melissa/0000-0002-6595-883X; Chapon, Emilien/0000-0001-6968-9828; Yang, Siqi/0000-0002-0204-984X; Alderweireldt, Sara/0000-0002-8224-7036; Snyder, Scott/0000-0001-8610-8423; Kourlitis, Vangelis/0000-0001-6568-2047; Bortoletto, Daniela/0000-0002-1287-4712; Wu, Xin/0000-0001-7655-389X; Dong, Qichen/0000-0002-0117-7831; Burdin, Sergey/0000-0003-4831-4132; Mondal, Santu/0000-0002-6965-7380; Cepaitis, Vilius/0000-0002-4809-4056; Worm, Steven/0000-0002-3865-4996; Zhang, Zhicai/0000-0002-1630-0986; Sato, Koji/0000-0001-8988-4065; Leblanc, Matt/0000-0001-5977-6418; Khwaira, Yahya/0000-0001-8538-1647; Escobar Ibanez, Carlos/0000-0003-4442-4537; Zenz, Seth/0000-0002-9720-1794; Cadamuro, Luca/0000-0001-8789-610X; Shah, Aashaq/0000-0002-6157-2016; Haley, Joseph/0000-0002-6938-7405; Cerri, Alessandro/0000-0002-1904-6661; Gonski, Julia/0000-0003-2037-6315; Mcgovern, Robert/0000-0001-9139-6896; Yorita, Kohei/0000-0003-1988-8401; Sciandra, Andrea/0000-0001-7163-501X; Robson, Aidan/0000-0002-1659-8284; Shabalina, Elizaveta/0000-0003-4849-556X; Camarda, Stefano/0000-0003-0479-7689; Saibel, Andrej/0000-0002-9932-7622; Panwar, Lata/0000-0003-2461-4907; Vormwald, Benedikt/0000-0003-2607-7287; Islam, Wasikul/0000-0002-5624-5934; Munoz Sanchez, Francisca/0000-0002-6374-458X; Tishelman-Charny, Abraham/0000-0002-7332-5098; De Almeida Dias, Flavia/0000-0001-6882-5402; Stupak Iii, John/0000-0001-9610-0783; Haas, Andrew/0000-0002-4832-0455; Pianori, Elisabetta/0000-0001-9233-5892; Li, Liang/0000-0001-6411-6107; Lister, Alison/0000-0002-1552-3651; Qian, Jianming/0000-0003-4813-8167; Thomson, Evelyn/0000-0001-6031-2768; Gonzalez Suarez, Rebeca/0000-0002-6126-7230; Grivaz, Jean-Francois/0000-0003-4793-7995; Hays, Chris/0000-0003-2371-9723; Deliot, Frederic/0000-0003-0777-6031; Farrington, Sinead/0000-0001-5350-9271; Fox, Harald/0000-0003-3089-6090; Brandt, Oleg/0000-0001-5219-1417; Romain, Madar/0000-0002-6875-6408; Hopkins, Walter/0000-0001-7814-8740; Lopez Solis, Alvaro/0000-0002-0511-4766; Duperrin, Arnaud/0000-0002-5789-9825; Grinstein, Sebastian/0000-0002-6460-8694; Tu, Yanjun/0000-0002-5865-183X; Introzzi, Gianluca/0000-0002-1314-2580This Letter presents a constraint on the total width of the Higgs boson (Gamma(H)) using a combined measurement of on-shell Higgs boson production and the production of four top quarks, which involves contributions from off-shell Higgs boson-mediated processes. This method relies on the assumption that the tree-level Higgs-top Yukawa coupling strength is the same for on-shell and off-shell Higgs boson production processes, thereby avoiding any assumptions about the relationship between on-shell and off-shell gluon fusion Higgs production rates, which were central to previous measurements. The result is based on up to 140 fb(-1) of proton-proton collisions at a centre-of-mass energy of root s = 13 TeV collected with the ATLAS detector at the Large Hadron Collider. The observed (expected) 95% confidence level upper limit on Gamma(H) is 450 MeV (75 MeV). Additionally, considering the constraint on the Higgs-top Yukawa coupling from loop-induced Higgs boson production and decay processes further yields an observed (expected) upper limit of 160 MeV (55 MeV).We gratefully acknowledge the support of ANPCyT, Argentina; Yer-PhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo CNRC, Canada enter, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSTDI, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, United States of America. Individual groups and members have received support from BCKDF, Canarie, CRC and DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes c-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelser, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from Armenia: Yerevan Physics Institute (FAPERJ); CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1230812, FONDECYT 1230987, FONDECYT 1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700, MOST-2023YFA1609300), National Natural Science Foundation of China (NSFC - 12175119, NSFC 12275265, NSFC-12075060); Czech Republic: Czech Science Foundation (GACR - 24-11373S), Ministry of Education Youth and Sports (FORTE CZ.02.01.01/00/22_008/0004632), PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC - 101002463); European Union: European Research Council (ERC-948254, ERC 101089007), Horizon 2020 Framework Programme (MUCCA-CHIST-ERA-19-XAI-00), European Union, Future Artficial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21CE310022, ANR-22-EDIR-0002), Investissements d'Avenir Labex (ANR11LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW StiftungPostdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG 469666862, DFG -CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca (PRIN -20223N7F8K -PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAKENHI JP23KK0245); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020 -VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Ministry of Science and Higher Education (IDUB AGH, POB8, D4 no 9722), Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, NCN ; H2020 MSCA 945339, UMO-2020/37/B/ST2/01043, UMO2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920); Slovenia: Slovenian Research Agency (ARIS grant J1-3010); Spain: Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN ; NextGenEU PCI2022-135018-2, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021031273I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/027); Sweden: Carl Trygger Foundation (Carl Trygger Foundation CTS 22:2312), Swedish Research Council (Swedish Research Council 2023-04654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and Alice Wallenberg Foundation (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF -PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIF-R1-231091); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; BMBF, Germany; MPG, Germany; Hong Kong SAR, China; ISF, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; MICINN, Spain; Wallenberg Foundation, Sweden; SERI, Switzerland; MOST, Taiwan; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; Czech Republic [PRIMUS 21/SCI/017, UNCE SCI/013]; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Investissements d'Avenir Idex , France; ANR, France; DFG , Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF, Greece; Thales programme - EU-ESF, Greece; Aristeia programme - EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN, Poland; NAWA, Poland; La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC, United Kingdom; TENMAK, Turkiye; Canton of Geneva, Switzerland; Canton of Bern, Switzerland; SNSF, Switzerland; SRC, Sweden; DSI/NRF, South Africa; NWO, Netherlands; Benoziyo Center, Israel; RGC, China; GSRI, Greece; HGF, Germany; SRNSFG, Georgia; Minciencias, Colombia; MOST, China; CAS, China; ANID, Chile; CERN; NRC, Canada; CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo [FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987]; China: National Natural Science Foundation of China [NSFC - 12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; European Union: European Research Council [ERC - 948254]; European Union: Horizon 2020 Framework Programme [MUCCA - CHIST-ERA-19-XAI-00]; European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU) [PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); Marie Sklodowska-Curie Actions (EU H2020 MSC IF GRANT) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; France: Investissements d'Avenir Idex [ANR-11-LABX-0012]; France: Investissements d'Avenir Labex [ANR-11-LABX-0012]; Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme); Germany: Deutsche Forschungsgemeinschaft [DFG - 469666862, DFG - CR 312/5-1]; Italy: Istituto Nazionale di Fisica Nucleare (FELLINI) [754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [22KK0227, JP21H05085, JP22H01227, JP22H04944]; Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020) [VI.Veni.202.179]; Norway: Research Council of Norway [RCN-314472]; Poland: Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Poland: Polish National Science Centre [NCN 2021/42/E/ST2/00350, 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; Slovenia: Slovenian Research Agency (ARIS grant) [J1-3010]; Spain: BBVA Foundation [LEO22-1-603]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; La Caixa Banking Foundation [LCF/BQ/PI20/11760025]; Ministry of Science and Innovation (MCIN NextGenEU) [PCI2022-135018-2]; Ministry of Science and Innovation (MICIN FEDER) [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; PROMETEO Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; GenT Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; Sweden: Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-0468, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447]; Switzerland: Swiss National Science Foundation [SNSF - PCEFP2_194658]; United Kingdom: Leverhulme Trust [RPG-2020-004]; United States of America: Neubauer Family Foundatio