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Search for a Light Charged Higgs Boson in T → H±b Decays, With H±→ Cs, in Pp Collisions at √s=13 Tev With the Atlas Detector
No full textCalafiura, Paolo/0000-0002-1692-1678; /0000-0001-5765-1750; Gwilliam, Carl/0000-0002-9401-5304; Haley, Joseph/0000-0002-6938-7405; Stanislaus, Beojan/0000-0001-9007-7658A search for a light charged Higgs boson produced in decays of the top quark, t -> H(+/-)b with H-+/- -> cs, is presented. This search targets the production of top-quark pairs t (t) over bar. WbH(+/-)b, with W -> lv (l = e, mu), resulting in a lepton-plus-jets final state characterised by an isolated electron or muon and at least four jets. The search exploits b-quark and c-quark identification techniques as well as multivariate methods to suppress the dominant t (t) over bar background. The data analysed correspond to 140 fb(-1) of pp collisions at root s = 13 TeV recorded with the ATLAS detector at the LHC between 2015 and 2018. Observed (expected) 95% confidence-level upper limits on the branching fraction B(t -> H(+/-)b), assuming B(t -> Wb) + B(t -> H +/-(-> cs)b) = 1.0, are set between 0.066% (0.077%) and 3.6% (2.3%) for a charged Higgs boson with a mass between 60 and 168 GeV.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. [127]. We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFWandFWF, 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; MEXTand 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 andWallenberg 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 co-financed by EU-ESF and the Greek NSRF, Greece; BSFNSF 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 andTechnology(MOST2023YFA1605700), 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-XAI00), 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-CE310013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-00 02), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-469666862, DFGCR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca (PRIN20223N7F8K-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 and 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 and NextGenEU PCI2022-135018-2, MICIN and FEDERPID2021-125273NB, RYC2019-028510-I, RYC2020-030254I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/027); Sweden: Swedish Research Council (Swedish Research Council 202304654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 202303403, 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 (SNSFPCEFP2_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.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA) [127]; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFWandFWF, 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 and Benoziyo Center, Israel; INFN, Italy; MEXTand JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSSR, Slovakia; SRC andWallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; NSTC, Taipei; DOE; NSF, United States of America; BCKDF; CANARIE; CRC; DRAC, Canada; FORTE; PRIMUS, Czech Republic; ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSFNSF; NCN [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]; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO [CIDEGENT/2019/027]; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society [NIF-R1-231091]; Leverhulme Trust, United Kingdom; 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 andTechnology [MOST2023YFA1605700]; 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 [ERC -101002463]; European Union: European Research Council [ERC-948254, ERC 101089007, CHIST-ERA-19-XAI00]; European Union; France: Agence Nationale de la Recherche [ANR-20-CE310013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-00 02]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-469666862]; Ministero dell'Universita e della Ricerca; 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 (MCIN) [NextGenEU PCI2022-135018-2]; MICIN [FEDERPID2021-125273NB, RYC2019-028510-I, RYC2020-030254I, RYC2021-031273-I, RYC2022-038164-I]; Swedish Research Council (Swedish Research Council) [202304654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 202303403, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2018.0157, KAW 2018.0458, KAW 2019.0447]; Swiss National Science Foundation [SNSFPCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; United States of America [ECA DE-AC02-76SF00515]; Neubauer Family Foundatio
Search for Light Neutral Particles Decaying Promptly into Collimated Pairs of Electrons or Muons in pp Collisions at √s=13 TeV with the ATLAS Detector
No full textRocchi, Alessandro/0000-0002-3125-8333; Lanza, Agostino/0000-0003-4980-6032; Carmignani, Joseph (Joe)/0000-0002-1705-1061;A search for a dark photon, a new light neutral particle, which decays promptly into collimated pairs of electrons or muons is presented. The search targets dark photons resulting from the exotic decay of the Standard Model Higgs boson, assuming its production via the dominant gluon-gluon fusion mode. The analysis is based on 140fb(-1) of data collected with the ATLAS detector at the Large Hadron Collider from proton-proton collisions at a center-of-mass energy of 13 TeV. Events with collimated pairs of electrons or muons are analysed and background contributions are estimated using data-driven techniques. No significant excess in the data above the Standard Model background is observed. Upper limits are set at 95% confidence level on the branching ratio of the Higgs boson decay into dark photons between 0.001% and 5%, depending on the assumed dark photon mass and signal model.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 TNWO, Netherlandsier-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), theTier-2 facilities worldwide and largenon-WLCG resource providers. Major contributors of computing resources are listed in Ref. [112]. 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 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, 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 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, 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 Artificial 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-21-CE31-0022, ANR-22-EDIR-0002), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung(BWStiftung-Postdoc 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 ; H2020MSCA945339, 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: GeneralitatValenciana (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, RYC2021-031273I, RYC2022-038164-I), PROMETEO andGenT Programmes Generalitat Valenciana (CIDEGENT/2019/027); Sweden: Carl Trygger Foundation (Carl Trygger Foundation CTS 22:2312), Swedish Research Council (SwedishResearch Council 2023-04654, VR2018-00482, VR202203845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and AliceWallenberg Foundation (KAW2018.0157, KAW2018.0458, 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.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands); BNL (USA) [112]; ANPCyT, Argentina; 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; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA, Romania; MSSR, Slovakia; SRC; Wallenberg Foundation, Sweden; SNSF; NSTC, Taipei; STFC/UKRI, United Kingdom; DOE; NSF, United States of America; BCKDF; CANARIE; CRC; DRAC, Canada; FORTE; PRIMUS, Czech Republic; ERC; ERDF; Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society [NIF-R1-231091, ECA DE-AC02-76SF00515]; Leverhulme Trust, United Kingdom; 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, MOST-2023YFA1609300]; National Natural Science Foundation of China [NSFC -12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: Czech Science Foundation; 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]; European Union [FAIR-NextGenerationEU PE00000013]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG - 469666862, DFG - CR 312/5-2]; Ministero dell'Universita e della Ricerca; Japan Society for the Promotion of Science (JSPS KAKENHI) [JP22H01227, JP22H04944, JP22KK0227, RCN-314472]; 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, H2020MSCA945339, 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]; Slovenian Research Agency [J1-3010]; FEDER [IDIFEDER/2018/048, NextGenEU PCI2022-135018-2]; MICIN [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273I, RYC2022-038164-I, CIDEGENT/2019/027]; Swedish Research Council (SwedishResearch Council) [2023-04654, VR2018-00482, VR202203845, VR 2022-04683, VR 2023-03403, 2021-03651]; AliceWallenberg Foundation [KAW2018.0157, KAW2018.0458, KAW 2019.0447, SNSF -PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; Neubauer Family Foundation; FEDER; Canton of Geneva, Switzerland; Canton of Bern, Switzerland; v; NWO, Netherlands; PIC (Spain
Fair Cost Allocation for Collaborative Hub Networks
No full textCollaboration in logistics is an effective tool not only for cost savings but also for reducing the carbon footprint. Hub networks take advantage of scale economies by bundling flows. Merging hub networks through horizontal collaboration unlocks further economic and environmental advantages. We consider the problem of designing a collaborative hub network as a cooperative game and show that the core of the game might be empty, meaning that an efficient and stable cost allocation does not exist. Our key novelty lies in formulating this design problem as a cooperative game and demonstrating the potential absence of a fair and stable solution. Various game theoretical approaches are used for the allocation of joint costs due to the collaboration. Each approach is also tested through extensive numerical experiments to gain insight into the features and behavior of the corresponding cost allocation game. These experiments are conducted on both randomly generated and also real-world hub location instances. Achieving a stable and also fair cost allocation among collaborators is critical for the future of the organization. Finally, we compare the performance of the nucleolus, the Shapley value and the least core cost allocation methods based on different fairness measures such as relative savings, stability concepts and coalition satisfaction. This work ultimately paves the way for more efficient and sustainable logistics operations by measuring the value of collaboration in hub network design and minimizing the operating costs and also environmental footprint of the logistics industry. © The Author(s) 2025.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITA
Ultra-Miniaturized Bloch Mode Metasplitters for One-Dimensional Grating Waveguides
No full textSakin, Ahmet Oguz/0009-0009-0787-4644; Unlu, Mehmet/0000-0002-6594-0904We present, for the first time, to our knowledge, power splitters with multiple channel configurations in one-dimensional grating waveguides (1DGWs) that maintain crystal lattice- sensitive Bloch mode profiles without perturbation across all output channels, all within an ultra-miniaturized footprint of just 2.1 x 2.2 mu m2. This novel capability reduces the need for transition regions, simplifies multi-channel configurations of 1DGWs, and maximizes the effective use of chip area. The pixelated metamaterial approach, integrated with a time-domain heuristic algorithm, is utilized to concurrently achieve broadband operation, optimized dispersion control, and minimal loss. We experimentally demonstrate that the 1 x 2 and 1 x 3 metasplitters achieve average minimum losses per channel of 3.80 dB and 5.36 dB, respectively, which are just 0.80 dB and 0.59 dB above ideal splitting. The measurements for both designs demonstrate a 1 dB bandwidth of 15 nm, with excellent uniformity across all output channels. These versatile metasplitter designs can serve as fundamental building blocks for ultrahigh-bandwidth, densely integrated photonic circuits and in scenarios where slow light is essential. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.Funding. Turkiye Bilimsel ve Teknolojik Ara ; scedil;t ; imath;rma Kurumu (122E566, BIDEB 2210A) .Turkiye Bilimsel ve Teknolojik Arascedil;timath;rma Kurumu [122E566, BIDEB 2210A
Two Modified Forms of the SAIR Model With a Fuzzyfied Vaccination Effectiveness Parameter
No full textIn 2019, the emergence of COVID-19 underscored the critical role of mathematical modeling in understanding and forecasting global health crises. The rapid and often unnoticed spread of infectious diseases by asymptomatic carriers poses a significant challenge to public health efforts worldwide. Understanding and accurately modeling this transmission is crucial for developing effective vaccination strategies and controlling outbreaks. We address this critical issue by enhancing the SAIR model, a Susceptible-Asymptomatic-Infected-Recovered compartmental model, to better capture the dynamics of asymptomatic spread and vaccination effectiveness. This study focuses on the SAIR models to investigate the dynamics of COVID-19 transmission, with a particular emphasis on asymptomatic individuals, who can unknowingly transmit the disease. In this paper, we present two modifications to the SAIR model. The first modification assumes that individuals gain lifelong immunity after recovering from the infection. The second modification, known as the SAIRS model, considers the possibility of reinfection, meaning recovered individuals can become susceptible again. By applying these enhanced models to real-world data on daily reported COVID-19 cases in T ; uuml;rkiye, we aim to gain a deeper understanding of the pandemic's behavior and progression in the country. The novelty of this work lies in the integration of a vaccine effectiveness parameter into the SAIR model, uniquely considering the delayed immunity of vaccinated individuals and the distinct transmission dynamics of both symptomatic and asymptomatic cases. Analyzing this parameter within a fuzzy environment enhances the accuracy of predictions, providing more dependable estimations of future disease scenarios. This approach offers a new dimension to epidemic modeling, contributing valuable insights to public health strategies and vaccination policies
A Machine Learning Approach for Predicting Familial and Sporadic Disease Cases Based on Clinical Symptoms: Introduction of a New Dataset
No full textAmaç: Nörofibromatozis tip 1 (NF1), hem genetik hem de çevresel faktörlerden etkilenen, oldukça değişken bir klinik sunumla karakterize, yaygın ancak karmaşık bir nörogenetik bozukluktur. Genetik temeli iyi anlaşılmış olsa da, hastalar arasındaki semptomların değişkenliği tanı ve yönetim için önemli zorluklar ortaya koymaktadır. Bu çalışma, sporadik ve ailesel NF1 vakaları arasındaki klinik özelliklerdeki farklılıkları incelemeyi amaçlamıştır. Ayrıca, makine öğrenimi tekniklerinin klinik semptomlara dayalı olarak sporadik NF1 vakalarını tahmin etme potansiyelini değerlendirerek, hesaplamalı yaklaşımların geleneksel tanı yöntemlerini nasıl tamamlayabileceğine dair içgörüler sunulmuştur. Yöntem: 121 sporadik ve 120 ailesel vaka dahil olmak üzere 241 NF1 hastasının tıbbi kayıtları üzerinde retrospektif bir analiz yapılmıştır. Lisch nodülleri, psödoartroz ve hipertansiyon gibi çeşitli klinik özelliklerin sıklığı gruplar arasında karşılaştırılmıştır. Sporadik vakaları ailesel olanlardan ayıran en önemli özellikleri belirlemek için varyans analizi (ANOVA) kullanılmıştır. Ayrıca, belirlenen özelliklere dayanarak sporadik vakaları tahmin etmek için k-en yakın komşular, yapay sinir ağları, destek vektör makineleri, karar ağaçları ve XGBoost dahil olmak üzere çoklu makine öğrenimi algoritmaları kullanılmıştır. Bulgular: Test edilen makine öğrenimi modelleri arasında XGBoost algoritması %62,86 ile en yüksek tahmin doğruluğunu göstermiş ve sporadik vakaların belirlenmesinde orta düzeyde güvenilirliğe işaret etmiştir. Bu sınırlamaya rağmen, analiz iki grup arasında klinik belirtiler açısından önemli farklılıklar olduğunu ortaya koymuştur. Bu farklılıklar, paylaşılan genetik değiştiricilerin NF1’de gözlenen genotip-fenotip ilişkisini şekillendirmede kritik bir rol oynayabileceğini düşündürmektedir. Sonuç: Bu çalışma, sporadik ve ailesel NF1 vakaları arasında geniş bir klinik semptom spektrumunun ilk ayrıntılı karşılaştırmasını temsil etmektedir. Makine öğrenimi modelleri tahminde yalnızca orta düzeyde başarı gösterirken, bulgular NF1’in fenotipik değişkenliği hakkında değerli bilgiler sağlamakta ve tahmin doğruluğunu artırmak için daha büyük, daha çeşitli veri kümelerinin öneminin altını çizmektedir. Bu sonuçlar, NF1 hastaları için kişiselleştirilmiş tanı ve tedavi stratejilerine rehberlik etme konusunda önemli bir potansiyele sahiptir
Bifurcation Analysis, Chaos Control, and Fast Approach for the Complex Dynamics of a Discrete-Time Predator-Prey System With a Weak Allee Effect
No full textIn this paper, we analyze the complex dynamics of a discrete-time, Leslie-type predator-prey system that exhibits a weak Allee effect where the prey population has a mate-finding Allee effect. This discrete mathematical model has been obtained by applying the forward Euler scheme to its continuous-time counterpart. First, stability and bifurcation analyses are performed to explore the stability of positive equilibrium points and to identify critical points where dynamic behavioral transitions occur. Then, center manifold theory and normal form theory are used to classify bifurcations, including Flip bifurcation and Neimark-Sacker bifurcation, revealing the emergence of chaos and periodic orbits in the system by choosing integral step size as a bifurcation parameter. Specifically, numerical examples are presented to illustrate and support the theoretical results, thereby demonstrating the practical application of the theory. Next, we implement chaos control strategies, namely the feedback control method, to control chaotic dynamics. In addition, we apply the FAST method to improve the efficiency of our numerical simulations, allowing a more detailed exploration of the parameter space. Our results contribute to a better understanding of ecological interactions under the influence of the Allee effect and provide valuable insights for biodiversity management in predator-prey systems, especially for endangered populations
Healthcare Users’ Experience With E-Health: Benefits, Drawbacks, and the Future
No full textTurkey has undergone significant structural changes in its healthcare system through the implementation of the Health Transformation Programme (H.T.P.) since 2003. This initiative aimed at enhancing accessibility to health services for all citizens, with universal health coverage (U.H.C.) being a cornerstone. However, despite these efforts, disparities exist in the distribution of healthcare infrastructure and professionals throughout the country, which impede the effectiveness of U.H.C. alone in improving accessibility. One pivotal aspect of the H.T.P. is the integration of e-health applications, playing a crucial role in supporting U.H.C. objectives. E-health, or electronic health, refers to the use of information technologies in the health sector aiming at ensuring the effective and efficient delivery of health services, providing rapid access for citizens, and sustaining data sharing with relevant stakeholders. To fully harness the benefits of e-health, it is essential not only for healthcare professionals but also for users to be knowledgeable about these applications and have access to them. Consequently, this study aims to assess the awareness and usage of e-health applications among healthcare users in Turkey. The study has realized that awareness and utilization of e-health applications vary among users from diverse demographic and socio-economic backgrounds. © 2025 selection and editorial matter, Dilek Başar and Selcen Öztürk
Spatial Frequencies and Degrees of Freedom: Their Roles in Near-Field Communications
No full textAs wireless technology begins to utilize physically larger arrays and/or higher frequencies, the transmitter and receiver will reside in each other's radiative near field. This fact gives rise to unusual propagation phenomena, such as spherical wavefronts and beam focusing, creating the impression that new spatial dimensions - called degrees of freedom (DOF) - can be exploited in the near field. However, this is a fallacy because the theoretically maximum DOF are already achievable in the far field. This article sheds light on these issues by providing a tutorial on spatial frequencies, which are the fundamental components of wireless channels, and by explaining their role in characterizing the DOF in the near and far fields. In particular, we demonstrate how a single propagation path utilizes one spatial frequency in the far field and an interval of spatial frequencies in the near field. We explain how the array geometry determines the number of distinguishable spatial frequency bins and, thereby, the spatial DOF. We also describe how to model near-field multipath channels and their spatial correlation matrices. Finally, we discuss the research challenges and future directions in this field. © 1991-2012 IEEE.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Vetenskapsrådet, VR; Stiftelsen för Strategisk Forskning, SSF, (2022-04222); Stiftelsen för Strategisk Forskning, SS
