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Search for Heavy Majorana Neutrinos in i>e/I>sup>± and i>e/I>sup>± Final States Via Ww Scattering in i>pp/I> Collisions at √i>s/I>=13 Tev With the Atlas Detector
No full textSala, Alessandro/0000-0003-0824-7326; Mazzeo, Elena/0000-0002-8406-0195; Carbone, Antonio/0000-0002-4117-3800; Ragusa, Francesco/0000-0002-4064-0489; D'Auria, Saverio/0000-0003-3393-6318; Nasella, Laura/0000-0002-4871-784X; Stanislaus, Beojan/0000-0001-9007-7658A search for heavy Majorana neutrinos in scattering of same-sign.. boson pairs in proton-proton collisions at root s = 13 TeV at the LHC is reported. The dataset used corresponds to an integrated luminosity of 140 fb(-1), collected with the ATLAS detector during 2015-2018. The search is performed in final states including a same-sign ee or e mu pair and at least two jets with large invariant mass and a large rapidity difference. No significant excess of events with respect to the Standard Model background predictions is observed. The results are interpreted in a benchmark scenario of the Phenomenological Type-I Seesaw model. New constraints are set on the values of the vertical bar V-eN vertical bar(2) and vertical bar VeNV mu N*vertical bar parameters for heavy Majorana neutrino masses between 50 GeV and 20 TeV, where V-eN is the matrix element describing the mixing of the heavy Majorana neutrino mass eigenstate with the Standard Model neutrino of flavour l = e, mu. The sensitivity to the Weinberg operator is investigated and constraints on the effective ee and e mu Majorana neutrino masses are reported. The statistical combination of the ee and e mu channels with the previously published mu mu channel is performed.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; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZS, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; TENMAK, Turkiye; STFC, 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, PRIMUS 21/SCI/017, 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; Norwegian Financial Mechanism 2014-2021, Norway; 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 CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), 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); 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-CE310013, ANR-21-CE31-0022), Investissements d'Avenir Labex (ANR-11LABX-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); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020 -VI.Veni.202.179); Norway: Research Council of Norway (RCN314472); Poland: 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, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085); 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, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (Swedish Research Council 2023-04654, VR 201800482, 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
Interplay Between Sensing and Communication in Cell-Free Massive Mimo With Urllc Users
No full text25th IEEE Wireless Communications and Networking Conference, WCNC 2024 -- 21 April 2024 through 24 April 2024 -- Dubai -- 200793This paper studies integrated sensing and communication (ISAC) in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with multi-static sensing and ultra-reliable low-latency communication (URLLC) users. We propose a successive convex approximation-based power allocation algorithm that maximizes energy efficiency while satisfying the sensing and URLLC requirements. In addition, we provide a new definition for network availability, which accounts for both sensing and URLLC requirements. The impact of blocklength, sensing requirement, and required reliability as a function of decoding error probability on network availability and energy ef-ficiency is investigated. The proposed power allocation algorithm is compared to a communication-centric approach where only the URLLC requirement is considered. It is shown that the URLLC-only approach is incapable of meeting sensing requirements, while the proposed ISAC algorithm fulfills both sensing and URLLC requirements, albeit with an associated increase in energy consumption. This increment can be reduced up to 75% by utilizing additional symbols for sensing. It is also demonstrated that larger blocklengths enhance network availability and offer greater robustness against stringent reliability requirements. © 2024 IEEE.VINNOVA; EU Horizon 2020 and Vinnova; Swedish Innovation Agency; ECSEL, (876124, RAI-6Green, 2020- 1.2.3-EUREKA-2021-000006
Transformation of Fly Ash-Based Oxide Particles Into a Functional Silica-Alumina Aerogel and Its Potential Application as an Anti-Icing Surface
No full textLightweight, surface hydrophobic, highly insulating, and long-lasting aerogels are required for energy conservation and ice-repellent applications. Here, we present the conversion of fly ash to a silica-alumina aerogel (SAA) by utilizing its high silica content. The extracted silica component replaces expensive precursors typically used in conventional aerogel production. Ice adhesion performance was compared to that of polypropylene (PP), an insulating commodity polymer. First, we removed some salt impurities and heavy metals via water and alkaline washing protocols. Then, we produced SAA via the ambient pressure drying method by using trimethylchlorosilane (TMCS) as an adhesion promoter. The newly produced SAA has a surface area of 810 m(2) g(-1) and shows hydrophobic properties with a contact angle of 140 +/- 5 degrees. The thermal conductivity of SAA is 0.0238 W m(-1) K-1 with C-P = 1.1922 MJ m(-3) K-1. The ice adhesion strength of the PP substrate was calculated as 188.30 +/- 51.24 kPa, while the ice adhesion strength of the SAA was measured as 1.21 +/- 0.40 kPa, which was about 150 times lower than that of PP. This indicated that SAA had icephobic properties since ice adhesion strength was less than 10 kPa. This study demonstrates that fly ash-based SAA can be utilized as an economical material with a large surface area and exceptional thermal insulation capacity and is free of harmful compounds (heavy metals), making it potentially suitable as an anti-ice thermal insulation material.TUBITAK TEYDEB [5220156]; COST Action - COST (European Cooperation in Science and Technology) [CA20126]H.D. and S.C. gratefully acknowledge TUBITAK TEYDEB for the financial support of project 5220156 and COST Action CA20126 Network on Porous Semiconductors and Oxides supported by COST (European Cooperation in Science and Technology)
Energy-Efficient Cell-Free Massive MIMO with Wireless Fronthaul
No full textCell-free massive MIMO improves the fairness among the user equipments (UEs) in the network by distributing many cooperating access points (APs) around the region while connecting them to a centralized cloud-computing unit that coordinates joint transmission/reception. However, the fiber cable deployment for the fronthaul transport network and activating all available antennas at each AP lead to increased deployment cost and power consumption for fronthaul signaling and processing. To overcome these challenges, in this work, we consider wireless fronthaul connections and propose a joint antenna activation and power allocation algorithm to minimize the end-to-end (from radio to cloud) power while satisfying the quality-of-service requirements of the UEs under wireless fronthaul capacity limitations. The results demonstrate that the proposed methodology of deactivating antennas at each AP reduces the power consumption by 50% and 84% compared to the benchmarks based on shutting down APs and minimizing only the transmit power, respectively.Celtic-Next project RAI-6Green - Swedish funding agency Vinnova; 2232-B International Fellowship for Early Stage Researchers Programme - Scientific and Technological Research Council of Turkiye; SSF [FFL18-0277]This work has been funded by Celtic-Next project RAI-6Green partly supported by Swedish funding agency Vinnova. The work by 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. E. Bjornson was supported by the FFL18-0277 grant from SSF
Azimuthal Angle Correlations of Muons Produced Via Heavy-Flavor Decays in 5.02 Tev Pb+pb and Pp Collisions With the Atlas Detector
No full textAngular correlations between heavy quarks provide a unique probe of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions. Results are presented of a measurement of the azimuthal angle correlations between muons originating from semileptonic decays of heavy quarks produced in 5.02 TeV Formula Presented and Formula Presented collisions at the LHC. The muons are measured with transverse momenta and pseudorapidities satisfying Formula Presented and Formula Presented, respectively. The distributions of azimuthal angle separation Formula Presented for muon pairs having pseudorapidity separation Formula Presented, are measured in different Formula Presented centrality intervals and compared to the same distribution measured in Formula Presented collisions at the same center-of-mass energy. Results are presented separately for muon pairs with opposite-sign charges, same-sign charges, and all pairs. A clear peak is observed in all Formula Presented distributions at Formula Presented, consistent with the parent heavy-quark pairs being produced via hard-scattering processes. The widths of that peak, characterized using Cauchy-Lorentz fits to the Formula Presented distributions, are found to not vary significantly as a function of Formula Presented collision centrality and are similar for Formula Presented and Formula Presented collisions. This observation will provide important constraints on theoretical descriptions of heavy-quark interactions with the quark-gluon plasma. © 2024 CERN, for the ATLAS Collaboration.Australian Research Council, ARC; Centre National pour la Recherche Scientifique et Technique, CNRST; Fundação para a Ciência e a Tecnologia, FCT; Cooperative Research Centres, Australian Government Department of Industry, CRCs; National Stroke Foundation, NSF; Science and Technology Facilities Council, STFC; H2020 Marie Skłodowska-Curie Actions, MSCA; HORIZON EUROPE Marie Sklodowska-Curie Actions, MSCA; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministry of Science and Technology, Taiwan, MOST; Israel Science Foundation, ISF; Leverhulme Trust; Baden-Württemberg Stiftung, BWS; Neubauer Family Foundation, NFF; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Generalitat de Catalunya; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; 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; Karlsruhe Institute of Technology, KIT; Canarie; Göran Gustafssons Stiftelser; European Commission, EC; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; U.S. Department of Energy, USDOE; European Cooperation in Science and Technology, COST; International Council of Shopping Centers, ICSC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Natural Sciences and Engineering Research Council of Canada, NSERC; Research Grants Council, University Grants Committee, 研究資助局; General Secretariat for Research and Innovation, GSRI; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Irish Rugby Football Union, IRFU; Chinese Academy of Sciences, CAS; Defence Science Institute, DSI; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Minerva Foundation; National Research Foundation, NRF; Royal Society of South Australia, RSSA; Generalitat Valenciana, GVA; CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; 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; Horizon 2020 Framework Programme, H2020: MUCCA-CHIST-ERA-19-XAI-00; Horizon 2020 Framework Programme, H2020; Norges Forskningsråd: RCN-314472; Norges Forskningsråd; Agence Nationale de la Recherche, ANR: ANR-20-CE31-0013, ANR-11-LABX-0012, ANR-21-CE31-0022; Agence Nationale de la Recherche, ANR; SCI/013; 101033496; CIDEGENT/2019/027, CIDEGENT/2019/023; Center for Advancing Research Impact in Society, ARIS: J1-3010; Center for Advancing Research Impact in Society, ARIS; 21/SCI/017; Japan Society for the Promotion of Science, JSPS: JP21H05085, JP22H04944, 22H01227; Japan Society for the Promotion of Science, JSPS; Fundación BBVA, FBBVA: LEO22-1-603; Fundación BBVA, FBBVA; CC-IN2P3; National Natural Science Foundation of China, NSFC: NSFC 12275265, NSFC–12175119; National Natural Science Foundation of China, NSFC; Narodowa Agencja Wymiany Akademickiej, NAWA: PPN/PPO/2020/1/00002/U/00001; Narodowa Agencja Wymiany Akademickiej, NAWA; Vetenskapsrådet, VR: VR 2022-03845; Vetenskapsrådet, VR; Ministerio de Ciencia e Innovación, MCIN: RYC2019-028510-I, RYC2020-030254-I; Ministerio de Ciencia e Innovación, MCIN; IN2P3-CNRS; CHIST-ERA-19-XAI-00; Deutsche Forschungsgemeinschaft, DFG: DFG–CR 312/5-1; Deutsche Forschungsgemeinschaft, DFG; Narodowe Centrum Nauki, NCN: 2022/47/B/ST2/03059, 2021/42/E/ST2/00350, UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043; Narodowe Centrum Nauki, NCN; Instituto Nazionale di Fisica Nucleare, INFN: 754496; Instituto Nazionale di Fisica Nucleare, INFN; European Research Council, ERC: 948254; European Research Council, ERC; European Regional Development Fund, ERDF: IDIFEDER/2018/048, LCF/BQ/PI20/11760025; European Regional Development Fund, ERDF; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT: 1230987, 1210400, 1190886; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF: RPG-2020-004, SNSF–PCEFP2_194658; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SN
Design of Aircraft Composite Structures Against Ballistic Impact Threats
No full textGünümüzde savunma sektöründe yaşanan teknolojik gelişmeler, askeri hava araçlarının güvenliği ve dayanıklılığı üzerinde yeni zorluklar ortaya çıkarmıştır. Bu bağlamda, yüksek kinetik enerjiye sahip mühimmatlara karşı uçak yapısallarının tasarımı önemli bir araştırma ve geliştirme alanı haline gelmiştir. Özellikle uçakların balistik tehditlerine karşı hazırlanmış MIL-PRF-46103E standardında yer alan 12.7x99 AP gibi büyük kalibreye sahip mühimmatlar, hava araçlarının güvenliği ciddi bir tehdit oluşturmaktadır. Kompozit uçak zırh yapılarında kullanılan teknik seramik ve kompozit (yüksek performanslı fiberler ve onları bir arada tutan polimerler) malzemeler geleneksel metal zırhlara göre çok daha hafiftir. Bu, uçakların daha fazla manevra kabiliyeti kazanmasına, daha yüksek hızlara ulaşmasına ve daha fazla yakıt tasarrufu sağlamasına yardımcı olur. Seramik malzemeler yüksek sertlikleri sayesinde büyük kalibre mühimmatların kırılmasını ve böylece mühimmatı, kinetik enerjileri düşük şarapneller haline getirmeye yararken, kompozit malzemeler ise kinetik enerjileri düşmüş şarapnelleri yüksek enerji sönümleme kabiliyetleri yardımıyla durdurur. Bu çalışmada balistik çarpışmalara karşı dayanıklı ve hafif yapılar tasarlamak amacıyla, çarpışma anında meydana gelen deformasyonu tahmin edebilen bir sonlu elemanlar modeli geliştirilmiştir. Malzeme karakterizasyon testleriyle ve literatür verileriyle elde edilen malzeme modellerinin doğruluğu her malzeme için ayrı ayrı kontrollü balistik testlerle doğrulanmış olup sonlu elemanlar analizlerinin gerçeğe en yakın olması hedeflenmiştir. Bu çalışma kapsamında kompozit uçak zırhlarının sonlu elemanlar modelleri LS-DYNA yazılımı kullanılarak geliştirilmiştir. Sonlu eleman modelleri verileri ışığında 12.7x99 AP mühimmatı tehdidi için optimum kompozit uçak zırhı üretilmiş ve V50 testleri gerçekleştirilmiştir.The technological advancements in the defense sector today have introduced new challenges regarding the safety and durability of military aircraft. In this context, the design of aircraft structures against high-kinetic-energy ammunition has become a significant area of research and development. Especially, large-caliber ammunition, such as the 12.7x99 AP listed in the MIL-PRF-46103E standard, poses a serious threat to the safety of aircraft against ballistic threats. Technical ceramic and composite materials (high-performance fibers and polymers that hold them together) used in composite aircraft armor structures are much lighter than traditional metal armors. This helps aircraft gain greater maneuverability, reach higher speeds, and improve fuel efficiency. Due to their high hardness, ceramic materials enable the fragmentation of large-caliber ammunition, converting it into shrapnel with lower kinetic energy, while composite materials stop these fragments with reduced kinetic energy by utilizing their high energy-absorbing capacity. In this study, a finite element model capable of predicting the deformation occurring at the moment of impact has been developed to design structures that are both resistant to ballistic impacts and lightweight. The accuracy of the material models, obtained through material characterization tests and literature data, was validated for each material through controlled ballistic tests to ensure that finite element analyses closely reflect reality. Under this study, finite element models of composite aircraft armors were developed using LS-DYNA software. Based on the finite element model data, an optimized composite aircraft armor for the 12.7x99 AP ammunition threat was produced, and V50 tests were conducted
Investigation of the Cytotoxic and Antimetastatic Effect of Quetiapine Fumarate on Pancreatic Cancer Cells
No full textConvolutional Encoder-Decoder Network Using Transfer Learning for Topology Optimization
No full textGorguluarslan, Recep/0000-0002-0550-8335; Ates, Gorkem Can/0000-0002-3424-8587State-of-the-art deep neural networks have achieved great success as an alternative to topology optimization by eliminating the iterative framework of the optimization process. However, models with strong predicting capabilities require massive data, which can be time-consuming, particularly for high-resolution structures. Transfer learning from pre-trained networks has shown promise in enhancing network performance on new tasks with a smaller amount of data. In this study, a U-net-based deep convolutional encoder-decoder network was developed for predicting high-resolution (256 x 256) optimized structures using transfer learning and fine-tuning for topology optimization. Initially, the VGG16 network pre-trained on ImageNet was employed as the encoder for transfer learning. Subsequently, the decoder was constructed from scratch and the network was trained in two steps. Finally, the results of models employing transfer learning and those trained entirely from scratch were compared across various core parameters, including different initial input iterations, fine-tuning epoch numbers, and dataset sizes. Our findings demonstrate that the utilization of transfer learning from the ImageNet pre-trained VGG16 network as the encoder can improve the final predicting performance and alleviate structural discontinuity issues in some cases while reducing training time
İndirekt Oftalmoskopik muayene (İndirekt oftalmoskop optik prensipleri, Kullanılan Lensler İndirekt oftalmoskop ile arka kutup ve perifer muayenesi)
No full textTOD Prematüre Retinopatisi Beceri Aktarım Kursu, 25-27 Nisan 2024 TODEM Ankara / Retinopathy of Prematurity Skills Transfer Course[No Abstract Available
Resilient Cooling of the Mediterranean Office Spaces Under Climate Change
No full textResilient cooling strategies can minimize overheating risks by reducing energy demands and providing healthy indoor environments. Envelope-based resilient cooling strategies are particularly crucial for limiting external heat gain through conduction, convection, and radiation. This paper examines the impact of these strategies on the indoor environment under climate change. Using a case study approach, we simulate various scenarios for single and combined envelope-based resilient cooling strategies in a hypothetical, free-running office building located in the Mediterranean across five K ; ouml;ppen-Geiger climate zones (BSh, BSk, BWh, Csa, and Cfa) for both historical and 2050 weather data. We calculate indoor overheating degree and evaluate occupants' adaptive comfort. The findings suggest that combining envelope-based resilient cooling strategies significantly reduces indoor overheating risks, particularly in Southern European and North-Western African cities. Strategies that effectively control solar exposure are more influential in mitigating these risks. Among the strategies examined, a ventilated double skin with low-SHGC or chromogenic glazing is the most climate-change-resilient. This study contributes to the field by assessing the effectiveness of envelope-based resilient cooling strategies and providing recommendations for their application in the Mediterranean climate. It also evaluates how climate change may impact the performance of these strategies, offering insights for design and policymaking