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Türkiye Bağlamında Toprak Yapı Teknikleri ve Malzemelerinin Potansiyelinin Açığa Çıkarılması
No full textDoğal yapı malzemeleri ve yapım tekniklerinin yapılı çevrenin sürdürülebilirlik bağlamında katkı koyabileceği hem üretim hem de kullanım süreçleri değerlendirildiğinde sektörde halihazırda yaygın olarak kullanılan inşaat malzemelerine göre karbon ayak izlerinin düşük olmasından dolayı bilinmektedir. Doğal yapı malzemeleri ve tekniklerinden, geçtiğimiz son on yılda toprak malzemenin yeniden gündeme geldiği görülmektedir. Ancak hem Türkiye'de hem de dünyada, özellikle kentsel alanlarda, kullanımlarının çok yaygın olmadığı ifade edilebilir. Üretim ve imalat aşamalarındaki standardizasyonunun zorluğu bu teknik ve malzemelerin, sürdürülebilirlik tartışmaları arasında yeniden gündemimize gelmişlerdir. Son zamanlarda, 3B baskı gibi yeni yeniliklerin entegrasyonu konusundaki araştırmalar artmış ve kalite kontrolü, emek yoğun süreçleri ve karakteristik zayıflıkların ele alınması konusunda umut verici sonuçlar elde edilmiştir. Tez, gelecekte toprak malzemenin Türkiye'deki kullanımının yaygınlaşmasını sağlayabilmek amacıyla, küresel literatürü Türkiye'ye özgü süreçlerle birleştirerek, toprak yapıları ve malzemelerinin bugünkü teknolojiler ve olanaklar ışığında Türkiye'deki yerini yeniden değerlendirmeyi, yaygınlaşmasının önündeki sorunları ve yaygınlaşmasını sağlayabilecek etkenleri tespit etmeyi amaçlamaktadır. Tez, modern toprak yapılarının anket yoluyla elde ettiği verilerle, Türkiye'deki coğrafi haritalaması yoluyla bu sektörün sınırlı örneklerine dair bir kesit sunmaktadır. Toprak malzemenin kullanımına ilişkin etkenler ve engeller, saha uygulayıcılarıyla yapılan yarı yapılandırılmış mülakatlardan elde edilen verilerin nitel yöntemlerle analizi sonucunda ortaya çıkarılmış ve geleneksel ve modern toprak yapım tekniklerinin üretim ve malzeme temini sırasındaki gereksinimlerini irdeleyen çalışmalarla desteklenmiştir. Konunun çok katmanlı doğası ve yeni inovasyonlarla ilgili bağlamı nedeniyle, araştırma bulgularını analiz etmek ve yorumlamak amacıyla inovasyon odaklı analitik bir çerçeve olan çok düzeyli perspektif (multi-level perspective) kullanılmıştır. Bu analitik yaklaşım tarafından tanımlanan ve bir dışsal çerçeve olarak sosyo-teknik görünüm, sosyo-teknik rejim (mevcut inşaat sektörü), niş (yeniliklerin doğduğu alanlar) ve sosyo-teknik bağlam arasındaki ilişkiler incelenmiş ve mevcut dünya literatürü ile karşılaştırılmıştır. Bu tekniklerin kullanımını engelleyen nedenlere dayanarak, bu tekniklerin gelecekte sektörde nasıl bir yer bulabileceğine dair bir öngörü çerçevesi sunulmuştur. Yapılan analizler, Türkiye'nin bir deprem bölgesi olmasının ve mevcut deprem yönetmeliğinin, bu malzemenin ve tekniklerin benimsenmesindeki ana engellerden biri olduğunu ve bu durumun Türkiye'yi literatürde incelenen diğer ülkelerden tamamen farklı bir konuma yerleştirdiğini ortaya koymuştur. Ayrıca, mevcut yapı kodları üzerindeki etkisi, toprak karışımının test edilmesi için standart eksiklikleri, toprak malzeme uygulayıcıları için teşvik olarak özel fonların eksikliği ve mimarlık ve inşaat mühendisliği bölümleri dahil olmak üzere formel öğrenme ortamlarında kümülatif bilginin bulunmaması gibi konular da vurgulanmıştır. Tez, yalnızca niş uygulayıcılarının bakış açısına odaklanmış olup, gelecekteki araştırmaların son kullanıcılar ve politika yapıcılar üzerinden yapılabilecek bir çalışmaya altlık oluşturabilecek ve fayda sağlayabilecektir.Natural building materials and construction techniques are recognized for their capacity to enhance the sustainability of the built environment, as their carbon footprint, both at the production and use phase, are lower compared to the commonly used construction materials in the industry. Among these materials, earth material has regained significant attention over the last decade. Yet their use is scarce, especially in urban zones not only in Turkey but also across the world. Regardless of the difficulty of standardization in the production and manufacturing stages of these techniques and materials sustainability issues have brought them back to our agenda. Recently, research on topics such as integrating innovative production techniques like 3D printing has increased, yielding promising results for addressing quality control, labor-intensive processes, and characteristic weaknesses. The thesis aims to reassess the role of earthen structures and materials in Turkey, considering current technologies and opportunities. This will be achieved by integrating global literature with Turkey-specific processes. The objective is to identify the barriers hindering the widespread use of earth materials and structures, as well as the factors that can promote their adoption. This thesis first provides a snapshot of this niche sector, with a particular emphasis on a small number of instances gathered through an online survey distributed to professionals in the field, utilizing a geographical mapping approach to identify contemporary earthen structures. Second, through the analysis of traditional and modern earthen techniques and the qualitative analysis of data obtained from semi-structured interviews with field practitioners, the thesis examines the drivers and barriers to the use of these techniques in Turkey. Due to the multi-layered nature of the topic and the context involving innovations, an innovation-focused analytical framework, the Multi-Level Perspective, is used to read and analyze the research findings. The relationships between 'The Landscape,' 'Regime,' 'Niche,' and the socio-technical context as described by this theoretical approach are examined and finally cross analyzed with the existing documentation across the world. By drawing on the reasons that hinder the use of these techniques, a prospective framework on how these techniques would possibly find their place in the upcoming future is provided. The analysis revealed that Turkey being an earthquake zone and the current earthquake regulations are one of the main obstacles to the adoption of this material and techniques, and this situation places Turkey in a completely different position from other countries examined in the literature. In addition, the impact on the current building codes, the lack of standards for testing earth mixtures, the lack of special funds as incentives for earth material practitioners and the lack of courses on these materials in formal learning environments including architecture and civil engineering departments are also highlighted. The thesis only focused on the perspective of niche practitioners; therefore, further research is needed based on the analysis of may benefit from an in-depth study of end-users and policymakers
Observation of Electroweak Production of W+w- in Association With Jets in Proton-Proton Collisions at √s=13 Tev With the Atlas Detector
No full textGwilliam, Carl/0000-0002-9401-5304; Mitsou, Vasiliki A./0000-0002-1533-8886; Mckee, Shawn/0000-0002-4551-4502; Bhamjee, Muaaz/0000-0002-2697-4589; Bianco, Gianluca/0000-0003-4473-7242; Smirnova, Oxana/0000-0003-2517-531X; Butterworth, Jonathan/0000-0002-5905-5394; Bernlochner, Florian/0000-0001-8153-2719; Khwaira, Yahya/0000-0001-8538-1647; Stanislaus, Beojan/0000-0001-9007-7658; Ali, Hafiz Muhammad/0000-0002-9885-5933; Potti, Harish/0000-0002-0800-9902; Herde, Hannah/0000-0001-8926-6734; Ventura, Andrea/0000-0002-3368-3413; Saoucha, Kamal/0000-0001-9150-640X; Ahmadov, Faig/0000-0003-3644-540X; Nellist, Clara/0000-0002-5171-8579; Haley, Joseph/0000-0002-6938-7405; Fiorini, Luca/0000-0002-5070-2735; Lagouri, Theodota/0000-0001-7509-7765; Carratta, Giuseppe/0000-0002-8846-2714; Gonnella, Francesco/0000-0003-0885-1654; Etzion, Erez/0000-0001-6871-7794; Konstantinidis, Nikolaos/0000-0002-4140-6360; /0000-0001-5765-1750; Petersen, Troels/0000-0003-0221-3037; Oh, Alexander/0000-0001-9025-0422A measurement of the production of W bosons with opposite electric charges in association with two jets is presented based on 140 fb(-1) of data collected by the ATLAS detector in proton-proton collisions at root s = 13 TeV. The analysis is sensitive to the scattering of W bosons, which is of particular interest in the ATLAS physics programme as it can be used to probe the electroweak symmetry breaking mechanism of the Standard Model. This signal is observed with a significance of 7.1 standard deviations above the background expectation, while 6.2 standard deviations were expected. The measured cross-section is determined in a signal-enriched fiducial volume and is found to be 2.7 +/- 0.5 fb, which is consistent with the theoretical prediction of 2.20(-0.13)(+0.14) fb.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 (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [85].r 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; IN2P3CNRS 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; MEiN, 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.r Individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; CERN-CZ, PRIMUS 21/SCI/017 and UNCE SCI/013, 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.r In addition, individual members wish to acknowledge support from 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); EU: H2020 European Research Council (ERC -101002463); European Union: European Research Council (ERC -948254), 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 NO 101033496); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022), Investissements d'Avenir Idex (ANR-11-LABX-0012), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG -CR 312/5-1); Italy: Istituto Nazionale di Fisica Nucleare (FELLINI G.A. n. 754496, ICSC, NextGenerationEU); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI 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), Polish National Science Centre (NCN 2021/42/E/ST2/00350, 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, 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 (VR 2018-00482, VR 2022-03845, VR 2022-04683, VR grant 202103651), 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 (Leverhulme Trust RPG-2020004); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1; BNL (U.S.A.); 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; 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; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS; MIZS, Slovenia; MICINN, Spain; SRC; Wallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taipei; DOE; NSF, United States of America; BCKDF; CANARIE; CRC; DRAC, Canada [PRIMUS 21/SCI/017, UNCE SCI/013]; 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 [CIDEGENT/2019/023, CIDEGENT/2019/027]; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society; Leverhulme Trust, United Kingdom; Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1190886]; FONDECYT [1230987]; China: National Natural Science Foundation of China [NSFC -12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; EU [ERC -101002463]; European Union: European Research Council [ERC -948254, MUCCA -CHIST-ERA-19-XAI-00]; European Union [FAIR-NextGenerationEU PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC); Marie Sklodowska-Curie Actions (EU) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; Investissements d'Avenir Idex [ANR-11-LABX-0012]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG -CR 312/5-1, 754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [JP21H05085, JP22H01227]; Research Council of Norway [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, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; Slovenian Research Agency [J1-3010]; BBVA Foundation [LEO22-1-603]; Generalitat Valenciana; FEDER [IDIFEDER/2018/048]; Ministry of Science and Innovation [NextGenEU PCI2022-135018-2]; MICIN FEDER [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-04683, 202103651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, SNSF PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020004]; United States of America [ECA DE-AC02-76SF00515]; Neubauer Family Foundatio
Test of Cp-Invariance of the Higgs Boson in Vector-Boson Fusion Production and in Its Decay Into Four Leptons
No full textA search for CP violation in the decay kinematics and vector-boson fusion production of the Higgs boson is performed in the H -> ZZ* -> 4l ( l = e, mu) decay channel. The results are based on proton-proton collision data produced at the LHC at a centre-of-mass energy of 13TeV and recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb(-1). Matrix element-based optimal observables are used to constrain CP-odd couplings beyond the Standard Model in the framework of Standard Model effective field theory expressed in the Warsaw and Higgs bases. Differential fiducial cross-section measurements of the optimal observables are also performed, and a new fiducial cross-section measurement for vector-boson-fusion production is provided. All measurements are in agreement with the Standard Model prediction of a CP-even Higgs boson.ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; ANID, Chile; CAS, China; MOST, China; NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRI, Greece; RGC, China; Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TENMAK, Turkiye; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; CRC, Canada; PRIMUS, Czech Republic [21/SCI/017]; UNCE, Czech Republic [SCI/013]; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Sklodowska-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 KingdomWe 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, The Netherlands; RCN, Norway; MEiN, 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. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and DRAC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, 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
Differential Cross-Sections for Events With Missing Transverse Momentum and Jets Measured With the Atlas Detector in 13 Tev Proton-Proton Collisions
No full textSawada, Ryu/0000-0002-2226-9874; Farooque, Trisha/0000-0003-1363-9324; Gonzalez Sevilla, Sergio/0000-0003-4458-9403; Principe Martin, Miguel Angel/0000-0002-5085-2717; Cheng, Hok-Chuen/0000-0002-8912-4389; Nikolopoulos, Konstantinos/0000-0002-3048-489X; Benjamin, TROCME/0000-0001-9500-2487; Orellana, Gonzalo Enrique/0000-0002-4753-4048; Ju, Xiangyang/0000-0002-9745-1638; Rossi, Eleonora/0000-0002-2146-677X; Worm, Steven/0000-0002-3865-4996; /0000-0001-5765-1750; Bernlochner, Florian/0000-0001-8153-2719; Marti-Garcia, Salvador/0000-0002-3897-6223; Dova, Maria Teresa/0000-0001-6113-0878; Walkowiak, Wolfgang/0000-0002-0385-3784; Lazzaroni, Massimo/0000-0002-4094-1273; Sun, Shaojun/0000-0001-5295-6563; Munoz Sanchez, Francisca/0000-0002-6374-458X; Cristinziani, Markus/0000-0003-3893-9171; Carbone, Antonio/0000-0002-4117-3800; Saibel, Andrej/0000-0002-9932-7622; Francescato, Simone/0000-0001-5315-9275; Zhang, Rui/0000-0002-8265-474X; Lopez Solis, Alvaro/0000-0002-0511-4766; Chu, Ming-chung/0000-0002-1971-0403; Sankey, David/0000-0003-0955-4213; Scharf, Christian/0000-0002-0294-1205; BOUMEDIENE, Djamel/0000-0002-7809-3118; Stevenson, Thomas/0000-0003-2399-8945; Baron, Petr/0000-0002-5170-0053; Bachas, Konstantinos/0000-0002-9047-6517; Loch, Peter/0000-0002-2005-671X; Sopczak, Andre/0000-0001-6981-0544; McKee, Shawn/0000-0002-4551-4502; Liu, Yanlin/0000-0001-9190-4547; Longo, Luigi/0000-0002-2357-7043; Moskalets, Tetiana/0000-0001-6508-3968; Alexa, Calin/0000-0003-0922-7669; Bogavac, Danijela/0000-0003-2138-9062; Verissimo de Araujo, Micael/0000-0001-8060-2228; Dong, Qichen/0000-0002-0117-7831; Tudorache, Alexandra/0000-0001-6307-1437; Henkelmann, Lars/0000-0001-8231-2080; Olivares, Sebastian/0000-0003-4616-6973; Valero, Alberto/0000-0002-9776-5880; Kharlamova, Tatyana/0000-0002-0387-6804; Anisenkov, Alexey/0000-0002-7201-5936; Dao, Valerio/0000-0003-1645-8393; Zanzi, Daniele/0000-0002-1222-7937; Zerradi, Soufiane/0000-0001-9101-3226; Jones, Eleanor/0000-0001-6289-2292; Giuli, Francesco/0000-0002-8506-274X; El Sawy, Mai/0000-0002-3012-9986; Auriol, Adrien/0000-0002-3623-1228; Sadrozinski, Hartmut/0000-0003-0019-5410; Komarek, Tomas/0000-0002-3047-3146; Parajuli, Santosh/0000-0003-1499-3990; Hugging, Fabian/0000-0002-7472-3151; Smolek, Karel/0000-0002-5996-7000; Tlou, Humphry/0000-0002-4934-1661; Kiryunin, Andrey/0000-0001-7490-6890; Gaudio, Gabriella/0000-0002-6833-0933; Iuppa, Roberto/0000-0001-5038-2762; Beacham, James/0000-0003-3623-3335; Kennedy, Philip David/0000-0002-8491-2570; Keeler, Richard/0000-0002-0510-4189; Fox, Harald/0000-0003-3089-6090; Duehrssen-Debling, Michael/0000-0002-5833-7058; Starz, Steffen/0000-0002-2908-3909; Li, Zhelun/0000-0001-7096-2158; Ezhilov, Alexey/0000-0002-7520-293X; wei, Yingjie/0000-0001-9725-2316; Lawrence, Zak/0000-0002-9035-9679; Lloyd, Stephen/0000-0002-5073-2264; Barberio, Elisabetta/0000-0002-3111-0910; Beretta, Matteo Mario/0000-0002-7026-8171; Mitsou, Vasiliki A./0000-0002-1533-8886; Oide, Hideyuki/0000-0002-2173-3233; Lapertosa, Alessandro/0000-0001-6246-6787; 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A Brief History and Milestones of Psychiatric Ethics
No full textThis chapter aspires to adopt a comprehensive perspective to encompass the intricate and interwoven complexities inherent in psychiatric ethics. In pursuit of this objective, it is dedicated to a thorough exposition of the historical trajectory of psychiatry ethics. The purpose of this section is to demonstrate how the epistemological trajectory of approaching mental health disorders was intertwined with the dominant social, religious, cultural, and economic paradigms of the time and how they cultivated ethical problems within this trajectory. When reading this section, it becomes evident that traces of ancient Greek, Roman, and medieval etiology and treatment approaches and their ethical indications for mental disorders still persist in some of our current practices. This awareness can assist in reevaluating and eliminating some of the irrational and unscientific beliefs and patterns in our contemporary approaches and their ethical implications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
The Effects of Infrared Sensor Wavelength on Panchromatic Image Colorization Performance
No full textAkin, Suleyman Emir/0009-0002-7684-4763Infrared (IR) imaging sensors, designed to detect the wavelength range between 0.9 mu m and 14 mu m, offer unique advantages over daylight cameras in consumer, industrial, and defense applications. However, IR images lack natural color information and can be challenging for individuals without sensor-specific training to interpret. Consequently, transforming IR images into perceptually realistic color images represents a valuable research endeavor with significant commercial potential. Recently, various studies utilizing deep neural networks for colorizing single-mode (near-IR or thermal) infrared images have been reported. This article will apply a common neural network architecture to images captured with different imaging modes (near-IR, thermal IR, and low-light) for colorization and compare the results. These experiments will examine the influence of perceived wavelength on the colorization process
Makine Öğrenmesi Teknikleri ile Frezeleme İşlemlerinde Takım Aşınmasının Tahmini
No full textEndüstri 4.0'ın temel birleşenlerinden biri Büyük Veri Analitiği (Big Data Analytics) ve Makine Öğrenme Teknikleri(Machine Learning Techniques) ile imalat süreçlerinin Akıllı İmalat (Smart Manufacturing) haline dönüştürülmesidir. Dünyada otomotiv, havacılık, sağlık, finans vb. gibi sektörlerde hızla popülerliği artan Makine Öğrenme tekniklerinin en yenisi ve etkilisi kabul edilen Derin Öğrenme (Deep Learning), bu sektörlerde devrim niteliğinde yeni ürün ve uygulamalara yol açmaya devam etmektedir. İmalat alanında ise en önde gelen araştırma potansiyelini, ileri durum gözetleme (Advanced Condition Monitoring), işlem eniyilenmesi (Process Optimization), ve kestirimci bakım(Predictive Maintenance) alanlarında bulunduğu öne sürülmektedir. İleri işlem gözetleme, günümüzde tamamen bilgisayar denetimli olan makine ve sistemlerde üretilen verinin zamanında, yeterli hassasiyette, çözünürlükte ve güvenilirlikte toplanmasına bağlıdır. Bu amaç doğrultusunda tez kapsamında ivmeölçer, mikrofon ve enerji metre kullanılarak sırasıyla titreşim, akustik ve makinenin çektiği güç verileri toplanmıştır. Aynı zamanda kesici takımda meydana gelen kenar aşınması değeri mikroskop aracılığıyla toplanmış ve işlenen parçanın yüzey pürüzlülüğü de yüzey pürüzlülüğü ölçüm cihazı ile toplanmıştır. Burada tez kapsamında yapılan çalışmaların amacı toplanan verilerle birlikte derin öğrenme tekniklerini talaşlı imalat süreçlerine uygulayarak, işlem kontrol ve gözetleme parametreleri ile işlenen parçanın yüzey pürüzlülüğü, tüketilen enerji ve kesici takım aşınması arasında makine öğrenme (Machine Learning) temelli modeller geliştirmektir. İşlenen parçanın yüzey pürüzlülüğü ve özgül kesme enerjisi (ÖKE) tahmin eedebilmek için Derin Çok Katmanlı Algılayıcı (DMLP) tabanlı tahmin modeli geliştirilmiştir. Ayrıca kesici takımda meydana gelen aşınmayı da tahmin etmek amacıyla Konvolüsyonel Sinir Ağı (CNN) ve Uzun Kısa Süreli Bellek (LSTM) tabanlı iki farklı tahmin modeli geliştirilmiştir. Tez kapsamında geliştirilen DMLP tabanlı tahmin modeli kullanılarak elde edilen özgül kesme enerjisi ve yüzey pürüzlülüğü sonuçları ile hedef çıktılar arasındaki hata oranının %10'un altında olduğu görülmektedir. Ayrıca kesici takım aşınmasının tahmini için geliştirilen CNN ve LSTM tabanlı modelleri eğitebilmek için 8 farklı deney yapılmıştır. Deneylerden elde edilen titreşim ve akustik verileri kullanılarak modeller eğitilmiştir. Eğitim sırasında kullanılmayan deney verileri test verisi olarak kullanılmıştır. Burada iki farklı tahmin modeli için de 5 farklı senaryo üzerinde çalışılmıştır. Senaryolarda amaç farklı farklı kesme hızları (m/dk) ve farklı kesici takım çaplarına göre kesici takımda meydana gelen aşınmaları tahmin etmeye çalışmaktır. Senaryolardaki en iyi sonuçlara bakıldığı zaman Wavelet-CNN tabanlı tahmin modelinin ortalama hata karesinin (MSE) değeri 0.031 olup WLSTM-DMLP tabanlı tahmin modelinin ortalama hata karesinin değeri 0.004 olarak elde edilmiştir.One of the fundamental components of Industry 4.0 is the transformation of manufacturing processes into Smart Manufacturing through Big Data Analytics and Machine Learning Techniques. Deep Learning, considered the latest and most impactful of the Machine Learning techniques, is rapidly gaining popularity in sectors such as automotive, aerospace, healthcare, and finance, leading to revolutionary new products and applications in these sectors. In the field of manufacturing, it is suggested that the most prominent research potential lies in the areas of Advanced Condition Monitoring, Process Optimization, and Predictive Maintenance. Advanced process monitoring relies on the timely collection of machine and system generated data with sufficient accuracy, resolution, and reliability. In this context, vibration, acoustic, and machine power consumption data were collected using an accelerometer, microphone, and energy meter, respectively. At the same time, the value of flank wear occurring on the cutting tool was collected using a microscope, and the surface roughness of the machined part was also collected using a surface roughness measuring device. The aim of the studies conducted within the scope of this thesis is to develop Machine Learning-based models for process control and monitoring parameters, surface roughness of the machined part, consumed energy, and cutting tool wear by applying deep learning techniques to the collected data together with machining processes. A Deep Multi-Layer Perceptron (DMLP) based prediction model has been developed to predict the surface roughness and specific cutting energy (ÖKE) of the machined part. In addition, two different prediction models based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have been developed to predict the wear occurring on the cutting tool. Using the DMLP-based prediction model developed within the scope of the thesis, it is observed that the error rate between the obtained specific cutting energy and surface roughness results and the target outputs is below 10%. In addition, 8 different experiments were conducted to train the CNN and LSTM-based models developed for the prediction of cutting tool wear. The models were trained using the vibration and acoustic data obtained from the experiments. The experimental data not used during training was used as test data. Here, 5 different scenarios were studied for both prediction models. The aim in the scenarios is to try to predict the wear occurring on the cutting tool for different cutting speeds (m/min) and different cutting tool diameters. When the best results in the scenarios are examined, the average error value of the Wavelet-CNN based prediction model is 0.031, while the average error value of the WLSTM-DMLP based prediction model is 0.004
Yokluk Ekonomisinden Enerji İhracatçılığına İki Ayrı Boyut
No full textYedinci Uluslararası İsrail Ve Yahudilik Çalışmaları Konferansı, 7-8 Aralık 2023 / Seventh International Conference On Israel And Judaism Studies, December 7-8, 2023İsrail yakın zamana kadar kendi enerji kaynaklarının bulunmaması sebebiyle net ithalatçı konumundaydı ve enerji politikası verimlilik üzerine odaklandırmaktaydı. Dolayısıyla, İsrail’in enerji güvenliğine ilişkin iki ayrı boyut veya iki ayrı dönem ele alınmaktadır. İlk boyut (dönem), geçmişteki kıt kaynakları yönetme ve kalkınma odaklı anlayışıyla ülkenin tarım, sulama, enerji verimliliği ve yenilenebilir enerji, inovasyon ve yeni teknolojilerde önemli bir ülke olmasının avantajını vurgulamaktadır. Girişimcilik alanında girişimci ülke (‘startup nation’) (Senor ve Singer, 2011) ve ‘chutzpah’ (Arieli, 2019) gibi kavramlarla anılan İsrail’in, yeşil ekonomi alanında önemli firmaları ve teknolojileri bulunmaktadır. Bu boyut, yokluk içerisinde kaynakların optimum kullanma gereksinimden ortaya çıkmıştır. Buna mukabil, ikinci boyut, İsrail’in son 10 yılda net ihracatçı konumuna geçmesi sonucu ortaya çıkmıştır. Doğu Akdeniz’deki Leviathan gibi doğalgaz sahaları sayesinde konvansiyonel enerji kaynağına sahip olması sonucu bölgesel jeopolitik dengeler, kaynak milliyetçiliği ve ihraç edebileceği yakın pazarların önemi gibi meseleler ortaya çıkmaktadır. İsrail teknolojik ürünlerde önemli bir ülke olmaya devam etmektedir. 1973 Arap-İsrail savaşından sonra petrol üreticisi olan Arap devletlerinin petrol üretimini azaltıp, ambargo uygulaması neticesinde, küresel enerji politikalarında kaynak ve pazar çeşitlendirme ihtiyacı belirginleşmiştir. Enerji politikalarında alternatif enerji kaynaklarının kullanımının yanı sıra su, tarım gibi sektörlerdeki verimlilik ve teknolojiye öncelik verilmiştir. Son 10 yılda bulunan gaz sahaları, fosil yakıtların gelecek vadetmemesi sebebiyle, İsrail’in alternatif enerji kaynaklarına olan ilgisini azaltmamıştır. İsrail’in gazının tükenmesi durumunda, yeniden ilk boyuttaki enerji ve kaynak verimliliği üzerine edinilen know-how sayesinde gelecekte potansiyel avantajların kullanabilmesi mümkün olacaktır. Bu bağlamda, iki farklı boyuttaki yaklaşımları ele aldığımızda, uzun yıllar İsrail’in kıt kaynaklarla kısıtlı bir coğrafyada kendi kendine yetinmeye ve diğer kaynakları uluslararası piyasalardan tedarik etmeye çalışması, ülkenin ekonomisinin belirgin bir özelliği olmuştur. Bu bağlamda, doğalgaz ihracatçısı olmadan önceki dönemle günümüzü ayırmak gerekmektedir, ancak her ikisi de İsrail politik-ekonomisini etkilemeye devam etmektedir. Açıklama: Startup Nation, Israil’in istatistiksel bakımdan nüfusuna nazaran en çok yeni firma kuran ülkelerden biri olmasını ve İsrail’in girişimci ruhuna atıftır. Bu bağlamda özel sektör ve serbest ticaretin ne derece bu ülke için önemli olduğunun vurgulanması. Chutzpah ise cüretkarlık yani bireylere yani yeni girişimlere başlama hevesi (hatta bazen küstahça inatla bildiğini yaparak) anlamına kullanılır ve özellikle Inbal Arieli’nin aynı isimli kitabıyla İsrail’deki girişimci ruhu özetleyen bir kavramdır.Until recently, Israel was a net importer due to the lack of its own energy resources, and its energy policy focused on efficiency. Therefore, two separate dimensions or two separate periods regarding Israel's energy security are discussed. The first dimension (period) emphasizes that the country is an important country in agriculture, irrigation, energy efficiency and renewable energy, innovation and new technologies. Israel’s past understanding of managing scarce resources and focusing on development is an advantage. Israel, known in the field of entrepreneurship with concepts such as 'startup nation' (Senor and Singer, 2011) and 'chutzpah' (Arieli, 2019), has important companies and technologies in the field of green economy. This dimension emerged from the need for optimum use of resources in times of scarcity. On the other hand, the second dimension emerged as a result of Israel becoming a net exporter in the last 10 years. As a result of having a conventional energy source thanks to natural gas fields such as Leviathan in the Eastern Mediterranean, issues such as regional geopolitical balances, resource nationalism and the importance of nearby markets to which it can export arise. Israel continues to be an important country in technological products. After the 1973 Arab-Israeli war, the need for resource and market diversification in global energy policies became evident as oil producing Arab states reduced oil production and imposed an embargo. In energy policies, priority is given to efficiency and technology in sectors such as water and agriculture, as well as the use of alternative energy sources. The gas fields discovered in the last 10 years have not reduced Israel's interest in alternative energy sources, as fossil fuels do not have a promising future. If Israel runs out of gas, it will be possible to use the potential advantages in the future thanks to the know-how gained on energy and resource efficiency in the first dimension. In this context, when we consider approaches from two different dimensions, for many years Israel's attempt to be self-sufficient in a geography limited by scarce resources and to supply other resources from international markets has been a distinctive feature of the country's economy. In this context, it is necessary to distinguish between the period before it became a natural gas exporter and the present, but both continue to influence the Israeli political-economy. Explanation: Startup Nation is a reference to Israel's entrepreneurial spirit and the fact that it is statistically one of the countries that establishes the most new companies relative to its population. In this context, emphasizing how important the private sector and free trade are for this country. Chutzpah, on the other hand, is used to mean audacity, that is, the enthusiasm to start new ventures (sometimes even arrogantly, stubbornly doing what one knows) and is a concept that summarizes the entrepreneurial spirit in Israel, especially with Inbal Arieli's book of the same name
