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Digital Transformation In Product Design Tests
No full textBAİBÜİLEF-İG Bolu Abant İzzet Baysal Üniversitesi İletişim Fakültesi 1. uluslararası iletişim günleri Dijital Dönüsüm Ve Yeni Medya Sempozyumu 13-14-15 Mayıs 2024 / Bolu Abant İzzet Baysal University Faculty of Communication 1st International communication days Dıgıtal Transformatıon;New Medıa Symposıum May 13-14-15 2024Product design encompasses idea validation and product enhancement, while product testing ensures technical functionality and alignment with intended purposes. Testing aids in understanding user needs and satisfaction. This study highlights the advantages and disadvantages of digital product testing. It outlines six common testing types: concept testing, QA testing, A/B testing, market testing, user testing, and regression testing. Concept testing explores the feasibility of a product idea and evaluates how it may perform in the marketplace. QA testing ensures the functionality and reliability of interconnected industrial products. E-commerce platforms prefer A/B testing to optimize the user interface, and checkout processes due to the continuous testing and refinement of design elements to improve conversion rates and user satisfaction. Market testing enables testing prototypes, gathering user feedback, and validating product concepts before full-scale production. User testing conducts remote usability testing with real users for creating test scenarios, recruiting participants, and analyzing user feedback. The practice of regression testing ensures that new changes or updates to a product do not adversely impact existing functionalities. In conclusion, digital transformation in product testing offers numerous advantages, including Rapid Iteration, Analytics and Data Insights, Simulation and Virtual Testing, Real-Time Monitoring, Collaborative QA Platforms, IoT-Enabled Quality Monitoring, Digital Documentation and Traceability, Personalization, Machine Learning for User Behavior Prediction, Cloud-Based Testing Environments, Continuous Integration, and Deployment. However, digital product testing has disadvantages. These are respectively Initial Implementation Costs, Complex Test Scenarios, Limited Physical Interaction, and Data Privacy Concerns.Ürün tasarımı fikir doğrulamayı ve ürün geliştirmeyi kapsarken, ürün testi teknik işlevselliği ve amaçlanan amaçlarla uyumu sağlamaktadır ve kullanıcı ihtiyaçlarının ve memnuniyetinin anlaşılmasına yardımcı olmaktadır. Bu çalışmada dijital ürün testlerinin avantajları ve dezavantajları açıklanmaktadır. Altı yaygın ürün test türünü özetlenmiştir. Bunlar sırasıyla konsept testi, QA testi, A/B testi, pazar testi, kullanıcı testi ve regresyon testi. Konsept testi, bir ürün fikrinin fizibilitesini araştırır ve pazarda nasıl performans gösterebileceğini değerlendirir. QA testi, birbirine bağlı endüstriyel ürünlerin işlevselliğini ve güvenilirliğini sağlar. E-ticaret platformları, dönüşüm oranlarını ve kullanıcı memnuniyetini artırmak amacıyla tasarım öğelerinin sürekli test edilmesi ve iyileştirilmesi nedeniyle kullanıcı arayüzünü optimize etmek için A/B testini ve ödeme süreçlerini tercih ediyor. Pazar testi, tam ölçekli üretim öncesinde prototiplerin test edilmesine, kullanıcı geri bildirimlerinin toplanmasına ve ürün konseptlerinin doğrulanmasına olanak tanır. Kullanıcı testi, test senaryoları oluşturmak, katılımcıları işe almak ve kullanıcı geri bildirimlerini analiz etmek için gerçek kullanıcılarla uzaktan kullanılabilirlik testleri gerçekleştirir. Regresyon testi uygulaması, bir üründe yapılan yeni değişikliklerin veya güncellemelerin mevcut işlevleri olumsuz etkilememesini sağlar. Sonuç olarak, ürün testindeki dijital dönüşüm, Hızlı Yineleme, Analitik ve Veri İçgörüleri, Simülasyon ve Sanal Test, Gerçek Zamanlı İzleme, İşbirliğine Dayalı Kalite Güvence Platformları, IoT Etkin Kalite İzleme, Dijital Belgeleme ve İzlenebilirlik, Kişiselleştirme, Makine Öğrenimi, Kullanıcı Davranışı Tahmini, Bulut Tabanlı Test Ortamları, Sürekli Entegrasyon ve Dağıtım olmak üzere çok sayıda avantaj sunar. Ancak dijital ürün testinin dezavantajları vardır. Bunlar sırasıyla ilk uygulama maliyetleri, karmaşık test senaryoları, sınırlı fiziksel etkileşim ve veri gizliliği kaygılarıdır
Search for Short- and Long-Lived Axion-Like Particles in H → Aa → 4γ Decays With the Atlas Experiment at the Lhc
No full textTeixeira-Dias, Pedro/0000-0001-9977-3836Presented is the search for anomalous Higgs boson decays into two axion-like particles (ALPs) using the full Run 2 data set of 140 fb(-1) of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS experiment. The ALPs are assumed to decay into two photons, providing sensitivity to recently proposed models that could explain the (g -2)(mu) discrepancy. This analysis covers an ALP mass range from 100 to 62 GeV and ALP-photon couplings in the range 10(-7) TeV-1 C-a gamma gamma/Lambda 1 TeV-1, and therefore includes signatures with significantly displaced vertices and highly collinear photons. No significant excess of events above the Standard Model background is observed. Upper limits at 95% confidence level are placed on the branching ratio of the Higgs boson to two ALPs in the four-photon final state, and are in the range of 10(-5) to 3x10(-2), depending on the hypothesized ALP mass and ALP-photon coupling strength.CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA); ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF; Benoziyo Center; INFN, Italy; MEXTand 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; CERCAProgramme Generalitat deCatalunya; PROMETEO; 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]; European Union: European Research Council [ERC -948254, MUCCA - CHIST-ERA-19-XAI-00]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC); Marie Sklodowska-Curie Actions [EUH2020MSCIFGRANTNO101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; Investissements d'Avenir Idex [ANR11-LABX-0012]; Investissements d'Avenir Labex; Germany: Baden-Wurttemberg Stiftung; Deutsche Forschungsgemeinschaft [DFG-CR 312/51]; Istituto Nazionale di Fisica Nucleare [754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [JP21H05085, JP22H01227, JP22H04944, NWO Veni 2020 -VI.Veni.202.179, RCN-314472]; Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Polish National Science Centre [NCN2021/42/E/ST2/00350, NCNUMO-2019/34/E/ST2/00393, 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]; LaCaixa Banking Foundation [LCF/BQ/PI20/1 1760025, PCI2022-135018-2, RYC20 19-028510-I, RYC2020-030254-I, CIDEGENT/2019/023, CIDEGENT/2019/027]; Swedish Research Council [VR 2018-00482, VR2022-03845, VR 2022-04683, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458]; Swiss National Science Foundation [SNSF-PCEFP2_194658]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004]; United States of AmericaWe 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. [70]. 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, Is rael; INFN, Italy; MEXTand 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. 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, CERCAProgramme Generalitat deCatalunya 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 Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230987); China: National Natural Science Foundation of China (NSFC-12175119, NSFC 12275265, NSFC-12075060); European Union: European Research Council (ERC -948254), Horizon 2020 Framework Programme (MUCCA - CHIST-ERA-19-XAI-00), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU), Marie Sklodowska-Curie Actions (EUH2020MSCIFGRANTNO101033496); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022), Investissements d'Avenir Idex (ANR11-LABX-0012), Investissements d'Avenir Labex (ANR-11-LABX0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG-CR 312/51); 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 (NCN2021/42/E/ST2/00350, NCNUMO-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), LaCaixa Banking Foundation (LCF/BQ/PI20/1 1760025), Ministry of Science and Innovation(MCINandNextGenEU -PCI2022-135018-2, MICINandFEDER-PID2021-125273NB, RYC20 19-028510-I, RYC2020-030254-I), PROMETEOandGenT Programmes GeneralitatValenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (VR 2018-00482, VR2022-03845, VR 2022-04683, VR grant 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 (Leverhulme Trust RPG-2020-004); United States of America: Neubauer Family Foundation
Integrating UN Sustainable Development Goals into Campus Planning: Pathways for Higher Education Institutions
No full textThis research article explores the significant role of higher education institutions (HEIs) in contributing to the Sustainable Development Goals (SDGs) by analyzing their sustainability initiatives through the lens of the GreenMetric University Sustainability Rating. It scrutinizes the commitment of prominent universities across various continents to environmental stewardship, focusing on essential areas such as infrastructure, energy efficiency, waste management, water conservation, and sustainable transportation. The study examines the sustainability practices at notable institutions including the University of Indonesia (UI), Wageningen University ; Research (WUR), the American University in Cairo (AUC), Lincoln University, the University of California, Davis (UC Davis), and the University of São Paulo (USP). It organizes these efforts into six main domains: Campus and Infrastructure, Energy and Climate Change, Waste Management, Water Conservation, Transportation, and Education and Research, providing a detailed analysis and recommendations for enhancing campus sustainability. Through a comparative analysis, presented in a structured format, the article highlights exemplary practices and aims to serve as a blueprint for other universities seeking to integrate sustainability into their operations. This research underscores the pivotal role of higher education institutions (HEIs) in driving sustainable solutions and aligning with global environmental objectives, advocating for a systematic and scientific integration of sustainability principles across university operations and curricula. It offers an in-depth analysis of current sustainability trends in higher education, stressing the critical need to motivate institutions towards greater sustainability commitments and to foster the development of leaders and innovative approaches for a sustainable future
Bir Bağlantılı Tarih Örneği Olarak İbrahim Müteferrika'nın Çok Kimlikli Düşünce Dünyası
No full textİbrahim Müteferrika, Erdel bölgesinde Kolozsvár'da doğup, Hristiyan bir eğitim almıştır. Transilvanya bölgesinin kültürel çeşitlilik fikirleri ile 18. yüzyıl başlarında Osmanlı topraklarına gelen Müteferrika, kendini Osmanlı dili ve kültüründe geliştirerek Osmanlı kültür dünyasında önemli bir yer edinmiştir. Osmanlı yönetiminin resmi onayıyla kurduğu matbaada bastığı eserlere yaptığı katkılarla çok kimlikli yapısı ve kendi etkileşimlerini sergilemiştir. Eserlerinde tüm bu fikir dünyalarını sentezleyerek sunmuştur. Aynı anda ve çatışmadan birlikte var olabilen kimlikleri ile her iki kültürden de faydalanarak her iki kültüre de etki etmiştir. Tezde Müteferrika "kültürlerarası geçişlilik" (transculturality) kavramı üzerinden incelenmiştir. Bu tez, Osmanlı kültür dünyasının yekpare bir anlayışla var olduğu anlatının dışına çıkılmasını amaçlamakta ve farklı kültürlerden beslenen bu yapıyı Müteferrika üzerinden örneklendirmektedir.Ibrahim Müteferrika was born in Kolozsvár in the Transylvanian region and have received Christian education. Müteferrika, who came to Ottoman lands at the beginning of the 18th century with the ideas of cultural diversity of the Transylvania region, developed himself in the Ottoman language and culture and gained an important place in the Ottoman cultural world. He exhibited his multi-identity structure and his own interactions with his contributions to the published works in the printing house established with the official approval of the Ottoman administration. He synthesized all these worlds of ideas in his works. It has influenced both cultures by benefiting from both cultures with its identities that can co-exist simultaneously and without conflict. In the thesis, Müteferrika has been examined through the concept of "transculturality". This thesis aims to go beyond the narrative that the Ottoman cultural world exists with a monolithic understanding and exemplifies this structure, which is nourished by different cultures, through Müteferrika
Machine Learning Based Severity Classification of Obstructive Sleep Apnea Patients Using Awake Eeg
No full textObstructive sleep apnea (OSA) is one of the most widespread breathing-based sleep disorders. Previous studies reported daytime sleepiness, mental fatigue, and cognitive decline in patients with OSA. The diagnosis of OSA is done with laborious overnight polysomnography (PSG) recording. This study aims to classify the severity of OSA patients according to the Apnea-Hypopnea Index (AHI) into mild and moderate to-serve groups (AHI >= 15) without using recorded signals during sleep, non-PSG signals and investigate the relevant features. For this purpose, 25 OSA patients participated in 3-minute eyes closed resting state EEG session on the following morning of overnight PSG recording. Time, spectral domain, and nonlinear features were extracted from the delta, theta, and alpha subbands' of EEG signals. Several machine learning algorithms were used to classify patients' severity. To investigate optimal feature combinations features were grouped according to their types (Time domain/ spectral domain and nonlinear), their electrodes (frontal/central/parietal/occipital), and their subbands (delta/ theta/alpha). Also, the Relief method was applied to select the most relevant features. A 5-fold cross validation method was used to generalize the model behaviour. Optimal feature combinations were selected according to classifiers' performances that were evaluated by accuracy, sensitivity, specificity, and area under curve (AUC) parameters when feature sets were used as input. The 15 selected features by the Relief method showed the best performances in the K-Nearest Neighbours (K = 5) classifier [accuracy: 93.33 % +/- 5.27 %; sensitivity: 92.30 % +/- 4.97 %; specificity: 94.14 % +/- 4.24 %; AUC:0.98]. The findings establish that awake resting state EEG records might be used as a faster tool to use OSA severity level.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Kadın Sürücülere Yönelik Tutumlar Ölçeğinin Geliştirilmesi
No full textBu araştırmanın amacı, trafikte kadın sürücülere yönelik tutumların anlaşılabilmesi adına bir ölçek geliştirerek bu ölçeğin geçerlik ve güvenirlik çalışmalarının tamamlanmasıdır. Bu amaçla, ilgili alanyazın incelenerek 59 maddeden oluşan bir madde havuzu oluşturulmuştur. Araştırmaya 160’ı erkek ve 173’ü kadın olmak üzere toplam 333 kişi katılmıştır. Yapılan açıklayıcı ve doğrulayıcı faktör analizi sonuçları Kadın Sürücülere Yönelik Tutumlar Ölçeğinin toplam varyansın %57.60’ını açıklayan 15 maddelik tek faktörlü bir yapıda olduğunu göstermiştir. Ölçeğin Cronbach’s alpha iç tutarlık katsayısı .94 olarak bulgulanmıştır. Uyum geçerliğin test edilmesi amacıyla Sürücü Davranışları Ölçeği, Sürüş Becerileri Ölçeği ve Trafikte Öfke İfadesi Ölçeği; benzeme geçerliği için ise BEM Cinsiyet Rolleri Ölçeği ve Çelişkili Duygulu Cinsiyetçilik Ölçeği kullanılmıştır. Yapılan korelasyon ve regresyon analizlerinin sonuçları Kadın Sürücülere Yönelik Tutumlar Ölçeğinin ilgili alanyazında ve uygulamalarda kullanılabilecek geçerli ve güvenilir bir ölçek olduğunu göstermiştir. Araştırma sonuçlarının trafik ortamında kadın sürücülere yönelik tutumlarda değişim meydana getirecek programlarının geliştirilmesine ve trafikte istenmeyen davranışların azaltılmasına aracılık etmesi hedeflenmektedir
Search for Decays of the Higgs Boson Into a Pair of Pseudoscalar Particles Decaying Into B(b)over-Barτ+τ Using Pp Collisions at √s=13 TeV with the ATLAS Detector
No full textWang, Zirui/0000-0002-0928-2070; Majersky, Oliver/0000-0001-8857-5770; Boudet, Leo/0000-0002-3613-3142; Dell'Acqua, Andrea/0000-0003-2453-7745; Bhatta, Somadutta/0000-0002-9045-3278; Bahmani, Marzieh/0000-0003-4173-0926; Gutierrez Zagazeta, Luis Felipe/0000-0003-0374-1595; Wang, Xiaoning/0000-0002-2411-7399; Aoki, Masato/0000-0001-7498-0097; Moskalets, Tetiana/0000-0001-6508-3968; Bruschi, Marco/0000-0002-4319-4023; Schultz-Coulon, Hans-Christian/0000-0002-0860-7240; Belfkir, Mohamed/0000-0001-9974-1527; Fanti, Marcello/0000-0002-8773-145X; Sinha, Supriya/0000-0002-3600-2804; Mohapatra, Soumya/0000-0003-3006-6337; Leban, Blaz/0000-0003-1501-7262; Robson, Aidan/0000-0002-1659-8284; Onofre, Antonio/0000-0003-3471-2703; Chou, Yuan-Tang/0000-0002-2204-5731; Sykora, Ivan/0000-0003-3447-5621; , Sascha/0000-0003-2941-2829; Cheong, Sanha/0000-0002-2797-6383; Proklova, Nadezda/0000-0002-5237-0201; Rotaru, Marina/0000-0003-4088-6275; Grancagnolo, Sergio/0000-0001-8490-8304; Danninger, Matthias/0000-0002-7807-7484; Dao, Valerio/0000-0003-1645-8393; Abdelrazek Aboelela, Mohammed/0009-0003-6578-220X; Valente, Marco/0000-0002-0486-9569; Sampsonidou, Despoina/0000-0003-0384-7672; Betti, Alessandra/0000-0003-0839-9311; Haas, Andrew/0000-0002-4832-0455; Wielers, Monika/0000-0001-9232-4827; Nikiforou, Nikiforos/0000-0003-1267-7740; Deliot, Frederic/0000-0003-0777-6031; Cranmer, Kyle/0000-0002-5769-7094; Moenig, Klaus/0000-0002-3169-7117; Liu, Xiaotian/0000-0003-1366-5530; Pizzini, Alessio/0000-0001-8891-1842; Santra, Arka/0000-0003-4644-2579; Vermeulen, Jos/0000-0003-4378-5736; Vickey Boeriu, Oana/0000-0002-6497-6809; Alexa, Calin/0000-0003-0922-7669; Saito, Masahiko/0000-0001-5564-0935; Dziedzic, Bartosz/0000-0002-0805-9184; Sinha, Sukanya/0000-0002-2438-3785; Coelli, Simone/0000-0002-5145-3646; Ballabene, Eric/0000-0001-9700-2587; Sawyer, Craig/0000-0002-2027-1428; Bouquet, Romain/0000-0001-9683-7101; Khoda, Elham E/0000-0001-8720-6615; Vale, Tiago/0000-0001-8855-3520; Balek, Petr/0000-0002-0942-1966; Lewicki, Maciej Piotr/0000-0002-8972-3066; Falda Ulhoa Coelho, Luis Felipe/0000-0002-2298-3605; Klein, Lucas/0000-0002-0145-4747; Wu, Sau Lan/0000-0001-5866-1504; Romain, Madar/0000-0002-6875-6408; Burlayenko, Oleksandr/0000-0001-8283-935X; Garcia Navarro, Jose Enrique/0000-0002-0279-0523; Van Daalen, Tal/0000-0002-2254-125X; Warburton, Andreas/0000-0002-2298-7315; Da Fonseca Pinto, Joao Victor/0000-0003-1746-1914; Biswal, Jyoti Prakash/0000-0001-8361-2309; Hugging, Fabian/0000-0002-7472-3151; Mazzeo, Elena/0000-0002-8406-0195; Kempster, Jacob/0000-0003-4168-3373; Mondal, Santu/0000-0002-6965-7380; Nielsen, Jason/0000-0002-9175-4419; Khwaira, Yahya/0000-0001-8538-1647; Wiedenmann, Werner/0000-0003-3605-3633; Muller, Roman/0000-0002-5835-0690; Rohne, Ole Myren/0000-0001-7744-9584; Kumari, Neelam/0000-0001-9174-6200; Behr, J. Katharina/0000-0002-5501-4640; Mezquita, Costa/0000-0002-2064-2954; Martinelli, Luca/0000-0002-4466-3864; Genest, Marie-Helene/0000-0002-4098-2024; Boumediene, Djamel/0000-0002-7809-3118; Zenz, Seth/0000-0002-9720-1794; Gee, Carolyn/0000-0002-3271-7861; Barak, Liron/0000-0002-3436-2726; Dado, Tomas/0000-0002-7050-2669; Lysak, Roman/0000-0003-2990-1673; Pereira Sanchez, Laura/0000-0001-7913-3313; Delitzsch, Chris Malena/0000-0001-7021-3333; Sadrozinski, Hartmut/0000-0003-0019-5410; Redlinger, George/0000-0002-6437-9991; Xu, Lailin/0000-0001-8997-3199; Masetti, Lucia/0000-0002-0038-5372; Liu, Kun/0000-0001-5807-0501; Vivarelli, Iacopo/0000-0003-0097-123X; Weiser, Christian/0000-0002-6456-6834; Nechaeva, Serafima/0000-0002-0623-9034; Gorisek, Andrej/0000-0002-3903-3438; Mijovic, Liza/0000-0003-0162-2891; Heinrich, Jochen Jens/0000-0002-0253-0924; Lister, Alison/0000-0002-1552-3651; Maniatis, Ioannis/0000-0002-4362-0088; Baron, Petr/0000-0002-5170-0053; Price, Darren/0000-0003-2750-9977; Xella, Stefania/0000-0002-0988-1655; 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