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Asymptotic Expansions for the Stationary Moments of a Modified Renewal-Reward Process With Dependent Components
No full textAbstract: In this paper, a modification of a renewal-reward process with dependent components is mathematically constructed and the stationary characteristics of this process are studied. Stochastic processes with dependent components have rarely been studied in the literature owing to their complex mathematical structure. We partially fill the gap by studying the effect of the dependence assumption on the stationary properties of the process. To this end, first, we obtain explicit formulas for the ergodic distribution and the stationary moments of the process. Then we analyze the asymptotic behavior of the stationary moments of the process by using the basic results of the renewal theory and the Laplace transform method. Based on the analysis, we obtain two-term asymptotic expansions of the stationary moments. Moreover, we present two-term asymptotic expansions for the expectation, variance, and standard deviation of the process. Finally, the asymptotic results obtained are examined in special cases. © Pleiades Publishing, Ltd. 2024
Search for Heavy Neutral Higgs Bosons Decaying Into a Top Quark Pair in 140 FB−1 of Proton-Proton Collision Data at √s = 13 TeV With the ATLAS Detector
No full textA search for heavy pseudo-scalar (A) and scalar (H) Higgs bosons decaying into a top-quark pair (tt¯) has been performed with 140 fb−1 of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a centre-of-mass energy of s = 13 TeV. Interference effects between the signal process and Standard Model (SM) tt¯ production are taken into account. Final states with exactly one or exactly two electrons or muons are considered. No significant deviation from the SM prediction is observed. The results of the search are interpreted in the context of a two-Higgs-doublet model (2HDM) of type II in the alignment limit with mass-degenerate pseudo-scalar and scalar Higgs bosons (mA = mH) and the hMSSM parameterisation of the minimal supersymmetric extension of the Standard Model. Ratios of the two vacuum expectation values, tan β, smaller than 3.49 (3.16) are excluded at 95% confidence level for mA = mH = 400 GeV in the 2HDM (hMSSM). Masses up to 1240 GeV are excluded for the lowest tested tan β value of 0.4 in the 2HDM. In the hMSSM, masses up to 950 GeV are excluded for tan β = 1.0. In addition, generic exclusion limits are derived separately for single scalar and pseudo-scalar states for different choices of their mass and total width. © The Author(s) 2024.Agencia Nacional de Investigación y Desarrollo; BSF-NSF; Australian Research Council, ARC; La Caixa Banking Foundation; Centre National pour la Recherche Scientifique et Technique, CNRST; NAWA; Center for African Studies, CAS; Fundação para a Ciência e a Tecnologia, FCT; European Union, Future Artificial Intelligence Research; European Organization for Nuclear Research; Polish National Science Centre; Georgia Health Initiative, HGF; National Science Foundation, NSF; Baden-Württemberg Stiftung; Science and Technology Facilities Council, STFC; Horizon 2020, ICSC-NextGenerationEU; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Ministerio de Ciencia e Innovación, MCIN; Ministry of Science and Innovation; Istituto Nazionale di Fisica Nucleare; Japan Society for the Promotion of Science; PROMETEO; Spine Education and Research Institute, SERI; The Slovenian Research and Innovation Agency, ARIS; Ministry of Education Youth and Sports; Neubauer Family Foundation; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Austrian Science Fund, FWF; BCKDF; ERDF; Agencia Nacional de Investigación y Desarrollo, ANID; Bundesministerium für Bildung und Forschung, BMBF; Slovenian Research Agency; Canada Foundation for Innovation, FCI; Danmarks Grundforskningsfond, DNRF; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Göran Gustafssons Stiftelse; MIZŠ; Generalitat de Catalunya; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MEST; U.S. Department of Energy, USDOE; EU-ESF; COST; CRC; Generalitat Valenciana; RGC; Duchenne Research Fund, DRF; Netherlands Organisation for Scientific Research; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Islamic Scholarship Fund, ISF; ICSC; ANR; Institutul de Fizică Atomică, IFA; Ministry of Science and Technology of the People's Republic of China, MOST; Natural Sciences and Engineering Research Council of Canada, CRSNG; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; GenT Programmes Generalitat Valenciana, Spain; Marie Skłodowska-Curie Actions; EU; MINERVA, Israel; Irish Rugby Football Union, IRFU; Cantons of Bern and Geneva; Defence Science Institute, DSI; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Horizon 2020 Framework Programme; MNE; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Marcus och Amalia Wallenbergs minnesfond, MMW; CERN-CZ; National Research Foundation, NRF; Ministerstwo Edukacji i Nauki, MNiSW; European Research Council; CERN, CERN; Ministerstvo Školství, Mládeže a Tělovýchovy, MSMT; European Union; National Research Council Canada, NRC; Multiple Sclerosis Scientific Research Foundation, MSSRF; DFG; AvH Foundation; Istituto Nazionale di Fisica Nucleare, INFN; CANARIE; Ministry of Education, Culture, Sports, Science and Technology, MEXT; FAIR-NextGenerationEU, (PE00000013); FORTE, (CZ.02.01.01/00/22_008/0004632, PRIMUS/21/SCI/017); H2020 European Research Council, (ERC — 101002463); Chinese Ministry of Science and Technology, (MOST-2023YFA1605700); National Natural Science Foundation of China, NNSF, (12275265, NSFC-12075060); National Natural Science Foundation of China, NNSF; Agence Nationale de la Recherche, (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022); Knut and Alice Wallenberg Foundation, (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); MCIN, (RYC2021-031273-I, RYC2019-028510-I, PID2021-125273NB, RYC2022-038164-I, RYC2020-030254-I, PCI2022-135018-2); U.S. Department of Energy, (ECA DE-AC02-76SF00515); Deutsche Forschungsgemeinschaft, (DFG — CR 312/5-2, DFG — 469666862); Polish National Agency for Academic Exchange, (PPN/PPO/2020/1/00002/U/00001); DNSRC, (IN2P3-CNRS); Royal Society, (NIF-R1-231091); National Natural Science Foundation of China, (NSFC — 12175119); MUCCA, (CHIST-ERA-19-XAI-00); Leverhulme Trust, (RPG-2020-004); Czech Science Foundation, (GACR — 2411373S); DRAC, (21/SCI/017); GenT Programmes Generalitat Valenciana, (CIDEGENT/2019/027, CIDEGENT/2019/023); ERC, (948254, 101089007); Japan Society for the Promotion of Science, JSPS, (JP21H05085, JP22KK0227, JP22H04944, JP22H01227); Japan Society for the Promotion of Science, JSPS; Norwegian Financial Mechanism, (2014-2021); ARIS, (J1-3010); FONDECYT, (1230987, 1210400, 1190886, 1230812); Swiss National Science Foundation, (SNSF — PCEFP2_194658); Swedish Research Council, (VR 2018-00482, VR 2022-03845, VR 2023-03403, 2023-04654, 2021-03651, VR 2022-04683); FEDER, (IDIFEDER/2018/048); NCN, (UMO-2020/37/B/ST2/01043, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2021/40/C/ST2/00187, 2022/47/B/ST2/03059, 2021/42/E/ST2/00350, UMO-2019/34/E/ST2/00393); Research Council of Norway, (RCN-314472); Investissements d’Avenir Labex, (ANR-11-LABX-0012
Çoklu Hava-Hava Angajmanı Problemine Bir Atış Bölgesi Tabanlı Modelleme ve Kontrol Yaklaşımı
No full textAtış zarfı belirlenmesi problemi askeri havacılık tarihinin başlangıcından beri genel bir uygulama problemi olmuştur. Bu alandaki ilk çalışmalar atış cetvelleri/çizelgeleri yardımıyla gerçekleştirilmişken günümüze gelindiğinde bu problemler için karmaşık hesaplama yöntemleri ve ileri seviye uçuş benzetimlerinden faydalanılmaktadır. Hava görevlerinde güdümlü silahların veya füzelerin atış sonrası erişebilecekleri silah angajman alanı (WEZ) bilgisine geçek zamanlı olarak sahip olunması, görev başarımı için ve bilhassa hava-hava görevlerinde pilotlar için hayati bir önem taşımaktadır. Hızlı değişen dinamikleri sebebiyle hava-hava angajmanlarında WEZ bilgisine gerçek zamanlı ya da gerçek zamana yakın bir şekilde ihtiyaç duyulmaktadır. Bunun için hızlı çalışan fakat düşük detay seviyeli uçuş benzetimleri kullanılabileceği gibi, önceden elde edilmiş yüksek doğruluktaki altı serbestlik dereceli uçuş benzetim verileri kullanılarak da modelleme yapılabilir. Benzetim verilerinin kullanıldığı hava-yer görevlerinde bu teknik olası tüm uçuş koşullarındaki benzetim sonuçlarının modellemesi şeklinde uygulanabiliyorken, hava-hava problemlerinde tüm koşulları kapsamak için hedef irtifası, Mach sayısı ve görüş açıları gibi artan angajman parametrelerine ihtiyaç duyulmaktadır. Bu parametreler eklendiğinde WEZ modellemesi için gereken benzetim sayısı katlanarak artmaktadır. Bu problemin çözümü için, tez çalışmanın ilk kısmında genel kapsamlı bir hava-hava füzesi için yüksek doğrulukta ve detay seviyesinde altı serbestlik dereceli bir benzetim modelli geliştirilmiştir. Daha sonra yüksek sayıdaki benzetim ihtiyacının azaltılması amacıyla modern ardışık deney tasarımı yöntemleri ve sinir ağlarının yinelemeli kullanımına dayalı yeni bir modelleme yöntemi sunulmuştur. Bu yöntemde, yinelemeli bir şekilde deney tasarımı yoluyla elde edilen noktalarda uçuş benzetimleri ve modelleme yapılarak matematiksel WEZ modelleri istenen performans seviyesine gelene kadar devam edilerek türetilmiştir. Çalışmanın ikinci kısmında ise çok etmenli bir hava-hava angajmanı için uçak modelleri geliştirilerek otonom angajman manevraları problemi ele alınmış, elde edilen WEZ modellerinin kontrol kısıtı veya başarım ölçütü şeklinde angajman problemine tanıtılması değerlendirilmiştir. Bu noktada yüksek dinamikler içeren bu sistem için geleneksel sürü kontrolü yöntemlerinin aksine son yıllarda hızla gelişen takviyeli öğrenme ile kontrol yöntemi uygulanmıştır. Çok etmenli hava-hava angajmanları için, önce tekli angajmanda otonom manevra kararı alacak bir algoritma hazırlanmıştır. Daha sonra, etmenlerin istenen sistem durumlarına hareket etmelerini sağlamak için bir hedef tahsis algoritması ve WEZ alanlarını dikkate alan bir çoklu angajman mimarisi oluşturulmuştur Çalışmanın sonucunda elde edilen öğrenme performansı ve angajman sonuçları benzetim ortamında farklı senaryoları için doğrulanmıştır.The problem of launch envelope generation has been a general application problem since the beginning of military aviation history. While the first studies in this field were carried out with the help of firing tables/charts, today, complex computational methods and advanced flight simulations are used for these problems. In air missions, having real-time weapon engagement zone (WEZ) information for guided weapons or missiles is of vital importance for mission success, especially for pilots in air-to-air missions. Real time WEZ information can be obtained through fast but low-fidelity flight simulations, or it can be modeled using previously obtained high accuracy six-degree-of-freedom flight simulation data. In this technique of using simulation data, while air-ground missions can be implemented by modeling simulated results in all possible flight conditions, the number of simulations needed to cover all conditions of air-to-air engagement increases exponentially with increasing parameters such as target altitude, Mach number, and aspect angles. In the first part of this thesis, in order to solve this problem a six-degree-of-freedom simulation model for a generic air-to-air missile with high fidelity and detail level is developed. Then, in order to reduce the need for a large number of flight simulations, a new modeling method based on modern sequential design of experiments and iterative use of neural networks is presented. In this method, flight simulations and modeling are performed at the design points iteratively and mathematical WEZ models are derived by continuing until the desired performance level is reached. In the second part of the study, the problem of autonomous engagement maneuvers is addressed by developing aircraft models for a multi-agent air-to-air engagement environment, and the introduction of the obtained WEZ models to the problem in the form of control constraints and performance indices is evaluated. In contrast to traditional swarm control methods, control with reinforcement learning, which has developed rapidly in recent years, has been applied for this highly dynamic system. For multi-agent air-to-air engagements, first an algorithm to take autonomous maneuver decisions in single engagement is prepared. Then, a target allocation algorithm and a multi-engagement architecture that considers WEZ domains are established to ensure that the agents move to the desired system states. The learning performance and engagement results obtained from the study are validated in a simulation environment for different scenarios
Search for Leptoquark Pair Production Decaying Into i>te/I>sup>- or i>t/I>μsup>- in Multi-Lepton Final States in Pp Collisions at √i>s/I>=13tev With the Atlas Detector
No full textHaley, Joseph/0000-0002-6938-7405; Konstantinidis, Nikolaos/0000-0002-4140-6360; KHWAIRA, Yahya/0000-0001-8538-1647; Camplani, Alessandra/0000-0002-6386-9788; Potti, Harish/0000-0002-0800-9902; Butterworth, Jonathan/0000-0002-5905-5394; Petersen, Troels/0000-0003-0221-3037; Mitsou, Vasiliki A./0000-0002-1533-8886; Smirnova, Oxana/0000-0003-2517-531X; Saoucha, Kamal/0000-0001-9150-640X; Tian, Yusong/0000-0001-8739-9250; McKee, Shawn/0000-0002-4551-4502; Herde, Hannah/0000-0001-8926-6734; Calafiura, Paolo/0000-0002-1692-1678; Ahmadov, Faig/0000-0003-3644-540X; /0000-0001-5765-1750; Follega, Francesco Maria/0000-0003-2317-9560; Stanislaus, Beojan/0000-0001-9007-7658A search for leptoquark pair production decaying into te(-)(t) over bare(+) or t mu(-)(t) over bar mu(+) in final states with multiple leptons is presented. The search is based on a dataset of pp collisions at root s = 13TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb(-1). Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from a b-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into te(-) (t mu(-)), the corresponding lower limit on the scalar mixed-generation leptoquark mass m(LQmixd) is at 1.58 (1.59) TeV and on the vector leptoquark mass m((U) over tilde1) at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang-Mills scenario.We thank CERN for the very successful operation of the LHC and its injectors, as well as the support staff at CERN and at our institutions worldwide without whom ATLAS could not be operated efficiently. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [129]. 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; 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; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skodowska-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 DOCT); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1210400); China: National Natural Science Foundation of China (NSFC - 12175119); EU: H2020 European Research Council (H2020-MSCA-IF-2020: HPOFHIC - 10103); European Union: European Research Council (ERC - 948254), Horizon 2020 Framework Programme (MUCCA - CHIST-ERA-19-XAI-00), 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); Germany: Deutsche Forschungsgemeinschaft (DFG - CR 312/5-1); Italy: Istituto Nazionale di Fisica Nucleare (FELLINI G.A. n. 754496); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI 22H01227, JSPS KAKENHI JP21H05085, 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 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 (La Caixa Foundation, LCF/BQ/PI20/11760025), Ministry of Science and Innovation (RYC2019-028510-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027, CIDEGENT/2019/029, GVA-SEJI/2020/037); Sweden: Swedish Research Council (SRC - 2017-05160, VR 2017-05092), Knut and Alice Wallenberg Foundation (KAW 2017.0100, KAW 2018.0157, KAW 2019.0447); Switzerland: Swiss National Science Foundation (SNSF - PCEFP2_194658); UAE: Arab Fund for Economic and Social Development (AFESD-2021-994); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004).CERN; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands), PIC (Spain); BNL (USA) [129]; ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW; FWF, Austria; ANAS; CNPq; FAPESP, Brazil; NSERC; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF; DNSRC, Denmark; IN2P3-CNRS; CEA-DRF/IRFU, France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF; Benoziyo Center, Israel; INFN, Italy; MEXT; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; 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 Skodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex; ANR, France; DFG; AvH Foundation, Germany - EU-ESF; Greek NSRF, Greece; BSF-NSF; NCN; La Caixa Banking Foundation; CERCA Programme Generalitat de Catalunya; PROMETEO; Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society; Leverhulme Trust, United Kingdom; CERN: European Organization for Nuclear Research; Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT) [1190886, FONDECYT 1210400]; China: National Natural Science Foundation of China [NSFC - 12175119]; EU [H2020-MSCA-IF-2020: HPOFHIC - 10103]; European Union: European Research Council [ERC - 948254, MUCCA - CHIST-ERA-19-XAI-00]; 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]; Germany: Deutsche Forschungsgemeinschaft [DFG - CR 312/5-1]; Istituto Nazionale di Fisica Nucleare [754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [22H01227, JP21H05085, 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 [NCN UMO-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 (Artemisa, FEDER) [IDIFEDER/2018/048]; La Caixa Banking Foundation (La Caixa Foundation) [RYC2019-028510-I]; GenT Programmes Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027, CIDEGENT/2019/029, GVA-SEJI/2020/037, SRC - 2017-05160, VR 2017-05092]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, SNSF - PCEFP2_194658]; UAE: Arab Fund for Economic and Social Development [AFESD-2021-994]; United Kingdom: Leverhulme Trust (Leverhulme Trust) [RPG-2020-004
Attainment and Gender Equality in Higher Education: Evidence From a Large-Scale Expansion
No full textWe examine the causal effects of the drastic expansion in Turkish higher education on the attainment disadvantage of women by using the variation in exposure intensity across cohorts and regions. The expansion increased the attainment rates of both genders but did not significantly reduce the gender gap after controlling for time trend. Studying the mechanisms, we observe that the expansion in social sciences, more than half of additional slots, benefited men and women evenly, but the expansion in engineering, about 25% of additional slots, benefited men more. The results are robust to a wide range of checks for alternative specifications, samples, and policies
Diagnostic Machine Learning Applications on Clinical Populations Using Functional Near Infrared Spectroscopy: a Review
No full textFunctional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (Delta HbO) based features were used more than concentration changes in deoxy-hemoglobin (Delta b) based ones and the most popular Delta HbO-based features were mean Delta HbO (n = 11) and Delta HbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.Institute of Photonic SciencesWe would like to thank Prof. Dr. Turgut Durduran from the Institute of Photonic Sciences (ICFO, Barcelona, Spain) for his valuable and constructive suggestions during the planning and development of this review
Güvene Dayalı Mesleklerde Markalaşma Kriterleri
No full textThis study aims to investigate personal branding criteria in credence based professions, with a particular focus on doctors, university professors and auto mechanics. The research identifies important attributes that contribute to successful personal branding in these professions. Using a mixed methods approach, the study conducted preliminary surveys with 80 respondents to identify key attributes, followed by detailed surveys with 303 respondents to assess the importance of these attributes. The findings show that while doctors favour qualities such as being good at their job, innovative and well-groomed, university professors favour qualities such as being good at their job, innovative and fair. For auto mechanics, qualities such as being good at their job, experienced and meticulous come to the fore. The qualifications obtained for each occupation and the value attributed to these qualifications in the specific occupational group are different. These findings emphasise the importance of certain personal branding attributes in enhancing professional reputation and success in credence based professions.Bu çalışma, güvene dayalı mesleklerde kişisel markalaşma kriterlerini araştırmayı amaçlamakta olup, özellikle doktorlar, üniversite profesörleri ve oto tamircilerine odaklanmaktadır. Araştırma, bu mesleklerde başarılı kişisel markalaşmaya katkıda bulunan önemli nitelikleri belirlemektedir. Karma yöntem yaklaşımı kullanılarak gerçekleştirilen çalışmada, temel nitelikleri belirlemek için 80 katılımcı ile ön anketler yapılmış, ardından bu niteliklerin önemini değerlendirmek için 303 kişiye detaylı anketler uygulanmıştır. Bulgular, doktorlar için işinde iyi, yenilikçi ve bakımlı olma gibi nitelikleri desteklerken üniversite profesörleri için işinde iyi, yenilikçi ve adaletli olma gibi niteliklerin önemli olduğunu göstermektedir. Oto tamircileri için ise işinde iyi, deneyimli ve titiz olma gibi nitelikler ön plana çıkmaktadır. Her meslek için elde edilen nitelikler ve bu niteliklere o meslek grubu spesifiğinde atfedilen değer farklıdır. Bu bulgular, güvene dayalı mesleklerde profesyonel itibar ve başarının artırılmasında belirli kişisel markalaşma niteliklerinin önemini vurgulamaktadır
Sufficiency of Struggling With the Current Criminal Law Rules on the Use of Artificial Intelligence in Crime
No full textEvery new technology affects crime, which is a social phenomenon. This interaction is in the form of either the emergence of new forms of crime or the facilitation of committing the crime. Based on the definition of intelligence as the ability to adapt to changes, artificial intelligence is defined as “the ability to perceive a complex situation and make rational decisions accordingly”. Based on this definition, in cases where the decisions taken constitute a crime, it is necessary to determine the responsibility in terms of criminal law. The criminal responsibility of artificial intelligence may immediately come to mind. However, holding artificial intelligence, which does not form a legal personality, responsible in terms of criminal law is a controversial situation. Secondly, the responsibility of the software developer who created the artificial intelligence algorithm can be discussed here as well. And yet, in this second case, the willful or negligent responsibility of the software developer should be examined separately. In terms of the negligent responsibility of the software developer who created the artificial intelligence algorithm, the issue of whether artificial intelligence can be used in committing a crime is predictable should be addressed. In this paper, it will be examined whether the existing regulations will be sufficient to determine the responsibility in terms of criminal law where the artificial intelligence algorithm is used in the commission of a crime. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024
A Trade-off Based Assessment Study on Possible Coating Alternatives for Armored Combat Vehicles
No full textSTO-MP-AVT-373 Research Specialists' Meeting "Emerging Technologies for Proactive Corrosion Maintenance"Armored combat vehicles often encounter harsh service conditions, especially in environments like tropical weather, which in turn necessitates high corrosion resistance regarding durability and/or reliability requirements. Consequently, several solutions are employed to prevent corrosion, beginning from the design phase and extending to maintenance procedures. In many cases, appropriate coating processes are utilized during the design activities, and they can be complemented by specialized painting processes like Chemical Agent Resistant Coating. The selection of suitable coating systems depends on various parameters, including the material of the workpiece, design requirements, and cost-effectiveness. In this regard, the armored combat vehicle industry possesses a distinctive character due to its widespread use of high-strength materials. This reality highlights the phenomenon of hydrogen embrittlement, which can occur due to electroplating or environmental corrosion. If the dissolved hydrogen in the workpiece material reaches a certain threshold, it significantly impairs overall mechanical performance and leads to sudden catastrophic failures. This study aims to compare different coating alternatives in terms of their impact on hydrogen embrittlement in high-strength materials used in armored combat vehicles. To achieve this goal, samples of high-strength steel substrates coated with various alternatives were examined for their susceptibility to hydrogen embrittlement following ASTM F519 standards. To understand the effect of environmental corrosion on hydrogen embrittlement, advanced tests were designed based on preliminary results obtained from ASTM F519 tests, including salt spray tests according to ASTM B11