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From Diversity To Discrimination: a Comparative Study of Blue-, Grey- and White-Collars’ Perceptions and Experiences
No full textBases of discrimination are also dimensions of diversity. However, not every dimension of diversity automatically becomes a ground of discrimination. Thus, this paper aims to identify the dimensions of diversity which are also bases for discrimination in a workplace. As different-collar workers may have different perceptions and experiences, this study aims to compare blue-, grey-, and white-collar workers. The study adopted an exploratory approach, and semi-structured interviews were conducted with eleven workers. The questions were based on a diversity wheel which includes a wide range of diversity dimensions. The findings show that any dimension of diversity can also be a basis for discrimination, with peer-to-peer discrimination being the most prevalent among blue- and grey-collars. This research contributes to a more comprehensive understanding of discrimination, as many existing papers on diversity only cover a few dimensions or bases. Additionally, this study sheds light on an under-researched area of discrimination among blue- and grey-collar workers
Moment-Based Approximations for Stochastic Control Model of Type (s, S)
No full textIn this study, we propose an approximation for a renewal reward process that describes a stochastic control model of type (s, S) based on the first three moments of demand random variables. Various asymptotic expansions for this model exist in the literature. All these studies rely on the condition of knowing the distribution function of demand random variables and require obtaining the asymptotic expansion of the renewal function produced by them. However, obtaining a renewal function can be challenging for certain distribution families, and in some cases, the mathematical structure of the renewal function is difficult to apply. Therefore, in this study, simple and compact approximations are presented for the stochastic control model of type (s, S). The findings of this study rely on Kambo's method, through which we obtain approximations for the ergodic distribution, and the nth order ergodic moments of this process. To conclude the study, the accuracy of the proposed approximate formulas are examined through a specialized illustrative example. Moreover, it has been noted that the proposed approximation is more accurate than the approximations existing in the literature
Computational Modeling and Investigation of the Vibro-Acoustic Effects Induced by Intracranial Stenosis in a Simplified Head Model
No full textPurpose Intracranial stenosis is a critical cardiovascular disorder which may lead to recurrent ischemic stroke. The flow in a stenosed artery tends to have a chaotic pattern, resulting in fluctuating pressures on the artery wall. These acoustic pressures are known as the sources of arterial noise and vibration. The generated vibro-acoustic signals on the stenosed artery propagate through the surrounding body tissues and reach the epidermal surface. In this study, the stenosis-induced vibration on the epidermal surface is aimed to be utilized for the diagnosis of occlusion in the intracranial artery. Methods An intracranial stenosis is modeled considering a simplified head geometry consisted of brain, skull, and skin. The simplified head model has an idealized spherical form. Three different stenosis severities are employed to elucidate the effects of mild, moderate, and severe intracranial stenosis. ADINA finite element analysis software package is used to perform harmonic and modal analyses to determine the biodynamic responses on the epidermal surface of a human head. Results According to the results obtained, vibration amplitudes on the epidermal surface tend to increase with increasing stenosis severity. The stenosis-induced effects on the vibration responses are particularly prominent in the frequency range of 400-1000 Hz. Abnormal increase in vibration amplitudes between 400 and 1000 Hz can be an indicator of further occlusion in the intracranial artery. Conclusions The stenosis-induced vibro-acoustic effects provide important clues about arterial health. Excessive vibration amplitudes indicate a potential arterial occlusion and can be used as an early diagnostic tool for intracranial stenosis detection.TuBTAK (The Scientific and Technological Research Council of Turkiye) 3501-Career Development Program [221M001]This study is funded by TuBTAK (The Scientific and Technological Research Council of Turkiye) 3501-Career Development Program (Project number: 221M001)
19. Yüzyıl Osmanlı Sosyo-ekonomik Tarihinin Bir Parçası Olarak Selanik Bölgesi
No full text[No Abstract Available
Statistical Analysis of Infant Thermal Support Devices: Performance Evaluation in Heating and Energy Consumption
No full text2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703The occurrence of premature birth or specific challenges during the neonatal phase is far from being a recent concern. As the impact of birth and postnatal care on neonatal morbidity and mortality rates notions became evident, the necessity for specialized units dedicated to newborns emerged within hospitals. Following birth, newborns encounter several challenges; among these, regulating their own body temperature at an optimum level is the most common and critical one. To address this, multiple devices have been designed explicitly for newborns, providing thermal energy support to stabilize their body temperature. In this study, these devices have been categorized based on their heating functionalities. Consequently, the devices underwent standardized test procedures to analyze their thermal performance and electrical consumption. Although the purposes of the devices are considered to be different on newborns, when the data obtained from conducted analyses are taken into account, the devices with the least energy consumption is the warmer bed device (0.290 kWh), and the device with the most effective heating performance (mid-surrounding points mean temperature difference: $0. 5150 ± 0. 0461 ° C is found to be the incubator. © 2023 IEEE
Sosyal Medyada Kullanıcı Gizliliğini Korumak için Taraf Tespiti Görevinde Dönüştürücü Dil Modellerini Yanıltma Yöntemleri
No full textThe recent advances in natural language processing have led to a better understanding of language complexities and more successful outcomes in text analysis and comprehension models. Social media platforms, which offer large datasets for natural language processing models, have become an integral part of people's daily lives. Individuals express their emotions, thoughts, experiences, and various personal information through the text they share on social media platforms. However, the ability of artificial intelligence models to track and analyze this data has raised significant privacy concerns among users. In this thesis, we investigate what individuals using social media platforms can do to avoid being detected by artificial intelligence models. We address the task of stance detection on various topics using Turkish and English datasets. We propose methods for BERT and BERTurk-based transformer models to deceive the models by rephrasing and introducing intentional spelling errors. We investigate the effectiveness of the 13 different methods based on their impact on the models' performances. Our experiments demonstrate that intentional spelling error methods significantly reduce the performance of BERT and BERTurk-based models for stance detection. The most effective methods for spelling errors in both languages involve using visually similar characters, splitting words by adding spaces and shuffling the order of letters in words. However, paraphrasing methods are found to be unsuccessful in affecting the models' performances. Two different approaches, manual and automatic, were used for applying the methods. The automatic application of the methods aimed to retain the readability and original meanings of the resulting texts. Two evaluators ensured these checks, and some methods were found to result in reduced readability and changes in semantics due to automatic modifications. Hence, caution is advised in applying shuffle, delete hashtag and adding space. Another point is that in methods based on hashtags, hashtag selections are very important and can cause models to perform better. However, removing or not using hashtags has been more effective in most cases. The proposed methods and the results obtained serve as a guiding reference for users who want to protect their information and privacy from artificial intelligence models.Doğal dil işleme alanındaki heyecan verici yeni gelişmeler dillerin karmaşıklıklarının daha iyi anlaşılmasını, metinler üzerinden yapılan anlam çıkarımları ve analizlerle daha başarılı sonuçlar ortaya koyulmasını sağlamıştır. Doğal dil işleme modelleri için geniş veri kümeleri sunan sosyal medya platformlarının kullanımı her geçen gün artarak insanların günlük hayatlarının önemli bir parçasına haline gelmiştir. İnsanlar, sosyal medya platformları üzerinden paylaştıkları metinlerde duygularını, düşüncelerini, deneyimlerini ve kendileriyle ilgili kişisel birçok bilgiyi ifade edebilmektedir. Yapay zekâ modellerinin, bu verileri insanların takip edilmesinde kullanabilmesi, kullanıcılarda önemli gizlilik endişelerini de beraberinde getirmiştir. Bu tez çalışmasında, sosyal medya platformlarını kullanan bireylerin yapay zekâ modelleri tarafından tespit edilememeleri için yapabileceklerini araştırıyoruz. Araştırmamızda birçok konuda kullanıma açık olan taraf tespiti görevini çeşitli konulardaki Türkçe ve İngilizce veri kümeleriyle ele alıyoruz. BERT ve BERTurk tabanlı dönüştürücü modellerini, yanıltmak amacıyla yeniden ifade etme ve kasıtlı yazım hataları yapma tabanlı yöntemler öneriyoruz. Önerilen 13 farklı yöntemin modellerin performanslarını etkileme seviyelerine göre etkinliklerini araştırıyoruz. Deneylerimiz sonucunda, yazım hataları karşısında BERT ve BERTurk tabanlı modellerin performanslarının belirgin bir şekilde düştüğü gösterilmiştir. Yazım hatalarına yönelik yöntemlerden iki dilde de en etkili yöntemlerin görsel olarak benzer karakterleri birbirleri yerine kullanma, boşluk ekleyerek kelimeyi bölme ve kelimelerdeki harflerin sıralarını karıştırma olduğu sonucuna ulaşılmıştır. Fakat bunula birlikte, yeniden ifade etme yöntemlerinin bu modellerin performanslarını etkileme konusunda başarılı olmadığı görülmüştür. Yöntemlerin uygulanmasında manuel ve otomatik olmak üzere iki farklı yöntem kullanılmıştır. Yöntemlerin otomatik uygulanması sonucunda elde edilen metinlerin hâlâ eski anlamlarını koruyarak okunabilir olması istenmiştir. İki değerlendirici tarafından bu kontroller sağlanmış olup harf sıralarını karıştırma, hashtag silme ve boşluk ekleme yöntemleri kullanılarak yapılan otomatik değişiklikler sonucunda okunurluğun azalması ve anlam değişmeleri gibi durumlar tespit edilmiştir. Bu sebeple bu yöntemlerin uygulanması konusunda daha dikkatli olunması gerektiği sonucuna ulaşılmıştır. Diğer bir nokta ise hashtag'lere dayalı yöntemlerde hashtag seçimleri oldukça önemli olup modellerin daha iyi performans göstermesine de sebep olabilmektedir. Bununla birlikte hashtag silme ve hashtag kullanılmaması çoğu durumda daha etkili sonuçlar vermiştir. Önerdiğimiz yöntemler ve elde ettiğimiz sonuçlar, bilgi ve gizliliklerini yapay zekâ modellerinden korumak isteyen kullanıcılar için yol gösterici nitelik taşımaktadır
Combined Measurement of the Higgs Boson Mass From the Formula Presented and Formula Presented Decay Channels With the Atlas Detector Using Formula Presented, 8, and 13 Tev Formula Presented Collision Data
No full textA measurement of the mass of the Higgs boson combining the Formula Presented and Formula Presented decay channels is presented. The result is based on Formula Presented of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of Formula Presented. This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics. © 2023 CERN, for the ATLAS Collaboration.IN2P3-CNRS; CC-IN2P3; 2014-2021; SCI/013; U.S. Department of Energy, USDOE; Alexander von Humboldt-Stiftung, AvH; Canarie; Karlsruhe Institute of Technology, KIT; H2020 Marie Skłodowska-Curie Actions, MSCA; Multiple Sclerosis Scientific Research Foundation, MSSRF; CERN; Compute Canada: 21/SCI/017; Göran Gustafssons Stiftelser; Natural Sciences and Engineering Research Council of Canada, NSERC; National Research Council Canada, NRC; Canada Foundation for Innovation, CFI; Science and Technology Facilities Council, STFC; Leverhulme Trust; European Research Council, ERC; European Cooperation in Science and Technology, COST; Australian Research Council, ARC; National Stroke Foundation, NSF; Neurosurgical Research Foundation, NRF; Helmholtz-Gemeinschaft, HGF; Minerva Foundation; Deutsche Forschungsgemeinschaft, DFG; Agence Nationale de la Recherche, ANR; Japan Society for the Promotion of Science, JSPS; Ministry of Education, Culture, Sports, Science and Technology, MEXT; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Danmarks Grundforskningsfond, DNRF; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; National Natural Science Foundation of China, NSFC; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Fundação para a Ciência e a Tecnologia, FCT; Bundesministerium für Bildung und Forschung, BMBF; Chinese Academy of Sciences, CAS; Austrian Science Fund, FWF; Generalitat de Catalunya; Ministry of Science and Technology of the People's Republic of China, MOST; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Israel Science Foundation, ISF; Instituto Nazionale di Fisica Nucleare, INFN; Narodowe Centrum Nauki, NCN; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; Ministerio de Ciencia e Innovación, MICINN; Centre National pour la Recherche Scientifique et Technique, CNRST; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; British Columbia Knowledge Development Fund, BCKDF; European Regional Development Fund, ERDF; Defence Science Institute, DSI; Narodowa Agencja Wymiany Akademickiej, NAWA; Institutul de Fizică Atomică, IFA; Agencia Nacional de Investigación y Desarrollo, ANID; Royal Society of South Australia, RSSA; Irish Rugby Football Union, IRF
Search for Flavour-Changing Neutral-Current Couplings Between the Top Quark and the Photon With the Atlas Detector at √s=13 Tev
No full textThis letter documents a search for flavour-changing neutral currents (FCNCs), which are strongly sup-pressed in the Standard Model, in events with a photon and a top quark with the ATLAS detector. The analysis uses data collected in pp collisions at ; RADIC;s =13 TeV during Run 2 of the LHC, corresponding to an integrated luminosity of 139 fb-1. Both FCNC top-quark production and decay are considered. The final state consists of a charged lepton, missing transverse momentum, a b-tagged jet, one high-momentum photon and possibly additional jets. A multiclass deep neural network is used to classify events either as signal in one of the two categories, FCNC production or decay, or as background. No significant ex-cess of events over the background prediction is observed and 95% CL upper limits are placed on the strength of left-and right-handed FCNC interactions. The 95% CL bounds on the branching fractions for the FCNC top-quark decays, estimated (expected) from both top-quark production and decay, are B(t ; RARR; u ; gamma; ) 0.85 (0.88+0.37 -0.25) x 10-5 and B(t ; RARR; c ; gamma; ) 4.2 (3.40+1.35-0.95) x 10-5 for a left-handed tq ; gamma; cou-pling, and B(t ; RARR; u ; gamma; ) 1.2 (1.20+0.50 -0.33) x10-5 and B(t ; RARR; c ; gamma; ) 4.5 (3.70+1.47 -1.03) x10-5 for a right-handed coupling. ; COPY; 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3.CERN; 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, Tuerkiye; 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; 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; Thales programme; 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; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goeran Gustafssons Stiftelser, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; [21/SCI/017]; [UNCE SCI/013]We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We 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 Canton of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Tuerkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, Canarie, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 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; Goeran Gustafssons Stiftelser, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada) , NDGF (Denmark, Norway, Sweden) ,CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy) , NL-T1 (Netherlands) , PIC (Spain) , ASGC (Taiwan) , 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. [109]