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Third-Party Funding in Investment Arbitration: How To Define and Disclose It
No full textThe provision of funds by non-parties to pursue or defend a dispute, namely third-party funding, has become a focal point in investment arbitration because of participation of States, and the high value of disputes and thus expenses. However, since there are different kinds of external financing such as insurance contracts, contingency and conditional fee arrangements and loans by financial institutions, there is no consensus with regard to the definition of the concept either in literature or in practice. Besides, due to the possibility of connection between the funders and the arbitrators, disclosure of any funding arrangement is considered necessary to refrain from potential conflicts of interest that may harm the integrity of the arbitration process. Yet again, there are different approaches to the issue. After examining various definitions used in national and international regulations, investment treaties, related reports, rules of arbitration institutions and academic literature, this article seeks to offer an operational definition of the concept. While doing that, it suggests a perspective of differentiating agreements with professionals whose motivation is to make an investment from other means of external financing. Considering the importance of independence and impartiality of arbitrators and the need for statistical data to evaluate the link between third-party funding and the number of cases in investment arbitration, the article acknowledges the necessity for disclosure of third-party funding. Going one step further, it modestly proposes a model provision regarding definition and disclosure of third-party funding to clarify the requirement of disclosure
Halk Sağlığı Uzmanlık Eğitiminde Sorunlara Çözüm Önerileri
No full text7. Uluslararası ve 25. Ulusal Halk Sağlığı Kongresi 14-17 Aralık 2023, Antalya / 7th International and 25th National Public Health Congress December 14-17, 2023, Antalya[No Abstract Available
Superconducting quantum electronics
No full textSuperconducting quantum electronics is formally born in 1911, three years after Heike Kamerlingh Onnes was able to liquefy helium at the University of Leiden in the Netherlands. Josephson junctions are simple devices made of two superconductors coupled through a thin barrier, which can be made of one of several layers of insulating, normal metal, or ferromagnetic material. This chapter discusses the surface impedance of superconductors and their behavior in the radiofrequency (RF) regime. After the Josephson junction discovery, many new applications surfaced and the Josephson junction has become to date the most versatile active element used for superconducting analogue, digital and quantum electronics. These applications include magnetometers and gradiometers based on RF and DC superconducting quantum interference devices (SQUIDs), Josephson voltage standards, analogue-to-digital converters, quasi-one-junction SQUIDs, digital logic circuits, quantum computing, quantum communication and quantum sensing. The chapter discusses some of its applications. © ISTE Ltd 2023. All rights reserved
Cross-Section Measurements for the Production of a Z Boson in Association With High-Transverse Jets in Pp Collisions at ?s = 13 Tev With the Atlas Detector
No full textCross-section measurements for a Z boson produced in association with high-transverse-momentum jets (p T ? 100 GeV) and decaying into a charged-lepton pair (e + e ? , ? + ? ?) are presented. The measurements are performed using proton–proton collisions at s = 13 TeV corresponding to an integrated luminosity of 139 fb?1 collected by the ATLAS experiment at the LHC. Measurements of angular correlations between the Z boson and the closest jet are performed in events with at least one jet with p T ? 500 GeV. Event topologies of particular interest are the collinear emission of a Z boson in dijet events and a boosted Z boson recoiling against a jet. Fiducial cross sections are compared with state-of-the-art theoretical predictions. The data are found to agree with next-to-next-to-leading-order predictions by NNLOjet and with the next-to-leading-order multi-leg generators MadGraph5_aMC@NLO and Sherpa. [Figure not available: see fulltext.]. © 2023, The Author(s).IN2P3-CNRS; 2014-2021; SCI/013; National Science Foundation, NSF; U.S. Department of Energy, USDOE; Alexander von Humboldt-Stiftung, AvH; CRC Health Group, CRC: 21/SCI/017; Canarie; H2020 Marie Sk?odowska-Curie Actions, MSCA; Multiple Sclerosis Scientific Research Foundation, MSSRF; CERN; Compute Canada; 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, KAKEN; 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, Mláde?e a T?lov?chovy, 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; Horizon 2020; British Columbia Knowledge Development Fund, BCKDF; European Regional Development Fund, ERDF; Defence Science Institute, DSI; Narodowa Agencja Wymiany Akademickiej, NAWA; Institutul de Fizic? Atomic?, IFA; Agencia Nacional de Investigación y Desarrollo, ANID; Royal Society of South Australia, RSSA; Irish Rugby Football Union, IRFUWe 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 MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; 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 Sk?odowska-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; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom
Genel İşletmecilik Bilgileri
No full textGözden geçirilmiş ve genişletilmiş 12. baskı[No Abstract Available
Automated Cell Viability Analysis in Tissue Scaffolds
No full textImage analysis of cell biology and tissue engineering is time-consuming and requires personal expertise. However, evalu - ation of the results may be subjective. Therefore, computer-based learning and detection applications have been rapidly developed in recent years. In this study, Confocal Laser Scanning Microscope (CLSM) images of the viable pre-osteoblastic mouse MC3T3-E1 cells in 3D bioprinted tissue scaffolds, captured from a bone tissue regeneration study, were analyzed by using image processing techniques. The aim of this study is to develop a reliable and fast algorithm for the automated analysis of live/dead assay CLSM images. Percentages of live and dead cell areas in the scaffolds were determined, and then, total cell viabilities were calculated. Furthermore, manual measurements of four different analysts were obtained to evaluate subjectivity in the analysis. The measurement variations of analysts, also known as the coefficient of variation, were determined from 13.18% to 98.34% for live cell images and from 9.75% to 126.02% for dead cell images. Therefore, an automated algorithm was developed to overcome this subjectivity. The other aim of this study is to determine the depth profile of viable cells in 3D tissue scaffolds. Consequently, cross-sectional image sets of three different types of tissue scaf - folds were analyzed
Time Series Prediction With Hierarchical Recurrent Model
No full textIn this paper, we investigate the capability of modeling distant temporal interaction of Long Short-Term Memory (LSTM) and introduce a novel Long Short-Term Memory on time series problems. To increase the capability of modeling distant temporal interactions, we propose a hierarchical architecture (HLSTM) using several LSTM models and a linear layer. This novel framework is then applied to electric power consumption, real-life crime and financial data. We demonstrate in our simulations that this structure significantly improves the modeling of deep temporal connections compared to the classical architecture of LSTM and various studies in the literature. Furthermore, we analyze the sensitivity of the new architecture with respect to the hidden size of LSTM
Firma Büyümesinde Finans Disi Etkiler Analizi: Tobb Türkiye 100 Firmalari Üzerine Bir Çalisma
No full textEkonomik nedenlerden dolayı firmalar için büyüme önem taşımaktadır. Bu çalışma, TOBB Türkiye 100 programına 2020 ve 2021 dönemi başvuru yapan firmaların net satış geliri büyümesi esas alınarak büyümelerine ve firmaların başarılı olmasına etki eden dinamiklerin incelenmesini amaçlamaktadır. Bu amaç doğrultusunda, büyüme ve başarılı olma durumlarını anlamaya yönelik 2020 ve 2021 için bağımsız değişkenler ile regresyon ve lojistik regresyon modelleri kurulmuştur. Daha sonra 2020 dönemine başvuruda bulunan firmaların verileriyle elde ettiğimiz regresyon ve lojistik regresyon modelleriyle 2021 dönemime başvuruda bulunan firmaların büyüme oranı ve başarı durumu tahmin edilmiştir. Elde edilen sonuçlara göre çalışan sayısındaki değişimin her iki dönemde de ciro artışını pozitif yönde etkilediği görülmektedir. Firma yaşı arttığında büyüme oranı azalmaktadır. Tarım, ormancılık ve balıkçılık sektöründe faaliyet gösteren firmaların diğer sektörlerdeki firmalara oranla daha az büyüdüğü ve başarılı olma olasılığının daha az olduğu görülmüştür. Doğu Anadolu Bölgesi'nde yer alan firmaların büyüme oranının ve başarılı olma olasılığını İstanbul, Batı Marmara ve Batı-Doğu Karadeniz Bölgelerine göre daha az olduğu tespit edilmiştir. Başarı durumu tahmini için ise oluşturulan 2020 dönemi başarı durumu lojistik regresyon modelinin 2021 dönemi doğru tahmin etme oranı %77 olarak hesaplanmıştır. 2021 döneminde başvuran firmaların verileri ile 2021 yılı başarı durumu lojistik regresyon modelinin doğruluk oranı %81,6 olarak bulunmuştur. Doğruluk oranları kıyaslandığında 2021 dönemi lojistik regresyon modelinin daha iyi bir tahmin modeli olduğu ortaya çıkmaktadır.Due to economic pressures, growth is given importance for companies. This study aims to examine the dynamics that affect the growth and success of companies applying to the TOBB Turkey 100 program for the 2020 and 2021 periods, based on their net sales revenue growth. In line with this goal, regression and logistic regression models with independent variables for 2020 and 2021 were established for the growth and success section. Then, with the regression and logistic regression models we obtained with the data of the companies applying for the 2020 period, the growth rate and success status of the companies applying for the 2021 period were estimated. According to the results obtained, it is seen that the change in the number of employees has a positive effect on the increase in turnover in both periods. The growth rate decreases as the age of the firm increases. It has been observed that the companies operating in the agriculture, forestry and fisheries sectors have grown less than the companies in other sectors and are less likely to be successful. It has been determined that the growth rate and probability of success of the companies in the Eastern Anatolia Region are lower than those in Istanbul, West Marmara and West-East Black Sea Regions. For the estimation of the success situation, the correct prediction rate of the 2021 period of the 2020 period logistic regression model was calculated as 77%. The accuracy rate of the logistic regression model of the success status of 2021 with the data of the companies applying in the 2021 period was found to be 81.6%. When the accuracy rates are compared, it turns out that the 2021 logistic regression model is a better forecasting model
Search for Displaced Photons Produced in Exotic Decays of the Higgs Boson Using 13 Tev Pp Collisions With the Atlas Detector
No full textA search is performed for delayed and nonpointing photons originating from the displaced decay of a neutral long-lived particle (LLP). The analysis uses the full run 2 dataset of proton-proton collisions delivered by the LHC at a center-of-mass energy of pffisffi 1/4 13 TeV between 2015 and 2018 and recorded by the ATLAS detector, corresponding to an integrated luminosity of 139 fb-1. The capabilities of the ATLAS electromagnetic calorimeter are exploited to precisely measure the arrival times and trajectories of photons. The results are interpreted in a scenario where the LLPs are pair produced in exotic decays of the 125 GeV Higgs boson, and each LLP subsequently decays into a photon and a particle that escapes direct detection, giving rise to missing transverse momentum. No significant excess is observed above the expectation due to Standard Model background processes. The results are used to set upper limits on the branching ratio of the exotic decay of the Higgs boson. A model-independent limit is also set on the production of photons with large values of displacement and time delay.France; BMBF; MPG, Germany; RGC and Hong Kong SAR, China; ISF and 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; MICINN, Spain; SRC; Wallenberg Foundation, Sweden; SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; STFC, United Kingdom; DOE; NSF, United States of America; BCKDF; CANARIE; CRC, Canada [UNCE SCI/013]; Czech Republic; ERC; ERDF; 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; Gran Gustafssons Stiftelse, Sweden; Royal Society; Leverhulme Trust, United Kingdom; NDGF (Denmark, Norway, Sweden); KIT/GridKA (Germany); INFN-CNAF (Italy); NL-T1 (Netherlands) , PIC (Spain); ASGC (Taiwan); BNL (USA)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 Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Turkiye; STFC, United Kingdom; and 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 U CE 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 programs cofinanced 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 Stiftelse, Sweden; and 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.[55]