648 research outputs found

    Environmental, Social and Governance Exchange-Traded Funds: Do They Attract More Cash Flows Than Their Conventional Counterparts?

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2020. 8. Lee Jeong-Dong.The integration of environmental, social and governance (ESG) factors into the investing process is underpinned by the growing popularity of sustainable development strategies and global concerns regarding the environment. However, scholars are not in agreement about whether ESG investing obliges an investor to sacrifice financial returns, does not affect the performance of financial entities, or can improve it. Such investing can be done using different financial instruments. This study deals with one of those instruments โ€“ exchange-traded funds or ETFs. These funds are one of the most prospective investment vehicles because of their special properties. Moreover, ESG ETFs are being popularised, and they demand attention from both practitioners and academia. However, existing studies concerning ESG ETFs are rather scarce, and they focus on performance measured by risk-return characteristics. This paper addresses that gap in the literature and investigates a special financial issue that has not been discovered in the case of ESG ETFs โ€“ whether such funds can attract more financial flow than their conventional counterparts โ€“ which may reflect investors demand for ESG ETFs. In particular, this study examines the relationship between the inflows to such funds and their compliance with ESG criteria, using two types of regression models. First, it exploits cross-sectional data on bond and equity ETFs traded in the U.S., and it investigates the relationship between higher ESG scores and higher inflows. Second, the study uses historical data on U.S. bond and equity ETFs in 2- and 3- year periods, and it applies pooled OLS and mixed effects models to panel data samples of ESG and non-ESG ETFs distinguished by a dummy variable. The positive relationship between financial flows and ESG score was partially proved: a statistically significant and positive coefficient of variables representing ESG scores was observed in the case of the equity ETF model. However, its value was too modest to draw any meaningful conclusions. In the model for bond ETFs, no such evidence was found. This might be because of the small size of the sample and other methodological weaknesses. On the contrary, in all specifications operating different samples of panel data dummy variables representing ESG labelling had relatively high, positive and statistically significant coefficients. To be precise, on average, compliance of ETFs with ESG criteria may promise 2.1-3.5% of additional inflows. This is in line with other ESG fund literature, e.g. studies dealing with mutual funds. This result gives a strong market signal to ETF providers, other market participants and policy makers; it contributes to the dilemma regarding the financial impact of ESG factors, and it opens up many directions concerning financial properties and other specificities of ESG ETFs. Several implications for the broader literature on exchange-traded funds also are identified and discussed. At present, there are still many limitations to this type of research. However, as more ESG ETFs emerge โ€“ those limitations can be overcome. This paper provides a strong background for further studies in this area, and it suggests possible improvements for later studies.ESG ์š”์†Œ์˜ ํˆฌ์ž ํ”„๋กœ์„ธ์Šค ํ†ตํ•ฉ์€ ์ง€์†๊ฐ€๋Šฅํ•œ ๋ฐœ์ „ ์ „๋žต ๋ฐ ํ™˜๊ฒฝ ๋ฌธ์ œ์™€ ๊ด€ํ•œ ์ „์„ธ๊ณ„์  ๊ด€์‹ฌ์˜ ์ฆ๋Œ€์™€ ํ•จ๊ป˜ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ESG ํˆฌ์ž์™€ ๊ธˆ์œต ์ˆ˜์ต์˜ ๊ด€๊ณ„ ๋ฐ ๊ธˆ์œต ๊ธฐ์—…์˜ ์„ฑ๊ณผ ์˜ํ–ฅ ๋“ฑ์— ๋Œ€ํ•ด์„œ๋Š” ํ•ฉ์˜๋œ ๊ฒฐ๋ก ์ด ์กด์žฌํ•˜์ง€ ์•Š๋Š”๋‹ค. ESG ํˆฌ์ž๋Š” ๋‹ค์–‘ํ•œ ๊ธˆ์œต ์ƒํ’ˆ์„ ํ†ตํ•ด ์ˆ˜ํ–‰๋  ์ˆ˜ ์žˆ์œผ๋‚˜, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒ์žฅ์ง€์ˆ˜ํŽ€๋“œ(ETF)๋ฅผ ์ค‘์ ์ ์œผ๋กœ ์‚ดํŽด๋ณธ๋‹ค. ์ƒ์žฅ์ง€์ˆ˜ํŽ€๋“œ๋Š” ๊ณ ์œ ์˜ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ๊ฐ€์žฅ ์œ ๋งํ•œ ํˆฌ์ž ์ˆ˜๋‹จ ์ค‘ ํ•˜๋‚˜๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ESG ETF ๋Š” ๋Œ€์ค‘ํ™”๋˜๊ณ  ์žˆ์œผ๋ฉฐ ์‹ค๋ฌด์ž ๋ฐ ํ•™๊ณ„์˜ ๋†’์€ ๊ด€์‹ฌ์„ ์–ป๊ณ  ์žˆ์œผ๋‚˜, ๊ด€๋ จ ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๋Œ€๋ถ€๋ถ„ ์œ„ํ—˜-์ˆ˜์ต ํŠน์„ฑ์— ์˜ํ•ด ์ถ”์ •๋œ ์ƒํ’ˆ์˜ ์„ฑ๊ณผ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ESG ETF ์™€ ๊ด€๋ จํ•˜์—ฌ ๊ธฐ์กด์— ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•˜๋˜ ํŠน๋ณ„ํ•œ ๊ธˆ์œต ์ด์Šˆ๋ฅผ ์ œ์‹œํ•˜์—ฌ ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋ฅผ ๋ณด์™„ํ•œ๋‹ค. ์ด๋Š” ESG ETF ์— ๋Œ€ํ•œ ํˆฌ์ž์ž๋“ค์˜ ์ˆ˜์š”๋ฅผ ํ‘œํ˜„ํ•˜์—ฌ ๋‹ค๋ฅธ ์ „ํ†ต์ ์ธ ํˆฌ์ž ์ˆ˜๋‹จ์— ๋Œ€๋น„ํ•˜์—ฌ ๋” ๋งŽ์€ ๊ธˆ์œต ํ๋ฆ„์„ ์œ ๋„ํ•˜๋Š” ESG ETF ์˜ ํŠน์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‘ ๊ฐ€์ง€ ์œ ํ˜•์˜ ํšŒ๊ท€๋ถ„์„์„ ์‚ฌ์šฉํ•˜์—ฌ ESG ๊ธฐ์ค€์— ๋”ฐ๋ฅธ ์ž๊ธˆ์˜ ์กฐ๋‹ฌ๊ณผ ์œ ์ž… ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํ™•์ธํ•œ๋‹ค. ๋จผ์ €, ๋ฏธ๊ตญ์—์„œ ๊ฑฐ๋ž˜๋˜๋Š” ์ฑ„๊ถŒ ๋ฐ ์ฃผ๊ฐ€ ETF ์˜ ์ž๋ฃŒ๋ฅผ ์กฐ์‚ฌํ•˜์—ฌ ๋†’์€ ESG ์ ์ˆ˜์™€ ETF ๊ด€๋ จ ํ˜„๊ธˆ ํ๋ฆ„ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌํ•œ๋‹ค. ์ด์–ด, ๋ฏธ๊ตญ์˜ ์ฑ„๊ถŒ ๋ฐ ์ฃผ๊ฐ€ ETF ์— ๊ด€ํ•œ 2~3 ๋…„์˜ ๊ณผ๊ฑฐ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ESG ์™€ ๋น„-ESG ๊ฐ„ ํšจ๊ณผ์˜ ์ฐจ์ด๋ฅผ ์ตœ์†Œ์ž์Šน๋ฒ•(OLS) ๋ฐ ํ˜ผํ•ฉ ํšจ๊ณผ ๋ชจํ˜•์œผ๋กœ ์ถ”์ •ํ•œ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ธˆ์œต ํ๋ฆ„๊ณผ ESG ์ ์ˆ˜ ์‚ฌ์ด์—๋Š” ์–‘์˜ ๊ด€๊ณ„๊ฐ€ ๋ถ€๋ถ„์ ์œผ๋กœ ์กด์žฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜ ๊ฐ•ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์ฆ๋ช…ํ•˜๊ธฐ์—๋Š” ๋ถ€์กฑํ•˜์˜€๋‹ค. ๊ฒฐํ•ฉ ETF ๊ฐ€ ์žˆ๋Š” ๋ชจํ˜•์—์„œ๋Š” ์ƒ˜ํ”Œ ์ˆ˜์˜ ๋ถ€์กฑ ๋ฐ ๋ฐฉ๋ฒ•๋ก ์  ํ•œ๊ณ„๋กœ ์ธํ•ด ์œ ์˜ํ•œ ๊ด€๊ณ„๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ๋ฐ˜๋ฉด, ESG ๋ผ๋ฒจ๋ง์„ ๋‚˜ํƒ€๋‚ด๋Š” ๋”๋ฏธ ๋ณ€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•œ ๋ถ„์„์—์„œ๋Š” ํ†ต๊ณ„์ ์œผ๋กœ 101 ์œ ์˜ํ•œ ์–‘์˜ ๊ด€๊ณ„๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ๋Š”, ESG ๊ธฐ์ค€์— ๋”ฐ๋ฅธ ETF ์˜ ํ‰๊ท  ์ž๊ธˆ ์กฐ๊ฐˆ์€ ์ถ”๊ฐ€ ์œ ์ž…์˜ 2.1 ~3.5%๋ฅผ ์ฐจ์ง€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ์ด๋Š” ๋ฎค์ถ”์–ผ ํŽ€๋“œ์™€ ๊ด€๋ จํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ๊ฒฐ๋ก ๊ณผ ์ผ์น˜ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ์ •์ฑ… ์ž…์•ˆ์ž ๋ฟ ์•„๋‹ˆ๋ผ ETF ์˜ ์ œ๊ณต ์—…์ฒด ๋ฐ ๊ธฐํƒ€ ์‹œ์žฅ ์ฐธ์—ฌ์ž์—๊ฒŒ ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ESG ์š”์†Œ์˜ ์žฌ์ •์  ์˜ํ–ฅ์— ๋Œ€ํ•œ ๋”œ๋ ˆ๋งˆ๋ฅผ ํ‘œํ˜„ํ•˜๊ณ , ESG ETF ์˜ ์žฌ๋ฌด ํŠน์„ฑ ๋ฐ ๊ธฐํƒ€ ํŠน์„ฑ๊ณผ ๊ด€๋ จํ•œ ๋ฐฉํ–ฅ์„ฑ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค. ์ƒ์žฅ์ง€์ˆ˜ํŽ€๋“œ์™€ ๊ด€๋ จํ•œ ๋‹ค์–‘ํ•œ ๋ฌธํ—Œ์—์„œ ์–ธ๊ธ‰๋œ ๊ฒฐ๋ก  ์—ญ์‹œ ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ํ•ด์„๋  ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฌ ESG ETF ์™€ ๊ด€๋ จํ•œ ๋งŽ์€ ์ œํ•œ์ด ์žˆ์ง€๋งŒ, ๋” ๋งŽ์€ ์ƒํ’ˆ์˜ ๋“ฑ์žฅ์„ ํ†ตํ•ด ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๊ด€๋ จ ๋ถ„์•ผ์˜ ํ›„์† ์—ฐ๊ตฌ์— ์žˆ์–ด์„œ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•œ ๊ฐœ์„ ์ ์„ ์ œ์‹œํ•˜๋Š” ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค.1. Introduction 1 2. Theory and Hypothesis 6 2.1. Basics of Investment Funds and Passive Investing 6 2.2. Exchange-Traded Funds as the disruptive invention 10 2.3. Environmental, Social and Governance Exchange-Traded Funds: main properties and description of the market 17 3. Literature review 23 4. Methodology and Approach 35 4.1. Common methods in funds literature 35 4.2. Cross-sectional regression model 37 4.3. Panel data model 39 5. Data 43 5.1. Cross-sectional data 43 5.2. Panel data 47 6. Findings 58 6.1. Models with cross-sectional data 58 6.2. Models with panel data 61 7. Discussion and conclusion 75 Bibliography 87 Appendix A 97 Appendix B 98 Appendix C 99 Abstract (Korean) 100Maste

    Existence results for regularized equations of second-grade fluids with wall slip

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    In this paper, we study the equations governing the steady motion of a class of second-grade fluids in a bounded domain of Rn\mathbb{R}^n, n=2,3{n=2,3}, with the nonlinear slip boundary condition. We prove the existence of a weak solution without assuming smallness of the data. Moreover, we give estimates for weak solutions and show that the solution set is sequentially weakly closed

    Exploring the Intersection of Multi-Omics and Machine Learning in Cancer Research

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    Cancer biology and machine learning represent two seemingly disparate yet intrinsically linked fields of study. Cancer biology, with its complexities at the cellular and molecular levels, brings up a myriad of challenges. Of particular concern are the deviations in cell behaviour and rearrangements of genetic material that fuel transformation, growth, and spread of cancerous cells. Contemporary studies of cancer biology often utilise wide arrays of genomic data to pinpoint and exploit these abnormalities with an end-goal of translating them into functional therapies. Machine learning allows machines to make predictions based on the learnt data without explicit programming. It leverages patterns and inferences from large datasets, making it an invaluable tool in the modern era of large scale genomics. To this end, this doctoral thesis is underpinned by three themes: the application of machine learning, multi-omics, and cancer biology. It focuses on employment of machine learning algorithms to the tasks of cell annotation in single-cell RNA-seq datasets and drug response prediction in pre-clinical cancer models. In the first study, the author and colleagues developed a pipeline named Ikarus to differentiate between neoplastic and healthy cells within single-cell datasets, a task crucial for understanding the cellular landscape of tumours. Ikarus is designed to construct cancer cell-specific gene signatures from expert-annotated scRNA-seq datasets, score these genes, and distribute the scores to neighbouring cells via network propagation. This method successfully circumvents two common challenges in single-cell annotation: batch effects and unstable clustering. Furthermore, Ikarus utilises a multi-omic approach by incorporating CNVs inferred from scRNA-seq to enhance classification accuracy. The second study investigated how multi-omic analysis could enhance drug response prediction in pre-clinical cancer models. The research suggests that the typical practice of panel sequencing โ€” a deep profiling of select, validated genomic features โ€” is limited in its predictive power. However, incorporating transcriptomic features into the model significantly improves predictive ability across a variety of cancer models and is especially effective for drugs with collateral effects. This implies that the combined use of genomic and transcriptomic data has potential advantages in the pharmacogenomic arena. This dissertation recapitulates the findings of two aforementioned studies, which were published in Genome Biology and Cancers journals respectively. The two studies illustrate the application of machine learning techniques and multi-omic approaches to address conceptually distinct problems within the realm of cancer biology.Die Krebsbiologie und das maschinelle Lernen sind zwei scheinbar kontrรคre, aber intrinsisch verbundene Forschungsbereiche. Insbesondere die Krebsbiologie ist auf zellul ฬˆarer und molekularer Ebene hoch komplex und stellt den Forschenden vor eine Vielzahl von Herausforderungen. Zu verstehen wie abweichendes Zellverhalten und die Umstrukturierung genetischer Komponente die Transformation, das Wachstum und die Ausbreitung von Krebszellen antreiben, ist hierbei eine besondere Herausforderung. Gleichzeitig bestrebt die Krebsbiologie diese Abnormalitรคten zu nutzen zu machen, Wissen aus ihnen zu gewinnen und sie so in funktionale Therapien umzusetzen. Maschinelles Lernen ermรถglicht es Vorhersagen auf der Grundlage von gelernten Daten ohne explizite Programmierung zu treffen. Es erkennt Muster in groรŸen Datensรคtzen, erschlieรŸt sich so Erkenntnisse und ist deswegen ein unschรคtzbar wertvolles Werkzeug im modernen Zeitalter der Hochdurchsatz Genomforschung. Aus diesem Grund ist maschinelles Lernen eines der drei Haupthemen dieser Doktorarbeit, neben Multi-Omics und Krebsbiologie. Der Fokus liegt hierbei insbesondere auf dem Einsatz von maschinellen Lernalgorithmen zum Zweck der Zellannotation in Einzelzell RNA-Sequenzdatensรคtzen und der Vorhersage der Arzneimittelwirkung in prรคklinischen Krebsmodellen. In der ersten, hier prรคsentierten Studie, entwickelten der Autor und seine Kollegen eine Pipeline namens Ikarus. Diese kann zwischen neoplastischen und gesunden Zellen in Einzelzell-Datensรคtzen unterscheiden. Eine Aufgabe, die fรผr das Verst ฬˆandnis der zellulรคren Landschaft von Tumoren entscheidend ist. Ikarus ist darauf ausgelegt, krebszellenspezifische Gensignaturen aus expertenanotierten scRNA-seq-Datensรคtzen zu konstruieren, diese Gene zu bewerten und die Bewertungen รผber Netzwerkverbreitung auf benachbarte Zellen zu verteilen. Diese Methode umgeht erfolgreich zwei hรคufige Herausforderungen bei der Einzelzellannotation: den Chargeneffekt und die instabile Clusterbildung. Darรผber hinaus verwendet Ikarus, durch das Einbeziehen von scRNA-seq abgeleiteten CNVs, einen Multi-Omic-Ansatz der die Klassifikationsgenauigkeit verbessert. Die zweite Studie untersuchte, wie Multi-Omic-Analysen die Vorhersage der Arzneimittelwirkung in prรคklinischen Krebsmodellen optimieren kรถnnen. Die Forschung legt nahe, dass die รผbliche Praxis des Panel Sequenzierens - die umfassende Profilierung ausgewรคhlter, validierter genomischer Merkmale - in ihrer Vorhersagekraft begrenzt ist. Durch das Einbeziehen transkriptomischer Merkmale in das Modell konnte jedoch die Vorhersagefรคhigkeit bei verschiedenen Krebsmodellen signifikant verbessert werden, ins besondere fรผr Arzneimittel mit Nebenwirkungen. Diese Dissertation fasst die Ergebnisse der beiden oben genannten Studien zusammen, die jeweils in Genome Biology und Cancers Journalen verรถffentlicht wurden. Die beiden Studien veranschaulichen die Anwendung von maschinellem Lernen und Multi-Omic-Ansรคtzen zur Lรถsung konzeptionell unterschiedlicher Probleme im Bereich der Krebsbiologie

    Fluctuation-Stimulated Variable-Range Hopping

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    Qualitatively new transport mechanism is suggested for hopping of carriers according to which the variable-range hopping (VRH) arises from the resonant tunneling between transport states brought into resonance by Coulomb potentials produced by surrounding sites with fluctuating occupations. A semiquantitative description of the hopping transport is given based on the assumption that fluctuations of energies of hopping sites have spectral density 1/f

    Lattice Delone simplices with super-exponential volume

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    In this short note we give a construction of an infinite series of Delone simplices whose relative volume grows super-exponentially with their dimension. This dramatically improves the previous best lower bound, which was linear.Comment: 7 pages; v2: revised version improves our exponential lower bound to a super-exponential on

    Simulation of the phononless hopping in a Coulomb glass

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    The phononless hopping conductivity of a disordered system with localized states is studied in a broad range of frequencies by straightforward computer simulations taking into account Coulomb interactions. At sufficiently low temperatures, the conductivity is determined by the zero-phonon absorption of the photon by pairs of states. The laser frequency dependence of the conductivity is examined and compared with the analytical model of Efros and Shklovskii and with recent experimental data obtained on Si:P. The range of parameters is determined, for which the conductivity dependence on photon energy best reproduces the experimental results.Comment: Presented as a poster at TIDS11, to be published in pss(c

    Formation Energies of Antiphase Boundaries in GaAs and GaP: An ab Initio Study

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    Electronic and structural properties of antiphase boundaries in group III-V semiconductor compounds have been receiving increased attention due to the potential to integration of optically-active III-V heterostructures on silicon or germanium substrates. The formation energies of {110}, {111}, {112}, and {113} antiphase boundaries in GaAs and GaP were studied theoretically using a full-potential linearized augmented plane-wave density-functional approach. Results of the study reveal that the stoichiometric {110} boundaries are the most energetically favorable in both compounds. The specific formation energy ฮณ of the remaining antiphase boundaries increases in the order of ฮณ{113} โ‰ˆ ฮณ{112} < ฮณ{111}, which suggests {113} and {112} as possible planes for faceting and annihilation of antiphase boundaries in GaAs and GaP

    Design principles of mobile railway networks

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    The basic principles of GSM railway network planning are examined. The method of the frequency channels distribution between cells is described. The development of frequency channel reuse scheme is performed. The procedure of an operational planning is proposed. The principles of even radio coverage are considered for cells with mainly free radio propagation and for urban areas
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