Relevant bibliographies by topics / S.Z. Goldberg (Firm)

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Academic literature on the topic 'S.Z. Goldberg (Firm)'

Author: Grafiati
Published: 27 February 2023

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Journal articles on the topic "S.Z. Goldberg (Firm)"

1

Micek, Grzegorz. "Problematyka funkcjonowania firm informatycznych w ujęciu przestrzennym." Studies of the Industrial Geography Commission of the Polish Geographical Society 8 (January 1, 2006): 139–51. http://dx.doi.org/10.24917/20801653.8.13.

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Według raportu końcowego ze spotkania przedstawicieli ważnych gremiów ponadnarodowych (OECD, Banku Światowego, EBOiR oraz EBI), strategia budowy gospodarki opartej na wiedzy musi opierać się na czterech filarach. Jednym z nich powinna być „budowa dynamicznej infrastruktury informacyjnej oraz konkurencyjnego i innowacyjnego sektora informacyjnego w gospodarce, prowadzącego do powstania różnorodnych efektywnych i konkurencyjnych usług w dziedzinie informacji i komunikacji” (Budowanie... 2003, s. 11)....
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2

Gross, Zeev. "Electronic and Size Effects, in Electrocatalysis By Minimally Substituted Corrole Metal Complexes." ECS Meeting Abstracts MA2022-01, no. 14 (July 7, 2022): 956. http://dx.doi.org/10.1149/ma2022-0114956mtgabs.

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The last 20 years have evidenced an enormous increase of activity on introducing first row transition metal complexes of corroles as sustainable catalysts for clean production of energy from renewable resources. While triarylcorroles were used almost exclusively for those purposes, contemporary research exposed the demand for corroles that are much smaller in size. We now report the progress in that direction, which resulted in corrole metal complexes of the size required for strong absorbance onto electrodes used for fuel cells and other energy-relevant redox reactions.1 This aspect is shown to come into play in the most important variables for efficient electrocatalytic hydrogen gas production from water: earlier onset potential, larger yield and longer durability. References Yadav, S. Khoury, N. Fridman, V. K. Sharma, A. Kumar, M. Majdoub, A. Kumar, Y. Diskin-Posner, A. Mahammed and Z.Gross, Angew. Chem. Int. Ed. 2021, 60, 12829 –12834. Yadav, I. Nigel-Etinger, A. Kumar, A. Mizrahi, A. Mahammed, N. Fridman, S. Lipstman,I. Goldberg, and Z. Gross, Iscience 2021, 24, 102924 Kumar, P. Yadav, M. Majdoub, I. Saltsman, N. Fridman, S. Kumar, A. Kumar, Mahammed and Z. Gross, Angew. Chem. Int. Ed. 2021, 60, 25097-25103. Figure 1
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3

Duma, Paweł, and Daniel Wojtucki. "Die Abdeckerei und die Hinrichtungsstätte in Kamienna Góra (Landeshut) und Złotoryja (Goldberg), Polen. Zwei Beispiele dargestellt an Hand archäologischer und historischer Forschungen." Anthropologia integra 10, no. 1 (June 28, 2019): 33–43. http://dx.doi.org/10.5817/ai2019-1-33.

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Cílem příspěvku je představení výsledků výzkumů, které byly prováděny na bývalých popravištích v Kamenné Hoře a Zlotoryji (Dolní Slezsko, Polsko). V průběhu archeologických výzkumů v obou lokalitách byly nalezeny pozůstatky zděných šibenic o průměru od 5,2 do 7,5 m a také soubor různých kosterních částí. Ty byly předány k antropologické a archeozoologické analýze. Nejvíce kostí bylo objeveno ve velké jámě u šibenice v Kamenné Hoře. V případě Zlotoryji se nejvíce pozůstatků nacházelo uprostřed konstrukce. Během prací nebyly ani na jednom stanovišti nalezeny anatomické hroby přesto, že o ukládání těl na těchto místech informovaly písemné zdroje. Po ukončení výzkumů se započalo s bádáním v historických archívech. Současně analýza kostí ukázala, že jak kosti z vnitřku šibenice tak i z okolí patří hlavně zvířatům. V případě Kamenné Hory bylo lidských pozůstatků méně než 5 % z celého souboru, v případě Zlotoryji méně než 6 %. Celkově dominovaly kosti koní, psů, koček a skotu. Výzkumy ukázaly velmi důležitou sanitární funkci bývalých popravišť, která byla v mnoha městech využívána k zakopávání padlých zvířat z okolních obcí.
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4

Basak, Suleyman, and Anna Pavlova. "Monopoly power and the firm?s valuation: a dynamic analysis of short versus long-term policies." Economic Theory 24, no. 3 (October 1, 2004): 503–30. http://dx.doi.org/10.1007/s00199-004-0499-z.

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5

Sriram, M. "Do firm specific characteristics and industry classification corroborate voluntary disclosure of financial ratios: an empirical investigation of S&P CNX 500 companies." Journal of Management and Governance 24, no. 2 (March 27, 2018): 431–48. http://dx.doi.org/10.1007/s10997-018-9414-z.

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6

Kiracı, Kasım. "Determinants of Financial Risk: An Empirical Application on Low-Cost Carriers." Scientific Annals of Economics and Business 66, no. 3 (2019): 335–49. http://dx.doi.org/10.47743/saeb-2019-0025.

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The airline industry has entered a rapid development and transformation process, especially after the Second World War. In this process, it is seen that the market structure changed and many private airlines were established. Due to increased competition, airlines have begun to follow various strategies and business models in order to gain a competitive advantage over each other. One of the business models successfully applied recently is the low-cost business model. Therefore, this study focuses on airline companies that applied the low-cost business model. The study aims to reveal the factors that determine the financial risk in airlines, which implements the low-cost business model. For this purpose, firstly, airline companies that implement the low-cost business model have been identified according to the classification in the literature. The study included an analysis of 13 airlines with the low-cost business model that was fully accessible to financial data for the 2004-2017 period. Panel data analysis was used in the study and Altman (1968) Z-Score and Springate (1978) S-Score were used in measuring financial risk. Empirical findings of the study reveal that firm leverage, asset structure, firm size, firm profitability, and liquidity ratio have an effect on financial risk.
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7

Sharma, Bhu Dev, and Ankita Gaur. "Codes Correcting Limited Patterns of Random Errors Using S-K Metric." Cybernetics and Information Technologies 13, no. 1 (March 1, 2013): 34–45. http://dx.doi.org/10.2478/cait-2013-0004.

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Abstract Coding is essential in all communications and in all multi-operation devices, and errors do occur. For error control, the method in vogue is to use code words with redundant digits. The number of redundant digits is determined based on two things − the number of messages and the kind of errors that need to be controlled. For efficient coding the redundant digits have to be kept to the minimum. In this paper we introduce the idea of limited error patters while using the code alphabet {0,1, 2,..., 1},mod , q Z = q − q when q > 3. We define limitations of the errors in a position by substitution of the character there by a specified number of other characters, rather than by any other character. This is not possible through Hamming approach, because there a character in an error could be substituted by any other of the q-1 characters. The firm mathematical base is provided by use of a metric from the class of S-K metrics, Hamming metric being one of these. The paper gives upper bounds on the codeword lengths for various kinds of “random limited error patterns”. Examples and discussion bring out the tremendous improvement and generalization of Rao Hamming bound.
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8

Omrani, Hamed, Saber Samadi ., Ahmad Kazemi Margavi ., Hamid Asadzadeh ., and Hemad Nazari . "Corporate Life Cycle and the Explanatory Power of Risk Measures versus Performance Measures." Journal of Education and Vocational Research 2, no. 6 (December 15, 2011): 199–206. http://dx.doi.org/10.22610/jevr.v2i6.40.

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The major aim of this paper is to compare the explanatory power of risk measures versus performance measures in different life-cycle stages. To test the hypotheses, first, sample firms were classified into three life-cycle stages (Growth, Mature and Decline). Then, using regression models and Vuong's Z-statistic, the hypotheses were investigated. In this study, financial information of 75 firms which were accepted at Tehran’s Stock Exchange (TSE) from 2003 to 2008 (450 firm-years) was examined. The results of this study show that in growth and decline stages, the explanatory power of risk measures is significantly higher than performance measures and in mature stage, the opposite is true.
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9

Maria Arcari, Anna, and Daniele Grechi. "Predicting the Risk of Insolvency in Small Enterprises: Critical Analysis of the Predictive Model Associated with the New Italian Code of Business Crisis." International Journal of Business and Management 16, no. 7 (June 8, 2021): 41. http://dx.doi.org/10.5539/ijbm.v16n7p41.

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Numerous studies have been conducted to verify whether, and under what conditions, Altman's Z-Score model can also be applied to unlisted, non-US companies. The response of numerous studies confirms the substantial validity of this algorithm. However, in Italy, the legislator, in launching the new Business Crisis Code in 2019, in adherence to an important European recommendation, did not adopt the aforementioned model but approved a different one. In order to find a justification for this choice, the present work intends to test the effectiveness of the warning indices that will be adopted in Italy by comparing them with the Altman predictive model in the Z'' Score version. To this end, the two models were applied to the balance sheets of 789 Italian firms that went bankrupt in the period 2016-2018 and, at the same time, to a control sample, equal in number and composition, of non-bankrupt firms. The results of this analysis produced two distinct findings. The Italian method proved to be less effective in predicting a crisis than the Z'' score. but more effective in determining whether a firm is truly healthy. This evidence is useful to confirm once again the effectiveness of the Z'' Score in a non-American context but also, and above all, to provide suggestions to the Italian legislator so that it can refine the predictive model currently in force.
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10

Rydz, Eugeniusz. "Proces kształtowania się Słupskiej Specjalnej Strefy Ekonomicznej." Studies of the Industrial Geography Commission of the Polish Geographical Society 7 (January 1, 2004): 9–24. http://dx.doi.org/10.24917/20801653.7.1.

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Recesja gospodarcza z początku lat dziewięćdziesiątych spowodowała, że ważnym zagadnieniem stało się poszukiwanie sposobów wsparcia obszarów zagrożonych recesją gospodarczą i rosnącym bezrobociem. Możliwości takie dawała idea, sprawdzona na Zachodzie, jaką było powoływanie specjalnych stref ekonomicznych, na terenie których inwestujące przedsiębiorstwa mogą liczyć na ulgi i przywileje w zamian za budowę zakładów i tworzenie nowych miejsc pracy. Wzorcem w tym zakresie stała się specjalna strefa ekonomiczna w Shaanon, w Irlandii, która przed wejściem do Unii Europejskiej borykała się z podobnymi jak Polska problemami. Należy nadmienić, iż Shaanon było kiedyś lotniskiem obsługującym ruch atlantycki. Po wprowadzeniu odrzutowców, które nie musiały lądować w celu uzupełnienia paliwa na dalszą drogę, Shaanon przestało spełniać swoją funkcję. Perspektywą była utrata pracy przez kilka tysięcy ludzi oraz zmarnowanie całej infrastruktury tworzonej przez wiele lat. Powstał więc pomysł, aby stworzyć tu sieć sklepów wolnocłowych. W 1959 r. powołano w tym celu strefę ekonomiczną, na terenie której umożliwiano podejmowanie działalności produkcyjnej firmom prywatnym i stwarzano warunki dla przyciągania zagranicznych inwestorów. Dla podniesienia atrakcyjności strefy uproszczono przepisy zakładania nowych firm, zniesiono podatki i zwolniono z opłat celnych wszystkie importowane i eksportowane towary oraz materiały do produkcji na eksport. W efekcie tych działań w 1962 r. liczba nowych miejsc pracy przekroczyła liczbę wcześniej utraconych stanowisk, a trzy lata później ze strefy pochodziło 30% irlandzkiego eksportu. Efekty były więc stosunkowo szybkie i dobrze widoczne (Polskie Specjalne Strefy..., 2000, s. 30).
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Book chapters on the topic "S.Z. Goldberg (Firm)"

1

Anderson, Raymond A. "Business Credit." In Credit Intelligence & Modelling, 121–58. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0004.

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This chapter covers modelling of business-credit risk, whether retail or wholesale. (1) Risk 101—i) data sources—variations by firm or loan size (financial statements, traded securities prices, environmental assessments); ii) assessment tools—rating agency grades, business-report scores, public and private firm, hazard, portfolio, and exposure models; iii) rating grades—internal and external (Moody’s, Standard and Poor (S&P), Fitch; S&P provided further insights); iv) small and medium enterprises (SME) lending—including reviewing principals in the personal capacities. (2) Financial-ratio scoring—i) pioneers—including Altman’s Z score and Moody’s commercially successful RiskCalc; ii) predictive ratios—that have appeared; iii) agency usage—for the development of public- and private-firm models; iv) Moody’s RiskCalc—basics and results when first launched; v) non-financial factors—those typically considered, and how objectivity can be improved. (3) Forward-looking data—most provided by human judgment, even the ‘wisdom of the crowd’ inherent in market prices. Rating transitions and functional versus reduced-form models are also used.
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