scholarly journals Condition and problems of entrepreneurial subjects lending

2021 ◽  
pp. 3-10
Author(s):  
Olha Chernenko ◽  
Iryna Vdovenko
Keyword(s):  
Specific Weight ◽  
Added Value ◽  
Credit Policy ◽  
Medium Term ◽  
Short Term ◽  
Access To Credit ◽  
Agrarian Sector ◽  
Main Instrument ◽  
Bank System

Lending is a main instrument of bank institutions’ influence on the development of economy and its subjects. The aim of the paper is to analyze the condition of entrepreneurial subjects lending by the bank system, especially agrarian enterprises, and separation of main restraining factors of its development. During 2017-2020 there is observed an essential reduction of volumes of medium-term and long-term credits, given to entrepreneurial subjects. The use of short-term credits for less than 1 year, the most specific weight (80.5 %) of which is possessed by microentrepreneurial subjects with annual income less than 50 thousand euro, prevails. It has been established, that high cost of credit resources, absence of correspondent guarantee and insufficient competitiveness of most entrepreneurial subjects prevent the development of credit relations for all participants (borrowers, creditors and state). Agricultural economy that produces more than 12 % of GDP and provides more than 40 % of Ukrainian currency receipts, demonstrates positive financial results of activity, is really underfinanced at the expanse of bank credits. A share of credits, directed to the agrarian sector during last years, is essentially less than the contribution of the branch in the gross added value formation in the country. A bank credit policy, acceptable for all entrepreneurial subjects and directed on credit cost decrease and long-term lending increase, is necessary. Studies of the influence of arrangements in the AIC by reduction of credit prices on effective indices (pure profit of agrarian enterprises) has testified a close connection (R=0,9803), comparing with other factors, that is why the practice of using the preferential lending mechanism must be continued, but by stable, not continuously changing approaches and by direct state support of just small and medium entrepreneurial subjects, which are most limited in access to credit resources of bank institutions.

scholarly journals A Baseline Roadmap for Advanced Wireless Research Beyond 5G

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 351 ◽  
Author(s):  
Alain Mourad ◽  
Rui Yang ◽  
Per Hjalmar Lehne ◽  
Antonio De La Oliva
Keyword(s):  
Performance Indicators ◽  
Key Performance Indicators ◽  
Research Community ◽  
Use Cases ◽  
Added Value ◽  
Medium Term ◽  
Short Term ◽  
New Radio ◽  
Technology Trends

This paper presents a baseline roadmap for the evolution of 5G new radio over the next decade. Three timescales are considered, namely short-term (2022-ish), medium-term (2025-ish), and long-term (2030-ish). The evolution of the target key performance indicators (KPIs) is first analyzed by accounting for forecasts on the emerging use cases and their requirements, together with assumptions on the pace of technology advancements. The baseline roadmap is derived next by capturing the top-10 and next the top-5 technology trends envisioned to bring significant added value at each timescale. Being intrinsically predictive, our proposed baseline roadmap cannot assert with certainty the values of the target KPIs and the shortlisting of the technology trends. It is, however, aimed at driving discussions and collecting feedback from the wireless research community for future tuning and refinement as the 5G evolution journey progresses.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

scholarly journals Do the Green Credit Guidelines Affect Renewable Energy Investment? Empirical Research from China

2021 ◽  
Vol 13 (16) ◽  
pp. 9331
Author(s):  
Kexian Zhang ◽  
Yan Wang ◽  
Zimei Huang
Keyword(s):  
Renewable Energy ◽  
Natural Experiment ◽  
Energy Transformation ◽  
Credit Policy ◽  
Short Term ◽  
Energy Investment ◽  
Mediating Role ◽  
Green Development ◽  
Renewable Energy Investment

How to promote renewable energy investment is central to energy transformation and green development. To take China’s “green credit guidelines” policy as a quasi-natural experiment, we investigate the impacts of green credit policy on renewable energy investment. Using the samples of 1021 Chinese listed enterprises during 2007–2017, we find that: Firstly, the introduction of the green credit guidelines has promoted renewable energy investment. Secondly, short-term debts play a mediating role in the impacts of green credit guidelines on renewable energy investment, while long-term debts play a masking role, and financing constraints do not play a significant role. Thirdly, the heterogeneous impacts on renewable energy investment are reflected in different ownerships and enterprise scales, with significant impacts on the state-owned enterprises and small ones.

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

scholarly journals Intralymphatic immunotherapy for allergic rhinoconjunctivitis: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
M.P. Hoang ◽  
K. Seresirikachorn ◽  
W. Chitsuthipakorn ◽  
K. Snidvongs
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Specific Ige ◽  
Allergic Rhinoconjunctivitis ◽  
Medium Term ◽  
Short Term ◽  
Intralymphatic Immunotherapy ◽  
Specific Igg4 ◽  
Igg4 Level

BACKGROUND: Intralymphatic immunotherapy (ILIT) is a new route of allergen-specific immunotherapy. Data confirming its effect is restricted to a small number of studies. METHODOLOGY: A systematic review with meta-analysis was conducted. The short-term (less than 24 weeks), medium-term (24-52 weeks), and long-term (more than 52 weeks) effects of ILIT in patients with allergic rhinoconjunctivitis (ARC) were assessed. The outcomes were combined symptom and medication scores (CSMS), symptoms visual analog scale (VAS), disease-specific quality of life (QOL), specific IgG4 level, specific IgE level, and adverse events. RESULTS: Eleven randomized controlled trials and 2 cohorts (483 participants) were included. Compared with placebo, short term benefits of ILIT for seasonal ARC improved CSMS, improved VAS and increased specific IgG4 level but did not change QOL or specific IgE level. Medium-term effect improved VAS. Data on the long-term benefit of ILIT remain unavailable and require longer term follow-up studies. There were no clinical benefits of ILIT for perennial ARC. ILIT was safe and well-tolerated. CONCLUSION: ILIT showed short-term benefits for seasonal ARC. The sustained effects of ILIT were inconclusive. It was well tolerated.

scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

The impact of the reform of PIS/COFINS on the Brazilian industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pedro Gomes Vasconcelos ◽  
Nelson Leitão Paes
Keyword(s):  
Design Methodology ◽  
Added Value ◽  
Neoclassical Model ◽  
Short Term ◽  
Content Type ◽  
Macroeconomic Indicators ◽  
Tax Distortions ◽  
The Impact ◽  
Two Sectors

PurposeIn an attempt to reduce tax distortions and increase economic efficiency, in 2002 and 2003 Brazil promoted changes in the PIS/COFINS tax, the main federal tax on consumption. Thus, in addition to the old cumulative regime calculated on company revenues, the noncumulative regime was created with higher rates and the added value as a tax basis.Design/methodology/approachThis paper analyzes the effects of the PIS/COFINS reform in a context of deindustrialization in the Brazilian economy, using a neoclassical model with two sectors.FindingsThe results suggest that after a small improvement in the aggregate economy in the short term, in the long term there was a worsening of the macroeconomic indicators. From the sector perspective, the PIS/COFINS reform may have contributed to the loss of industry participation in the Brazilian economy.Originality/valueThe study of the impact of the PIS/COFINS reform on industry through a neoclassical model is unprecedented in the national literature and contributes to the investigation of changes in the tax regime that occurred in the country.

Sign in / Sign up

Export Citation Format

Share Document