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Found 6319 publications

The assessment of financial risks of municipally owned public utility companies in hungary between 2009 and 2018

Laszlo Vasa Csaba Lentner Szilárd Hegedűs

Publication Name: Montenegrin Journal of Economics

Publication Date: 2020-01-01

Volume: 16

Issue: 4

Page Range: 29-41

Description:

The aim of this study is to assess the financial risks, as interpreted by us, of Hungarian corporations fully owned by municipalities across a national dimension after the global economic crisis broken out in 2008. In this study, financial risk was measured by profitability, liquidity and the equity ratio. We were seeking an answer to the question as to how more stringent state controls had influenced the companies’ ability to provide public services and their financial situation behind in a more robust public financial regulatory and control environment created in Hungary after 2010, that is, how the going concern principle of accounting had been implemented. Indirectly, we were also seeking an answer to the question as to how operational risks had been affected by the “breaking” effect on net income exerted by the administrative price regulation (reduction of utility costs on the consumers’ side) imposed on the services of public utility companies in 2013 (as part of the public finance re-form introduced in 2010), that is, whether a more robust regulatory environment of public finances compelled the promotion of corporate efficiency. With the complex (and inter-related) methodology of the assessments carried out, we could establish that financial risks improved, but we propose even stricter controls due the economic shock caused by the COVID-19 pandemic. Our research results present a comprehensive situational picture of municipally owned companies providing public services in an emerging market economy, which can be compared with the data of other countries as well.

Open Access: Yes

DOI: 10.14254/1800-5845/2020.16-4.3

Numerical study on the micro-mechanical behaviour of artificial granular materials

Majid Movahedi Rad M. Amine Benmebarek

Publication Name: Fib Symposium

Publication Date: 2020-01-01

Volume: Unknown

Issue: Unknown

Page Range: 86-93

Description:

Numerical models for the simulation of the micro-mechanical behaviour of granular assemblies have a wide range of applications, for instance in material science, process engineering, environmental engineering, railway engineering and geotechnical engineering (in this study we examined one macro-grain but what important is behaviour of granular assemblies). In this examination, experimental tests and numerical computations using the discrete element method (DEM) are carried out to evaluate the micro-mechanical behviour of the granular materials. For this purpose, artificial materials are taken into consideration for experimental Brazilian laboratory tests, and then according to the experimental results the DEM model is calibrated. Artificial crushable materials are produced by mixing cement and silt according to their mass ratio, in which cement can provide bonding and silt is the main filling material. In the DEM model, a 3D crushable granular material ‘macro-grain’ is built up from a large number of micro-grains which are associated according to crushable parallel bond properties. The behaviour of the single crushable grains and the fragmentation patterns under different contact configuration and load position are studied. The DEM simulation results show that the contact configuration type and load position affect the fragmentation patterns and loading capacity.

Open Access: Yes

DOI: DOI not available

Challenges of Corporate Ecological Footprint Calculations in the SME Sector in Hungary: Case Study Evidence from Six Hungarian Small Enterprises

Cecília Szigeti Áron Szennay L. Radácsi J. Lisányi Endréné Beke J. R.Polák Weldon

Publication Name: Agroecological Footprints Management for Sustainable Food System

Publication Date: 2020-01-01

Volume: Unknown

Issue: Unknown

Page Range: 345-363

Description:

Scientific and social discourse examines primarily the environmental performance of large enterprise actors. Although these large enterprises usually operate on an international level, over half of the added value created in the European Union and thus over half of the environmental damage are generated by small- and medium-sized enterprises (SME). Nevertheless, while the tools and expertise required to measure environmental performance are available for large enterprises, the SME sector has only limited access to these tools. s part of our research, we have developed an ecological footprint (EF) calculator applicable to the specificities of the SME sector, which has been tested on six Hungarian companies operating in different sectors and organisational frameworks. The test results indicate that the managerial information system of partnerships includes all the main inputs that are necessary to estimate a company’s EF. However, in the case of sole proprietorships, most of the required data can only be acquired by estimation. Our EF calculations on analysed firms cannot be considered as representative data. But on the base of the case studies, we can suggest that our EF calculator for SMEs is suitable to take a more comprehensive survey on EF of Hungarian and international firms, in order to generate sectoral benchmarks. Ecological footprint among analysed enterprises ranged between 5102 and 263,589 global square metres. It is caused mainly by (1) the sector (e.g. constructions have generally larger footprints than office activities) and (2) the size, expressed in number of employees or value added. To increase transparency of the environmental performance of the SME sector, we recommend that the supplementary annex of partnerships includes the main input data necessary for the calculation of the EF in a comparable and consistent way, in natural units of measurement. With such information and our calculator, it would be possible to determine the average environmental impact of the individual sectors, which would provide an appropriate starting point for the environmental investments of enterprises.

Open Access: Yes

DOI: 10.1007/978-981-15-9496-0_11

The effects of autonomous buses to vehicle scheduling system

Balazs Horvath Viktor Nagy

Publication Name: Procedia Computer Science

Publication Date: 2020-01-01

Volume: 170

Issue: Unknown

Page Range: 235-240

Description:

We are more and more closer to time when mass value of autonomous vehicles is appearing in road traffic. The number of unanswered questions does not diminish but growing. One such issue is the role of autonomous vehicles in public transport. When talking about autonomous vehicles we often think of only cars and we think less about self-driving buses. But the economic potential inherent in autonomous buses is huge. In the Hungarian vehicle and crew scheduling practice the one driver-one vehicle control is typical. This method closely links the vehicles and the drivers. Vehicles should therefore adapt to the rest time of the crew and the employment rules. Unused reserves are generated in the system. Autonomous vehicles can release this overcapacity. Thanks to that, fewer vehicles can carry out public transport tasks and we can save extra rides. It also provides a solution to the lack of drivers, which is a basic problem in many countries. In our study we show the reserves that can be recovered from the system in a case of a Hungarian city, Eger. We show how much savings can be achieved by running autonomous buses in a European city with a population of 50,000 inhabitants. Our suppositions are only unsure statements what try to help in the preparing of the future.

Open Access: Yes

DOI: 10.1016/j.procs.2020.03.035

Comparison of magnesium determination methods on Hungarian soils

Csaba Centeri Renato Kalocsai G. Milics Zsolt Biró Viktoria Vona A. J. Kovács Gergely Jakab Zsolt Giczi

Publication Name: Soil and Water Research

Publication Date: 2020-01-01

Volume: 15

Issue: 3

Page Range: 173-180

Description:

Magnesium is one of the most important nutrient elements. Soils are tested for magnesium in many countries with several extractants. Each country has its own validated methods, best-suited for its soils. The current study was designed to compare different magnesium content measuring methods with 80 Hungarian samples. The magnesium content was determined by the potassium chloride (1 M KCl 1:10), Mehlich 3 and CoHex (cobalt hexamine trichloride) methods. The maximum, mean and median values resulting from all the Mg determination methods showed the following order of measured magnitude: KCl < CoHex < M3.

Open Access: Yes

DOI: 10.17221/92/2019-SWR

Automated synthesis of process-networks by the integration of P-graph with process simulation

Ákos Orosz Raymond R. Tan Jean Pimentel Ferenc Friedler

Publication Name: Chemical Engineering Transactions

Publication Date: 2020-01-01

Volume: 81

Issue: Unknown

Page Range: 1171-1176

Description:

Chemical process simulation has become one of the most important tools for the analysis of process networks. The simulation software currently available are not capable of automatically generating the process structure, the designer must provide it as an input for the simulation. This limits the contribution of simulation to the latter stages of design after the structure has been clearly defined. Since the P-graph methodology was originally conceived to generate process structures systematically, it can be used to produce the topology of the problem automatically based on rigorous combinatorial axioms and algorithms. In this work, the properties of two P-graph algorithms are exploited to automatically generate alternative structures in a commercial simulator, conferring the latter an improved capacity to assist during the early stage of design. Initially, the maximal structure generation (MSG) algorithm is employed to identify a rigorous superstructure from the initial set of plausible operating units. The solution structure generation (SSG) algorithm is then used to enumerate all combinatorially feasible processes included in the superstructure. Each process structure is individually exported to Aspen Plus®, where rigorous models are used to simulate its performance. A set of alternative processes ranked by their economic performance can be generated. This integrated methodology is employed in a case study for producing methyl lactate from methanol and lactic acid. This work demonstrates that integration of P-graph with rigorous simulation constitutes an enhanced tool for process synthesis that automates the generation of process alternatives, providing useful information and additional insight of the synthesis problem.

Open Access: Yes

DOI: 10.3303/CET2081196

The Acquisition of Phonological Awareness in Children with Mild General Learning Difficulties: Delayed or Disordered Speech Development?

Enikő Csertán Krisztina Zajdó

Publication Name: On Under Reported Monolingual Child Phonology

Publication Date: 2020-01-01

Volume: Unknown

Issue: Unknown

Page Range: 274-323

Description:

No description provided

Open Access: Yes

DOI: DOI not available

Covid-19 and the food chain? Impacts and future research trends

John G. Keogh Abderahman Rejeb Karim Rejeb

Publication Name: Logforum

Publication Date: 2020-01-01

Volume: 16

Issue: 4

Page Range: 475-485

Description:

Background: Throughout history, the world has witnessed natural disasters affecting businesses and societies with varying degrees of disruption. COVID-19 (henceforth C19) constituted a significant system shock and a stark reminder of the fragility and sensitive nature of supply chains. The pandemic has exerted considerable societal and economic pressure and has had an adverse impact on food supply chains in particular. Many food processing operations were forced to alter activities or close temporarily due to outbreaks. Societal lockdowns, travel restrictions, business closures and quarantine have led to structural changes in the productivity of economies and impacted the mental health and financial wellbeing of citizens. Methods: We present a critical review of the literature to explore the impact of C19 on the food supply chain. We collected data from journal articles retrieved from a leading scientific database (i.e., Scopus), books, chapters, conference proceedings, reports, and a variety of Internet websites. For the literature search, we used the following words; "COVID-19" and "food". Results and conclusions: The findings of the review suggest that the C19 pandemic poses unprecedented challenges for food supply chains. We reveal that C19 has raised food insecurity and food safety concerns, increased supply chain and logistics costs and radically changed consumer behavior. On the positive side, the pandemic has improved awareness of food waste and the importance of self-grown foods. We generated nine research propositions to foster future academic research. Our study also highlighted the need to advance this literature and calls for increased attention from the supply chain management and logistics community to further analyse and quantify the impact of C19 on the food chain.

Open Access: Yes

DOI: 10.17270/J.LOG.2020.502

Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care

B. Benyo J. Geoffrey Chase Bálint Szabó K. Kovacs Yahia Anane Ákos Szlávecz Béla Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2020-01-01

Volume: 53

Issue: 2

Page Range: 16335-16340

Description:

Stress-induced hyperglycaemia is a frequent complication in the intensive therapy that can be safely and efficiently treated by using the recently developed model-based tight glycaemic control (TGC) protocols. The most widely applied TGC protocol is the STAR (Stochastic-TARgeted) protocol which uses the insulin sensitivity (SI) for the assessment of the patients state. The patient-specific metabolic variability is managed by the so-called stochastic model allowing the prediction of the 90% confidence interval of the future SI value of the patients. In this paper deep neural network (DNN) based methods (classification DNN and Mixture Density Network) are suggested to implement the patient state prediction. The deep neural networks are trained by using three years of STAR treatment data. The methods are validated by comparing the prediction statistics with the reference data set. The prediction accuracy was also compared with the stochastic model currently used in the clinical practice. The presented results proved the applicability of the neural network based methods for the patient state prediction in the model based clinical treatment. Results suggest that the methods' prediction accuracy was the same or better than the currently used stochastic model. These results are the initial successful step in the validation process of the proposed methods which will be followed by in-silico simulation trials.

Open Access: Yes

DOI: 10.1016/j.ifacol.2020.12.659