Search in Publications

Found 6342 publications

The role of democracy and institutional quality on environmental performance: empirical evidence using a stochastic directional distance model

Cristian Barra Pasquale Marcello Falcone

Publication Name: Economics of Governance

Publication Date: 2026-12-01

Volume: 27

Issue: 1

Page Range: Unknown

Description:

This paper investigates the role of institutional quality and democratic governance in enhancing environmental performance across global economies. As countries confront escalating environmental challenges, understanding these factors is crucial for achieving sustainable development. Despite existing literature emphasizing the importance of high-quality institutions and democratic governance in improving environmental performance, empirical evidence remains inconsistent. Our study refines this understanding by exploring how specific dimensions of institutional quality and varying degrees of democracy impact environmental performance. Basically, we use a stochastic directional distance model to assess how democracy and institutional quality directly affect environmental performance in an unbalanced heterogeneous sample (127 economies) between 1996 and 2018. We find that high institutional quality generally improves environmental performance, though its impact varies with different degrees of democracy. The synergy between higher institutional quality and democratic governance enhances environmental performance, though the effects’ magnitude and direction are context-dependent. This paper provides new insights into how institutional quality and democratic governance work together to enhance environmental performance, offering valuable implications for policy development aimed at balancing economic growth with ecological sustainability.

Open Access: Yes

DOI: 10.1007/s10101-026-00353-7

Examining Tertiary Education Amid the War in Ukraine: A Synthetic Control Approach

Marianna Kichurchak Olha Hrynkevych Halyna Mishchuk Yuriy Bilan Iryna Paslavska

Publication Name: European Journal of Interdisciplinary Studies

Publication Date: 2024-01-01

Volume: 16

Issue: 2

Page Range: 95-115

Description:

War consistently imposes significant challenges to the functioning and advancement of higher education. To identify the key trends in the development of tertiary education in Ukraine during 2014-2021 amid the war, the synthetic control method (SCM) was employed. The outcome variable for assessing tertiary education development is the gross enrolment ratio of the relevant age group. The broadest set of predictors influencing the dependent variable, for which statistical data is available on the World Bank website, consists of eighteen indicators. Through statistical and expert analysis, sixteen countries were selected for inclusion in the control group. The pre-war period was defined as 2000-2013, with 2014 marking the war’s onset, and 2015-2021 representing the war years. In the first stage, a synthetic model is constructed using the broadest possible dataset. In the second stage, the model’s sensitivity is analyzed, leading to the reduction of predictors to thirteen and the control group to ten countries. Consequently, the adequate synthetic model for the development of tertiary education in Ukraine from 2014 to 2021 was established. A placebo test confirmed that the observed gap between actual and synthetic values for tertiary education in Ukraine is not coincidental. The SCM analysis revealed that, without the war, a decline in demand in tertiary education would have been predicted for the 2014-2021 period. The observed gap underscores the significant impact of the war on Ukraine’s higher education system, providing valuable insights for shaping policy initiatives aimed at advancing tertiary education in the post-war era.

Open Access: Yes

DOI: 10.24818/ejis.2024.13

Using Tensor-Type Formalism in Causal Networks

Claudiu Radu Pozna Áron Ballagi Radu Emil Precup

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2024-01-01

Volume: 21

Issue: 10

Page Range: 75-91

Description:

The causal network is a possible description of complex phenomena, and several domains, for example, Machine Learning, Social Science, and Artificial Intelligence. Although a successful solution is referred to in this paper, the field inherently faces challenges. Among these, the work identified that the formalism used is time-consuming and difficult to understand. Consequently, the approach proposed in this paper consists in transcribing this formalism in a tensor form. This goal is accomplished in three steps: first common tensor formulas are proposed for direct and inverse models; second these formulas are adapted for the network primitives; in the end the primitive and consequently the formula composition is analysed. To facilitate the understanding of the proposed formalism, the paper describes several examples. This paper is dedicated to Prof. Imre J. Rudas, to celebrate his 75th anniversary.

Open Access: Yes

DOI: 10.12700/APH.21.10.2024.10.5

Fuzzy Decision Support Methodology for Sustainable Packaging System Design

Adrienn Buruzs Dániel Pup Kata Vöröskoi Ĺaszĺo T. Ḱoczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2022-01-01

Volume: 955

Issue: Unknown

Page Range: 163-173

Description:

The aim of the present paper is to develop an integrated method that provides assistance to decision makers during packaging system planning, design, operation and evaluation from an environmental perspective. The role of the packaging system is to provide a cover for the handling and communication functions surrounding the product. Single-use and reusable packaging are known based on the time it participates in the goods trade. The purpose of the authors is to develop an evaluation model for the selection of packaging systems from an environmental and sustainability point of view in the supply chain.

Open Access: Yes

DOI: 10.1007/978-3-030-88817-6_19

Preface

Rafal Wisniewski Janusz Kacprzyk Piotr Kulczycki Radko Mesiar Ĺaszĺo T. Ḱoczy

Publication Name: Advances in Intelligent Systems and Computing

Publication Date: 2020-01-01

Volume: 945

Issue: Unknown

Page Range: v-vii

Description:

No description provided

Open Access: Yes

DOI: DOI not available

The Spiral Discovery Network as an Automated General-Purpose Optimization Tool

Adam Csapo

Publication Name: Complexity

Publication Date: 2018-01-01

Volume: 2018

Issue: Unknown

Page Range: Unknown

Description:

The Spiral Discovery Method (SDM) was originally proposed as a cognitive artifact for dealing with black-box models that are dependent on multiple inputs with nonlinear and/or multiplicative interaction effects. Besides directly helping to identify functional patterns in such systems, SDM also simplifies their control through its characteristic spiral structure. In this paper, a neural network-based formulation of SDM is proposed together with a set of automatic update rules that makes it suitable for both semiautomated and automated forms of optimization. The behavior of the generalized SDM model, referred to as the Spiral Discovery Network (SDN), and its applicability to nondifferentiable nonconvex optimization problems are elucidated through simulation. Based on the simulation, the case is made that its applicability would be worth investigating in all areas where the default approach of gradient-based backpropagation is used today.

Open Access: Yes

DOI: 10.1155/2018/1947250

Identification of the nonlinear steering dynamics of an autonomous vehicle

Gabor Rodonyi R. Toth M. Schoukens Péter Korös Dániel Pup Adam Kisari G. I. Beintema Zsombor Vigh Alexandros Soumelidis József Bokor

Publication Name: IFAC Papersonline

Publication Date: 2021-07-01

Volume: 54

Issue: 7

Page Range: 708-713

Description:

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results.

Open Access: Yes

DOI: 10.1016/j.ifacol.2021.08.444

Creating and using key network-performance indicators to support the design of change of enterprise infocommunication infrastructure

No authors available

Publication Name: Proceedings of the 2012 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS'12 - Part of SummerSim 2012 Multiconference

Publication Date: 2012-10-08

Volume:

Issue:

Page Range: Unknown

Description:

Nowadays, an increasing number of organisations have to make decisions about the change and optimization of their enterprise infocommunication infrastructure. The usual approach of performance (using QoS, SLA) is not user (enterprise) centred and complex enough to help ICT (Information and Communication Technology) experts to support management decisions. © 2012 Society for Modeling & Sim.

Open Access: No

DOI: DOI not available

Regression and statistical analysis of heat transfer enhancement in water/ethylene glycol (40/60) base molybdenum carbide (Mo2C) MXene nanofluid using a transient fractional model

Nadeem Ahmad Sheikh Zulkhibri Bin Ismail S. W. Teklu Ilyas Khan Ghada R. Elnaggar

Publication Name: Discover Nano

Publication Date: 2026-12-01

Volume: 21

Issue: 1

Page Range: Unknown

Description:

To investigate the effects of fractional order (), nanoparticle volume fraction (), magnetic field strength (), and Brinkman permeability () on both flow and heat transfer characteristics, a detailed parametric and statistical analysis is conducted. The statistical regression analysis shows that the volume fraction of nanoparticles and temperature have a strong positive correlation (coefficient = 0.94, p = 0.021) indicating that Mo2C MXene is an excellent heat absorption. On the other hand, the fractional parameter α has a strong negative effect on temperature field (coefficient = − 0.086, p < 0.001), which emphasizes its importance in describing the effects of thermal memory. The findings also indicate that, although MXene nanoparticles significantly increase thermal transport, an augmentation in magnetic field strength and Brinkman resistance cause a resistive Lorentz force and frictional drag, respectively, to prevent fluid flow. These results are physically informative about non-Fourier heat transfer in MXene-based nanofluids as well as offer invaluable information to developing high-performance thermal management systems and solar-energy applications.

Open Access: Yes

DOI: 10.1186/s11671-026-04645-z