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Found 6319 publications

Crisis, institutional change and peripheral industrialization: Municipal-central state relations and changing dependencies in three old industrial towns of Hungary

Ernő Molnár Gergő Gajzágó Melinda Mihály Erika Nagy

Publication Name: Applied Geography

Publication Date: 2021-11-01

Volume: 136

Issue: Unknown

Page Range: Unknown

Description:

This paper aims to discuss radical changes, institutional responses and their socio-spatial consequences by focusing on reorganisation of institutional settings of local economic development after the global financial crisis (2008). We focus on the complexity of institutional change and social relations driving those in three old industrial towns (Dunaújváros, Martfű and Tatabánya in Hungary) that faced a functional, cognitive and political lock-in in the 1990s, and emerged as spaces of encounter of global production networks, governmental development policies and local society in the 2000s. This entailed a complex and dynamic assembly of various interests and strategies, providing a scope for local institutional experimentations that were interrupted by the global crisis and the resulting macro-structural changes. We place municipal agency, its uneasy, contested and changing relation to the central state in the focus. We discuss how the introduction of a new regulative system and institutional-spatial hierarchies in Hungary after the 2008 crisis enhanced central state power, and how that was mobilized to develop a new regime in which communities were losing control over their resources, and local assets were being channelled in peripheral industrialization orchestrated by the central government. Discussing municipal agency in a strategic-relational approach allows us to highlight the depth and multiple consequences of the crisis locally beyond market relations, giving an insight in the spatial rearrangement of power in relation to peripheral industrialization.

Open Access: Yes

DOI: 10.1016/j.apgeog.2021.102576

DOES TELEWORK WORK? GAUGING CHALLENGES OF TELECOMMUTING TO ADAPT TO A “NEW NORMAL”

Agota Giedrė Raišienė Tibor Dory Violeta Rapuano Kristina Varkulevičiūtė

Publication Name: Human Technology

Publication Date: 2021-10-31

Volume: 17

Issue: 2

Page Range: 126-144

Description:

The paper aims to contribute to a deeper understanding of organisation management while telecommuting. With exploratory factor analysis (EFA), we define the specific set of telework organising efficiency characteristics. We determined the number of factors with Kaiser Eigenvalues rule as well as Cattel’s scree criterion. We conducted the study in Lithuania, the country with a low percentage of teleworkers until organisations have been urged to properly implement their performance to remote means after the COVID-19 quarantine was announced. This paper reveals that the fundamental challenges of teleworking are the feedback issues related to working accomplishment, especially to the task and process overload, and individual self-organisation ability. Moreover, the flexibility of work organisation represents a unique characteristic of telework, and managers should cooperate more effectively with teleworkers to keep them motivated.

Open Access: Yes

DOI: 10.14254/1795-6889.2021.17-2.3

Progress and Tradition

Miklós Könczöl András Lapsánszky Ákos Tussay András Koltay

Publication Name: Public Governance Administration and Finances Law Review

Publication Date: 2021-10-29

Volume: 6

Issue: 1

Page Range: 5-6

Description:

No description provided

Open Access: Yes

DOI: 10.53116/pgaflr.2021.1.1

Case study on the tactical level of an autonomous vehicle control

Claudiu Radu Pozna Csaba Antonya Ernö Horváth

Publication Name: International Conference on Electrical Computer Communications and Mechatronics Engineering Iceccme 2021

Publication Date: 2021-10-07

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

In this paper, a case study is presented on the tactical level of autonomous vehicle control. Inspired by the human driver behavior, vehicle control is structured in a hierarchy on three levels: the strategic, the tactical, and the operational level. These are connected by specific links, and based on prior information and models, are transforming the input data from the environment into actuator commands on the various degree of freedom of the vehicle. The case study is presenting a simulation of a scenario, detailing the vehicle models with sensors, the collection of behavior, and the behavior selector.

Open Access: Yes

DOI: 10.1109/ICECCME52200.2021.9590868

Impact of agricultural drought on sunflower production across hungary

Tamás Ratonyi Endre Harsányi A. Széles Muhammad Habib Ur Rahman Bashar Bashir István Bácskai Karam Alsafadi Safwan Mohammed Firas Alsilibe Csaba Juhász Abdullah Alsalman

Publication Name: Atmosphere

Publication Date: 2021-10-01

Volume: 12

Issue: 10

Page Range: Unknown

Description:

In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CAR-BATCLIM database (1961–2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as −3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.

Open Access: Yes

DOI: 10.3390/atmos12101339

Rényi 100, Quantitative and qualitative (in)dependence

J. Pintz Miklós Arató Gy Michaletzky T. Rudas T. F. Móri G. Tusnády G. J. Székely Gy O.H. Katona

Publication Name: Acta Mathematica Hungarica

Publication Date: 2021-10-01

Volume: 165

Issue: 1

Page Range: 218-273

Description:

We discuss recent developments in the following important areas of Alfréd Rényi’s research interest: axiomatization of quantitative dependence measures, qualitative independence in combinatorics, conditional qualitative independence in statistics/data science and in measure theory/probability theory, and finally, prime gaps that are responsible for Rényi’s early career reputation. Most authors of this paper are main contributors to the new developments.

Open Access: Yes

DOI: 10.1007/s10474-021-01164-4

Comparative and phylogenomic analysis of nuclear and organelle genes in cryptic Coelastrella vacuolata MACC-549 green algae

B. Pap Attila Farkas Torda Varga Gergely Maróti Prateek Shetty Bettina Hupp Vince Ördög Tibor Bíró

Publication Name: Algal Research

Publication Date: 2021-10-01

Volume: 58

Issue: Unknown

Page Range: Unknown

Description:

The nuclear, chloroplast and mitochondrial genomes of a unicellular green algal species of the Coelastrella genus was sequenced, assembled and annotated. The strain was previously classified as Chlamydomonas sp. MACC-549 based on morphology and partial 18S rDNA analysis. However, the proposed multi-loci phylogenomic approach described in this paper placed this strain within the Coelastrella genus, therefore it was re-named to Coelastrella vacuolata MACC-549. The strain was selected for de novo sequencing based on its potential value in biohydrogen production as revealed in earlier studies. This is the first thorough report and characterization for green algae from the Coelastrella genus. The whole genome annotation of Coelastrella vacuolata MACC-549 (including nuclear, chloroplast and mitochondrial genomes) shed light on interesting metabolic and sexual breeding features of this algae and served as a basis to taxonomically classify this strain.

Open Access: Yes

DOI: 10.1016/j.algal.2021.102380

The potentials of augmented reality in supply chain management: a state-of-the-art review

Horst Treiblmaier Samuel Fosso Wamba John G. Keogh Abderahman Rejeb

Publication Name: Management Review Quarterly

Publication Date: 2021-10-01

Volume: 71

Issue: 4

Page Range: 819-856

Description:

This paper examines the potentials of augmented reality (AR) technology in supply chain management (SCM) and logistics. Specifically, we provide an overview of the technology’s various value propositions and its ability to support companies’ business processes. Although the emergence of Industry 4.0 has renewed the interest in AR, and how it can address several issues challenging existing business models, rigorous studies investigating the potentials of AR for SCM and logistics activities are scant. To bridge this knowledge gap, we conducted a systematic literature review to compile existing literature, identify current research gaps, and systematize AR research in SCM and logistics activities. In total, forty-three (43) papers were thoroughly analyzed. The findings of this study reveal that AR can add value in five main areas, namely warehousing, manufacturing, sales and outdoor logistics, planning and design and human resource management. Moreover, we discuss organizations’ challenges when deploying AR in SCM and logistics and propose exploratory research opportunities for further investigation. In this paper, we highlight numerous practical implications of AR in SCM and recommend that organizations consider AR as a potential solution for enhancing business processes, improving operational efficiencies, and increasing overall competitiveness. This study represents one of the first attempts to synthesize AR’s literature from an SCM and logistics perspective.

Open Access: Yes

DOI: 10.1007/s11301-020-00201-w

Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods

Endre Harsányi G. Milics Bálint Daróczy C. Kerepesi A. J. Kovács J. Nagy A. Nyéki M. Neményi András Benczúr

Publication Name: Precision Agriculture

Publication Date: 2021-10-01

Volume: 22

Issue: 5

Page Range: 1397-1415

Description:

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account.

Open Access: Yes

DOI: 10.1007/s11119-021-09833-8