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Publications - 6374

Women's Empowerment Dimensions and Child Stunting in Ethiopia: A Multilevel Analysis of Demographic and Health Surveys 2011–2016

Seyoum Teffera Mengesha Sándor Remsei Amare Zerihun Yohannes Éva Berde

Publication Name: Maternal and Child Nutrition

Publication Date: 2026-01-01

Volume: 22

Issue: 1

Page Range: Unknown

Description:

Child stunting affects 38.3% of children under five in Ethiopia as of 2016. Women's empowerment, defined through both fundamental capabilities and household decision-making authority, has emerged as a critical determinant of child nutritional outcomes. Drawing on Sen's capability approach and Kabeer's empowerment framework, we examined the associations between two distinct dimensions of women's empowerment and child stunting across Ethiopia's diverse regions. We analyzed 18,466 mother–child pairs from the 2011 and 2016 Ethiopia Demographic and Health Surveys. Validated empowerment indices were constructed using factor analysis. We employed hierarchical multilevel models as our primary specification to examine the associations between women's empowerment and child stunting across Ethiopia's 11 administrative regions. Between 2011 and 2016, stunting declined from 42.3% to 36.4%. Women's decision-making authority increased (mean score: 0.70–0.78), while capabilities remained stable (0.17– 0.16). Higher capabilities were significantly associated with lower odds of stunting (β = −0.141, aOR = 0.87, 95% CI: 0.83, 0.91), whereas decision-making showed no association (β = 0.013, aOR = 1.01, 95% CI: 0.98, 1.05). A significant interaction between capabilities and decision-making was observed (β = 0.050, aOR = 1.05, 95% CI: 1.01, 1.09). Regional heterogeneity was substantial: Amhara maintained high stunting rates despite empowerment gains, while Somali saw improvements with low capabilities but increased decision-making. The study findings suggest that interventions should prioritize capability development through region-specific strategies reflecting diverse pastoral, agrarian, and urban contexts; promote multi-sectoral programs linking education and nutrition services; and develop monitoring frameworks to track both dimensions of empowerment at the regional level.

Open Access: Yes

DOI: 10.1111/mcn.70136

ICE Relevant Physical-chemical Properties and Air Pollutant Emission of Renewable Transport Fuels from Different Generations – An Overview

György Szabados Jan Knaup Ákos Bereczky

Publication Name: Periodica Polytechnica Transportation Engineering

Publication Date: 2022-01-01

Volume: 50

Issue: 1

Page Range: 11-22

Description:

The fuel demand in transport sector seems to be raised on a short and also on a long term base in the European Union and worldwide as well. A constantly growing trend is foreseen through 2050 worldwide as for using bio-based energy or fuels. Questions can arise before using these kinds of fuels in connection with the use of clean water or in terms of soil degradation, plant nutrients. It is also questionable whether they can be useful regarding their usage. First-, and second generation liquid as well as third generation gaseous bio-based fuels will be in focus in this article. They will be analyzed from physical-chemical properties and pollutant emission points of view.

Open Access: Yes

DOI: 10.3311/PPtr.14925

First Report of 'Candidatus Phytoplasma asteris' Associated with Cyclamen Little Leaf in Hungary

János Ágoston Orsolya Viczián Emese Mergenthaler József Fodor

Publication Name: Plant Disease

Publication Date: 2023-08-01

Volume: 107

Issue: 8

Page Range: 2515

Description:

No description provided

Open Access: Yes

DOI: 10.1094/PDIS-12-22-2870-PDN

Mezogazdasagi nyugdijak finanszirozasa az europai kontinensen

Andras Giday Tatay Tibor

Publication Name: Public Finance Quarterly

Publication Date: 2022-01-01

Volume: 67

Issue: 1

Page Range: 82-98

Description:

No description provided

Open Access: Yes

DOI: 10.35551/PSZ_2022_1_5

Robust scheduling of waste wood processing plants with uncertain delivery sources and quality

Máté Hegyháti Balazs David Olivér Osz

Publication Name: Sustainability Switzerland

Publication Date: 2021-05-01

Volume: 13

Issue: 9

Page Range: Unknown

Description:

While the study of reverse wood value chains has become an important topic recently, optimization-focused studies usually consider network-level problems and decisions, and do not address the individual processes in the network. In the case of waste wood, one such important process is the scheduling of the various machines in a waste wood processing facility to treat incoming wood deliveries with multiple sources and varying quality. This paper proposes a robust multi-objective mixed-integer linear programming model for the optimization of this process that considers the uncertain origins and compositions of the incoming deliveries, while aiming to minimize both lateness and energy consumption. An exhaustive study is performed on instance sets of different sizes and structures to show the efficiency and the limits of the proposed model both in single-and multi-objective cases.

Open Access: Yes

DOI: 10.3390/su13095007

Short-term cognitive fatigue effect on auditory temporal order judgments

Gábor Orosz Endre Takács Júlia Simon Borbála Berki István Winkler

Publication Name: Experimental Brain Research

Publication Date: 2020-02-01

Volume: 238

Issue: 2

Page Range: 305-319

Description:

Fatigue is a core symptom in many psychological disorders and it can strongly influence everyday productivity. As fatigue effects have been typically demonstrated after long hours of time on task, it was surprising that in a previous study, we accidentally found a decline of temporal order judgment (TOJ) performance within 5–8 min. After replicating prior relevant findings we tested whether pauses and/or feedback relating the participant’s performance to some “standard” can eliminate or reduce this short-term performance decline. We also assessed whether the performance decline is specific to the processes evoked by the TOJ task or it is a product of either general inattentiveness or the lack of willingness to thoroughly follow the task instructions. We found that both feedback and introducing pauses between successive measurements can largely reduce the performance decline, and that these two manipulations likely mobilize overlapping capacities. Performance decline was not present in a similar task when controlling for the TOJ threshold and it was not a result of uncooperative behavior. Therefore, we conclude that the TOJ threshold decline is either specific to temporal processing in general or to the TOJ task employed in the study. Overall, the results are compatible with the notion that the decline of TOJ threshold with repeated measures represents a short-term cognitive fatigue effect. This objective fatigue measure did not correlate with subjective fatigue. The latter was rather related to perceived difficulty/effort, the reduction of positive affectivity, heightened sensitivity to criticism, and the best TOJ threshold.

Open Access: Yes

DOI: 10.1007/s00221-019-05712-x

Using Machine Learning Models to Predict and Reduce Noise Levels in Gear Systems

Ambrus Zelei Krisztián Horváth

Publication Name: Advances in Science and Technology

Publication Date: 2025-01-01

Volume: 165 AST

Issue: Unknown

Page Range: 215-221

Description:

Machine learning models are effective tools for predicting and reducing noise levels in industrial gear systems. In this study, we compare different machine learning methods to investigate the effects of different gear modification parameters on noise levels. Four different predictive models was used. Random Forest Regressor, XGBoost, Gradient Boosting Machines and neural network. The study concluded that Random Forest and Gradient Boosting Machines models were the most effective. Both models achieved low mean squared error values 6.10 and 6.67. Further tests with synthetic data confirmed the stability of these models. Current sustainability trends show that the integration of machine learning into industrial applications fits well with manufacturers' objectives. However, it is currently challenging to determine which machine learning methods are most effective in optimizing noise reduction. This paper seeks to address this gap by comparing the accuracy and reliability of these models. Based on the results, the use of machine learning models is recommended to reduce noise levels in geared systems.

Open Access: Yes

DOI: 10.4028/p-0GDArj

Generalised weighted relevance aggregation operators for hierarchical fuzzy signatures

János Botzheim B. Sumudu U. Mendis Tamás D. Gedeon Ĺaszĺo T. Ḱoczy

Publication Name: Cimca 2006 International Conference on Computational Intelligence for Modelling Control and Automation Jointly with Iawtic 2006 International Conference on Intelligent Agents Web Technologies

Publication Date: 2006-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Hierarchical Fuzzy Signatures are generalizations of the Vector Valued Fuzzy Set concept introduced in the 1970s. A crucial question in the Fuzzy Signature context is what kinds of aggregations are applicable for combining data with partly different substructures. Our earlier work introduced the Weighted Relevance Aggregation method to enhance the accuracy of the final results of calculations based on Hierarchical Fuzzy Signature Structures. In this paper, we further generalise the weights and the aggregation into a new operator called Weighted Relevance Aggregation Operator (WRAO). WRAO enhances the adaptability of the fuzzy signature model to different applications and simplifies the learning of fuzzy signature models from data. We also show the methodology of learning these aggregation operators from data. © 2006 IEEE.

Open Access: Yes

DOI: 10.1109/CIMCA.2006.110

Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection

Peter Foldesi György Istenes Viktor Nagy

Publication Name: Applied Sciences Switzerland

Publication Date: 2024-05-01

Volume: 14

Issue: 9

Page Range: Unknown

Description:

On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where the driver’s eyes are off the road through binarizing frames under different conditions and iteratively recognizing markers. It represents a significant improvement over traditional methods. The study shows that BAIT performs better than other software in identifying a driver’s focus on the windscreen and dashboard with higher accuracy. This study highlights the potential of our method to enhance the analysis of driver attention in real-world conditions, paving the way for future developments for application in naturalistic driving studies.

Open Access: Yes

DOI: 10.3390/app14093838