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Publications - 6278

AI-Powered Digital Transformation of Government Human Resource Management: A Bibliometric and Systematic Literature Review

Roland Zsolt Szabó Aweewan Panyagometh Xiangyu Bian Bin Wang

Publication Name: Journal of Innovation Management

Publication Date: 2025-01-01

Volume: 13

Issue: 3

Page Range: 66-95

Description:

Recent developments in modern artificial intelligence (AI) have driven profound changes in public sector human resource management systems, offering remarkable opportunities alongside intricate challenges. Governments across the globe are progressively integrating AI tools to modernize HR operations, enhance workforce planning, and respond to evolving socio-economic demands. This research utilizes the PRISMA framework for systematic literature review to explore the role of AI in transforming government HR practices. By analyzing 47 peer-reviewed articles published from 2019 to 2023, the study identifies five central themes: ethical and governance models for AI in public administration; AI’s influence on HR functions and organizational behavior; implementation barriers and potential benefits; AI applications in digital governance and policy formulation; and innovations in HR technologies driven by big data. The findings highlight critical success factors such as strong data infrastructure, structured employee training initiatives, and well-defined ethical standards. Key challenges identified include concerns around data privacy, biased algorithms, workforce adaptation, and wider societal implications like employment shifts and changing competency needs. The study underscores the importance of: (1) adaptive regulatory frameworks that support innovation while safeguarding public interest; (2) robust data governance strategies to manage confidentiality and cybersecurity risks; (3) tailored training programs aimed at improving AI understanding among government staff; and (4) collaborative efforts across sectors to promote ethical AI adoption and mitigate socio-economic disruptions.

Open Access: Yes

DOI: 10.24840/2183-0606_013.003_0003

NeuralODE-based Parameter Identification of the Three Chamber Model of the Circulatory System

B. Benyo Ákos Antal J. Geoffrey Chase Bálint Szabó K. Kovacs J. Geoffrey Chase Ákos Szlávecz

Publication Name: Iccc 2025 IEEE 12th International Joint Conference on Cybernetics and Computational Cybernetics Cyber Medical Systems Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 161-166

Description:

In cases of Acute Circulatory Failure, fluid therapy is a commonly used intervention to stabilize heart function. However, the effectiveness of fluid therapy is not directly predictable, and the therapy can also be harmful. Physiological models can be used to predict fluid responsiveness - describing the effectiveness of fluid therapy for the patient - but require solving complex parameter identification problems. The current study aims to develop a Physics Informed Neural Network, specifically a NeuralODE-based parameter identification method for the Three Chamber cardiovascular model, which has the potential to be used to define a novel perfusion marker. The method is developed and validated on a clinical data set collected in model animal experiments.

Open Access: Yes

DOI: 10.1109/ICCC64928.2025.10999147

Big data for sustainable agri‐food supply chains: a review and future research perspectives

Suhaiza Zailani Abderahman Rejeb Karim Rejeb

Publication Name: Journal of Data Information and Management

Publication Date: 2021-09-01

Volume: 3

Issue: 3

Page Range: 167-182

Description:

Research on agri-food supply chains (AFSCs) has attracted significant attention in recent years due to the challenges associated with sustainably feeding the global population. The purpose of this study is to review the potentials of big data for sustainable AFSCs. One hundred twenty-eight (128) journal articles were selected to identify how big data can contribute to the sustainable development of AFSCs. As part of our focus, a framework was developed based on the conceptualization of AFSCs in the extant literature to analyse big data research in the context of AFSCs and to provide insights into the potentials of the technology for agri-food businesses. The findings of the review indicate that there is a noticeable growth in the number of studies addressing the applications of big data for AFSCs. The potentials of big data for AFSC sustainability were synthesized in a summary framework, highlighting the primary resources and activities that are ready for improvement with big data. These include soil, water, crop and plant management, animal management, waste management and traceability management. The challenges of big data integration in AFSCs, the study’s implications, contributions, and the future research directions are highlighted in detail.

Open Access: Yes

DOI: 10.1007/s42488-021-00045-3

Spatial and spectral properties of the dummy-head during measurements in the head-shadow area based on HRTF evaluation

György Wersényi

Publication Name: Institute of Noise Control Engineering of the Usa 35th International Congress and Exposition on Noise Control Engineering Inter Noise 2006

Publication Date: 2006-12-01

Volume: 7

Issue: Unknown

Page Range: 4477-4486

Description:

In accurate and repeatable measurements dummy -heads are often used to model the average human head and body. They are suited for standardized measurements and for investigating the human spatial hearing and localization performance. The monaural Head-Related Transfer Functions (HRTFs) of the dummy-head can be used for various investigations. This paper uses the HRTF-set of a Brüel & Kjaer head and torso simulator focusing on the so called monaural head-shadow area, where one of the ears is shadowed by the head itself. Based on long-term measurements using the bare torso as well as other accessories (glasses, clothing etc.) on it, the extent of the head-shadow area will be presented in frequency and space. The head-shadow area is investigated in connection with the overall SNR of the measurement and sensitivity domains of the ears. Conclusions are drawn for binaural recognition in human spatial hearing using low-frequency 'bright spots' and high-frequency information during lateral-contralateral evaluation.

Open Access: Yes

DOI: DOI not available

Elasto-Plastic limit analysis of reliability based geometrically nonlinear bi-directional evolutionary topology optimization

János Lógó Majid Movahedi Rad Muayad Habashneh

Publication Name: Structures

Publication Date: 2021-12-01

Volume: 34

Issue: Unknown

Page Range: 1720-1733

Description:

This paper presents elasto-plastic limit analysis of reliability-based geometrically nonlinear topology optimization. For this purpose, by reason of uncertainties the volume fraction is considered randomly during optimization. Thus reliability-based design has been considered for solving the problems. To perform reliability-based topology optimization design, the Monte-Carlo simulation method has been applied to calculate the probability of failure, thus the reliability index. Besides, bi-directional evolutionary structural optimization (BESO) method is used to consider the effect of geometrically nonlinear design for elasto-plastic analysis. Plastic behavior is controlled by applying a bound on the plastic limit load multipliers using limit analysis. The adequacy of the proposed method is exhibited by three 2D benchmark problems. 2D models of L-shape beam and U-shaped plate are considered for reliability-based design and geometrically nonlinear analysis topology optimization in case of elastic material. Additionally, 2D and 3D elasto-plastic material models have been considered to demonstrate that the proposed method can find the optimal topology of elasto-plastic models for reliability-based design and geometrically nonlinear analysis.

Open Access: Yes

DOI: 10.1016/j.istruc.2021.08.105

Notes on the rescaled algorithm for fuzzy cognitive maps

István Harmati László T. Kóczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2020-01-01

Volume: 819

Issue: Unknown

Page Range: 43-49

Description:

Fuzzy Cognitive Maps are network-like decision support tools, where the final conclusion is determined by an iteration process. Although the final conclusion relies on the assumption that the iteration reaches a fixed point, it is not straightforward that the iteration will converge to anywhere, since it can produce limit cycles or chaotic behaviour also. In this paper, we briefly analyse the behaviour of the so-called rescaled algorithm for fuzzy cognitive maps with respect to the existence and uniqueness of fixed points.

Open Access: Yes

DOI: 10.1007/978-3-030-16024-1_6

Effect of Internal Structural Design on Stress Distribution in 3D-Printed Subperiosteal Implants Under Mechanical Loading

Ibolya Zsoldos J. Kónya Ádám Vörös Balázs Lőrincz

Publication Name: Bioengineering

Publication Date: 2026-03-01

Volume: 13

Issue: 3

Page Range: Unknown

Description:

Custom-made subperiosteal implants are increasingly used in clinical cases where significant bone loss due to trauma or disease renders conventional endosseous implant placement unfeasible. This study investigated how different internal structural designs affect the deformation and stress distribution in mandibular subperiosteal implants under clinically relevant loading conditions. An idealized implant geometry was defined based on average human mandibular dimensions, and four configurations with identical outer shape and connection features were created, differing only in sidewall architecture (solid, top-relieved, top-relieved with lateral perforations, and top-relieved lattice framework). All specimens were manufactured by metal additive manufacturing and evaluated using cone-beam computed tomography (CBCT). Mechanical testing was performed in two stages: (i) cyclic loading consisting of 500 bite cycles at an overall force of ~326–350 N and (ii) a single static high-load event of 2000 N, applied parallel to the fixation pin axes. CT datasets acquired before and after each stage were compared to detect permanent deformation. No measurable residual deformation was identified in any configuration; the only observed macroscopic change was an adhesive-bond limitation in one case, rather than structural yielding of the implant. Finite element analysis further supported these findings by identifying localized stress concentrations mainly at the implant–prosthetic interface and by revealing the load-transfer zones that govern the mechanical response. Overall, the results indicate that lightweight, perforated, and lattice-based internal designs can preserve global structural integrity across physiological and supra-physiological load ranges while enabling design optimization to improve stress distribution.

Open Access: Yes

DOI: 10.3390/bioengineering13030368

Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors

Dragan Pamucar Nallakaruppan Kailasanathan Rajesh Kumar Dhanaraj Gangadevi Ezhilarasan Nathan Shankar Shitharth Selvarajan Gangadevi Ezhilarasan Rajesh Kumar Dhanaraj Nallakaruppan Kailasanathan

Publication Name: Frontiers in Digital Health

Publication Date: 2025-01-01

Volume: 7

Issue: Unknown

Page Range: Unknown

Description:

Introduction: Cardiovascular health is increasingly at risk due to modern lifestyle factors such as obesity, smoking, stress, hypertension, and sedentary behavior. Post-pandemic health practices and medication side effects have further contributed to rising cases of early heart failure, particularly among individuals aged 25–40 years. This highlights the need for an automated and interpretable framework to predict heart disease at an early stage. Methods: In this study, body vitals acquired from a secondary dataset. Machine learning models including Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression were employed for classification. Model performance was evaluated using accuracy, F1-score, and k-fold cross-validation. Results: Among the tested models, the Random Forest classifier demonstrated superior performance with an accuracy and F1-score of 0.955. The interpretability is enhanced with model predictions were explained using Local Interpretable Model-Agnostic Explanations (LIME) for local surrogates and SHAP values for global surrogates. SHAP decision plots provided clear insights into classification behaviour and feature contributions. Discussion/Conclusion: The proposed interpretable machine learning framework successfully predicts heart disease with high accuracy while maintaining transparency in decision-making. With the integration of sensor data with cloud-based analysis and explainable AI techniques, this study contributes to reducing the incidence of early heart failures and supports more reliable decision-making in healthcare applications.

Open Access: Yes

DOI: 10.3389/fdgth.2025.1612915

Impact of green fiscal policy on the collaborative reduction of pollution and carbon emissions: Evidence from energy saving and emission reduction policy in China

Lóránt Dávid Dong Xu Kai Zhu Tingting Bai Shenghao Bi

Publication Name: Oeconomia Copernicana

Publication Date: 2024-12-30

Volume: 15

Issue: 4

Page Range: 1263-1302

Description:

Research background:Since China is facing the dual challenges of environmental pollution and climate change, how to effectively deal with the collaborative reduction of pollution and carbon emissions (CRPCE) has become an important problem. Energy saving and emission reduction fiscal policy (ESER), as a green fiscal policy, plays an important role in solving China's environmental problems. Purpose of the article: The aim of this study is to analyze the direct impacts, mechanisms and spatial spillover effects of the ESER policy on the CRPCE through theoretical and empirical analyses, thereby providing practical and feasible fiscal-related policy proposals for developing countries like China to achieve low-carbon development. Methods: Difference-in-differences method (DID), spatial DID. Findings & value added: Based on panel data from 274 Chinese cities, this study analyzes the impact of ESER policy on the CRPCE. The findings demonstrate that the ESER policy effectively enhances the CRPCE. The mechanism analysis demonstrates that the impact of the ESER policy is realized by promoting green technology innovation, improving energy efficiency, and increasing industrial structure upgrading. The heterogeneity analysis demonstrates that the ESER policy can be more effective in enhancing the CRPCE when it is implemented in northern, resource-based, and high fiscal self-sufficiency cities. The spatial analysis results suggest that ESER policy attenuates the CRPCE of neighboring cities. In addition, the co-implementation of the ESER policy and the innovation policy is more effective in enhancing the CRPCE, but cities are required to implement the innovation policy first. This study broadens the research perspective on the synergistic effects of green fiscal policy in reducing pollutant and carbon emissions, and offers a useful guide for other developing countries on green fiscal policy.

Open Access: Yes

DOI: 10.24136/oc.3159