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Virtual reality headsets for employee training in enterprises: fuzzy SRP data-driven framework for a comprehensive evaluation

Dragan Pamucar Fatih Ecer Alptekin Ulutaş Ömer Faruk Görçün Ejder Ayçin Hakan Aşan

Publication Name: Virtual Reality

Publication Date: 2026-03-01

Volume: 30

Issue: 1

Page Range: Unknown

Description:

Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research’s novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.

Open Access: Yes

DOI: 10.1007/s10055-025-01282-2

Human–GenAI-based agent collaboration: How employee perceptions shape knowledge sharing, thriving, and well-being

Rsha Alghafes Gabriella Imre Tuan Phong Nham Mai Nguyen Obaidur Rahman Obaidur Rahman

Publication Name: Acta Psychologica

Publication Date: 2026-03-01

Volume: 263

Issue: Unknown

Page Range: Unknown

Description:

The growing pace of the introduction of generative artificial intelligence (GenAI) into organizational processes is changing the way workers cooperate with technology. Based on Social Exchange Theory, we propose that the perception of employees regarding the value of GenAI-based agents, their vulnerability and privacy, and their self-concern would determine the collaboration with GenAI agents, which subsequently would predict the knowledge sharing, job thriving, and well-being. The findings show that perceived GenAI-based value has a strong positive impact on human-GenAI-based agent collaboration, but data vulnerability and privacy concerns have no significance. Interestingly, self-concern has a positive effect, which implies that identity-based fears can be used to drive active use of GenAI-based agents. Knowledge sharing, job thriving, and well-being are highly predicted by human-GenAI-based agent collaboration, and organizational exploratory innovation moderates these correlations. These results extrapolate the Social Exchange Theory to human-AI situations, dispel the assumptions of the consistently negative impact of risk perception, and emphasize the relevance of organizational climate to the implementation of the advantages of AI cooperation. The paper provides both theoretical and practical understanding of the way in which employees interact with GenAI-based agents to ensure that organizational learning and psychological outcomes of employees are achieved.

Open Access: Yes

DOI: 10.1016/j.actpsy.2026.106271

Shared Heritage, Divergent Paths: Heritage Tourism Development in UNESCO Fortified Church Villages of Transylvania, Romania

János György NAGY Enikő Nemes Tímea Csizmadia Melinda Nagyné Molnár

Publication Name: Heritage

Publication Date: 2026-03-01

Volume: 9

Issue: 3

Page Range: Unknown

Description:

Romania joined the UNESCO Convention in 1990. The fortified church of Biertan was inscribed on the World Heritage List in 1993, followed by six additional Transylvanian fortified church villages in 1999. An interesting feature of this heritage landscape is that settlements with different demographic and development trajectories share the same World Heritage designation. In our research, we collected demographic and tourism data from these seven municipalities. Subsequently, a standard questionnaire was sent to municipal decision-makers (mayors) in 2023 to map tourism development in their municipalities. The communication activities of the municipalities were analysed using a content analysis method, which was observation-based and based only on online content. In our experience, there is no common strategy to turn this heritage into a tourist attraction; each of the seven municipalities has faced this challenge separately. The main result of the research was to explore how heritage tourism works in municipalities with different demographic, linguistic-cultural heritage and with different levels of management.

Open Access: Yes

DOI: 10.3390/heritage9030116

Special Issue: Exploring Abiotic Stress in Plants—Mechanisms, Adaptations, and Mitigation Strategies

Tarek Alshaal

Publication Name: International Journal of Molecular Sciences

Publication Date: 2026-03-01

Volume: 27

Issue: 5

Page Range: Unknown

Description:

No description provided

Open Access: Yes

DOI: 10.3390/ijms27052182

Optimal scheduling of electric vehicle charging and discharging using two optimization paradigms

Basem Abd Elhamed Rashad Basem Abd Elhamed Rashad Mustafa M. Abdelaziz Ragab A. El-Sehiemy Almoataz Y. Abdelaziz

Publication Name: Results in Engineering

Publication Date: 2026-03-01

Volume: 29

Issue: Unknown

Page Range: Unknown

Description:

Electric Vehicles (EVs) play a pivotal role in advancing environmental sustainability and accelerating the transition toward clean energy systems. However, large-scale EV adoption poses significant operational challenges, particularly when charging and discharging activities are uncoordinated, potentially leading to elevated peak demand and increased grid stress. Effective scheduling techniques are therefore essential to ensure reliable integration of EVs into modern power systems. This study provides a rigorous comparative evaluation of two metaheuristic optimization paradigms for EV charging and discharging scheduling: the traditional Particle Swarm Optimization (PSO) algorithm and the more recent Transit Search Optimization (TSO) algorithm. Using an identical system configuration and EV dataset, the study assesses the performance of both approaches based on peak power reduction, cost minimization, and overall system efficiency. Results demonstrate that while enhanced PSO scenarios exhibit noticeable improvements over earlier literature, TSO consistently achieves superior outcomes due to its stronger exploration-exploitation balance. In particular, TSO attains a 46.23 % reduction in average EV charging cost and achieves the lowest power-loss levels across all tested scenarios. Relative to the best previously published benchmarks, TSO further improves peak power consumption by 1.6 % and total charging cost by 6.1 %. These findings highlight TSO’s strong potential as a high-efficiency scheduling tool for large-scale EV integration in future smart grid environments.

Open Access: Yes

DOI: 10.1016/j.rineng.2025.108768

The dynamic impact of oil price volatility on China's green bond market: An empirical analysis during economic shocks

Andrea Gatto Hyoungsuk Lee Xin Zhao Xin Shu Xiaolei Zong

Publication Name: Energy Strategy Reviews

Publication Date: 2026-03-01

Volume: 64

Issue: Unknown

Page Range: Unknown

Description:

The progressive financialization of oil, in tandem with the advancement of economic globalization, has led to a sharp increase in oil prices. The growing volatility in the global economic and financial landscape has had some impact on the green bond market. Emerging markets, such as China, are particularly interesting due to their rapid evolution. This paper empirically analyzes the dynamic impact of oil market price uncertainty on China's Green Bond (GB) using the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) model. The empirical findings indicate that the uncertainty of oil has a remarkable time-varying influence on China's green bonds. Specifically, when oil prices rise, the yields on green bonds decrease. Dynamic correlation analysis reveals that oil market uncertainty exhibits a negative correlation with green bonds, with a more pronounced impact during the COVID-19 pandemic. Furthermore, an impulse response analysis shows that long-term interactions between oil prices and green bonds gradually stabilize, and short-term fluctuations are frequent and complex due to market factors. These fluctuations were more pronounced during the COVID-19 pandemic, consistent with the above conclusions. Oil market uncertainty increases risk levels in the overall financial market, which may affect investors' perceptions of green bonds. Drawing on the research outcomes, this study presents targeted policy recommendations aimed at promoting the stable and sustainable development of China's GB market. These measures are designed to bolster the nation's transition toward a green economy and align with its long-term sustainability goals.

Open Access: Yes

DOI: 10.1016/j.esr.2026.102112

Pathologic abnormalities of deep placentation in the great obstetrical syndromes: Implications for understanding the pathophysiology, risk assessment in early pregnancy, and personalized prevention

Offer Erez Adi L. Tarca Nándor Gábor Than Pierre Yves Robillard Zoltán Papp Gergő Leipold Zsófia Benkő Sándor Nagy Roberto Romero Petra Merkely Máté Posta Nándor Ács Beáta Hargitai Sándor Valent Tamás Marton Gábor Szalai

Publication Name: Journal of Reproductive Immunology

Publication Date: 2026-03-01

Volume: 174

Issue: Unknown

Page Range: Unknown

Description:

AbstractThe concept of the Great Obstetrical Syndromes was introduced to explain the unique nature of obstetrical disease, which differs fundamentally from disorders in other areas of medicine. These syndromes, including preeclampsia, fetal growth restriction, fetal death, and spontaneous preterm birth, represent clinical endpoints rather than single diseases and share defining characteristics: they arise from multiple etiologies, have a prolonged subclinical phase, involve the fetus as an active participant, are adaptive in nature, and result from complex genetic and environmental interactions between the mother and fetus. Among the diverse mechanisms leading to these syndromes, abnormalities of the maternal supply line to the placenta constitute one major etiology and are often caused by vascular disorders affecting the maternal cardiovascular system and uterine spiral arteries, resulting in placental lesions of maternal vascular malperfusion. The most severe spiral artery lesion is atherosis, which closely resembles atherosclerosis and links obstetrical syndromes to maternal vascular disease. Disorders of deep placentation associated with maternal vascular malperfusion are accompanied by characteristic alterations in angiogenic balance, and the ratio of placental growth factor to soluble fms-like tyrosine kinase-1 in the maternal circulation serves as a biomarker of this pathophysiologic process. Importantly, each obstetrical syndrome is associated with a stereotypic temporal pattern of angiogenic imbalance that reflects differences in disease burden, timing, and clinical expression. While substantial progress has been made in the prediction and prevention of preeclampsia, these concepts extend to other obstetrical syndromes, including fetal growth restriction, fetal death, and spontaneous preterm labor, supporting a unified biologic framework for early risk assessment and personalized prevention.

Open Access: Yes

DOI: 10.1016/j.jri.2026.104846

First Successful Treatment Reported with Pembrolizumab in a Patient Diagnosed with Choriocarcinoma in Hungary

Vilmos Fülöp Kornél Lakatos László Kalmár Erzsébet Gyöngyvirág Bíró Erika Lahm

Publication Name: Life

Publication Date: 2026-03-01

Volume: 16

Issue: 3

Page Range: Unknown

Description:

Pembrolizumab is a programmed cell death protein (PD-1) inhibitor, humanized antibody widely used in cancer immunotherapy. Choriocarcinoma is an aggressive type of gestational trophoblastic neoplasia. Its treatment is based on surgical removal of the tumorous tissue and systemic chemotherapy; however, in some chemoresistant cases, immunotherapy can also be a valid option. Here, we report the first successful programmed death inhibitor-based treatment of a patient diagnosed with stage IV, ultra-high-risk choriocarcinoma in Hungary.

Open Access: Yes

DOI: 10.3390/life16030481

Recent advances in MPPT techniques for photovoltaic systems: A review of classical (P&O, IC), intelligent (ANN), optimization (PSO) and hybrid (ANN-PSO) methods

Gusztáv Fekete Sumit Pundir Yang Song Abhishek Sharma Tej Singh Jagdish Chandola

Publication Name: Results in Engineering

Publication Date: 2026-03-01

Volume: 29

Issue: Unknown

Page Range: Unknown

Description:

As global energy consumption rises and fossil fuel resources continue to diminish, the use of effective renewable energy technologies becomes increasingly critical. This comprehensive analysis examines maximum power point tracking (MPPT) approaches for solar photovoltaic (PV) installations, evaluating both classical methods, such as perturb and observe and incremental conductance, alongside advanced intelligent approaches, including artificial neural networks (ANNs). The study also explores optimization-based approaches such as particle swarm optimization (PSO) and combined frameworks that integrate ANN with PSO, assessing their effectiveness across different solar irradiance and temperature scenarios. Classical MPPT approaches are known for their ease of implementation; however, they are reported to demonstrate limitations, including slow response times and steady-state oscillations during rapid changes in environmental conditions. In contrast, artificial intelligence and swarm intelligence methodologies show enhanced flexibility, precision, and stable performance across various irradiance conditions. The integration of intelligent algorithms with optimization techniques results in accelerated convergence rates, improved tracking precision, and enhanced stability, consistently maintaining efficiency levels exceeding 99 % while minimizing oscillations. Recent developments in MPPT technology underscore the exceptional adaptability and energy harvesting potential of hybrid methodologies, emphasizing their crucial role in optimizing PV system performance and supporting sustainable power generation.

Open Access: Yes

DOI: 10.1016/j.rineng.2026.109395