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Found 6278 publications
Shared Heritage, Divergent Paths: Heritage Tourism Development in UNESCO Fortified Church Villages of Transylvania, Romania
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
The dynamic impact of oil price volatility on China's green bond market: An empirical analysis during economic shocks
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
Effect of Internal Structural Design on Stress Distribution in 3D-Printed Subperiosteal Implants Under Mechanical Loading
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
Hybrid Brown-Bear and Hippopotamus Optimization with Quasi-Opposition-Based Learning for Optimal Power Flow with Renewable Energy Integration
Publication Name: Computers and Electrical Engineering
Publication Date: 2026-03-01
Volume: 131
Issue: Unknown
Page Range: Unknown
Description:
The optimal power flow (OPF) problem is a highly nonlinear and complex multi-dimension optimization problem, especially with the increased penetration of uncertain renewable energies (RES). In this line, this paper presents the Hybrid Brown-Bear and Hippopotamus Optimization Algorithms with Quasi-Opposition-Based Learning (HBOA-QOBL) to enhance multi-dimension OPF solution. The algorithm combines the strengths of Brown-Bear optimizer, which excels in exploration and adaptive search mechanisms, and the Hippopotamus optimizer, known for its social behavior modeling and localized search strategies. By integrating QOBL, the HBOA-QOBL improves exploration through the generation of quasi-opposite solutions, allowing for a wider search of the solution space and reducing the risk of premature convergence. Adaptive search mechanisms embedded in HBOA-QOBL enhance exploitation by dynamically adjusting search behaviors during iterative power dispatch tuning, enabling improved fine-tuning of generation schedules and voltage profiles. The effectiveness of the proposed method is evaluated on the IEEE 30-bus, 57-bus, and 118-bus test systems for multiple dimension OPF objectives, including fuel cost minimization, emission reduction, power loss reduction, voltage deviation minimization, reactive power loss reduction and the voltage stability indicator (L-index). Simulation results indicate faster convergence compared to conventional techniques, achieving near-optimal solutions within 200 iterations, with a standard deviation of 63.8%, demonstrating superior technical and economic performance relative to previous research. Key convergence parameters such as population size, maximum iterations, and learning factor are explicitly tuned to enhance both exploration and exploitation. Simulation results confirm that HBOA-QOBL outperforms conventional optimization techniques in terms of solution quality, convergence speed, and stability, establishing significant improvement in the technical and economic issues.
Open Access: Yes
Foot Progression Angle Modulates Three-Dimensional Lower-Limb Biomechanics in Flexible Flatfoot: Kinematic–Kinetic Patterns and Clinical Implications
Publication Name: Journal of Foot and Ankle Research
Publication Date: 2026-03-01
Volume: 19
Issue: 1
Page Range: Unknown
Description:
Introduction: Foot progression angle affects gait and lowerlimb alignment. Altered angles may increase knee and ankle loading and produce tissue loading patterns previously linked to musculoskeletal injury. This study investigates how different foot progression angles modify knee and ankle biomechanics in young adults with flexible flatfoot. Methods: 28 participants (aged 18–35 years) with flexible flatfoot completed gait trials under three foot progression angle conditions. Kinematic and kinetic variables were analyzed using one-dimensional statistical parametric mapping. A 1D convolutional neural network was applied to classify progression angle patterns based on flexible flatfoot severity and gait biomechanics. Results: Decreasing foot progression angle reduced the ankle eversion/inversion range and knee abduction and external rotation (p < 0.05). Increasing foot progression angle lowered early stance ankle plantarflexion and increased knee abduction/external rotation (p < 0.05). Kinetically, a smaller foot progression angle reduced peak ankle plantarflexion moment and knee extension moment but increased the first peak of the knee adduction moment and rotational moment fluctuations (p < 0.05). A larger foot progression angle reduced rotational fluctuations and terminal stance knee extension moment (p < 0.05). The convolutional neural network model was most accurate for moderate flexible flatfoot cases, and ankle coronal and knee transverse biomechanics showed the strongest discriminative power. Conclusion: Modifying the foot progression angle can meaningfully alter knee and ankle loading in young adults with flexible flatfoot. Neutral or mild toe-in angles may help mitigate excessive eversion and rotational stress, suggesting a simple noninvasive adjustment that clinicians can incorporate during gait retraining or orthotic prescription. Because biomechanical responses vary across individuals, FPA modification may be the most effective when tailored to patient-specific gait characteristics. In addition, deep-learning-based gait classification shows promise for supporting personalized monitoring and guiding clinical decision-making during rehabilitation.
Open Access: Yes
DOI: 10.1002/jfa2.70126
Reproductive Success Beyond Pollinators: Microhabitat Effects and Pollen Dynamics in Epipactis bugacensis, a Traditionally Obligately Autogamous Orchid
Publication Name: Plants
Publication Date: 2026-03-01
Volume: 15
Issue: 5
Page Range: Unknown
Description:
Orchid pollination is traditionally considered to rely on intact pollinarium transfer by animal vectors. Species lacking a functional viscidium are generally classified as obligately autogamous. In this study, we investigated the reproductive biology of Epipactis bugacensis, a taxon long regarded as strictly self-pollinating. Floral visitor activity was assessed through repeated field observations, and pollinator dependence was tested using a pollinator-exclusion (net-covering) experiment at two Hungarian populations, combined with measurements of fruit set, capsule volume, seed number, and seed density. We documented a previously unreported pollen-transfer mechanism in E. bugacensis, whereby halictid bees fragment pollinia and transfer these fragments in their scopa to neighboring flowers enabling geitonogamous deposition and suggesting the potential for xenogamous pollen transfer. Other visitor taxa showed no evidence of effective pollen transport. Mesh coverage increased fruit set, capsule volume, and seed number, while seed density remained unchanged. Reproductive output declined from basal to apical positions along flowering shoots, revealing strong internal resource-allocation constraints. Overall, E. bugacensis is predominantly self-pollinating but not strictly obligate autogamous, and its reproductive success is governed primarily by microhabitat quality rather than pollinator availability.
Open Access: Yes
Building safe organisations: using machine learning to decode safety habits of blue-collar workers in the construction industry
Publication Name: Engineering Management in Production and Services
Publication Date: 2026-03-01
Volume: 18
Issue: 1
Page Range: 42-59
Description:
This study aims to provide a framework for categorising safety behaviours of construction workers, recognising the importance of employee safety in the competitive business environment. Employee safety is crucial to overall efficiency, productivity, and well-being, and the study seeks to contribute to understanding and managing workplace safety in the construction industry. This study utilises machine learning (ML) algorithms, like logistic regression, support vector machine, and decision trees, to develop a categorisation framework for the safety behaviours of construction workers. The framework is validated using frequent safety behaviours observed in a random sample of construction professionals. The study finds that workplace safety behaviours (WSB) are primarily influenced by supervisor support, reckless habits, and safety motivation. Limiting workplace accidents, enforcing safety laws, properly documenting safety processes, and organising sessions to educate staff are identified as critical sub-factors. Advancements in technology have resulted in significant improvements across construction organisations in allied domains. Additional considerations include education, preempting the possibility of accidents in different workplace situations, and enforcing strong disciplinary measures. The framework proposed can serve as a valuable tool for organisations to tailor safety interventions. By recognising the diverse influences on safety behaviours, companies can implement targeted measures to address specific root causes of unsafe practices. The practical implications of these findings for safety management in the construction industry are noteworthy.
Open Access: Yes
Mapping municipal debt risks: A spatiotemporal analysis of China's prefecture-level cities
Publication Name: International Review of Economics and Finance
Publication Date: 2026-03-01
Volume: 106
Issue: Unknown
Page Range: Unknown
Description:
Addressing the risks associated with local government debt is crucial for economic development and fiscal security. This paper analyzes the spatiotemporal distribution of municipal government debt risks using panel data from 271 prefecture-level cities in China from 2015 to 2021, employing Exploratory Spatial Data Analysis (ESDA) and the Spatial Durbin Model. The prime objective of this research is to analyze the spatiotemporal distribution of municipal government debt in different regions of China, including the central, western, eastern regions. Key findings include: (1) Local government debt risk exhibits a fluctuating upward trend characterized by significant regional, administrative, and debt-type disparities. (2) Risk levels in central and western regions have increased, while major urban agglomerations have maintained medium or lower risk levels. (3) Local government debt risk demonstrates significant global spatial correlation, with low-low (LL) agglomerations evolving from multi-centered to dual-centered distributions. (4) Notably, a 1 % increase in neighboring debt risk leads to a 0.2467 % rise in local debt risk. (5) Fiscal pressure, urbanization rates, and economic scale are primary drivers of local government debt risk, whereas industrial structure, land transfer income, and financial development serve to mitigate it. These findings underscore the intra-regional and inter-regional heterogeneity and geographical differences, providing valuable insights for managing municipal government debt risk.
Open Access: Yes
Fractal geometry-based Klein-Gordon model for heat and mass transfer in a cylindrical cavity with variable thermal conductivity
Publication Name: Propulsion and Power Research
Publication Date: 2026-03-01
Volume: 15
Issue: 1
Page Range: 179-196
Description:
This study presents a generalized framework of vector calculus for non-integer dimensional spaces, motivated by the prevalence of fractals in nature. The work formulates first- and second-order differential operators, including gradient, divergence, and scalar and vector Laplacian, for scalar and rotationally covariant vector functions. This framework is applied to the thermoelastic response of an infinite fractal medium with a cylindrical cavity, a problem that incorporates thermoelastic mass diffusion and variable thermal conductivity through the Kirchhoff transformation. The system is analyzed under combined thermal and chemical shocks at the boundary, with the medium remaining mechanically fixed. The governing equations are solved using the Laplace transform method, and Zakian technique is employed for numerical inversion. The computational results indicate that parameters such as delay time and fractal dimension significantly influence the material's response. The graphical analysis visually examines the effects of different kernel functions, fractal dimension, variable thermal conductivity, nonlocal length and time scales on the thermoelastic response, providing a clear illustration of their impact. Specifically, an increase in fractal dimension leads to a more pronounced reduction in the thermoelastic response near the cylindrical cavity. Furthermore, an examination of different memory-dependent kernel functions reveals that nonlinear kernels demonstrate superior performance compared to linear kernels within this theoretical framework.
Open Access: Yes
