bluuebunny/crossref_metadata_2025 huggingface dataset:
Approaches to Prognosing the European Economic Crisis Through a New Economic–Financial Risk Sensitivity Model
Authors: Zlati Monica Laura , Fortea Costinela , Meca Alina , Antohi Valentin Marian
Published: December 2024

This paper presents a novel approach to prognosing European economic crises through the development of an economic–financial risk sensitivity model. The model integrates key macroeconomic indicators such as government deficit (NETGDP), GINI coefficient, social protection expenditure (ExSocP), unemployment rate (UNE), research and development spending (RDGDP), and tax structures (TXSwoSC), assessing their role in predicting economic vulnerability across European countries. By applying the Kruskal–Wallis non-parametric test on data from 324 observations across multiple countries, significant differences were identified in the distribution of these variables. The results show that government policies related to social protection, R&D, and taxation play an important role in a country’s resilience to economic shocks. On the other hand, indicators such as income inequality and unemployment exhibit less variation, reflecting global economic conditions. The model provides a comprehensive risk assessment framework, allowing for the early detection of potential economic crises and guiding policy adjustments to mitigate risks. This methodology offers valuable insights into the sensitivity of European economies to financial disruptions, emphasizing the importance of fiscal policies and social expenditure in maintaining economic stability.
Subject-Specialized Chatbot in Higher Education as a Tutor for Autonomous Exam Preparation: Analysis of the Impact on Academic Performance and Students’ Perception of Its Usefulness
Authors: Sánchez-Vera Fulgencio
Published: December 2024

This study evaluates the impact of an AI chatbot as a support tool for second-year students in the Bachelor’s Degree in Early Childhood Education program during final exam preparation. Over 1-month, 42 students used the chatbot, generating 704 interactions across 186 conversations. The study aimed to assess the chatbot’s effectiveness in resolving specific questions, enhancing concept comprehension, and preparing for exams. Methods included surveys, in-depth interviews, and analysis of chatbot interactions. Results showed that the chatbot was highly effective in clarifying doubts (91.4%) and aiding concept understanding (95.7%), although its perceived usefulness was lower in content review (42.9%) and exam simulations (45.4%). Students with moderate chatbot use achieved better academic outcomes, while excessive use did not lead to further improvements. The study also identified challenges in students’ ability to formulate effective questions, limiting the chatbot’s potential in some areas. Overall, the chatbot was valued for fostering study autonomy, though improvements are needed in features supporting motivation and study organization. These findings highlight the potential of chatbots as complementary learning tools but underscore the need for better user training in “prompt engineering” to maximize their effectiveness.
Rethinking Economics Education: Student Perceptions of the Social and Solidarity Economy in Higher Education
Authors: Arcos-Alonso Asier , Fernandez de la Cuadra-Liesa Itsaso , Garcia-Azpuru Amaia , Barba Del Horno Mikel
Published: December 2024

This article emphasises the critical role higher education institutions (HEIs) play in fostering critical knowledge and social cohesion by exploring students’ perceptions of the social and solidarity economy (SSE). It addresses the need for a more diverse curricular approach within HEIs, particularly in economics and business education, to challenge the dominance of the neoclassical model. This study, conducted at the Faculty of Economics and Business (FEB) of the University of the Basque Country, investigates whether students perceive the SSE as a viable alternative to the current economic paradigm and its necessity and innovation in higher education. Utilizing a free-association questionnaire and Iramuteq software analysis, the findings reveal a strong demand for a more inclusive educational framework that incorporates heterodox economic theories, especially the SSE. This research contributes to the discourse on integrating SSE into economics and business courses as part of university social responsibility (USR), highlighting its unique social values.
Optimal Implementation of d-q Frame Finite Control Set Model Predictive Control with LabVIEW
Authors: Iranian Mohamad Esmaeil , Zamiri Elyas , de Castro Angel
Published: December 2024

Finite Control Set Model Predictive Control emerges as a promising method for controlling power electronics inverters, outperforming traditional linear techniques. However, implementing Finite Control Set Model Predictive Control on conventional processors faces a significant computational burden due to its repetitive nature. This paper presents a novel approach that utilizes LabVIEW & Field Programmable Gate Arrays to address this computational bottleneck. By capitalizing on the inherent parallelism and suitability of Field Programmable Gate Arrays for discrete control problems, substantial computational advantages are achieved for Finite Control Set Model Predictive Control. The use of LabVIEW, a well-established platform in industrial and commercial solutions, ensures that this work is relevant not only academically but also for real-world industrial applications of FCS-MPC in power electronics and motor drives. This research successfully demonstrates the application of Finite Control Set Model Predictive Control for controlling the current of a motor-like load for a three-phase Voltage Source Inverter system in LabVIEW. To simplify the traditionally complex Field Programmable Gate Arrays programming process, user-friendly toolkits such as LabVIEW Control Design & Simulation, LabVIEW Real-Time, and LabVIEW FPGA Module are employed. This LabVIEW-based integration facilitates the execution of both concurrent and sequential Field Programmable Gate Arrays algorithms, leading to efficient Field Programmable Gate Arrays resource management and user-defined restrictions on maximum switching frequency, obviating the need for resource-intensive control methods for fast switches such as SiC and GaN IGBTs. The proposed controller is validated using an off-the-shelf computer turned into a real-time system but also on Field Programmable Gate Arrays for comparison purposes.
Multi-Objective Optimization of Municipal Solid Waste Collection Based on Adaptive Large Neighborhood Search
Authors: Li Wenbin , Wang Peiyang , Xu Yunsheng , Pan Li , Nie Chuhui , Yang Bo
Published: December 2024

To address the dual challenges of reducing carbon emissions and operating costs in municipal solid waste collection, this paper investigates the vehicle routing problem (VRP) for municipal solid waste collection and transportation, which significantly affects the transportation costs and efficiency. An analysis has shown substantial disparities in workload distribution across different routes, highlighting the need to consider both the workload balance and cost efficiency. Therefore, considering the factors of workload disparities and costs, a multi-objective VRP model is formulated, and a multi-objective adaptive large neighborhood search (MOALNS) algorithm based on balance remove and balance insert heuristics is proposed to solve the above problem. The proposed algorithm is compared with two classical multi-objective algorithms, and the results show its competitiveness. The designed model can effectively reflect the conflict between minimizing costs and balancing disparities in employee workloads.
Heterogeneous Multi-Agent Risk-Aware Graph Encoder with Continuous Parameterized Decoder for Autonomous Driving Trajectory Prediction
Authors: Sun Shaoyu , Wang Chunyang , Xiao Bo , Liu Xuelian , Shi Chunhao , Sun Rongliang , Han Ruijie
Published: December 2024

Trajectory prediction is a critical component of autonomous driving, intelligent transportation systems, and human–robot interactions, particularly in complex environments like intersections, where diverse road constraints and multi-agent interactions significantly increase the risk of collisions. To address these challenges, a Heterogeneous Risk-Aware Graph Encoder with Continuous Parameterized Decoder for Trajectory Prediction (HRGC) is proposed. The architecture integrates a heterogeneous risk-aware local graph attention encoder, a low-rank temporal transformer, a fusion lane and global interaction encoder layer, and a continuous parameterized decoder. First, a heterogeneous risk-aware edge-enhanced local attention encoder is proposed, which enhances edge features using risk metrics, constructs graph structures through graph optimization and spectral clustering, maps these enhanced edge features to corresponding graph structure indices, and enriches node features with local agent-to-agent attention. Risk-aware edge attention is aggregated to update node features, capturing spatial and collision-aware representations, embedding crucial risk information into agents’ features. Next, the low-rank temporal transformer is employed to reduce computational complexity while preserving accuracy. By modeling agent-to-lane relationships, it captures critical map context, enhancing the understanding of agent behavior. Global interaction further refines node-to-node interactions via attention mechanisms, integrating risk and spatial information for improved trajectory encoding. Finally, a trajectory decoder utilizes the aforementioned encoder to generate control points for continuous parameterized curves. These control points are multiplied by dynamically adjusted basis functions, which are determined by an adaptive knot vector that adjusts based on velocity and curvature. This mechanism ensures precise local control and the superior handling of sharp turns and speed variations, resulting in more accurate real-time predictions in complex scenarios. The HRGC network achieves superior performance on the Argoverse 1 benchmark, outperforming state-of-the-art methods in complex urban intersections.
Modelling of Lake Wind Parameters to Simulate Environmental Disturbances for a Scaled Ship Model
Authors: Miller Anna , Rak Andrzej
Published: December 2024

In the field of vessel motion control system design, it is of essential importance to consider the environmental disturbances which affect a ship’s hull, including wind, waves and currents. The conventional modelling techniques for wind disturbance simulation are designed to represent the behaviour of winds over open sea areas. However, before being implemented on a real seagoing vessel, its motion control systems are often tested on scale models built for a ship. Such tests are usually carried out on small bodies of water, where wind and wave models built for the open sea cannot be used. Therefore, a wind and wave model for a small body of water is required so that the effects of external disturbances on the motion of the scale model can be properly considered in the early stages of control system design. This paper presents the construction of a wind speed and direction simulator for a small lake, which was used as a test area for a free-floating, manned training vessel built at a 1:24 scale. This paper describes modifications made to the model, employing a von Karman spectrum to characterise the wind. These modifications were designed to align the model with the observed phenomena in a lake test area. The resulting model exhibits a clear structure and can be readily implemented both in software and on a microprocessor-based hardware platform.
SelTZ: Fine-Grained Data Protection for Edge Neural Networks Using Selective TrustZone Execution
Authors: Jeong Sehyeon , Oh Hyunyoung
Published: December 2024

This paper presents an approach to protecting deep neural network privacy on edge devices using ARM TrustZone. We propose a selective layer protection technique that balances performance and privacy. Rather than executing entire layers within the TrustZone secure environment, which leads to significant performance and memory overhead, we selectively protect only the most sensitive subset of data from each layer. Our method strategically partitions layer computations between normal and secure worlds, optimizing TrustZone usage while providing robust defenses against privacy attacks. Through extensive experiments on standard datasets (CIFAR-100 and ImageNet-Tiny), we demonstrate that our approach reduces membership inference attack (MIA) success rates from over 90% to near random guess (50%) while achieving up to 7.3× speedup and 71% memory reduction compared to state-of-the-art approaches. On resource-constrained edge devices with limited secure memory, our selective approach enables protection of significantly more layers than full layer protection methods while maintaining strong privacy guarantees through efficient data partitioning and parallel processing across security boundaries.
Optimal Layout Planning of Electric Vehicle Charging Stations Considering Road–Electricity Coupling Effects
Authors: Deng Minghui , Zhao Jie , Huang Wentao , Wang Bo , Liu Xintai , Ou Zejun
Published: December 2024

With the advancement of dual-carbon goals and the construction of new types of power systems, the proportion of electric vehicle charging stations (EVCSs) in the coupling system of power distribution and transportation networks is gradually increasing. However, the surge in charging demand not only causes voltage fluctuations and a decline in power quality but also leads to tension in the power grid load in some areas. The complexity of urban road networks further increases the challenge of charging station planning. Although laying out charging stations in areas with high traffic flow can better meet traffic demands, it may also damage power quality due to excessive grid load. In response to this problem, this paper proposes an optimized layout plan for electric vehicle charging stations considering the coupling effects of roads and electricity. By using section power flow to extract dynamic data from the power distribution network and comparing the original daily load curves of the power grid and electric vehicles, this paper plans reasonable capacity and charging/discharging schemes for EVCSs. It considers the impact of the charging and discharging characteristics of EVCSs on the power grid while satisfying the peak-shaving and valley-filling regulation benefits. Combined with the traffic road network, the optimization objectives include optimizing the voltage deviation, transmission line margin, network loss, traffic flow, and service range of charging stations. The Gray Wolf Optimizer (GWO) algorithm is used for solving, and the optimal layout plan for electric vehicle charging stations is obtained. Finally, through road–electricity coupling network simulation verification, the proposed optimal planning scheme effectively expands the charging service range of electric vehicles, with a coverage rate of 83.33%, alleviating users’ charging anxiety and minimizing the impact on the power grid, verifying the effectiveness and feasibility of the proposed scheme.
A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism
Authors: Hua Qingbo , Fan Zengliang , Mu Wei , Cui Jiqiang , Xing Rongxin , Liu Huabo , Gao Junwei
Published: December 2024

This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load.
Impact of Coolant Operation on Performance and Heterogeneities in Large Proton Exchange Membrane Fuel Cells: A Review
Authors: Cornet Marine , Tardy Erwan , Poirot-Crouvezier Jean-Philippe , Bultel Yann
Published: December 2024

PEMFCs’ operation entails the presence of heterogeneities in the generation of current, heat and water along the active surface area. Indeed, PEMFCs are open systems, and as such, operating heterogeneities are inherent to their operation. A review of the literature reveals numerous attempts to achieve uniform current density distribution. These attempts are primarily focused on bipolar plate design and operating conditions, with the underlying assumption that uniform current density correlates with enhanced performance. Most studies focus on the influence of gas flow-field design and inlet hydrogen and air flow conditioning, and less attention has been paid to the coolant operating condition. However, uncontrolled temperature distribution over a large cell active surface area can lead to performance loss and localized degradations. On this latter point, we notice that studies to date have been confined to a narrow range of operating conditions. It appears that complementary durability studies are needed in order to obtain in-depth analyses of the coupled influence of temperature distribution and gas humidification in large PEMFCs.
Examining Microplastics Along the Calabrian Coastline: Analysis of Key Characteristics and Metal Contamination
Authors: Brunetti Luana S. , Piersante Costanza , La Russa Mauro F. , Cellini Emilio , Bolea Eduardo , Laborda Francisco , Ruffolo Silvestro A.
Published: December 2024

Plastic pollution is a major concern today. Microplastics (MPs), due to their small size, can enter the food chain and cause serious harm to living organisms. The Mediterranean Sea is the sixth largest accumulation area for plastic waste, including MPs, worldwide. In this study, we analyzed the distribution, shape, color, size, and polymer composition of MPs (having dimensions between 330 µm and 5 mm), collected from the water surface in six areas along the Calabrian coast, Italy. A prevalence of polyethylene was detected, with higher concentrations of MPs found in the Gioia Tauro and Cetraro areas. Additionally, heavy metals were identified within the MPs, suggesting that these particles could act as environmental carriers of such elements into the food chain.
Enhancing Circularity in Urban Waste Management: A Case Study on Biochar from Urban Pruning
Authors: Pavesi Rocco , Orsi Luigi , Zanderighi Luca
Published: December 2024

This study investigates the potential of biochar production from urban pruning waste as a sustainable solution within a circular economy framework. Urban green waste, often underutilized, typically increases landfill burden and greenhouse gas emissions. Converting pruning waste into biochar reduces landfill reliance while enabling stable carbon sequestration. Utilizing the circular triple-layered business model canvas (CTLBMC), biochar’s impact is evaluated across economic, environmental, and social dimensions. This structured analysis is based on a theoretical framework and uses secondary data to illustrate the model’s applicability. As a result of the conducted studies, it was found that biochar derived from urban green waste not only improves soil phytotoxicity and enables long-term carbon sequestration, but also offers economic benefits, including municipal cost savings in waste management and diversified revenue streams from biochar sales. Socially, biochar production promotes community engagement in sustainable practices and supports urban greening initiatives, enhancing local ecosystems. The findings suggest that biochar production, assessed through the CTLBMC framework, represents a viable circular business model. This approach provides significant environmental, economic, and social benefits over conventional disposal, offering valuable insights for policymakers, waste management professionals, and urban planners advancing circular economy solutions.
Evaluation of Petroleum Hydrocarbon-Contaminated Soil Remediation Technologies and Their Effects on Soybean Growth
Authors: Jiang Dengyu , Li Tao , Liang Xuanhe , Zhao Xin , Li Shanlong , Li Yutong , Oh Kokyo , Liu Haifeng , Cao Tiehua
Published: December 2024

The application of persulfate (PS) for the remediation of petroleum hydrocarbon contamination is among the most widely employed in situ chemical oxidation (ISCO) techniques, and it has received widespread attention due to its limited impact on soil integrity. This study employed a FeSO4-activated PS oxidation method to investigate the feasibility of remediating soil contaminated with total petroleum hydrocarbons (TPHs). The factors tested included the TPH concentration, different PS:FeSO4 ratios, the reaction time for remediation, soil physical and chemical property changes before and after remediation, and the effect of soil before and after remediation on soybean growth. The TPH degradation rate in soil was highest for high-, medium-, and low-TPHs soils—81.5%, 81.4%, and 72.9%, respectively, with minimal disruption to the soil’s physicochemical properties—when PS:FeSO4 = 1:1. The remediation verification results indicated that the condition of the soybeans was optimal when PS:FeSO4 = 1:1. Under this condition, the net photosynthetic rate, stomatal conductance, intercellular CO2 concentration, and transpiration rate all remained high. Therefore, the best remediation effect was achieved with PS:FeSO4 = 1:1, which also minimized the damage to the soil and the effects on crop growth.
Spatiotemporal Variations of Vegetation NPP Based on GF-SG and kNDVI and Its Response to Climate Change and Human Activities: A Case Study of the Zoigê Plateau
Authors: He Li , Yuan Yan , He Zhengwei , Pang Jintai , Zhao Yang , Zeng Wanting , Cen Yuxin , Xiao Yixian
Published: December 2024

Net primary productivity (NPP) is a key metric for evaluating ecosystem carbon sink capacity and defining vegetation. Despite extensive research on vegetation NPP, much relies on coarse spatial resolution data, which often overlooks regional spatial heterogeneity, causing inaccuracies in NPP estimates. Therefore, this study employed the improved CASA model, based on GF-SG and kNDVI methods, to estimate vegetation NPP at a 30 m spatial resolution on the Zoigê Plateau from 2001 to 2020. The effects of anthropogenic and climatic factors on NPP were quantified through residual and partial correlation analyses. These results indicated the following: (1) NDVI derived from the GF-SG fusion method aligns closely with Landsat NDVI (R2 ≈ 0.9). When contrasted with using NDVI alone, incorporating kNDVI into the CASA model enhances NPP assessment accuracy. (2) Vegetation NPP on the Zoigê Plateau has fluctuated upward by 2.09 gC·m−2·a−1 over the last two decades, with higher values centrally and lower at the edges. (3) Monthly partial correlation analysis indicates almost no temporal effects in NPP response to temperature (97.42%) but significant cumulative effects in response to precipitation (80.3%), with longer accumulation periods in the south. Annual analysis reveals that NPP correlates more strongly with temperature than precipitation. (4) NPP changes are jointly influenced by climate change (48.46%) and human activities (51.54%), with the latter being the dominant factor. This study deepens the understanding of NPP dynamics in the Zoigê Plateau and offers insights for estimating NPP at high spatial-temporal resolutions.
Evaluation of Height Changes in Uneven-Aged Spruce–Fir–Beech Forest with Freely Available Nationwide Lidar and Aerial Photogrammetry Data
Authors: Pintar Anže Martin , Skudnik Mitja
Published: December 2024

Tree height and vertical forest structure are important attributes in forestry, but their traditional measurement or assessment in the field is expensive, time-consuming, and often inaccurate. One of the main advantages of using remote sensing data to estimate vertical forest structure is the ability to obtain accurate data for larger areas in a more time- and cost-efficient manner. Temporal changes are also important for estimating and analysing tree heights, and in many countries, national airborne laser scanning (ALS) surveys have been conducted either only once or at specific, longer intervals, whereas aerial surveys are more often arranged in cycles with shorter intervals. In this study, we reviewed all freely available national airborne remote sensing data describing three-dimensional forest structures in Slovenia and compared them with traditional field measurements in an area dominated by uneven-aged forests. The comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data, but they are still useful due to their temporally dense data. Up-to-date data are very important for forest management and the study of forest resilience and resistance to disturbance. Based on field measurements (2013 and 2023) and all remote sensing data, dominant and maximum heights are statistically significantly higher in uneven-aged forests than in mature, even-aged forests. Canopy height diversity (CHD) information, derived from lidar ALS and DAP data, has also proven to be suitable for distinguishing between even-aged and uneven-aged forests. The CHDALS 2023 was 1.64, and the CHDCAS 2022 was 1.38 in uneven-aged stands, which were statistically significantly higher than in even-aged forest stands.
The Importance of Stand Structure in Narrow-Leaved Ash (Fraxinus angustifolia Vahl) Dieback—Insights from an Extensively Managed Stand on a Humogley Soil in Serbia
Authors: Kabiljo Milan , Bobinac Martin , Andrašev Siniša , Milenković Ivan , Šušić Nikola
Published: December 2024

Ash dieback is a major issue affecting European ash populations, including narrow-leaved ash (Fraxinus angustifolia Vahl). An important factor contributing to the decline of narrow-leaved ash is the fungal disease caused by Hymenoscyphus fraxineus. However, the mortality of trees also depends on stand structure that may influence the disease dynamics. In 2020, we analysed the stand structure of middle-aged, extensively managed, narrow-leaved ash stands growing on Humogley soil (Cariceto remotae-Fraxinetum angustifoliae, Jov., et Tom. 1979). This permanent sample plot is located in Posavina (Serbia), where we observed reduced tree vitality and mortality. The stand originates from natural regeneration after a succession of marsh habitats. At ages 20–25 years (1996) and 30–35 years (2006), selective thinning was carried out. Until the age of 45–50 years (2020), the stand was left unthinned, and the presence of ash dieback fungus was recorded in Serbia. In 2020, we measured the diameter at breast height (DBH) of each tree on the plot and assessed their crown class and degree of isolation. These parameters were evaluated in relation to the crown defoliation of the trees. The results indicate that reduced vitality and mortality of trees manifest in conditions of strongly expressed intraspecific competition in the stand, particularly during the stem exclusion stage. Healthy trees were observed primarily within the predominant/dominant crown class and exhibited the highest mean DBH. Trees classified as dead or dying (81%–100% defoliation) had a lower mean DBH compared to both healthy trees (<25% defoliation) and significantly defoliated trees (26%–80%). This was observed at both the stand level and in predominant/dominant trees, suggesting that tree mortality is primarily linked to their poor growth.
Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
Authors: Ma Ping , Peng Jian , Zheng Jianghua , Liu Liang , Yu Xiaojing , Li Wei
Published: December 2024

Stress events induced by global warming pose severe threats to vegetation health. Assessing the impact of these stress events on the health and growth of vegetation ecosystems in China is crucial. This study constructed three vegetation health assessment systems and selected the one that most effectively reflects vegetation health. By identifying the characteristics of stress events, and employing trend analysis, sensitivity analysis, anomaly change analysis, and modified residual analysis, this study explores the dynamic changes in vegetation health and their responses to stress events across China from 2001 to 2020. The results indicate that the Pressure–State–Response (PSR) model has the best evaluation performance, achieving the highest fit to Solar-Induced Chlorophyll Fluorescence (SIF) with an goodness of fit (R2) of up to 0.74. Overall, vegetation health exhibits more negative anomalies than positive ones and shows the strongest positive sensitivity to Cumulative Precipitation Anomaly (CPA) and the strongest negative sensitivity to Cumulative Heat (CH). Among different vegetation types, alpine vegetation has the highest stability in health, while meadows and grasslands are the most sensitive to stress events. Additionally, stress events have a greater contribution rate to vegetation health than other events. Our findings will provide important data for climate change adaptation policies and extreme environmental early warning while also contributing to the formulation of policies aimed at improving vegetation health. These results are of significant importance for enhancing carbon sequestration capacity, refining carbon market policies, and promoting the sustainable development of ecosystems.
Fuel Load Models for Different Tree Vegetation Types in Sichuan Province Based on Machine Learning
Authors: Wang Hongrong , Chen Haoquan , Sheng Hanmin , Chen Kai , Dong Chen , Min Zhiqiang
Published: December 2024

(1) Objective: To improve forest fire prevention, this study provides a reference for forest fire risk assessment in Sichuan Province. (2) Methods: This research focuses on various forest vegetation types in Sichuan Province. Given data from 6848 sample plots, five machine learning models—random forest, extreme gradient boosting (XGBoost), k-nearest neighbors, support vector machine, and stacking ensemble (Stacking)—were employed. Bayesian optimization was utilized for hyperparameter tuning, resulting in machine learning models for predicting forest fuel loads (FLs) across five different vegetation types. (3) Results: The FL model incorporates not only vegetation characteristics but also site conditions and climate data. Feature importance analysis indicated that structural factors (e.g., canopy closure, diameter at breast height, and tree height) dominated in cold broadleaf, subtropical broadleaf, and subtropical mixed forests, while climate factors (e.g., mean annual temperature and temperature seasonality) were more influential in cold coniferous and subtropical coniferous forests. Machine learning-based FL models outperform the multiple stepwise regression model in both fitting ability and prediction accuracy. The XGBoost model performed best for cold coniferous, cold broadleaf, subtropical broadleaf, and subtropical mixed forests, with coefficient of determination (R2) values of 0.79, 0.85, 0.81, and 0.83, respectively. The Stacking model excelled in subtropical coniferous forests, achieving an R2 value of 0.82. (4) Conclusions: This study establishes a theoretical foundation for predicting forest fuel capacity in Sichuan Province. It is recommended that the XGBoost model be applied to predict fuel loads (FLs) in cold coniferous forests, cold broadleaf forests, subtropical broadleaf forests, and subtropical mixed forests, while the Stacking model is suggested for predicting FLs in subtropical coniferous forests. Furthermore, this research offers theoretical support for forest fuel management, forest fire risk assessment, and forest fire prevention and control in Sichuan Province.
Stand Density Management of Cypress Plantations Based on the Influence of Soil Hydrothermal Conditions on Fine Root Dynamics in Southwestern China
Authors: Hou Guirong , Zhang Jinfeng , Fan Chuan , Li Xianwei , Chen Gang , Zhao Kuangji , Zhang Yunqi , Zheng Jiangkun , Wang Yong
Published: December 2024

The mechanisms by which the soil physical structure, nutrient conditions, understory vegetation diversity and forest meteorological factors influence fine root (<2 mm diameter) characteristics mediated by soil moisture content (SMC) and soil heat flux (SHF) remain uncertain under climate change. Therefore, in this research, continuous observations were made of the fine root growth, death and turnover of cypress plantations, as well as the SMC and SHF under the management of four thinning intensities in hilly areas in central Sichuan from 2021 to 2023. The fine root data were obtained using the microroot canals (minirhizotron) in the study, and the soil hydrothermal data were obtained using the ECH2O soil parameter sensor and the PC-2R SHF data logger. In the time series, the fine root growth, death and turnover of the cypress plantations with different thinning intensities first increased and then decreased throughout the year; the vertical center of the gravity of the fine roots of cypress was concentrated in the 30–50 cm range. This research also revealed that the variability in the SMC decreased with increasing soil depth. Additionally, the SHF was transmitted from greater soil depths to the surface in unthinned cypress plantation at a rate of 0.036 per year, which decreased the heat in the fine root region. However, SHF was transmitted from the soil surface to greater depths at rates of 0.012 per year, 0.08 per year and 0.002 per year, which increased the heat in the fine root area. The redundancy analysis (RDA) and structural equation model (SEM) results indicated that the SMC and soil heat energy distribution pattern obviously affected fine root growth, death and turnover in the cypress plantation. However, the climate conditions in the forest, the characteristics of vegetation in the understory and the physical and chemical characteristics of the soil directly or indirectly affect the characteristics of the fine roots of cypress plantations with changes in thinning intensity. This research provides a basis for understanding ecosystem structure, nutrient cycling and carbon balance and may guide artificial plantation development and management.
Nature Conservation and Tourism Sustainability: Tikvara Nature Park, a Part of the Bačko Podunavlje Biosphere Reserve Case Study
Authors: Štetić Snežana , Ristić Vladica , Trišić Igor , Tomašević Vladimir , Skenderović Ibro , Kurpejović Jasmina
Published: December 2024

Ecosystems, water supplies, and tourism all benefit from the protection of forest regions. All the above affect the possibility for tourism to prosper in forested areas. Tikvara Nature Park (TNP) has significant tourist potential for the development of specific and sustainable forms of tourism because it has direct contact with the Danube River, which forms the Upper Danube Region. This nature park has a significant forested area, inhabited by rare species of flora and fauna. In addition, there are wetlands, which are inhabited by rare species of birds, aquatic animals, and plants. Ecological and socio-cultural sustainability are the subjects of research in this article. It was possible to observe all possibilities for the growth of tourism based on nature by analyzing sustainable tourism by applying an extended PoS study method. The results of this research indicate that the two examined dimensions have a significant impact on the state and perspective of tourism development in this nature park. Also, sustainable tourism has an impact on the satisfaction of respondents through ecological and socio-cultural factors of the destination. Analysis of the results indicates that the respondents are ready to harmonize their activities with ecological principles in this protected area. Researching these two dimensions of sustainable tourism development (STuD) is important for tourism planning, growth, and nature protection control. This study’s significant results demonstrate the importance of environmental and socio-cultural elements for tourism development (ToD), and their substantial influence on tourism sustainability (ToS) and local and visitor satisfaction. Plans for the growth of sustainable tourism might be significantly influenced by research findings.
Suitable Tree Species for Afforestation in Semiarid China: Evidence from Tree Stem Growth Monitoring
Authors: Wu Mei , Tian Di , Shi Liang , Xu Chongyang , Mo Yien , Zhang Guochen , Gao Yongliang
Published: December 2024

Each tree species occupies its own ecological niche along biogeographic gradients. Selecting suitable tree species with the principle of matching specific habitats is therefore of vital importance for ensuring the success and efficiency of afforestation, especially in harsh ecoregions. Therefore, if the ecological suitability of trees to the target area is not carefully considered in the selection of afforestation species, the establishment of plantation may not achieve the desired effect. Hence, to evaluate trees’ fitness to different environments along an altitude gradient and then select suitable tree species for afforestation in semiarid China, we investigated stem growth of the most common tree species in typical afforestation types, including larch (Larix principis-rupprechtii), pine (Pinus sylvestris) and birch (Betula pendula), at low, middle and high altitudes (~1400 m, 1500 m and 1600 m, respectively), via high-resolution dendrometers in 2021. We found that pine had the highest growth rate and cumulative stem growth amount at lower, rather than higher, altitude, while larch showed the opposite pattern. Compared to the two conifers, the growth rate of the secondary species birch is much lower. Water stress-related climatic variables during the growing season, including relative humidity and vapor pressure deficit, explained the most variation of tree stem growth among these three species (33%~44%). Specifically, larch revealed higher sensitivity to atmospheric water demand factors while pine indicated stronger drought tolerance. These results indicated higher potential mortality and decline risk of larch plantations with the climate becoming warmer and drier, especially at low altitudes in semiarid China. There are challenges in using larch for reforestation in areas with harsh environmental conditions.
Contrasting Effects of Broadleaf Thinning Treatments on Spruce Growth in Central British Columbia, Canada
Authors: Griesbauer Hardy , Hawkins Chris
Published: December 2024

As forest managers increasingly seek to develop and maintain mixedwood forests, more information is required on the potential facilitative and competitive interactions between tree species. We present data from a broadleaf thinning study established in a mixedwood stand in Central British Columbia, Canada, to examine how residual trembling aspen and paper birch competitively affect spruce growth after thinning but may also concurrently protect spruce from attack by the white pine weevil. Tree-level data collected at a stand age of 36 years, 19 years after broadleaf trees were thinned, show that spruce height and diameter growth declined with broadleaf competition, particularly from taller trees, resulting in a competition-related reduction in stand-level spruce volume yields. The fastest spruce growth occurred in treatments where all broadleaf trees were removed, but complete broadleaf removal also resulted in higher rates of weevil attack on spruce, which also caused height and diameter growth reductions. Our results suggest that maintaining a density of approximately 500 broadleaf trees per hectare may achieve a stand condition that balances spruce growth reductions from competitive interactions with broadleaf trees while providing some protection from white pine weevil attacks.
Utilizing Machine Learning and Geospatial Techniques to Evaluate Post-Fire Vegetation Recovery in Mediterranean Forest Ecosystem: Tenira, Algeria
Authors: Souane Ali Ahmed , Khurram Abbas , Huang Hui , Shu Zhan , Feng Shujie , Belgherbi Benamar , Wu Zhiyuan
Published: December 2024

This study investigated post-fire vegetation recovery in Algeria’s Tenira forest using statistical traits (PCA), RFM, and LANDIS-II spatial analysis. The dataset included satellite imagery and environmental variables such as precipitation, temperature, slope, and elevation, spanning over a decade (2010–2020). Tenira forest is composed of Mediterranean species (36.5%); the biological types encountered are dominated by therophytes (39.19%). Ninety fire outbreaks were recorded, resulting in a loss of 1400.56 ha of surface area. Following the PCA results, precipitation, temperature, slope, and elevation were the main drivers of recovery (PC1 explained 43% alone, with the first five principal components accounting for 90% of observed variance, reflecting significant environmental gradients). Based on these components, an RFM predicted the post-fire recovery with an overall accuracy of 70.5% (Cost-Sensitive Accuracy), Quantity Disagreement of 3.1%, and Allocation Disagreement of 76%, highlighting spatial misallocation as the primary source of errors. The evaluation also identified PC4 (species richness) and PC3 (elevation) as significant predictors, collectively accounting for >50% of the variation in post-fire recovery. In the spatial analysis using LANDIS-II, the growth of vegetation, mainly in mid-altitude areas, was shown to be stronger, with the species consisting of those areas being more diverse. As a result, it demonstrated the connection between species richness and recovery capability. These findings can be useful in developing a management and development strategy, as well as proposing actions for species recovery after fire, such as the construction of firebreaks or the introduction of fireproof species, to make the forest more resistant to weather changes in Mediterranean ecosystems.
Urban Trees and Elderly Well-Being: Species-Specific Strategies for Thermal Comfort in Heat-Stressed Cities
Authors: Elsadek Mohamed , Nasr Ahmed , Guo Li , Gong Xueqian , Hassan Ahmad , Zhang Deshun
Published: December 2024

The dual challenges of global aging and intensifying urban heat demand innovative, evidence-based strategies to foster thermally and psychologically comfortable environments for vulnerable populations, particularly the elderly. Despite the documented benefits of urban greenery, the species-specific impacts of urban trees on thermal comfort and well-being remain underexplored. This study investigates how distinct tree species—Camphora officinarum (camphor), Platanus acerifolia (London plane), and Ginkgo biloba (ginkgo)—regulate urban microclimates and support elderly well-being during hot summer days. Conducted at five sites in Shanghai, including a control site and four vegetated plots, this study engaged 210 elderly participants. Microclimatic variables were measured using the physiological equivalent temperature (PET) alongside air temperature, humidity, and wind speed. Physiological responses, assessed through heart rate variability (HRV), and psychological outcomes, evaluated via validated self-report scales, were analyzed. The results revealed that dense-canopy trees significantly reduced PET, enhanced thermal comfort, and improved ROS and SVS scores, while lower LF/HF ratios indicated reduced physiological stress. Correlation analyses underscored the pivotal role of canopy density (SVF) in fostering psychological and physiological well-being. Camphor and London plane trees consistently provided the greatest benefits, emphasizing the importance of species selection in urban greening strategies. These findings underscore the critical role of species selection in urban forestry to mitigate heat stress and foster age-friendly resilience. Practical implications emphasize integrating dense-canopy species into urban landscapes to enhance microclimate regulation and public health.
Biofunctional Characterization of Collagen from Orange-Spotted Grouper, Epinephelus coioides: Maintenance of Cartilage in Aged Zebrafish
Authors: Hu Shao-Yang , Tsou Chen-Han , Tseng Chung-Chih , Han Tai-Wei , Liu Chun-Hung
Published: December 2024

In this investigation, collagen was successfully extracted from the skin and fins of the orange-spotted grouper, Epinephelus coioides, with recovery rates of 4.45% and 23.65% (dry weight basis), respectively. UV–Vis spectrophotometric analysis demonstrated distinct absorbance peaks at 222 nm and 217 nm for collagen isolated from the skin and fins, correspondingly. Both collagen sources were confirmed to be type I, characterized by the presence of two α-chains (α1 and α2), with glycine as the predominant amino acid, an absence of tryptophan, and a notable content of proline and hydroxyproline. The enzymatic hydrolysis of fin-derived collagen using pepsin yielded low-molecular-weight peptides (PHC), which were subsequently incorporated into the diet of 1.5-year-old zebrafish, either alone or in combination with glucosamine (GC), to assess their biological effects. After eight weeks of dietary supplementation, zebrafish fed PHC or the combined PHC + GC diets exhibited a significant upregulation of sox9a (jef) expression in spinal tissues, accompanied by a marked downregulation of runx2. Although the differences in swimming performance among the groups were not statistically significant, zebrafish that received PHC or the combined supplements demonstrated enhanced endurance compared to the control group. These results suggest that collagen sourced from grouper may have advantageous effects in supporting cartilage health in aged zebrafish. Furthermore, utilizing fish by-products for collagen extraction enhances resource efficiency and aligns with circular economy principles.
Decoding Vitellogenin Subtype Responses: A Molecular Approach to Biomarkers of Endocrine Disruption in Scatophagus argus
Authors: Wu Meiqin , Zhang Jun , Wu Di , Moss Amina S. , Wang Weilong
Published: December 2024

Vitellogenins (Vtgs) are key yolk precursor proteins in fish, serving as critical indicators of gonadal maturation in females and reliable biomarkers for detecting xeno-oestrogenic pollution, particularly through their expression in juveniles or males. The vtg gene family comprises multiple subtypes that are species-specific, necessitating precise characterisation and quantification for effective use as biomarkers in studies on estrogenic endocrine-disrupting chemicals (EEDCs). In this study, we successfully cloned and characterised the full-length cDNAs of three vtg subtypes (vtgAa, vtgAb, and vtgC) from Scatophagus argus. Differential expression analysis revealed that vtgAb exhibited the highest responsiveness to 17α-ethynylestradiol (EE2) exposure, with a 3-fold increase in vivo at 10.0 μg/g EE2 and a 30-fold increase in vitro at 10−7 mol/L EE2. The expression patterns were dose- and time-dependent, with peak expression observed 72 h post-exposure. While in vivo assays indicated moderate upregulation, in vitro experiments demonstrated significantly higher expression, attributed to direct hepatocyte interaction with EE2. These findings confirm vtgAb as the most responsive subtype to oestrogen exposure in S. argus and highlight the species’ tolerance to EE2, as compared to more sensitive species like Danio rerio. This study shows the evolutionary conservation of vtg transcripts across teleost species and reinforces the importance of subtype-specific characterisation to advance their application as biomarkers for EEDCs, with significant implications for environmental monitoring and pollution regulation.