Search Results (2,417)

Background/Objectives: Forest bathing (Shinrin-yoku) is a nature-based approach with potential preventive health relevance. This review summarizes evidence on its effects on immune function, stress physiology, and neuroprotective pathways. Methods: A narrative review of peer-reviewed studies was conducted using major scientific databases, including observational and interventional research assessing physiological or neurocognitive outcomes following forest exposure. Results: Forest bathing is associated with enhanced natural killer (NK) cell activity, modulation of inflammatory cytokine profiles, reductions in cortisol levels, and shifts toward parasympathetic autonomic dominance. Evidence also suggests a contributory role of tree-derived biogenic volatile organic compounds and phytoncides in immune and stress-regulatory effects. Emerging findings indicate potential benefits for cognitive restoration, emotional regulation, and neurotrophic signaling; however, substantial heterogeneity in study design, exposure characteristics, and outcome measures limits direct comparability and causal inference. Conclusions: Current evidence supports forest bathing as a promising, low-risk strategy for supporting immune resilience, stress regulation, and neurocognitive well-being within a preventive health framework. Preliminary findings also suggest potential benefits in chronic neurological conditions, supporting its neuroprotective role within multimodal neurorehabilitation strategies. Standardized intervention protocols, mechanistic biomarkers, and longitudinal studies are required to strengthen clinical relevance and guide evidence-based integration into public health and lifestyle medicine. Full article

Electrochemical energy storage is pivotal in constructing new-type power systems. However, the large-scale deployment of energy storage stations poses severe safety challenges, particularly the risk of deflagration. The coupling of combustible accumulation within battery systems and the confined structure of storage units can trigger cascading thermal runaway and deflagration accidents. Existing research still falls short in systematically analyzing the deflagration risks and process evolution mechanisms in energy storage stations. To address this gap, this study develops a probabilistic risk assessment model that enables analysis of risk propagation through the integration of fault tree analysis (FTA) with a static fuzzy Bayesian network (BN). The proposed approach delineates the complete risk evolution pathway from battery thermal runaway to deflagration in a confined space. Diagnostic reasoning identifies a dominant risk escalation path initiated by internal short circuits, leading to thermal runaway, flammable gas release, and pressure accumulation due to inadequate pressure relief. Sensitivity analysis highlights gases ejected during thermal runaway (C22) and lack of pressure relief devices or insufficient venting area (C31) as the most influential risk drivers. This study thus offers a practical, model-based framework for enhancing targeted risk prevention and safety resilience in electrochemical energy storage station infrastructure. Full article

Old trees function as enduring ecological legacies that preserve historical biodiversity within intensively human-modified landscapes, yet the relative influence of environmental versus anthropogenic drivers on their diversity remains unclear. Here, we aim to disentangle the joint effects of climate, urbanization intensity and cultural preservation on old-tree density and community composition. We analyzed a province-wide census of 21,733 old-tree individuals across 115 counties in Shanxi Province, China, encompassing species origin (native vs. nonnative) and growth form (trees vs. shrubs). Old-tree density was assessed using spatial simultaneous autoregressive error models, while compositional dissimilarity was quantified using generalized dissimilarity modeling. In total, 131 species were recorded, with four dominant species comprising more than 75% of all individuals. Old-tree density increased with mean annual temperature, human population density, and cultural heritage abundance, but declined sharply with cropland coverage. Driver importance varied among groups: native species were primarily governed by climatic conditions, nonnative species by land-use intensity, and tree-form old trees were positively associated with cultural heritage abundance, an effect absent in shrub-form old trees. Compositional dissimilarity was driven mainly by climatic gradients and spatial distance, with additional contributions from human-related variables, particularly for nonnative assemblages. Our findings demonstrate that climate and spatial processes establish the regional framework of old-tree community composition, while cultural and demographic contexts promote local retention of old trees. By explicitly integrating ecological filters with socio-cultural drivers, this study advances old-tree research through a large-scale empirical framework, providing both scientific insight and socially relevant guidance for conservation under land-use intensification and climate warming. Full article

Priority habitat 91E0* (alluvial forests with Alnus glutinosa and Fraxinus excelsior) constitutes a key riparian biodiversity hotspot, yet it is increasingly threatened by woody invasions that alter the community composition and reduce the habitat’s heterogeneity. Ten permanent plots (15 m radius) were surveyed in the Nestos River delta (NE Greece) in 2019 and 2023, following a manual control campaign conducted in 2021, targeting Amorpha fruticosa and Acer negundo. Because systematic plot-level vegetation data were collected only in 2019 and 2023, the study evaluates before–after changes rather than continuous annual dynamics. Woody species composition and diversity, community turnover (Bray–Curtis dissimilarites/PCoA; PERMANOVA), invasive dynamics (negative binomial GLMs), and community-weighted Ellenberg-type indicator values and their relationships with the soil properties (0–30 cm) were assessed. Across the surveys, 18 woody taxa were recorded, dominated by native riparian trees and shrubs, together with four established alien species. The total alien abundance declined from 943 to 385 individuals between 2019 and 2023, driven by A. negundo (−68%) and A. fruticosa (−39%). The woody community composition differed significantly between years (R² = 0.12; p = 0.013) and river banks, whereas plot-scale diversity indices changed modestly and evenness increased. The mean community-weighted moisture affinity increased (CWM_F: 6.28 → 7.07), nutrient affinity remained high, and reaction values declined slightly. The soil’s properties did not differ between the treated and control plots; nevertheless, Shannon diversity was positively correlated with organic C, total N, exchangeable Ca and K, and clay content. Permanent plot resurveys thatintegrate soil properties and indicator-based community metrics provide robust baselines to support Article 17 reporting under the EU Habitats Directive and to guide spatially targeted invasive-species management in Mediterranean alluvial forests (habitat 91E0) undergoing restoration actions. Full article

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

In the context of global efforts to promote energy conservation and emission reduction, geopolitical conflicts have intensified the challenges of mitigating marine climate change, posing increasingly severe economic and climatic pressures on the shipping industry worldwide. Research on multi-objective route optimization is of great significance for addressing climate challenges and enhancing economic efficiencies. This field focuses on constructing multi-objective optimization models that aim to reduce voyage time, fuel consumption, navigational risks, and carbon emissions and solving them using various algorithms. However, determining the optimal route and sailing speed under complex and variable meteorological conditions remains a significant challenge owing to the presence of numerous unevenly distributed feasible solutions within a vast solution space, making it difficult for traditional intelligent algorithms to effectively explore this space. To address this issue, this study proposes a hybrid algorithm named NRRT by integrating the Rapidly exploring Random Tree (RRT) algorithm with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By improving the sampling logic of the RRT algorithm and combining the vessel’s voluntary speed loss with the sampling step size, the algorithm efficiently explored the feasible route set, enhancing the quality and diversity of the solutions. Subsequently, the NSGA-III algorithm treats sailing speed and heading as direct decision variables to perform multi-objective optimization on the explored routes and generate Pareto-optimal solutions. The optimization results demonstrate that the proposed method excels at generating route plans that effectively reduce costs, minimize emissions, and mitigate risks compared with the 3D Dijkstra algorithm and the improved NSGA-III algorithm. Full article

This study investigated land degradation sensitivity in Southern Tunisia’s Jeffara region and examined the effectiveness of water harvesting techniques (WHTs) as countermeasures. Land Degradation Sensitivity Index was calculated using a modified MEDALUS framework, in which thematic quality indices were derived from normalized indicators (climate, soil, vegetation, and management) and combined through a geometric mean within a GIS environment. The model is validated with field observations. The research found that almost the entire study area (≈99%) was classified as critically sensitive under the baseline scenario. Contributing factors include extreme aridity, limited vegetation cover, significant soil erosion, and human pressures. The most severely degraded areas are found in mountainous zones, desert plains, and mining areas, whereas regions dominated by olive orchards showed moderate sensitivity levels. This lower sensitivity is associated with the drought tolerance and deep root systems of olive trees, which enhance resistance to prolonged dry periods. This study modeled the impact of implementing traditional WHTs, notably Jessour and Tabias. Under this scenario, a clear qualitative improvement was observed, with the proportion of land classified as critical decreasing from 99% to 77.3%, indicating a measurable reduction in land degradation sensitivity associated with the implementation of WHTs. Despite their environmental benefits, such as enhancing soil moisture and stabilizing agricultural yields, the spatial expansion of WHTs remains limited. Full article

Background: Anterior open bite (AOB) is a complex malocclusion characterized by different vertical craniofacial growth and heterogeneous skeletal patterns, making objective diagnosis challenging using conventional cephalometric assessment alone. Recent advances in machine learning offer new opportunities to improve phenotypic characterization and diagnostic accuracy in orthodontics. Methods: This retrospective study analyzed lateral cephalometric records from 1056 orthodontic patients, comprising 621 patients with an anterior open bite and 435 healthy controls, all of whom were from the Arab population in Israel. Five clinically relevant cephalometric parameters related to vertical skeletal relationships were evaluated: the mandibular plane angle (ML-NSL), palatal plane angle (NL-NSL), posterior to anterior facial height ratio (PFH/AFH), gonial angle, and the facial axis. Statistical comparisons were made between the open bite and healthy subgroups, and these analyses were conducted in an exploratory framework to support hypothesis generation. A decision tree classifier was developed to distinguish AOB from healthy subjects using these features, and model performance was evaluated on a hold-out test set. Additionally, agglomerative hierarchical clustering was applied to explore latent craniofacial phenotypes. Results: Significant differences in vertical skeletal parameters were observed between open-bite and healthy subjects across various subgroups. The decision tree classifier achieved a test accuracy of 96.2%, with a precision, recall, and F1-score of approximately 0.97. ML-NSL emerged as the most influential feature, followed by facial axis and PFH/AFH. Unsupervised clustering identified ten distinct craniofacial clusters, including pure open bite and pure healthy phenotypes, as well as mixed clusters representing borderline or intermediate skeletal patterns. Clusters dominated by open bite cases exhibited steep mandibular planes, reduced PFH/AFH ratios, increased gonial angles, and decreased facial axis values, consistent with known vertical dysplasia patterns. Conclusions: Machine learning applied to cephalometric data enables accurate classification and meaningful phenotypic stratification of anterior open bite malocclusion. Beyond binary diagnosis, clustering analysis reveals clinically relevant subgroups that reflect varying degrees and types of vertical skeletal imbalance. These findings support the potential role of interpretable machine learning models as decision-support tools in orthodontic diagnosis and personalized treatment planning. Full article

Mass M (kg) and peak intrusion L (mm) are jointly minimized for a CFRP-enabled battery pack enclosure under the GB 38031-2025 −40° side-pole extrusion condition. A 50-run explicit FE design of experiments is conducted and deterministically partitioned into 37/5/5/3 for initial training, two sequential enrichment batches, and an independent hold-out test. Bayesian additive regression trees are trained as the primary surrogates for M, L, and Stress, and stress acceptability is enforced through a probability-of-feasibility (PoF) gate anchored to a baseline-scaled cap, σ_lim = 1.2 σ_base = 410.4 MPa. NSGA-II performed on the feasible surrogate landscape yields a bimodal feasible non-dominated set. The two branches correspond to two discrete levels of a key thickness variable x₄: a low-mass regime (n = 106) with M = 100.61–104.81 kg and L = 5.430–5.516 mm at x₄ ≈ 5.60 mm, and a stiffer regime (n = 94) with M = 110.69–115.08 kg and L = 5.362–5.430 mm at x₄ ≈ 8.00 mm. PoF screening eliminates part of the intermediate region where feasibility confidence is insufficient. Independent FE reruns further indicate that the PoF gate reduces deterministic misclassification near the stress boundary (e.g., one near-threshold candidate exceeds σ_lim, whereas others satisfy the cap with margin). Overall, the proposed workflow offers a traceable lightweighting route under extreme-cold uncertainty within a constrained FE budget. Full article

To address the computational-efficiency bottlenecks of Hybrid A* and its bidirectional variant in long-distance parking and narrow-slot scenarios, an improved bidirectional Hybrid A* algorithm is presented. First, the cohesion cost is reformulated in a vector-space representation. Distance and heading-consistency terms are evaluated using dot products, which eliminates trigonometric operations and reduces the overhead of node evaluation. Second, an RS (Reeds–Shepp) cost template is constructed on a sparse grid of key nodes. Neighborhood costs are approximated with Euclidean-distance correction. In addition, a geometry reachability-based trigger is designed for analytic RS connections to avoid redundant analytic linking and unnecessary RS curve computations. Third, a KD-tree spatial index is introduced to accelerate nearest-neighbor queries in the Voronoi potential field, and vehicle corner coordinates are updated in a vectorized manner to improve the efficiency of potential-field evaluation. Simulation results in parallel and perpendicular parking show that, compared with the baseline bidirectional Hybrid A* algorithm, RS computations are reduced by 98.7% and 97.8%, respectively, while total planning time is shortened by 63.2% and 57.5%, with stable path quality. These results indicate that the proposed method effectively mitigates the dominant computational costs of bidirectional Hybrid A* in complex parking tasks and improves the efficiency and real-time performance of automatic parking path planning. Full article

Punchouts distress represents a major structural deficiency in Continuously Reinforced Concrete Pavements (CRCPs), contributing to premature deterioration, reduced ride quality, and increased maintenance demands. To improve the prediction of punchout occurrence, this study develops a hybrid data-driven modeling approach that combines Gradient Boosting Machines (GBMs) with Particle Swarm Optimization (PSO). The proposed framework utilizes 395 observations obtained from 33 CRCP sections in the Long-Term Pavement Performance (LTPP) database, incorporating structural, climatic, traffic, and performance-related variables. PSO was applied to systematically tune key GBM hyperparameters, including the number of boosting iterations, learning rate, and tree complexity, in order to enhance predictive accuracy. Model performance was evaluated using five-fold cross-validation, where the optimized PSO-GBM model achieved an average RMSE of 1.09 and an R² value of 0.947, outperforming conventional GBM as well as Random Forest, Support Vector Regression, Artificial Neural Networks, and Linear Regression models. Variable importance and sensitivity analyses revealed that Layer 3 thickness, pavement age, annual average daily traffic, and precipitation play dominant roles in punchout development. The consistency of residual distributions and the stability of hyperparameter sensitivity trends further confirm the robustness of the proposed framework. Overall, the results demonstrate that integrating evolutionary optimization with ensemble learning provides an effective tool for modeling complex pavement distresses and offers practical support for proactive maintenance planning and long-term management of CRCP infrastructure. Full article

The post-genomic era has transformed medical genetics, raising renewed debate over the role of medical cytogenetics in clinical practice. High-throughput sequencing and chromosomal microarray technologies now dominate cancer diagnostics, prenatal testing, and rare disease evaluation by enabling rapid detection of gene-level variation, often leading to the perception that cytogenetics is obsolete. However, this view overlooks the unique and complementary strengths of cytogenetic analysis. Although the relationship between cytogenetics and current NGS technologies can be compared to that between forests and trees versus leaves—both of which are necessary for clinical diagnosis—cytogenetic methods uniquely enable direct in situ visualization of chromosomes, allowing detection of large-scale structural and numerical genome alterations at the level of individual cells and cell populations. These system-level features that are frequently invisible or difficult to interpret using sequencing-based approaches alone yet are critical in disease contexts where genome architecture itself carries biological and clinical significance beyond individual genes. This article, therefore, advances a new perspective based on Genome Architecture Theory: that karyotype-level information organizes gene-level function and that many previous gene-centric genetic concepts require reexamination within a unified framework of clinical genomics. Rather than being replaced, cytogenetics is increasingly integrated with sequencing within a unified framework of clinical genomics that combines high-resolution molecular detail with system-level insight into genome organization. Reassessing the role of cytogenetics, therefore, has important implications for medical education, diagnostic strategy, and healthcare policy, as cytogenetics provides the appropriate platform for understanding system-level inheritance through karyotype coding and for advancing molecular medicine from a genome systems perspective. Full article

Globally, coffee-based agroforestry systems are recognized for their capacity to integrate agricultural production with biodiversity conservation, particularly in tropical landscapes under intense anthropogenic pressure. However, significant knowledge gaps remain regarding floristic composition, arboreal structure, and the ecological importance of woody species in Andean agroforestry systems of the Peruvian Amazon, especially along altitudinal gradients. The objective of this study was to characterize the diversity, floristic composition, arboreal structure, and ecological value of woody species in coffee-based agroforestry systems in the Department of Amazonas, Peru. Forest inventories were conducted in twelve one-hectare plots, recording dasometric variables, estimating diversity indices, analyzing floristic affinity, and calculating the Importance Value Index of species. A total of 57 tree species belonging to 41 genera and 25 families were recorded, with moderate diversity levels and a marked dominance of species from the Fabaceae family. The structure showed a predominance of young individuals, concentrated in low and intermediate diameter and height classes, and a moderate shade cover suitable for coffee cultivation. The species with the highest ecological and productive value were Pinus tecunumanii, Colubrina glandulosa, Clitoria juninensis, Inga edulis, and Inga mendozana, which perform key functions related to shade provision and soil fertility. These results are transferable to other coffee agroforestry systems in tropical montane regions and provide relevant evidence for sustainable forest management, biodiversity conservation, and productive optimization, issues of international interest in the agricultural and agroforestry sectors. Full article

Springback control in Multi-Point Stretch Forming (MPSF) is significantly hindered by the computational intensity of Finite Element Analysis (FEA) and the limited predictive robustness of traditional regression methods. This study develops a hybrid GWO-CatBoost model acting as a data-driven surrogate for MPSF simulations by integrating the Grey Wolf Optimizer (GWO) with the CatBoost algorithm for high-precision springback forecasting. An FEA model of the MPSF process was initially validated through experimental comparison under a representative working condition to assess modeling accuracy. A comprehensive dataset comprising 1200 scenarios was generated via a full factorial design, incorporating key variables: curvature radius, sheet thickness, cushion thickness, and pre-stretching rate. In this study, the GWO was employed to perform automated hyperparameter tuning for CatBoost by optimizing the learning rate, tree depth, and number of iterations, thereby enabling accurate modeling of the complex nonlinear relationship between process inputs and numerical springback values. Numerical evaluations demonstrate that the GWO-CatBoost model outperforms GWO-XGBoost and GWO-Random Forest benchmarks, achieving a Coefficient of Determination (R²) of 0.9293, a root mean square error (RMSE) of 0.0274 mm and mean absolute error (MAE) of 0.0189 mm. Sensitivity analysis identifies sheet thickness as the dominant factor (46% contribution), with cushion thickness as the secondary driver (23%). This predictive framework serves as a computationally efficient auxiliary surrogate, designed to assist iterative finite element analyses and support process optimization in the manufacture of complex-curved panels. Full article

Reliable fault diagnosis of induction motors from current signals is critical for preventing failures in industrial systems. However, deep learning models often exhibit performance degradation when the torque load and other operating conditions change. Although a lot of research has been completed on supervised fault classification using current signals, the investigation of the behaviour of these datasets for unsupervised learning has not been done. This study quantifies and analyses the “shadowing effect” of operational variability, demonstrating that a baseline 1D-CNN achieving 100% accuracy under static 0 Nm loads drops to 53.19% accuracy when subjected to 4 Nm load in the KAIST dataset using a stator current. Similar trends were validated using the Paderborn University (PU) bearing dataset. Using 1D-CNN feature extraction followed by Principal Component Analysis (PCA), t-SNE, and hierarchical clustering, we show that standard linear mitigation strategies, such as removing high-variance principal components, are ineffective because fault and load features are deeply entangled. Hierarchical clustering analysis confirms that the feature space is organised by load dominance, with the primary tree split consistently occurring by torque load rather than fault type. Crucially, we identify that internal geometric metrics, such as “spread” and “diameter”, correlate with external purity metrics like the proposed “Dominance Score”. The findings establish a quantitative basis for developing unsupervised, load-invariant diagnostic models that utilise geometric stopping criteria to isolate fault clusters without using ground-truth labels. Full article