Search Results (3,958)

(1) Background: The recent decade appears to be the hottest since the beginning of modern measurements. Changes in climate patterns related to extreme events and disturbances in forest ecosystems are well documented. Six prominent protected areas (PAs), mountainous forest ecosystems in Serbia, were assessed from the perspective of species potential distribution and vulnerability. (2) Methods: Seven different machine learning models were employed, evaluated using AUC, the maximum F-measure, and TSS and joined into an ensemble model for each of the eight tree species/groups taken from the National Forest Inventory. Representatives from four groups of environmental variables were included: 1. climate (Ellenberg’s Climate Quotient), 2. soil (soil organic carbon), 3. topography (elevation), and 4. remotely sensed indices (NDVI). Future climate was derived from four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Stable/gain/loss areas and species vulnerability were calculated with a focus on the end of the 21st century. (3) Results: By the 2090s, generally, contraction of Silver fir, Norway spruce, and European beech is expected, together with the promotion of Downy oak and Sessile oak, in all climate scenarios at all PAs. Two high-mountain PAs expect to see promotions in average forest suitability, one PA both a promotion and a reduction in two scenarios, and three PAs reductions in forest ecosystems in general. (4) Conclusions: National parks “Kopaonik” and “Tara” appear to be the least endangered, followed by “Golija”, while “Stara planina”, “Djerdap”, and “Fruska gora” are expected to experience overall reductions in forest habitats. Full article

Class imbalance is a persistent challenge in supervised machine learning, particularly in biological datasets where minority classes represent functionally critical categories. Synthetic data generation has emerged as a principal strategy for mitigating this problem, yet systematic comparisons of classical and modern deep generative approaches remain limited. This study presents a comprehensive benchmark evaluation of four synthetic data generation methods—SMOTE, CTGAN, TVAE, and TabDDPM—across two well-established biological datasets from the UCI Machine Learning Repository: the E. coli protein localization dataset (307 samples, 6 features, 4 classes) and the yeast protein localization dataset (1299 samples, 8 features, 4 classes). Synthetic data quality was rigorously assessed using a multi-dimensional evaluation framework encompassing distributional fidelity (Fréchet Distance, Wasserstein Distance), machine learning utility (Train-on-Synthetic-Test-on-Real and Train-on-Real-Test-on-Real protocols using XGBoost version 3.2.0, Logistic Regression, Support Vector Machines, and Random Forest), and distinguishability (Classifier Two-Sample Test). The datasets are rather imbalanced. During the experiments, the dataset size increased to three times its original size while preserving the imbalanced class-sample ratio. To evaluate the quality of synthetic data, the max(AUC,1−AUC) score is proposed. This score is inversely proportional to classification performance, indicating that synthetic data are not easily distinguishable from real data. Per-class analysis reveals that minority classes remain the primary challenge across all generative methods. SMOTE and TabDDPM obtained the highest predictive utility F1-scores across both datasets. TVAE offers the strongest distributional fidelity among deep generative models, producing synthetic samples that are most difficult to distinguish from real data (lowest C2ST scores). CTGAN exhibits significant performance degradation on both small- and medium-scale datasets, with F1 utility ratios below 0.50. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Promoting the construction of National Forest Cities to enhance urban ecological environmental quality and foster green and sustainable development has become an important policy pathway in China’s ecological civilization agenda. This study employs panel data for 214 Chinese cities over the period 2003–2023 and adopts a difference-in-differences (DID) approach to empirically examine the impact of National Forest City construction—a policy implemented in China since 2004—on urban ecological environments and its underlying mechanisms. The results indicate that National Forest City construction significantly improves urban ecological environmental quality. The findings remain robust after a series of robustness checks. Mechanism analysis shows that National Forest City construction primarily promotes urban environmental improvement by enhancing urban green innovation and optimizing adjustments to the urban industrial structure. Further heterogeneity analysis reveals that the environmental effects of the policy are more pronounced in non-resource-based cities, non-central cities, large cities, and cities with stronger governance capacity and higher levels of environmental concern. The conclusions provide policy implications and mechanistic insights from China’s experience for other cities around the world seeking to jointly address environmental pollution and climate change through comprehensive ecological interventions and to advance green and sustainable development. Full article

Forest harvesting transportation planning must balance operational efficiency with environmental sustainability, because timber transportation can cause both soil disturbance and carbon emissions. However, most vehicle routing studies primarily focus on economic objectives such as distance or cost minimization, whereas environmental impacts are often considered separately. The integrated optimization of ecological disturbance and carbon emissions remains limited in forest transportation planning. To address this gap, this study formulates a multi-vehicle routing optimization model for timber transportation that simultaneously minimizes transportation distance, makespan, soil disturbance, and CO₂ emissions within a hierarchical forest road network. An enhanced evolutionary algorithm, Eco-Constrained Lévy-flight Local Search NSGA-II (ECLS-NSGA-II), is proposed to improve convergence and maintain environmentally favorable routing solutions. Simulation experiments comparing ECLS-NSGA-II with NSGA-II, MOPSO, MOEA/D, and WS-GA demonstrate that the proposed method achieves superior performance across all objectives, producing shorter routes, lower completion times, and reduced CO₂ emissions while maintaining minimal ecological disturbance. Additional experiments on randomly generated networks further confirm the robustness of the proposed approach. These results indicate that the proposed framework provides an effective methodological tool for environmentally sustainable timber transportation planning in forest operations. Full article

Accurate prediction of a ship’s Estimated Time of Arrival (ETA) is of great significance for port scheduling, logistics management, and navigation safety. Traditional ETA prediction approaches often rely on manual experience for parameter tuning, which tends to be inefficient and susceptible to subjective factors. To address this issue and improve prediction accuracy, this study proposes a hybrid modeling framework, integrating Bayesian Optimization (BO), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. In this approach, Automatic Identification System (AIS) data is leveraged to predict the total voyage duration before departure, thereby deriving the vessel’s ETA. The model, referred to as BO-CNN-LSTM, utilizes BO for automatic hyperparameter tuning, employs CNN for extracting local features, and applies LSTM network to capture temporal dependencies. The model is developed using a dataset of 32,972 distinct voyage records, among which 23,947 are retained as valid samples after data cleaning. Pearson correlation analysis is conducted to select key input variables, including navigation speed, ship type, sailing distance, and deadweight tonnage. Additionally, sailing distance is processed using the Ramer–Douglas–Peucker algorithm. Experimental evaluation indicates that the BO-CNN-LSTM model achieves a coefficient of determination of 0.987, along with a mean absolute error and root mean square error of 6.078 and 8.730, respectively. These results significantly outperform comparison models such as CNN, LSTM, CNN-LSTM, random forest, AdaBoost, and Elman neural networks. Overall, this study validates the effectiveness and superiority of the proposed BO-CNN-LSTM model in ship ETA prediction, providing an efficient and effective prediction solution for intelligent maritime transportation systems. Full article

In the context of the rapid development of smart horticulture, a deep remote sensing-based dual detection method for horticultural crop growth anomalies and safety risks was proposed to address the limitations of existing remote sensing monitoring approaches. These conventional methods, which predominantly focused on growth vigor assessment or single-task anomaly detection, had difficulty distinguishing anomalies from actual production risks and exhibited insufficient sensitivity to weak anomalies and complex temporal disturbances. Within a unified framework, a growth state modeling branch and an anomaly perception branch were constructed, enabling the joint modeling of normal growth trajectories and anomalous deviation features. By further introducing a risk joint discrimination mechanism, an integrated analysis pipeline from anomaly identification to risk assessment was achieved. Multi-temporal remote sensing features were used as inputs, through which normal crop growth patterns were characterized via trend perception, texture modeling, and temporal aggregation, while sensitivity to local disturbances and weak anomaly signals was enhanced by anomaly embeddings and energy representations. Systematic experiments conducted on multi-regional and multi-crop horticultural remote sensing datasets demonstrated that the proposed method significantly outperformed comparative approaches, including traditional threshold-based methods, support vector machines, random forests, autoencoders, ConvLSTM, and temporal transformer models. In the dual task of horticultural crop growth anomaly detection and safety risk identification, an accuracy of approximately 0.91 and an F1 score of 0.88 were achieved, indicating higher anomaly recognition accuracy and more stable risk discrimination capability. Further anomaly-type awareness experiments showed that consistent performance was maintained across diverse real-world production scenarios, including climate stress, disease-induced anomalies, and management errors. Full article

Indigenous territories in Amazonia sustain forest cover through the practice of swidden-fallow agriculture, yet declining land availability threatens both the ecological sustainability of this agricultural system and its contributions to community livelihoods. While scholars recognize land scarcity’s potential to drive transformations in shifting cultivation systems, we lack a systematic understanding of how local institutional frameworks shape heterogeneous responses to resource constraints. This study examines how land access mechanisms, distribution dynamics and property regimes among Indigenous communities mediate experiences of and adaptations to land scarcity in the Peruvian Amazon. We conducted a comparative case study of Solidaridad and Tamboruna, two land-scarce Indigenous communities in Peru’s Napo River basin, employing mixed methods including household surveys (n = 74), plot-level assessments, and qualitative interviews with community leaders. Our findings reveal three critical pathways through which institutions mediate scarcity outcomes. First, land access mechanisms determine whether scarce resources produce equitable constraint or acute land inequality. Second, land use intensification emerges not from scarcity alone but from accumulated inequality and household labor capacity, with land accumulated over lifecycles showing stronger associations with management practices than initial endowments. Third, where scarcity manifests as extreme polarization, it precipitates renegotiation of land property norms shaped by Indigenous sociability and moral economies, defying straightforward trajectories toward either resource privatization or collective governance. These results demonstrate that land scarcity produces divergent trajectories mediated by community-specific institutions, with swidden-fallow systems likely diminishing their capacity to sustain forest regeneration in Indigenous communities where scarcity leads to acute land inequality. Rather than uniform solutions, sustainability policy must therefore tailor interventions to local institutional contexts—prioritizing territorial expansion, facilitating communities’ own governance development, and supporting household adaptive capacity to resource scarcity. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, field experiments were conducted over two consecutive years, applying four N-application rates (0, 150, 300, and 450 kg N ha⁻¹) to ZA. At each phenological stage, hyperspectral imagery and LiDAR point clouds were collected via three UAV flight altitudes (60 m, 80 m, and 100 m), and canopy nitrogen concentration (CNC) and aboveground nitrogen accumulation (AGNA) were measured. This study developed a framework by synergistically fusing UAV-derived hyperspectral imaging (HSI) and LiDAR data for CNC and AGNA monitoring. Results showed that the response of nitrogen status indicators to fertilization was phenology-specific: CNC showed no significant difference (p > 0.05) among treatments during the vigorous vegetative growth stage (VGS) but differed significantly (p < 0.05) during the fruit expansion stage (FES); AGNA differed significantly among treatments at VGS and FES (p < 0.05). The two-step screening yielded NDSI _{(732, 879)} and NDSI _{(560, 690)} as the optimal CNC indicators at VGS and FES, respectively (r = 0.83 and 0.93), whereas the NDSI _{(711, 986)} and NDSI _{(515, 736)} were identified as the optimal AGNA indicators at VGS and FES, respectively (r = 0.91 and 0.71). Across all phenological stages, Random Forest Regression consistently delivered the highest accuracy for CNC (R² = 0.93–0.98, RMSE = 0.87–1.02 g kg⁻¹) and AGNA (R² = 0.95–0.97, RMSE = 1.92–2.55 g plant⁻¹), outperforming MLR, PLSR, and SVR. This synergistic framework provides a high-precision, non-destructive methodology for the precision N monitoring of woody crops. Full article

The musk gland of male forest musk deer (Moschus berezovskii) secretes musk enriched with sex steroid hormones. The testes mainly produce these hormones; however, whether glandular cells can autonomously synthesize them remains unexplored. This study aimed to utilize an in vitro-cultured musk gland cell model to investigate whether musk gland cells possess the capability for autonomous synthesis of sex steroid hormones. We used single-cell RNA sequencing (scRNA-seq), reverse transcription quantitative real-time polymerase chain reaction, and liquid chromatography–mass spectrometry (LC-MS) to verify the steroidogenic potential of musk gland cells. scRNA-seq revealed that during the secretion period, 18 cholesterol and 6 sex steroid hormone biosynthesis genes were significantly expressed in the cells. In vitro experiments demonstrated that these genes were expressed without exogenous cholesterol supplementation. LC-MS analysis confirmed stable synthesis of nine sex steroid hormones. Increasing cholesterol concentration to 20 mg/L significantly upregulated SRD5A3 and AKR1D1, with AKR1C3 expression showing an upward trend. Elevated cholesterol increased several sex steroid hormone levels: pregnenolone, progesterone, 17α-hydroxypregnenolone, androstenedione, androsterone, and etiocholanolone by 4.12-, 1.46-, 33.42-, 2.06-, 3.11-, and 5.65-fold, respectively. These results collectively indicate that the musk glandular cells can synthesize sex steroid hormones de novo and suggest that cholesterol may regulate their biosynthesis in these cells. Full article

Topsoil features within a depth of 0–10 cm are vital for making soil management decisions that affect crop production. However, the use of these soil features to predict soil organic carbon (SOC) at 10–20 cm requires further investigation. The study aims to predict SOC at 10–20 cm using total nitrogen (total N), pH, cation exchange capacity (CEC), and SOC at 0–10 cm and select a suitable model for predicting SOC. This study was conducted using data from a long-term tillage, winter wheat (Triticum aestivum L.)-fallow experiment established in autumn 1970. Treatments included moldboard plow, stubble mulch, no-till, and native sod, each replicated three times. Soil samples were collected from each plot at depths of 0–10 cm and 10–20 cm in April of 2010 and 2011. Models were fit using ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), random forests, and Bayesian additive regression trees (BART). Using root mean square error (RMSE), SOC was predicted with an accuracy of 1.44 g kg⁻¹ or relative RMSE (rRMSE) of 13.5%. This was achieved with the OLS model that used total N, pH, and CEC as predictors. The good performance of the OLS model over more flexible machine learning approaches suggests that the information predictors provide about the response variable (SOC) is approximately linear. As the agricultural dataset was small (n = 24), the less complex model reduced the chances of overfitting while keeping the variance relatively low. Random forests and BART had an rRMSE greater than 21%. Statistical models could be used to estimate SOC at 10–20 cm and reduce destructive soil analysis methods. Full article

Accurate individual tree crown identification is essential for urban forestry, yet existing datasets often lack exhaustive annotations and multi-temporal diversity. To address this limitation, an exhaustively annotated dataset was curated for crown instance segmentation, comprising 47,754 labeled individual crowns from approximately 110 species across three temporal phases. Anchored in a “crown geometry” labeling criterion focusing on upper-canopy individuals visible in the imagery, and the high-resolution imagery captured seasonal variations in shape, color, and texture, providing an empirical basis for within-site robustness. Utilizing this dataset, this study (1) compared five instance segmentation models; (2) evaluated their generalization capabilities across different temporal phases; and (3) tested a multi-temporal joint training strategy and a non-maximum suppression (NMS)-based fusion. The experiments revealed significant overfitting in single-temporal models. While ConvNeXt-V2 achieved a high segmentation mean Average Precision (Segm_mAP) of 0.852 within the same temporal phase, its performance dropped sharply to 0.361 across phases. Bi-temporal joint training significantly mitigated this issue, improving cross-temporal performance to 0.665 and further increasing within-phase accuracy to 0.874. In contrast, tri-temporal training reduced accuracy (0.748), demonstrating that effective generalizability depends on the strategic selection of complementary temporal phases rather than the mere accumulation of data. The multi-temporal training framework provided in this study could serve as a practical reference and a foundational benchmark for further urban forest structural monitoring research. Full article

Intrusion Detection Systems (IDSs) operate under asymmetric misclassification costs: false alarms (FP) consume analysts’ time and erode trust, whereas missed attacks (FN) carry business risks. This paper presents a complete pipeline for network anomaly detection on the CIC-IDS2017 dataset using Histogram-Based Gradient Boosting (HGB), with a particular focus on cost-aware threshold selection on a validation split for representative operating regimes $w_{FP}:w_{FN}\in \{1:1, 1:2, 1:3, 1:4, 1:5, 1:10\}$—treated as scenario-based proxies for varying risk posture, attack severity, and analyst workload rather than as universally fixed costs—and on the role of isotonic calibration. The results indicate that (i) under 1:1, the cost-optimal operating point aligns with the F1/MCC optimum; (ii) for 1:k cost regimes, the optimum shifts to lower thresholds, reducing FN at the expense of FP and increasing the alert rate; and (iii) isotonic calibration improves PR/ROC (ranking separation), but in the reported 1:5 experiment it did not reduce the final TEST-set operational cost relative to the uncalibrated run, despite using a separately selected post-calibration threshold. The evaluation includes PR/ROC curves, Cost–Threshold and Alert–Threshold sweeps, per-class recall, and permutation importance. In addition, the proposed approach is compared with unsupervised baselines (Isolation Forest, LOF). The results provide practical guidance for SOC decisions on how to choose thresholds consistent with alert budgets and risk profiles. In deployment, these operating points can be indexed to context (e.g., user type, service class, or time of day), yielding a small library of adaptive thresholds rather than one immutable global threshold. Full article

Background: Young men experience substantial mental health and mortality-related risks, yet they often do not engage in conventional health promotion programs. This highlights the need for gender-specifc interventions that are acceptable, engaging, and feasible for young men. Purpose of Research: Guided by self-determination theory, this single-group proof-of-concept study evaluated the feasibility and acceptability of a dual-component intervention combining an in-person nature-based intervention (NBI; two days of group activities and guided reflection in a forested park) and a subsequent virtually supported resistance training (RT) program for young men and explored secondary, exploratory pre- to post-changes in depressive and anxiety symptoms. Methods: Eight men aged 18–34 not meeting RT recommendations (i.e., <2 sessions/week) completed a two-day, in-person NBI followed by six weeks of virtually supported RT with weekly group check-ins. Primary feasibility outcomes were satisfaction and qualitative acceptability for NBI/RT, recruitment, retention, and adherence. Secondary, exploratory quantitative outcomes were pre- to post-changes in depressive and anxiety symptom scores. Brief semi-structured exit interviews were conducted at the study end and audio-recorded for analysis. Results: Satisfaction met a priori thresholds for both components (NBI = 3.4/4; RT = 4.3/5; criteria ≥ 3.0 and ≥ 3.5). Recruitment was 46% and retention 100%, exceeding the 42% and 80% criteria, respectively. Exit interview themes highlighted guided learning, accountability, and feeling more connected to nature as acceptability drivers, with the scheduling burden noted but manageable. Depressive and anxiety symptom scores were lower post-intervention. Conclusions: Challenges in recruitment, group dynamics, and participant selection require refinement before the next phase; however, high satisfaction with both the NBI and RT segments, together with improvements in anxiety and depression symptom scores, supports progressing to a feasibility trial once these enhancements are in place. Full article