Search Results (8,188)

Bio-based and biodegradable polymers such as short-chain-length (scl) poly(3-hydroxybutyrate) (PHB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) are widely adopted in diverse areas such as healthcare, manufacturing, and packaging. However, high production costs and the complexity of tailoring their thermal properties, such as glass transition temperature (Tg), melting temperature (Tm), and crystallization temperature (Tc), hinder further adoption. The current study reported on the development of a raw dataset of PHB and PHBV materials compiled from 572 instances collected from the literature (558 instances) and in-house experiments (14 instances). The dataset encompassed compositional physicochemical parameters, molecular features, and corresponding thermal characteristics. After assessing data quality and filtering for completeness and available features, curated datasets were created for machine learning (ML) analysis. Two ML models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were utilized to predict values of Tg, Tc, and Tm using feature engineering methods that integrated chemistry-based descriptors with polymer-specific and experimental variables. The predictive performance of the models was systematically investigated using different combinations of input features to identify the most informative descriptor sets for each target property. The best-performing models were obtained using 118 data points for Tg and Tm and 201 data points for Tc, achieving R² values of 0.77, 0.76, and 0.82 for Tg, Tc, and Tm, respectively. Despite the reliable prediction of the thermal properties of scl-PHAs, the main limitations of the study were the relatively small dataset size for certain targets and incomplete or missing reporting of experimental conditions in the literature sources, which may introduce variability in the compiled data. The findings implied that curated polymer datasets and interpretable ML models can support the rational design of sustainable polymers with tailored properties for specific applications. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

Introduction: The global ornamental fish trade is a rapidly expanding sector and a major pathway for the introduction of non-native species, particularly in freshwater ecosystems in developed countries. The introduction of non-native species can result in a range of ecological impacts, including predation, competition, hybridization, and disease transmission, often leading to ecosystem degradation and biotic homogenization. Therefore, it represents a clear ecological risk, especially serious in freshwater systems with a high endemism rate, such as the Iberian Peninsula. The occurrence of ornamental non-native species in the Iberian Peninsula has been common, yet little has been done to describe the overall ornamental fish trade as a first step to evaluate invasion risk. Objective: This study characterizes the import dynamics of ornamental freshwater fish in Portugal between 2020 and 2024 and evaluates its potential role as a pathway for species introductions. Methodology: Data were obtained from the Institute for Nature Conservation and Forests database, including information on species composition, quantities, sizes, prices, and countries of origin. A total of 431 records were analyzed, resulting in 27,689 validated entries of imported freshwater fish, which were taxonomically verified and filtered to retain only freshwater species. Results: A total of 666 species from 88 families were identified, with an average of 380 species imported annually, reflecting high taxonomic diversity. Import volumes increased from approximately 1.25 million individuals in 2020 to 1.75 million in 2024, while total import value nearly doubled from €300,000 to €600,000. Imports were predominantly from five Southeast Asian countries, particularly Indonesia and Vietnam, and largely supported by aquaculture production (88%). A stable core of highly traded species, including Carassius auratus, Poecilia reticulata, and Paracheirodon innesi, suggests a sustained and very high propagule pressure, while some species variability was observed on yearly basis, suggesting the importance of monitoring programs on actual imports. Conclusions: Overall, the ornamental fish trade represents a significant and growing pathway for biological invasions in Portugal. The combination of increasing trade volume, high species diversity, and persistent dominance of key taxa highlights the need for improved monitoring, regulatory frameworks, and public awareness to mitigate ecological risks. Full article

Radiation use efficiency (RUE) is closely associated with cotton biomass and yield, yet the synergistic regulation of phenotypic structure and physiological potential remains unclear. A field experiment (2024–2025) in Anyang, China, utilized three independent trials: six sowing dates (from 12 April to 12 May at 6-day intervals, S1–S6), six planting densities (1.5, 3.3, 5.1, 6.9, 8.7, and 10.5 × 10⁴ plants·ha⁻¹, D1–D6), and ten cultivars with distinct architectures (V1–V10). Feature importance and structural relationships were quantified via random forest (RF) and partial least squares structural equation modeling (PLS-SEM). Results indicated that delaying sowing reduced true leaf number (TLN) and plant height (PH), with the April 24 sowing (S3) optimizing leaf area index (LAI, 2.57) and light interception rate (iPAR, 0.61). Increasing density significantly enhanced population-level LAI, above-ground biomass, and RUE, despite a progressive decline in TLN. Among cultivars, CCRI 60 (V6) exhibited superior structural traits (PH: 72.94 cm; iPAR: 0.61), while CCRI 113 (V8) exhibited the highest maximum carboxylation rate (V_cmax, 88.9 μmol·m⁻²·s⁻¹) and RUE (4.88 g·MJ⁻¹). Across the comprehensive dataset (integrating the density, sowing date, and cultivar trials), iPAR exhibited the highest relative importance (42.01%) for RUE variation, while associated structural traits (PH, LAI, TLN) yielded a cumulative relative importance of 41.69%. RUE was strongly associated with biomass accumulation (path coefficient > 0.97), which subsequently optimized yield components. Conversely, within the cultivar-comparison subset, the relative importance of iPAR decreased to 17.95%, while V_cmax rose significantly to 19.20%. PLS-SEM indicated that canopy structure exerted a significant negative association with photosynthetic potential (V_cmax, J_max) within this cultivar subset (path coefficient ≈ −0.51), whereas enhanced physiological potential was positively associated with resource allocation to yield components (path coefficient ≈ 0.57). Consequently, mitigating the inherent trade-off between canopy structure and leaf photosynthetic capacity is critical for further improving RUE and cotton yield under similar production environments. Full article

Reliable carbon emission data are essential for the effective operation of emissions trading systems (ETS), especially as China’s ETS expands to include energy-intensive industries. This study proposes a hybrid, risk-based anomaly detection framework for high-frequency CO₂ emission data by cross-validating material-based emissions with flue gas-based monitoring data. Under normal operating conditions, the ratio of material-based to flue gas-based emissions is expected to remain within a relatively stable distribution. Potential high-risk periods can therefore be identified when this relationship is distorted or when local temporal patterns deviate from expected behavior. The framework combines Hartigan’s dip test with a window-based Random Forest (RF) classifier, which is suitable for continuous monitoring data that may exhibit temporal dependence. The framework was evaluated using 15-min CO₂ emission data from a cement production facility, with simulations of anomaly magnitude, duration, and mode. Results show that the dip test performs well for long-lasting or strong anomalies, whereas the RF model is more sensitive to subtle, short-term deviations. In the integrated framework, 94.7% of anomalous periods were detected by at least one method and flagged as potential data-quality risks, whereas normal periods were not flagged, supporting its use to prioritize verification efforts. Full article

Protected areas increasingly require functional zoning approaches that integrate biodiversity conservation, ecosystem service provision, and human use. This study developed a data-driven functional zoning framework for Jirisan National Park, South Korea, by combining ecosystem service assessment with Self-Organizing Map (SOM)-based spatial typology. Five ecosystem services—water yield, sediment retention, carbon storage, net ecosystem productivity, and habitat quality—were assessed using InVEST, RUSLE, and locally derived carbon-related coefficients. These indicators were integrated with topographic and anthropogenic disturbance variables, including distances to roads and trails. The SOM analysis classified the park into seven functional spatial types with distinct environmental and ecosystem service characteristics. High-altitude areas near major trails were characterized by strong visitor pressure and mismatches among regulating services, whereas interior forest areas showed high multifunctionality and evenness, indicating stable ecosystem service provision. Low-altitude facility-dense and disturbance-adjacent zones showed relatively low habitat quality or service imbalance, highlighting the need for restoration-oriented management. These results suggest that ecosystem service bundles, multifunctionality, and evenness can provide a useful basis for functional zoning and evidence-based management of mountainous national parks. Full article

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques. Full article

African soils face increasing levels of metal pollution due to industrialization, artisanal mining activities, improper waste management, and enhanced agricultural productivity. However, unlike many organic pollutants, heavy metals do not degrade naturally and therefore persist in environmental systems for prolonged periods. Heavy metals accumulate over many decades in the soil and bioaccumulate through the food chain causing severe health complications such as cancer, kidney problems, and neurological impairment. This paper reviews the current literature on the origin, prevalence, and behavior of the main pollutants Pb, Cd, Cr, As, Hg, and Cu. The major phytoremediation methods including phytoextraction, rhizofiltration, phytostabilization, and phytovolatilization are highlighted alongside in planta screening methods for hyperaccumulating plants including Berkheya coddii (Ni) and Haumaniastrum robertii (Co). The paper evaluates various enhancement techniques such as the use of chelators, Rhizobium inoculations, and genetic modifications. The significance of these approaches in tropical and subtropical climates is discussed. The paper suggests a holistic framework involving empirical kinetic modeling, geospatial machine learning (random forest, kriging), and molecular omics in prediction modeling. Major hurdles in such predictions include lack of field-based verification of the models, biotechnology safety of genetically modified (GM) organisms, and inadequate regulations. Future perspectives emphasize community-driven phytomining, biomass recycling, and resilient phytoremediation solutions. Full article

In Chile, Eucalyptus globulus stands out as a significant forest species, yielding around 2 million tonnes of bark; this by-product is a valuable source of phenolic compounds. This research evaluated the valorization of E. globulus bark using alkali-assisted extraction (AAE) and obtained extracts intended to protect the wood against fungal degradation and ultraviolet (UV) radiation. The chemical and thermal properties of the extracts were characterized using total phenolic content (TPC), antioxidant capacity, FTIR spectroscopy, LC-LTQ-Orbitrap-MS, and thermal analyses (TGA and DSC). Pine wood samples were impregnated using the Bethel process, and their absorption, retention, leaching, UV resistance, gloss, and antifungal efficacy were evaluated. The AAE showed an extraction yield of 8.79%, almost double that of aqueous extraction, with a phenolic content of 970 mg GAE/100 g dry bark and good antioxidant capacity. The MS/MS analysis tentatively identified low-molecular-weight organic acids, phenolic acids, a hydrolyzable tannin derivative, ellagic acid, methylated flavonol glycosides, and an iridoid non-phenolic metabolite. Thermal analysis indicated greater stability of the alkaline extracts, with a mass loss of less than 10% up to 200 °C, and significant degradation between 220 and 300 °C. Leaching tests showed a lower release of polyphenols from alkali-treated wood, indicating reduced mobility and/or greater retention of the extractives within the wood structure. Biological assays demonstrated effective inhibition of stain fungi and strong resistance to brown rot. Furthermore, UV aging tests showed less color change (Delta E*) and greater resistance to surface degradation. These results demonstrate the potential of alkaline extracts from E. globulus bark as sustainable additives for wood protection. Full article

Aboveground biomass (AGB) mapping is fundamental to tropical forest carbon monitoring, yet national-scale estimation remains challenging because field plots are sparse and model performance is often sensitive to predictor choice and validation design. This study assessed whether geospatial embeddings improve national AGB mapping in Guyana when combined with environmental and topographic predictors. Predictor selection was undertaken using repeated grouped resampling at the plot-cluster level, and model performance was evaluated across 100 independent train–test repeats. Three final random forest models were compared. The environmental baseline model (Env + SRTM-derived elevation; 8 predictors) achieved a mean R² of 0.179, an RMSE of 148.5 Mg/ha and a relative RMSE of 36.1%. A retained 8-predictor model combining environmental variables with a selected embedding subset (Env + Emb*) improved performance slightly, with a mean R² of 0.189, an RMSE of 147.6 Mg/ha and a relative RMSE of 35.9%. The best performance was obtained with a 22-variable full-stack model combining environmental, topographic and embedding predictors, after all Sentinel-2 predictors had been eliminated during feature selection; this model achieved a mean R² of 0.203, an RMSE of 146.3 Mg/ha and a relative RMSE of 35.5%. Across models, isothermality, a measure of how day-to-night temperature variation compares to annual temperature variation, and precipitation of the coldest quarter were consistently the most influential predictors. Mean ensemble coefficient of variation, representing relative model disagreement, ranged from 0.336 to 0.361. These results indicate that geospatial embeddings provide useful complementary information, but predictive performance remained modest overall, with the best model explaining only about one-fifth of plot-level AGB variance. The resulting maps are therefore best interpreted as broad-scale decision-support products rather than high-precision local estimates of AGB. Full article

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

Water quality monitoring is a fundamental element of sustainable aquaculture management, as changes in parameters of physicochemical and biological properties directly affect the health, growth performance, and productivity of the aquaculture systems. Although traditional machine learning (ML) methods have demonstrated effectiveness in water quality classification, their performance often depends on large amounts of labeled data, which can be challenging and expensive to collect in real-world aquaculture environments. This study explores a few-shot learning (FSL) framework for data-efficient water quality classification under limited supervision to address this limitation. Several FSL models, including prototypical networks (ProtoNet), Siamese Networks, and Matching Networks were developed and evaluated in a comparative experimental framework against the traditional machine learning classifiers logistic regression, random forest, support vector machine and extreme gradient boosting. Low-data learning scenarios were simulated using a structured episodic evaluation approach. Experimental results demonstrate FSL techniques outperform traditional machine learning methods across all evaluated scenarios. Among the tested methods, ProtoNet achieved the highest performance, attaining an accuracy of 94.46% and an ROC-AUC score of 98.65%, indicating superior discriminative capability and robustness. Siamese Networks also demonstrated competitive performance under highly constrained data conditions. Furthermore, latent-space visualization, confusion matrix analysis, paired t-test statistical analysis, and ablation studies confirmed that episodic meta-learning enables the learning of highly discriminative latent representations with strong generalization capability under limited labeled data conditions. The findings highlight that FSL provides a robust and scalable framework for intelligent water quality classification in aquaculture systems, particularly in scenarios where labeled data are scarce, offering significant potential for sustainable aquaculture monitoring applications. Full article

Pollen viability is a crucial determinant of reproductive success in plants. Given the enormous threat posed to the common ash (Fraxinus excelsior L.) by ash dieback, it is important to investigate the potential disease’s effects on pollen viability and germination. Thus, we conducted an analysis of these pollen characteristics across three distinct forest stands in southern Bavaria, with up to 23 ash trees per study site. These ash trees exhibited varying degrees of ash dieback-related damage symptoms, enabling us to assess differences between mildly and severely affected trees (via Mann–Whitney-U/Wilcoxon tests, complemented by linear mixed-effects modelling). Pollen viability was assessed using the TTC test, while pollen germination capacity was evaluated on a sucrose–agar medium. Our findings revealed no statistically significant differences in pollen viability between mildly affected and severely diseased trees, as indicated by both the TTC test and pollen germination assay when applying non-parametric analyses (Mann–Whitney U and Kruskal–Wallis tests). Nevertheless, a consistent tendency towards higher pollen viability was observed in healthier ash trees. When accounting for the hierarchical structure of the data using linear mixed-effects modes, tree vitality showed a significant effect on pollen viability, whereas a substantial proportion of the observed variation was explained by interannual differences. These results indicate that ash trees generally retain the capacity to produce viable pollen across different levels of disease severity, but vitality-related effects are subtle and context-dependent. However, severely diseased trees produced few or no flowers, substantially reducing the likelihood that their pollen contributes to fertilization. We therefore conclude that ash dieback primarily limits reproductive success in common ash mainly by reducing flower and pollen production, whereas pollen viability itself is strongly driven by interannual differences. Consequently, no consistent pattern of declining pollen viability with increasing disease severity emerged. Full article

Accurate assessment of carbon storage dynamics and their driving factors is important for ecological sustainability and land management on the Loess Plateau under China’s dual carbon goals. In this study, the InVEST and PLUS models were integrated to evaluate carbon storage changes from 2000 to 2020 and simulate future carbon storage patterns for 2030 under four development scenarios, including natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP). In addition, the XGBoost-SHAP framework was employed to identify the dominant drivers and nonlinear response relationships controlling spatial variation in carbon storage. During 2000–2020, ecosystem carbon storage across the Loess Plateau generally increased, rising from 5.780 Pg to 5.893 Pg. Spatially, carbon storage displayed a pronounced pattern characterized by higher levels in the southeast and lower levels in the northwest, aligning with forest–grassland restoration belts. Scenario simulations showed that EP produced the largest carbon storage gain, with total carbon storage projected to reach 5.962 Pg in 2030. In contrast, RD reduced carbon storage to 5.858 Pg because of intensive construction land expansion. XGBoost-SHAP results identified net primary productivity (NPP) as the most influential factor controlling spatial variation in carbon storage, accounting for 57.3% of the total explanatory importance, whereas soil erosion (SE) exhibited a strong negative effect on carbon storage. Population density (POPD) also exerted a negative effect, whereas gross domestic product (GDP) showed positive contributions in economically developed counties. These findings enhance understanding of the spatial response characteristics of carbon storage under environmental gradients and human disturbance across the Loess Plateau. They further provide scientific support for differentiated ecological management and regionally adapted carbon mitigation planning. Full article

In Mediterranean small-scale forestry, the adoption of highly mechanized CTL systems remains limited by fragmented forest lots, variable stand conditions, and high machine costs. This case study evaluated the operational productivity and economic feasibility of harvester-based felling and processing in two Mediterranean conifer stands in Southern Italy. A harvester was monitored in Calabrian pine and silver fir stands using a time-motion approach. Processing represented the dominant productive phase, while moving accounted for about one-third of productive machine time. Under the observed site conditions, the Calabrian pine showed higher gross productivity and lower unit time consumption than silver fir. The economic analysis indicated that feasibility was strongly dependent on gross productivity, benchmark motor-manual costs, and harvested lot volume, with more favourable break-even conditions in Calabrian pine. Full article