Search Results (1,423)

Microplastics (MPs) and nanoplastics (NPs) have emerged as pervasive contaminants across diverse environments—including soil, water, and the atmosphere—posing substantial risks to resident organisms. Concurrently, alien plant invasion represents a significant driver of environmental change, introducing considerable ecological risks to terrestrial ecosystems. Synthesizing evidence from 26 original research articles, this review examines the bidirectional interactions between micro(nano)plastics (MNPs) and terrestrial invasive plants. A growing body of evidence indicates that MNPs alter the growth and performance of both invasive and native plants. In most documented cases, MNPs appear to enhance the competitive ability of invasive plants, thereby elevating invasion potential. However, counterexamples exist wherein MNPs strengthen the competitiveness of native plants, consequently mitigating invasion risk. These divergent outcomes are likely attributable to a suite of influencing factors, notably the characteristics of the MNPs (e.g., type, size, concentration), the specific invasive and native plant species involved, and variations in experimental conditions. Key mechanistic pathways involve MNPs-induced disturbances in soil microecology—particularly nutrient dynamics and rhizosphere microbiomes—and allelopathic interactions. Conversely, invasive plants may adsorb/absorb MNPs and subsequently modify their environmental fate and behaviors (e.g., degradation, transport). Finally, we delineate critical knowledge gaps and propose prioritized directions for future research. This review advances our understanding of the ecological risks associated with plant invasions in an era of pervasive MNP pollution and offers a scientific foundation for developing informed management strategies. Full article

Urban wetlands are assumed to contribute to nitrous oxide (N₂O) emissions; however, the microbial mechanisms underlying enhanced N₂O fluxes in urban wetlands and differences in microbial responses between aquatic and soil compartments have not been clearly identified. Here, we characterized the nitrogen (N) cycling microbial communities and their functional metabolic pathways in urban and rural wetlands using metagenomics and N₂O flux measurements. Results showed that urbanization drove a 6~8-fold increase in N₂O fluxes from urban wetlands compared to rural wetlands. Structural equation modeling (SEM) confirmed that urbanization intensity was a primary driver (standardized coefficients: 0.72 for soil and 0.92 for water). In wetland water, N₂O emissions were negatively correlated with inorganic nutrient concentrations (coefficient = −0.62). Aquatic microbial communities exhibited substantial taxonomic shifts but preserved network connectivity, indicating adaptive strategies for surviving urban perturbations at the cost of reduced functional redundancy. In wetland soil, microbial communities maintained stability under urbanization, which was attributed to environmental buffering from heterogeneous microenvironments. Soil N₂O emissions were positively linked to microbial alpha diversity (coefficient = 0.79). Furthermore, urban wetlands enriched genes mediating nitrification and denitrification while depleting genes associated with N fixation and organic N metabolism. This functional shift reflects microbial specialization in processing elevated reactive N (Nr) inputs from urban sources, trapping urban wetlands in an “N loss loop” that reinforces high N₂O fluxes. This study elucidates the microbial mechanisms governing wetland N₂O emissions under urbanization, thereby enhancing understanding of microbially mediated N cycling in the urban wetland ecosystem. Full article

Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity prediction is formulated as a supervised regression problem, and six widely-used tree ensemble models (Random Forest, XGBoost, CatBoost, LightGBM, Extra Trees, and Decision Tree) are systematically evaluated using large-scale Spotify data. Among these models, Random Forest achieves the best predictive performance on this dataset (RMSE = 6.79, MAE = 5.10, and $R^2$ = 0.6658), followed by Extra Trees ($R^2$ = 0.6378) and Decision Tree ($R^2$ = 0.6328). Bayesian hyperparameter optimization based on a Tree-structured Parzen Estimator with an Expected Improvement acquisition function is conducted over 50 trials with 5-fold cross-validation to ensure robust model selection. Shapley value decomposition via SHAP analysis reveals that temporal recency dominates feature importance, far surpassing traditional musical attributes, while acoustic intensity (loudness) exhibits a U-shaped contribution pattern with optimal values at moderate intensity levels. Further SHAP dependence analysis uncovers non-linear relationships, indicating substantial popularity advantages for recent releases and optimal loudness levels around $-5$ to 0 dB. These findings suggest that streaming popularity is primarily governed by temporal exposure dynamics and production-related characteristics rather than intrinsic musical structure, offering both theoretical insights for music information retrieval research and suggestive empirical patterns that may inform future investigations into digital music ecosystems. Full article

The digital divide between large enterprises and SMEs (Small and Medium-sized Enterprises) within industrial clusters poses a significant challenge to achieving collective digital transformation, exacerbated by the quasi-public goods, attributes of digital inclusion ecosystems, and the prevalence of free-riding behavior. This paper investigates whether platform enterprises, as core actors occupying structural holes in cluster networks, can foster the co-construction of a digitally inclusive ecosystem. We developed a complex network public goods game model, incorporating performance feedback into a modified Fermi learning to capture firms’ adaptive decision-making based on historical and social aspirations. The model simulates strategic interactions on both small-world and scale-free networks, characteristic of industrial clusters. Numerical simulations reveal that: (1) The core driver of co-construction is the investment return coefficient; (2) Performance feedback amplifies individual rationality, accelerating the formation or collapse of cooperation depending on the investment return coefficient; (3) Platform empowerment—specifically, selectively connecting and incentivizing cooperative firms—effectively promotes ecosystem co-construction, with this strategy proving most impactful when investment returns are moderate. Furthermore, while this selective empowerment strategy benefits the cluster overall, its effect on the platform’s own revenue is network-dependent, showing a more pronounced decline in small-world structures. This study provides a novel analytical framework for understanding strategic interactions in digital inclusion and offers practical insights for policymakers and platform leaders in orchestrating collaborative digital transformation. Full article

Lanternfishes (Myctophidae) dominate mesopelagic ecosystems and play a central role in pelagic food webs through their high biomass and diel vertical migration, yet detailed information on their age structure and growth dynamics remains limited in the Northwest Pacific Ocean. This study reconstructs age, growth patterns, and life-history strategies of D. gigas and D. perspicillatus using sagittal otolith microstructure analysis. Specimens were collected during oceanographic surveys conducted in 2023 and 2024, and individual ages were estimated by counting daily otolith growth increments. Somatic growth trajectories were evaluated using multiple nonlinear growth models, including the von Bertalanffy, Gompertz, and Logistic functions, and growth dynamics were further assessed through derivative-based growth speed analyses. The results reveal pronounced interspecific differences in growth strategy and longevity. D. perspicillatus exhibited rapid early somatic growth, a compressed age structure, and an early approach to asymptotic length, indicating a short-lived life-history strategy characterized by early growth deceleration and high population turnover. In contrast, D. gigas showed faster early growth, prolonged somatic development, greater inter-individual variability, and substantially larger maximum body size, reflecting delayed maturation and extended lifespan. Otolith microstructural zonation clearly corresponded to larval, juvenile, and adult growth phases in both species. The predominance of younger age classes in the catch and interannual differences in size structure were primarily attributed to ontogenetic habitat shifts, cohort composition, and sampling availability rather than intrinsic changes in growth dynamics. Full article

Coilia nasus, a typical species with migratory–sedentary polymorphism, shows different intestinal microbiota characteristics among its different ecotypes. This is attributed to differences in feeding habits and habitat environments (such as water temperature, salinity, etc.). This study constructed a database of intestinal microbiota for three ecological types of C. nasus, namely migratory type (comprising marine populations and freshwater populations), sedentary type and aquaculture-reared type, through 16S rRNA amplicon sequencing technology. This study investigates the ecological mechanisms underlying microbiota differentiation, focusing on three key drivers: environmental selection, host nutritional metabolism requirements, and host life history strategies. The results showed that the core flora of C. nasus consisted of Firmicutes, Proteobacteria, and Actinobacteria. Both the depletion of microbial taxa and the enrichment of marine-adapted bacterial lineages—including Proteobacteria and Psychrobacter—are associated with elevated salinity in the migratory marine population of C. nasus. In contrast, the elevated relative abundance of Actinobacteria in aquaculture-reared C. nasus is likely attributable to dietary supplementation with protein- and lipid-rich artificial feed. Functional correlation analysis holds promise for partially predicting the microbiota’s metabolic functional succession patterns. The dominance of Pseudomonas_E in the migratory freshwater population is consistent with its well-documented physiological versatility and adaptive capacity in dynamically fluctuating aquatic habitats. The elevated abundance of Cyanobacteria in the sedentary population C. nasus coincides with the water bloom in their habitat, suggesting that the structure of the microbiota may serve as a novel biomarker for indicating the ecosystem. In conclusion, this study identifies potential molecular markers for tracing genetic resources and distinguishing ecological types of C. nasus, while establishing a theoretical foundation for elucidating the co-evolutionary dynamics between fish hosts and their associated microbiota—and thereby informing both conservation strategies for wild populations and microbiota-informed aquaculture practices. Full article

Human-driven land-use change has significantly altered forest ecosystems and carbon dynamics in mountainous regions. This study aims to quantify land cover transitions and associated forest carbon stocks changes in Bhutan. It also seeks to support the development of a national measurement, reporting, and verification system. Using Landsat-based satellite imagery and object-based image classification techniques, we assessed forest cover transitions, stand structure variations, and forest type changes across temporal intervals. The analysis revealed a consistent increase in agricultural and built-up areas. It also showed a concomitant decline in coniferous forest cover. In particular, agricultural land increased by approximately 0.77 million ha, while coniferous forest decreased by approximately 0.19 million ha over the study period. These changes were driven by both climatic shifts and socio-economic factors. Approximately 57% of Bhutan’s population depends on agriculture. Correspondingly, forest carbon stocks declined from approximately 570 million tC in 1995 to 405 million tC in 2017. This decline was largely attributed to coniferous forest loss and climate-induced mortality. Bhutan has made significant preparations for the implementation of the Warsaw REDD+ framework under the United Nations Framework Convention on Climate Change. These preparations include the establishment of a forest reference emission level for submission. However, challenges remain in detecting small-scale land use changes. Additional challenges include addressing spectral misclassification in mountainous regions. Our study provides a scientific baseline to support national forest monitoring and carbon accounting systems. It also offers policy-relevant insights for achieving Bhutan’s nationally determined contributions and enhancing its carbon sink potential. Full article

AI-enabled technology (AI) has a transformational role in our modern society because it is increasingly used as an interaction partner, making anthropomorphism (tendency to ascribe human features to non-human agents) a central mechanism shaping how people evaluate, accept or resist AI systems. Existing technology acceptance models and anthropomorphism frameworks, however, offer limited guidance on how human-like attributes of AI translate into perceptions of usefulness, perceived control, perceived opportunity or threats, particularly across different levels of AI autonomy. Building on the theory of planned behavior, the technology acceptance model and threat rigidity model, this paper develops a mid-range conceptual framework of AI anthropomorphism grounded in universal social perception dimensions of warmth and competence. We integrate fragmented research to derive three core propositions and four corollaries that specify how warmth and competence attributions shape evaluative cognitions in relation to AI. The framework further identifies AI autonomy as a boundary condition under which anthropomorphic cues may either facilitate acceptance or trigger perceptions of pseudo-empathy, cognitive superiority and identity threat. By offering a parsimonious, theoretically informed model, this paper clarifies when anthropomorphism fosters acceptance versus resistance in human–AI interaction and provides a structured agenda for future empirical research and AI design aimed at fostering synergies and resilience in human–AI ecosystems. Full article

Existing soil remediation approaches are lacking in cost-effectiveness, environmental impacts or societal acceptance. Environmental remediation techniques are often characterized by considerable time requirements and may leave residual effects on natural ecosystems, thereby potentially compromising net environmental benefits. This study investigated the oil adsorption capacity of aerogels produced from waste orange peels. Aerogels are highly porous three-dimensional materials made from organic and inorganic materials, with low density and high adjustable specific surface area. Orange peel aerogel was produced from waste orange peels using combined methods of physical, chemical, and thermal modification processes and was dried using the freeze-drying method. Adsorption and reusability tests were conducted after characterization of the aerogel. Surface characterization of the orange peel aerogel indicated it has an ultra-light density of 0.010417 g/cm³, high porosity of 99%, and a measured contact angle of 102°. An adsorption experiment was conducted with sandy and clay soils, and the maximum oil adsorption capacities of the orange peel aerogel were 13.55 mg/g and 9.60 mg/g for sandy and clay soils respectively. High oil adsorption capacity was shown by the produced aerogel and was attributed to its ultra-light density of 0.010417 g/cm³ and high porosity of 99%. In conclusion, the higher oil adsorption capacity of the orange peel aerogel in sandy soil compared with clay soil indicated that soil texture and aerogel properties influenced its oil remediation capacity. The reusability test in three adsorption trials indicated that orange peel aerogel is a sustainable material for the remediation of oil-contaminated soil. Full article

Cancer metabolism is a cornerstone of tumor biology, characterized by profound alterations in cellular energy production and biosynthetic pathways that drive malignancy. The seminal discovery of the “Warburg effect”, the preference of cancer cells for aerobic glycolysis even under oxygen-rich conditions, provided the first major insight into this field. Historically, this observation was attributed to defective mitochondria, but modern research has revealed a far more complex picture of metabolic reprogramming that is actively driven by oncogenes, tumor suppressor genes, and the tumor microenvironment (TME). This review advances a unifying framework for understanding cancer metabolism as a dynamic ecosystem defined by three interconnected adaptations: metabolic plasticity, oncometabolite-driven epigenetic remodeling, and immune-metabolic crosstalk. These adaptations extend beyond glycolysis to encompass glutamine metabolism, lipid synthesis, amino acid utilization, and mitochondrial dynamics, all coordinated to fuel rapid proliferation, promote survival, and enable metastasis. By examining the drivers, consequences, and therapeutic barriers within this framework, we highlight emerging strategies for precision intervention. Although understanding the mechanistic basis of these pathways has unveiled new therapeutic avenues, clinical translation has been limited by metabolic redundancy, microenvironmental buffering, and patient heterogeneity. Strategies such as metabolic inhibitors, dietary interventions, and immuno-metabolic combinations offer promising prospects for disrupting tumor growth when guided by biomarker-driven patient selection and emerging technologies, including spatial metabolomics and AI-driven network modeling. Full article

This study examines intelligent judgment formation in blockchain-based public digital wallet systems within smart city environments. Drawing on an integrated framework that combines cognitive evaluation, social influence, and trust–risk appraisal, this research conceptualizes intelligent decision-making as a socially embedded and contextually enacted evaluative process rather than a fixed cognitive attribute. A structural equation modeling approach is employed to analyze the interrelationships among perceived usefulness, perceived ease of use, subjective norms, social electronic word of mouth, trust–risk appraisal, attitude, and behavioral intention. The findings indicate that socially distributed information signals play a dominant role in shaping evaluative integration and decision readiness, while cognitive and institutional appraisals operate primarily through mediated pathways. The results suggest that intelligent action in public digital service ecosystems emerges from the coordinated interaction of usability perception, institutional confidence, and socially calibrated information flows. These findings contribute to theoretical extensions of technology acceptance models in public governance contexts and offer implications for the design of socially responsive digital service infrastructures. Full article

Monoculture and mixed sowing are common practices for restoring degraded alpine meadow grasslands. To investigate the effects of different sowing patterns on soil bacterial community characteristics in alpine artificial grasslands, this study examined a 20-year-old established artificial grassland, systematically analyzing plant community attributes, soil physicochemical properties, and the diversity and functional structure of soil bacterial communities under various monoculture and mixed-sowing treatments. The results showed that: (1) Mixed-sowing treatments significantly improved soil physicochemical properties and plant community characteristics. The P4 (Elymus nutans + Poa pratensis + Festuca sinensis + Poa crymophila) mixed-sowing treatment notably enhanced vegetation performance and soil conditions. Compared with the monoculture P1 (Elymus nutans) treatment, aboveground biomass (AGB) and soil organic matter (SOM) content increased by 57.23% and 68.25%, respectively, indicating that perennial grass mixtures improve soil water and nutrient retention, thereby promoting plant growth. (2) Microbiome analysis revealed that mixed sowing significantly optimized the structure of rhizosphere bacterial communities. Operational Taxonomic Units (OTUs), which represent sequence-based taxonomic units and their abundance information, were most abundant in the P4 mixed-sowing treatment, reaching a total of 5685 OTUs. In terms of bacterial diversity indices, the OTU richness, Ace index, and Chao1 index in the P4 mixed-sowing treatment were 26.12%, 25.81%, and 24.34% higher, respectively, than those in the monoculture P1 treatment, with all differences being statistically significant (p < 0.05). (3) Mantel test and redundancy analysis (RDA) revealed that soil electrical conductivity (SEC) and pH were negatively correlated with bacterial diversity indices, while soil organic matter (SOM) was identified as the key environmental driver shaping bacterial community assembly. In summary, appropriate grass mixtures effectively enhance “plant–soil–microbe” interactions, leading to improved soil fertility and optimized bacterial communities, representing a viable strategy for long-term ecological restoration and sustainability of alpine artificial grassland ecosystems. The P4 treatment—comprising a four-species mixture of Elymus nutans, Poa pratensis, Poa crymophila, and Festuca sinensis—achieved the best overall performance. Full article

Forest ecosystems are increasingly degraded by natural disasters and human activities, intensifying the need for large-scale restoration. Because restoration outcomes are long-term, uncertain, and largely non-market, understanding how environmental attitudes relate to public preferences and willingness to pay (WTP) is important for socially acceptable and financially feasible policy design. Using a discrete choice experiment in Korea, this study applies a hybrid choice framework that incorporates latent attitudinal variables into a mixed logit structure, allowing attitudes to interact with preference heterogeneity across restoration attributes. Results show significant heterogeneity in choices and WTP. The model identifies two segments with distinct trade-off patterns: one is more sensitive to risk and payment burden, while the other places relatively greater value on restoration and access-related improvements. Although attitudinal indicators are statistically relevant, segment differentiation is more strongly associated with risk sensitivity and cost aversion than with attitudes alone. Compared with conventional choice models, the latent-attitude specification improves behavioural interpretability and model fit, and yields policy-relevant WTP estimates. Overall, the findings indicate that attitudinal information is complementary to economic and risk-related factors, supporting more targeted and publicly aligned forest-restoration policies. Full article

Land-use change strongly affects soil microbiota, yet the role of agroecological systems in shaping soil fungal communities remains poorly understood in tropical soils. We evaluated the diversity, trophic modes, community composition, and co-occurrence networks of culturable soil fungal taxa across a land-use gradient in the Colombian Andes–Amazon transition zone. Agroecological systems—including improved pasture (IP), cacao and copoazu agroforestry systems (CaAS and CoAS), secondary forest with agroforestry enrichment (SFAE), and a moriche palm swamp ecosystem (MPSE)—were compared with dominant land-uses (degraded pasture, DP and old-growth forest, OF). Fungi were isolated using the soil dilution plate method and identified based on morphological and molecular characteristics, and soil physicochemical properties were measured to evaluate their relationships with fungal community patterns. A total of 420 isolates were assigned to 93 fungal species. Alpha-diversity metrics revealed significantly higher fungal richness in OF and MPSE, and higher Shannon diversity in agroforestry and forest-based systems, whereas DP exhibited the lowest values. Ordination analyses showed clear differences in fungal community composition, with CoAS displaying the most distinct assemblage. Agroecological and forest-based systems favored saprotrophic and symbiotrophic modes. Co-occurrence network analyses indicated that MPSE, OF, and IP supported more complex and modular fungal networks. Soil pH and total phosphorus (TP) were key drivers of fungal community composition, whereas exchangeable calcium, TP, soil organic carbon, and base saturation were associated with network attributes. Overall, our findings highlight the importance of agroecological management for soil fungal diversity and network organization in Amazonian transition landscapes. Full article

Lake heatwaves (LHWs) pose a growing threat to freshwater ecosystems, yet the drivers of these short-term extremes versus long-term warming are often conflated. This study aims to disentangle these mechanisms for US lakes. We analyzed daily surface temperature data from 43 representative US lakes (1980–2020), combining Mann–Kendall trend analysis, Sen’s slope estimation, and Random Forest attribution modeling. Results reveal a significant overall warming trend of 0.14 °C/decade, a pattern primarily driven by lakes outside the Western Cordillera region. Concurrently, LHWs intensified in frequency, duration, and cumulative intensity, occurring most frequently in summer but with the greatest intensity in spring. Crucially, the attribution analysis identified distinct controlling mechanisms: short-term LHWs were predominantly governed by meteorological factors (57.3% contribution), whereas long-term warming trends were modulated by geographical factors (47.5% contribution) that dictate the lake’s thermal sensitivity. These findings establish that lakes respond differently to short-term weather extremes versus long-term climate shifts. This distinction is critical for developing more accurate meteorologically based LHW forecasts and tailored, region-specific ecosystem management. Full article