Search Results (1,121)

Industrial emissions of large amounts of CO₂ have seriously affected human health, making it imperative to reduce atmospheric CO₂ concentrations. However, carbon capture technologies such as chemical absorption and membrane separation are still limited by high regenerative energy costs, corrosion, and low efficiency in diluting flue gas. Within this technological landscape, physical adsorption separation technology, due to its advantages such as a wide operating temperature range, low equipment corrosivity, and low regeneration energy consumption, has gradually become a research hotspot in carbon capture technology. The core of physical adsorption lies in finding high-quality adsorbents. Metal–organic frameworks (MOFs), with their ultra-high specific surface area, tunable pore structure, and abundant functionalization sites, are considered highly promising next-generation CO₂ adsorbent materials. This review summarizes strategies for modifying MOFs to improve CO₂ adsorption performance, focusing on aperture adjustment, doped metal ions, functional group doping, and computational screening. Performance enhancements are mechanism-dependent rather than simply additive. Moderate aperture adjustment and defect engineering can improve gas selectivity and CO₂ capture capacity, while excessively narrow pores sacrifice available pore volume and gas diffusion. Doped metal ions, particularly in MOF-74 and related materials, can enhance CO₂ capture capacity while controlling framework integrity and dopant composition. Functional group Doping remains an effective method for capturing low-partial-pressure CO₂. Computational screening is shifting from ranking based on single adsorption capacity to a comprehensive consideration that includes humidity tolerance, stability, and regenerability. Overall, under industrial conditions, modified MOFs should be evaluated by balancing affinity, selectivity, capacity, stability, and energy efficiency. This review provides guidance for the rational design of MOF-based carbon capture adsorbents. Full article

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by developing and evaluating a novel data-centric framework that synergistically integrates Graph Neural Networks (GNNs) with zero-shot superpixel segmentation derived from the Segment Anything Model (SAM) applied to Sentinel-2 imagery. A cornerstone of our methodology is a rigorous assessment of OpenStreetMap (OSM) data, refined through temporal NDVI stability analysis to generate high-quality ground truth. We propose an optimized UrbanGraphSAGE model, enhanced with spectral data augmentation and trained using a robust loss function with label smoothing to mitigate label noise. In the complex urban landscape of Algiers, Algeria, our approach achieves a Test F1-Score of 0.7131, demonstrating highly competitive performance with standard pixel-based baselines like U-Net while offering significant topological and computational advantages. Specifically, our model operates with merely 19,585 parameters—orders of magnitude fewer than pixel-based CNNs. A rigorous Gold Standard evaluation against manually labeled imagery confirms the model’s high recall (0.8484) and reliability for automated urban monitoring. Full article

Chemotherapy resistance remains a major obstacle to durable cancer control, yet its underlying mechanisms cannot be fully explained by genetic mutations alone. Increasing evidence suggests that therapeutic stress induces dynamic adaptive programs that reshape tumor phenotypic landscapes. Here, we propose a systems-level framework in which chemotherapy resistance emerges from the stabilization of interconnected stress-response circuits integrating redox signaling, metabolic reprogramming, and transcriptional plasticity. In this model, cytotoxic therapies function as state-generating perturbations that elevate oxidative stress and activate adaptive buffering systems, including NADPH-dependent redox homeostasis, replication stress tolerance, and integrated stress response (ISR)-mediated translational reprogramming. These adaptive modules collectively expand the accessibility of therapy-tolerant phenotypic states within tumor cell populations. Importantly, these circuits coordinate mitochondrial redox homeostasis, metabolic NADPH regeneration, and epigenetic–transcriptional plasticity to sustain cellular survival under persistent oxidative pressure. Such adaptive redox networks not only stabilize stress-tolerant phenotypes but also create vulnerabilities that can be therapeutically exploited. From a translational perspective, this framework suggests that effective strategies to overcome chemotherapy resistance should move beyond single-target inhibition and instead focus on circuit-guided therapeutic interventions that simultaneously destabilize redox buffering systems, constrain phenotypic plasticity, and disrupt metabolic stress adaptation. By conceptualizing therapy resistance as a dynamic redox-regulated state-space phenomenon, this model provides a mechanistic foundation for the development of evolution-aware and plasticity-constraining therapeutic strategies. Targeting the coordinated redox–metabolic–translational circuits that maintain tumor adaptability may therefore represent a promising direction for next-generation redox therapeutics in cancer. Full article

In late Ming China, landscape (shanshui 山水) painting could function not only as a scenic representation but also as a pictorial means of making sacred space perceptible. This article examines Wu Bin’s hanging scroll Fanghu Tu 方壺圖 (1626; Palace Museum, Beijing) and asks how the painting renders Daoist sacred space visible through relations of distance, access, concealment, and uneven disclosure. To avoid treating “Daoist aesthetics” as a general label, the analysis uses schema and pictorial organization as limited descriptive terms for the structuring of spatial experience within the image. The close reading identifies two recurrent pictorial formations brought into relation in Fanghu Tu: a sea-boundary, distant-view configuration that emphasizes separation and delay, and a pavilion-centered enclosure that produces a more concentrated middle field. It then shows how layered waves and broken shoreline, cloud and mist, middle-zone enclosure, and the thinning legibility of the upper peaks prevent the scene from stabilizing into a single resolved destination. Read in relation to late Ming discussions of cultivated “strangeness” (qi 奇) in landscape painting, these features suggest that Daoist sacred space in Fanghu Tu takes shape as an uneven and mediated experience, structured through provisional concentration, interrupted visibility, and renewed distance. The article argues that late Ming landscape painting could render Daoist-inflected sacred spatial experience visible not only through iconography, but also through the pictorial distribution of visibility, access, and reorientation. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

In this work, density functional theory (DFT) was used to comparatively investigate the thermodynamic and electronic factors governing the association of Cd(II), Hg(II), and Pd(II) with native chitosan (CTS) and functionalized derivatives (CTS–COOH, CTS–COO−, CTS–NH³⁺, and CTS–SH). Representative acid–base states were considered to approximate changes in site availability, and a uniform explicit microhydration scheme was adopted to enable controlled relative comparisons across metals and materials. Within this framework, the calculated free energies suggest metal-dependent affinity regimes: the carboxylic microenvironment favors Cd(II), the thiolated microenvironment provides the most favorable association for Hg(II), and native CTS affords the strongest calculated stabilization for Pd(II). Geometry optimizations show that most complexes retain the first hydration sphere of the metal, indicating that stabilization is dominated by outer-sphere association rather than by systematic first-sphere ligand substitution. ESP/MEP maps reveal that the heterogeneity and directionality of the electrostatic landscape govern selectivity. In contrast, NCI analysis supports a cooperative contribution of weak interactions and second-sphere organization. These results provide a comparative electronic framework to guide future experimental validation of selective metal capture by functionalized chitosan materials. Full article

The eukaryotic translation initiation factor 4E (eIF4E) family plays a dual role in plants, regulating cap-dependent protein synthesis and mediating susceptibility to viruses in the family Potyviridae. In cowpea (Vigna unguiculata (L.) Walp.), an economically important legume cultivated worldwide, the structural determinants of these isoforms remain largely unexplored. This study characterizes the genomic organization, evolutionary history, and conformational dynamics of eIF4E, eIF(iso)4E, and nCBP in cowpea using a multi-omics approach. Genome mining identified three paralogous genes located on chromosomes 4, 6, and 7, showing high synteny with Phaseolus vulgaris. Phylogenetic analysis confirmed nCBP as the ancestral Class I lineage, distinct from the Class II eIF4E and eIF(iso)4E clades. Theoretical models for the isoforms were generated and subsequently validated by molecular dynamics simulations, revealing that while all isoforms preserve the canonical tertiary architecture and an electropositive cap-binding pocket, eIF(iso)4E exhibits superior structural compactness and hydrogen-bond stability. These biophysical features highlight their role as a stable anchor for viral VPg proteins. By elucidating the atomic-level landscape of these factors, we provide a robust structural framework to guide allele mining and genome-editing strategies aiming to engineer virus-resistant cowpea cultivars without compromising agronomic performance. Full article

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare. Full article

Through assessing the roles of artificial intelligence (AI), Internet of Things (IoT), and blockchain in augmented finance, a critical synthesis of the literature for addressing the complex financial challenges that accompany climate change is provided. This systematic review synthesizes the existing literature to identify how these technologies may help in the context of sustainable finance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we reviewed and analyzed 42 peer-reviewed studies published between 2018 and 2025. Our results are applicable in three general areas: (1) increased measurement, reporting, and verification (MRV) of environmental impacts through employing IoT and blockchain to ensure transparency and traceability; (2) better physical and transition risk control using predictive AI modeling; and (3) better environmental, social, and governance (ESG) analysis and detection of greenwashing and risk reduction via alternative data. We highlight the power of these technologies to address problems such as information asymmetry and transparency gaps in impact chains. However, significant challenges such as algorithmic bias, difficulties associated with data governance, and regulatory delays persist. This study addresses this critical gap by synthesizing the evidence into a cohesive overview of the augmented finance landscape, identifying key challenges and priorities for future research. It also proposes a future research agenda with emphasis on impact assessment, algorithmic transparency, and impact on financial stability. Full article

Caves are fragile subterranean ecosystems whose conservation depends on accurate microclimatic zonation. Traditional fixed-distance sampling often overlooks non-linear thermodynamic transitions at geomorphological thresholds, hindering sustainable management of subterranean biodiversity. This study introduces the Adaptive Morpho-Statistical Protocol (AMSP), a novel, resource-efficient framework for functional cave profiling. The methodology integrates high-precision atmospheric monitoring with adaptive spatial positioning to identify three distinct sectors (S1–S3) based on thermodynamic homeostasis rather than linear distance. Validated across five diverse cave archetypes in the Vratsa Karst Region (Bulgaria), the AMSP demonstrated exceptional predictive power using second-order polynomial regressions (R² > 0.92). A key finding is the definition of a standardized reference threshold for deep-reach stability (Sector 3), consistently characterized by a Dew Point Standard Deviation (SD_DP < 0.40) and stabilized thermal coupling (∆T → 0). Furthermore, the adaptive strategy successfully captured extreme hygrometric jumps at morphological bottlenecks—critical inflection points for protecting sensitive biota. By providing a cost-effective and replicable standard, the AMSP bridges the gap between spatial resolution and logistical feasibility in challenging environments. These results confirm that morphological isolation is the primary driver of microclimatic inertia, offering a robust tool for sustainable subterranean heritage management and high-precision ecological monitoring in protected karst landscapes. Full article

Invasive plant species frequently dominate contaminated ecosystems and are increasingly reported as accumulators of heavy metals and potentially toxic elements (PTEs). While this phenomenon is widely documented, its functional implications for contaminant dynamics and remediation-oriented management remain insufficiently synthesized. This review provides a comprehensive assessment of heavy metal and PTE accumulation in invasive plants across terrestrial and aquatic environments, with emphasis on tissue-specific partitioning, environmental context, and species-level variability. Based on field surveys, controlled experiments, and biomonitoring studies, we synthesize evidence for the accumulation of key elements (As, Cd, Cr, Cu, Ni, Pb, and Zn) in the roots and above-ground tissues of terrestrial and aquatic invasive plants. The available literature reveals consistent patterns of root-dominated sequestration in many terrestrial invaders, contrasted with enhanced shoot accumulation in fast-growing aquatic species. These patterns underpin divergent functional roles, ranging from contaminant stabilization in soils and sediments to conditional phytoextraction under managed harvesting. Rather than promoting invasive plants as remediation tools, this review frames them as unavoidable functional components of contaminated landscapes. We critically evaluate their advantages, limitations, and ecological risks, identify key research gaps, and propose a context-aware framework for interpreting invasive plant–PTE interactions in environmental management. Full article

Metaheuristic optimization algorithms often suffer from structural imbalance between exploration and exploitation, leading to premature convergence and performance degradation in high-dimensional or constrained problems. To address this issue, a symmetry-enhanced Improved Enterprise Development Optimization Algorithm (IEDOA) is proposed. The algorithm establishes a dynamic symmetry between global exploration and local exploitation through three coordinated strategies: a performance-feedback-based adaptive activity selection mechanism, a multi-elite-guided structural evolution strategy, and a lifecycle-aware exploration mechanism inspired by technological scheduling dynamics. The proposed symmetric regulation framework improves population diversity while preserving convergence stability, thereby enhancing search efficiency in complex landscapes. To validate its performance, IEDOA is evaluated on CEC2017 (30/50 dimensions) and CEC2022 (10/20 dimensions) benchmark suites and compared with several advanced metaheuristic algorithms. Experimental results demonstrate superior convergence accuracy, robustness, and scalability. Statistical analyses using the Wilcoxon signed-rank and Friedman tests further confirm its significant performance advantages. To demonstrate practical applicability, IEDOA is applied to a grid-connected microgrid economic dispatch problem involving renewable generation units, controllable generators, and energy storage systems under 24 h operational constraints. Simulation results show that the proposed method achieves lower operational costs and smaller performance variance across independent runs. Overall, IEDOA provides an effective symmetric optimization framework for complex engineering systems characterized by nonlinearity, multi-constraints, and high dimensionality. Full article

Human motor control has long been described within traditionally deterministic frameworks that emphasize consistency and error minimization. However, accumulating evidence across motor learning, coordination dynamics, and information theory suggests that variability and uncertainty are not merely sources of noise but fundamental resources for adaptive behavior. This review synthesizes theoretical, empirical, and methodological advances to propose an integrative probabilistic framework for motor control. Drawing on complex-systems theory, entropy-based analyses, and hierarchical coordination models, motor behavior is conceptualized as a self-organizing process that continuously balances stability and flexibility under uncertainty. Variability is reinterpreted as functionally regulated, supporting exploration, reorganization, and context-sensitive adaptation rather than reflecting control failure. To formalize this perspective, a Probabilistic Landscape Model is introduced, in which motor behaviors are represented as trajectories within a dynamic landscape of multiple attractors. Within this framework, entropy captures the structured organization of uncertainty, metastability enables rapid transitions between coordination states, and probabilistic stability characterizes the system’s capacity to maintain effective performance across changing constraints. Beyond synthesizing existing research, this review introduces the Probabilistic Landscape Model (PLM), a conceptual framework that integrates nonlinear coordination dynamics, entropy-based variability analysis, and probabilistic interpretations of motor behavior. By integrating insights from motor learning, sports performance, rehabilitation, and predictive processing, this review provides a unified account of adaptive motor control as an inherently probabilistic and self-organizing system. The proposed framework offers conceptual and practical implications for training design, rehabilitation strategies, and human–machine interaction in uncertain environments. Full article

Climate change threatens the stability of temperate grassland ecosystems in Inner Mongolia, a core part of the Eurasian Steppe, by driving widespread shifts in plant species distributions. Agropyron cristatum (L.) Gaertn., a dominant native perennial herb in Inner Mongolian steppes, is ecologically vital for degraded grassland restoration and forage supply, but its response to future climate change is unclear. Here, we used an optimized MaxEnt model to assess its potential distribution under current and future climate scenarios. We processed 228 initial occurrence records into 112 valid points, selected 11 non-collinear environmental variables, optimized model parameters with the R package ENMeval, and projected distributions for the 2050s and 2100s under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios, while quantifying habitat fragmentation with landscape metrics. We found that annual mean temperature and annual precipitation dominate A. cristatum distribution (total contribution ~87%), with current highly suitable habitats concentrated in central-eastern Inner Mongolia. Future scenarios show stable core suitable habitats with northward and westward shifts, habitat fragmentation will slightly increase. Our findings clarify the climate response of A. cristatum and support its conservation and adaptive grassland management. Full article

Urban expansion in Europe is accelerating, increasing impermeable surfaces and intensifying climate-related pressures, while reducing the capacity of natural and semi-natural habitats to regulate climate. Despite growing interest in ecosystem service (ES), the assessment of resilience, and thus the stability of ES providers, as well as their integration into spatial planning tools, remain limited. This study develops and tests a comprehensive assessment framework that (i) evaluates the current performance of selected ecosystem functions underpinning key regulating ES important for climate adaptation using a look-up table method; (ii) assesses ecosystem resilience by quantification its preconditions; and (iii) applies spatial prioritization to identify and prioritize climate adaptation measures that enhance ecosystem functions and strengthen resilience. The framework was applied to the cadastral area of Liberec (Czech Republic). Results indicate that areas with the highest urgency for intervention were identified consistently across urban and peri-urban zones. However, proposed measures were more diverse and spatially differentiated in peri-urban and rural areas, whereas a single dominant measure prevailed in urban areas, suggesting higher practical applicability outside densely built environments. The approach supports evidence-based spatial planning and contributes to the implementation of the EU Adaptation Strategy by promoting resilient green infrastructure in urban and peri-urban landscapes. Full article