Search Results (83,862)

Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography. Full article

Energy poverty, as an emerging form of poverty, is key to consolidating the achievements of poverty alleviation and is also an important cornerstone for promoting energy transformation, social equity, and people’s well-being. Based on data from the China Family Panel Studies (CFPS) for 2018 to 2022, we use the head of household’s subjective happiness to proxy for family happiness. Using a two-way fixed-effects model, we analyze the impact of energy poverty on family happiness and its mechanism from the theoretical and empirical aspects. The conclusions are as follows: (1) Energy poverty has a significant negative impact on family happiness, and the estimated results of instrumental variables after solving endogeneity are consistent. (2) Heterogeneity analysis finds that for families with relatively disadvantaged economic conditions, such as non-relatively poor families, urban families, and families with loans, energy poverty significantly reduces their happiness, which contradicts our conventional understanding. (3) Mechanism analysis shows that energy poverty affects income gaps, health status, and economic status, which in turn affect family happiness. The respective percentages coming from the mechanisms of income gap, health status, and economic status are 43.31%, 26.11%, and 9.55%. We directly link energy sustainability, a core area of sustainable development, with residents’ well-being. It fills the systematic research gap on how energy poverty affects household happiness and deepens our understanding of its underlying transmission mechanism. Furthermore, it enriches research on the implementation pathways of energy policy and common prosperity, broadens the boundaries of research in energy economy and social welfare, and provides important practical implications for advancing energy inclusion and rural revitalization within the sustainable development framework. Full article

Cotton, as a globally significant economic crop, is intricately regulated in its growth and development by the key genes for SA (Salicylic acid) biosynthesis. In the present study, a systematic analysis of genes related to SA biosynthesis was conducted across four cotton species, leading to the identification of 70 genes. Specifically, the tetraploid species Gossypium hirsutum and G. barbadense were found to harbor 22 and 23 genes, respectively, representing a substantial expansion compared to the 12 and 13 genes identified in the diploid progenitors G. arboreum and G. raimondii. Comprehensive characterization of chromosomal localization, phylogeny, domain architecture, and promoter cis-elements revealed a uniform distribution of key genes involved in SA biosynthesis across A/D sub-genomes of tetraploids with extensive interspecific collinearity; whole-genome and segmental duplication act as the dominant drivers for the expansion of this gene family, while partial gene loss following polyploidization results in non-doubled gene copy numbers in tetraploids relative to diploids, which reflects the evolutionary selection for genomic dosage balance. The key genes for SA biosynthesis demonstrate a high degree of conservation in protein sequences, protein structures, and conserved motifs, which constitute the structural basis for the stable maintenance of their core functions in the SA biosynthesis pathway during plant evolution. This is closely related to their core function in the salicylic acid (SA) synthesis pathway and serves as the structural basis for the stable maintenance of gene functions during evolution. Analysis of cis-elements revealed that the expression of key genes involved in SA biosynthesis is governed by a complex interplay of phytohormones, stress signals, and transcription factors. Yeast one-hybrid (Y1H) assays confirmed the interaction between the GhPAL and GhICS gene and predicted candidate transcription factors, specifically the binding of GhWRKY21 to GhICS2-1 promoter and GhMYB12 to GhPAL1-2 promoter, thus elucidating their stage-specific regulatory mechanisms in cotton fiber development and reflecting their evolution. This study provides a fundamental basis for investigating the role of the SA signaling pathway in cotton development and offers support for cotton molecular breeding. Full article

Continuous urban expansion and the resulting land use and land cover (LULC) changes significantly exacerbate the urban heat island effect and intensify heatwaves. While the cooling effects of blue–green spaces are widely documented, most studies focus on single landscape types or specific time frames. Few investigations systematically explore the comprehensive thermal regulation mechanisms of gray–green spaces, or their nonlinear driving factors and interactive effects across coupled seasonal and diurnal scales. To address these gaps, this study focuses on Chengdu, a typical expanding city in China, to establish a comprehensive indicator system for urban gray–green spaces. This system encompasses four key dimensions: coverage, fragmentation, aggregation, and morphological spatial pattern. After evaluating 12 machine learning models, the optimal model was selected for further analysis using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP). This research investigates the nonlinear thresholds and interactive effects of composite gray–green space indicators on land surface temperature (LST) across varying seasonal and diurnal cycles. The results indicate that: (1) The impact of gray–green spaces on LST varies significantly across seasonal and diurnal contexts. Green spaces primarily exert a cooling effect during spring, summer, and autumn, whereas gray spaces dominate heat retention during winter and across all nocturnal periods. (2) The morphological spatial pattern of green spaces (GMSPA) outperforms traditional coverage indicators (G1) in providing cooling benefits across multiple scenarios. (3) The cooling efficiency of GMSPA peaks between −0.8 and −0.5, reaching saturation at 0.53. Conversely, LST exhibits a sharp, step-like increase when gray space aggregation (B3) exceeds −0.58. (4) Optimizing areas with high GMSPA can significantly mitigate heat exposure risks in expanding cities. These findings offer robust theoretical insights and actionable guidelines for spatial planning aimed at thermal resilience, urban thermal environment management, and building energy conservation in rapidly growing urban areas. Full article

Urban innovation spaces are crucial to stimulate innovative thinking and facilitate the integration of science, technology, and humanities. On the one hand, existing research on urban innovation spaces focuses on spatial patterns, associated networks, and spillover effects. They are limited to the macro scale and lack of innovation subject perspective. On the other hand, few studies have explored factors influencing the distribution by examining the needs of innovative talent. This study aimed to identify the evolution mechanism of urban innovation spaces. In total, 36,519 high-tech enterprises in Guangzhou from 2008 to 2023 were selected to represent urban innovation spaces. Spatial analysis methods and statistical methods were employed to investigate the spatiotemporal dynamic characteristics. Furthermore, employing multiscale geographically weighted regression, the study identifies multiple factors influencing the development of innovation spaces from the dual perspectives of the innovation environment and services. The results indicated that characterized by a southeast-northwest orientation, the urban innovation spaces of Guangzhou have displayed an apparent point–axis–face structural evolution, expanding from the central district into sparsely distributed in the suburbs. The factors influencing the distribution of urban innovation spaces, ranked by their degree of impact, were as follows: vehicle carrying, research institutions, public park, living convenience, university resources, business hotel, industrial structure height, and metro station. These findings facilitated the understanding of urban innovation space development and grasped the influencing factors and their functioning mechanisms. They provided references for innovation space planning amidst urban stock development. Full article

School dropouts remain a complex challenge for educational systems globally, with economic, social and psychological consequences for the individual and society at large. Evidence from the literature supports the high prevalence of school dropouts in rural communities, resulting in teenage pregnancy, exposure to drugs, and early marriage, among others. The study employed an exploratory approach to contribute to existing knowledge on the challenges of school disengagement through the lenses of community and peer-influence among high school students in rural South Africa. A qualitative research design employing semi-structured interviews was used, with a total of 20 interviews conducted (3 parents, 2 community leaders, 5 teachers, and 10 students, including dropouts). A thematic analysis procedure was employed for theme identification and analysis. There was evidence of a lack of community support in ensuring learners remain in school. Peer pressure was prevalent, given the influences and attachments students form with peers. This condition influences students to resort to drug abuse, teenage pregnancies, and early marriages as coping mechanisms for school dropouts. The overarching effect is a decline in academic comprehension, leading to school dropout rates. Parents and guardians play an active and collaborative role in discouraging practices that contribute to school dropout. Parent and community members must also be sensitised regarding the long-term negative effects of peer pressure and early marriage on education and future opportunities, especially for girls. Full article

Achieving resilient consensus in adversarial environments often requires extending the W-MSR algorithm to multi-hop communication. While the robustness guarantees of multi-hop W-MSR are now well understood, the problem of how to determine the minimal hop radius $h^{*}$ that ensures these guarantees has remained largely unaddressed. Existing work typically assumes a fixed h, leaving practitioners without a systematic way to balance robustness requirements against communication and computational cost. This paper introduces a new hop-selection framework that identifies the smallest communication horizon capable of satisfying the robustness assumptions underlying MW-MSR consensus. The framework combines exact robustness verification—when tractable—with a hierarchy of computationally efficient proxy tests based on local feasibility, normalized algebraic connectivity, and adversary-dilution criteria. These components provide a practical and scalable mechanism for establishing $h^{*}$ in both synchronous and bounded-delay asynchronous settings. Design-time and runtime procedures, complexity analysis, and validation on IEEE 14-, 30-, and 57-bus networks demonstrate that the proposed approach reliably detects resilience thresholds and substantially improves consensus behavior under stealthy and burst-type adversaries. The results show that systematic hop selection is essential for avoiding failure at small h while preventing unnecessary communication overhead at large h. The framework thus offers an implementable and deployment-oriented strategy for resilient distributed coordination in sparse and adversarial multi-agent networks. Full article

Antioxidant biopolymeric films (ABFs) have emerged as promising bio-based and biodegradable polymer materials for sustainable food packaging, combining environmental sustainability with functional performance. This study identifies convergent design principles governing ABFs through a systematic mapping of research published between 2015 and 2025, organized into thematic discussions covering global trends, material strategies, processing technologies, and structure–property relationships. The analysis reveals a clear transition from biodegradable substitution materials toward performance-driven polymer systems engineered to modulate mass transport phenomena. Polysaccharide- and protein-based matrices dominate current developments due to their chemical functionality and compatibility with natural bioactive compounds; however, their inherent hydrophilicity introduces trade-offs between barrier resistance and controlled release. Recent advances increasingly employ blends, composites, and multilayer architectures to decouple mechanical stability from antioxidant migration. Processing technologies, including casting, extrusion, and multilayer assembly, are shown to play a decisive role in defining diffusion pathways and release kinetics. The findings demonstrate that the effectiveness of ABFs depends primarily on polymer–bioactive interactions and structure–property relationships rather than additive concentration alone. Future progress toward industrial implementation requires scalable fabrication strategies and predictive processing–structure–performance frameworks aligned with circular economy principles. This perspective positions ABFs as functional bio-based polymer systems capable of synchronizing antioxidant release with food oxidation kinetics, contributing to sustainable food packaging solutions. Full article

Accurate prediction of forest fires is crucial for enhancing regional fire prevention and control. Existing models frequently rely on static factors such as weather and terrain, while insufficiently taking into account the Fuel Moisture Content (FMC), a critical internal factor that directly determines fire behavior. Instead, proxies like the Normalized Difference Vegetation Index (NDVI) are commonly employed, which weakens the physical foundation of predictions. This study assesses the marginal contribution of integrating dynamic FMC into fire prediction models. Concentrating on California, we developed a random-forest-based model that incorporates high-resolution FMC products retrieved by our team, along with meteorological, topographic, vegetation, and anthropogenic data. Through comparative experiments and SHapley Additive exPlanations (SHAP) analysis, we evaluated model improvements and the contribution mechanisms of key drivers. The results indicated that: (1) Incorporating FMC significantly enhanced model performance, with precision and specificity increasing by 3.93% and 3.60%, respectively, and the Area Under the Curve (AUC) showing improvements, suggesting heightened sensitivity in detecting actual fire occurrences. (2) SHAP analysis disclosed nonlinear effects and threshold dynamics: temperature was the dominant positive driver (the fire risk soared above 20 °C); FMC demonstrated a negative correlation with fire risk, with 100% serving as a potential threshold; elevation presented an inverted U-shaped pattern (the peak risk occurred at 1000–1500 m); and population density exhibited a shifting influence from positive to negative. (3) The monthly risk maps for California in 2023 captured the seasonal progression of fire risk and spatial patterns consistent with historical fire points. The fire risk map for 9 September 2020 also demonstrated consistency with the spatial distribution of the actual fire points on that day. This study validates that the integration of dynamic FMC strengthens the mechanistic foundation and early-warning capacity of fire prediction models, providing scientific backing for targeted fire management. Full article

Inter-basin water transfer projects (IBWTPs) play a pivotal role in alleviating the spatiotemporal imbalances of water resources. However, their operation is exposed to multiple, highly interdependent safety risks that can significantly undermine system stability and water supply reliability. Existing studies predominantly focus on isolated risk factors or rely heavily on subjective data, which limits their ability to capture the complex interrelationships among risks and reveal their underlying propagation mechanisms. To address these limitations, this study proposes a novel risk correlation analysis framework that integrates Interpretive Structural Modeling (ISM) with copula functions. ISM is first employed as a preprocessing tool to structure expert knowledge and develop an initial risk correlation framework. It is then used to hierarchically organize the complex interrelationships among risks. Subsequently, copula functions are utilized to model nonlinear dependencies and tail behaviors among risk variables. This enables a quantitative assessment of correlation strengths and facilitates the construction of a risk topological network. An empirical case study is conducted based on the Middle Route of the South-to-North Water Diversion Project. The results reveal 13 significant correlations among six second-level risk categories. Natural risks (e.g., floods and geological hazards) are identified as the primary driving factors. They exhibit a strong positive correlation (0.6155) with engineering risks and serve as the most critical nodes for proactive risk prevention and control. Engineering risks function as central intermediary hubs in the risk transmission process, whereas water quality and economic risks are characterized as terminal endpoints. Furthermore, three principal risk propagation pathways are identified: (1) natural risks → engineering risks → economic risks; (2) natural risks → operational scheduling risks → social risks; and (3) engineering risks → water quality risks → economic risks. The resulting risk topological network demonstrates significant small-world properties, indicating highly efficient risk transmission within the system. Ultimately, this study provides a robust quantitative approach for analyzing risk interactions in complex engineering systems and enriches the theoretical framework of engineering risk management. It also identifies critical nodes and key transmission pathways for risk prevention and control in IBWTPs, thereby offering significant practical implications for operational safety. Full article

The increasing demand for high-purity lead sulfide (PbS) for optoelectronic applications necessitates efficient methods to remove residual antimony sulfide (Sb₂S₃) from complex ores—a challenge due to their chemical similarity and fine intergrowth. This study presents a hybrid purification strategy combining vacuum distillation pretreatment with oxygen-free alkaline selective leaching. Thermodynamic analysis using Eh-pH diagrams revealed significant differences in the behavior of trace Sb₂S₃ and bulk PbS under alkaline conditions (pH 9–11), identifying a suitable window for selective dissolution. The process begins with mechanical ball milling to break Sb₂S₃ inclusions and improve reaction kinetics, followed by anaerobic leaching in a sealed reactor under inert atmosphere using a NaOH solution at a controlled potential (Eh 0.1–0.35 V vs. SHE). Multiple characterization techniques confirmed that Sb₂S₃ undergoes dissolution and conversion while the PbS phase remains intact. Notably, zeta potential measurements (−12.3 mV) and high conductivity (204 mS/cm) indicated the formation of a stable colloidal dispersion system favorable for interfacial reactions. Under optimal conditions, antimony removal exceeded 99% with lead loss below 1%. Overall, the proposed strategy offers a technically viable route to produce ≥99.9% pure PbS from polymetallic sources, addressing a longstanding separation challenge. Full article

The management of plastic packaging waste needs to be optimized to improve recycling rates. In this article, fourteen categories of non-bottle polyethylene terephthalate (PET) packages were mechanically recycled at laboratory bench scale; the generated data were assessed using a multi-criteria decision analysis (MCDA) approach to identify the categories most suited for the mechanical recycling process from social, technical and legislative viewpoints. Recycling yields varied between 75% and 92% across the 14 categories. The intrinsic viscosity (IV) values of the produced recycled PET (rPET) corresponded to molecular weights ranging from 28,000 to 35,000 g/mol. The MCDA recyclability assessment showed that 7 of the 14 categories (accounting for 72% of the sorted products by mass flow) are often composed of multiple, inseparable materials, resulting in the lowest-quality rPET. Furthermore, only 4 categories (approximately 28% of the categories) were found suitable for closed-loop mechanical recycling. The stakeholders involved in the PET packaging value chain could use these results to support decision-making and the development of a well-organized framework to valorize even the most complex types of plastic waste. Full article

Tree architecture is a critical determinant of plant performance, light capture, biomechanical stability, and resource allocation. However, the multidimensional functional trait space and multiscale allometric scaling mechanisms underlying different architectural types in Malus remain poorly understood. This study investigates the multidimensional functional trait space and multiscale allometric scaling relationships among three typical architectural types (weeping, upright, and spreading) in Malus. A total of 206 germplasm accessions were analyzed by integrating nine core functional traits spanning macro-architectural, branch biomechanical, and leaf economic dimensions. Principal component analysis revealed that architectural differentiation is primarily driven by macro-architectural and branch biomechanical traits, alongside coordinated contributions from leaf economic traits. Functional diversity analysis indicated that the upright and spreading types exhibited higher functional richness, while the weeping type displayed the highest functional divergence but minimal or no functional overlap with the upright and spreading type, reflecting strong niche specialization under artificial selection. Multiscale allometric analyses demonstrated significant divergence in resource allocation strategies across hierarchical levels. At the whole-tree level, architectural types differed markedly in height–diameter and height–crown scaling relationships. At the branch level, conserved positive allometric scaling was observed, with the weeping type showing higher intercepts indicative of increased mechanical investment. At the leaf level, consistent negative allometry between petiole length and leaf area suggested optimized resource allocation for light capture. These pronounced differences suggest distinct ecological adaptation strategies: the weeping type prioritizes biomechanical compensation for pendulous branches and optimized light capture in loose canopies; the upright type emphasizes vertical light competition and mechanical compactness; the spreading type balances lateral expansion and spatial filling efficiency, reflecting differentiated resource allocation patterns shaped by artificial selection. Overall, this study reveals that tree architecture in Malus is shaped by coordinated trait interactions across multiple scales, leading to distinct ecological strategies and resource allocation patterns. These findings provide new insights into the structure–function co-evolution of woody plants and offer a theoretical framework for functional trait-assisted breeding of ornamental tree architectures. Full article

Low-temperature stress severely restricts the engineering application of anaerobic ammonia oxidation (anammox) technology in municipal mainstream wastewater treatment, leading to its slower large-scale implementation relative to industrial wastewater and reject water treatments. The inhibitory effects of low temperatures on the anammox process cannot be merely ascribed to conventional microbial metabolic responses. Elucidating the specific mechanisms underlying low-temperature impacts on anammox bacteria is therefore critical for formulating targeted mitigation strategies. In this study, a meta-analysis was performed to compare the response patterns of specific anammox activity (SAA) and nitrogen removal rate (NRR) to temperature variations. SAA declines gradually with decreasing temperature, while NRR displays a more dramatic and stepwise reduction. The T₅₀ values (temperature corresponding to 50% of the performance at 30 °C) for these two parameters are 20 °C and 15 °C, respectively. Low-temperature inhibition of anammox is a multifaceted process, encompassing direct physiological disturbances to individual anammox cells and impaired nitrite bioavailability within the microbial community. To address these temperature-related bottlenecks, a conceptual hybrid nitrogen removal system was rationally optimized by integrating conventional strategies with an innovative split-flow influent regulation strategy. This hybrid system is anticipated to enhance the stability and treatment efficiency of anammox under low-temperature conditions, thus facilitating its broader engineering application in cold climate regions. Full article

Background: Triple−negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. Methods: Here, we present an AI−integrated benchmarking framework combining virtual drug discovery, molecular dynamics simulations, machine learning−driven QSAR modeling, and quantum−mechanical electronic structure analysis to identify potent CK2 inhibitors from natural product chemical space. Results: A validated XP docking protocol (ROC–AUC = 0.748) screened ~480,000 compounds, yielding seven hits, with superior binding to the reference inhibitor CX−4945. Among these, Anastatin B, 3,4,8,9,10−pentahydroxy−dibenzo−[b,d]pyran−6−one, Rhein, and aloe emodin acetate exhibited highly favorable docking scores (−11.6 to −13.1 kcal mol⁻¹) and stable 200 ns binding dynamics, reflected by RMSD ≤ 2 Å and compact Rg trajectories. MM−PBSA/MM−GBSA analyses confirmed robust thermodynamic stability, while DFT−derived HOMO–LUMO gaps (3.8–4.3 eV) suggested optimal electronic reactivity for kinase inhibition. Machine learning QSAR models demonstrated strong predictive performance, with the best stacking models achieving test R² ≈ 0.69 and consistent cross−validation performance (CV R² ≈ 0.66–0.69), supporting reliable prediction of pIC₅₀ values and prioritization of top−ranked scaffolds. Conclusions: Collectively, this integrative framework bridges AI−based learning and biophysical validation, establishing a reproducible paradigm for de novo CK2 inhibitor discovery in TNBC. Full article