Search Results (952)

Non-destructive diagnostic approaches play a crucial role in cultural heritage studies by enabling the assessment of artworks’ conservation conditions without causing damage. This work presents the case study of the tempera painting Cristo in Trono, by Domenico Morelli and Paolo Vetri, located in Amalfi (SA, Italy). A multi-analytical approach combining portable and non-invasive techniques was employed. Hygrometric tomography revealed anomalous surface moisture values (up to 23 units) along the edges and wooden joints, identifying areas requiring particular attention during conservation. Infrared reflectography (IR-R) detected preparatory drawings, compositional changes, and evidence of previous restoration interventions. X-ray fluorescence spectroscopy (XRF) identified the elemental composition of the pigments, indicating the use of cinnabar (or vermilion), ultramarine (or lapis lazuli), ochres, lead white (Biacca), and brass (Orone) for the golden background. Multivariate statistical analysis of the XRF data (PCA and HCA) revealed compositional heterogeneity and chemically distinct areas within apparently similar chromatic regions. Micro-invasive analyses complemented these results by providing information on the stratigraphy and material composition. Fourier-transform infrared (FT-IR) spectroscopy identified polyvinyl acetate (PVA), attributed to previous restoration treatments and associated with the yellowing of lead white, together with protein-based binders. Microscopic observations revealed the woven structure of the support, pigment-layer cracking, and degradation features, while the interaction of the brass with atmospheric agents and marine chlorides was found to be responsible for the alteration of the original golden background to a greenish-gold hue. The lower part of the canvas exhibited the most severe deterioration, resulting from higher moisture levels, unsuitable past restoration treatments, and prolonged exposure to humidity, atmospheric pollutants, and marine aerosols, highlighting the importance of integrated diagnostic investigations for planning effective conservation and restoration strategies. Full article

Regional sea-level trends derived from satellite altimetry deviate substantially from the global mean, but the relative roles of externally forced change and internally generated climate variability remain difficult to separate from the short satellite record. Here, we examine the 32-year Data Unification and Altimeter Combination System (DUACS) gridded multi-mission satellite altimetry product (January 1993–December 2024) together with 100 100-year samples from an unforced Community Earth System Model (CESM) pre-industrial control simulation. Empirical orthogonal function (EOF) analysis of satellite sea-level anomalies reveals a leading mode explaining 10.9% of total variance, with an Interdecadal Pacific Oscillation (IPO)-like dipolar pattern and high correlation with the IPO index (r = 0.92). A similar IPO-like mode appears consistently in the unforced CESM samples. Because previous large-ensemble studies indicate that the externally forced sea-level response is generally broader and structurally distinct from this dipolar internal mode, this agreement supports the interpretation that the satellite-observed leading pattern is strongly consistent with internally generated variability, although a partial forced contribution, particularly in the tropical Pacific, cannot be excluded. Based on CESM simulations, the empirical contribution of internal variability to regional trend uncertainty decreases approximately inversely with record length. The resulting location-specific estimate can be scaled by the local EOF amplitude and is largest in regions where the dominant internal-variability mode has large amplitudes, including the western tropical Pacific and Indian Ocean. However, this estimate represents only the internally generated component inferred from a single unforced CESM simulation. It does not include DUACS mapping errors, inter-mission calibration uncertainty, geophysical correction uncertainty, glacial-isostatic-adjustment-related bias, or uncertainty in the forced sea-level response. Thus, this study provides a model-based framework for estimating the internal-variability contribution to regional sea-level trend uncertainty, rather than a formal detection-and-attribution separation or a complete uncertainty bound for satellite-altimeter-derived regional sea-level trends. Full article

Identifying homogeneous flash flood regions through regionalization is essential for effective mitigation and prevention. However, most existing regionalization methods focus primarily on attribute similarity (e.g., meteorological and underlying factors), while ignoring structural similarity that reflects topological network and flow relationships among catchments. In this study, we developed a new graph-clustering-neural-network-based flash flood regionalization (GFFR) method to address these limitations and improve the homogeneous region delineation. Catchments were first represented as a directed graph. Within GFFR, we then designed a graph convolutional autoencoder to learn latent representations that capture both catchment structure and attributes, while a decoder grouped the catchments into clusters. GFFR was applied in Jiangxi province, China, where it outperformed three typical clustering methods. Historical flash flood events were used to validate the GFFR map, presenting strong spatial consistency with dense event clusters and achieving a determinant power of 81%. Furthermore, the GFFR achieved a 24% higher determinant power than the average performance of the three compared methods. Overall, GFFR provides a valuable tool for flash flood regionalization, while the delineated regions offer critical guidance for governmental flash flood prevention and mitigation strategies. Full article

Background: Same-day or post-admission cancellation of elective surgery is associated with inefficient operating room utilization, patient distress, and disruption of coordinated perioperative care. Functional neurosurgery is particularly vulnerable to late-stage cancellation because procedural readiness depends on parallel clinical, anatomical, medication-related, device-related, and systemic considerations. Objective: This paper aims to describe the implementation of a structured preoperative review framework for functional neurosurgery and to evaluate its association with late-stage surgical cancellation after patient admission. Methods: We performed a retrospective single-center before-and-after study of consecutive functional neurosurgical procedures scheduled at Sheba Medical Center between January 2020 and December 2025. The ABCDE framework was introduced in 2023 as a structured workflow model organized around five domains: Anatomy, Bacteria, Clotting, Devices, and Elsewhere. Included procedures were deep brain stimulation, vagus nerve stimulation, focused ultrasound, intrathecal pump implantation or replacement, implantable pulse generator replacement, spinal cord stimulation, peripheral nerve stimulation, and other palliative neuromodulation procedures. The primary endpoint was late-stage cancellation, defined as cancellation after hospital admission and initiation of operative preparation. The primary comparison was between 2020–2022 and 2023–2025. A sensitivity analysis excluded 2020 because of pandemic-era disruption. Results: A total of 867 procedures were scheduled. Late-stage cancellation occurred in 16 of 407 pre-implementation procedures and 5 of 460 post-implementation procedures, corresponding to rates of 3.9% and 1.1%, respectively. The absolute risk difference was −2.8 percentage points (95% CI, −5.2 to −0.8), and the relative risk for cancellation after implementation was 0.28 (95% CI, 0.10 to 0.75). A two-sided Fisher’s exact test comparing cancellation versus non-cancellation across the pre-implementation and post-implementation periods was statistically significant (16/407 vs. 5/460; p = 0.0074). After excluding 2020, the direction of effect remained similar, but the difference was not statistically significant: 8 of 302 procedures in 2021–2022 were canceled compared with 5 of 460 in 2023–2025 (2.6% vs. 1.1%; p = 0.15). Detailed source-level attribution was incomplete for 10 of 21 cancellation events, and domain-level analyses should therefore be interpreted descriptively. Conclusions: Implementation of the ABCDE framework was associated with fewer late-stage surgical cancellations in this single-center before-and-after study. Because the design is observational, the number of events is small, and the study period overlaps with pandemic-era disruption and post-pandemic service stabilization, the findings should be interpreted as hypothesis-generating rather than causal. The framework may provide a practical structure for standardizing preoperative review in functional neurosurgery, but prospective studies with formal adherence tracking and procedure-specific analyses are needed. Full article

The middle reaches of the West Liaohe River Basin, a typical semi-arid to semi-humid transition and agro-pastoral ecotone in northern China, exhibit high ecological sensitivity, low resilience, and pronounced fragility. Despite growing concerns, existing studies in this region lack a comprehensive assessment paradigm that effectively couples inherent ecological attributes with nonlinear predictive modeling. To fill this gap, we developed an integrative framework that innovatively combined the SRP conceptual model with a stacking ensemble learning technique. This coupling is methodologically novel because it moves beyond linear assumptions, enables the detection of complex nonlinear response surfaces, and establishes a seamless analytical chain from historical evaluation to future projection. By selecting 13 indicators, including topography, climate, soil, vegetation, and socio-economic factors, the weight was determined by the comprehensive application of the analytic hierarchy process and entropy weight method, and the ecological fragility of the middle reaches of the West Liaohe River Basin from 2000 to 2020 was evaluated at multiple scales. The spatial differentiation driving factors were analyzed using a geographic detector. Therefore, an Ensemble Learning Regression model was used to simulate and predict the ecological fragility pattern in 2030. The results show that from 2000 to 2020, the ecological fragility of the study area showed a decreasing trend overall, with the Ecological Vulnerability Synthetical Index (EVSI) decreasing from 3.48 to 2.68, and the spatial pattern gradually shifting from “high in the northwest, low in the southeast” to “overall stability, local optimization.” The spatial agglomeration of ecological fragility gradually weakened, indicating that high-fragility areas tend to disperse and low-fragility areas expand in contiguous areas, and the ecosystem structure tends to develop towards equilibrium. The driving mechanism shows an evolution characteristic from “soil erosion dominated” to “biological abundance dominated,” with the impact of climate factors first increasing and then stabilizing, and the direct pressure from human activities continuously weakening. Under the assumption that historical trends continue, the ensemble learning model projects that by 2030, the ecological vulnerability pattern will be dominated by Mild and Moderate levels, with the area of extremely vulnerable regions significantly reduced to 0.36%. This study verified the applicability of the SRP model in transitional river basins, and the constructed “evaluation-driving mechanism-prediction” framework can provide a scientific basis for the ecological protection and adaptive management of the West Liaohe River Basin and provide a methodological reference for ecological fragility research in similar areas. However, limitations persist: the indicator system and weight assignment are subject to inherent subjectivity, and the 2030 scenario projection based on the Stacking ensemble learning model relies on the BAU (Business-As-Usual) assumption, which fails to account for abrupt climate extremes or major policy shifts. Future studies should incorporate multi-scenario constraints to reduce predictive uncertainty. Full article

Existing image–text retrieval methods often compute cross-modal similarity using global single-vector representations. Although efficient for coarse semantic alignment, such compressed representations are limited when textual queries involve fine-grained semantics, including objects, attributes, relations, and their compositional structures. This paper focuses on fine-grained text-to-image retrieval and proposes Q-GrAM, a retrieval-oriented adaptation of the BLIP-2 Q-Former. Instead of treating Q-Former queries as a homogeneous set, Q-GrAM partitions a fixed query budget into semantically differentiated groups. A text-guided router assigns token-level semantic demands to query groups, while query conditional initialization modulates each group according to group-level textual summaries. The resulting grouped visual query features are matched with text tokens through a group-aware late interaction scorer, and auxiliary routing balance and inter-group diversity regularization are introduced to stabilize semantic specialization. Experiments on MS-COCO 5K, Flickr30K, and Flickr30K-CFQ show that Q-GrAM achieves strong text-to-image retrieval performance against both global embedding baselines and representative fine-grained image–text matching methods, while maintaining competitive bidirectional retrieval performance. These results demonstrate the effectiveness of structured, text-conditioned Q-Former query specialization for fine-grained text-driven image search. Full article

Designing sustainable closed-loop supply chain (CLSC) networks requires jointly assessing node-level operational attributes (recovery efficiency, processing capacity, unit cost) and inter-node spatial structure. Existing methods, including mixed-integer programming, multi-objective metaheuristics, and graph-matching, typically optimize a single cost dimension and do not decompose structural connectivity from attribute-level inefficiency. We propose a Fused Gromov–Wasserstein (FGW) diagnostic framework that combines the Wasserstein distance (attribute similarity) and the Gromov–Wasserstein distance (structural alignment) via a convex trade-off parameter α, solved using the conditional gradient algorithm. Supply–capacity imbalances are resolved by marginal rescaling, with residual unabsorbed mass reported as a diagnostic indicator of infrastructure shortfall. The framework is applied to an eight-echelon PET bottle recovery and filament manufacturing network across 24 synthetic benchmark instances at three scale classes. The FGW cost decomposes exactly into feature and structural components, allowing bottleneck arcs to be diagnosed as attribute-driven or structure-driven. Under this benchmark, bottleneck cost decreases with network size, the most frequent bottleneck arc shifts from the collection interface in small networks to the mid-chain processing handoff in large networks, and attribute heterogeneity accounts for the majority of FGW cost (57.9%, conditional on the normalization and weighting scheme used) across all 144 arc–instance combinations. These results position FGW as a tractable, interpretable diagnostic layer for circular supply chain analysis, complementing rather than replacing classical CLSC design models. Full article

The evaluation of optimization algorithms for electric vehicle routing problems depends strongly on the quality of the benchmark instances used during experimentation. However, many synthetic instances simplify the joint effects of geometry, topography, operation, and energy, which can distort algorithmic assessment. This article proposes a modular and reproducible pipeline for generating synthetic instances of the Electric Vehicle Steiner Traveling Salesman Problem (EV-STSP), calibrated from a real urban reference network based on publicly available Madrid data. The pipeline combines directed graph construction, geometric control, attribute enrichment, charging-infrastructure placement, structured export, and explicit traceability mechanisms. To assess the realism of the generated instances, a three-level validation protocol is introduced, covering marginal distributional similarity, physical coherence among dependent variables, and structural–operational consistency. A controlled ablation design is then used to quantify the contribution of individual modules to overall benchmark realism. Within the experimental domain explored here, the results show that benchmark realism is not supported uniformly by all modules; instead, it depends primarily on arc-length generation, topographic alignment, and energy modeling. The proposed framework therefore offers not only a reproducible way to generate EV-STSP benchmarks, but also an explicit methodology for verifying whether such benchmarks are suitable for comparative algorithmic experimentation. Full article

Targeting the joint challenges posed by sparse graph topology, limited semantic expressiveness, and scarce annotation resources that commonly afflict knowledge graphs in the oil and gas pipeline safety domain, this paper presents a Multi-Information Fusion Unsupervised Entity Alignment model (MIF-UEA). The proposed method constructs high-quality initial alignment pairs by integrating multi-source similarity computation with a structure-aware seed generation mechanism and performs representation learning by fusing structural features and semantic attribute information. Furthermore, a pseudo-label augmentation and denoising strategy is introduced to enhance the effectiveness of self-training. Finally, entity matching is achieved through an optimal transport model. Experimental results confirm that MIF-UEA surpasses existing baselines across both the specialized oil and gas pipeline safety dataset and multiple general-domain benchmarks, demonstrating its effectiveness and generalization capability. Full article

Attributed graph clustering partitions nodes in an unsupervised manner by leveraging graph topology and node attributes. Existing deep methods face challenges including local structural bias, high noise in unsupervised graph editing, and insufficient discriminative ability for hard samples. To address these issues, we propose a deep graph clustering framework based on confidence-guided graph enhancement and dual-negative sample contrastive learning (CGEN). CGEN constructs a local–global dual-view representation learning module to fuse local neighborhood attributes with high-order global topological information. It then utilizes a confidence-guided conservative graph editing mechanism that integrates multiple constraints, specifically feature similarity, intra-cluster consistency, multi-view consistency, and pairwise node confidence, using a progressive update strategy for stable structural optimization. Furthermore, a dual-negative sample contrastive learning strategy dynamically adjusts the weights of attribute-confused and inter-cluster-confused negative samples to enhance discriminative ability near adjacent cluster boundaries. Extensive experiments on four benchmark datasets demonstrate that CGEN achieves highly competitive performance, outperforming the majority of state-of-the-art methods across core clustering metrics, thereby validating its effectiveness in addressing local structural bias, graph editing noise, and hard sample discriminative limitations. Full article

This exploratory study examines the heterogeneous spatial evolution of cities in a fragmented non-metropolitan region of Southern Brazil and develops an original functional-symbolic typological framework that integrates functional performance and symbolic production in the classification of cities. Grounded in the theoretical contributions of Lefebvre, Santos, and Corrêa, the framework was designed by the authors to simultaneously incorporate economic, territorial, cultural, and identity-related dimensions that are typically analysed separately in conventional urban typologies. The research adopts a qualitative and inductive approach to analyse secondary data from municipalities in the state of Santa Catarina. Rather than treating urbanisation as a homogeneous process, the study conceptualises urban typologies as analytical devices capable of revealing differentiated urban trajectories, uneven capacities of territorial articulation, and distinct modes of governance in non-metropolitan contexts. The findings show that cities with similar demographic scales perform diverse social, cultural, and economic roles shaped by historically and symbolically produced spatial relations. Five urban typologies were identified: Multifunctional Metropolises, Industrial Regional Capitals, Agroindustrial Cities, Cultural Tourist Cities, and Local Centres of Basic Function. The results demonstrate that urban centrality in non-metropolitan regions is not determined solely by economic performance or demographic scale, but also by symbolic attributes such as cultural heritage, territorial identities, festivals, and religious functions. By integrating material and symbolic dimensions within a single analytical structure, the proposed framework advances the understanding of fragmented urban systems, contributes to contemporary debates on non-metropolitan urbanisation and territorial governance, and offers a transferable approach for the analysis of urban diversity beyond the Brazilian context. The findings also provide practical implications for regional planning and public policy by highlighting the role of symbolic production in shaping territorial organisation and regional influence. Full article

Conservation-oriented renewal of traditional rural settlements increasingly requires evidence-based color control that considers both natural environmental backgrounds and built-cultural interfaces. This study examined whether built-cultural colors in a traditional mountain village are differentiated from natural environmental colors in hue composition while remaining proximate in NCS attribute space and explored how such quantitative findings can inform carrier-specific architectural color-control guidance. Taking Qingmuchuan Village in the Qinba Mountain region as a case study, 145 representative color samples were recorded, including 59 natural environmental samples and 86 built-cultural environmental samples. The samples were encoded using the Natural Color System (NCS) and their hue composition, blackness–whiteness–chroma attributes, nonparametric differences, exploratory structural order assessment, and attribute-space proximity were analyzed. Among the retained carrier-oriented samples, natural environmental samples were dominated by green-yellow hues (54.2%), whereas built-cultural environmental samples mainly contained yellow-red, red-blue, and neutral hues (31.4%, 18.6%, and 12.8%, respectively). Blackness did not differ significantly between the two systems, while whiteness and chroma differed significantly; the mean pairwise cosine similarity was 0.824, indicating attribute-space proximity rather than direct hue correspondence. Based on these empirical results, the study proposes provisional, carrier-specific guidance for facade renewal, roof and eave replacement, paving repair, and signage regulation. Full article

This study experimentally investigates the structural behavior of steel–concrete composite beams with circular web openings under monotonic loading to evaluate the effects of opening location and number on structural performance while maintaining feasibility for integrating mechanical, electrical, and plumbing (M.E.P.) systems. Six simply supported composite beam specimens were tested, including one reference beam without openings and five beams with 80 mm diameter circular web openings. The investigated variables were limited to the presence, number, and longitudinal location of the openings, while the beam dimensions (IPE160 section, 2.8 m clear span), material properties, reinforcement details, shear connector arrangement, and loading conditions were kept constant. The study addresses a specific research gap: Previous studies have primarily focused on the effects of opening number and size on ultimate load capacity, with limited systematic investigation of how opening location influences not only ultimate load but also stiffness and ductility. Openings were strategically placed in three critical zones: the shear zone (low stress region), the bending zone (high moment region at mid-span), and the region under load points. The experimental results demonstrated that opening location is more critical than opening number. Openings in the shear zone achieved the best performance with only 2.13% reduction in ultimate load capacity, making it the preferred location for service openings. Openings in the bending zone (mid-span) or under load points caused reductions ranging from 9.62% to 11.70%, attributed to interference with high bending stresses. Notably, the configuration with ten openings achieved a load reduction similar to the two-opening configurations when located in the shear zone, confirming the dominant role of location over opening number within the experimental program. These results support a location-driven design philosophy for composite beams with web openings. However, these findings are restricted to the present experimental configuration—specifically 80 mm circular openings, IPE160 steel section, 2.8 m clear span, and the tested loading condition—and should not be generalized to composite beams with different geometric parameters, material properties, or loading conditions without additional research. Full article

Partial discharge (PD) source-type classification is essential for condition-based maintenance of high-voltage apparatus. Existing approaches based on grid discretizations of phase-resolved partial discharge (PRPD) patterns suffer from performance degradation under stochastic interference and multi-source conditions. This paper proposes a graph-based framework that integrates the morphological characterization of raw high-frequency PD waveforms with the phase-amplitude position of individual discharge events to enable multi-label classification, identifying multiple PD sources coexisting within a single test. The framework operates through three stages: a multi-task neural network extracts per-pulse embeddings and confidence scores; a construction procedure establishes selective graph connectivity based on spatial proximity and morphological similarity; and an edge-conditioned graph neural network performs classification via message passing weighted by multimodal edge attributes. Experimental evaluation on PD measurements acquired in accordance with IEC 60270 shows that the proposed framework achieves a Matthews correlation coefficient (MCC) of 0.98 and an exact match ratio of 0.97 across single-source, noisy, and multi-source conditions, substantially outperforming histogram- and set-based baselines. The framework maintains an MCC of 0.97 in multi-source scenarios, where its advantage over existing methods is most pronounced. Cross-domain evaluation on an independent dataset acquired with different laboratory equipment confirms the approach’s robustness, achieving an MCC of 0.93 without retraining. Finally, an ablation study demonstrates that the joint removal of morphological similarity filtering and confidence-based node filtering and edge gating reduces the MCC by 0.25, confirming the critical role of the waveform-informed relational structure. Full article

Aquatic and semi-aquatic plant assemblages, water quality, riparian habitat, and landscape conditions were assessed for 140 ponds located in the Ozarks region in Arkansas and Missouri in order to better describe their occurrences and distributional patterns. Local environmental and landscape-level determinants that shape their diversity and influence their respective distributions, particularly in light of urbanization, were also assessed. Ozark ponds are highly variable in terms of physical structure, habitat quality, and plant diversity. Urban ponds were generally of lower quality in terms of environmental attributes compared to those in non-urban areas, but they had similar plant taxa richness as well as numbers of non-native species compared to their non-urban counterparts. Ponds had high plant diversity (N = 204 taxa, $\overline{\mathrm{x}}$ = 9.89, range = 0–33). Taxa richness increased with increasing pond size, and urban ponds had slightly more species on average compared to non-urban ponds (10.38 vs. 9.58, respectively). Spatial beta diversity of plants showed a high dissimilarity among ponds, with turnover being the dominant fraction. Beta diversity also followed a significant distance-decay model. These findings show that urban Ozark ponds serve as important habitats for a broad variety of aquatic plants. Full article