Search Results (3,186)

The formation and development of gullies are pervasive drivers of hillslope degradation, yet forecasting where and at what elevation gullies begin remains challenging. This study proposes a morphometric–energetic framework to anticipate gully-initiation zones in catchments developed on low-permeability lithologies and limited tectonic control across contrasting climatic and geomorphic settings. Using GIS analyses and morphometric parameters, with some derived from hypsometric curves, our objective is to link basin-scale morphology and energy distribution to the propensity for linear incision, thereby defining a statistically representative initiation belt and stream network positions most susceptible to gully initiation. The study results show that the altitudinal range most susceptible to gully development is at the mean basin’s elevation, and that this range can be associated with an energy potential (Şen’s “Energy Index”) similar to those used to calculate hydroelectric potential in a river basin. Furthermore, the study highlights that the contributing area required to activate these erosive processes varies within fairly narrow limits, between 1 and 3 ha. The framework is designed to be quantitative, transferable among landscapes, and parsimonious in data requirements, even if applicable, as mentioned, in basins with low-permeability lithology and limited tectonic control, and as a first-level predictive tool. By prioritizing diagnostics that can be computed from standard topographic datasets, the approach aims to support land-use planning and sediment-risk mitigation, offering a practical pathway for early identification and management of areas vulnerable to gullying. Full article

Nanozymes, nanomaterials that mimic enzymatic activity, offer superior stability, tunability, and lower production costs compared to natural enzymes. To date, most nanozyme-based point-of-care (PoC) diagnostic systems have relied on oxidation reactions, such as oxidation of 3,3′,5,5′-tetramethylbenzidine, which often suffer from limited substrate stability and high background signal. This study investigates reduction reactions, particularly those involving tetrazolium salts, as an alternative route for signal generation in PoC devices. For this purpose, monometallic and bimetallic gold, palladium, and platinum nanoparticles were synthesized via chemical reduction using poly(vinyl alcohol) as a stabilizing agent. The resulting nanoparticles were uniform in size and morphology. Their catalytic performance was confirmed through the reduction of 4-nitrophenol. The tetrazole salts were selected as promising substrates for application in PoC settings and further explored by examining the nanozyme-based reduction of 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT). The nanozymes catalyzed the reduction of MTT in the presence of sodium borohydride, producing a distinct colorimetric signal under selected conditions. The effects of reducing agent concentration, buffer pH, and potential interferents were evaluated, with performance suitable for PoC devices achieved at basic pH and low borohydride concentration. Interference studies showed negligible MTT reduction in the presence of physiological levels of ascorbic acid, human serum albumin, and 10% concentration of human serum. Finally, a proof-of-concept lateral flow assay demonstrated successful signal generation through nanozyme-catalyzed MTT reduction. Results establish tetrazolium salts as suitable substrates for nanozyme-enhanced PoC diagnostics and highlight reduction-based chromogenic systems as a viable alternative to traditional oxidation-based assays. Full article

Intelligent tunnel defect detection is essential for structural safety and efficient operation and maintenance. However, manual inspection is inefficient, subjective, and risky, while existing deep learning methods often show unstable performance under practical conditions involving small targets, large-scale variations, and severe background interference, limiting their accuracy and real-time deployment on edge devices. To address these issues, this paper proposes CAMD-RTDETR, an end-to-end real-time multi-defect detection method based on RT-DETR. Cross-attention feature mining is introduced to enable bidirectional interaction between shallow spatial details and deep semantic information, enhancing the perception of weak-texture defects such as fine cracks. Multi-scale contextual pooling is designed to aggregate features from different receptive fields and improve the unified representation of cracks, seepage, and spalling with diverse morphologies. In addition, decoding enhancement and query optimization are incorporated to improve query updating and localization discrimination, thereby enhancing detection stability and boundary accuracy in complex tunnel scenes. Experiments on a field-collected tunnel defect dataset show that CAMD-RTDETR achieves an average inference latency of 15.855 ms per image and a processing speed of 63.06 FPS under the batch-size-1 testing setting. Compared with the baseline RT-DETR, Precision, Recall, mAP50, and mAP50-95 are improved by 6.3%, 13.5%, 14.7%, and 15.8%, respectively. Comparisons with seven representative detectors further demonstrate its superior accuracy and real-time performance, demonstrating its preliminary feasibility for edge-side inference and its potential for future integration into vehicle-mounted tunnel inspection systems. Full article

This paper presents Geo-Esthetics, an application-oriented workflow that uses remote sensing imagery as source morphology for generative design. The study addresses a design problem: how can large-scale terrestrial textures be extracted, abstracted, and organized as pattern references for parametric and visual design? Nine representative geomorphological settings were selected. For each case, Sentinel-2 imagery was cropped into a 2 km × 2 km geographic window, enhanced using spectral-index selection and Contrast Limited Adaptive Histogram Equalization (CLAHE), and used as an image prompt in Midjourney v6.0. A consistent prompt structure and parameter setting were applied. Four variants were generated for each case and screened according to topological fidelity, level of abstraction, and design applicability. Box-counting dimension and lacunarity were calculated to compare morphological complexity between source images and generated patterns. The cases show that hydrological, tectonic, desert, agricultural, and reef morphologies can be translated into design-oriented pattern prototypes for paving, façades, interfaces, acoustic elements, and biomimetic surfaces. The contribution of this work lies mainly in design methodology: it provides a documented workflow for connecting Earth observation data, generative AI, and design ideation, while retaining clear boundaries around model reproducibility, prompt sensitivity, case representativeness, and perceptual evaluation. Full article

Multilithologic interbedded reservoirs commonly consist of frequent alternations of fine-grained rocks, carbonate rocks, and soluble evaporite interlayers. The contrasts in mechanical properties and fluid–rock interactions tend to induce hydraulic-fracture deflection, height containment, and complex cross-interface propagation. To elucidate fracture initiation and cross-layer connectivity, a self-developed true-triaxial hydraulic fracturing simulation system was used to systematically investigate the effects of lithologic configuration, fracturing-fluid viscosity, injection rate, interface position, and injection-fluid type on fracture morphology and cross-interface behavior. Integrated analyses were performed by jointly interpreting injection-pressure responses and three-dimensional fracture reconstructions. The interactions between hydraulic fractures and lithologic interfaces/natural fractures can be categorized into three modes: (i) deflection with restricted growth, (ii) penetration without activation, and (iii) penetration with synchronous activation. Under water-based fluids, soluble evaporite interlayers predominantly develop dissolution-induced conductive pathways, which reduce stress concentration at the fracture tip and weaken interface strength, thereby promoting activation of interfaces or natural fractures. Moderately increasing viscosity and injection rate enhances cross-layer connectivity while lowering the probability of passive activation of interfaces/natural fractures; however, excessively high injection rates may induce fluid diversion and increase the likelihood of complex fracture growth. The injection-fluid type exerts a pronounced control on breakdown pressure and connectivity patterns: supercritical CO₂ yields the lowest initiation pressure, water-based fluids the highest, and alcohol-based fluids an intermediate response. In the pressure curves, attenuation of propagation pressure corresponds to enhanced cross-layer penetration, whereas a sustained pressure increase indicates dominant diversion or restricted propagation. These findings provide experimental support for parameter optimization and fracture-control design in multilithologic interbedded reservoirs in Southwest China and analogous geological settings. Full article

Non-invasive methods used for PH detection in clinical practice have several limitations. The combination of high spatiotemporal sensitivity magnetocardiography (MCG) and artificial intelligence algorithms may offer an accurate approach for PH detection. In this study, we develop a convolutional neural network (CNN) model based on the 64-channel MCG time-series data. This exploratory study enrolled patients undergoing 64-channel MCG, including right-heart-catheterization confirmed PH patients and symptomatic controls with low echocardiographic probability of PH. After data preprocessing, a temporal CNN integrating MCG signals with age, sex, and body mass index was developed and compared with conventional machine learning models. The CNN model achieved strong discrimination, with area under the curve (AUC) values of 0.939 (95% confidence interval [CI]: 0.913–0.961) in the development out-of-fold evaluation and 0.974 (95% CI: 0.944–0.994) in the hold-out test set, outperforming conventional machine learning models. Decision curve analysis showed the greatest net benefit at clinically relevant thresholds. Attribution analysis indicated that spatial QRS morphology redistribution contributed substantially to PH classification. The temporal CNN model based on raw 64-channel MCG signals showed promising performance for non-invasive PH detection and outperformed conventional machine learning approaches in this exploratory single-center cohort enriched for PAH and CTEPH. Full article

Traumatic fractures are common in small animal emergency care, yet subtle fracture lines may be difficult to identify accurately using routine three-dimensional reconstruction workflows, particularly when access to specialized software is limited. This study describes the use of the open-source platform Three-Dimensional Slicer for computed tomography-based reconstruction and three-dimensional printing in a small dog with cranial trauma, with emphasis on documenting a practical and reproducible workflow through voxel resampling. Imaging data were imported into the software, bone structures were segmented using a rapid workflow, voxel spacing was resampled for smoother surface visualization by volume resampling, and the reconstructed model was processed for physical printing. Digital models of different resolutions were generated within minutes, and a life-size skull model was successfully fabricated using fused deposition modeling in less than three hours at a material cost of under one United States dollar. The enhanced model provided an intuitive representation of fracture morphology and spatial relationships compared with routine reconstruction alone. These findings demonstrate that open-source software combined with low-cost printing can provide a rapid, affordable, and user-friendly approach for practical skeletal reconstruction in small animals, with practical value for fracture assessment, preoperative planning, and broader use in resource-limited veterinary settings. Full article

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

High friction and drag are among the challenging subjects for constructing water-based drilling fluids available in horizontal drilling. Lubricants play a major role in mitigating friction of water-based drilling fluids, and thus, developing new lubricants is necessary for efficient horizontal drilling. In this work, a generation 1.5 (1.5G) hyperbranched polyamide-amine P(EDA-MA-OA), which serves as a candidate for a traditional lubricant with linear conformation, was newly synthesized via a divergent approach. A set of physicochemical characterizations was carried out on P(EDA-MA-OA) to confirm its effective synthesis. The results indicated that P(EDA-MA-OA) has a nanoparticulate morphology with a size of approximately 100 nm. Its molecular structure shows strong thermal stability, with initial thermal decomposition occurring at 146 °C. The water-based drilling fluid formulated with P(EDA-MA-OA) as the lubricant exhibits effective comprehensive properties and, in particular, the lubrication coefficient was 0.067, comparable to that of the oil-based drilling fluid, indicating enhanced lubricity by the incorporation of the hyperbranched polymer. The results of molecular simulations show that P(EDA-MA-OA) possesses a unique “basket-like” architecture, with C18 long chains enveloping the central active segments, namely the carbonyl (-C=O) and amide (-CO(NH₂)) groups. When interacting with montmorillonite (MMT) particulates, the active groups can interact with MMT, allowing the eight C18 branched terminal chains to form a “molecular brush” with a normal orientation toward the MMT interface, which can serve as a hydrophobic lubricating film to improve lubricity. A lubrication model was finally proposed to rationalize the enhanced lubricity from the hyperbranched polymers in the water-based drilling fluid. Full article

Accurate urban building energy modeling (UBEM) is constrained by mismatches between standard climate inputs and actual urban microclimate conditions. This study introduces a scalable, bottom-up, framework that integrates EnergyPlus building energy modeling simulation outputs with Earth observational and geographical-based urban morphology data, which are enhanced through machine learning techniques to improve energy demand predictions in urban settings. Applied to Los Angeles (LA), California, we evaluate the representativeness of typical meteorological year (TMYx) sampling sites against actual urban environmental conditions. We find that while satellite-derived surface temperatures show reasonable alignment with average city conditions, significant discrepancies are observed in urban form metrics such as tree cover, street cover, and building density, suggesting that TMYx stations should be placed in denser urban areas. We augment EnergyPlus simulations for 19 single-family buildings, with remote sensing data using machine learning models, to generate city-wide residential energy consumption heatmaps corrected for microclimate conditions. Models capture substantial intra-urban variation, with predicted energy use differing by approximately 10% between neighborhoods. Feature importance analysis highlights land surface temperature as a key predictor, underscoring its relevance to building energy research. We also find the majority of TMY3 sampling sites to be in low-vulnerability areas, underscoring the structural mismatch that is embedded in urban form and climate. This framework offers a scalable path for integrating urban microclimate effects into energy modeling to enable more precise and equitable energy policy and planning. Full article

Background: It is rare in clinical practice to encounter a common arterial trunk when the ventricular septum is intact. In this setting, other clinical diagnoses, such as hypoplastic left heart syndrome with aortic atresia, may be mistaken for a common arterial trunk. Data for this combination is largely limited to case reports and small case series. We have conducted a systematic review of reported cases, performing cluster analyses to provide an objective grouping of the cases. Methods: A systematic review of the literature was performed to identify cases of a common arterial trunk with an intact ventricular septum. Cases for which individual data were available were included in the final analyses. Cluster analysis using K-means clustering was conducted to provide an objective grouping of the hearts based on morphologic findings. Results: K-means clustering identified three distinct groups among hearts with a common arterial trunk with intact ventricular septum. The commitment of the common ventriculo-arterial junction to the left, right, or both ventricles was the defining feature of each group. Hearts with a common trunk committed to one of the ventricles demonstrated significant hypoplasia or atresia of structures related to the other ventricle. Conclusions: Distinct patterns can be identified when a common arterial trunk is found with an intact ventricular septum. They depend on the ventricle or ventricles, which support the common ventriculo-arterial junction. Full article

Seagrass meadows are important blue carbon habitats, but their patchy distribution in intertidal zones makes accurate UAV mapping difficult under shallow water cover and complex sediment backgrounds. This study developed a fine-grained semantic segmentation framework with explainability analysis to improve intertidal seagrass extraction from high-resolution UAV visible and multispectral imagery. Exposed seagrass (ESG) and shallow-submerged seagrass (SSG) were mapped separately to represent two observable intertidal states. Visible bands, multispectral bands, and vegetation indices were used as model inputs. U-Net and DeepLabV3+ served as baseline models, while UPerNet-ConvNeXtV2-Tiny was tested under the same settings. Kernel SHAP and permutation importance were used to assess feature contributions. UPerNet-ConvNeXtV2-Tiny achieved the best performance, with an overall accuracy (ACC), mean Intersection over Union (mIoU), and F1 score of 97.45%, 94.63%, and 97.23%, respectively. It outperformed the baseline models in suppressing background interference, preserving patch morphology, and reducing omission errors in weak response and boundary areas, while demonstrating better cross-scenario applicability in independent test areas. Explainability analysis showed that model discrimination was mainly associated with red and green-related features, especially RGB-R, MS-R, MS-G, RGB-G, and NGRDI. ESG and SSG showed different feature dependence patterns, indicating that high-resolution UAV imagery can support accurate seagrass mapping and reveal spectral differences between intertidal seagrass states. These findings provide a practical framework for UAV-based intertidal seagrass mapping and monitoring and offer guidance for feature selection and model explainability analysis. Full article

Background and Clinical Significance: Peristomal pyoderma gangrenosum (PPG) is a rare subtype of pyoderma gangrenosum, most commonly associated with inflammatory bowel disease or haematologic disorders. Its occurrence in patients with solid malignancies is uncommon. PPG in an oncologic setting poses diagnostic and therapeutic challenges because systemic immunosuppressive therapy, wound care, and ongoing chemotherapy must be carefully balanced; Case Presentation: We report the case of a Japanese man in his 50s with stage IVB rectal adenocarcinoma who developed rapidly progressive peristomal ulceration clinically consistent with PPG around a colostomy 12 weeks after initiation of panitumumab-containing systemic chemotherapy. The diagnosis was made on clinical grounds and was strongly supported by the clinical morphology, exclusion of major mimickers, and response to systemic corticosteroid therapy, although histopathological confirmation was not obtained. Because existing diagnostic criteria for pyoderma gangrenosum are not specifically designed for peristomal disease, they were used as supportive rather than definitive diagnostic tools. Skin biopsy was avoided due to the risk of pathergy at the peristomal site. Superficial cultures were not obtained because frequent cleansing and faecal contamination were likely to compromise diagnostic accuracy. To minimise mechanical pathergy, the stoma appliance was changed from a one-piece soft convex system to a two-piece flat system. Multidisciplinary management, including systemic corticosteroids, meticulous stoma care, and selective ultrasonic debridement, resulted in complete epithelialisation by Week 26. Chemotherapy was temporarily withheld during the active inflammatory phase and later resumed. Despite successful control of the peristomal ulceration, the patient died from progressive malignancy at Week 34; Conclusions: This case highlights the clinical challenge of balancing immunosuppressive therapy for clinically suspected PPG with ongoing oncologic treatment. Mechanical pathergy related to stoma appliance use was considered a more likely precipitating factor than chemotherapy alone, although panitumumab may have contributed to impaired cutaneous repair. Close collaboration among dermatologists, oncologists, surgeons, WOC nurses, and family caregivers is essential for multidisciplinary decision-making in complex oncologic settings. Full article

Prior to this study, knowledge on the evolutionary lineage of Garra remained inadequate, as previous phylogenetic investigations were primarily based on partial gene sequences. Although several mitogenomes of Garra species have been reported, their structural organization and comprehensive genomic characteristics have not been thoroughly evaluated. In this study, Garra manipurensis, endemic to the Indo-Burma biodiversity hotspot, was identified based on its detailed morphology and meristic counts. The circular mitogenome of G. manipurensis is 16,776 bp in length and contains the canonical set of 37 genes, along with duplicated control regions separated by tRNA-Proline. The comparative assessments across Garra species indicate predominantly conserved GTG start codons, occasional alternative ATA initiation codons, and incomplete stop codons. The selection pressure examinations within Garrini taxa reveal a purifying selection across all protein-coding genes. The control region comprises four conserved sequence blocks and species-specific tandem repeats, reflecting a balance between functional constraint and lineage-dependent evolutionary dynamics. The phylogenetic inference supports the monophyly of Garra and places G. manipurensis in close affinity with Garra flavatra, which is native to the western slope of Rakhine Yoma in Myanmar and Mizoram State in northeastern India. The genetic diversity analyses revealed haplotype differentiation, with shallow intraspecific genetic distances (0.000–0.011) observed samples between two distinct drainage systems in Manipur and Mizoram, northeastern India. The observed pattern of haplotype divergence in G. manipurensis may reflect the historical or seasonal hydrological connectivity among the western-slope drainages of the Chin Hills, with the subsequent geographic isolation potentially contributing to the emergence of distinct genetic lineages. Nevertheless, the extent and evolutionary significance of this differentiation remain uncertain and warrant further investigation through expanded geographic sampling and the incorporation of additional molecular data. Collectively, these findings provide in-depth insights into the mitogenomic architecture, comparative gene arrangements, phylogenetic patterns, and matrilineal evolutionary history of G. manipurensis and other congeners, thereby improving our understanding of the systematics and genetic diversity of this important cyprinid fish lineage. 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