Search Results (175)

We present a material distribution topology optimization (TO) framework that directly generates capacity-specific radial trims for severe-service control valves. The method uses an out-of-plane resistance modified two-dimensional turbulence model and objective functions that maximize directional change to create tortuous pressure-staging geometries at predefined channel depths. Four trims targeting non-dimensional capacities ($C_V$) of 0.672, 0.96 (two objectives), and 1.248 were optimized, MSLA-printed, and tested in a globe valve using IEC 60534 procedures. The measured capacities ranged from −13.7% to +4.8% of the targets for a fully 2D optimization process, dropping to a maximum of 7.8% when coupled with a hybrid 3D tuning step. Acoustic detection indicated incipient cavitation at a pressure drop ratios greater than $0.87$ for the most highly staged design and $0.73$ for the highest capacity design, which is consistent with our simulations of the flow field before fabrication. These results demonstrate that TO can deliver fit-to-service, capacity-tuned trims with excellent cavitation suppression, reducing reliance on large parametric design libraries. Full article

Massive Internet of Things (IoT) and sensor-network services in 5G/6G systems increasingly rely on network slicing to support large-scale sensing, monitoring, and mission-critical applications. In such sliced infrastructures, Proportional Fair (PF) allocation assigns resources according to slice-reported demands. This reliance on trusted demand reporting makes coexisting slices, including mMTC-based IoT sensor slices, vulnerable to resource exhaustion attacks, where a malicious slice inflates its demand to monopolize shared resources and induce Service Level Agreement (SLA) violations. Existing unsupervised defenses mainly focus on anomaly detection, while the translation of detection results into resource-level mitigation remains insufficiently addressed. To bridge this gap, this paper proposes AutoGuard-Hybrid, an unsupervised detection-to-mitigation framework that combines complementary anomaly detectors with allocation-aware mitigation policies to preserve slice-level service availability. Unlike prior detection-only approaches, AutoGuard-Hybrid converts unsupervised anomaly evidence into allocation-aware demand purification before PF scheduling. Its key design is a closed-loop integration of Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) as spatial and temporal front-end detectors with Adaptive Clipping and a Safety Cap, which translate anomaly scores into demand purification actions. Experiments show that AutoGuard-Hybrid remains comparable to Isolation Forest under Continuous attacks and improves the mean system-wide SLA violation rate by 27.6% under Adaptive Probing attacks. Stage activation analysis further shows that LSTM-AE activations increase from 9.3 under Continuous attacks to 29.4 under Adaptive Probing attacks. Ablation results show that Adaptive Clipping alone reduces the system-wide SLA violation rate by 75.0%, while the full mitigation pipeline achieves an 84.6% total reduction. AutoGuard-Hybrid operates within the 1 ms Transmission Time Interval (TTI) constraint and provides a practical defense framework for next-generation network slicing-enabled IoT and sensor-network services. Full article

Peri-implantitis treatment is challenging because of the complex micro- and nanostructured topography of implant surfaces. No standard debridement protocol exists. In this study, we compared five debridement methods used on heavily contaminated titanium implants that were explanted due to peri-implantitis. Twenty-five explanted implants (five per group) were treated with a carbon fiber ultrasonic insert, a polyetheretherketone (PEEK) ultrasonic insert, a rotating titanium brush, an erbium, chromium-doped yttrium, scandium, gallium, and garnet (Er,Cr:YSGG) laser, or an erbium-doped yttrium, aluminum, and garnet (Er:YAG) laser. Five pristine implants were used as controls. Surface morphology was assessed by scanning electron microscopy (SEM). The Modified-Implant Debridement Visual Index (M-IDVI) was used to assess the debridement effectiveness according to SEM images. Surface elemental composition was assessed for atomic percentage (at. %) of carbon, titanium, oxygen and nitrogen using energy-dispersive X-ray spectroscopy (EDS). Mechanical methods were more effective at removing debris than laser methods. The titanium brush showed the lowest residual debris (2.33 ± 0.33) and the greatest reduction in surface carbon (Δ = −7.77 at. %). Surface titanium increased after debridement for all methods except for Er,Cr:YSGG (Δ = −5.9 at. %). Er:YAG best preserved SLA microtopography but exhibited a lower debridement efficacy (3.27 ± 0.83) than mechanical methods. No method resulted in a pristine surface. Full article

In recent years, the YOLO series has emerged as a widely adopted framework for real-time object detection because of its favorable balance between detection accuracy and inference efficiency. Nevertheless, accurate recognition and localization of tiny defects in industrial inspection remain challenging. These challenges mainly arise from the extremely small scale of defect targets, low image contrast, and the limited capability of conventional models in feature representation under uniform backgrounds. To address these issues from a mathematically optimized perspective and via feature modeling optimization, we develop a dedicated framework for tiny defect detection, termed SLA-YOLO. The main contributions of this work are as follows. First, we adopt a slicing-based processing strategy inspired by the SAHI framework, referred to as Image Slicing Processing (ISP) in this work, and extend it to both training and inference stages. This design enhances the relative scale of tiny defects within local regions, improving detection sensitivity and data diversity without introducing additional model complexity. Second, we introduce a Large Receptive-Field Selective Context (LRSC) module. By leveraging large-receptive-field selective convolution kernels, this module adaptively captures contextual information around critical defect regions via feature modeling optimization of scale-dependent representations. Third, we incorporate a Transformer-based High-level Feature Enhancement (THFE) module to improve global dependency modeling in high-level semantic representations, thereby enhancing feature discriminability for complex defect patterns. Experimental results on the CCB defect dataset show that SLA-YOLO improves mAP@50:95 by 2.7% and mAP@50 by 3.3%. In addition, the proposed method demonstrates strong generalization capability on other tiny object detection tasks. Full article

Plant functional traits help us understand forest carbon storage. We quantified eight functional traits that reflect plant life history strategies: leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), leaf carbon (LC), nitrogen (LN), phosphorus (LP), leaf carbon–nitrogen ratio (LCNR), and wood density (WD). But their role across successional stages is still unclear. We set up sixteen permanent plots in Pu’er, Yunnan, China. Each plot was 60 m × 60 m. The plots covered four successional stages. Stage one was early-successional Simao pine forests. Stage two was mid-successional mixed forests. Stage three was mid-to-late-successional mature mixed forests. Stage four was late-successional mature broad-leaved forests. We measured aboveground carbon storage (CS). We measured carbon growth rates (CAR). We also measured plant traits, soil nutrients, and topography. Carbon storage increased step by step during succession. It became stable in the late stage. Carbon accumulation rate stayed similar across all stages. A key trait axis (LPC2) directly increased carbon storage. LPC2 represents the trade-off between nitrogen use efficiency and leaf construction costs. Environmental factors only affected carbon storage indirectly. They influenced traits first. These results support the metabolic trade-off hypothesis. They also support the leaf economics spectrum theory. Early-successional traits help forests gain biomass quickly. Late-successional traits help forests store carbon for a long time. We suggest protecting mature forests. We also suggest using pioneer species in restoration. This dual strategy can enhance carbon sequestration in subtropical production forests. Full article

In the boreal winter of the Northern Hemisphere, a weakening of the surface Indonesian throughflow (ITF) is commonly observed. The intraseasonal mechanism of the weakening, namely, the impact of the atmospheric Madden–Julian Oscillation (MJO), is well-known and has been extensively studied. However, a significantly low volume transport of ITF (<100 m in depth) was also observed in the Makassar Strait during the traverse of tropical cyclones (TCs). The observed transport decrease is 0.31 Sv (1 Sv = 10⁶ m³/s) on average, which is ~70% of the estimated influence of the MJO. The time scale of the incurred variation is up to 30 days, comparable to the time of 20–90 days caused by the MJO. The winds in the TC circulation have a major impact on the Makassar Strait’s ITF transport reduction. Numerical experiments reveal that the reduction is due to the along-strait sea level anomaly (SLA) variability that is forced by the winds from the upstream region. The mechanism involves the propagation of coastal Kelvin waves along the Sulawesi Sea generated by the TCs and is confirmed by theoretical analysis. Based on the numerical experiments, this mechanism contributes ~40% to the total ITF transport reduction, while the large-scale guiding circulation surrounding the TCs may contribute to the remaining ITF transport reduction. These results support that TCs are also important forcing components in the intraseasonal variation in surface ITF. Full article

Understanding diurnal canopy orientation in crops is important for interpreting plant responses to light and environmental conditions, yet field-based quantification remains limited. In this study, we present Heliocot, a field RGB imaging approach that converts time-resolved images into reference-area standardized projected leaf area (PLA) time series to quantify within-day canopy orientation dynamics in early-season cotton. Leaf instance segmentation was performed using YOLOv8m-seg and refined through a 144-combination post-processing optimization. On the held-out early-stage validation/tuning set, the selected workflow showed strong agreement with manual ground truth (R² = 0.948; NRMSE = 0.082) and destructive leaf area measurements (R² = 0.836). Derived diurnal metrics, including Daily Orientation Amplitude (DOA) and Peak Orientation Index (POI), consistently revealed a midday maximum (13:15) in canopy projection. Exploratory genotype-level analysis suggested negative associations between orientation indices and selected plant traits, including specific leaf area (SLA) versus DOA (r = −0.71, p = 0.021, R² = 0.508), destructive leaf area (LA) versus DOA (r = −0.69, p = 0.028, R² = 0.471), and stem dry weight (SDW) versus POI (r = −0.74, p = 0.014, R² = 0.554), while plant height was not significantly associated with POI and DOA (p > 0.05). Although currently limited to early-season conditions and two field-imaging dates, this approach provides a practical workflow for field-based monitoring of canopy projection dynamics in cotton, while broader temporal and environmental validation remains necessary. Full article

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

Electrically conductive photopolymers enable the fabrication of functional 3D-printed components with customized electrical properties, expanding additive manufacturing applications beyond traditional structural uses. This study reports the formulation and characterization of electrically conductive, water-washable photopolymer resins for masked stereolithography (MSLA) through the incorporation of nano-graphite, PEDOT:PSS, and dimethyl sulfoxide (DMSO) as a secondary dopant. Single filler and hybrid resin systems were prepared and processed via MSLA printing, then subjected to sequential thermal treatments, 25 °C curing for 48 h followed by annealing at 80 °C and 120 °C, to investigate conductivity enhancement and microstructural evolution. Electrical characterization via current–voltage (I–V) measurements, referenced to the transversal conductivity (${\sigma }_{TRA}$), showed that the hybrid formulation containing PEDOT:PSS, graphite, and DMSO achieved the highest conductivity (9.40 × 10⁻² S·cm⁻¹), outperforming PEDOT:PSS/graphite systems (2.6 × 10⁻³ S·cm⁻¹) and graphite-only samples (9.76 × 10⁻⁴ S·cm⁻¹). Conductivity increased consistently after each thermal step, indicating enhanced charge transport. Scanning electron microscopy further revealed improved filler dispersion and interconnectivity within the polymer matrix. The synergistic combination of PEDOT:PSS, graphite nanofillers, and DMSO enables MSLA printed components with tunable and reproducible electrical performance. This work demonstrates a scalable strategy for producing functional, water-washable photopolymer resins suitable for applications in sensors, soft electronics, and lightweight conductive structures. Full article

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments. Full article

Skeleton-based action recognition is a critical technology for intelligent sports analysis. Although the human skeletal structure exhibits inherent bilateral symmetry, sensor noise on resource-constrained edge devices frequently induces geometric distortion and topological asymmetry. Consequently, achieving a balance between high accuracy and real-time performance remains a significant challenge. To this end, we propose EMS-GCN, an Efficient Multi-scale Shift Graph Convolutional Network that integrates geometric priors. Specifically, we design a Gaussian kernel-driven topology refinement module to mitigate structural noise inherent in sensor data. By leveraging geometric symmetry and Gaussian distances among nodes, this module dynamically constrains graph topology learning, thereby effectively rectifying the structural asymmetry and ambiguity induced by noise. Furthermore, we construct a Multi-scale Shift Linear Attention (MSLA) module to replace computationally intensive temporal convolutions. Leveraging temporal shift invariance, this module captures multi-scale contexts via parameter-free shift operations. Furthermore, we introduce a linear temporal attention mechanism to model global temporal dependencies with linear complexity, effectively resolving the information asymmetry inherent in long-range interactions. Finally, EMS-GCN incorporates a dual-branch attention structure to adaptively calibrate feature responses. Extensive experiments demonstrate that our model maintains high recognition accuracy with only 0.56 M parameters, representing a reduction of over 60% compared to mainstream baselines. These results validate the efficacy of leveraging geometric and temporal symmetries to enhance real-time sports analysis. Full article

The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity. Full article

Glaciers in the Canadian Arctic Archipelago (CAA) contribute significantly to sea-level rise, yet sparse in situ data limit regional climate assessments. This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six CAA glaciers, utilising 9920 Landsat 8/9 and Sentinel-2 scenes. Glacier surface cover types (snow and bare ice) were mapped via machine learning, and SLA was extracted using elevation-binning and Snow-Elevation Histogram Analysis (SEHA). Elevation data were obtained from ArcticDEM v3; positive degree days (PDD) from Eureka, Pond Inlet, and Pangnirtung were used to characterize melt-season forcing. Satellite-derived SLA was validated against equilibrium-line altitude (ELA) observations from White Glacier. All glaciers exhibit a characteristic seasonal SCA cycle: maximum extent in June, minimum in August, and partial recovery in September, with extreme anomalies in 2020. Annual peak SLA correlates positively with summer warmth; sensitivities to PDD were 2.56, 0.67, and 0.83 m (°C d)⁻¹ for White, Highway, and Turner glaciers, respectively. Hypsometry strongly modulates climatic sensitivity: glaciers with limited high-elevation area (e.g., BylotD20s, Turner) frequently lose their accumulation zones in warm years. At White Glacier, SLA replicates interannual ELA variability with high correlation and lower error using the elevation-bin method (mean bias +53 m; RMSE 177 m) compared with SEHA (+165 m; 339 m). Meteorological records indicate significant summer and winter warming at Eureka, with increasing PDD; precipitation trends are spatially variable. A regionally calibrated, quality-assured elevation-bin method produces objective and transferable SLA time series, suitable for ELA estimation in data-sparse Arctic settings. The SLA–PDD relationship and hypsometry-dependent responses highlight increasing stress on accumulation zones under continued warming. Reporting SLA uncertainty and image quality, alongside expanded field observations, will enhance Arctic-wide glacier monitoring. Full article

Background/Objectives: The formation of a soft tissue seal through mucosal integration around dental implants is critical for potentially achieving long-term peri-implant health and clinical success. Understanding how different implant and abutment surfaces interact with individual layers of the oral mucosa remains limited. This study aimed to compare the differential attachment of tissue-engineered oral epithelium, connective tissue, and full-thickness human oral mucosa to various implant and abutment materials and surface topographies. Methods: Sand-blasted, large-grit, acid-etched (TiZr-SLA), machined TiZr (TiZr-M), machined zirconia (ZrO₂-M), polished zirconia (ZrO₂-P), and machined PEEK rods, along with commercially available titanium and ZrO₂ healing abutments, were inserted into 3D oral mucosal models following a 4-mm punch biopsy. Inflammation was induced using Escherichia coli lipopolysaccharide. Analyses included histology, PrestoBlue viability assay, scanning electron microscopy, and ELISA quantification of cytokines IL-1β, IL-6, and IL-8. Results: Epithelial attachment was greater on TiZr-SLA, ZrO₂-P, and PEEK-M (p < 0.05 and p < 0.01) surfaces compared with TiZr-M and ZrO₂-M. TiZr-SLA exhibited the highest connective tissue attachment (p < 0.05). Commercial titanium and ZrO₂ healing abutments demonstrated the highest post-pull PrestoBlue viability and overall soft tissue attachment. SEM confirmed cell retention on all implant surfaces. Elevated IL-1β levels were detected in models exposed to ZrO₂-M and PEEK-M, whereas IL-6 and IL-8 levels were not influenced by any material or surface topography. Conclusions: In vitro epithelial and connective tissue responses are influenced by implant material, surface topography, and design. Rough TiZr-SLA surfaces promote superior connective tissue attachment, while smooth commercial abutments support optimal overall soft tissue integration. These findings highlight the importance of surface engineering in preclinical optimization of peri-implant soft tissue attachment. Full article

Microneedles (MNs) offer a transformative platform for transdermal drug delivery, though balancing structural precision with mechanical robustness remains challenging. This study utilized SLA 3D printing to fabricate high-resolution MN masters, systematically evaluating printing angles (0° to 60°) and aspect ratios to optimize fidelity. A 45° printing angle was found to significantly enhance tip sharpness and insertion efficiency. These optimized structures served as templates for flurbiprofen (FLU)-loaded dissolving MNs (DMNs) fabricated via a bilayered casting method. We investigated the impact of geometric architectures—conical, pyramidal, and star-type—on functional performance. Mechanical testing using Parafilm^® M and ex vivo rat skin revealed that the star-type design, possessing the highest vertex count, exhibited superior strength and a 100% penetration rate by effectively concentrating stress at tip edges. Consequently, star-type DMNs achieved the highest cumulative drug permeation (86.9 ± 9.9% in 12 h), outperforming pyramidal (77.8 ± 9.0%) and conical (64.4 ± 10.2%) designs. These findings underscore geometric design as a critical determinant of clinical efficacy, providing a robust framework for precision manufacturing of task-specific MNs for poorly soluble drugs. Full article