Search Results (666)

Centella asiatica (L.) Urban extracts are widely used in food, cosmetic, and pharmaceutical products. Conventional solvent extraction requires organic solvents and often produces dark green extracts due to chlorophyll co-extraction, limiting their industrial applications. This study developed an environmentally friendly aqueous two-phase system (ATPS) for the simultaneous extraction of triterpenoids of Centella asiatica. Response surface methodology with a central composite design was employed to optimize ethanol concentration, ammonium sulfate concentration, and temperature. A rapid and validated HPLC method for quantification of four triterpenoids—madecassoside, asiaticoside, madecassic acid, and asiatic acid—was developed and validated on a Poroshell 120 SB-C18 column (3.0 × 150 mm, 2.7 microns) at 40 °C. The developed method for all four triterpenoids was rapid (14 min run time) with acceptable system suitability, specificity, linearity (R > 0.9995), accuracy (98.42–101.52%), precision (0.16–1.36%), LOD (0.04–0.64 µg/mL), and LOQ (0.13–1.93 µg/mL). The ANOVA data from the central composite design of response surface methodology for total triterpenoid content were fitted with quadratic models, yielding acceptable R² (>0.97), adjusted R² (>0.95), and predicted R² (>0.80). The optimum extraction conditions were 18.29% w/w ethanol, 33.93% w/w ammonium sulfate, and 60.04 °C, yielding a total triterpenoid content of 30.53 mg g⁻¹ dry weight. The optimized ATPS produced higher yields of madecassoside (13.15 mg g⁻¹), asiaticoside (9.02 mg g⁻¹), madecassic acid (5.26 mg g⁻¹), and asiatic acid (3.10 mg g⁻¹) than conventional ethanol extraction while simultaneously reducing chlorophyll-derived coloration. This ATPS extraction strategy provides a sustainable approach for producing suitable Centella asiatica extracts for cosmeceutical and pharmaceutical applications. Full article

This study addresses the design challenges of daily-use smart glasses by proposing a user-centered design and evaluation framework that integrates the KANO model, the Analytic Hierarchy Process (AHP), Fuzzy Comprehensive Evaluation (FCE), and Artificial Intelligence Generated Content (AIGC)-assisted concept generation. User requirements were identified through interviews with 20 participants and user journey map analysis, classified using a KANO questionnaire survey with 309 valid responses, weighted through AHP, and used to guide AI-assisted visual generation based on Low-Rank Adaptation (LoRA) fine-tuning and a ComfyUI workflow. The results show that Functionality and Performance had the highest criterion weight, while multi-scenario mode switching, misoperation recovery, and lightweight structural design were identified as the most critical design attributes. Three design schemes, including a commercial reference product, a conventional 3D-rendered non-AIGC concept, and the proposed AIGC-assisted scheme, were evaluated using FCE. The AIGC-assisted scheme achieved the highest comprehensive score. The study demonstrates that combining quantitative requirement modeling with generative design can support a systematic workflow from user demand identification to design scheme evaluation. The proposed framework provides a practical reference for smart wearable product design and may also inform wearable interface applications in digital cultural heritage and museum interpretation contexts. Full article

Objectives: To assess the technical feasibility and interobserver reliability of transrectal biplane ultrasound with external cotton-swab marking of the urethral orifice after transobturator mid-urethral sling surgery, and secondarily to describe dynamic measurements and exploratory associations with chart-derived postoperative leakage. Methods: In this single-center retrospective cross-sectional imaging study, 112 consecutive women examined between January 2024 and February 2026 underwent a standardized protocol. The swab tip was placed lightly against, but not inserted into, the external urethral orifice. Sling width (W), distance from the sling upper edge to the internal urethral orifice (D-I), and distance from the sling lower edge to the external urethral orifice at rest (D-O) and during Valsalva (V-D-O) were measured. Forty randomly selected examinations were independently measured by two blinded readers. Agreement was evaluated using ICC(2,1), Bland–Altman bias and 95% limits of agreement (LoA), standard error of measurement (SEM), and minimal detectable change (MDC95). Missingness patterns and age-adjusted exploratory logistic models were examined. Results: W, D-I and D-O were measurable in all 112 women; V-D-O was available in 95 (84.8%). Postoperative leakage status was available in 101 women (62 without and 39 with leakage). V-D-O was shorter than D-O in paired examinations (median difference, −3.00 mm; 95% CI, −4.90 to −1.00; p < 0.001). ICCs were 0.845 for W, 0.942 for D-I, 0.936 for D-O and 0.970 for V-D-O. Bland–Altman biases (reader A minus reader B) were −0.18, −0.93, 0.70 and −0.23 mm, respectively; corresponding MDC95 values were 1.53, 2.96, 3.72 and 2.33 mm. Missing V-D-O was associated with a shorter postoperative interval after Holm correction. None of six age-adjusted ultrasound models was associated with chart-derived leakage after Holm correction. Conclusions: The protocol permitted highly available static measurements and good-to-excellent interobserver reliability. Because the study lacked an unmarked comparator, intraobserver repeats and a standardized clinical endpoint, it supports technical feasibility and measurement reliability, not superiority, diagnostic validity or prognostic utility. Full article

High Friction Surface Treatment (HFST) relies heavily on wear-resistant aggregates to ensure roadway safety, yet the conventional evaluation of aggregate abrasion resistance is time-consuming and resource-intensive. In this study, a machine learning framework was developed to predict the abrasion-induced angularity evolution of recycled high-alumina aggregates from their initial morphological characteristics, thereby enabling rapid abrasion-resistance screening. Six regression models were compared under leave-one-group-out cross-validation, and an improved whale optimization algorithm (IWOA) was proposed to tune the Gaussian process regression (GPR) model, incorporating five enhancements and a regularized fitness function to restrain overfitting. The models were trained on 42 samples from six aggregates, whose angularity, Form 2D, micro-texture, sphericity, and F:E ratio were measured with the AIMS II device before and after successive abrasion cycles. The IWOA-GPR model achieved the best performance, with an R² of 0.8909, an RMSE of 150.98, an MAE of 120.55, and a MAPE of 4.60%. The SHAP analysis identified the abrasion revolutions, the initial Form 2D, and the initial angularity as the dominant contributors to the worn angularity. Moreover, the early angularity loss after the first 500 revolutions correlated strongly with the measured Los Angeles abrasion value (r = 0.935), which allows the LAA of a candidate aggregate to be estimated after a single abrasion cycle. The proposed framework therefore provides a rapid and reliable tool for screening wear-resistant aggregates for HFST applications and supports the clean utilization of recycled solid wastes in anti-skid pavements. Full article

Accurate satellite component segmentation plays a fundamental role in numerous on-orbit perception tasks, including spacecraft pose estimation, autonomous robotic servicing, and space situational awareness. However, existing segmentation methods still face three major challenges: the scarcity of fine-grained annotations, the difficulty of preserving structural continuity under large pose variations, and the severe structural imbalance caused by extremely small and slender components such as antennas. To address these issues, this paper proposes LRSRNet, a low-resource spatial reconstruction network for satellite component segmentation. First, a LoRA-adapted DINOv3 foundation model is employed to efficiently transfer large-scale visual priors to the satellite domain, enabling robust structural representation under limited supervision. To exploit the complementary information encoded at different semantic levels, multi-level transformer features are uniformly aggregated for structural representation learning. Subsequently, a Converse2D-based spatial reconstruction decoder progressively restores the spatial continuity of satellite components, facilitating the recovery of fine structural details lost during hierarchical feature encoding. Furthermore, a structure-aware optimization strategy is introduced by jointly considering the geometric characteristics and category imbalance of satellite components during training, thereby improving the learning of geometrically fragile structures without sacrificing overall segmentation performance. Experimental results on a public satellite component segmentation benchmark demonstrate the effectiveness of the proposed method. LRSRNet achieves an mIoU of 77.10% on foreground categories and 82.45% over all categories. Ablation studies and visualization results further validate the contributions of the proposed representation adaptation, spatial reconstruction, and structure-aware optimization strategies. Full article

This work presents a modular, sensor-aware, end-to-end simulation framework for optical camera communication (OCC). The framework combines modified Monte Carlo ray tracing with pixel-level camera modeling, including optical blur, shutter timing, noise, digitization, and modulation-aware signal processing. It produces physically consistent synthetic images from simulated optical propagation and enables communication performance to be estimated through image-domain signal-to-noise ratio (SNR) and theoretical bit-error-rate (BER) calculations. The simulator is applied to intra-satellite optical links as a representative case study involving confined three-dimensional geometries, line-of-sight (LOS) visibility, partial occlusion, and rolling-shutter image formation. Experimental validation under LOS conditions shows good agreement in the dominant spatial-temporal characteristics of rolling-shutter imagery, with a structural similarity index measure (SSIM) of approximately $0.80$. Simulated SNR values range from approximately $20.5$ to $22.6$ dB, compared with measured values between $20.8$ and $24.4$ dB. No bit errors are observed in the experimental sequences, corresponding to finite-length BER upper confidence bounds, while the BER values derived from the simulated images are theoretical estimates obtained from the image-domain SNR under ideal receiver assumptions. Additional simulations using a detailed 12U CubeSat model demonstrate the capability to assess emitter–receiver placement and partial geometric occlusion, including cases in which the visible portion of the source remains sufficient for bitstream decoding. By jointly modeling optical propagation, camera acquisition, and communication metrics, the proposed framework supports early-stage OCC system analysis and configuration trade-offs without requiring immediate hardware implementation. The approach is applicable to other OCC scenarios in which spatial image formation and sensor dynamics influence communication performance. Full article

Human biomonitoring in environmentally complex regions requires not only matrices that are easy to collect but also careful consideration of external contamination, non-detects, and analytical uncertainty. Inductively coupled plasma mass spectrometry (ICP–MS) was used to measure the concentrations of 24 elements in occipital scalp-hair samples from 134 adults living in Campania, southern Italy (67 women and 67 men; age range: 20–72 yr). Continuous models were restricted to elements detected in at least 70% of adults and were fitted using HC3 heteroskedasticity-consistent standard errors, with Benjamini–Hochberg correction for the false discovery rate (FDR). Ca, Zn, Mg, K and Cu were the most abundant elements in the hair profile. The most consistent adjusted association was lower concentrations of Ca, Mg, Cu, Cr and Zn in men. Pb showed a strongly right-skewed distribution, whereas Cd was detected in only 22.4% of adults; this low detection frequency did not support a population-level elevation claim. Spatial summaries and clustering were retained for exploratory purposes: Kruskal–Wallis tests indicated a zone-level difference only for Zn, whereas patterns in Pb, Cr, As, Cd, and V were interpreted as priorities for confirmatory sampling rather than as evidence of territorial risk. Sensitivity analyses based on the limit of detection (LOD) supported retaining As and Cd as descriptive outcomes; V associations were not promoted to primary findings because the V detection frequency was below the pre-specified threshold. These findings support the use of hair biomonitoring as a non-invasive screening approach in mixed geogenic–anthropogenic settings, provided that sex, sample mass, non-detects, outliers, and external contamination are considered explicitly and that confirmatory matrices are used where appropriate. Full article

Automated three-dimensional (3D) mapping with a survey-grade terrestrial laser scanner (TLS) is the canonical instance of robotic stop-and-scan light detection and ranging (LiDAR) mapping: the mobile platform must remain stationary for minutes per scan, so the environment must be covered with as few scans as possible. Where to stop hinges on a coverage model predicting what a scan pose will observe. The conventional isotropic-disk model counts every free cell within sensing range as covered—including cells behind walls—and, therefore, skips scans that genuine observation requires. We replace the disk with a ray-cast visibility region computed on the robot’s live two-dimensional (2D) occupancy map, admitting only cells in direct line of sight; define a line-of-sight (LOS) coverage metric over mapped free space and drive a marginal-gain scan/skip rule embedded in frontier exploration. The planner thus performs 2D scan-station placement for subsequent 3D mapping. In a controlled paired ablation in simulation, the visibility rule raises LOS coverage from about 77% to 84–85% for one to two additional scans, and it also outperforms a disk baseline governed by the identical marginal-gain rule, isolating the coverage model itself as the decisive factor. In a fully autonomous on-hardware comparison in an industrial test room, with each rule driving a mobile platform carrying a Leica BLK360 G1, the visibility rule raised LOS coverage from 77.6% to 92.1%; every visibility run’s scans registered offline at survey grade (4–5 mm bundle error, 84–88% overlap), whereas disk runs yielded at best a single-link network and once a single unregistrable scan. The results indicate that, for the room-scale indoor environments studied, occlusion-aware visibility is a sounder basis than Euclidean proximity for stop-and-scan placement. Full article

Ganoderma lucidum’s anticancer potential is largely attributed to its polysaccharides, yet their preclinical translation requires rigorous systematization. Following PRISMA 2020 guidelines, this systematic review evaluated the anticancer efficacy of G. lucidum polysaccharides (GLPs) in preclinical models (PROSPERO: CRD42021296200). Systematic searches spanning 2011 to 2026 were conducted in PubMed, Embase, and Scopus. Eligible peer-reviewed articles investigated GLPs in cancer cell lines or animal models. The Cochrane Risk of Bias 2 (RoB 2.0) tool was used to assess the risk of bias for animal studies. Across 14 included studies, GLPs were primarily extracted from fruiting bodies or spores via hot-water extraction and ethanol precipitation. Preclinical models predominantly involved colorectal cancer cell lines (HCT-116, HT-29, LoVo, SW480) and BALB/c, ICR, or Swiss albino mouse strains. GLPs consistently reduced tumor volume in vivo and decreased tumor cell viability in vitro. Mechanistic outcomes encompassed the induction of apoptosis, senescence, and immunomodulatory markers, frequently converging on MAPK/NF-κB signaling pathways. However, a major methodological limitation was the widespread negligence or superficiality in the chemical characterization of the tested fractions; only one study achieved complete purification of a β-D-glucan. In conclusion, while GLPs demonstrate potent tumor-suppressive and immunomodulatory properties, the pervasive structural ambiguity and lack of extract purification profoundly hinder translational reliability and the establishment of robust structure–activity relationships. Full article

Background and Objectives: Three-dimensional printing is increasingly used to fabricate dental restorations; however, limited evidence is available on the mechanical performance and fracture behavior of printable resin materials used for endocrown restorations. Fracture load alone may not fully describe material performance, particularly when brittle or defect-sensitive failure occurs. This in vitro study aimed to compare the fracture resistance, Weibull parameters, experimental Mode I fracture toughness parameter, and failure patterns of three 3D-printed resin materials used for endocrown restorations. Materials and Methods: Thirty anatomically identical molar replicas were produced from a single prepared tooth model and restored with endocrowns fabricated from NextDent C&B MFH (NextDent B.V., Soesterberg, The Netherlands), SprintRay Crown (SprintRay Inc., Los Angeles, CA, USA), and BEGO VarseoSmile Crown Plus (BEGO GmbH & Co. KG, Bremen, Germany) (n = 10/group). The restorations were cemented and subjected to compressive loading until fracture. Maximum fracture force values were analyzed using Welch’s ANOVA and Weibull statistics. In parallel, single-edge-notched bend (SENB) specimens were fabricated from the same materials and tested using an ASTM D5045-based configuration to calculate an experimental Mode I fracture toughness parameter. Representative fractured crowns and standardized specimens were examined using stereomicroscopy to assess visible failure morphology. Results: No statistically significant difference in maximum fracture force was found among the three materials (Welch’s ANOVA, p = 0.217). The mean fracture force values were 862.37 N for NextDent C&B MFH, 804.37 N for SprintRay Crown, and 699.43 N for BEGO VarseoSmile Crown Plus. In the complementary analyses, BEGO VarseoSmile Crown Plus showed the highest Weibull modulus (m = 9.36), indicating a narrower distribution of fracture values, and the highest mean experimental Mode I fracture toughness parameter (5.250 MPa·m^0.5) under the present experimental conditions. Qualitative stereomicroscopic analysis revealed material-dependent visible failure patterns: SprintRay Crown exhibited more extensive fragmentation, whereas BEGO VarseoSmile Crown Plus showed a more defined visible fracture pattern with less secondary fragmentation. Conclusions: Fracture load alone was insufficient to characterize the mechanical behavior of the tested materials fully. Weibull parameters, the experimental fracture toughness parameter, and failure morphology provided complementary information and should be considered when evaluating 3D-printed resin materials for endocrown restorations. Full article

The advancement of precision agriculture and smart tillage relies on high-precision, real-time perception of unmanned ground vehicle (UGV) positions. In large-scale farmland operations, conventional Global Navigation Satellite System (GNSS)-based positioning may suffer from short-term signal loss, degrading accuracy. Multi-unmanned aerial vehicle (UAV)-assisted vehicle localization based on direction-of-arrival (DOA) estimation can provide critical positioning compensation for UGV, where compressed sensing (CS)-based DOA-assisted localization algorithms are commonly employed. However, existing schemes neither account for the bias induced by the local positional oscillation of UAVs, nor address the limited accuracy and real-time performance of CS-based DOA estimation, restricting their agricultural deployment. To this end, this paper first develops an assisted-localization architecture that explicitly incorporates the local positional offsets of multiple UAVs, together with a corresponding array signal reception model. To overcome the accuracy–efficiency trade-off of conventional CS-DOA methods, an adaptive local overcomplete dictionary (LOD) is then constructed to robustly refine the angular resolution around the region of interest. With the number of sources K assumed to be known and fixed, a particle swarm optimization (PSO)-based local refinement algorithm is further introduced to adaptively optimize the DOA estimates within the constructed local dictionary, thereby improving estimation robustness under low-SNR and coherent-source conditions. Consequently, the proposed method improves robustness while maintaining favorable localization accuracy and computational efficiency in the simulated scenarios. Simulation results show that it substantially reduces localization error compared with state-of-the-art algorithms, suggesting its potential as a localization-assistance approach for UGV navigation in sustainable tillage. Full article

Insulator types and materials vary substantially across power system inspection scenarios, while damage defects occur infrequently; consequently, defect images that match a target insulator type and operating environment are often difficult to obtain. Existing open-source insulator image datasets provide limited coverage of equipment types, scene backgrounds, and defect morphologies. Their direct use for detector training may therefore cause domain mismatch and poor generalization. To address these limitations, this study proposes a scene-domain-adaptive sample expansion method for few-shot damaged-insulator detection. The method adapts a general-purpose pretrained diffusion model to the insulator inspection domain and incorporates three-dimensional (3D) structural constraints to generate targeted samples of damaged insulators. First, low-rank adaptation (LoRA) is used for scene-domain adaptation, enabling the generation model to learn the characteristic geometry, appearance, and material properties of insulators. Second, a 3D model of the target insulator is constructed, and physical damage simulation and edge extraction are applied to obtain geometric guidance maps containing shed boundaries and fracture contours. These maps constrain the locations and shapes of the generated defects. Finally, the geometric guidance is injected into the diffusion process to synthesize damaged-insulator images, which are combined with limited real samples to train detectors for damaged-insulator instances, which were evaluated exclusively on real validation images. Experimental results show that adding a moderate number of generated samples enriches the scarce defect features in the real dataset and improves detector performance. In the mixture-ratio experiment using YOLOv8, mAP@0.5 increased by 8.2 percentage points. Additional experiments with multiple detectors yielded performance gains of varying magnitudes, demonstrating that the generated samples provide an effective supplement to limited, real-world data. The proposed method alleviates the scarcity of insulator defect samples and offers a practical data-augmentation strategy for intelligent inspection of power equipment. Full article

Sustainable urban disaster response requires Earth observation (EO) resources to be selected according to event demands, environmental conditions, sensing capabilities, and urban targets. This study proposes an Observation Linked Open Data (O-LOD)-based Urban Disaster Observation Task (UDOT) enhancement framework for semantic EO resource configuration. O-LOD organizes heterogeneous data through four dimensions, Event, Context, Subject, and Object, which an instantiation algorithm populates as task graphs. Layered GeoSPARQL queries then match thematic and analytical capabilities, qualify orbit-derived observation opportunities against context and object constraints, and rank feasible alternatives using the Observation Capability Evaluation Model (OCEM). Evaluation on the 2020 Khartoum flood and Bobcat wildfire narrowed 202 satellite–sensor pairs to three flood-capable and four wildfire-capable pairs and returned two Khartoum and eight Bobcat ranked opportunities, with complete queries executing in 3.0 s and 0.07 s. Constraint ablation and resolution sensitivity analyses identified the conditions governing the feasible set. Publicly accessible Sentinel, Landsat, and MODIS products corroborated both retained and excluded results, supporting water-extent and burn-severity mapping where coverage, spectral bands, and image quality met the task requirements. O-LOD therefore provides a traceable semantic link from disaster observation demand to qualified and ranked EO opportunities and subsequent image assessment, supplying task-oriented inputs for downstream scheduling and supporting context-aware sensing for sustainable urban disaster response. Full article

The integration of temporary support systems with permanent structural frameworks remains a complex challenge in high-rise construction, where traditional building information modeling (BIM) workflows often lack the coordination needed to optimize both simultaneously. This study addresses that gap through a federated BIM workflow using Tekla Structures, Revit, Navisworks, and Dynamo on a fourteen-story building with a five-level basement. The temporary shoring, permanent concrete frame, and formwork were modeled at Level of Development (LOD) 300–350. A parameter-driven methodology in Dynamo managed large-scale clash detection. Steel cutting-list optimization reduced sheet-cutting waste from 4.5% to below 3% across 18 tons of steel. Clash detection between the truss guard and permanent rebar initially returned over 6000 conflicts; categorizing these via Dynamo made resolution tractable. This data optimized plywood formwork, reducing requirements by 15% (3350 to 2850 square meters), and refined rebar scheduling, recovering 51 of 103 additional tons, 32.8 tons of which are directly attributable to the truss-rebar interaction-zone workflow presented in this study. The study demonstrates that federating temporary and permanent models generates reusable coordination data, yielding savings beyond simple clash avoidance. It offers a transferable workflow for temporary-to-permanent structural integration that the current literature has not yet examined quantitatively. Full article

Architectural heritage faces multiple degradation risks, while traditional preventive conservation often falls short in information integration, dynamic analysis, and regional management. Moreover, existing HBIM research lacks systematic digital approaches for hybrid Sino-Western vernacular heritage. This study addresses these gaps by constructing a preventive conservation HBIM framework for Kaiping Diaolou. A customized parametric component library and a three-level coding system are developed, enabling systematic HBIM application in non-standardized contexts. Moving beyond digital archiving, a closed-loop workflow of “data acquisition → HBIM → multi-physics simulation → targeted intervention” is established, shifting HBIM’s core function from passive documentation to proactive risk forecasting. A value- and risk-driven principle is proposed to define LOD/LOI levels, allowing appropriate modeling resource allocation according to heritage significance and risk grades. A direct data-interaction workflow is developed between HBIM and multi-physics simulations (structural FEM, wind/CFD), enabling quantitative identification of structural weak points and high-risk environmental degradation areas. Enhanced visual expression methods, including 3D defect mapping and a “shadow-element and displacement-decomposition” structural exploded-view technique, are introduced to overcome the limitations of conventional 2D representations. The proposed framework and methods are validated through the case study of Ruishi Tower, demonstrating the feasibility of HBIM in heritage preventive conservation and providing a replicable pathway for the digital preservation of similar vernacular heritage. Full article